Altz-Stamm-Dissertation-2016

The Dissertation Committee for Amelia Elizabeth Altz-Stamm Certifies that this is the approved version of the following dissertation:

Factors that Facilitate Cooperative Efforts in the Management of Irrigation Water: An Assessment of Water User Associations in the Jordan Valley

Committee:

David J. Eaton, Supervisor

Robert H. Wilson

Sheila Olmstead

Suzanne A. Pierce

Samer A. Talozi

François Molle Factors that Facilitate Cooperative Efforts in the Management of Irrigation Water: An Assessment of Water User Associations in the Jordan Valley

Amelia Elizabeth Altz-Stamm, A.B.; M.A.

Dissertation Presented to the Faculty of the Graduate School of

The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

The University of Texas at Austin May 2016 Dedication

For Mom and Dad.

Acknowledgements

There are many for whom I am grateful for their help and support and without whom I could not have conducted the research for this dissertation. Dr. Samer Talozi offered me the initial opportunity to travel to Jordan and explore its water sector. Since our first meeting, I have benefited greatly from his knowledge of Jordan’s water situation, his guidance in my fieldwork, his constant encouragement and optimism, and our friendship.

During my initial field visit to Jordan in 2012, it was in lengthy conversations with Engineer Haidar Malhas in Amman, Jordan, and a subsequent visit to the Jordan Valley with him for a Ramadan meal with a Jordanian farmer, that I became aware of the issues surrounding water user associations and decided upon this topic for my dissertation. Not only did Haidar start me along this path but when I returned to Jordan in 2014 for my fieldwork, he was instrumental in directing my research strategy.

There are a host of actors in the Jordan Valley who made my work possible and who made it enjoyable. Engineer Shafiq Habash at the water user association at PS 91 was the first association official to fully welcome me in and support my research endeavors, allowing me constant access and much of his time and energy. Hussam Ali, a ditchrider at PS 91, also deserves the utmost gratitude for his hours of taking me around the field to find farmers and for giving me an honest assessment of his work and the valley environment.

My work in PS 33 was made entirely possible by Engineer Mohammed al-Mnezil. He always greeted me with a smile and displayed a never-ending capacity to bring farmers to the association office to talk with me or to bring me out to the farmers in the field. Fouad Ijbarat, a ditchrider in PS 55, likewise was my one conduit into the inner-workings of PS 55 and its farmers. He graciously took me on his motorbike around the fields for hours

v and hours and all of this in his free time. And in Mazraa-Haditha, I am similarly indebted to ditchrider Abu Shakir who accompanied me to the field on multiple occasions to speak with farmers.

A special thanks goes to select individuals in the valley who not only helped with my research but became my good friends. Engineer Anas Bani Hamdan opened my eyes to the reality of the valley, never hesitating to answer my endless series of questions and never backing away from the harsh truth. Whenever I ran into a wall, I could count on Anas to find a way over or around it. Abdallah al-Sa’adi, a farmer in the north of the valley, invited me on multiple occasions to his farm with his father so that I could learn first-hand what was involved in farming citrus trees. They too displayed amazing honesty and forthrightness in their views on the valley and its farming community.

The Jordan Valley Authority and its officials and employees in the Jordan Valley also deserve many thanks. In particular, Ali al-Omari of the WUA Unit did not hesitate, from our first encounter, to help me and to make my first introduction with an association head. Khalid Abu Husayyan, an employee at a stage office in the north, also never doubted my intentions from the beginning and provided assistance not only in my research but supported my presence in the valley and introduced me to his lovely family.

A general thanks must be made to the farmers of the Jordan Valley as well. Their opinions and thoughts are at the heart of my research and I feel fortunate to have been able to speak with them and learn more about their daily lives and issues of concern. Indeed, I delighted in their company and commentary; they are what made my research worth doing. I was also tremendously humbled by their invitations to their households to share tea or meals and to meet their families. These experiences, in and of themselves, were the most enriching and informative of my time in Jordan.

vi With regard to my Arabic language skills in particular, I would like to thank Bashar Shunnaq for teaching me the Jordanian dialect. He also reviewed and edited my interview questions and survey questions, thus enabling me to sound legitimate and precise in my use of the Jordanian dialect.

At The University of Texas at Austin, I must thank Dr. Eaton for his unrelenting encouragement and constant faith in my research path and capabilities. He has been my cheerleader from the start and without his support, this venture would have been quite difficult. I’d also like to thank fellow PhD Candidates Miha Vindis and Emily Kao for their support, advice, comradery, and the many enjoyable meals.

Finally, and most importantly, I want to thank my mom and dad. Their unflagging belief in my abilities, understanding of the difficulties, and wisdom in all life endeavors have, above all else, made everything that I have and do in my life possible.

vii Factors that Facilitate Cooperative Efforts in the Management of Irrigation Water: An Assessment of Water User Associations in the Jordan Valley

Amelia Elizabeth Altz-Stamm, Ph.D. The University of Texas at Austin, 2016

Supervisor: David J. Eaton

User participation in the management of water resources has garnered much support over the past several decades. Questions remain for its feasibility and suitability as an alternative to state-led management. To examine the circumstances and contexts in which user-based management works well, studies from around the world have determined the kinds of factors related to the physical environment, community, institutions and users that can lead to its success or failure. This dissertation similarly examines Jordan’s experience with water user associations in the Jordan Valley through both qualitative and quantitative research methods. Potential influential factors that facilitate or hinder the performance of and participation in these associations are assessed through the Institutional Analysis and Development (IAD) Framework. Results indicate that institutional factors play a particularly large role in determining the level of farmer satisfaction with the associations, and less so does the adequacy of the water supply. Despite Jordan’s extreme water scarcity, factors unrelated to the water supply have a stronger impact on association performance. For determining membership in the association, user-related factors demonstrate the most significance. Community-related factors remain unquantified but likely influence associations to a large degree. This research adds to the growing literature that identifies important factors and the magnitude

viii of their effects on user management. The IAD framework acts as a useful tool to dissect user management and reveals where policy interventions will be most impactful.

ix Table of Contents

List of Tables ...... xvii

List of Figures ...... xxxi

Chapter One: Introduction ...... 1 The World’s Water Resources ...... 1 Combating Water Scarcity ...... 4 Managing Water Scarcity ...... 8 Research Question ...... 13 Dissertation Outline ...... 14

Chapter Two: Jordan’s Water Conditions...... 16 Geography and Climate of Jordan ...... 16 Available Water Resources in Jordan ...... 20 Use of Water Resources in Jordan ...... 24 Water Management in the Agricultural Sector in Jordan ...... 35 Water Management in the Jordan Valley...... 38 Water Distribution in the Jordan Valley ...... 49 Summary ...... 54

Chapter Three: The Trend of Participation in Development ...... 56 Overview of Previous Development Trends ...... 56 Participation in Irrigation Management ...... 59 Purported Benefits of User Participation in Irrigation Management ...64 Questions and Doubts about User Management ...... 69 The Body of Case Studies and Evaluations ...... 74 Summary ...... 81

Chapter Four: Evaluating Water User Associations ...... 83 Relevant Outcomes for the Implementation of WUAs ...... 83 WUA Performance...... 85 WUA Participation...... 87

x Potential Factors Affecting the Outcomes ...... 88 Physical and Environmental Factors ...... 88 Status of the Infrastructure ...... 88 Water Scarcity ...... 90 Water Predictability ...... 92 System Size ...... 93 Crop/Farm Diversification ...... 94 Weather and Natural Events ...... 95 Community Factors ...... 96 Preexisting Community Organization...... 96 Heterogeneity of Farmers ...... 98 Political Environment and Support ...... 102 Market Environment and Support ...... 103 Institutional Factors ...... 104 Legal Authority ...... 104 Collective-Choice Arrangements and Rules ...... 106 Monitoring, Sanctioning and Conflict Resolution ...... 108 User Factors ...... 110 Leadership ...... 110 Level of Dependence on Water Source and Agriculture ...... 112 Socioeconomic Status ...... 113 Land-Holding Status ...... 113 Education ...... 114 Perceived Benefits to and Incentives for Membership ...... 115 Summary ...... 118

Chapter Five: Methodology ...... 121 Contextual Assessment ...... 122 Interviews with WUAs Heads ...... 124 In-depth Case Study Analyses ...... 125 Farmer Survey ...... 126 xi Sampling Issues ...... 127 Survey Design ...... 128 Survey Issues ...... 130 Detailed Follow-up Interviews with Farmers ...... 132 Interviews with Donor and Government Agency Officials ...... 133 Data Analysis ...... 134 Statistical Analysis ...... 134 Independent Variables ...... 136 Water Scarcity ...... 136 Water Predictability ...... 137 Status of the Infrastructure ...... 139 System Size ...... 139 Crop/Farm Diversity ...... 140 Climate and Natural Events ...... 141 Preexisting Community Organizations ...... 142 Heterogeneity of Farmers ...... 142 Political Environment and Support ...... 143 Market Environment and Support ...... 143 Legal Authority ...... 144 Collective-choice Arrangements and Rules ...... 145 Monitoring ...... 145 Sanctioning ...... 146 Conflict Resolution ...... 146 Leadership ...... 147 Level of Dependence on Water Source and Agriculture ...... 148 Socioeconomic Status ...... 149 Land-holding Status ...... 150 Education ...... 151 Perceived Benefits to and Incentives for Membership ...... 152 Dependent Variables ...... 152

xii Opinion of the WUA ...... 152 Comparison between WUA and JVA ...... 153 Water Stealing ...... 154 Fairness of the WUA ...... 154 Membership in the WUA ...... 155 Summary ...... 156

Chapter Six: Water User Associations in Jordan ...... 157 The Idea of Water User Associations ...... 158 Reaction to the Idea of Water User Associations ...... 159 Implementation of Water User Associations in the Jordan Valley ...... 162 Current Status of Water User Associations in the Jordan Valley ...... 168 Water User Associations with Task Transfer Agreements ...... 173 Personal Characteristics of WUA Heads ...... 178 Management and Operations of the WUAs ...... 182 Financial Affairs of the WUAs ...... 184 WUA Membership ...... 185 Summary ...... 187

Chapter Seven: The Case Studies ...... 189 Selection of Case Studies ...... 189 PS 91 – Al-Baladna Water User Association ...... 192 PS 33 Water User Association ...... 199 PS 55 Water User Association ...... 205 Mazraa-Haditha Water User Association ...... 210 Summary ...... 216

Chapter Eight: Physical Factors ...... 218 Status of the Infrastructure ...... 221 Water Scarcity ...... 245 Secondary Water Resources ...... 254 Special Crops ...... 259

xiii Water Predictability ...... 262 Equality across the network ...... 269 System size...... 279 Crop/Farm Diversity ...... 280 Climate and Natural Events ...... 282 Summary ...... 287

Chapter Nine: Community Factors ...... 288 Preexisting Community Organizations ...... 291 Heterogeneity of Farmers ...... 296 Endowments ...... 296 Identity ...... 302 Interests ...... 304 Political Environment and Support ...... 305 Market Environment and Support ...... 310 Summary ...... 328

Chapter Ten: Institutional Factors ...... 330 Legal Authority ...... 333 Collective-choice Arrangements and Rules ...... 343 Monitoring, Sanctioning and Conflict Resolution ...... 346 Monitoring ...... 346 Sanctioning ...... 355 Conflict Resolution ...... 364 Summary ...... 375

Chapter Eleven: User Factors ...... 377 Leadership ...... 380 Level of Dependence on Water Source and Agriculture ...... 386 Socioeconomic Status ...... 393 Land-holding Status ...... 402 Education ...... 408

xiv Perceived Benefits to and Incentives for Membership ...... 416 Summary ...... 420

Chapter Twelve: Outcomes and All Factors ...... 422 Opinion of WUA...... 424 Physical Factors ...... 426 Institutional Factors ...... 429 User Factors ...... 431 All Factors ...... 433 Comparison between WUA and JVA ...... 437 Physical Factors ...... 441 Institutional Factors ...... 443 User Factors ...... 446 All Factors ...... 448 Water Stealing ...... 451 Physical Factors ...... 460 Institutional Factors ...... 461 User Factors ...... 461 All Factors ...... 463 Fairness of the WUA ...... 465 Physical Factors ...... 473 Institutional Factors ...... 475 User Factors ...... 477 All Factors ...... 478 Membership in WUAs ...... 480 Member Activities ...... 488 Physical Factors ...... 490 Institutional Factors ...... 492 User Factors ...... 492 All Factors ...... 494 Summary ...... 497 xv Chapter Thirteen: Conclusions ...... 498 The Community ...... 498 The Institutions ...... 502 The Water Users ...... 507 The Physical Structures...... 509 Limitations and Future Steps ...... 510

Appendix A – Contextual Assessment Interviews...... 515

Appendix B – WUA Head Interview Questions ...... 518

Appendix C – Farmer Survey Questions ...... 525

Appendix D – Variable Key ...... 527

Appendix E – Supplementary Stata Results ...... 531

Appendix F – OLS Regression Models ...... 655

Appendix G – WUA Contract Example ...... 676

Appendix H – Determination of WUA Salaries ...... 687

Glossary of Abbreviations ...... 690

Bibliography ...... 691

xvi List of Tables

Table 1.1: Total renewable water resources per capita by world region for 2014 ...2 Table 1.2: Annual precipitation and renewable water resources for countries in the

Middle East in 2014...... 3 Table 1.3: Gross domestic product per capita from 2009 to 2013 in ESCWA member

countries...... 5

Table 1.4: Percent of water withdrawal by sector among world regions...... 9

Table 1.5: Percent of water withdrawal by sector within the Middle East...... 10 Table 1.6: GDP, agriculture’s contribution to GDP, and population involved in

agriculture in the Middle East...... 12

Table 2.1: Sources of water available in Jordan...... 22

Table 2.2: Projected water demand and water deficit from 2013 to 2030 in Jordan.24

Table 2.3: Number of registered Syrian refugees in the Middle East by country. 26

Table 2.4: Jordanian and non-Jordanian populations in 2015...... 27

Table 2.5: Safe yield and actual abstraction from Jordan’s 12 groundwater basins.29

Table 2.6: Water use by sector in Jordan in MCM and percentages...... 30 Table 2.7: GDP, GDP per capita and the contribution of the agricultural sector to

GDP in Jordan...... 32 Table 2.8: Water tariffs in the Jordan Valley for surface water and the Highlands for

groundwater...... 37

Table 4.1: Outcome variables for WUA performance and participation...... 84

Table 4.2: Hypotheses for the four main categories of factors...... 119

Table 4.2: (continued) ...... 120

Table 5.1: Number of survey participants within each of the four WUAs...... 126

xvii Table 5.2: Rubric used for quantification/tabulation of leadership rankings among

WUA heads...... 148 Table 6.1: Location and stage of development of water user association in the Jordan

Valley...... 170 Table 6.2: Year established, number of farm units, land area and water source of

WUAs...... 175

Table 6.3: Number of farmers, members and non-Jordanian presence in WUAs.176

Table 6.4: Number of greenhouses and dunums of major crops in WUAs...... 177 Table 6.5: Personal characteristics of the WUA heads of WUAs with task transfer

agreements...... 179

Table 6.5: (continued) ...... 180

Table 7.1: Select characteristics of the four WUAs chosen for in-depth analysis.191

Table 8.1: Summary of physical factors, hypotheses and conclusions reached. .219

Table 8.1: (continued)...... 220

Table 8.2: Regression results of water adequacy’s effect on opinion of the WUA.247 Table 8.3: Regression results of water adequacy’s effect on farmer opinion of the

WUA in comparison to the JVA...... 248 Table 8.4: Regression results of water adequacy’s effect on reporting of water

stealing...... 249 Table 8.5: Regression results of water adequacy’s effect on opinion of fairness of the

WUA...... 250 Table 8.6: Regression results for water adequacy’s effect on membership in the

WUA...... 251 Table 8.7: Percentage of farmers surveyed in each of the four WUAs who admitted to

having a secondary source of water...... 255 xviii Table 8.8: Regression results of effect of having a secondary water source on water

adequacy...... 257 Table 8.9: Regression results of having a secondary source of water and individual

WUAs on water adequacy...... 258 Table 8.10: Regression results for the effect of growing citrus or date palm trees on

water adequacy...... 260 Table 8.11: Regression results for the effect of crop type, farm size, greenhouses and

exporting crops on water adequacy...... 262 Table 8.12: Regression results of water reliability’s effect on farmer opinion of the

WUA...... 264 Table 8.13: Regression results of water reliability’s effect on farmer opinion of the

WUA in comparison to the JVA...... 265 Table 8.14: Regression results of water reliability’s effect on reporting of water

stealing...... 266 Table 8.15: Regression results of water reliability’s effect on opinion of fairness of

the WUA...... 267 Table 8.16: Regression results for water reliability’s effect on membership in the

WUA...... 268 Table 8.17: Regressions results for the effects of network type, lateral position and

WUA on water reliability...... 272 Table 8.18: Regression results of the effects of water reliability, network type and

lateral position on farmer opinion of the WUA...... 274 Table 8.19: Regression results of the effects of water reliability, network type and

lateral position on farmer opinion of the WUA versus JVA...... 276

xix Table 8.20: Regression results of the effects of water reliability, network type and

lateral position on whether farmers are members in the WUA...... 278 Table 8.21: Land area and number of farmer units in each of the four surveyed

WUAs...... 279

Table 8.22: Summary of outcome variable statistics between WUAs...... 280 Table 8.23: Percentage of surveyed farmers in each WUA growing one, two or more

than two crops...... 281 Table 8.24: Percentage of farmers in the four WUAs growing different crops (farmers

can be in more than one category)...... 281

Table 9.1: Summary of community factors, hypotheses and conclusions reached.289

Table 9.1: (continued)...... 290

Table 9.2: Interviewed WUA Heads and their tribal status...... 292 Table 9.3: Percentage of farmers with greenhouses within each of the surveyed

WUAs...... 298

Table 9.4: Number of greenhouses owned by farmers in the four surveyed WUAs.298 Table 9.5: Number of farmers selling to local or international markets in the four

surveyed WUAs...... 298

Table 9.6: Summary of outcome variable statistics between WUAs...... 301 Table 9.7: Percentage of Jordanians, Egyptians and Pakistanis in the four surveyed

WUAs...... 302

Table 9.8: Percentage of owners, renters and agents in the four surveyed WUAs.303 Table 9.9: Percentage of farmers in the four WUAs who have secondary work or

income...... 304 Table 9.10: Types of secondary work or income for farmers in the four surveyed

WUAs...... 305 xx Table 9.11: Percentage of rise or fall from previous year in consumer price indices in

Jordan...... 321 Table 9.12: Seasonal costs, selling price and fixed costs for one farm unit of

vegetables for one season...... 323 Table 9.13: Seasonal costs, selling price and fixed costs for one farm unit of date

palm trees for one season...... 324 Table 9.14: Seasonal costs, selling price and fixed costs for one farm unit of citrus for

one season...... 325

Table 10.1: Summary of institutional factors, hypotheses and conclusions reached.331

Table 10.1: (continued)...... 332 Table 10.2: Regression results of the effect of where a farmer seeks help (WUA, JVA

or both) on farmer opinion of the WUA...... 337 Table 10.3: Regression results of the effect of where a farmer seeks help (WUA, JVA

or both) on farmer opinion of the WUA as compared to the JVA. .339 Table 10.4: Regression results of the effect of where a farmer seeks help (WUA, JVA

or both) on farmer reporting of water stealing...... 340 Table 10.5: Regression results of the effect of where a farmer seeks help (WUA, JVA

or both) on farmer opinion of the fairness of the WUA...... 341 Table 10.6: Regression results of the effect of where a farmer seeks help (WUA, JVA

or both) on farmer membership in the WUA...... 342 Table 10.7: Membership fees and use of elections in the WUAs with task transfer

agreements...... 344

Table 10.8: Summary of outcome variable statistics between WUAs...... 346 Table 10.9: Regression results of the effect of monitoring the field on farmer opinion

of the WUA...... 350 xxi Table 10.10: Regression results of the effect of monitoring the field on farmer opinion

of the WUA as compared to the JVA...... 351 Table 10.11: Regression results of the effect of monitoring the field on farmer

reporting of water stealing...... 352 Table 10.12: Regression results of the effect of monitoring the field on farmer opinion

of the fairness of the WUA...... 353 Table 10.13: Regression results of the effect of monitoring the field on farmer

membership in the WUA...... 354 Table 10.14: Regression results of the effect of sanctioning on farmer opinion of the

WUA...... 358 Table 10.15: Regression results of the effect of sanctioning on farmer opinion of the

WUA as compared to the JVA...... 360 Table 10.16: Regression results of the effect of sanctioning on farmer reporting of

water stealing...... 361 Table 10.17: Regression results of the effect of sanctioning on farmer opinion of the

fairness of the WUA...... 362 Table 10.18: Regression results of the effect of sanctioning on farmer membership in

the WUA...... 363 Table 10.19: Regression results of the effect of the WUA’s conflict resolution

abilities on farmer opinion of the WUA...... 371 Table 10.20: Regression results of the effect of the WUA’s conflict resolution

abilities on farmer opinion of the WUA as compared to the JVA. .372 Table 10.21: Regression results of the effect of the WUA’s conflict resolution

abilities on farmer reporting of water stealing...... 373

xxii Table 10.22: Regression results of the effect of the WUA’s conflict resolution

abilities on farmer opinion of the fairness of the WUA...... 374 Table 10.23: Regression results of the effect of the WUA’s conflict resolution

abilities on farmer membership in the WUA...... 375

Table 11.1: Summary of user factors, hypotheses and conclusions reached...... 378

Table 11.1: (continued)...... 379 Table 11.2: Personal and farm characteristics of the heads of the four surveyed

WUAs...... 380 Table 11.3: Opinions of surveyed WUA heads on the future of the WUA and JVA.

...... 384 Table 11.4: Tabulation of WUA head characteristics and opinions as an overall score.

...... 385 Table 11.5: Regression results of the effects of secondary work and secondary water

resources on farmer opinion of the WUA...... 387 Table 11.6: Regression results of the effects of secondary work and secondary water

resources on farmer opinion of the WUA as compared to the JVA.388 Table 11.7: Regression results of the effects of secondary work and secondary water

resources on farmer reporting of water stealing...... 390 Table 11.8: Regression results of the effects of secondary work and secondary water

resources on farmer opinion of the fairness of the WUA...... 391 Table 11.9: Regression results of the effects of secondary work and secondary water

resources on farmer membership in the WUA...... 392 Table 11.10: Regression results of the effects of farm size, greenhouses and exporting

on farmer opinion of the WUA...... 394

xxiii Table 11.11: Regression results of the effects of farm size, greenhouses and exporting

on farmer opinion of the WUA as compared to the JVA...... 396 Table 11.12: Regression results of the effects of farm size, greenhouses and exporting

on farmer reporting of water stealing...... 398 Table 11.13: Regression results of the effects of farm size, greenhouses and exporting

on farmer opinion of the fairness of the WUA...... 400 Table 11.14: Regression results of the effects of farm size, greenhouses, exporting

and WUA on farmer membership in the WUA...... 402 Table 11.15: Regression results of the effect of ownership status on farmer opinion of

the WUA...... 404 Table 11.16: Regression results of the effect of ownership status on farmer opinion of

the WUA as compared to the JVA...... 405 Table 11.17: Regression results of the effect of ownership status on farmer reporting

of water stealing...... 406 Table 11.18: Regression results of the effect of ownership status on farmer opinion of

the fairness of the WUA...... 407 Table 11.19: Regression results of the effect of ownership status on farmer

membership in the WUA...... 408 Table 11.20: Regression results of the effects of education level on farmer opinion of

the WUA...... 410 Table 11.21: Regression results of the effects of education level on farmer opinion of

the WUA as compared to the JVA...... 411 Table 11.22: Regression results of the effects of education level on farmer reporting

of water stealing...... 413

xxiv Table 11.23: Regression results of the effects of education level on farmer opinion of

the fairness of the WUA...... 414 Table 11.24: Regression results of the effects of education level on farmer

membership in the WUA...... 416 Table 12.1: Summary of all outcomes, overall statistics and influential categories of

factors and factors...... 423 Table 12.2: Percent of variation explained in outcome variables by factor categories.

...... 424 Table 12.3: Regression results of the effects of the physical factors on farmer opinion

of the WUA...... 428 Table 12.4: Regression results of the effects of the institutional factors on farmer

opinion of the WUA...... 431 Table 12.5: Regression results of the effects of user factors on farmer opinion of the

WUA...... 433 Table 12.6: Regression results of the effects of all factors on farmer opinion of the

WUA...... 436

Table 12.6: (continued) ...... 437

Table 12.7: Farmer opinion of why WUA is either better or worse than the JVA.440 Table 12.8: Regression results for the effects of physical factors on farmer opinion of

the WUA as compared to the JVA...... 443 Table 12.9: Regression results of the effects of institutional factors on farmer opinion

of the WUA as compared to the JVA...... 445 Table 12.10: Regression results of the effects of user factors on farmer opinion of the

WUA as compared to the JVA...... 447

xxv Table 12.11: Regression results of the effects of all factors on farmer opinion of the

JVA as compared to the JVA...... 450 Table 12.12: Regression results of the effects of physical factors on farmer reporting

of water stealing...... 461 Table 12.13: Regression results for the effects of user factors on farmer reporting of

water stealing...... 462 Table 12.14: Regression results of the effects of all factors on farmer reporting of

water stealing...... 465 Table 12.15: Regression results of the effects of physical factors on farmer opinion of

the fairness of the WUA...... 475 Table 12.16: Regression results of the effects of institutional factors on farmer

opinion of the fairness of the WUA...... 477 Table 12.17: Regression results of the effects of all factors on farmer opinion of the

fairness of the WUA...... 480 Table 12.18: Regression results of the effects of physical factors on farmer

membership in the WUA...... 492 Table 12.19: Regression results of the effects of user factors on farmer membership in

the WUA...... 494 Table 12.20: Regression results of the effects of all factors on farmer membership in

the WUA...... 496 Table 8.4A: OLS regression results of water adequacy’s effect on reporting of water

stealing...... 655 Table 8.5A: OLS regression results of water adequacy’s effect on opinion of fairness

of the WUA...... 655

xxvi Table 8.6A: OLS regression results for water adequacy’s effect on membership in the

WUA...... 655 Table 8.9A: OLS regression results of having a secondary source of water and

individual WUAs on water adequacy...... 656 Table 8.10A: Regression results for the effect of growing citrus or date palm trees on

water adequacy...... 656 Table 8.11A: OLS regression results for the effect of crop type, farm size,

greenhouses and exporting crops on water adequacy...... 657 Table 8.14A: OLS regression results of water reliability’s effect on reporting of water

stealing...... 657 Table 8.15A: OLS regression results of water reliability’s effect on opinion of

fairness of the WUA...... 657 Table 8.16A: OLS regression results for water reliability’s effect on membership in

the WUA...... 658 Table 8.17A: OLS regressions results for the effects of network type, lateral position

and WUA on water reliability...... 658 Table 8.20A: OLS regression results of the effects of water reliability, network type

and lateral position on whether farmers are members in the WUA.659 Table 10.4A: OLS regression results of the effect of where a farmer seeks help

(WUA, JVA or both) on farmer reporting of water stealing...... 659 Table 10.5A: OLS regression results of the effect of where a farmer seeks help

(WUA, JVA or both) on farmer opinion of the fairness of the WUA.660 Table 10.6A: OLS regression results of the effect of where a farmer seeks help

(WUA, JVA or both) on farmer membership in the WUA...... 660

xxvii Table 10.11A: OLS regression results of the effect of monitoring the field on farmer

reporting of water stealing...... 660 Table 10.12A: OLS regression results of the effect of monitoring the field on farmer

opinion of the fairness of the WUA...... 661 Table 10.13A: OLS regression results of the effect of monitoring the field on farmer

membership in the WUA...... 661 Table 10.16A: OLS regression results of the effect of sanctioning on farmer reporting

of water stealing...... 662 Table 10.17A: OLS regression results of the effect of sanctioning on farmer opinion

of the fairness of the WUA...... 662 Table 10.18A: OLS regression results of the effect of sanctioning on farmer

membership in the WUA...... 662 Table 10.21A: OLS regression results of the effect of the WUA’s conflict resolution

abilities on farmer reporting of water stealing...... 663 Table 10.22A: OLS regression results of the effect of the WUA’s conflict resolution

abilities on farmer opinion of the fairness of the WUA...... 663 Table 10.23A: OLS regression results of the effect of the WUA’s conflict resolution

abilities on farmer membership in the WUA...... 663 Table 11.7A: OLS regression results of the effects of secondary work and secondary

water resources on farmer reporting of water stealing...... 664 Table 11.8A: OLS regression results of the effects of secondary work and secondary

water resources on farmer opinion of the fairness of the WUA. ....664 Table 11.9A: OLS regression results of the effects of secondary work and secondary

water resources on farmer membership in the WUA...... 665

xxviii Table 11.12A: OLS regression results of the effects of farm size, greenhouses and

exporting on farmer reporting of water stealing...... 665 Table 11.13A: OLS regression results of the effects of farm size, greenhouses and

exporting on farmer opinion of the fairness of the WUA...... 665 Table 11.14A: OLS regression results of the effects of farm size, greenhouses,

exporting and WUA on farmer membership in the WUA...... 666 Table 11.17A: OLS regressions results of the effect of ownership status on farmer

reporting of water stealing...... 667 Table 11.18A: OLS regressions results of the effect of ownership status on farmer

opinion of the fairness of the WUA...... 667 Table 11.19A: OLS regressions results of the effect of ownership status on farmer

membership in the WUA...... 667 Table 11.22A: OLS regression results of the effects of education level on farmer

reporting of water stealing...... 668 Table 11.23A: OLS regression results of the effects of education level on farmer

opinion of the fairness of the WUA...... 668 Table 11.24A: OLS regression results of the effects of education level on farmer

membership in the WUA...... 669 Table 12.12A: OLS regression results of the effects of physical factors on farmer

reporting of water stealing...... 670 Table 12.13A: OLS regression results for the effects of user factors on farmer

reporting of water stealing...... 670 Table 12.14A: OLS regression results of the effects of all factors on farmer reporting

of water stealing...... 670

xxix Table 12.15A: OLS regression results of the effects of physical factors on farmer

opinion of the fairness of the WUA...... 671 Table 12.16A: OLS regression results of the effects of institutional factors on farmer

opinion of the fairness of the WUA...... 672 Table 12.17A: OLS regression results of the effects of all factors on farmer opinion of

the fairness of the WUA...... 672 Table 12.18A: OLS regression results of the effects of physical factors on farmer

membership in the WUA...... 673 Table 12.19A: OLS regression results of the effects of user factors on farmer

membership in the WUA...... 674 Table 12.20A: OLS regression results of the effects of all factors on farmer

membership in the WUA...... 674

xxx List of Figures

Figure 2.1: Location of Jordan in the Middle East...... 17

Figure 2.2: The climatic/agricultural zones of Jordan...... 18

Figure 2.3: Depiction of average monthly rainfall in millimeters (mm) in Jordan.20 Figure 2.4: Domestic population of Jordan from 1952 to 2015 (dots represent years

where data is present*)...... 25 Figure 2.5: Percentage of use of water supply in Jordan by the agricultural and

domestic sectors from 2000 to 2013...... 31

Figure 2.6: Map of the Jordan Valley above the Dead Sea...... 43

Figure 2.7: Map of the Jordan Valley below the Dead Sea...... 44 Figure 2.8: Management diagram of the MWI, JVA, directorates, stage offices and

WUAs...... 46

Figure 2.9: Map of the Jordan Valley with the directorates and stage offices...... 48 Figure 2.10: Diagram of the Jordan Valley depicting the King Abdullah Canal, water

gates (red ticks), and pump stations (PS)...... 51 Figure 2.11: Diagram of water distribution through the pumping station to the main

line, lateral lines and farms...... 52 Figure 2.12: Farm turn assembly (FTA) with water regulator, flow limiter, and water

meter...... 53

Figure 2.13: The two varieties of a flow limiter device on the FTA...... 53

Figure 2.14: The water meter in an FTA...... 54

Figure 3.1: Diagram of the Institutional Analysis and Development framework..78

Figure 3.2: Diagram of the Institutions of Sustainability (IoS) framework...... 80

xxxi Figure 5.1: Types of crops grown by farmers in the four surveyed WUAs (farmers

could choose more than one category)...... 141

Figure 7.1: Map of four case study water user associations in the Jordan Valley.190 Figure 7.2: Development Area (DA) 27 and the details of the area administered by the

WUA at PS 91...... 193 Figure 7.3: Map of the area in DAs 12, 13 and 14 under the administration of the

WUA at PS 33...... 200 Figure 7.4: Map of the area within DAs 20 and 21 under the administration of the

WUA at PS 55...... 206 Figure 7.5: Plot areas of DAs 45, 46 and 47 that comprise the area under the

administration of the WUA at Mazraa-Haditha...... 212

Figure 8.1: King Abdullah Canal near the beginning of its length...... 222 Figure 8.2: The KAC and fencing along its sides in the area of PS 55 (a) and PS 91

(b)...... 223 Figure 8.3: The KAC near PS 33 with no fencing on either side (a) and the KAC near

PS 91 with no fencing on one side (b)...... 224 Figure 8.4: Trash build-up at PS 33 KAC intake point (a) and a ditchrider manually

clearing-out trash from a subsequent intake point (b)...... 225 Figure 8.5: Trash floating in the KAC near PS 91 (a), trash build-up at PS 91 intake

point (b) and the trash removed by ditchriders (c)...... 226

Figure 8.6: Sheep grazing alongside the KAC at PS 41 (a) and PS 91 (b)...... 227

Figure 8.7: The black blob is a dead goat floating in the KAC near PS 91...... 228

Figure 8.8: The spring near Mazraa-Haditha that supplies water to farmland. ...229

Figure 8.9: Open-air reservoirs in the Mazraa-Haditha area...... 229

xxxii Figure 8.10: On-farm holding pools lined with plastic (a and b), lined with concrete

level is low...... 232 Figure 8.12: The removed mud from the KAC that the JVA dumps alongside the road

next to the KAC...... 233 Figure 8.13: Water in the KAC in the area of PS 55 after major flooding from heavy

rains...... 234

Figure 8.14: Main pumps at PS 33 (a) and PS 91 (b) with water leakages...... 235

Figure 8.15: A lateral line laying above ground in the area of PS 91...... 236 Figure 8.16: New lateral lines being placed below ground in the rehabilitation of the

network in Mazraa-Haditha...... 237 Figure 8.17: A pool of water has formed over where the lateral line runs in the area of

PS 91, signaling that there is a leakage in the piping...... 238 Figure 8.18: a) Uncovered lateral valves in the area of PS 91; b) a ditchrider

unlocking a covered lateral valve...... 239 Figure 8.19: A covered and enclosed FTA (a); an FTA filled with trash (b); an FTA

overgrown by weeds (c); an FTA without a box and leaking (d). ..240 Figure 8.20: Depiction of elevation differences between the Highlands and the Jordan

Valley areas of Al Ghor and Al Zhor...... 242 Figure 8.21: Above-ground plastic piping connecting some farms in PS 91 directly to

the KAC due to pressure issues in the lateral lines...... 244 Figure 8.22: Percentage of farmers among surveyed WUAs who think that the water

supply is adequate or not adequate...... 245

xxxiii Figure 8.23: Percentage of farmers within each of the four WUAs who think that the

water supply is adequate or not adequate...... 246 Figure 8.24: Percentage of farmers among surveyed WUAs who think that the water

supply is reliable or not reliable...... 263 Figure 8.25: Percentage of farmers within each of the four WUAs who think that the

water supply is reliable or not reliable...... 263

Figure 8.26: Damage from sinkholes in the area of Mazraa-Haditha...... 286 Figure 9.1: Princess Alia’s date farm in the zhor area beyond the purview of the

WUA at PS 91...... 294 Figure 9.2: The plastic pipe that directly connects Princess Alia’s farm to the KAC.

...... 295

Figure 9.3: Farm size among farmers in the four surveyed WUAs...... 297

Figure 9.4: Education level of farmers in the four surveyed WUAs...... 299 Figure 9.5: Prices for tomatoes in their harvesting season in the Jordan Valley from

1998 to 2012...... 314 Figure 9.6: Prices for cucumbers in their harvesting season in the Jordan Valley from

1998 to 2012...... 315 Figure 9.7: Prices for eggplants in their harvesting season in the Jordan Valley from

1998 to 2012...... 315 Figure 9.8: Prices for sweet peppers in their harvesting season in the Jordan Valley

from 1998 to 2012...... 316 Figure 9.9: Prices for string beans in their harvesting season in the Jordan Valley

from 1998 to 2012...... 316 Figure 9.10: Prices for oranges in their harvesting season in the Jordan Valley from

1998 to 2012...... 318 xxxiv Figure 9.11: Prices for lemons in their harvesting season in the Jordan Valley from

1998 to 2012...... 318 Figure 9.12: Prices for grapefruit in their harvesting season in the Jordan Valley from

1998 to 2012...... 319 Figure 9.13: Prices for guava in its harvesting season in the Jordan Valley from 2007

to 2014...... 319

Figure 9.14: Monthly prices for bananas in the Jordan Valley from 2007 to 2014.320 Figure 10.1: Percentage of farmers who seek help from the WUA, the JVA, both or

neither among all surveyed WUAs...... 334 Figure 10.2: Percentage of farmers who seek help from the WUA, the JVA, both or

neither in the four surveyed WUAs...... 335

Figure 10.3: Level of monitoring reported by surveyed farmers...... 347

Figure 10.4: Level of monitoring reported by surveyed farmers in the four WUAs.348

Figure 10.5: Level of sanctioning as reported by surveyed farmers...... 356 Figure 10.6: Level of sanctioning as reported by surveyed farmers in the four WUAs.

...... 357 Figure 10.7: WUA’s ability to resolve conflicts among farmers, including whether

problems exist...... 365 Figure 10.8: WUA’s ability to resolve conflicts among farmers, including whether

problems exist, among the four surveyed WUAs...... 366

Figure 10.9: Nature of relations among farmers within the four surveyed WUAs.367

Figure 10.10: WUA’s ability to resolve conflicts among farmers...... 368 Figure 10.11: WUA’s ability to resolve conflicts among farmers within the four

surveyed WUAs...... 369

xxxv Figure 11.1: Percentage of farmers in all surveyed WUAs who do and do not see a

benefit to their membership in the WUA...... 417 Figure 11.2: Percentage of farmers within each of the four surveyed WUAs who do

and do not see a benefit to their membership in the WUA...... 418

Figure 12.1: Overall opinion from all surveyed farmers regarding their WUA. .425 Figure 12.2: Opinion from surveyed farmers in PS 33, PS 55, PS 91 and MH

regarding their WUA...... 426 Figure 12.3: Overall opinion of farmers comparing the WUA to the JVA, whether it is

better, the same thing or worse than the JVA...... 438 Figure 12.4: Opinion of farmers comparing their WUA’s performance to that of the

JVA in PS 33, PS 55, PS 91 and MH...... 439 Figure 12.5: Percentage of surveyed farmers who say that water stealing is happening

among farmers...... 451

Figure 12.6: Reporting of water stealing in all four WUAs...... 452 Figure 12.7: Evidence of illegal siphoning of water from the KAC in the area of PS

33...... 455

Figure 12.8: The small wire clasp to “lock” the FTA’s flow limiter...... 456 Figure 12.9: Percentage of farmers in all surveyed WUAs who believe the WUA is

fair, not fair or don’t have an answer...... 466 Figure 12.10: Views of farmers in the four surveyed WUAs with regard to the

fairness of the WUA...... 467 Figure 12.11: Overall membership among farmers within the four surveyed WUAs

according to data from the JVA and WUAs...... 481 Figure 12.12: Membership rates within the four WUAs according to data from the

JVA and WUAs...... 482 xxxvi Figure 12.13: Percentage of WUA members and non-members among all surveyed

farmers...... 483 Figure 12.14: Membership rates among surveyed farmers in PS 33, PS 55, PS 91 and

MH...... 484 Figure 12.15: Membership rates among surveyed farmers who are eligible to be

members in a WUA...... 485 Figure 12.16: Membership rates among surveyed farmers within each of the four

WUAs who are eligible to be members in the WUA...... 486

xxxvii Chapter One: Introduction

THE WORLD’S WATER RESOURCES

Amid the impassioned talk about water scarcity in this world, sometimes we fail to also embrace the fact that 71 percent of the Earth’s surface is covered by water. We are absolutely surrounded by water in so many shapes and forms, from ice caps, oceans, seas, lakes and rivers to groundwater and even the water held in the atmosphere and within soils.

Unfortunately, about 96.5 percent of all water resides in oceans, laden with dissolved salts

(USGS, 2015). Human access to fresh water is source-dependent; for example, people cannot easily access and use the polar ice cap. Water availability depends on where on Earth you live, as some countries are more blessed than others with rain and bodies of standing fresh water. Table 1.1 below lists by location the volume of available renewable and usable water resources in the world. While Southern and Eastern Asia, South and North America and Sub-Saharan African are all fairly rich in water resources, Northern

Africa, Central Asia and the Middle East have less water per capita per year. Of course, “infrastructural” and “institutional” aspects of water scarcity (Ross-Larson et al., 2006) are important, but some areas of the world start with a greater deficit in the first place due simply to geographical location.

Table 1.1: Total renewable water resources per capita by world region for 2014 Region* Total renewable water resources (m3/capita/year) Africa | Northern Africa Northern Africa 477 Sub-Saharan Africa 12,989 Americas Central America and Caribbean 9,814 Northern America 31,645 Southern America 81,170 Asia Middle East 1,716 Central Asia 3,626 Southern and Eastern Asia 17,831 Oceania Australia and New Zealand 46,337 Pacific Islands 55,099 Europe Western and Central Europe 24,644 Eastern Europe 11,424 Source: Aquastat Database, Food and Agriculture Organization, http://www.fao.org/nr/water/aquastat/main/index.stm. *Missing data from the following countries: Sub-saharan Africa -- Seychelles; Oceania/Pacific Islands -- Cook Islands, Kiribati, Marshall Islands, Micronesia, Nauru, Niue, Palau, Samoa, Tokelau, Tonga, Tuvalu, Vanuatu; Western Europe -- Faroe Islands, Liechtenstein, Montenegro, Holy See, Monaco, San Marino.

The Middle East is the exemplar of a particularly dry region. However, within the Middle East, there is quite a bit of variation in water availability (see Table 1.2). The average amount of rainfall and renewable water resources among countries in the Middle East region, here including the Caucasus and Iran along with the Arabian Peninsula and the Levant, has variability over three orders of magnitude in the volume of water available per capita and one and a half order of magnitude in millimeter per year (mm/yr) of precipitation. The per capita amount of water resources is a key value. According to the United National Development Programme (UNDP), the “national threshold for meeting water requirements for agriculture, industry, energy and the environment” is 1,700 cubic 2 meters per person per year (m3/p/y) (Ross-Larson et al., 2006, p. 135). Water scarcity, according again to the UNDP (Ross-Larson et al., 2006), occurs at 1000 m3/p/y and absolute scarcity at only 500 m3/p/y. Some countries, such as Georgia and Turkey, fall well above these minimum requirements, whereas several fall far below, especially those in the Gulf region (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, the UAE and Yemen) and the Levant (Jordan, Israel and the Palestinian Territories).

Table 1.2: Annual precipitation and renewable water resources for countries in the Middle East in 2014. Average Average annual Total Total annual precipitation renewable renewable precipitation (MCM/yr) water water (mm/yr) resources resources (MCM/yr) per capita (m3/p/y) Armenia 562 16,710 7,769 2,604 Azerbaijan 447 38,710 34,680 3,645 Bahrain 83 64 116 86 Georgia 1,026 71,510 63,330 14,650 Iran 228 397,900 137,000 1,746 Iraq 216 94,010 89,860 2,584 Israel 435 9,600 1,780 228 Jordan 111 9,915 937 125 Kuwait 121 2,156 20 6 Lebanon 661 6,907 4,503 907 Pal. Terr. 402 2,420 837 189 Oman 125 38,690 1,400 357 Qatar 74 859 58 26 Saudi Arabia 59 126,800 2,400 82 Syria 252 46,670 16,800 764 Turkey 593 464,700 211,600 2,790 UAE 78 6,521 150 16 Yemen 167 88,170 2,100 84 Source: Aquastat Database, Food and Agriculture Organization, http://www.fao.org/nr/water/aquastat/main/index.stm.

COMBATING WATER SCARCITY

The old adage is that “water flows to money” and some water supplies can be created and distributed with sponsoring governments willing to invest. From conservation to reuse to desalination, there are ways to combat a lack of natural resources with financial resources. Countries with money may not experience water shortage as frequently as poor nations, as those with wealth can invest in alternative sources of supply or water conservation. Table 1.3 below lists the gross domestic product per capita per year from 2009 to 2013 in member countries of the United Nation’s Economic and Social Commission for Western Asia (ESCWA), which is generally the Middle East region. The top half, countries within the Gulf Cooperation Council (GCC), are markedly better off; they can invest in relatively expensive technologies, such as desalination, wastewater treatment and reuse, and agricultural production abroad, sometimes referred to critically as “land-grabbing” (Von Braun and Meinzen-Dick, 2009).

Table 1.3: Gross domestic product per capita from 2009 to 2013 in ESCWA member countries. Gross Domestic Product Per Capita* Country 2009 2010 2011 2012 2013 Bahrain 13,447 13,358 13,206 13,420 13,988 Kuwait 21,209 19,723 20,702 21,225 20,778 Oman 9,653 9,611 8,809 8,610 8,163 Qatar 33,135 34,568 35,778 35,349 35,539 Saudi Arabia 11,050 11,671 12,441 12,919 13,177 UAE 20,377 18,934 18,785 19,063 19,754 GCC countries 14,197 14,408 14,983 15,364 15,611 Egypt 2,979 3,081 3,083 3,099 3,113 Iraq 730 750 804 861 871 Jordan 2,466 2,415 2,376 2,343 2,321 Lebanon 6,188 6,540 6,398 6,336 6,290 Libya 8,457 8,710 3,340 6,562 6,317 Morocco 1,793 1,837 1,904 1,927 1,981 Palestine 1,472 1,561 1,693 1,765 1,755 Sudan 682 710 708 696 707 Syria 1,461 1,487 1,415 1,102 854 Tunisia 3,063 3,119 3,025 3,108 3,160 Yemen 649 655 544 543 547 ESCWA countries 3,839 3,949 3,943 4,074 4,115 Source: UN-ESCWA, 2014. *At constant prices and in US dollars with base year as 2000.

Indeed, together Saudi Arabia and the United Arab Emirates account for around 30 percent of the world’s desalination production (WWAP, 2012; Alterman and Dziuban, 2010). In 2010, both countries were spending over three billion dollars on desalination per year (Alterman and Dziuban, 2010). Israel, for its part, is also at the forefront of desalination in the region; with the completion of its fifth desalination plant, it will be producing almost 600 million cubic meters (MCM) per year of desalinated water (Israel Water Authority, 2015a). According to the website Water Technology (water- technology.net), Israel has spent $163, $212 and 400 million on three of these plants, payments that are not feasible for all countries. 5

It should also be noted that outside of the GCC and Israel, where more funding is available, desalination is still taking place in Egypt, Algeria, Iraq, and Jordan, albeit at a smaller scale (WWAP, 2012) and not necessarily for seawater but sometimes for brackish groundwater. Even in the Gaza Strip in the Palestinian Territories, a portion of households have small reverse osmosis desalination units where the municipal water supply is falling short (WWAP, 2012). Jordan has used small-scale desalination of brackish groundwater for industrial, commercial or small-farm use (Mohsen, 2007). Jordan also recently signed with Israel in February of 2015 to build a large desalination plant at its port city of Aqaba to jointly supply 80 million cubic meters per year of freshwater for itself, Israel and the Palestinian Territories (Al-Khalidi, 2015), a $900 million project sponsored by the World Bank, which may explain why it can be done at all considering Jordan’s poverty. It is largely within the capacity of countries with greater financial capital to rely on desalination as a means to solve water scarcity.

Treated wastewater represents another growth arena for combatting water shortages, particularly in the irrigation sector. Within the Middle East region, both GCC countries (such as Kuwait, Saudi Arabia, the UAE, Qatar and Oman) as well as Israel and Jordan produce and reuse treated wastewater (WWAP, 2012; FAO, 2011). According to the Food and Agriculture Organization (FAO, 2011) report on the state of the world’s water resources, the top five producers of treated wastewater per capita are Kuwait, the United Arab Emirates, Qatar, Israel and Cyprus. Jordan does treat some of its wastewater, around

121 million cubic meters (MCM) per year (Jordan Ministry of Water and Irrigation, 2013), it has still been at a significant cost. According to Water Technology, Jordan’s largest treatment plant at Khirbet as-Samra cost $169 million and along with other smaller treatment plants, the country is able to produce around 121 MCM per year of treated

6 wastewater (Jordan Ministry of Water and Irrigation, 2013). Israel has spent roughly $750 million on its many wastewater treatment plants that produce about 400 MCM per year of treated wastewater (Israel Water Authority, 2015b). Wealthier water-scarce countries in the region can indirectly supply their water needs through the acquisition of farmland in other countries, mostly developing countries, or through “land-grabbing.” Farming of land in other nations can occur through a number of contractual processes, between the investor government and the host government, the private sector and the host government, or the private sector to the private sector (von Braun and Meinzen-Dick, 2009). For example, the Bahraini government has signed a contract with the Philippines for the purchase of farmland, the Qatari government with Kenya, Sudan and the Philippines, the Saudi Arabian government with Tanzania, Sudan and Indonesia, and the United Arab Emirates with Sudan and Pakistan (von Braun and Meinzen-Dick, 2009). The Saudi Arabian dairy company Almarai has bought farmland in

Arizona to grow thousands of acres of alfalfa to meet its livestock needs and potentially to sell elsewhere (NPR, 2015). Instead of continuing to draw down on its dwindling groundwater resources, Saudi Arabia invests in and uses the freshwater resources of other countries. An important factor working in tandem with exploiting water resources is the considerable amount of energy required. The United Nation’s World Water Assessment Programme (2012) maintains: “Energy is needed for extraction (surface water, groundwater), transformation (treatment to drinking water standards, desalination), water resource delivery (municipal, industrial and agricultural supply), reconditioning (wastewater treatment) and release” (p. 57). The water sector is costly in its use of energy to meet the demands of all sectors of the economy, including additional costs when using

7 more advanced technologies such as desalination and treated wastewater. In the US, for example, treatment of surface water and groundwater uses, respectively, 60 kilowatt hours per million liters (kWh/million L) and 160 kWh/million L, whereas wastewater can use anywhere from 250 to 400 kWh/million L and seawater desalination uses 2600 to 4400 kWh/million L (WWAP, 2012). Depending on a country’s wealth, this is a smaller or larger struggle.

MANAGING WATER SCARCITY

At the end of the day, regardless of what wealth can do, water is still scarce in some regions of the world, especially the Middle East. In the 1980s and 1990s, literature referred to the potential for “water wars” in the Middle East over shared, transboundary rivers and aquifers. As Wolf (1994) recounts in his overview of the history of “hydro-conflict” in the Nile, Jordan and Euphrates River Basins, the 20th century witnessed several instances of near-conflict over water resources and in the least, water resources factored into overarching tensions, conflicts, crises and wars between nations. But as Alterman and Dziuban (2010) point out, “few states in the Middle East have fought each other over water” and in fact, “states tend to cooperate over water,” (p. 2). Israel and Jordan agreed to terms to share the waters of the Jordan River in the 1994 Water Annex to the larger peace agreement between the two countries, and Turkey, Syria and Iraq, despite consistent tension between them, manage to share the Tigris and Euphrates Rivers. Alterman and

Dziuban (2010) point out a potential reason for this peaceful coexistence, that governments are more “inward looking” than concerned with external actors. They state: “The decisions that truly matter are the ones that have to do with politics, not diplomacy. Through

8 diplomacy, land can be won or lost, but it is through politics that power is mediated” (p. 2). Remaining in power is the goal and requires tough domestic political decision. The domestic decisions over water revolve around how the government apportions water among its use sectors. In much of the world, agriculture remains the major water user. In Table 1.4, in most regions of the world, agriculture uses roughly 70-90 percent of the total water supply. In the US and areas of Europe, this percentage is much lower, between around 30-40 percent.

Table 1.4: Percent of water withdrawal by sector among world regions. Percent for Percent for Percent for Municipal Industrial Agricultural Africa 10 4 86 Northern Africa 9 6 85 Sub-Saharan Africa 10 3 87 Americas 16 35 49 Northern America 15 43 43 Central America and 26 11 64 Caribbean Southern America 19 13 68 Asia 9 9 82 Western Asia 9 7 83 Central Asia 3 5 92 South Asia 7 2 91 East Asia 14 22 64 Southeast Asia 7 9 84 Europe 16 55 29 Western and Central Europe 16 56 28 Eastern Europe and Russian 18 51 32 Federation Oceania 17 10 73 Australia and New Zealand 17 10 73 Pacific Islands 14 14 71 World 11 19 70 Source: Table 1.4, FAO (2011), Table 1.4, p. 42.

Within the Middle East, as in the world at large, the largest percentage of water resources are used in the agricultural sector (see Table 1.5).

Table 1.5: Percent of water withdrawal by sector within the Middle East. Year of Agriculture Municipal Industry Data (Percent of (Percent of (Percent of total water total water total water use) use) use) Armenia 2006 66 30 4 Azerbaijan 2005 76 4 19 Bahrain 2003 45 50 6 Georgia 2005 65 22 13 Iran 2004 92 7 1 Iraq 2000 79 7 15 Israel 2004 58 36 6 Jordan 2005 65 31 4 Kuwait 2002 54 44 2 Lebanon 2005 60 29 11 Pal. Terr. 2005 45 48 7 Oman 2003 88 10 1 Qatar 2005 59 39 2 Saudi Arabia 2006 88 9 3 Syria 2003 88 9 4 Turkey 2003 74 15 11 UAE 2005 83 15 2 Yemen 2000 90 8 2 Source: Frenken, 2009.

Despite agriculture’s strong hold on water resources in the Middle East, the situation is gradually changing. In 2011, the percentage of the overall population in the

Arab world living in an urban environment was 56.6% and this percentage is expected to grow to 61.6% by 2025 (UN-ESCWA, 2013). This burgeoning urban population signals growing pressure from the municipal sector for water resources and the squeezing of agriculture’s share of water to service the drinking water supply. At the same time, a

10 growing population requires more food, requiring more water to produce more food, and with changing dietary patterns more meat, requiring even more water to produce. This is the squeeze that agriculture feels: the population demands more water from its share but at the same time demands higher agricultural production that requires more water. Unfortunately, because agriculture contributes to less than 10% of the GDP in many Middle Eastern countries (see Table 1.6), there can be a default notion that its use is inefficient and should be allocated elsewhere. But Molle and Berkoff (2009) refute this notion by addressing four inherent assumptions of the need for water reallocation from the agricultural sector to the domestic sector: 1) agriculture gets the “lion’s share” of a country’s water resources; 2) farmers waste a lot of water; 3) agriculture reaps lower water productivity than urban uses; and 4) cities are very water short. To the first point, they posit that agriculture is a “biophysical process that inherently needs a lot of water,” (p. 7) more so than other uses. It use of water is more dominant largely when other activities have not yet demanded as much water; when human uses do start to compete, agriculture takes second place. Referring to the second point, water wastage by farmers is exaggerated in a number of ways: sometimes water used for agriculture would not be used anywhere else; sometimes water used in one place in the basin cycles back to another place and is recycled; wasteful practices by farmers that actually lead to serious losses “are not the general rule” (p. 8); and much of the time farmers have little say in the water allocated to them in the first place. On the third point, agriculture’s use of water should not be compared to that of the urban sector. The two sectors’ uses cannot be compared in terms of production rates. And for the fourth point, there is only a “loose causal link between physical water availability and actual supply” in cities. Some cities are water-short regardless of their size throughout history and some cities in very water-rich regions still

11 have significant water deficiency. Overall, water scarcity in cities is more involved with economic, political and social issues, not just the water quantity.

Table 1.6: GDP, agriculture’s contribution to GDP, and population involved in agriculture in the Middle East. GDP per Agriculture, Population Population Percentage of capita (current value added to economically economically economically US$/person)** GDP (%)*** active* active in active agriculture* population involved in agriculture (%) Bahrain 25,200 0.86 663,000 3,000 0.5% Kuwait 50,589 0.32 1,723,000 17,000 1% Oman 20,835 1.29 1,667,000 451,000 27% Qatar 93,474 1,481,000 8,000 1% Saudi 25,401 1.92 11,317,000 440,000 4% Arabia United Arab 42,558 0.66 5,947,000 154,000 3% Emirates Yemen 1,440 10.15 6,380,000 2,215,000 35% Armenia 3,647 21.93 1,559,000 140,000 9% Azerbaijan 7,903 5.69 5,048,000 1,071,000 21% Georgia 3,824 9.20 2,397,000 323,000 13% Iran 5,289 10.06 33,071,000 6,621,000 20% Iraq 6,356 8.57 9,137,000 402,000 4% Israel 38,865 3,176,000 47,000 1% Jordan 4,774 3.78 2,195,000 118,000 5% Lebanon 9,209 5.54 1,894,000 26,000 1% Palestinian 2,871 4.83 1,528,000 103,000 7% Territory Syria 3,366 17.94 7,363,000 1,352,000 18% Turkey 10,549 8.03 25,660,000 7,607,000 30% Source: Aquastat Database, Food and Agriculture Organization, http://www.fao.org/nr/water/aquastat/main/index.stm. *Data from 2014. **Data from 2014 except Kuwait and Yemen (2013), Syria (2012). **Data from 2014 except Kuwait, Oman, UAE and Palestine (2013), Iran (2008), Syria (2007), Yemen, (2006), Iraq (2003), Bahrain (1995).

Thus, assuming that reallocations from the agricultural sector to the urban sector are “no-brainer” solutions is faulty. Agriculture has an important place and is not always guilty of certain accusations. Furthermore, there are people who depend on agriculture for employment and their livelihoods. The percentage of those economically active in the agriculture sector is low in many countries (see Table 1.6) but depending on the country, it can represent a politically, socially and culturally influential part of the population.

Ross-Larson et al. (2006, Table 5.1, p. 175) also point out many reasons why securing water for irrigation purposes is vital for any country. On-farm production rates increase in terms of yield, area, intensity and diversification, and thus on-farm income increases and can stabilize for farm families. Food prices decrease and food consumption increases. More opportunities for on-farm employment and off-farm employment (in related industries) arise and wages rise for these positions. Allotting sufficient water for irrigation purposes also leads to higher national food security, with greater in-country production and fewer instances of crop failure and seasonality effects. This leads to less reliance on other countries for a steady food supply and decreased susceptibility to consumption shortfalls. And moreover, as Molle and Berkoff (2009) point out, agriculture is using less dependable and “marginal” water sources and flows that cannot be used elsewhere, such as flood waters and wastewater reuse.

RESEARCH QUESTION

The situation is complex. Water is scarce. Not all countries have the financial resources to combat this scarcity. Populations are booming and require more water that is taken away from agriculture. Yet agriculture remains of fundamental importance to national security and stability. This dissertation examines one approach to better manage 13 irrigation water in light of these stresses: the inclusion of water users in the management of irrigation water resources. The approach asks water users to carry some or all of the management burden with the hope that they can do a better and more effective job than the government and do it cheaper. But is this user-based form of management effective? This dissertation uses Jordan’s experience over the last decade and a half to establish user management of irrigation water in the Jordan Valley, in the form of water user associations (WUAs), as a way to explore this question and add new questions. In particular, what specific factors are leading to better or worse performance of and participation in WUAs? Are these factors related to the physical environment, the surrounding community, the institutions at work or the water users themselves? By understanding where Jordan’s WUAs meet with difficulty, avenues for improvement are found.

DISSERTATION OUTLINE

In Chapter Two, Jordan’s water situation is reviewed in detail in terms of geography, climate and water resources. This chapter also outlines how water resources are used in Jordan and how they are managed, specifically within the agricultural sector and the Jordan Valley. Chapters Three provides a literature review of the development trends that led to participatory approaches in general and within the agricultural sector, particularly as embodied in the concept of water user associations. Chapter Four is an extension of the literature review, examining how WUAs can be evaluated in terms of outcomes and influential factors. This chapter includes the hypotheses that will be tested in the final analyses of the dissertation.

Chapter Five covers the research methodology, specifically with regard to the fieldwork conducted in Jordan from January 2014 to June 2015. The methods include contextual assessments, field observations, interviews, a survey and regression analysis. In Chapter Six, water user associations in Jordan are described in detail, including their management, operations and legal place within the Jordan Valley. Chapter Seven offers in-depth examination of the four WUAs that serve as the case studies.

Chapters Eight through Twelve cover the results. Hypothesis for factors are individually tested for their impact on the performance outcomes, grouped by chapter within their respective categories: physical factors (Chapter Eight), community factors (Chapter Nine), institutional factors (Chapter Ten) and user factors (Chapter Eleven). Chapter Twelve combines all factors from the previous four chapters into overarching tests of their relevance to the outcomes. Finally, Chapter Thirteen discusses what factors are of most importance, how stakeholders in the Jordan Valley view the future of the WUA project, and policy recommendations for how to improve the WUAs and ensure a more secure water future for the agricultural sector.

Chapter Two: Jordan’s Water Conditions

This chapter provides the country context in which the research subject, water user associations, is examined. A general overview of Jordan’s geographic and climatic conditions ensues, delving subsequently and in detail into the available surface, ground and nonconventional water resources in the country. How Jordan uses these water resources overall and within particular economic sectors is covered as well. Finally, there a discussion of how water is managed in Jordan’s agricultural sector, specifically within the Jordan Valley as it pertains to the system of water distribution.

GEOGRAPHY AND CLIMATE OF JORDAN

Jordan is located in the eastern Mediterranean region, bordered by Israel and the West Bank on its west, Syria to its north, Iraq to the east and Saudi Arabia to the east and south (see Figure 2.1). It is a largely land-locked country except for a narrow length of coastline on the Gulf of Aqaba at its very southern-most tip. The country’s land area is roughly 90,000 cubic kilometers (Nortcliff et al., 2008) and can be divided into three climatic zones: 1) the Jordan Rift Valley, comprising the narrow strip of land along the entire western border that is largely below sea level and stretches from where the Yarmouk

River empties into the Jordan River to the Gulf of Aqaba; 2) the Highlands, a higher- elevation plateau to the east of the Jordan Rift Valley separating it from the eastern desert that runs from Umm Qais in the north to Ras an-Naqab in the south; and 3) the Badia, the desert region encompassing most of the eastern portion of the country (Salameh and Haddadin, 2006) (see Figure 2.2 with these three regions depicted as the Jordan Valley, Highlands and Deserts, respectively). Jordan is predominantly desert, with 72% of its land

16 area categorized as in a desert climatic zone, 22% arid, 2% marginal between arid and semi-arid, 3% semi-arid and 1% semi-humid (Nortcliff et al., 2008).

Figure 2.1: Location of Jordan in the Middle East.

Source: Altz-Stamm, Amelia (2012).

Figure 2.2: The climatic/agricultural zones of Jordan.

Source: Talozi et al. (2015), Figure 1, p. 464.

The Jordan Rift Valley, the area of main concern for this research, runs from the Jordanian border south of Lake Tiberias (Sea of Galilee) to Aqaba and is about 400

18 kilometers in length. The width of the valley is around 5-10 kilometers, with Jordan’s portion stopping in the west at the border with Israel and the West Bank, and in the east 300-500 meters above sea level. Its elevation ranges mostly from 212 meters to 417 meters below sea level. Closer to Aqaba, it peaks to 250 meters above sea level (Ayadi, 2006). The Jordan River runs the length of the Jordan Rift Valley within the section above the Dead Sea and this is the part that is commonly referred to as the Jordan Valley (JV). Due to its low elevation, the JV is a natural green house, capable of producing fruits and vegetables in what would otherwise be the off-season. Even though the area of the JV is a sliver of land representing only 5% of Jordan’s land area, it produces more than 60% of the country’s produce (Ayadi, 2006). The average temperatures in Jordan vary due to the differences in elevation between the Highlands and the JV. There are generally two long seasons, summer and winter, with short interchanges between the two. The summer season runs from roughly April to

October and the highest monthly average maximum temperature in August in the Highlands (as measured at the Amman Civil Airport) is 32.4 degrees Celsius and in the JV (as measured at the central town of Deir Alla) is 39.5 degrees Celsius. Winter, in turn, lasts from roughly November to March and the lowest monthly average minimum temperature in the Highlands in December is 4.6 degrees Celsius and in the JV in January is 11.3 degrees Celsius (Jordan Department of Statistics (DoS), 2013; Nortcliff et al., 2008). As seen in Figure 2.3 below, most of the rainfall in Jordan occurs between November and

April and most of it is concentrated in the area of the JV and the Highlands in the northwest portion of the country.

Figure 2.3: Depiction of average monthly rainfall in millimeters (mm) in Jordan.

Source: Altz-Stamm, Amelia (2012).

AVAILABLE WATER RESOURCES IN JORDAN

Jordan’s water resources consist of rainfall, surface waters, groundwater and treated wastewater. For the 2012-2013 “rainy” or winter season, Jordan received an average of about 91 mm of rainfall with a total volume of 8,120 million cubic meters (MCM), ranging from as much as 529 mm in the north of the Jordan Valley to as little as 29 mm in the southern desert (Jordanian Ministry of Water and Irrigation (MWI), 2013). The MWI (2013) noted that in general, rainfall for the last ten years has been less than Jordan’s long- term average rainfall amount. Moreover, it is estimated that about 95% of this rainfall is lost to evaporation (MWI, 2013). This rainfall is included in the calculations below for surface waters (see floodwaters).

Surface water sources (see Table 2.1 for all water sources and their quantities) in Jordan are comprised of Jordan’s portion of its transboundary rivers and the water acquired from floodwater runoff, base flows and fresh springs. With regard to the transboundary resources, these are made up of Jordan’s share, as determined by the Water Annex within the 1994 peace agreement between Jordan and Israel, of the Yarmouk and Jordan River waters. In the 2012-2013 season, Jordan diverted 28.32 MCM from the Yarmouk River.

It received 52.91 MCM in total from the Tiberias Carrier Pipeline from Israel in the following four categories: 1) 10 MCM as replacement for the desalinated water that Jordan is to receive per the agreement; 2) 25 MCM as its Jordan River share; 0.49 MCM of water Jordan purchased from Israel in addition to its share; and 4) 13.4 MCM as its quantity of stored water in Lake Tiberias (MWI, 2013). In total, Jordan’s transboundary water resources amounted to 81.23 MCM. As for the other surface water, the 2012-2013 budget accounts for 187.18 MCM of water from floodwater that accumulates in the winter season and 262.32 MCM of water that consists of the base flow during the summer plus fresh spring water that bubbles to the surface and is not included in the portion of groundwater that is pumped (MWI, 2013). In total, these other non-transboundary surface flows amount to 449.5 MCM.

Table 2.1: Sources of water available in Jordan. Water Availability by Source MCM/year Surface water 530.73 Transboundary waters 81.23 Floodwater/base flow/surface flow 449.50 Groundwater 200.10 Renewable 57.10 Nonrenewable 143.00 Nonconventional water 133.41 Treated wastewater 121.31 Treated brackish water 12.10 Total* 864.24 Source: MWI (2013). *Total = surface water + groundwater + nonconventional water.

Groundwater is another key source of freshwater in Jordan. There are 12 groundwater basins in Jordan, two of which (Disi and Jafar) are nonrenewable and also extend underneath neighboring Saudi Arabia and are therefore shared. Four of the renewable groundwater aquifers receive some flow from Syria and are thus transboundary as well (MWI, 2013; Salameh and Haddadin, 2006). The MWI 2013 water budget calculates that the available groundwater resources in Jordan amount to 200.1 MCM per year, which is excluding the portion that has already been recorded for surface water as “base flow.” In the same report and another MWI report (2015), it is stated that the total yearly long-term safe yield of extracted groundwater in Jordan is 418.5 MCM (MWI, 2015;

MWI, 2013). The last category of water available in Jordan is from nonconventional sources, primarily treated wastewater. There are 31 wastewater treatment plants in Jordan of varying size and technology in use (waste stability ponds, extended aeration, trickling filter, oxidation ditches, aeration lagoons, and activated sludge). The largest facility is the Khirbet es-Samra treatment plant located just northwest of the Amman-Zarqa 22 metropolises. It has a design capacity of 364,000 cubic meters (m3). The other smaller treatment plants have design capacities as small as 50-1,200 m3 and as big as 52,000 m3. In total, Jordan’s wastewater treatment plants have 606,000 m3 of design capacity (MWI, 2015) although the total amount of treated wastewater in the 2012-2013 season was 121. 31 MCM (MWI, 2013). To be fair, this amount is somewhat misleading when included in Jordan’s total water availability in Table 2.1 as it is recycled and thus accounted for twice within the total. With regard to desalination of seawater, Jordan still cannot more widely engage in this endeavor due to the country’s limited financial resources, as noted in the previous chapter. Currently, there is some treatment of brackish water in Jordan on a small scale, used primarily for agricultural and industrial purposes, and this desalinated brackish water amounted to 12.1 MCM in the 2012-2013 season (MWI, 2013). As was also earlier mentioned, an agreement has been signed with Israel and Jordan for the construction of a desalination plant in Aqaba that will provide 80 MCM of desalinated water annually. It is also mentioned that the saltwater by-products leftover from the desalination processes may be transported via a 200 kilometer pipeline to the Dead Sea to replenish its rapidly depleting levels, but this part of the plan is still undecided. Israel will buy 30-50 MCM of the desalinated water and the rest will go to Aqaba in Jordan, with a further stipulation that

Israel release an extra 50 MCM from Lake Tiberias for Jordan to purchase in exchange for its share of the desalinated water from Aqaba (Al-Khalidi, 2015; Coren, 2015; Melhem,

2015; The Guardian, 2014). The plans for this desalination plant are ongoing and funding has yet to be entirely accounted for, meaning that there are still many years to wait before this is a viable source of water for Jordan.

USE OF WATER RESOURCES IN JORDAN

In the 2012-2013 season, total water use amounted to 901.59 MCM (MWI, 2013). That use amount is greater than the available amount (864.24 MCM) and means that Jordan is using roughly 104% of its available water resources, or overexploiting some of its water resources. At the same time, all of Jordan’s water demands are not even being met. The MWI (2015) estimated water demand in 2013 to be 1,213 MCM/year, meaning that in relation to the water actually used, Jordan suffered from a water deficit of around 311.41 MCM representing 25.7% of its total demand. In Table 2.2 below, Jordan’s current and future water deficits are predicted to 2030, with a predicted and eventual deficit of around 24%. As an important side note, Jordan’s water sector consumes roughly 14% of the country’s total electricity supply (MWI, 2015; DoS, 2013).

Table 2.2: Projected water demand and water deficit from 2013 to 2030 in Jordan. 2013 2014 2015 2020 2025 2030 Water Demand 1213 1243 1266 1384 1454 1532 Water Deficit 312 145 156 254 309 365 Deficit/Demand 25.7% 11.7% 12.3% 18.4% 21.2% 23.8% Source: MWI (2015).

The disparity between demand and supply is driven by Jordan’s still growing population and the continued flow of refugees into the country. Within the past half century, Jordan’s domestic population has grown from 586,000 in 1952 (DoS, 2013) to 6,613,587 in the latest census of 2015 (DoS, 2016), representing an increase more than tenfold in 55 years. Figure 2.4 below shows this upward population growth trend. Also according to the census of 2015 (DoS, 2016), the annual growth rate is 3.1%. Jordan’s entire population, comprising both Jordanian and non-Jordanians living in the country, is 9,531,712 (DoS, 2016). The population is concentrated mainly in the larger cities of

Amman, Zarqa and Irbid in the northwest of the country, with around 75% of the population residing in these three urban areas (DoS, 2016). From 2004 to 2015, the population in these three cities taken together has almost doubled from 3,635,008 to 7,142,562 (DoS, 2016).

Figure 2.4: Domestic population of Jordan from 1952 to 2015 (dots represent years where data is present*).

Source: Jordan Department of Statistics (2016, 2013). *Data from 1952, 1961, 1979, 1994 and 2004 is from census data; the other years’ data are estimates.

The issue more pressing than Jordan’s own population growth, especially with regard to the country’s limited water resources, is the presence of a large body of non-

Jordanians, both refugees and non-refugees. The total number of registered Syrian refugees in the Middle East region is close to five million, with Jordan housing around 13.4% of them as seen in Table 2.3 (UNHCR, 2016). Compared to some of its regional neighbors, this is a smaller percentage but with regard to water, Jordan is still not prepared. 25

Table 2.3: Number of registered Syrian refugees in the Middle East by country. Destination country Number of Percentage of registered registered Syrian refugees Syrian refugees Turkey 2,688,686 56.5% Lebanon 1,067,785 22.4% Jordan 637,859 13.4% Iraq 245,543 5.2% Egypt 118,512 2.5% Total in Middle East 4,758,385 100% Source: UNHCR, Syria Situation Map (2016).

Jordan’s latest census in 2015 (DoS, 2016) estimates a total of 1,265,514 Syrians living in Jordan (see Table 2.4). In addition to the number of registered Syrian refugees, MercyCorps (2014) estimates that Jordan is housing an additional 750,000 Syrians who have not claimed refugee status, which generally explains the difference between the number of Syrian refugees in Jordan listed in Table 2.3 and the total number of Syrians in Table 2.4. The 2015 Jordanian census (DoS, 2016) also estimates that there are 636,270

Egyptians, 634,182 Palestinians, and 130,911 Iraqis living in Jordan. UNHCR (Country Operations Profile, 2015) records the number of Iraqis in Jordan (as of March 2015) at 400,000, 29,300 of whom are refugees, but there is no known explanation for the discrepancy between UNHCR and the DoS with regard to the number of Iraqis in Jordan. The DoS (2016) estimates that there are an additional 31,163 Yemenis, 22,700 Libyans and 197,385 persons from other countries. Therefore, as per the 2015 census and listed in Table 2.4 (DoS, 2016), the total non-Jordanian population amounts to 2,918,125 persons, or around 31% of the total population living in Jordan.

Table 2.4: Jordanian and non-Jordanian populations in 2015. Population Source Number Jordanians 6,613,587 Non-Jordanians 2,918,125 Syrians 1,265,514 Egyptians 636,270 Palestinians 634,182 Iraqis 130,911 Yemenis 31,163 Libyans 22,700 Other 197,385 Total Population 9,531,712 Source: Jordan Department of Statistics (2016).

Jordan harnesses its limited water supply in a number of ways. With regard to the transboundary surface waters, Jordan siphons its portions from the Yarmouk River and the Jordan River (by way of the Tiberias Carrier Pipeline) into the King Abdullah Canal (KAC). The KAC is about 110 kilometers long and runs the length of the Jordan Valley from just south of Lake Tiberias down to just north of the Dead Sea. It carries roughly

500,000 to 800,000 cubic meters of water at any given time and its water depth is anywhere from 1.6 to 2.0 meters (Discussion with Director of North Directorate, August 2014). The KAC is highly regulated and monitored by a supervisory control and data acquisition (SCADA) system, with gates and gauges placed along its lengths and at its water sources to control how much water is in any given section at any given time. Only the northern half of the KAC carries water from the Yarmouk and Jordan Rivers and in this section, some of the water is used for agricultural production. In the central city of Deir Alla, the remaining freshwater is diverted and pumped from 200 meters below sea level up to 880 meters above sea level to the Zai Water Treatment Plant. The treated water is then pumped

27 up to 1,035 meters above sea level to the Dabouq holding tank and from there distributed to Amman and Zarqa for domestic and industrial use (Altz-Stamm, 2012). Floodwaters are also captured by dams that have been constructed along the side valleys, or wadis, that drain into the JV and eventually the Dead Sea. There are nine storage dams, ranging in capacity from 1 MCM to 75 MCM, with a total capacity in all of the dams of around 215 MCM per year, although in the 2012-2013 season, only about 83 MCM of runoff water was captured (MWI, 2013). The amount of actual storage heavily depends on the amount of rainfall Jordan receives in any given year. One of the dams, the King Talal

Dam (KTD), not only stores runoff water but also the treated wastewater that flows from Khirbet as-Samra Treatment Plant. The treated wastewater travels from the treatment plant to the KTD through the Zarqa River bed and sits in the dam for retention time, after which it is released into the JV and used for agricultural purposes. There is a tenth dam, Karak Dam, currently under construction on a wadi south of the Dead Sea that will add to Jordan’s water capture capacity. Also, an additional dam, Al Wehda Dam, is located along the Yarmouk River between Jordan and Syria and serves to regulate the river’s waters for a more controlled distribution between its riparians. Its capacity is 110 MCM and in the 2012-2013 season stored almost 24 MCM (MWI, 2013). With regard to groundwater, while it was stated above that in the 2012-2013 season, the available or safe yield groundwater amounted to 418.5 MCM, Jordan’s actual exploitation of groundwater exceeded this amount. The actual abstraction amounted to roughly 540 MCM, or 129% of the safe yield. As seen in Table 2.5, the percentage of abstraction of the safe yield is more problematic in some groundwater basins than in others. This excessive draw-down of groundwater has been both because of domestic needs in major population centers but also due to the needs of agriculture in the Highlands and the

Badia, which almost solely relies on groundwater. Groundwater wells are numerous in Jordan, with the MWI officially stating that in 2013, there were 3,034 wells in Jordan: 73% for agriculture, 20% for drinking water and 7% for industry (MWI, 2015). It is generally suspected that the number of wells is likely much larger as not all are registered or licensed and it is hard to count the unregistered ones.

Table 2.5: Safe yield and actual abstraction from Jordan’s 12 groundwater basins. Groundwater Basin Safe Yield Actual Abstraction Abstraction of (MCM) (MCM) Safe Yield (%) Jordan Side Valley 15.0 29.14 194 Jordan Valley 21.0 29.37 140 Yarmouk 40.0 45.85 115 Amman-Zarqa 87.5 156.30 179 Azraq 24.0 58.19 242 Hamamad 8.0 1.06 13 Dead Sea 57.0 79.00 139 Araba North 3.5 6.50 186 Sirhan 5.0 1.73 35 Jafer 27.0 28.83 107 Wadi Araba South 5.5 7.61 138 Disi 125.0 96.30 77 Total 418.5 539.88 129 Source: MWI (2015).

Of the afore-mentioned 902 MCM of total water used, roughly 42% is used in the domestic sector, 53% for irrigation, 4% for industry and 1% for other uses, as seen in Table 2.6 (MWI, 2015). The domestic sector acquires 68% of its water from groundwater and 32% from surface waters, with agriculture obtaining 53% of its water from groundwater and 47% from surface waters, and 82% of industry’s use comes from groundwater and 18% from surface water. Looking just at surface water, 34% is used by the domestic sector,

62% by agriculture, and 2% by industry, whereas 48% of groundwater is used by the domestic sector and 46% is used by the agricultural sector.

Table 2.6: Water use by sector in Jordan in MCM and percentages. Sector Water Use Percent of Total (MCM) (%) Domestic 381 42% Irrigation 475 53% Industry 39 4% Other 7 1% Total 902 100% Source: MWI (2015).

The domestic and agricultural sectors are in competition with each other for Jordan’s water resources, with agriculture continuing to lose ground to the growing domestic needs. From 2000 to 2013, agriculture’s use of the total water supply has decreased from 66% to 53%, while the domestic sector’s use has increased from 29% to

42% (MWI, 2015) (see Figure 2.5). With Jordan’s continually growing native and non- native population, the domestic sector is likely to continuing gaining ground over the agricultural sector in terms of water allotments. As in most countries, the first priority is allocation of water to “satisfy basic human needs,” interpreted as 100 liters per capita per day (Ayadi, 2006).

Figure 2.5: Percentage of use of water supply in Jordan by the agricultural and domestic sectors from 2000 to 2013.

66 65 64 64 63 63 64 63 62 58 56 57 54 53

42 42 38 39 39 32 33 33 32 32 34

29 31 31 Percentage Use of Water Supply UseWater of Percentage

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year

Agricultural Use Domestic Use

Source: MWI (2015).

Some might argue that agriculture is not reaping a sufficient advantage with regard to its large share of water resources. As seen in Table 2.7 below, agricultural production from 2006 to 2013 accounted for only about 3% of Jordan’s GDP each year (DoS, 2013). What is more, in 2012 for example, Jordan exported around 55% of its fruit and vegetable production that required around 206 MCM of Jordan’s virtual water, or the water used in the production processes of agricultural goods (Talozi et al., 2015). Jordan’s agricultural sector took a large portion of its water resources and essentially gave it to other countries. Furthermore, it has been reported that agriculture in Jordan only employs anywhere from

2% to 5% of the Jordanian labor force (Sidahmed et al., 2012; Courcier et al., 2005). From just these numbers, one could be convinced that perhaps not as much water should be funneled to this sector because it exploits too much water and contributes so little to the Jordanian economy. 31

Table 2.7: GDP, GDP per capita and the contribution of the agricultural sector to GDP in Jordan. 2006 2007 2008 2009 2010 2011 2012 2013 GDP 10.68 12.13 15.59 16.91 18.76 20.48 21.97 23.85 (million JD) GDP per 1906.3 2119.8 2665.5 2828.1 3069.2 3276.8 3438.6 3652.6 capita (JD) Agricultural 2.6% 2.5% 2.4% 2.7% 3.0% 2.9% 2.8% 3.0% percentage of GDP* Source: Jordan Department of Statistics (2013). All monetary amounts at market prices. *Includes activities involving hunting, forestry and fishing.

But that conviction would belie the larger reality of agriculture in Jordan. Agriculture is just as much a part of Jordan’s national security objectives as the domestic and industrial sectors. The Ministry of Agriculture’s 2012 annual survey reported that in fact, 7.9% of the entire workforce is employed in the agricultural sector and the ministry reported, at least in 2009, that agriculture is an income source for around 15% of Jordanians in rural areas and the Badia (MoA, 2009). Sidahmed et al. (2012) support the latter claim, considering that around 25% of Jordan’s poor live in rural areas and many depend on agriculture for their livelihoods. The Ministry of Agriculture (2013) also noted that roughly 31% of the migrant workers in Jordan (with work permits) are working in the agricultural sector. In addition, 5,930 out of the 7,749 companies registered with the

Ministry of Industry and Trade in 2009 operate in the agricultural sector (Ministry of Agriculture, 2009). Agricultural products accounted for about 14% of the value of Jordan’s total exports. While agriculture’s true contribution to the economy remains nebulous, it is still a significant player in Jordan’s economy and the livelihoods of many Jordanians. For their part, Courcier et al. (2005) suggest that when all agriculture-related activities are included 32 with agriculture, it can account for nearly 29% of Jordan’s GDP. Indeed, as Nortcliff et al. (2008) argue, when considering agriculture and how much employment and livelihoods it is responsible for, we must consider “a vast number of agricultural support services such as fertilizer manufacture, transportation, irrigation supply and maintenance” (p. 19), to name a few. With all of these jobs combined, agriculture is a busy and important hub of activity for Jordan’s employed workforce.

Van Aken (2004) also reminds us that agriculture in Jordan has a value other than economic, one that has political and social weight, equally important to a country like

Jordan struggling to maintain stability within a region of rapid upheaval and change. He references Richards (1993) who argues that agriculture is in fact “a source of patronage for key constituencies whose support is essential to achieve domestic stability” (p. 34). Van Aken elaborates in stating that water management is a realm in which the state-citizen or patron-client relationship is at work, where families and tribes “have often overlapped on water bureaucracies and on water delivery” (p. 34-35). This means that the Jordanian tribal network has influence within the water sector such that any water reform in the agricultural sector unfavorable to these tribes, such as a raise in the water price, could be met with resistance and could pose a threat to the country’s political stability at large. There is no convenient separation between water management and the social and political structure in

Jordan so it cannot be treated as a simple technical or economic matter to be easily controlled. For this reason, it is dangerous to assume that agriculture can simply and without opposition be drained of its large share of Jordan’s water resources with the expectation that the Jordanian monarchy will maintain the control that it enjoys. There are a few additional arguments to make with regard to the importance of agriculture in Jordan and thus its use of the largest portion of the country’s precious water

33 resources. Salman et al. (2008) argue that there are potentially negative impacts to the now increasing diversion of water away from irrigation and towards the urban areas. First, they make the same point as above, that agriculture and its related activities employ a significant portion of the labor force. But they also note that if laborers have to move from their agricultural jobs to industrial and other service jobs in light of a decrease in agricultural jobs, this shift will not be easy for them and will require retraining and perhaps more education. Second, Salman et al. posit that if there is a greater shift of the population to urban areas, “newcomers to urban areas will be more likely to form slums on the periphery of cities, which will become breeding grounds for crime,” and “evacuated rural areas will be attractive for activities hostile to the state” (p. 307). Third, they state that if less water is given to agriculture, then fewer agricultural products are produced in-country and there will thus be a greater dependency on food imports, something that Jordan’s “already highly indebted economy” can ill afford. Fourth, they suggest that with less agricultural production going on, the once-irrigated lands will dry up and “present an open invitation to desertification.” And Salman (Interview in January 2014), in his own personal commentary, made note that agriculture is presently the only user of treated wastewater in the country and is able to reap a 70% return on freshwater in this way. This is all to say, as Salman et al. (2008) conclude, that rather than simply taking water away from the agricultural sector, more thought should be put into better and more efficient water management in the sector.

WATER MANAGEMENT IN THE AGRICULTURAL SECTOR IN JORDAN

The agricultural sector is unlikely to see any additional and significant water resources developed for its use in Jordan, thus putting importance on better managing the remaining water resources. Thus far, Jordan’s record on water management, in any sector of water use, has not been stellar. As Bonn (2013) notes, to solve water scarcity issues, much of the time “Jordan opts for measures of supply enhancement; enormous infrastructure projects are either in planning stages or even already being constructed” (p. 731-732). The focus has typically been elsewhere than demand management. In looking closer at the weak state of Jordan’s water demand management strategy, annual water losses, or non-revenue water, in the domestic sector for the drinking water supply have amounted to 48% of the supply (MWI, 2015). These undocumented and unpaid water losses are largely the result of: water stealing through illegal connections; water meters that do not function reliably, are not read properly or do not exist; and leaks in the network piping (MercyCorps, 2014; Tarawneh et al., 2008; Mohsen, 2007). According to the MWI (2015), the efficiency of the irrigation distribution networks fare a better, reaching a use proportion of 87%. On-farm water use efficiency is markedly worse, reported at just 30-50% (USAID, 2005). This is not to say that Jordan has done nothing to better manage and save water throughout the country, particularly in the agricultural sector. Indeed, as Courcier et al. (2005) recount, since the early 1990s Jordan has attempted to enact many short- and long- term water saving strategies. These include: freezing authorizations or licenses to drill more wells (1992); installing water meters on wells (1994); enacting a groundwater control law (2002) that created a tax on pumped water; modernizing irrigation systems in the Jordan Valley to pressurized, drip-irrigation systems; replacing the use of freshwater with

35 treated wastewater in some areas of the JV; compensating farmers for letting land lay fallow and thus reducing water demand; and providing technical assistance to farmers in developing better demand management techniques. One measure that the Jordanian government has been resistant to is raising the price of water. This could be a significant problem because as USAID (2005) has argued, “ultimately, all the efforts to improve water use efficiency will have only marginal impact as long as water prices do not reflect the real cost of producing and delivering water” (p. 22). In the JV, the water tariff has not changed since 1995 and is minimal. As seen in

Table 2.8, for all tiered levels of use of surface water in the JV, the price is only 0.035 JD ($0.05) per m3 at most. For farms in the Highlands pumping groundwater, the water costs nothing for those with a former abstraction license (before abstraction licenses were frozen) for up to 150,000 m3 per year. Otherwise, water is anywhere from 0.005-0.070 JD ($0.007- 0.098) per m3. In sum, water for irrigation purposes is cheap and represents likely the lowest on-farm cost for a farmer in comparison to those associated with labor, marketing, pesticides and fertilizers, packaging, transportation, seeds and preparing the land (Van Aken, 2004).

Table 2.8: Water tariffs in the Jordan Valley for surface water and the Highlands for groundwater. Water consumption Water tariff (JD/m3) Jordan Valley (m3/month) 0-2500 0.008 ($0.01) 2501-3500 0.015 ($0.02) 3501-4500 0.020 ($0.03) Greater than 4500 0.035 ($0.05) Highlands (m3/year) Former license Non-former holders license holders 0-150,000 0 ($0) 0.025-0.030 ($.0.035-0.042) 151,000-200,000 0.005-0.025 0.035 ($0.05) ($0.007-0.035) Greater than 200,000 0.06 ($0.085) 0.07 ($0.098) Source: USAID (2013), Venot and Molle (2008).

While these water prices are low, it is likely that the Jordanian government, as Salman et al. (2006) state, considers “water in the hands of farmers to have a greater value than the farmers’ willingness to pay” (p. 117). It is a political and security calculation, favoring stability and contented farmers over any potential for unrest either from protesting farmers or from greater movement from rural to urban environments. The unfortunate consequence has been, again as Salman et al. (2006) add, that “longtime subsidization distorts people’s perception of water as a scarce and therefore valuable resource” (p. 132). It should be noted that the government did attempt to raise the price of water for farmers in the JV in the Spring of 2014, with a plan to raise the price over four years from 0.008 JD/m3 to 0.065 JD/m3 (Discussion with Ali al-Omari, 2/11/2014), but this was to no avail.

Farmers rejected this attempt and threatened more rebellious action if the government followed-through on their attempt. It is thus to other measures that Jordan has sought to curb and make more efficient its water use in the agricultural sector.

WATER MANAGEMENT IN THE JORDAN VALLEY

Before addressing the main topic at hand of water user associations, which represent one important way in which Jordan has attempted to better manage water demand in the agricultural sector, the historical and present context of water management and agriculture in the JV is covered. Up until the 1950s, the JV was home to small-scale “subsistence arable farming and nomadic pastoralism” (Nims, 2005, p. 5-6) that used simple earthen or masonry canals and small dams to supply water for irrigation and domestic purposes (Courcier et al., 2005). Water rights were established according to the Majallah, which was essentially the “codification of Islamic civil law under the Ottoman Empire” (The Law Library of Congress website). Thus, water rights were determined by land ownership; one had the right to the water on one’s land for irrigation if one owned that land (Haddadin, 2006). With the Hashemite Kingdom of Transjordan’s independence from the British

Mandate in 1946 and its absorption of around 450,000 Palestinian refugees in the aftermath of the 1948 Arab-Israeli war (Courcier et al., 2005), there arose a dire need for Jordan’s government to provide for, settle and stabilize its now greatly expanded and restive population. The subsequent and rapid development of the JV, through the establishment of the East Ghor Canal Authority (EGCA) in the late 1950s, answered this need and brought in much financial, technical and social aid to the area. The purpose of the EGCA was to “develop an agricultural base that would serve as the backbone for economic development of the Valley” (USAID, 2013, p. 9). To this end, the EGCA, according to the East Ghor Canal Law of 1959, claimed all of the land in the JV for the government and thereafter re- divvied it up among Jordanians and Palestinian refugees (Haddadin, 2006). A further land reform program in 1962 allowed for the creation of small farm plots of around 35 dunums

(3.5 hectares) each that would be given to each family (Molle et al., 2008). According to Law No. 38 of 1946, the Kingdom had already granted itself the arbiter of water rights. In this way, as Nims (2005) more cynically remarks, the EGCA “confiscated land…and redistributed it in units that were considered economically viable and better suited for irrigation” (p. 6), thus transforming “commonly managed resources into state-controlled systems” (p. 3).

In order to provide for the greater water demands of the newly-developing and growing agricultural sector in the JV, the EGCA began construction on the East Ghor Main

Canal, today known as the King Abdullah Canal (KAC), which would provide water from the Yarmouk River to farmland. The initial construction of the canal lasted from roughly 1958 to 1966 (Molle et al., 2008; Courcier et al., 2005). During this period, in 1959, the Central Water Authority (CWA) was also created to deal with all water issues in the Kingdom except those under the purview of the EGCA in the JV (Haddadin, 2006).

Therefore, in the beginning the EGCA acted with relative independence in the JV, even though officially, in 1966, it was put under the overall command of the Natural Resources Authority (NRA) along with the CWA and the Department of Mining (Haddadin, 2006). In the 1970s, in the aftermath of the Six Day War of 1967 and the absorption of 400,000 more Palestinian refugees (Courcier et al., 2005), the Jordanian government became further entrenched in its mission to rehabilitate and develop the JV. The Jordan Valley Commission (JVC) was established in 1972 with the administrative and financial independence to “enhance the economic and social revenues from the resources available in the Valley” (USAID, 2013, p. 9). This involved the development of water resources through the construction of dams and irrigation networks and the establishment of social and economic infrastructure, providing “electricity, telecommunications, agricultural

39 roads, towns, schools, health centers, local government buildings, domestic water supply and other infrastructure” (USAID, 2013, p. 9). Essentially, as Haddadin (2006) posits, irrigated agriculture was serving as the “backbone” to this social and economic development in the JV. In 1977, the Jordan Valley Authority (JVA) was established under the Jordan Valley Development Law No. 18 to replace the JVC. Like the JVC, its mission was to continue the economic and social development of the valley. From 1972 to 1988, the JVA developed 35,000 hectares of land and the required irrigation infrastructure for it, including doubling the length of the KAC and secondary canals along its length (Courcier et al., 2005). In addition, it constructed six dams for water storage, three marketing centers, two tomato processing plants, schools, health centers and hospitals (USAID, 2013). It was during this period from the late 1970s and into the early 1990s that a variety of other factors allowed for the rapid development and expansion of irrigated agriculture. These factors included: new production techniques such as the use of drip irrigation, greenhouses, plastic mulch and new varieties of fertilizers; the ready availability of cheap Egyptian migrant labor; and a healthy market for Jordanian production (up until the first Gulf War) (Courcier et al., 2005; Van Aken, 2004). With regard to the domestic supply in more populated centers in Jordan at this time, the Water Authority of Jordan (WAJ) was created in 1983, tasked with distributing the municipal water supply (Haddadin, 2006). It was in the late 1980s that water transfers from the KAC up to Amman, to WAJ, began (Haddadin et al., 2006; Courcier et al., 2005). In 1988, the JVA and WAJ came under the authority of the newly-created Ministry of Water and Irrigation (MWI), such that the JVA would handle irrigation water, the WAJ urban water, and the MWI higher-level policy and regulation (USAID, 2013).

Unfortunately for all of these decades of rapid development and growth for JV agriculture, its profitability took a hit in the 1990s with more competition from markets in Turkey, Lebanon and Syria and the loss of export markets in the Gulf (Molle et al., 2008). While agriculture accounted for 8.1% of Jordan’s GDP in 1991, it was reduced to 3.6% by 2003 (Molle et al., 2008). The JVA’s role in the JV was also set to change in 2001 with the Jordan Valley

Development Law No. 30. This law, as comprehensively reported by USAID (2013), “removed social development from JVA’s mandate, opened the way for private sector participation in projects developed or implemented by JVA, and removed constraints on land sale and ownership (within certain limit) to encourage investment in development projects with potential economic returns, and gave JVA more power to stop violation of water use regulations” (p. 10). It is to the point about private sector participation that warrants more attention because it was during the early 2000s that there was much discussion within JVA about the possibility of private sector management of irrigation water. Indeed, in 1999, WAJ had already awarded the French-Jordanian-United Kingdom private joint venture LEMA (Lyonnaise Des Eaux, Montgomery Watson-Arabtech Jardaneh) with a four-year management contract, which was later extended two years, to manage the municipal water and wastewater services in Amman (Water Authority of

Jordan website; Salman et al., 2008). JVA began preparing for a bid to award a management contract for the management of irrigation water in the JV but these efforts were eventually terminated because it was believed that they “would not be supported by the stakeholders,” nor would they be “popular among the general public” (Salman et al., 2008, p. 315). This is when the JVA started to consider more heavily the option of user participation in irrigation water management, or the use of water user associations.

Today, the JVA still presides over irrigation water management in the JV and is one branch of the MWI. While the JVA, as has been mentioned, was charged with more extensive duties at the time of its development in the 1970s, to include various aspects of developing the valley’s economy, today its responsibilities are focused on controlling the bulk water resources in the valley and water distribution to farms in areas where water user associations still do not operate. The JVA has numerous branches and offices in the JV and maintains a strong hold on water management in general. These offices are spaced throughout the valley in relation to the land management unit of the valley, which is the

Development Area. There are 54 DAs spanning the length of the valley. As demarcated in Figures 2.6 and 2.7, the JVA’s management scheme in the valley is divided into these two sections: one for the area above the Dead Sea that runs from the northwest corner of Jordan near Lake Tiberias to the Dead Sea (DAs 1-39 and 49-54) and one for the area below the Dead

Sea (DAs 40-48). The maps below include the DAs by number and are colored in green. For the purposes of this dissertation, the area above the Dead Sea consists of the northern, middle and southern (sometimes called Karama) sections of the JV. The area below the Dead Sea will be referred to as the southern ghor. There is a JVA director for the region of the JV above the Dead Sea, whose office is in Deir Alla and who presides over all matters concerning this region, and there is a separate director for the southern ghor region below the Dead Sea located in Safi. The Control Center, located in Deir Alla, works directly below the director for the JV above the Dead Sea and is responsible for closely monitoring and controlling the main water carrier for this region, the King Abdullah Canal.

Figure 2.6: Map of the Jordan Valley above the Dead Sea.

Source: Ayadi (2006).

Figure 2.7: Map of the Jordan Valley below the Dead Sea.

Source: Ayadi (2006). 44

Below the overarching director of the JV above the Dead Sea, there are three regional directorates: the northern (DAs 1-17 and 33-39), the middle (DAs 18- 25, 29-30 and 53-54) and the southern (DAs 26-28, 31-32 and 49-52). See Figure 2.8 for a pictorial representation of the general management scheme within the JVA. The northern directorate is located just north of the Sheikh Hussein Israel-Jordan border crossing, the middle directorate in the town of Deir Alla and the southern directorate just south of the town of South Shunneh. There are no regional offices below the director of the southern ghor region, presumably because this region is not as extensive, populated, or complex.

These regional directorates (and the overarching directorate in the southern ghor) are responsible for handling the more major operations and maintenance issues within their regional water distribution networks, recording and collecting ticketing fees and water fees from the lower level offices, and providing overall direction to the lower level offices on any new or changing procedures.

Figure 2.8: Management diagram of the MWI, JVA, directorates, stage offices and WUAs.

Source: Interviews with JVA employees.

Working directly under the regional directorates north of the Dead Sea are ten stage offices: three in the north, four in the middle and three in the south. The stage offices work more closely with the daily distribution of water to farms. See Figure 2.9 for a map including the locations of the stage offices (S.O. on the map) and the areas covered under the directorates (the south directorate is labeled as “Karama”). The southern ghor region has two stage offices. In specific, these stage offices fulfil the following duties: monitoring and ticketing of farmers for violations of water distribution rules; collection of water fees from farmers; recording of cropping pattern information from farmers (to aid in future decisions regarding water allotments); dealing with requests and complaints from farmers regarding the water supply; and taking care of minor maintenance tasks within the water distribution network. Where water user associations exist, the stage office takes on a more monitorial role over the WUA in its implementation of these tasks.

Figure 2.9: Map of the Jordan Valley with the directorates and stage offices.

Source: Ayadi (2006). 48

WATER DISTRIBUTION IN THE JORDAN VALLEY

Water distribution in the Jordan Valley begins with the overall determination of allocations to its various sections (the following concerning how the JVA’s allots water to the Jordan Valley is from a discussion with engineer at PS 91, 3/15/2014). The JVA’s Control Center in Deir Alla performs this most important duty of calculating the irrigation water distribution that each section of the valley will receive in the coming year. At the beginning of April of every year, a program called the Water Management Information System (WMIS) is run in order to forecast the coming crop season and year’s water needs as per the water resources available. This distribution schedule is based on historical data of needs from the past three to five years, with consideration of any current situations that might make this vary. At the end of April this forecasted data is checked with and compared to actual data from the month of April. In creating and calculating this distribution schedule, many factors and allocation priorities have to be taken into account.

The first priority to be considered is that a portion of the water supplied to the JV must be pumped to Amman to be treated and used for drinking water. The second priority is to meet the terms of the regional peace treaty between Jordan and its neighbors with regard to their quotas from the Yarmouk-Jordan River systems. After consideration of these priorities, the quantity of water storage in the dams along the side wadis (valleys) is calculated and this water must be considered not only for use on JV farms but also as strategic water storage for use in times of drought and potentially for use in Amman if needed. The quantity of water available in the Jordan River for agricultural uses, as well as the quantity of treated wastewater expected from the Khirbet As-Samra treatment plant, are added to the WMIS.

Of final consideration are the crop patterns of the farms in the JV and their water needs. The WMIS groups crops into types (vegetable, citrus, banana, nurseries, palms, other trees, etc.) because crops vary in their needs for water and the timing of these needs. The peaks of different crops occur at different times of the year, with some crops having more than one peak in a single year, and water needs vary in crops depending on the stage of production. According to the JVA’s 2004 quotas for specific crops, the annual quotas are: 3,600 m3 per hectare for vegetables, 7,650 m3 per hectare for citrus, and 12,550 m3 per hectare for bananas (World Bank, 2016). This represents a reduction of about 50% from former quota levels (Written correspondence with François Molle, 4/4/2016) and as reported by Molle et al. (2008), reductions in quotas for crops in the Jordan Valley have been implemented several times since the several years of drought in the late 1990s. From all of these calculations, the WMIS allocates a certain amount of water to each section of the JV. This allocation is then given to the JVA stage offices and WUAs in the form of a “water order,” or the amount of water that they will receive and in turn what they should allot to each individual farm unit. Figure 2.10 below shows the basic set- up of water distribution within any given section of the JV. Water is pumped from the KAC to a pump station (PS), where it is then pumped to the nearby farmland. The gates (denoted in red in the diagram) along the KAC regulate its flow in accordance with the water order and where water needs to be directed in order to supply areas with their allotted quota.

Figure 2.10: Diagram of the Jordan Valley depicting the King Abdullah Canal, water gates (red ticks), and pump stations (PS).

Source: Personal field observations.

After the pump station receives the water from the KAC, the water is pumped or moved by the force of gravity through a main line, then lateral lines and finally onto individual farm units (Figure 2.11). The main and lateral lines are connected by valves that can be opened or closed in order to provide water to a lateral only when it has a turn. Depending on the area and water order, along some laterals all farms take water at the same time and along others, farms take water from the lateral at different times.

Figure 2.11: Diagram of water distribution through the pumping station to the main line, lateral lines and farms.

Source: Personal field observations.

Each farm siphons water from the lateral line through a farm turnout assembly (FTA), which is the device pictured in Figure 2.12. The FTA includes a flow limiter that regulates how many liters per second of water the farm receives (Figure 2.13). This device can either be a simple variety consisting of a metal plate with a certain sized hole depending on the rate of flow desired (picture on the left) or a more advanced model with metal spokes and rubber that can expand or contract depending on the water pressure from the network

(picture on the right). The latter is preferred when network pressure is not always consistent (Discussion with Haidar Malhas, 2/9/2014). The FTA also typically includes a water meter, although it is not always used, and a valve that opens and closes the FTA. Figure 2.14 shows a close-up picture of the water meter. Depending on the area, the FTA remains open all of the time or else the farmer is responsible for opening and closing it at the right time according to the water order.

Figure 2.12: Farm turn assembly (FTA) with water regulator, flow limiter, and water meter.

Source: Personal photographs.

Figure 2.13: The two varieties of a flow limiter device on the FTA.

Source: Personal photographs.

Figure 2.14: The water meter in an FTA.

Source: Personal photographs.

On average, each farm receives two to three turns per week consisting of 5-12 hours of water for each turn. The flow rate for each farm is anywhere from three to nine liters per second depending on the network’s specific characteristics and whether the farm is supplied through the gravity-based or pressure-based network. Theoretically, this is the quantity per unit of time that water should reach each farm, although any problems in the distribution network could change this amount. Despite the potential variations in water quantities due to network problems, water allocation in the JV is still largely done through time. This reliance on time, instead of exact quantity, is due to the lack of or non-use of water meters at every farm plot.

SUMMARY

Jordan is a country dealing with extreme water scarcity due to its very arid climate and limited water resources. Its water demand constantly out-paces its water supply, with 54 significant pressure coming from the domestic sector and its rising population from within and from neighboring countries. While Jordan has enhanced its supply in all ways possible from natural and artificial sources that are within its financial means, working on the supply side will never be enough. Jordan has to focus more on the demand side within such arenas as non-revenue water losses, water use efficiency and proper water pricing. A demand side focus is necessary in all sectors, especially the agriculture sector that is increasingly being put in second place to domestic needs and has no choice but to literally make every drop count. Because agriculture in Jordan plays such an important part for so many rural livelihoods, its maintenance is a necessity but one that will require new methods for the future. In particular, in the Jordan Valley, the agricultural sector has witnessed a tremendous boom since the latter half of the 20th century with the creation of the King Abdullah Canal and the secondary water distribution networks. A concomitant management structure and detailed monitoring system were devised for the valley to accompany this growth of farmers and farms and their water demands. But the systems governing water management have proven insufficient, as will be seen in subsequent chapters. New tools such as participatory management have been implemented to try to deal with this imbalance in supply and demand and the evolution of this particular tool will begin in the next chapter.

Chapter Three: The Trend of Participation in Development

This chapter will review some development trends within irrigation water management, from more state-centered approaches to alternative strategies, especially those involving water users. Since the 1990s, participatory approaches have witnessed greater support in water management and to this day they are common. That being said, and while there are plenty of parties who still support this approach and sing its praises, there are many others who express profound doubts about this approach and its effects. Case studies have attempted to display the strengths and weaknesses of user-based management strategies but there have been few firm conclusions reached. The chapter offers an overview of the current status on management within the irrigation sector and provides the specific background on user participation in this field. Within this context, Jordan’s experience with user participation is more closely examined in subsequent chapters.

OVERVIEW OF PREVIOUS DEVELOPMENT TRENDS

While a more participatory, decentralized approach to water management, specifically in the agricultural sector, is common today, this is a relatively new trend. For the latter half of the 20th century, strong state-led management was the norm. Farmers in many parts of the world had managed their water in local schemes for some time

(Vermillion, 2006; Frederiksen and Vissia, 1998) but from around the 1950s and up until the 1970s-80s, central governments undertook large-scale irrigation development projects and capital investment, expanding irrigation infrastructure, taking responsibility for operations and maintenance of the systems, and in some cases nationalizing the formerly,

56 locally-owned irrigation systems (Ghazouani et al., 2012; Xie, 2007; Frederiksen and Vissia, 1998; de Graaf and van den Toorn, 1994). By one account, the world’s irrigated area expanded from around 98 million hectares in 1950 to over 270 million hectares by 2000 (Vermillion, 2006), largely as a result of this era of state-led development and investment in irrigation schemes. Perhaps the underlying push for this type of irrigation development in the developing world, in the mindset of those who supported it, is best expressed in Wittfogel’s book, Oriental Despotism, in which he argues that “a strong political and social structure was needed to mobilize the labor, financing, and other resources to construct and maintain large-scale hydraulic systems” (Meinzen-Dick, 2007, p. 15200). There was no way to manage irrigation at this scale apart from the central, command-and-control variety. Ostrom (1990) also takes note of this line of thinking in the realm of management of common-pool resources, citing Hardin as stating that “if ruin is to be avoided in a crowded world, people must be responsive to a coercive force outside of their individual psyches” (p. 9). In other words, Hobbes’ Leviathan is the only solution; the central government is the rightful source of control for natural resources. Despite the enthusiasm for state-led development in the middle of the 20th century, its base of support eventually weakened and people began to question the wisdom of relying solely on the state to accomplish tasks that were far more complex and difficult in the long-term. While the state had invested a considerable amount of money and effort into building large-scale irrigation schemes, the state was perhaps not the entity, in the end, most suited to managing these facilities. Not only were state employees seen to have few incentives to provide the best services but they also sometimes engaged in corrupt and rent-

57 seeking behaviors (Hodgson, 2009). De Graaf and van den Toorn (1994) put it most accurately in their depiction of the poor performance of irrigation bureaucracies:

The capability of irrigation bureaucracies to learn, adapt and develop has turned out to be quite limited. Their lack of responsiveness has been widely noted…They often are ‘empires’ not effectively accountable to public and professional control…Possibly not unrelated to this is the displayed limit to internal dynamism: the ability to innovate, to foster new ideas and to meaningfully link incentives to actual performance at field level. Careers in irrigation agencies tend to be determined by the progress of time or by merit with regard to design, construction and administration. Involvement in operation and maintenance is less rewarding, and largely left to lower level staff more vulnerable to local and political pressures (p. 42-43).

Seeking effective management of irrigation water through this type of state bureaucracy can lead to suboptimal outcomes. Vermillion (1991) offers two additional points with regard to the problem inherent in bureaucratic management of this sector. First, government bureaucrats are ineffective managers without the aid of farmers, who are the end-users and whose needs are particular to their local and variable circumstances. Second, not only is the bureaucracy a large drain on the government’s coffers but it also tends to favor financing new construction instead of maintenance and rehabilitation of the old systems, which is further costly to the government. In response to this perception of general government failure in the irrigation sector, many looked to getting the price mechanism right and the creation of private property rights as the solutions to more effective management for irrigation water distribution (Saleth and Dinar, 2004). These measures would correctly signal information and boundaries and would create the proper incentives for people to use water efficiently. A variety of nongovernmental and private organizations also came into being, such as consumer cooperatives, user associations, private utility companies, and contracting companies, to act as substitutions for the central government and as supposedly better means of managing 58 water (Vermillion, 1991). At the same time, the state was still seen to have a role to place in key regulatory functions within the water sector, such as providing a rights system, a means for pollution control and water quality standards, the regulation of well construction, accounting and auditing services, commissions to evaluate the viability of proposed projects, and the monitoring and enforcement of rules for managing water resources (Frederiksen, 2005; Frederiksen and Vissia, 1998; Merrey, 1996). In addition, the state provided a supporting or stewardship function by providing the overarching national water resource plan, data collection services, and conservation or preservation policies

(Frederiksen, 2005; Frederiksen and Vissia, 1998). But the state would no longer be sufficient as the sole and only manager.

PARTICIPATION IN IRRIGATION MANAGEMENT

As mentioned above, a number of non-governmental actors have been introduced within the field of irrigation water management and this dissertation is particularly concerned with involving water users within management. Several reasons explain this shift away from state-centered management towards user management. The most widely- cited one is the need of governments, especially starting in the 1980s and continuing into the 1990s and up to the present, to reduce their costs in the wake of the general economic downturn. Governments took a second and more critical look at the large budgets they financed for investment in the irrigation sector and the ongoing operations and maintenance costs necessary for these schemes (Ghazouani et al., 2012; Garces-Restrepo et al., 2007; Meinzen-Dick, 2007; Xie, 2007; Vermillion, 2006; Frederiksen, 2005; IFAD, 2001; Vermillion and Sagardoy, 1999; Frederiksen and Vissia, 1998; de Graaf and van den Toorn, 1994; Vermillion, 1991; Coward and Uphoff, 1986). With increasing demands 59 from greater urbanization and limited budgets, as Frederiksen (2005) notes, “governments will not be able, nor will they have the political mandate to continue paying significant subsidies for facilities operation and maintenance (O&M), much less finding funds for new investments in additional rural facilities” (p. 502). The limited funds available would need to be directed in the rural sector to “more inherently, governmental purposes (such as regulating water use along river basins)” (Vermillion, 1991, p. 8) and eventually, the responsibility for paying for these services would be taken up by the customers, or the farmers (Garces-Restrepo et al., 2007; Frederiksen, 2005).

Another key motivating reason for switching to user-based management systems was the generally poor perception of state-run systems and their bureaucratic workers. Despite the large amounts of investment by governments and aid agencies in irrigation systems in the previous decades, there was a general dismay with the performance of the government-run agencies due to their inability to collect water fees and the insufficient operations and maintenance of existing systems (Garces-Restrepo et al., 2007; Meinzen- Dick, 2007; Vermillion, 2006; Frederiksen and Vissia, 1998; de Graaf and van den Toorn, 1994; Vermillion, 1991). The result was deteriorating infrastructure, the wasting of water resources, uneven water distribution, and a reduction in the area of irrigated land with additional problems of salinity and water-logging (Vermillion and Sagardoy, 1999). The performance of the bureaucracy, in turn, was ineffective due to weak personnel policies, an overabundance of staff for the work needed, inadequate levels of compensation for staff and insufficient administrative capacity (Frederiksen and Vissia, 1998). Garces-Restrepo et al. (2007) summarize the vicious circle that can be created: “Countries experience the cycle of irrigation construction, followed by underinvestment in maintenance, followed by rapid deterioration, followed by pressures for ‘premature’

60 rehabilitation, which weigh heavily on the debt burdens of developing countries” (p. 18). The general decline in the scope of possibilities for expanding irrigation development, which had given initial impetus to the state agencies, did not help the state’s cause. As de Graaf and van den Toorn (1994) note, “agencies partly or wholly deriving their social prestige, the size of their budgets and their role and power in the political arena from their mandate to build new irrigation infrastructure face decline and sometimes deep-cutting changes in their mandate and political reach” (p. 40). Due to the seemingly hopeless ability to overcome the inefficiencies of the state bureaucracy and its restricted budget, there developed a sense of greater hope in nongovernmental organizations to remedy the situation. If they didn’t outperform government agencies, they perhaps would at least perform just as well and cost less (Vermillion, 1991). These thoughts were spurred on by liberalization policies of “rolling back the state” among donor agencies and the pressure they exerted, most especially on developing nations, to rid countries of their state-dominated structures and promote user association projects (Ghazouani et al., 2012; Garces-Restrepo, 2007; Vermillion, 2006). While this reform in irrigation management, from state-centered bureaucratic management to participatory management, began in earnest in the 1980s and reached its peak in the 1990s, countries experienced this change in previous decades as well. Indeed, in Germany, Italy, the Netherlands, Spain and France, farmers were already familiar with traditional, user-based systems. Taiwan and the United States began experimenting with participatory management schemes in the 1960s (Garces-Restrepo, 2007; Vermillion, 2006). But throughout the 1980s, 1990s and up to the present, participatory management in irrigation has more-widely and quickly spread to countries of all political and social ideologies in Latin America, Africa, Asia, Europe and Australia (Garces-Restrepo, 2007;

Xie, 2007; Vermillion and Sagardoy, 1999). Governments within these countries, as well as foreign agencies and organizations, have pushed this movement. Participatory management strategies have not been uniform in all of these various locations. Participation can range from farmers participating in name only or in some minimal tasks to situations where farmers fully own and operate water distribution systems. The common divide is between what is known as participatory irrigation management

(PIM) and irrigation management transfer (IMT), or turnover. Both typically involve the creation of water user associations (WUAs), or similar bodies, that deal with some degree of the operations, maintenance and financing of the irrigation systems. As Garces-Restrepo et al. (2007) point out, PIM is “more of a behavioral or attitudinal change than a reform” (p. 4) in that there is an increase in the involvement of the users in management alongside the government in the daily administration, maintenance and financing of irrigation systems, but it is more about strengthening this working relationship than changing it (Vermillion, 2006). It was already the case that users played a larger role at the tertiary, quaternary, and/or individual plot level and the government agency controlled the main lines. With PIM, this boundary of who controls what levels remains basically the same, with the government retaining its control of the system at large (Ghazouani et al., 2012). While PIM is a move towards, and perhaps a first step in, farmers taking over the entire operation, it is still a situation in which, as Ghazouani et al. (2012) report, “farmers sit at the table but are mostly [and only] consulted” (p. 11).

IMT, on the other hand, and at least in theory, reaps a different result and is more of a reform movement. It entails a significant reduction in the government’s role and a corresponding enlargement of the water users’ role. Authority and responsibility for the management of the irrigation system is transferred from a government agency to a private

62 or nongovernmental entity representing user interests (Garces-Restrepo, 2007; Vermillion, 2006; Vermillion and Sagardoy, 1999). Even within this more wholesale transfer, there can be variation, with the newly-created nongovernmental entity having all or partial decision-making powers in three key areas: water management, maintenance, and financial management (Ghazouani et al., 2012; Vermillion and Sagardoy, 1999). The level of authority of the new organization is dependent on the situation and what kind of authority with which the government is willing to part. IMT can involve: the creation of entirely new user associations with rules and procedures for how they operate, as well as legislation to give them legal authority; the training of farmers in operations and maintenance, finance/accounting and administrative tasks; farmer participation in the initial planning and designing of an IMT program; the expectation that farmers will invest in initial repairs and rehabilitation of systems or that the government will do this; plans for future monitoring and evaluation of the new organizations; and the reassignment of government staff who used to fulfill these tasks (Vermillion, 2006; Vermillion and Sagardoy, 1999). In essence, IMT represents a major change in the entire structure for water management in the irrigation sector and requires attention to many details. This gradient of potential losses of authority and responsibility for the government and potential gains of authority and responsibility for the water users, in either PIM or IMT, can permit a blurring of the lines between the two which raises important questions. While operations and maintenance can more clearly be transferred to water users, one particularly sticky issue is that of who will be responsible for the future financing of irrigation services: farmers or the government. There are also questions as to who will have the authority to apply sanctions, resolve disputes and handle rights of appeal to constitutional-level laws (Garces-Restrepo, 2007). These are functions that perhaps are best left to the central

63 government and yet questions might arise over a partial transfer of authority in terms of the nongovernmental organization’s ability to sufficiently carry-out its duties and farmers’ willingness to sustain such organizations (Coward and Uphoff, 1986). While countries are experimenting with both PIM and IMT at present, Vermillion (1991) suggests that there have been two general stages in the development of participation in irrigation management. The stage of “limited participation,” which primarily saw a promotion of PIM, occurred between the late 1970s and late 1980s. As Vermillion states, farmer participation acted “more or less as a complement to the main emphases of construction and technical improvements” done by the state, with the empowerment and autonomy of farmers not a particular goal. From the late 1980s and onward, IMT became the new trending development plan in irrigation, as seen in Mexico, Turkey, parts of India and Indonesia, with the full empowerment of farmers becoming the end goal (Vermillion, 2006).

Purported Benefits of User Participation in Irrigation Management

Many have stressed the benefits that have been brought about by user participation in irrigation management. These purported benefits deal with how user management can reduce some of the costs of state management, whether economic, political, or social. From the subsidiarity principle, as recounted by Ananda et al. (2009), it is posited that “any communal function should be performed by the smallest organizational unit possible” (p.

302). Decentralization in the water sector, in general, can be commonly associated with better performance because the smaller organizational unit is comprised of “those most directly affected…those best informed and…those best placed to deal with the consequences” of any decisions taken (Ananda et al., 2009, p. 302). But Ananda et al. 64 advise a closer look at what information is available to those making the decisions and whether they are capable of using that information most effectively in order to determine whether decentralization is a good idea or not. They further suggest, as per Dollery et al. (2006), the environmental conditions under which decentralization is optimal: when “decisions can be based on non-transferable local knowledge; where there is a need for community involvement, local empowerment and local participation; where there is a need to avoid concentration of power and risk of absence of power; [and] where there is a need for customization, innovation and flexibility” (Ananda et al., 2009, p. 304; Dollery et al.,

2006). Many of the same virtues of decentralization are those within the literature on collective action and resource management. While Olson’s (1982) “Logic of Collective Action” is now disputed with regard to its reflection of reality on the ground, the underlying thought remains. Essentially, it states that without some external, “selective” incentives, larger groups are less likely to act upon their common interests than smaller groups, with the additional caveat that this is more likely in homogeneous groups. This is generally the thought in turning to collective action among smaller groups of individuals, who are co- located and likely similar in some respects, in order to accomplish their goals instead of relying on the larger state or a private entity. Ostrom (1990), in her work on common-pool resources, adheres to this idea and bases her work on the notion that collective action among common-pool resources users is a way to effectively and efficiently manage and sustain natural resources. This is because users’ benefits from the resource acutely depend on the use and benefit that other users derive from the resource. If they can minimize the transaction costs among themselves by internalizing the administration of the resource, this will act in their mutual favor.

With the shift from state-centered to user-based management, there are several further intended benefits suggested in the theoretical and case study literature on the use of participatory irrigation management. The key problem of limited government budgets is resolved, with these new systems reducing the span of government responsibilities and thus its expenses. As farmers take over the day-to-day operations and maintenance of the irrigation distribution systems, the government will no longer be obliged to expend as much on the systems themselves, the staff required to administer and monitor the systems, and any monitoring and compliance measures requiring legal costs (Garces-Restrepo et al.,

2007; Vermillion, 2006; Hamdy, 2004; Lubell et al., 2002; IFAD, 2001; Coward and Uphoff, 1986). With farmers controlling this sector, costs in general should be lower because projects are better-designed and appropriate for the ground conditions, fees are collected at a higher rate, systems are more efficient, and there will be less destruction of the irrigation networks (Hamdy, 2004; IFAD, 2001). It could be said that this sector of the economy will in general be more efficient and therefore less of a burden on the government and actually a boon to the nation’s economy. Another benefit is stated by Hamdy (2004): “When farmers are clearly the owners of the physical system, so that the maintenance and rehabilitation cost are their own responsibility, they have a strong incentive to protect the physical integrity of the system to reduce their overall costs” (p. 15). Farmers will feel a direct need to perform up to higher standards because if they don’t, their livelihoods will suffer. It could even be posited that farmers would, in fact, be willing to pay the full price (considering many do not at present) and even more for irrigation services because, as Garces-Restrepo et al. (2007) note, “they will be empowered to take over the authority to define what their irrigation services will be, who will provide them, and how and at what costs these will be provided” (p. 11).

Farmers will be able to know how money is used because they will be deciding these matters, and in turn they will have greater faith in the expected results (Bhatt, 2013). An additional thought is that farmers will embody a type of management that is more transparent and accountable, again because they are managing something near and dear to their livelihoods and the livelihoods of those who live directly next to them. Due to their need to protect their own interests while at the same time respecting the interests of those with whom they jointly govern water resources, there would be a stronger, common understanding of what is desired with fewer conflicts of interests (Garces-

Restrepo et al., 2007; Vermillion, 2006; Vermillion and Sagardoy, 1999; Tang, 1992; Ghazouani et al., 2012). There would develop a certain “social legitimacy” for the required fees from farmers and support for equitable distribution of the fruits of these combined fees (Ghazouani et al., 2012; Vermillion, 1991). Farmers also know the ground situation better than any higher-level organization. Shifting management to their local level is assumed to result in more appropriate rules and procedures due to farmers’ greater access to key information and presumably reduced transaction costs (Lubell et al., 2002; IFAD, 2001; Tang, 1992). With regard specifically to the productivity of farmland, it is posited that the transfer of some management responsibilities to farmers will increase agricultural productivity. Users will have a greater interest than the state bureaucracy to ensure that the system is self-sufficient, in that it recovers all operations and maintenance costs, and the most efficient, or lower cost, because this will directly impact the productivity of their farms and thus their profits (Garces-Restrepo et al., 2007; Frederiksen and Vissia, 1998; Vermillion, 1991; Groenfeldt, 1988). Therefore, with greater availability and reliability in the supply of water, due to the better management, farmers will be able to increase their

67 cropped area, its intensity and/or diversity, and thus their eventual crop yields (Garces- Restrepo et al., 2007; Vermillion, 2006; IFAD, 2001). In sum, as Vermillion (2006) posits, irrigation management is made “more responsive to farmer economic aspirations” (p. 8). Beyond the more tangible benefits to devolution of irrigation management, there are other intangible advantages to shifting management to farmers. This is what Groenfeldt (1988) describes as the “logic of social development” and is similar to Korten’s (1984)

“people-centered development.” There are social benefits simply within these experiences of working with other farmers and cooperating with government agency officials: “Farmers gain skills, experience, and confidence in themselves both as individuals and as a group. Their relationship with irrigation officials in particular…may be changed qualitatively as their understanding of those officials increases, and as farmers begin to view those officials as co-workers and colleagues” (Groenfeldt, 1988, p. 252-254). The focus is on making farmers the actors in their lives instead of the “passive recipients of development benefits”

(Groenfeldt, 1988, p. 254; Korten, 1984), and this has unquantified value. Sen (1984) makes a similar case for including the concepts of human capability and freedom in development, despite the inability to quantify these concepts. Development should be equally concerned with improving the capabilities and freedoms of people, as this is what will enable people to better help themselves and improve the world and those around them. More user-based management strategies will give the power, freedoms and capabilities back to the people, making it more likely that they will experience deep and sustaining levels of development that they themselves can propagate.

Questions and Doubts about User Management

Despite the claimed benefits of transferring management to farmers, the movement has not resulted in the ultimate and immediate panacea that some expected. Debate over the viability of PIM and/or IMT in the face of a variety of issues puts the strategy of user management on shakier ground. As Hamdy (2004) asks, is management transfer “the final solution for launching the integrated management revolution we have all been waiting for or is it just the latest in a series of partial reforms which will lead to partial results and further imbalances in irrigation management?” (p. 4). Indeed, Vermillion and Sagardoy (1999) would agree that what we have seen thus far in devolving management to users is “only partial in nature” and these efforts “do not include all the changes that are really needed in order to permit WUAs to become viable organizations capable of discharging their essential functions” (p. 48). There are myriad concerns regarding the basic concepts of PIM and IMT and their use as a general solution in a wide variety of circumstances, the end goals of PIM/IMT, donor agency roles in this project arena, and the academic work thus far on PIM and IMT. A particular worry is how the WUA has become, in the words of Venot (2014), a type of “institutional fix” for a host of problems seen in the field of water management. He continues: “WUAs are considered inherently good…regardless of the processes followed for their establishment and of their outcomes” (p. 541). This is a type of “social engineering” approach, he states, where one follows some “linear model” of development and uses an IMT reform package to enact a specific result. But, as Mukherji et al. (2010) note, WUAs can’t be “engineered;” they actually require very specific contexts and conditions in order to be successful. It is unfortunate, Venot (2014) adds, that “WUAs have acquired (as have all development models) a positive discursive resonance” (p. 541)

69 that has not been sufficiently caveated and slowed by more detailed analysis of potential context-specific impediments. To the contrary, much of WUA development has involved a heavy use of several assumptions on the basic concept of WUAs when in reality, these assumptions have not panned-out in many situations. Mukherji et al. (2010) pose three such assumptions:

First, it is assumed that because traditional self governed irrigation systems have endured, therefore, WUAs in modern canal systems too will. Second, it is thought that most of these public systems have potential to be financially and economically viable, but government management is not the ideal way to achieve this. Third, it is assumed that resource users (i.e. the farmers) are ideally suited to manage these systems, because they have the largest stake in long term sustainability of the resource (p. 3).

To unpack the first assumption, a big push for the user-based resource management movement came from Ostrom’s (1990) original design principles for the management of common-pool resources, a product of her case study research. As Cox et al. (2010) warn, the concern arises of “whether they [the design principles] can be applied to a wide range of cases beyond those that were used to develop them.” Many of Ostrom’s case studies revolved around those “traditional” and long-established water, fishery or forest systems that are not always directly comparative to present-day situations where there is no such traditional basis for commons management and the same social, political or economic situations are not found. Even beyond Ostrom, looking at IMT experiences today, it is risky to think that because IMT has worked in some places, it will automatically work in others. McCornick and Merrey (2005), in their analysis of WUAs in sub-Saharan Africa, point out the difficulty in trying to “leapfrog” the success of IMT in countries like Mexico, Turkey, the US and New Zealand to other regions of the globe. They emphasize the

70 specific conditions that have enabled these more developed countries to reap success with IMT:

The irrigation system is central to a dynamic, high-performing agriculture; average farm size is large enough for a significant proportion of the farmers impacted to operate like agri-businessmen; backward linkages with input supply systems and forward linkages with output marketing systems are strong and well- developed; and the costs of self-managed irrigation are an insignificant part of the gross value of product of farming.

These conditions are not present and are highly difficult to attain in much of the developing world, rendering a fool’s errand the attempt to take lessons learned from these success stories and apply them elsewhere. With regard to the second assumption noted by Mukherji et al. (2010), that government management is simply not the way to achieve a financially and economically viable public system, this is wholesale discounting of agencies that have actually done a lot to bring services to people over many years. As Frederiksen and Vissia (1998) point out, “governments have played key roles in bringing public services to people” and there is the potential that “they remain the best providers” (p. 12). De Graaf and van den Toorn (1994) add that IMT might make things worse “by introducing an increasingly fluid multi- actor situation, not only beyond the traditional control of the traditional, central irrigation agencies, but beyond any control at all” (p. 48). Government agencies might still have a vital role to play when lower-level, farmer-based management entities need external support, resources and facilities for their survival (Meinzen-Dick, 2007; Tang, 1992).

Plusquellec (2002) would argue, in a similar vein, that the problem isn’t even institutional in nature so replacing one institution, the state, with another, the WUA, will not fix the problem. He further argues that the problem resides more so in the design of and

71 technology used in irrigation systems, or the physical deficiencies, and these are what need to be remedied, not the institutional or social arrangements. Mukherji et al.’s (2010) third assumption, which essentially states that the resource users are the best-suited to be the managers of resource systems, can also be disputed. This assumption exists because of a number of thoughts: farmers are closest to the ground reality and thus have the most knowledge about the reality; they want management to be transferred to them; and all farmers have the same characteristics and goals and can thus work well together. But these thoughts fail to recognize several points in direct contradiction to this assumption. First, farmers don’t necessarily have the managerial skills, capacity and knowledge necessary to run a WUA and oversee many of the large-scale irrigation networks that have been established in the past 50 years (Abernethy, 2010; Garces-Restrepo et al., 2007). Second, many times farmers do not want to take over management of irrigation schemes due to the potential for a higher cost burden, a higher risk of conflict among users and a more unreliable water supply (Garces-Restrepo et al., 2007; Hamdy, 2004; IFAD, 2001; de Graaf and van den Toorn, 1994). Sometimes farmers are never given a choice about whether they really wanted IMT or not (Mukherji et al., 2010). Third, farmers are not a neat, tidy, and homogenous group of water users, a concept upon which much of the support for user-based collective action relies. Berg (2014) notes that there is this common property regime mindset that sees users as homogenous and “characterized by shared norms and incentives to manage resources effectively and in a sustainable manner” (p. 549). In reality, as Groenfeldt (1988) much earlier observed, farmers may not have the aptitude for this degree of cooperation with each other.

Another arena of debate and contention within the field of PIM and IMT involves the disagreement over what should be considered the end goal or objective of the endeavor. Is the goal to achieve higher levels of operational and physical performance of the irrigation systems, as would be seen in greater on-farm cost recovery and agricultural production and efficiency? Or is it some sort of social development, as with what Groenfeldt and Sen advocate above, in which there is a good to be reaped for people’s personal, human growth in the mere cooperation, collective action and greater autonomy at the lower level even though no real performance improvement is seen? (Ghazouani et al., 2012; Vermillion,

1991; Groenfeldt, 1988). Mukherji et al. (2010) pose these dueling end goals as one of the many paradoxes of IMT. The initial goal behind IMT was to improve the performance of the system and indeed, farmers were very interested in this goal in that it would enhance the adequacy and reliability of their water supply and thus their production. But what happened is that “participation became the goal, rather than the means of IMT” (Mukherji et al., 2010, p. 42). Projects have focused on participation of farmers without also being equally concerned with what this participation yields in the end. To further complicate and convolute matters, the whole enterprise of PIM or IMT might not be about these goals that purportedly serve farmer interests. Rather, the enterprise could be about donor agency interests. Huppert (2013), in his review of rent- seeking in agricultural water management, offers some troubling and telling anecdotes from his time working with GTZ, the former name of the German development agency.

While plenty of sticky issues arose regarding rent-seeking and corruption within project arenas, he saw development agencies as not wanting to deal with these issues because they “might impinge in unpleasant ways on the outcomes of projects and programmes they were involved in and hence on their personal ambitions and reputations” (p. 268). This would

73 have interfered with their “logical framework” and their “goal-purpose-outputs-activities sequence” (p. 269) that neatly guided them from their initial objectives to a tidy outcome of their choosing. Huppert explicitly states that managers and board members of implementing organizations can sometimes “intentionally neglect or suppress a topic of core importance in favour of the success and well-being of the organisation they are responsible for” (p. 271). The insights from Huppert’s experiences with a donor agency reveal that PIM and IMT are part of an internationally-driven conceptualization of what is needed and appropriate, which could be in complete disregard for what is actually needed on the ground.

The Body of Case Studies and Evaluations

While the topic of user participation in irrigation management has gained wide appeal and publicity in the literature, there are critiques on the state of evaluations of

WUAs, PIM and IMT thus far. Much of the literature of the past few decades addresses the comparative performance of WUAs against state agencies, attempting to prove that there has been some level of improvement since the establishment of WUAs. These performance evaluations of WUAs are conducted either on individual case studies or as meta-analyses across a larger body of cases. For example, Ostrom (2002), in her review of farmer-managed irrigation systems in Nepal, found that these systems outperformed agency-managed irrigation systems with regard to the physical condition of the system, its technical efficiency, and agricultural productivity. Uysal and Atis (2010), in their examination of the Kestel WUA in Izmir Province, Turkey found that WUAs perform well with regard to irrigation efficiency, financial efficiency, productivity and sustainability. Bandyopadhyay et al. (2010) found 74 that transferring management to users in the Philippines resulted in an increase in maintenance activities and fee collection, higher rice yields, and signs of an increase in the timeliness of water availability and conflict resolution. And Vermillion (1997), in his examination of case studies from around the world on transfer of management to users, found that user participation had the potential to improve operational efficiency, decrease government expenses and prevent further deterioration of water distribution systems. Qiao et al. (2009) and Yami (2013) offer reviews of various other cases in which the use of WUAs has proven to reap beneficial results.

Despite the presence of positive reviews of WUA management, others have posited that management transfer has not had as large or impressive an impact or that similar problems remain after this administrative turnover. Akkuzu et al. (2007), in their study on the irrigation planning performance of WUAs in Turkey, reported that while the adequacy and efficiency of water delivery was generally good, measures of agricultural productivity, the dependability of the water supply, and equity were poor. Yami (2013) also reviewed cases of WUAs in Asia and Africa that resulted in few notable achievements and Bhatt (2013) witnessed mixed results in the performance of WUAs in western India. And Mollinga et al. (2007), in their study of WUAs in Andhra Pradesh, India, and Veldwisch and Mollinga (2013), in their study of WUAs in Uzbekistan, show that while there can be technical improvements to water management, they may be accompanied, paradoxically, by skewed, uneven and/or superficial user participation, which was the initial point of any change. On the whole, these case studies comparing government agency to user management have rarely reaped completely solid and generalizable results that can be taken as certain evidence of the superiority of one type of management over the other. As

75 both Mukherji et al. (2010) and Senanayake et al. (2015) argue, rarely do case studies include methods that would allow for any causal claims between management transfer and the positive or negative impacts observed. They remark that there is a distinct lack of before-and-after, with-and-without, or long-term impact studies. Especially with regard to where success has been seen with WUAs, there is little that allows for generalizability of such success. As Mukherji et al. (2010) state: “All failed cases of IMT failed due to similar reasons, while the successful cases are highly context specific” (p. 37). Senanayake et al. (2015) in particular go into the details of why case studies on

IMT and PIM have yielded such inadequate results thus far in their review and meta- analysis of 230 case studies that cover 181 different IMT or PIM projects. For one, case studies frequently rely on questionable data sources. Almost 90% of the studies relied on secondary sources, such as those from government or irrigation agencies, instead of gathering information from random samples of farmers and taking independent measures and observations of water distribution systems and infrastructure. In addition, as stated above, they found that most studies could make no legitimate causal claims. Many relied on “post-impact observations and data to infer cause and effect,” and many used only a “before and after” or a “with or without” design instead of both. Thus, there are a host of potential exogenous factors that could be of note but are not taken into consideration in most studies, rendering their causal claims weak. As mentioned above as well, few studies deal with a longer-term impact assessment either. They note that Vermillion (1997) and

Kloezen et al. (1997) state that IMT and PIM assessments should be done at least six years after establishment, with 10 years being even more ideal. Yet most studies are done in the very short term, less than six years after establishment, so the impacts have not had enough time to establish themselves.

Despite this rather dismal assessment of the status of case studies regarding IMT and PIM, research is still making headway and building upon itself, continuing to establish more causal links between the establishment of WUAs, for example, and certain outcomes. Much of the literature has moved past stating whether IMT has been an improvement over state management to now addressing specific factors that can enable or hinder user management. This is in the hopes of bettering global attempts to implement organizations for user management of common pool resources, a movement that will continue due to the entrenched nature of this particular development trend at present.

The literature regarding what factors enable or hinder collective action or cooperative efforts for the provision of public goods is extensive. Not only is this topic addressed with regard to water user associations for the management of irrigation water but also with regard to managing forests, fisheries, grazing land, drinking water and other common-pool or public goods. Several studies offer sizeable lists of the many potentially enabling factors for the successful operation of water user associations (Wade, 1988; Ostrom, 1990; Baland and Platteau, 1996; Vermillion and Sagardoy, 1999; Agrawal, 2001; Ostrom, 2000a; Kumnerdpet and Sinclair, 2011). Due to the large number of potentially influential factors, which are not all related under a single heading, many authors have introduced frameworks with which to better organize and examine these factors. Foremost among them is the one offered by Ostrom et al. (1994) and Ostrom (2010) called the Institutional Analysis and Development (IAD) framework, which breaks the many variables into groups of variables that can then be further parceled out and investigated. This framework entails clusters of variables related to the actors and their action arena (or organization set-up), which are affected by three

77 overarching elements: the physical attributes of the environment, the attributes of the community and the rules-in-use (see Figure 3.1).

Figure 3.1: Diagram of the Institutional Analysis and Development framework.

Source: Polski and Ostrom (1999) (cited from Ostrom et al., 1994).

Ostrom (1990) best expresses the benefit of the use of this framework:

The basic strategy is to identify those aspects of the physical, cultural, and institutional setting that are likely to affect the determination of who is to be involved in a situation, the actions they can take and the costs of those actions, the outcomes that can be achieved, how actions are linked to outcomes, what information is to be available, how much control individuals can exercise, and what payoffs are to be assigned to particular combinations of actions and outcomes (p. 55).

What the framework provides, in sum, is a way to visualize the characteristics of the physical world, the community, and the rules or institutions at work that can be considered in analyzing how actors are acting within the situations and organizations in which they find themselves. As Tang (1992) notes with regard to the use of this framework 78 in the irrigation sector, the idea is “to begin to understand those circumstances that allow farmers to solve their collective-action problems through self-organization and those that would benefit from government intervention” (p. 3). While this framework was originally created for the purpose of assessing the management of common-pool resources, it has continued to evolve and has come to also provide a way of organizing the exploration of a variety of institutions governing different kinds of goods (Blomquist and deLeon, 2011).

Ostrom is not the only one to have developed a framework for analyzing human management of environmental systems. Hagedorn (2008) created his Institutions of

Sustainability (IoS) framework (Figure 3.2) that mirrors many elements of Ostrom’s frameworks. At the center of the IoS framework sits actors within action situations, which can include situations related to both water resources and land resources, as well as the different levels of management from local to national. The actors and action situation will be influenced by the characteristics of the actors, the nature of the transactions among the actors, the types of rules in use and the governance structures.

Figure 3.2: Diagram of the Institutions of Sustainability (IoS) framework.

Source: Wang et al. (2013), p.11.

Araral (2009) uses a very similar logic and framework to Ostrom and Hagedorn to investigate collective action in the Philippines. He sees the outcomes of collective action as being influenced by the incentive structure of the players, and this structure is in turn created by their surrounding context, to include the physical characteristics of the resource, the attributes of the players and the internal and external institutional contexts. Other frameworks take different approaches. Stern et al. (2002) create categories of “moderating” and “intervening” variables that further shape the independent and dependent variables. Ghazouani et al. (2012) categorize variables as external and internal to the user organizations. These frameworks organize the potential variables at work in alternative fashions and are not better or worse than the IAD Framework. The use of the IAD framework is simply a preference due to the ease of inserting factors from the literature in its broad categories. The three categories of variables considered are: 1) the physical attributes of the resource itself, the environment and the resource systems; 2) the 80 social, economic and political aspects of the surrounding community; and 3) the institutions, or rules-in-use, to govern the resource. Additionally, attributes of the users are considered for their impact on outcomes and involved third parties (such as the German aid agency) will be examined for their roles in the performance WUAs.

SUMMARY

The development community has moved away from solely state-centered models and strategies to much more hybrid and multilayered approaches. Increasingly, there has been an urge to be more inclusionary, democratic and representative in decision-making, leading to models that include users in varied ways. Within the irrigation management sector, these trends have been observed and support for user and nongovernmental management stretches over a broad spectrum. There are many who praise participatory approaches in irrigation management for its greater efficiency, suitability, financial viability and productivity over state management but doubts remain. As seen from the brief review of case studies on water user associations in various countries around the world, some still question whether they represent an improvement over the past. Despite the mountain of data produced, the results are mixed, incomplete and/or unsatisfactory. This dissertation moves past the state versus user debate, one that is unlikely to have a single answer for every context around the globe. What will be sought is more clarification within the arena of factors that can influence user management. The use of the IAD Framework can act to broadly guide research and organize potential factors affecting WUA performance. It is possible that factors outside of these categories are of import but the categories are broad enough to serve as holding grounds for a great many of the potentially influential variables presented in the literature. 81

The following chapter will offer more details into how this dissertation uses the IAD Framework. First, the outcome variables used in this study are presented along with the literature that supports the use of such measures. Second, the potential influential factors for WUA performance and participation rates as found in the Jordan Valley and organized within the IAD Framework categories (the physical environment, the surrounding community, the institutions at work, and the water users) are presented. The choice of variables was guided by the literature as well as the reality found on the ground during fieldwork. A hypothesis is developed from the literature for each factor that will eventually be tested in subsequent chapters.

Chapter Four: Evaluating Water User Associations

There are myriad ways in which to evaluate water user associations. A large number of case studies support a variety of outcome measures as well as factors that can affect these outcomes. This chapter will review the outcomes suggested in the literature and which outcomes will be used in reference to the Jordan Valley’s specific context. Next, the chapter will discuss the factors that can influence these outcomes, also as suggested in the literature. Only those factors that were present within the ground reality of the Jordan Valley are presented although the literature suggests many other potential influential factors. The Institutional Analysis and Development (IAD) Framework serves as the guiding force to organize the large number of factors at work. Due to the impossibility of knowing exactly what factors would be most impactful before conducting the fieldwork, the framework allows for considering a wide variety of potentially influential elements within the subject domain. The factors are categorized in the IAD Framework as physical and environmental factors, community factors, institutional factors and user factors. Hypotheses are provided for each factor that will then be examined through various research methods within Chapter Five and analyzed and tested within the results in Chapters Eight through Twelve.

RELEVANT OUTCOMES FOR THE IMPLEMENTATION OF WUAS

The literature is awash in potential performance and outcomes measures for the implementation of WUAs, with indicators offered at the farmer, organizational and national levels. This study will mainly be attentive to those performance measures relevant to the farmer-level, as that is the main level of data collection, but measures at the

83 organizational level will also be of import and how they are perceived by farmers. Ruttan (2008) raises an important point when measuring the success of the WUAs, whether this is done “in terms of collective action, or in terms of the level of collective good provided” (p. 969). In other words, is success measured by the amount of participation of individuals in management and their obedience to rules, or is success measured by whether the good is adequately and efficiently provided and management is accountable, transparent and fair?

For this study, both measures will be considered. Of further note is that the relationship between the two outcome measures, whether for performance or participation rates, is not always straightforward. While, as Ruttan posits, some might presume that “collective action is a necessary precondition for successful provisioning of the collective good,” this is not always the case. Baland and Platteau (1997, 1999) support this claim in suggesting that low levels of collective action do not necessarily prevent a high level of collective good provision. Therefore, the two concepts will be used: the one pertaining to the classic performance measure of goods provision and the other to whether collective action, in the form of user participation, is actually happening. Table 4.1 lists the outcomes analyzed in this study and what variables are used to capture these outcomes. The following sections go into more detail as to why these outcomes are chosen.

Table 4.1: Outcome variables for WUA performance and participation. Outcomes Variable Used WUA Performance Operational efficiency and Farmer opinion of the WUA effectiveness Farmer opinion of the WUA in comparison to the JVA Rule-following Farmer reporting of water stealing Equitable water distribution Farmer opinion of the fairness of the WUA WUA Participation Membership in the WUA Farmer membership status

WUA Performance

To measure the performance of the WUA in terms of whether it is adequately providing the good, some studies use the level of satisfaction of farmers with WUA management (Sayin et al., 2013; Bhatt, 2013; Kadirbeyoğlu and Özertan, 2011; Uysal and Atiş, 2010; Gorton et al., 2009). Other more specific performance criteria are suggested in the literature and revolve around such categories as on-farm production, operational efficiency, financial efficiency, equity and environmental impacts. Some of these performance criteria, such as those relating to operational efficiency and equity, were attainable in Jordan’s water user association context. Others connected to financial matters, and technical production counts and measures were not attained. With regard to operational efficiency, this is the most extensive performance measure to cover and is cited frequently in the literature. It pertains to the effectiveness of WUA management in maintaining the networks, supplying water and keeping users in-line with the rules. In maintaining the networks, the physical condition of the network is pertinent, to include the state of the current infrastructure and the amount and kind of maintenance conducted on it (Bhatt, 2013; Sayin et al., 2013; Gunchinmaa and Yakubov, 2010; Bandyopadhyay et al., 2010; Garces-Restrepo, 2007; Vermillion, 2006; Lam, 1998; Ostrom et al., 1994; Tang, 1992). For measuring the water supply, whether the water delivery is reliable and dependable, of good quality, comes in a timely fashion and is adequate are relevant elements (Sayin et al., 2013; Huang et al., 2010; Gunchinmaa and

Yakubov, 2010; Kazbekov et al., 2009; Akkuzu et al., 2007; Garces-Restrepo et al., 2007; Vermillion, 2006; Ostrom et al., 1994). And in keeping users in-line with the rules-in-use, the number of occurrences of network manipulation and how they are dealt with, whether rules are enforced and followed, and whether conflicts arise and are managed are potential

85 measures (Bhatt, 2013; Gunchinmaa and Yakubov, 2010; Bardhan, 2000; Ostrom et al., 1994; Tang, 1992). The measures desired in this study for operational efficiency will be the farmer’s perspective on the performance of the WUA in general, and as compared to the JVA, as well as whether farmers follow the daily operational rules and do not irregularly manipulate the network to steal water. The other key arena of WUA performance to be assessed within Jordan’s water user associations is whether the water is distributed in an equitable manner to farmers, whether based on equal shares or proportional shares according to land holdings or crop requirements. Several studies highlight this measure (Bhatt, 2013; Sayin et al., 2013; Gunchinmaa and Yakubov, 2010; Kazbekov et al., 2009; Akkuzu et al., 2007; Garces- Restrepo et al., 2007; Vermillion, 2006; Dayton-Johnson, 2003; IFAD, 2001; Tang, 1992; Vermillion, 1991;). This study will be concerned with how farmers perceive their treatment by the WUA as compared to how other farmers are treated.

Measures excluded from this study include on-farm production and financial efficiency of the WUA. Many studies measure whether a change in on-farm production has occurred with the arrival of a WUA in terms, as Vermillion (1991) outlines, of “cropping intensities, yields, or profitability…per unit of land, water or labor” (Sayin et al., 2013; Zhang et al., 2013; Gunchinmaa and Yakubov, 2010; Córcoles et al., 2010; Uysal and Atiş, 2010; Akkuzu et al., 2007; IFAD, 2001; Vermillion and Sagardoy, 1999). But others argue that farm income, in general, is not the ideal indicator of WUA performance due to, as Garces-Restrepo et al. (2007) note, “a wide range of factors, such as farm location, ability to produce the adequate crops, access to inputs, access to markets, access to transport facilities, and farmer’s managerial skills” (p. 42).

Financial efficiency is another category of WUA performance indicators that is not addressed in this dissertation but is still widely viewed as a key arena to demonstrate WUA capabilities. Previous studies measure financial efficiency in terms of the rate of water fee collection (Bhatt, 2013; Uysal and Atiş, 2010; Huang et al., 2010; Gunchinmaa and Yakubov, 2010; Bandyopadhyay et al., 2010; Garces-Restrepo et al., 2007; Vermillion, 2006; Vermillion and Sagardoy, 1999) and/or the amount of cost recovery, whether revenues from water fees meet capital and operations/maintenance costs in the network (Sayin et al., 2013; Córcoles et al., 2010; IFAD, 2001). What makes this a factor unattainable in Jordan is that the WUAs still do not have financial independence. Thus, these measures in Jordan would still address the government agency’s amount of cost recovery and its rate of fee collection, not that of the WUA.

WUA Participation

As noted earlier, WUA performance can be assessed through the level of participation among farmers in WUAs and how much they are involved in WUA activities. The blunt measure is the number of farmers within the concerned area who have willingly joined the WUA as members (Yami, 2013; Qiao et al., 2009; Agrawal and Gupta, 2005; McCarthy et al., 2004). Some studies have looked into which activities farmers participate in within the WUA (such as decision-making, voting, lobbying and training courses) (Nisha, 2013; Bhatt, 2013; Khalkheili and Zamani, 2009; McCarthy et al., 2004; Agrawal and Goyal, 2001) and whether the farmer holds an administrative post in the WUA (Agrawal and Gupta, 2005). The amount of cash and labor contributions that a farmer makes is used as well (Tigabu et al., 2013; Hamada and Samad, 2011; Khalkheili and Zamani, 2009; Agrawal and Goyal, 2001). Fujita et al. (2005) take the provision of 87 contributions a bit further in their measure of active cooperation among small, user- managed irrigation systems in the Philippines. They fashion a composite collective action score based on how much collective work is conducted among farmers to clean the canals and laterals, how much coordination occurs among farmers when scheduling rice cropping, and whether water rotation schedules are followed by farmers. In sum, farmer participation in terms of whether they know the rules of the WUA and follow the rules is most relevant (Ostrom et al., 1994; Tang, 1992). More fundamentally is the question of whether farmers are aware of the WUA and what it is doing (Hamada and Samad, 2011).

POTENTIAL FACTORS AFFECTING THE OUTCOMES

The literature suggests many factors that could be affecting WUA performance or farmer participation in the WUA. The following sections catalogue these factors from the literature as they pertain to the physical, community, institutional or user domains. Each section concludes with a hypothesis regarding the likely effect of the factor as assessed from the literature. All of these hypotheses will be used in subsequent testing of the factors in the results chapters (Chapters Eight through Twelve).

Physical and Environmental Factors

Status of the Infrastructure

Water delivery and ease of management of the water network greatly depends upon infrastructure that is sound and operates efficiently, with little water wastage or delays due to facility and piping malfunctions. Unfortunately, as Huppert (2013), a former technical officer with the German development agency GTZ, remarks, it is quite common to see

88 maintenance fall by the wayside due to apathy on the part of all actors involved, thus rendering the infrastructure degraded and inefficient. He states that water users, for one, try to free-ride on the efforts of others and thus hope to benefit from the maintenance efforts of others; irrigation agency engineers shy away from maintenance chores because they reap little benefit towards their professional advancement in such activities as compared to larger construction projects; and higher-level management prefers to delay maintenance in order to get larger future budget allocations for such activities. With these attitudes, it is no surprise that many systems are, from the start, hindered by poor infrastructure.

With regard to efforts towards implementing PIM or IMT, Ghazouani et al. (2012), Asrar-Ulhaq (2010), Xie (2007), and Vermillion and Sagardoy (1999) all note that a key enabling and facilitating factor to successful performance of a WUA is a well-functioning infrastructure, with a very poor and dilapidated infrastructure leading to serious problems with irrigation network performance. In some cases, this requires rehabilitation or specific improvements before a WUA is implemented because requiring farmers to pay for these expenses themselves could create disincentives for them to participate in the first place. Mukherji et al. (2010) offer an important caveat to this discussion of the need for a rehabilitated network, stating that this is still no guarantee for success. What matters is how the system is managed and maintained over the long-term. Frija et al.’s (2009) study of WUAs in the Cap Bon area of Tunisia supports this point and notes the importance of the status of infrastructure past the stage of implementation. They see that as networks get older under WUA management, they get more expensive and harder to maintain and the WUA has to spend more money on them, thus leaving the WUA looking less efficient and performing worse if unable to make the repairs. And while WUA performance is many times the outcome of interest when looking at the status of infrastructure, Bassi et al. (2010)

89 also point out that in Gujarat, India, many farmers did not become members of the WUA because the physical condition of the network was so poor. Infrastructure problems can thus also be an issue for WUA participation rates.

Hypotheses: Where infrastructure is of higher quality and is operated and maintained at a higher level, cooperative efforts will meet with more success and farmers will be more likely to be members of the WUA.

Water Scarcity

One factor that is cited frequently in the literature as having an effect on the level of participation in cooperative efforts and the effects of those cooperative efforts is the level of water scarcity of the system within which users operate. Bardhan (2000), in a study on irrigation communities in South India, and Lubell et al. (2002), in a study on the formation of watershed partnerships in the US, see that in cases of more extreme water scarcity, cooperative efforts break down and are less likely.

On the other hand, many studies find the opposite to be the case. Regmi (2008), looking at farmer-managed irrigation systems in Nepal, finds that there are higher levels of rule-following in less-well-endowed resource systems. In an International Fund for Agricultural Development (IFAD) (2001) study on water user associations in worldwide projects, farmers are more likely to cooperate as water resources became scarcer. Pasaribu and Routray (2005) find that among farmer-managed irrigation systems for rice production in Indonesia that have greater water deficits, while they are more prone to conflict, they are also likely to have higher levels of agreement on water fees, a willingness to pay and satisfaction with the way fees are collected. This means that they have more of an interest in making sure that the water supply is steady despite the greater water scarcity. And

Keremane and McKay (2006) note, in their study of water user associations in India, that farmers view water scarcity as their number one reason for joining an association. Fujita et al. (2005) also observe that collective action within user-managed irrigation agencies in the Philippines is more difficult where the water supply is more abundant. An interesting twist on this debate between whether water scarcity leads to more or less cooperation among water users is that perhaps this relationship is non-linear. Some authors (Uphoff et al., 1990; Bardhan, 1993; Meinzen-Dick et al., 2002; Araral, 2009) discuss the possibility that there is an inverse U-shaped relationship between water scarcity and participation in collective efforts. When water is extremely scarce, users do not expect enough water and thus have fewer incentives to cooperate; when water is abundant, users do not feel the need to join in any efforts for a resource that already gives them what they need. So only when water is moderately scarce, it is thought, will users be motivated to participate together to maintain the resource.

The presence of alternative water sources should also be taken into account because they can be an important part of a farmer’s assessment of whether his supply of water resources is scarce. In the case of a user group that pulls its primary water from a river, some farmers might have private access to groundwater or other water supplies that are not open to others in the group or area. Ghazouani et al. (2012) find that if farmers have access to outside sources of water, this “contributes to undermine social cohesiveness” and leads farmers to value less the need for collective action (p.53). Bassi et al. (2010) similarly see that farmers in Gujarat, India with access to a secondary source of water, such as a tubewell, are less likely to be members of the WUA. And Pant (2008) as well finds that one of the most important factors in getting farmers to cooperate and work for a common good is that

91 they all greatly depend on the water source, such as a canal, for their crops and ultimate survival and have no workable alternative.

Hypotheses: Moderate water scarcity, versus extreme or minimal water scarcity, leads to more cooperation between farmers and better performance. If farmers have secondary water resources, they will be less likely to cooperate in an effective manner.

Water Predictability

A related concept to water scarcity is that of water predictability. The less common position taken in the literature is that if a resource is less predictable, there is more cooperation. McCarthy et al. (2004) argue this point in looking at collective action for natural resource management in villages in Burkina Faso. They argue that more climatic variability (less predictable water supply – rainfall) is associated with a higher value put on developing networks to maintain systems. Where a resource is unpredictable, there is a greater need for some sort of “mutual insurance” from the community, perhaps in the form of a collective management body. But the opposite position on water predictability is cited more often in the literature. As the resource becomes more unpredictable, cooperative efforts are less likely. Groenfeldt (1988) and Ghazouani et al. (2012) both note the difficulty of developing collective action around a resource that does not yield water on a predictable basis. Agrawal (2001) and Wang et al. (2013) bring in two related topics, mobility and storage of water resources, to further emphasize the point. If the resource is prone to greater mobility and cannot be easily stored, it will be hard for users to commit to rules that govern its use because of the high information, monitoring, rule enforcement and sanctioning costs that will be required. Collective efforts are aided by a more predictable resource and users are 92 more assured of their fair share when the resource is predictable, thus leading to their greater desire to be a part of these collective efforts. Network design in particular can have an effect on water predictability. In Mukherji et al.’s (2010) review of case studies of IMT in Asia, they find that pump-based lift irrigation networks are more likely to be turned over with success than gravity-based networks. The reason, they surmise, is that in pump-based networks, users have more control and can respond better to varying demands due to the presence of more storage capacity and the ability to divert water temporarily, meaning that water can be better controlled and rendered more predictable. Ghosh et al. (2010) come to the same conclusion in their study of WUAs in the Indian state of Orissa, finding that in the case of lift irrigation networks, as well as minor networks, as compared to major networks, farmers are more in control and feel more of a sense of ownership and financial independence. An added and important note is that where the water supply varies and is not uniformly predictably across different sections of a given area, collective action can suffer. This is seen by Fujita et al. (2005) in the Philippines where farmers cooperate less where the water supply is variable depending on the section of the lateral. Water has to be predictable and even across all parts of the network.

Hypotheses: A more predictable and reliable supply of water will make the WUA more successful and more farmers will be likely to join the WUA. Performance of the WUA will be enhanced where water is more equally reliable across all parts of the distribution network.

System Size

The size of the resource system, or the physical area covered by the farmers’ plots and the water distribution network, is also mentioned as potentially having an effect on the 93 ability to act collectively and produce successful outcomes. This factor does not refer to the number of individuals involved in an association but rather just the physical area. Wang et al. (2013), in looking at village-level irrigation governance in China, find that with a larger area serviced, which entails longer channels and more plots of land, monitoring costs increase, communication between users becomes more difficult, and users cannot as readily see what fellow farmers are doing. This all leads to fewer incentives to act collectively to manage water resources. Mushtaq et al. (2007), again in China in the Hubei Province among farmers managing ponds for secondary irrigation water sources, find that collective action levels were higher where the ponds were smallest. And Fujita et al. (2005) observe a negative impact on collective action in the Philippines for the larger areas over which a user-managed irrigation agency is established. But in contradiction to these three studies, Meinzen-Dick et al. (2002), when looking at farmer participation in irrigation management in two Indian states, see a positive relationship between the size of the command area and farmer participation. They find that it is easier to organize a larger group and easier to then attract the attention and resources of the government. In disagreement with both of the afore-mentioned positions, Mukherji et al. (2010), in their review of a large number of case studies from Asia, find that scheme size or area makes no difference in terms of success for an IMT or PIM project.

Hypothesis: WUAs with responsibility for a larger area will not perform as well as WUAs with smaller areas of responsibility.

Crop/Farm Diversification

This category acts as a catch-all for whether the variability in crops, farm design and farm operations among farmers has an impact on the performance of and participation

94 in water user associations. For example, the IFAD (2001) study on WUA projects worldwide finds that different irrigation technologies used on farms determine the level of complexity of operations and maintenance on a network, thus potentially requiring more specialized training and/or outside support services. These issues can signal that more advanced collective management are required or that more complex incentives need to be established to get farmers to be able to or want to participate. Berg (2014) takes the issue more broadly to discuss in general the impact that crop diversification can have for the operations of irrigation networks. Where crops are more uniform, allocation of irrigation water and general coordination among farmers can be much easier tasks. For example, in the case of cereals, in particular rice, the crop has a very particular schedule and farmers can more easily and jointly plan to use water in an efficient manner. But with crop diversification, so posits Berg (2014), farmers have varying irrigation schedules and more individualized water needs, making cooperation among them for water use more difficult.

Hypothesis: Where there is more diversity and complexity of cropping patterns, it will be harder for a WUA to perform well and it will be less likely for farmers to join a WUA.

Weather and Natural Events

A less commonly mentioned but potentially constraining factor for the successful performance of WUAs is the weather or other natural or ecological phenomena for a particular region. For example, Lansing and Miller (2003) show that the threat of rice pests is a large inducement for Balinese rice farmers to willingly cooperate to reduce their presence by coordinating their cropping patterns and water use to reduce the pest population. This unique ecological problem is at the heart of farmer participation in

95 collective action in this area. It could be imagined that weather or other natural occurrences could impede or facilitate farmer cooperation although not many specific examples are found in this domain.

Hypothesis: Extreme weather-related and natural events will have a negative impact on farmers’ ability and desire to cooperate in matters of water distribution.

Community Factors

Preexisting Community Organization

Several studies note the importance of prior experience with some form of collective organization and/or some sort of parallel system of community organization that can enhance efforts to collectively manage water resources. Ostrom (2000b), Regmi (2008), Lubell et al. (2002) and Ghazouani et al. (2012) emphasize the benefits that can accrue from having some sort of prior experience with, knowledge of or history of local organization. If farmers are already familiar with the types of rules and strategies needed to manage commonly-held resources, they will be more likely to agree upon these same types of rules and strategies to govern their shared resources. This would not be the case for farmers who have no prior experience and are likely introduced to these rules and strategies by external parties. The presence of some form of community organization in a different realm is also enhanced cooperative efforts, acting as the common-ground area for new organizations.

For example, among Balinese rice farmer communities, Lansing and Miller (2003) point out the parallel networks of shrines and temples created to worship agricultural deities. These temples also serve as places where farmers meet and coordinate their cropping patterns and labor requirements. Meinzen-Dick et al. (2002) similarly show, among 96 irrigation systems in India, that the number of temples in the area has a positive effect on farmer participation in irrigation management. Beyond the mere existence of prior or present community organizations that can parallel organizations for water management, water user associations work better when they are in agreement with local conditions and traditional community arrangements. This represents one of Ostrom’s (2010) fundamental design principles, that there be a congruence between whatever kind of organizational and distributional rules are agreed upon within an association and the local social conditions. This view is supported by

Vermillion and Sagardoy (1999), IFAD (2001) and Herath (2009). For example, Yami (2013) examines participation in irrigation systems in Ethiopia and finds that where there have been attempts to introduce “modern” irrigation schemes in the place of traditional systems and by-laws, there is a decrease in common understanding among users, more difficulty in enforcing rules and increased insecurity among users about their access to the system. Of course, there is a potential downside to high levels of influence and pressure from the local community (Ghazouani et al., 2012). For example, Gorton et al. (2009) find there to be a negative influence from previous associations on the present cooperative efforts among farmers in water communities in Macedonia. In this particular case, the concept of “associations” is too closely associated to those associations of the socialist era so farmers do not immediately think of these as good entities. Sometimes having prior experience with a type of collective action is not an automatically beneficial thing and has to be counteracted, making sure that any current associational concept is not related to a negative entity of the past.

There is also another potential danger to new cooperative institutions from preexisting organizational constructs. Theesfeld (2011) observes that in WUA councils in Kyrgyzstan, they tend to be made-up largely of the people who were the big actors in the village before the establishment of WUAs, so these new WUAs simply resemble the “existing local power asymmetries.” Kemerink et al. (2013) echo this sentiment in that the new management structure, meant to differentiate and separate itself from existing organizations and power structures, can instead “tend to reflect the existing power relations,” “benefit the haves over the have-nots,” and “have further reinforced existing inequities” (p. 251-253).

Hypotheses: Having prior or even present experience with some form of communal management, or having close agreement between WUA management and local customs and traditions, will be of benefit to WUA performance. At the same time, there is a potential danger that such prior or existing experience could harm newer efforts at communal management.

Heterogeneity of Farmers

The effect of heterogeneity on collective action is complex and covers many different angles. While some studies (Bardhan, 2000; Lubell et al., 2002; IFAD, 2001; Keremane and McKay, 2006) simply mention that the degree of heterogeneity within the group has a negative relationship with levels of cooperative effort. This is a more superficial position when compared to that of Baland and Platteau (1996), who categorize heterogeneities into three groups: heterogeneities of endowments, identity and interests. They posit that heterogeneities of endowments have a positive effect on collective action and heterogeneities of identity and interests have a negative effect. Differentiating between the different kinds of heterogeneities can be useful, although the evidence thus far is still

98 inconclusive with regard to the exact relationship between these types of heterogeneity and collective efforts. With regard to the effect of heterogeneity of identity, or heterogeneity in ethnicity, religion, village of origin, social grouping and/or culture, there is ample evidence to suggest that it will have a very negative effect on the ability of users to act collectively to manage their shared resources. The existence of a shared cultural or social identity leads to higher levels of social capital, which then leads to higher levels of trust and reciprocity and thus more collective action (Herath, 2009). Several studies emphasize this point (Groenfeldt,

1988; Ruttan, 2008). In her description of successful cases of user management of common-pool resources (communal tenure in high mountain meadows and forests in Torbel, Switzerland; common lands in Japanese villages; irrigation institutions in Spanish huertas; and Philippine zanjera irrigation communities), Ostrom (1990) finds that the existence of many norms of “proper” behavior and the similarity between participants, due to the populations remaining stable for such a long time and thus being culturally and socially homogeneous, are powerful and beneficial elements in their ability to cooperate and jointly manage their resources. On the other hand, where such longevity of cultural homogeneity and “social capital” does not exist, problems arise. Dayton-Johnson (2000) finds that user groups in

Mexico that have members from many different villages have lower levels of canal maintenance than groups with users all from the same village. In Egypt and Jordan, where in- and out-migration have resulted in a “new social fabric” (Ghazouani et al., 2012), establishing durable and stable water user associations has proven difficult. Similarly, Meinzen-Dick et al. (2002) find that in areas where farmers are from a greater number of different villages, it is harder for them to gather together and coordinate for maintenance

99 tasks. And Bassi et al. (2010) echo these thoughts in their study of WUAs in Gujarat, India, where more conflicts arise among farmers who belonged to different castes. Studies of the public provision of other goods have made the same point. Miguel and Gugerty (2005) look at the provision of school funding, school infrastructure and community water well maintenance in Kenya and find all to be negatively related to ethnic diversity. Habyarimana et al. (2007) examine the provision of public goods in Uganda and find that ethnically homogeneous groups are better able to provide such goods because they respond to “in-group reciprocity norms.” On the other hand, while these studies have shown a negative relationship between types of identity heterogeneity and collective action, other studies have not found any significant correlation between the two (Vermillion and Sagardoy, 1999; Fujita et al., 2000; Regmi, 2008; Nisha, 2013). Heterogeneity in terms of endowments, or wealth, socioeconomic status, landholdings and assets, provides a more complex and debated case. Cárdenas and Ostrom

(2004) find that heterogeneity in social position and wealth can be an obstacle to cooperative efforts. Place et al. (2004) review a number of studies that link this type of inequality with poor performance of collective action. And Dayton-Johnson (2003) observe that despite any shared social norms, they can be outweighed by inequalities that then affect users’ abilities to cooperate. In a study on irrigation communities in South

India, Bardhan (2000) finds a negative association between inequality of landholding and cooperative efforts, a position that Xie (2007) reiterates. And Baland and Platteau (1999) suggest that if the same contributions are needed from all members, wealthy and poor, then inequality among members could lead to very poor collective action outcomes. Economic inequalities can be present in the size of users’ land parcels, the quality or production potential of the land, and the status of users’ titles to the land (Bardhan, 2000).

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While it might seem intuitive that wealth inequality would lead to less collective action and agreement among resource users, some theoretical and empirical studies find the opposite to be the case. As a starting point, Olson (1965) posited that inequality could be a good thing for group effort. A dominant user who looks to gain the greatest benefits if a good is provided could very well pay most of the costs of providing the good, at least initially. In this sense, inequality among users could aid in the start-up of providing the good. Vaidyanathan (1986) and Bardhan (1993) remark on the useful role that such socioeconomic inequality has played in the past in that wealthier and larger land owners have been able to enforce more cooperative strategies on groups of lesser socioeconomic standing. In addition, there is the possibility that wealthy and poor members can make different yet needed contributions, such as in Varughese and Ostrom’s (2001) study on forest management in Nepal. Wealthier members contribute financial resources and poorer members contribute labor. And Ruttan (2008), in his study involving fishery and irrigation cases, finds that in some cases, there is a positive correlation between economic heterogeneity and two measures of successful collective action: ability to close access to the resource and implementing formal sanctions. This occurred because these two measures have positive externalities, leading wealthier members to value their achievement even at their personal expense. However, endowment heterogeneity could have negative consequences for participation and lead to more inequality between water users. As Ruttan and Borgerhoff Mulder (1999) note, the end result of this endowment heterogeneity might be generally less equitable and undemocratic in nature. Ruttan (2008) finds that while economic

101 heterogeneity has a positive relationship with performance level, as noted above, it has a negative relationship with participation rates. When addressing heterogeneity of interests, it is somewhat intuitive to imagine that differing interests in the group act as a significant obstacle to cooperation. If water users are not of the same mind when it comes to how the water should be used, when it should be used or how the water can be used more efficiently, this can cause tensions among them and lead to difficulties in acting together. Petrzelka and Bell (2000), in looking at common property resource organizations in two villages in Morocco, find that not only is solidarity in interests important in having people willing to cooperate with each other but also solidarity in sentiments. As one would think, people who like each other tend to get along better. They find that in one village where people spend time together after work, singing and dancing and celebrating important events, their cooperative institutions are stronger.

Hypotheses: Heterogeneity in endowments or socio-economic status could have a positive impact on WUA performance, although only if those wealthier parties support the WUA in additional ways even though others less-wealthy might be free-riding on their efforts, and a negative impact on WUA participation. On the other hand, socio-cultural or identity-related heterogeneity and heterogeneity in interests will negatively impact WUA outcomes and participation rates in WUAs.

Political Environment and Support

With regard to political support, Garces-Restrepo et al. (2007) remark that a lack of political support in some areas is a major impediment to successfully transferring irrigation management to user groups. De Graaf and van den Toorn (1994) and Tang (1992) also stress the need for this higher-level political backing of management transfer. And while high-level, ministerial support is essential, included in the idea of political support is that of the government irrigation agency and its willingness to pass on 102 responsibilities to the farmers. It is commonly seen that the government irrigation agency has a tendency to strongly resist this intended change whereas many have pointed out that this agency’s commitment to and valuation of IMT is a key factor in the success of any user-based organization (Suhardiman, 2013; de Graaf and van den Toorn, 1994; Coward and Uphoff, 1986). Government agency workers fear IMT and are uncertain of its consequences, viewing it as a threat to their jobs, budgets, and level of influence and power to make decisions (Garces-Restrepo et al., 2007; Vermillion, 2006; Hamdy, 2004). Sometimes state agencies are ignored in the IMT process when they should be a focus and given different tasks to substitute for their former tasks that are now given to users (Garces- Restrepo et al., 2007; Vermillion, 2006).

Hypothesis: WUAs can only succeed and farmers join them if there is strong political support from all levels of government for WUAs.

Market Environment and Support

For market conditions, De Graaf and van den Toorn (1994) emphasize the need for farmers to see advantageous market prospects and profits if they make contributions to the provision and maintenance of water resources. Market conditions for their products must be strong enough to provide the proper incentives. The state’s markets policies must be such that the agricultural sector in general is supported so that farmers see a reason to continue being farmers and managing their water well.

Hypotheses: Only with a strong national market for agricultural goods and marketing support for farmers will they be interested in joining WUAs and working collectively to manage their joint water resources.

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Institutional Factors

Legal Authority

The idea of PIM, leading to the ultimate goal of IMT, generally assumes that whatever body is created acts autonomously and with some sort of legitimate authority to carry out certain activities within a certain field. In IMT, it is assumed that farmers will eventually be making decisions independently with regard to water allocation and management. The goal is to get them to take over management and release the government agency from this burden. But, as Meinzen-Dick et al. (2002) point out, many times within a context of management devolution, state involvement continues apace and farmers do not act autonomously, especially in terms of setting the rules to which they must abide. Mukherji et al. (2010) also share this concern, stating that a WUA’s autonomy is frequently challenged by the state agency and a power struggle between the two ensues. Sometimes, in what they see as extreme cases, the WUA is so pressured and directed by the state agency that its organizational set-up purposefully mimics the bureaucratic set-up of the state agency. It is telling, in Bassi and Kumar’s (2011) look at PIM in India, that the government office actually fears a complete transfer of responsibilities, especially with regard to irrigation fee collection duties. They state that in this case, the WUAs “may start behaving like a political entity and then it will become much [more] difficult to monitor and supervise their work.” For eventual legitimization of the WUA to take place, Pant (2008) urges that the state irrigation agency, in the least, take note of and act on the demands of farmers. If their complaints are met with silence and are thus delegitimized, “the water users lose interest and the WUAs tend to become defunct” (p. 550). Moreover, the necessary laws and legal resolutions should be put in place in order to transfer power from higher to lower levels.

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Unfortunately, as Garces-Restrepo et al. (2007) note, “governments have not wanted to face the difficulties of changing the existing laws…and have tried to implement the reforms with existing, unsatisfactory legislation or with ministerial decrees that have lacked the necessary weight and authority” (p. 46). In particular, Frederiksen and Vissia (1998) argue that it is financial independence that is of utmost importance for a user-managed irrigation office as it is the ultimate form of autonomy and being able to run one’s own affairs.

Giving WUAs sufficient autonomy, while a common end goal, is often jeopardized by the current state irrigation agency and the government in general. Clement (2010) add that it is not sufficient to only officially devolve the necessary legal rights because the way that this kind of move is interpreted and accepted on the ground can differ. The “local structural and relational mechanisms” governing how official edicts and rules are implemented, she states, are the important elements that determine whether the end community can actually benefit from some newly-developed rights. For example, Dewan et al. (2014) look at participatory water management in coastal Bangladesh and find that all of the outward and loud support for participation is merely a “guise” being used to “give a ‘human face’ to depoliticized and technocratic projects.” Participation in this arena is only “public consultation” at best, with any real decision-making power remaining at the top. As Ghosh et al. (2010) posit, what is really needed is a paradigm shift from one of

“participatory irrigation management” to one of “participatory irrigation governance.” Governance would then imply more decision-making authority.

Hypothesis: The WUAs must possess legitimacy and autonomy in the eyes of all stakeholders in order to be successful and make farmers willing to participate in them.

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Collective-Choice Arrangements and Rules

Many studies stress the importance of user groups having sufficient and appropriate rules to guide their activities, facilitate their collective efforts, hold each other accountable and designate particular roles to their groups and to the government agencies and offices related to their work. Without these types of institutions, participation in user groups can be a risky proposition for farmers and the outcomes of efforts by such groups can be for naught. Some of Ostrom’s (2000a, 2010) fundamental design principles for user associations are of relevance to this topic and are supported by others in the literature. One important design principle is the need for some collective choice arrangements that give all involved users the right to participate in decision-making, rule-making and electing leaders. This requires, as Vermillion (2006) notes, that there be clear rules of who the members are and are not, democratically-decided procedural and operational rules as well as the right to elect and remove any leaders or service providers. Hunt (1989) and

Ghazouani et al. (2012) similarly voice the need for these kinds of arrangements. Agrawal and Chhatre (2006), in their particular study on forest management in the Indian Himalayas, find that with greater competition in the choice of officers (i.e. more democratic elections), forest conditions are better. On the other hand, as seen in India’s attempts to implement PIM by Swain and Das (2008), in areas where WUA officers are elected in an uncontested manner based on claimed consensus, the reality is that the elections are manipulated by the local elite. They see this manipulation of democratic proceedings as leading to a “lack of faith of water users in the credibility of office bearers,” which then threatens the sustainability and efficiency of WUAs. Ul Hassan (2011) also notes elite capture of key positions in WUAs in his look at case studies from India, Sri Lanka, Pakistan, Turkey and the Fergana Valley. Any

106 attempt at mobilizing members to de-seat a local elite requires a high “social cost” and resources, with a resultant lack of protection if others do not go along. In general, having elections assumes that all members have an equal say and equal opportunity to defend and support their interests when the reality is, as Hoogesteger (2012) points out, that there is potential serious harm being done by the “power differentials” between members in these organizations. In particular, he points out those economically, socially and politically marginalized groups who tend to consume less water. They are presupposed to have less interest in governance issues, to not be unified and to be

“backward.” Vermillion (2006) seconds these sentiments, positing that this negligence of less powerful groups is seen most especially in places where leaseholders and tenant farmers are not allowed to be members in associations. All parties have to feel invited to voice their opinions and to feel that their opinions matter. And yet, farmers sometimes have almost too much faith in their elected officers and this can be a bad thing, rendering them complacent when it comes to election time or participation in communal activities. Maleza and Nishimura (2007), in their look at irrigation systems in the Philippines, find that members tended to rely on their elected officers for all decisions and implementation of duties, making for very low participation in group discussions, monitoring activities and following irrigation schedules. Not only do leaders need to be elected democratically, but members still need to be actively involved in order for a WUA to function well.

As a side note on formal rules and “institutions,” Douglass North (2005, 1995) adds that institutions can also be informal norms, values, beliefs or codes of conduct. Ostrom’s (1990) “working rules” are similar in aim, realizing the need to look beyond the formal rules in any given situation and focus on what informal rules might be guiding the situation.

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In the arena of farmers managing water for irrigation, it is acceptable that there be some informal rules at work and this can be sufficient, although they would still need to be obvious and acceptable to all involved parties.

Hypothesis: The WUA will perform better and farmers will want to join the WUA at a higher rate when there are clear and democratic collective-choice arrangement, meaning that there are open and democratic procedures for determining membership and electing leaders that would allow farmers to participate in the decision-making.

Monitoring, Sanctioning and Conflict Resolution

Other commonly cited principles that reap better outcomes for collective management are the presence of solid monitoring, sanctioning and conflict resolution mechanisms. As Ostrom (2000a) remarks, “few long-surviving resource regimes rely only on endogenous levels of trust and reciprocity” (p. 151). What is needed to keep users in- line is a system that has users monitor each other or that makes users beholden to an outside monitor. Among irrigation communities in South India, Bardhan (2000) found a positive relationship between cooperative efforts and monitoring by guards, with Kadirbeyoğlu and Özertan (2011) finding a positive relationship between user satisfaction and monitoring within three WUAs in Turkey. Interestingly, Agrawal and Chhatre (2006) find a negative relationship between the use of guards and successful outcomes of collective management of forests. Their interpretation of this finding adds a potentially fruitful avenue of investigation. They posit that guards are hired mostly in cases where forests are not in good condition. Because many forests in the study are in good condition, the observed relationship does not accurately display this relationship. What can be taken away from their interpretation is that users employ monitoring where they see resource conditions

108 potentially floundering. In cases where resource conditions are already good and not threatened, the absence of monitoring cannot be used to predict collective action outcomes. With regard to sanctions, the assumption is that if there exist punishments for when violations of the resource network occur at the hands of water users, collective action will be more successful and users will join collective bodies with more assurance that rules will be followed by everyone. Vermillion (2006) emphasizes the need for strong sanctions to ensure compliance not only among water users but also water user association officers and management staff. Among community-based drinking water organizations in Costa Rica, the presence of clear rules regarding punishments for not paying water fees or leaving systems in disrepair provide the necessary incentives for users to stay within the rules (Madrigal et al., 2011). On the other hand, Araral (2011) finds among collective organizations for large-scale irrigation in the Philippines that informal incentives, such as “reputational pressure,” can play a more powerful role than official enforcement and sanctioning measures. Cox et al. (2010) echo this sentiment, suggesting that sanctions might not be necessary where there is “strong social capital,” adding that sanctions should not seek to replace such informal sanctions that already exist on the ground. As for conflict resolution mechanisms, there is an expected benefit to collective action outcomes when “rapid, low-cost, local arenas exist for resolving conflicts among users or between users and officials” (Ostrom, 2000a; Ostrom, 2010). If users have no recourse to a body or person who can adjudicate on disputes that arise among them, collective action is sure to run afield as users come into greater conflict and disagreement. Initial participation will also be hindered due to a lack of any assurance that they will have a way to voice their concerns about any conflicts with fellow users or officials.

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An interesting twist on this factor is introduced by Keremane and McKay’s (2006) study of water user associations in India. They observe that in some areas where formal conflict resolution measures exist, conflicts are more common than in other areas where no formal measures exist but where users rely on “social pressure” to keep members in-line. Similarly, in looking at WUAs in the Gash Irrigation Project in Sudan, Ngirazie et al. (2015) find that conflict resolution is very good because the tribal base in this area is so prevalent and strong. The tribal head is much respected and therefore farmers respect his decisions in these matters. Much as with the informal sanctioning measures mentioned above, conflict resolution mechanisms might not necessarily be formal and open but rather tucked within traditional and/or informal mediums.

Hypotheses: Where there is clear and effective monitoring, the WUA will be more successful and have higher membership. WUAs will perform better and incentivize more farmers to join when there are effective formal, or informal, sanctioning measures. Where there are formal, or informal, conflict resolution mechanisms to aid farmers with issues that arise between them, membership in WUAs will be higher and WUAs will more effectively manage water resources.

User Factors

Leadership

Many studies make note of the presence of strong and visionary leaders as influential factors in the success of user associations. In Regmi’s (2008) study of farmer- managed irrigations systems in Nepal, performance is positively influenced by an individual in the group willing to assume the leadership role and pursue entrepreneurial activities. Kazbekov et al. (2009) find that among water user associations in Kyrgyzstan, those with strong leaders who are able to more effectively communicate with users and bring a higher level of transparency to the association perform better. Similarly, in India, 110

Keremane and McKay (2006) observe that water user associations are driven in large part by one particular, influential local leader and his NGO. And both Ghazouani et al. (2012) and IFAD (2001) find that in the Middle East and worldwide, respectively, the quality of management is highly dependent on the personality, skills and initiative of leaders. In the same vein, Kumnerdpet and Sinclair (2011), in looking at water user organizations in Thailand, find that particular barriers to success in implementing organizations are poor leadership skills and an absence of motivators to make individuals want to be leaders. On the other hand, success is seen where leaders are dedicated to the project. Ostrom (2000a) points out that in the least, a leader can be an essential initial stimulus to cooperative efforts. But, as Pant (2008) states, good leadership does not necessarily mean “selfless commitment” on the part of that leader. The important thing is that the leader has a strong vested interest in the WUA, with large land-holdings and/or large water consumption rates, for example. In this way, their working for the common good concomitantly increases their own personal benefits. Also, some argue that a strong leader could be a crutch or dependence that, in the long run, hinders an association’s development. Merrey (1996) cautions that dependence on a leader without the establishment of strong institutions that will last beyond the life of the leader could jeopardize the sustainability of an association. Madrigal et al. (2011) add that strong leadership can result in more passive and apathetic participation of other members in the user group and this can lead to less than optimal long- term outcomes. Refuting all positions, Nisha (2013) sees no correlation between active leadership and levels of participation.

Hypothesis: A skilled, committed and visionary leader will be a boon for any WUA but a good leader is not a substitute for having a strong institution of leadership and active participation of other members.

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Level of Dependence on Water Source and Agriculture

Many have observed that where users are dependent on irrigation water resources for a large portion of their livelihoods, they are more likely to actively and constructively participate in user groups (Ostrom, 2000b; Vermillion and Sagardoy, 1999). Khalkheili and Zamani (2009) find that farmers in Iran with more irrigated land and thus a greater need for dam water participate more in the management of water resources. In their studies on forest management in Nepal and the Indian Himalaya, Agrawal and Gupta (2005) and Agrawal and Chhatre (2006), respectively, observe that those who subsist more on the products of the forest are more likely to expend greater efforts to protect the forests and participate in decision-making on matters related to the forest. And as seen by Mushtaq et al. (2007), with regard to ponds that serve as secondary water sources for communities in Hubei Province, China, among those farmers who depend on the pond water more for irrigation, they tend to manage the pond more effectively.

The existence of alternative incomes for some farmers, and thus their likely lesser dependence on farming and irrigation water for survival, can also be important. Khalkheili and Zamani (2009) find that for farmers who do not have a second job, they are more concerned about irrigation management than those with a second job. Fujita et al. (2000) use the proportion of non-farm households within the irrigation community as a proxy for this type of exit option and find that it is significantly and negatively associated with performance. Similar results are seen by Fujita et al. (2005); among irrigation systems in the Philippines, the number of non-farm households in the area is negatively associated with levels of cooperation among farmers. A somewhat conflicting position is seen by Agrawal and Gupta (2005) who find that households with alternative sources of income are more likely to participate in user groups.

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Hypothesis: When the users are more dependent on the joint water resource and agriculture for their livelihoods, they are more likely to actively participate in the WUA and the WUA is likely to perform better.

Socioeconomic Status

Most studies suggest a positive relationship between, on the one hand, wealth (income and/or landholdings) and socioeconomic status (caste/social status and/or being an office-holder), and on the other, participation levels. Tigabu et al. (2013) show, with regard to household participation in rural water supply systems in Ethiopia, that income positively affects the amount of cash and labor contributions, as would be intuitively expected. Nisha (2013) finds that household participation in rural water supply systems in India increases with per capita income. Qiao et al. (2009) observe that village cadres, or elites, and those with higher cropping incomes are more willing to join water user associations than peasant farmers and those with smaller incomes. And among users of local forests, Agrawal and Gupta (2005) find that richer and upper caste households are more likely to join user groups. They additionally posit that this could be due to “bias in existing forms of program implementation that encourages the economically and socially better-off to participate more” or “because of microlevel social and political connections with government officials and greater access to existing resources” (p.1110).

Hypothesis: For farmers with higher socioeconomic status, they are more likely to participate in the WUAs.

Land-Holding Status

Whether farmers are leaseholders or tenants versus farm owners is expected to have an effect on the likelihood of their participation in user groups and the performance of user

113 groups. As Vermillion (2006) notes, when a large portion of the farms are cultivated by tenants and leaseholders, the establishment of associations can be made difficult. This is due to the potential for these leaseholders and tenants to be excluded from membership, even though they might still be responsible for certain fees. Thus, these excluded farmers will not be as incentivized to actively and beneficially engage in the maintenance of the water networks. Vermillion and Sagardoy (1999) echo this notion that collective action is more enabled when all farmers are landowners or at least farming the land on multi-year leases. Ghazouani et al. (2012), though, bring up a potential difficulty in allowing non- landowners to be members. In particular, there may be issues that arise among non- landowners about why they have to pay certain water fees if they do not own the land.

Hypothesis: Where the majority of water users are owners, or at least more permanent renters, participation rates in the WUA will be higher and the WUA will perform better.

Education

Some studies find a positive relationship between the education level of farmers and their willingness to participate in user groups. Nisha (2013) shows that as the level of education increases, so does the rate of participation. Among community-based drinking water organizations in Costa Rica, Madrigal et al. (2011) find that education level is a contributor to the level of human capital, which in turn has a positive impact on the performance of the organization. Gorton et al. (2009) take a slightly different position on the positive impact of education level within their study of water communities in Macedonia. They observe that more educated farmers are more likely to be members because they can see the longer-term benefits and are willing to wait for them. On the

114 other hand, the less educated farmers are not as likely to be members because they cannot as readily understand these longer-term benefits and are more impatient to see improvements in the short term. At the same time, others have witnessed a negative relationship between education level and participation rates. For farmers in Iran, as observed by Khalkheili and Zamani (2009), educational background has a negative impact on farmer participation. They see this as possibly indicative of younger people being more educated and typically acquiring the bulk of their income in non-agricultural activities. This results in their lack of attention to and participation in irrigation management. Agrawal and Gupta (2005) also find a negative relationship between education level and participation rate. They posit that those with more education typically work outside of the resource system and thus are less available to participate and less interested in participating. Yet other studies report an insignificant relationship between education level and participation level (Tigabu et al.,

2013; Qiao et al., 2009).

Hypothesis: Better-educated farmers are more likely to participate in the WUA.

Perceived Benefits to and Incentives for Membership

One claimed advantage of farmers joining together in an association is that their costs will be reduced, or the effort and expenses that they put forth will be less in some sense. This follows first from Coase’s (1937) work “The Nature of the Firm,” in which he discusses the advantages of establishing a firm over relying on the market or the price mechanism. The firm is able to internalize some costs among individuals, especially those related to information availability and making contracts. Williamson (1996) furthers this idea in his position that whatever form of economic organization is chosen, it should seek 115 to economize on transaction costs or lead to the most efficient way that individuals can interact with each other and accomplish their goals. The idea in a water user association is that it should help farmers to lower their individual costs and make life easier for them in some way. Information acquisition should be easier, requests should be more convenient to make, and the like. Farmers should be able to see that there are advantages to joining the group or else they will have no incentive to join. There will also be some benefits that accrue over the short-term and others over the long-term, with people in general favoring those that arrive in the short-term. People tend to additionally give more weight to potential losses than potential gains (Ostrom, 1990). Indeed, as Hanatani and Fuse (2012) find in southern Senegal, farmers are simply more interested in the immediate benefits whereas the long-term, communal benefits are not very important to them. Many discuss the benefits-costs issue in relation to farmers’ willingness to participate in user associations and participate in a way that makes for better performance of the user association; the costs have to be outweighed by the benefits (Merry, 1996; Vermillion and Sagardoy, 1999; Xie, 2007; Tang, 1992). As Swain and Das (2008) succinctly state, farmers should be convinced that the benefits to their participation will be “substantial, tangible, quick-yielding, and also sustainable.” Hamdy (2004) more thoroughly delineates the benefits as being that of “physical system improvements, improved water supply, increased farm income, empowerment and conflict resolution obtained through WUAs,” while costs are the “substantial time, materials, cash and interpersonal transaction costs of being active in local irrigation organizations” (p.19). In their study of water communities in Macedonia, Gorton et al. (2009) observed that farmers become members of these communities in order to see improvements in the irrigation

116 systems, reduce water costs, and have more control over maintenance and water delivery. Abernethy (2010) points out that it is also better to make farmers a part of the initial thinking on and establishment of the WUAs. As he states, “when the system already exists, it is much more difficult to identify an incentive that will make people want to come together and make inputs to an organization.” Hunt (1989) posits that there can also be certain “intangible benefits” that need to be considered, such as is the case with head-enders in the systems in Sri Lanka. These head-enders gain a sort of “moral benefit” from helping those in greater need downstream.

In a similar vein, Kadiri et al. (2009) see that within the WUAs in the Moyen Sebou irrigation scheme in Morocco, farmers have appropriated, adapted and transformed the initial WUAs that they were given into organizations that are better suited to their needs and activities. They adapted the rules in use, the hiring procedures and the way they work and schedule water turns. An unexpected benefit has been that the WUAs have acted as a

“school in which these young people learned about associative life, collective action, and had contacts with the administration and the authorities.” This might be a very particular benefit that is unlikely to happen elsewhere but there is still much to be gain for farmers from participating and gaining greater control, although they do have to be allowed, as in this area in Morocco, to adapt and modify the rules.

Others note that where the benefits from management transfer are unequal among farmers, problems arise. Wegerich (2008), in discussing WUAs in south Kazakhstan, observes that WUAs end up serving only the interests of a few and are a way for those few to generate some extra income. Venot (2014) too finds this to be the case in looking in general at commons management in sub-Saharan Africa. The WUAs are established with a focus on downstream irrigators, who tend to be more productive and more structured into

117 groups from the beginning, leaving small-scale water users or women out of the picture. While some farmers may then be very satisfied with the performance and presence of WUAs, it is likely not sufficient for long-term sustainability or equity.

Hypotheses: Farmers will only join WUAs when they perceive that the benefits will be greater than the costs, especially when the benefits will accrue in the short-term but regardless of whether the benefits are necessarily tangible.

SUMMARY

There are a wide variety of elements within user management to examine when attempting to parse-out performance and participation levels. This chapter has produced a rather exhaustive list of physical, community, institutional and user-related factors that could be impacting how water user associations perform and whether farmers participate within them. The hypotheses associated with these factors are listed in full in Table 4.2. There is no clear indication in the literature that any factor or group of factors are the most important or relevant within any given context. For this reason, all must be examined in one way or another and the following chapter outlines how this is done within this dissertation.

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Table 4.2: Hypotheses for the four main categories of factors. Physical and Environmental Factors Status of the Where infrastructure is of higher quality and is operated and Infrastructure maintained at a higher level, cooperative efforts will meet with more success and farmers will be more likely to be members of the WUA. Water Scarcity Moderate water scarcity, versus extreme or minimal water scarcity, leads to more cooperation between farmers and better performance. If farmers have secondary water resources, they will be less likely to cooperate in an effective manner. Water A more predictable and reliable supply of water will make the WUA Predictability more successful and more farmers will be likely to join the WUA. Performance of the WUA will be enhanced where water is more equally reliable across all parts of the distribution network. System Size WUAs with responsibility for a larger area will not perform as well as WUAs with smaller areas of responsibility. Crop/Farm Where there is more diversity and complexity of cropping patterns, Diversification it will be harder for a WUA to perform well and it will be less likely for farmers to join a WUA. Weather and Extreme weather-related and natural events will have a negative Natural Events impact on farmers’ ability and desire to cooperate in matters of water distribution. Community Factors Preexisting Having prior or even present experience with some form of Community communal management, or having close agreement between WUA Organization management and local customs and traditions, will be of benefit to WUA performance. At the same time, there is a potential lurking danger of such prior or existing experience to harm newer efforts at communal management. Heterogeneity Heterogeneity in endowments or socio-economic status could have a of Farmers positive impact on WUA performance, although only if those wealthier parties support the WUA in additional ways even though others less-wealthy might be free-riding on their efforts, and a negative impact on WUA participation. On the other hand, socio- cultural or identity-related heterogeneity and heterogeneity in interests will negatively impact WUA outcomes and participation rates in WUAs. Political WUAs can only succeed and farmers join them if there is strong Environment political support from all levels of government for WUAs. and Support

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Table 4.2: (continued) Market Only with a strong national market for agricultural goods and Environment marketing support for farmers will they be interested in joining and Support WUAs and working collectively to manage their joint water resources. Institutional Factors Legal The WUAs must possess legitimacy and autonomy in the eyes of all Authority stakeholders in order to be successful and make farmers willing to participate in them. Collective- The WUA will perform better and farmers will want to join the Choice WUA at a higher rate when there are clear and democratic Arrangements collective-choice arrangement, meaning that there are open and and Rules democratic procedures for determining membership and electing leaders that would allow farmers to participate in the decision- making. Monitoring, Where there is clear and effective monitoring, the WUA will be Sanctioning more successful and have higher membership. WUAs will perform and Conflict better and incentivize more farmers to join when there are effective Resolution formal, or informal, sanctioning measures. Where there are formal, or informal, conflict resolution mechanisms to aid farmers with issues that arise between them, membership in WUAs will be higher and WUAs will more effectively manage water resources. User Factors Leadership A skilled, committed and visionary leader will be a boon for any WUA but a good leader is not a substitute for having a strong institution of leadership and active participation of other members. Level of When the users are more dependent on the joint water resource and Dependence on agriculture for their livelihoods, they are more likely to actively Water Source participate in the WUA and the WUA is likely to perform better. and Agriculture Socioeconomic For farmers with higher socioeconomic status, they are more likely Status to participate in the WUAs. Land-Holding Where the majority of water users are owners, or at least more Status permanent renters, participation rates in the WUA will be higher and the WUA will perform better. Education Better-educated farmers are more likely to participate in the WUA. Perceived Farmers will only join WUAs when they perceive that the benefits Benefits to and will be greater than the costs, especially when the benefits will Incentives for accrue in the short-term but regardless of whether the benefits are Membership necessarily tangible.

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Chapter Five: Methodology

A series of methods was applied during fieldwork conducted in Jordan from January 2014 to June 2015 in order to gather the data required to analyze the outcomes of water user association (WUA) performance and the factors that can affect these outcomes. No previous studies contained detailed information on the WUAs nor the inner-workings of the irrigation water distribution systems in the Jordan Valley. The Jordan Ministry of Irrigation and Water also appeared unlikely to offer what data they had on the WUAs, farming and farms in the Jordan Valley, and the water distribution systems. Thus, extensive field work was necessary to gather all of the relevant data first-hand. The first task, through a general contextual assessment, was to explore the environment in the Jordan Valley and the role of the WUAs in this environment. After determining the layout of water management in the Jordan Valley, where the WUAs were present, and what kinds of tasks the WUAs were undertaking, I focused on those WUAs working more independently on daily management tasks. Within these WUAs, I conducted interviews with their WUA heads to better understand the internal dynamics of the WUAs and how they differed among each other. These interviews aided in further categorizing the WUAs in terms of level of responsibility, the nature of their activities conducted in the field, and their physical surroundings.

From the details gathered on the WUAs in the interviews with their heads, I was able to determine which ones would be most conducive to undergoing a deep-dive investigation. In part, this was determined by ease of access and willingness of the WUA to accept my further exploration of their daily activities. The choice was also determined by the variation displayed among the four chosen WUAs with regard to their farm and

121 water characteristics and location in the Jordan Valley. Within these four WUAs, I conducted a survey among farmers to gather yet more detailed data on their personal and farm characteristics as well as their opinions on the WUAs’ activities. The survey provided the bulk of the data for the quantitative analyses. Finally, follow-up interviews were conducted with a handful of farmers to gather more detail on crop inputs and requirements. Interviews were also conducted with those who were involved in the initial implementation of the WUAs as well as with government agency officials still involved with the development of the WUAs.

After completion of the fieldwork, I compiled the data from the farmer interviews into a data set that was cleaned and coded for variables not yet in numerical format. As recounted in the sections below regarding how variables were fashioned, decisions were made to exclude certain observations or categorize variables in particular ways. For those factors without quantifiable data, I endeavored to explore them with different methods and sets of information as were available. The quantitative, qualitative and descriptive methods for exploring the different factors are imperfect but when dealing with a topic for which little data exists, this is the accepted reality. Fieldwork was funded by Boren and Fulbright Fellowships but no limitations were set by either of these fellowship organizations on my research and neither had any influence on the kinds of methods I applied or how I applied the methods. These fellowship organizations also had no impact on the conclusions that I have reached.

CONTEXTUAL ASSESSMENT

Upon arrival in Jordan, during January and February of 2014, I held meetings with: university professors; JVA staff in the Ministry of Water and Irrigation in Amman, the 122

WUA Unit and Control Center in Deir Alla in the Jordan Valley; the JVA directors of the Northern and Southern Jordan Valley regions; the directors of the directorates of the North, Middle and South of the Jordan Valley; engineers and employees in other JVA offices in the Jordan Valley; employees of the United States Agency of International Development’s (USAID) Institutional Support and Strengthening Program; employees of Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ), the German development agency; and officials working in the Ministry of Agriculture in the Jordan Valley (see Appendix A for a complete listing of these individuals). These discussions provided information on the current status and characteristics of the WUAs and water management and agriculture in the Jordan Valley. At this stage, general and high-level data was collected from JVA offices on such variables as: size of land area under each WUA; number of farms under each WUA; number of farmers within each WUA area; number of members in each WUA; date of establishment of each WUA; tasks transferred (none, distribution, or distribution and maintenance) to each WUA; name of the head of each WUA; the water ordered by, sold to and released to each WUA; money allotted to each WUA as stated within their contracts with the JVA; number of cases of water stealing, tampering of parts of the network, and maintenance within each WUA (data not present for all WUAs).

I also made initial visits to all functioning WUAs in the Jordan Valley to assess their general roles and functions and the situation on the ground through discussions with their heads, engineers and ditchriders. This stage included smaller-scale data gathering of a qualitative nature on particular WUAs and their internal rules, daily operations and farmer characteristics (see Appendix A for a listing of these WUA meetings).

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This period included a visit to the National Drip Irrigation Company located south of Amman. The company manufactures drip irrigation piping for farms in addition to providing consulting services for farmers on irrigation methods. Discussions were held with Engineer Ayman al-Idreesi and a tour of their facilities and produced pipes were conducted. This meeting provided information on the levels of technology that farmers can use on-farm based on their financial and technological capacities.

Throughout this research stage, shorter discussions were held with farmers in various areas of the Jordan Valley alongside other meetings held with WUA or JVA employees or in the field and at random. These discussions yielded information on: the types of crops grown in the Jordan Valley; the irrigation methods employed by farmers; the problems they face in agricultural production and marketing; and their thoughts on the WUAs. No formal or consistent line of questioning was employed in any of these initial discussions with the various actors involved with water management and farming in the

Jordan Valley. These discussions provided a baseline level of knowledge to more consistently and accurately employ the ensuing research methods.

INTERVIEWS WITH WUAS HEADS

I conducted interviews with all heads of WUAs that have some form of task transfer, either for water distribution or water distribution and light maintenance activities, in order to better understand the internal workings of the WUAs. These interviews occurred between March and June of 2014. The 13 WUAs included in this set are located at: PS (Pump Station) 28, PS 33, and PS 41 in the north; PS 50 and PS 55 in the middle; PS 81, PS 91, PS 95, Kafrein and Rama in the south; and Mazraa-Haditha, Khanizeera and Fifa in the southern ghor. 124

Each interview consisted of a structured questionnaire conducted in Arabic within the following domains: personal characteristics, management of the WUA, daily operations of the WUA, history of the WUA and the area in which it is located, financial matters within the WUA, the WUA members, the relationship with the JVA and the future outlook of the head for the WUA (see Appendix B for full interview questionnaire in English). Allowance was made for any additional topics of concern or tangents upon which the association head wished to speak. Much of the information in the following chapter about WUAs is gleaned from these interviews.

The interview questions made some assumptions that were not accurate but they thus provided a way to correct these presumptions and better understand the situation. For example, there were questions relating to the elections and these questions assumed that association members elect the president. In fact, association members elect the administrative council, which in turn elects the president.

From these interviews, the particular characteristics of each WUA were assessed and compared. From this process, four case studies were selected based on the variation found within these interviews among the WUAs.

IN-DEPTH CASE STUDY ANALYSES

Due to limited time and capabilities, only four WUAs were chosen for a more in- depth review, investigation and “deep dive.” These case study analyses were conducted at various points from April to December of 2014. The four WUAs are: PS 33 in the north, PS 55 in the middle, PS 91 in the south and Mazraa-Haditha (MH) in the southern ghor. They were chosen due to their different locations in the valley as well as the variation they display on a number of other factors: water source (freshwater, treated wastewater or spring 125 water); crop pattern (citrus, vegetables, palms, etc.); land area and number of farm units; number of greenhouses; membership rates; membership fees; nationality of farmers (Jordanians, Egyptians, or Pakistanis); and network infrastructure and operations (see Chapter Seven for details on each case). Time was spent in each of the four WUAs to: observe their daily routines, activities, elections, and meetings between farmers and WUA staff; accompany ditchriders on field tours; and conduct lengthy discussions with engineers and ditchriders.

FARMER SURVEY

After the deep dives into the four case study WUAs, a survey was conducted among farmers from each of the four WUAs between September of 2014 and February of 2015. This period represents the high season for agricultural activity in the Jordan Valley, with most farmers more actively involved on their farms during these months. A total of 197 farmers were surveyed; between 41 and 56 farmers were surveyed in each WUA (Table 5.1). Including a larger number of farmers would have been optimal but with limited time and abilities as a sole researcher, a survey of around 200 farmers was deemed adequate.

Table 5.1: Number of survey participants within each of the four WUAs. WUA Number of survey participants PS 33 52 PS 55 48 PS 91 56 MH 41 Total 197

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As a point of comparison, a different survey apparatus to assess the progress of the WUAs was created by USAID’s Institutional Support and Strengthening Program (ISSP), a body that works with the water ministry to improve management of water and wastewater services. This apparatus is today still used by the JVA and aims to talk with 10% of farmers in each area under a WUA or the JVA (Discussion with Nayef Seder at ISSP, 1/27/2014). The survey conducted for my study reached more than 10% of farmers in each of the four chosen WUAs. Thus, it was presumed that the sample size, while small compared to the total number of farmers under each WUA, still allows for sufficient understanding of the situation within these four specific WUAs. I also purposefully opted to only do the interviews myself because I wanted farmers to feel comfortable talking with me and not worry that the information would be imparted to others in the community. My goal was a random sample. For each area, I examined the map of the farming area and randomly chose farms at the beginning, middle and end of each lateral line within the pressure and gravity networks. My intention was to visit only the chosen farm units as much as possible. I then made many visits to each WUAs. The engineers and ditchriders helped me to find the farmers I had chosen, whether by bringing them to the WUA office to speak with me or by driving me out to the farm units to speak with the farmers. I always spoke with the farmer one-on-one without the presence of anyone from the WUA.

Sampling Issues

There were some complications for my sampling method. Some of the farm units randomly chosen were, when seen in the field, not being farmed at the time. I was forced to pick the farm next to it or within the vicinity. If I ran into a non-chosen farmer at the WUA office or in the field, I would take the opportunity to interview him even though he 127 was not on my chosen list. I also did not reach all of the chosen farms on my list due to time constraints and not wanting to tax the WUA further with my need for assistance. Therefore, the sample is not perfectly random but I did try to meet with farmers in all types of systems and locations, with agent, renters and owners, and with farmers of different nationalities. Because the WUA ditchrider or engineer helped me to find the farmers, there was a danger that the farmers would think that I worked for the WUA, JVA or a donor agency. At the beginning of my interviews, I presented myself as an independent researcher and discussed the purpose of my research, all with the intent of preventing any thoughts that I worked for another government or non-government agency. Suspicions of my intentions could have remained but it was not feasible, logistically or safety-wise, to enter the field by myself. The WUA’s assistance was therefore a necessary impairment. In the end, only a few farmers did not want to be interviewed.

Survey Design

The survey was structured with a set of 50 questions (see Appendix C for the English translation of the survey). I did allow farmers to elaborate on any answer or deviate into other related topics as they saw fit. The survey was done orally and in Arabic. I wrote the farmer’s answer to each question in a notebook and later put these answers into an Excel spreadsheet. Each survey took anywhere from 15 minutes to more than an hour, depending on how much the farmer wanted to discuss. There are many questions that require basic and minimal answers, thus explaining the short time frame for some interviews with farmers who only gave the most minimal answers and did not elaborate.

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Sometimes a farm tour and show-and-tell was also involved when the farmer was eager to make this presentation. My objective with the survey was to record the user factors that could potentially be influential factors for the outcomes, gather other pieces of personal and farm-related data, and hear their viewpoints on WUA performance. The first part of the survey dealt with the personal characteristics of the farmer: his farm location, ownership status, whether he has moved around to different farm units, age, education level, nationality, level of involvement with the farm and experience in farming. The second part of the survey dealt with the characteristics of the farm: land area, crops grown, greenhouses in use, irrigation and water systems on the farm, time and day of water turns, use of secondary water sources and where the farmer sells his produce, either locally or for export. Many of these questions indirectly relayed information about the farmer’s socioeconomic status and farming capacity.

The third and final part of the survey asked farmers to give their opinions on a host of performance-related issues regarding the WUA: whether the water supply is reliable and adequate; whether the water supply is distributed in a fair manner; whether the ditchriders are fulfilling their duties of monitoring, sanctioning, distributing water and conducting maintenance tasks; whether the WUA is able to handle farmer problems and conflicts on its own or requires the help of the JVA; whether water stealing and vandalism of the networks occurs; their general opinion of the WUA and comparing its performance to that of the JVA; whether they are members in the WUA or not; whether there are benefits to WUA membership; and whether they participate in WUA activities.

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Survey Issues

While my previous questioning of farmers did hone my line of questioning to more relevant and precise topics, complications and difficulties with some question still arose. Conducting a pilot survey would have been optimal but was not feasible due to a lack of time and not wanting to further drain the goodwill of the WUAs and their support for my research. Thus, after conducting a number of surveys, I realized that some questions were irrelevant and others needed more thorough explanations in order for farmers to understand the intent. For example, some farmers had trouble remembering the hours and days of their water turns. Some farmers in the same area, where they all theoretically received the same hours and days of water, reported different days and hours. In the end, I determined that this question was not really necessary although it was interesting to find such a variety of answers when there should have been only one answer. Asking about a secondary water source was a sensitive question. For the most part,

I felt that farmers were honest and admitted to having a well or using water directly from the KAC, even when these actions are illegal. It is still possible that others were using secondary water sources and did not share this with me. For water reliability, farmers did not always understand that this question differed from that of water adequacy. I had to explain that this question referred not to the amount but whether the water was coming at the right time during his water turn. Many farmers said that the water is reliable but they also said that the pressure was very weak. Thus, they would receive the appropriate hours of water but not necessarily the correct amount. The question of water adequacy attended to this issue by asking about the amount. While many farmers admitted to the WUA not being fair in its treatment of farmers, not all farmers were likely honest with me. Especially for those who benefit from special

130 favors from the WUA or farm agents who might be afraid to speak of such matters, their answers could have been self-censored. The question asking whether ditchriders open and close the laterals properly was an unnecessary one. There were no conflicting opinions in this arena; ditchriders attend to this task fully everywhere. Whether ditchriders attend to maintenance tasks was not a useful question either because maintenance is still largely the duty of the JVA, even on the farm turnout assemblies (FTAs), or is contracted out to a third party. In MH, its task transfer contract does not yet include maintenance.

Asking whether the WUA helps in resolving conflicts among farmers was problematic in that some farmers thought there were no conflicts between farmers. The WUA, for them, had no conflict resolution role. For this reason, I made a category for farmers who thought there were no problems between farmers in the first place. While most farmers stated that water stealing occurs in their area, not necessarily by them personally but in general by farmers, a smaller group of farmers still denied that water stealing happens. They could have honestly believed that no farmer in the area steals water but their denial could have been for another reason. One of three reasons could be at work: 1) they were afraid to tell the truth for fear of getting in trouble with a more powerful actor or the WUA; 2) they did not steal and did not fully understand that I was asking about farmers in general and not them in particular; or 3) they were not the ones stealing water and didn’t want me to think that they steal water.

For the overall outcomes of WUA performance and participation, answers were arguably contradictory. Some farmers would say that the WUA is “good” but at the same time say that it is the “same thing” as the JVA. Other farmers did not know whether they were members in the WUA or not and some farmers, especially in MH, had no idea that

131 there was even a WUA in the area. When asked how the WUA can improve, the most common response was that the farmer wanted more water. Only a few farmers had more particular suggestions and critiques.

DETAILED FOLLOW-UP INTERVIEWS WITH FARMERS

Second and third interviews were conducted between December of 2014 and

January of 2015 with a handful of farmers within each of the four surveyed WUAs who own a variety of types of farms (vegetables, palms and citrus). These interviews yielded information on the kinds and types of inputs costs that farmers face in the entire agricultural production process as well as the profits they achieve at the market. Questions to farmers revolved around both their seasonal and fixed costs. Seasonal costs included those for: mulch, seeds, nursery palates, pesticides and fungicides, manure, boxes and tape for transport, water, electricity, diesel, laborers (permanent and temporary), and transportation vehicles to get crops to the market. A few farmers also disclosed roughly how much they could expect to be paid for certain crops at the market. Fixed costs were for such things as: irrigation lines and their installation, materials for and installation of greenhouses or tunnels, tractors, and materials for and installation of holding ponds. To gain further insight into the general Jordanian socioeconomic and agricultural contexts, other data was collected to fact check price levels quoted by farmers. This data included: market prices in the central/Amman local market for select fruits and vegetables from the Jordan Ministry of Agriculture (monthly data from 2007-2014) and the Jordan Department of Statistics (DoS) (monthly data from 1998-2012); Jordan’s population, population growth rate, GDP, GDP per capita and agriculture’s percentage of the GDP (from 2006 to 2013) from Jordan’s DoS; consumer price indices on various goods (from 132

2008 to 2013) from Jordan’s DoS; overall and by economic sector average annual income from the DoS’ Household Expenditure and Income Survey of 2010; number of employed workers (Jordanian and non-Jordanian) in the agricultural sector from the Ministry of Agriculture’s 2012 annual survey; and the number of hired laborers in the agricultural sector (Jordanian and non-Jordanian as well as permanent, seasonal and casual) from 1998 to 2013 from DoS data.

INTERVIEWS WITH DONOR AND GOVERNMENT AGENCY OFFICIALS

The last stage of fieldwork involved meetings between March and June of 2015 with those who either worked with GIZ or the JVA in the initial implementation of WUAs and who are presently involved in the development of the WUAs. Three former employees of GIZ and two JVA employees who worked on GIZ’s WUA project during its initiation were interviewed as well as the current JVA director responsible for the future development of the WUAs. Those interviewed included:

 Jochen Regner, German GIZ team lead (2001-2011).

 Ali al-Adwan, Jordanian GIZ team lead (2003-2014).

 Fadi Abu Sahyoun, GIZ staff member on the WUA project (2001-2009) and currently a JVA employee in the Northern Directorate.

 Ali al-Omari, JVA employee who aided in GIZ’s project and is currently a JVA staff member in the WUA Unit.

 Ahmed al-Bukhari, head of a JVA stage office in the southern ghor at the time of GIZ’s project who aided GIZ and currently a JVA employee in its planning and contracts department.

 Khaled al-Qsous, not involved in the initial project but currently the head of JVA’s WUA Unit.

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These individuals were asked questions within the following topics: the origin of the idea of WUAs; the initial vision for the WUAs; how farmers and the JVA reacted to the initial idea of WUAs; how implementation of the WUAs began and progressed; how the WUAs operate at present; the future of the WUAs; and what GIZ and other aid agencies do today with regard to the development of the WUAs.

DATA ANALYSIS

After completion of the fieldwork, the data was exploited through a series of analytical methods to test the validity of the research hypotheses as developed in the literature review (Chapter Four, Table 4.2). The research hypotheses posit that various factors within four categories (physical, community, institutional or user) have certain impacts on the outcome variables. Below, the factors are considered as the independent variables. Each independent variable has a hypothesis that has already been developed with regard to its impact on the outcome variables (or dependent variables). These hypothesis are tested. Sometimes the methods for testing are statistical in nature but where quantitative data is not available, the methods are more descriptive and qualitative. The results of the analyses are featured in Chapters Eight through Twelve. The details of the data obtained through the survey are discussed herein as well as how each independent and dependent variable was determined and analyzed.

Statistical Analysis

For those research hypotheses that can be tested in a statistical fashion, the independent and dependent variables used or created from the survey data are listed in

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Appendix D in a variable key. The number of observations used in all statistical analyses was reduced from the original 197 surveys conducted. Observations of farmers who reported “I don’t know” for either the question asking them to state their opinion of the WUA (good, so-so or bad) or their opinion of the WUA in comparison to the JVA (better, same thing or worse) were removed. If farmers could not answer these questions, their answers to any others related to WUA performance were deemed irrelevant. This left 186 observations. More observations were removed in other particular instances depending on the variables in use, as discussed below.

Depending on the outcome variable, an ordered logistic regression or a logistic regression was used to determine the effect of the independent variable(s) on the dependent variable. All regression results are reported in odds ratios. STATA results are recorded in Appendix E for all regressions discussed in this section but not all are listed in the final results. For the logistic regressions, Ordinary Least Squares (OLS) regressions were also used to check the validity of the regressions. The results of these regressions are presented in Appendix F with the same table numbers as those tables of results that they correspond to in Chapters Eight through Twelve. With each independent variable used in a quantitative/statistical analysis, the variable is first assessed by itself in a regression predicting the five outcome/dependent variables. The level of significance of this one factor and the amount of variation explained by it are observed and recorded. These initial regressions are conducted in Chapter Eight

(physical factors), Chapter Ten (institutional factors) and Chapter Eleven (user factors). In Chapter Twelve, each variable/factor assessed for its individual impact on the outcome variables is considered in joint analyses with all other quantified variables in its category. All quantified physical factors are together used in regressions to predict the outcome

135 variables; the same joint regressions are conducted for the institutional and user factors. The effect size and significance of each variable is recorded again and the overall amount of variation explained by an entire category of variables is observed. Finally, all variables in all three categories are combined into final regression analyses to predict the outcome variables. Only those variables that continued to display significance or importance in the previous analyses are used in these final regressions. The final regressions show the entire amount of variation in the outcome variables that can be explained by all categories of variables together.

Independent Variables

There are independent variables associated with each of the original hypotheses outlined in Chapter Four and they are further explained in the following sections. Included in their explanations are how they were measured and how they were assessed for their impact on the dependent variables.

Water Scarcity

The variable indicating the level of water scarcity is adequacy, which is a binary variable where 1 is the water supply is adequate and 0 is the water supply is not adequate. Logistic regressions were run to determine if there are any differences among the four

WUAs with regard to adequacy (see Appendix E, Results 1). Ordered logistic and logistic regressions were run with the five outcome variables as dependent variables and adequacy as the independent variable to see if water adequacy has a significant impact on the outcomes (Appendix E, Results 2).

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Further logistic regressions were run with the dependent variable as adequacy to see whether having a secondary water source has an impact. The variable secondary water is used to indicate whether a farmer has a secondary water source (1 for if the farmer has a secondary source of water, 0 if he does not). Variables for the WUAs (ps33, ps55, ps91) and interaction terms between the WUAs and secondary water were included to see if that would have any impact (Appendix E, Results 3).

Water Predictability

The variable indicating the level of water predictability is reliability, which is a binary variable where 1 is the water supply is reliable or predictable and 0 is the water supply is not reliable or predictable. Logistic regressions were run to determine whether there are differences between the four WUAs with regard to reliability (Appendix E, Results 4).

Ordered logistic and logistic regressions were run with the five outcome variables as dependent variables and reliability as the independent variable to see if water reliability has a significant impact on the outcomes (Appendix E, Results 5). Further logistic regressions to assess whether water reliability is a product of the characteristics of the network (pressure or gravity) and lateral position (beginning, middle or end) were run. First, a logistic regression with reliability as the dependent variable and pressure network (having a farm in the pressure network) and both networks (having farms in both the pressure and gravity networks) as the independent variables is run, with gravity network (having a farm in the gravity network) as the excluded category. Second, a logistic regression with reliability as the dependent variable and beginning position (having a farm at the beginning of the lateral), middle position (having a farm at the middle of the lateral) 137 and multiple positions (having farms at various positions along the lateral) as the independent variables is run, with end position (having a farm at the end of the lateral) as the excluded category (Appendix E, Results 6). Third, regressions were run on some of the outcome variables to determine whether network type and lateral position, while including water reliability, have any significant impact on opinion of and participation in the WUA (Appendix E, Results 7).

To make sure that there is no possible significance when reliability is interacted with the network type or lateral position variables, interaction terms were generated and inserted into the logistic regression models predicting opinion of wua and comparison of wua to jva (further explanation of outcome variables in subsequent sections). The interaction terms are as follows: reliability*pressure network (interaction between reliable and sys1), reliability*both networks (interaction between reliable and sys3), reliability*beginning position (interaction between reliable and pos1), reliability*middle position (interaction between reliable and pos2) and reliability*multiple positions (interaction between reliable and pos4). Network types and lateral positions were also interacted with each other and added (pressure network*beginning position, pressure network*middle position, pressure network*multiple positions, both networks*beginning position, both networks*middle position, both networks*multiple positions) (Appendix E,

Results 8). Next, logistic regressions were used to assess whether network type and lateral position have an impact on membership in the WUA while taking into account water reliability. Logistic regressions were run with membership as the dependent variable and reliability, pressure network, both networks, beginning position, middle position, multiple

138 positions and their various interaction terms as the independent variables (Appendix E, Results 9). The same procedures were run with the outcomes of water stealing and fairness of wua with no significant results found for any of the network type or lateral position variables, nor for their interactions. For water stealing, this was further complicated by the fact that some of the independent variables were perfectly predicting the dependent variable so these analyses are not ideal (Appendix E, Results 10).

Status of the Infrastructure

Through observation of the WUAs and discussions with JVA and WUA employees, a more qualitative assessment of this factor was achieved. Unlike with other factors, the assessment is at the larger valley level, without any specific differences to be pointed out between the four surveyed WUAs. Differences were observed but are not quantifiable and are subtler in nature. Therefore, an overall assessment of the impact of infrastructure status on WUA performance and farmer participation in WUAs is achieved.

System Size

System size is examined at the WUA-level and is assessed by way of the land area and number of farm units under the purview of each of the four surveyed WUAs, as gathered from meetings with WUA employees and JVA officials. Because of the small number of observations (four WUAs only), a quantitative assessment is not possible.

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Crop/Farm Diversity

There are two ways that crop diversity is assessed, at the farmer level and at the WUA level: for the former, whether a farmer, on his own, is growing more than one type of crop; for the latter, whether all farmers in the WUA are growing the same type of crop. These crop categories are citrus, vegetables, date palms, grape leaves, herbs, olives, melons and bananas. See Figure 5.1 for a breakdown of the types of crops grown by surveyed farmers in the four WUAs. All of these crops differ in terms of the quantity and timing of their water needs and/or the way they are irrigated, making them distinct and diverse types of crops. This diversity, in turn, can make farmers differ in their water needs and affect other variables.

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Figure 5.1: Types of crops grown by farmers in the four surveyed WUAs (farmers could choose more than one category).

Source: Survey data.

Climate and Natural Events

This factor, as with the previous few, is analyzed on a larger scale for the Jordan

Valley and Jordan as a whole. Data from the literature on temperature and rainfall trends in Jordan are consulted. Additional discussions with farmers in the Jordan Valley and observation of the valley after storms provided insight into the effects of dramatic climatic events on their farms and the water networks. There are also some differences seen between the areas in which the four surveyed WUAs operate.

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Preexisting Community Organizations

This factor relies primarily on farmer commentary during initial meetings or the survey data and material gathered in interviews with WUA heads. Analysis is qualitative in nature.

Heterogeneity of Farmers

Heterogeneity in endowments among the surveyed farmers is assessed not through income (as this information was not able to be gathered) but through the number of dunums owned, whether the farmer has greenhouses, whether the farmer exports some of his production and his education level. If all farmers generally have the same land holdings, on-farm capabilities and education level, then there is little heterogeneity in existence between farmers. For farmers who have more dunums of farmed land, they are likely better off than farmers with very small plots of land. Additionally, farmers with greenhouses are typically able to farm more intensively and thus earn more per dunum. When a farmer exports goods, this is another sign that he has more capital and financial wherewithal. And finally, education is a type of endowment that could serve a farmer well in his general strategy-making and planning for his farm. Heterogeneity in identity is assessed through the nationality of farmers and their ownership status. If most farmers are of one nationality and/or are mostly agents, renters or owners, then their level of heterogeneity would be low.

Heterogeneity in interests is a more abstract concept related to how a farmer sees and uses his farm, whether for a livelihood, a hobby, to keep up his family’s heritage or simple as a salary in the case of agents. Due to the wide-ranging nature of this subject area, farmer interest is judged by whether the farmer has a secondary job or source of income.

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For those with a secondary job or income, it is presumed that his sole livelihood and interests are not just in agriculture under this WUA. If a larger portion of farmers have secondary jobs or incomes, then it will be assumed that they are more diverse in their interests.

Political Environment and Support

The assessment of the political environment and the support to farmers and the WUAs is based on commentary heard from farmers, those involved in GIZ’s WUA project, and higher-level analysis of the political situation in Jordan in light of the Arab Spring.

Market Environment and Support

Jordan’s market environment and marketing support for farmers is viewed first at a higher level, with Jordan’s membership in the World Trade Organization, and then at a lower level from farmers themselves and how they see the environment. An additional observation of market prices for select agricultural goods in the local market helps to put farmer complaints in perspective. Data is gathered from the Ministry of Agriculture from 2007 to 2014 and from the Department of Statistics (DoS) from 1998 to 2012. In order to consider the most pertinent crops and prices for farmers in the Jordan Valley, popularly- grown vegetables and fruits were chosen and only prices during those months when those particular crops are harvested from the Jordan Valley are considered. To put these selling prices into further perspective, I look to input prices in the form of consumer price indices from 2008 to 2013.

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The more in-depth and additional farmer interview material is used to give examples of the variety and breadth of the input prices that farmers face in growing vegetables, citrus or palms. Farmers were asked to list the prices per month per farm unit for a variety of inputs such as seeds, tractor rentals, water, electricity, labor, mulch, packaging boxes and transportation. These prices were relayed into per season costs for farmers for one crop on one farm unit. There are a range of costs because different farmers reported different costs. Farmers were also queried as to how much, on average, they earn from this crop for a season. These cost tallies were conducted for vegetable crops, citrus crops and date palm crops, considering that the farming needs for these three crop categories are fairly different. More perspective on the input prices that farmers are facing is given by considering the average income in the agricultural sector as compared to other sectors and the condition of agricultural extension services in the Jordan Valley.

Legal Authority

The domain of the WUA’s legal authority is viewed through the lens of the law, simply reviewing what the terms of the WUA’s authorities are in regulations and contracts. To bring this down to the farmer level, the survey question surrounding who farmers go to for help (the WUA or the JVA, or neither) is assessed. The variables are as follows: wua help (1 if a farmer calls the WUA for help, 0 otherwise); jva help (1 if the farmer calls the JVA for help, 0 otherwise); and wua-jva help (1 if the farmer calls both the WUA and the

JVA for help, 0 otherwise). An analysis of variance and logistic regressions are used to ascertain whether the differences shown between the WUAs in descriptive statistics are in fact significant (Appendix E, Results 11).

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Additional ordered logistic regressions and logistic regressions were conducted to determine whether who a farmer goes to for help significantly determines any of the outcomes. The farmers reporting “neither,” in that they ask for help from neither the WUA nor the JVA, are excluded because there are only four remaining observations in this category, not enough to be effectively included in the regressions. The two categories included in the regressions are wua help (those who go to the WUA for help) and wua-jva help (those who go to both the WUA and the JVA for help) in order to better compare them to those who are only going to the JVA for help (jva help). The choice of jva help as the left-out category is taken because it is hypothesized that those who only got to the JVA for help have the strongest views against the WUA and its abilities (Appendix E, Results 12).

Collective-choice Arrangements and Rules

To assess differences in collective-choice arrangements among WUAs, information gleaned from WUA head interviews on membership fees and whether they were elected or appointed is used.

Monitoring

The categorical variable touring was generated to mean how often a farmer thinks that the ditchriders are touring the field. The farmers who reported “I don’t know” were removed, resulting in the removal of three observations. This variable is configured such that the following categories apply: 1 is rarely, 2 is sometimes and 3 is always. Logistic regressions were first used to determine if there are any differences between the WUAs with regard to touring (Appendix E, Results 13).

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Further regression analyses were conducted to determine whether levels of monitoring have an effect on the outcomes. Ordered logistic regressions were conducted with the outcomes of opinion of wua and comparison of wua to jva and logistic regressions were conducted with water stealing, fairness of wua and membership (Appendix E, Results 14).

Sanctioning

The categorical variable punishing was generated to mean how often a farmer thinks that the ditchriders are ticketing farmers when they have broken the rules. The farmers who reported “I don’t know” were removed, resulting in the removal of ten additional observations. For punishing, there are three categories: 1 is rarely, 2 is sometimes and 3 is always. Anova and logistic regressions were used to determine whether there are differences between the WUAs (Appendix E, Results 15).

Further regression analyses were conducted to determine whether levels of punishing have an effect on the outcomes. Ordered logistic regressions were conducted with the outcomes of opinion of wua and comparison of wua to jva and logistic regressions were conducted with water stealing, fairness of wua and membership (Appendix E, Results 16).

Conflict Resolution

The categorical variable resolving conflict was generated to represent the farmer’s opinion of how involved the WUA is with conflict resolution among farmers. Those farmers who reported that there are simply “no problems” among them were removed,

146 leading to 60 additional observations being removed from the sample set. With the remaining observations, the following categories are used for the variable resolving conflict: 1 is the WUA does not help, 2 is the WUA sometimes helps and 3 is the WUA always helps. Anova and logistic regressions assess if there are differences between the WUAs on this count (Appendix E, Results 17). Further regression analyses were conducted to determine whether levels of conflict resolution have an effect on the outcomes. Ordered logistic regressions were conducted with the outcomes of opinion of wua and comparison of wua to jva and logistic regressions were conducted with water stealing, fairness of wua and membership (Appendix E, Results 18).

Leadership

The factor of leadership is another factor for which no quantitative analysis can is conducted. All of the data involved in the quality of leadership of the WUA heads was gathered in the WUA head interviews. In the final estimation of the quality of leadership in the four WUAs, each head was given a certain number of points within leadership categories as compared to each other. The rubric for this tabulation is presented in Table 5.2. With more points, the leader is more qualified; in any category, whatever answer is favorable to better leadership is given more points.

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Table 5.2: Rubric used for quantification/tabulation of leadership rankings among WUA heads. Leadership Characteristic Scoring Rubric Education level 1=high school, 2=diplome, 3=bachelors Size of farm 1=one farm unit, 2=two farm units, 3=more than two farm units Secondary work 1=has no secondary, 0=has secondary work Desire to be head 1=wanted to be head, 0=did not want to be head Salary sufficiency 1=salary is sufficient, 0=salary is not sufficient WUA as the solution 1=sees WUA as ultimate solution, 0=does not see WUA as ultimate solution View on privatization 1=does not want privatization instead of WUA, 0=does want privatization Personal initiatives 1=has personal initiatives, 0=has no personal initiatives View of independence for WUA 1=wants independence for WUA, 0=does not want independence for WUA Additional activities for WUA 1=wants additional activities for WUA, 0=does not want additional activities

Level of Dependence on Water Source and Agriculture

Level of dependence was determined by both whether the farmer has a secondary source of income or job (secondary work) and whether the farmer has a secondary source of water (secondary water). Logistic regressions were run on both of the variables for these two additional assets as determined by the four WUAs to see if there were significant differences among the WUAs (Appendix E, Results 19).

To further evaluate the effect of having a secondary income or source of water, ordered logistic and logistic regressions were run pitting the outcome variables against having a secondary income or water source. An additional interaction term secondary

148 work*secondary water was generated to interact secondary work with secondary water (Appendix E, Results 20). Because PS 33 has a significantly larger portion of farmers with a secondary source of water, whether a farmer is in the WUS at PS 33 (ps33) was included in the models and it was also interacted with secondary water to form a new variable secondary water*ps33 (Appendix E, Results 20).

Additional regressions have been conducted to determine whether having a secondary income or source of water affects whether a farmer is a member in the WUA or not and only for those farmers who are eligible to be WUA members (Jordanian renters or owners) (Appendix E, Results 21). Yet further regressions were conducted to determine whether having a secondary income or source of water affects levels of water stealing or fairness of the WUA. For fairness, the variable denoting the WUA PS 33 (ps33) is included due to PS 33’s significantly larger portion of farmers with a secondary income. Secondary income is also interacted with ps33 in this case to produce the variable secondary work*ps33 (Appendix E, Results 22).

Socioeconomic Status

Farmer socioeconomic status (SES) is determined by the number of dunums a farmer has (more dunums, higher SES), whether or how many greenhouses he has (with greenhouses, higher SES), and whether he is able to export any of his crops (ability to export, higher SES). Because income is not available for farmers, this indirect means to determining their SES is used. For the variable dunums, the log of dunums is taken to make it normal distributed. For the variable greenhouses, because it also has a highly 149 skewed distribution, the variable is turned into a binary one with the option of either having greenhouses or not. And the variable exporting is the binary variable denoting whether a farmer exports internationally (=1) or does not (=0). Interaction terms combine dunums and greenhouses, dunums and exporting, and exporting and greenhouses. Ordered logistic regressions are run to determine whether any of these three factors, and their interactions, have an impact on farmer opinion of the WUA and farmer opinion of the WUA in comparison to the JVA, including using interaction terms between each pair of the three factors (Appendix E, Results 23).

Further logistic regressions are run for the outcome member and PS 55 (ps55) is included, as well as an interaction between ps55 and the interaction term dunums*greenhouses because of the higher proportion of farmers with lots of land and greenhouses in PS 55 (Appendix E, Results 24). Finally, logistic regressions were run to determine whether any of these socioeconomic variables and their interactions have an effect on the outcomes of water stealing and fairness of wua (Appendix E, Results 25).

Land-holding Status

First, those farmers who are both owners and renters were made into owners. The categorical variable ownership was generated with the following categories: 1 is an agent, 2 is a renter and 3 is an owner (or owner-renter). To see whether the make-up of agents, renters and owners is significantly different among the four WUAs, an analysis of variance and logistic regressions were conducted on these variables between the WUAs (Appendix E, Results 26).

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Further ordered logistic and logistic regressions were run to see if being an agent, renter or owner significantly affects the outcomes (Appendix E, Results 27). For analyzing whether being an agent, renter or owner has an effect on membership, in particular, those farmers not Jordanians and not owners or renters were excluded and interactions between the WUA variables and whether the farmer is an owner or not (owner, 1=owner, 0=not owner) were included (Appendix E, Results 28).

Education

A categorical variable for education was generated as education with the following categories: 1 for none; 2 for elementary school, 3 for middle school, 4 for high school, 5 for diplome, 6 for bachelor’s degree and 7 for master’s degree or PhD. The “diplome” is like an extra certificate that can be obtained after high school in a certain technical field. To see whether there are statistically significant differences between the WUAs with regard to education level, analysis of variance and multinomial logistic regressions were used (Appendix E, Results 29). Ordered logistic regressions and logistic regressions were used to determine whether the outcomes are affected by education level. Because only two farmers have a master’s degree or PhD, these two observations are included in the category for bachelor’s degree, rendering the highest category of education as some type of higher education degree. The binary variable high school was also used to condense the education levels into two categories, high school and above (=1) and below high school (=0) (Appendix E, Results 30).

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Perceived Benefits to and Incentives for Membership

The analysis of whether farmers receive benefits from the WUA and whether this affects their membership status is examined qualitatively through the data collected from discussions with farmers.

Dependent Variables

All of the aforementioned independent variables were assessed in terms of their impacts on five dependent variables. These variables were determined in light of the discussion of potential dependent variables in Chapter Four. The dependent variables associated with WUA performance are farmer opinion of the WUA, farmer opinion of the WUA as it compares to the JVA, farmer reporting of water stealing, and farmer opinion of the fairness of the WUA. The dependent variable associated with participation in the WUA is farmer membership in the WUA. In the following sections, how these variables were measured and tested is explained.

Opinion of the WUA

The variable for opinion of the WUA (opinion of wua) is a categorical variable with the following categories: 1 is bad, 2 is so-so and 3 is good. Anova and logistic regressions were used to determine if there were differences between the four WUAs with regard to opinion of the wua (see Appendix E, Results 31).

Each of the individual independent factors have already been analyzed for their independent effects on the outcomes. Further joint analyses are run to determine the weight of all of the factors together when looking at each of the outcomes. For factors that

152 previously did not display any significance for particular outcomes, they are left out of these analyses. First, ordered logistic and logistic regressions with just the two quantifiable physical factors (water adequacy and water reliability) are run (Appendix E, Results 32), including other potentially important variables such as WUA, secondary water or work, network type and lateral position (Appendix E, Results 33). Second, the institutional factors (who the farmer goes to for help, levels of monitoring, levels of sanctioning, and levels of conflict resolution) are run in ordered logistic and logistic regressions to determine their significance, with the inclusion of the WUAs to see if they have an impact (Appendix E, Results 34). Third, the user factors (number of dunums, having greenhouses, exporting produce, ownership status, and education level) are run in ordered logistic and logistic regressions to assess their impact, again with the inclusion of variables for the four WUAs (Appendix E, Results 35). For community factors, there are no variables to readily use for regressions so they unfortunately cannot be included in these final analyses. Finally, all quantifiable variables from these three categories of physical, institutional and user categories are all combined in ordered logistic and logistic regressions to determine opinion of wua (Appendix E, Results 36), with various models produced, and the same is done with the other outcomes.

Comparison between WUA and JVA

The variable for farmer opinion of the WUA as compared to the JVA (comparison of wua to jva) is a categorical variable with the following categories: 1 is worse, 2 is same thing and 3 is better. Anova and logistic regressions were used to determine if there were differences between the four WUAs with regard to comparison of wua to jva (see Appendix 153

E, Results 37). Results for predicting comparison of wua to jva with physical factors (Appendix E, Results 38), institutional factors (Appendix E, Results 39) and user factors (Appendix E, Results 40) are determined. Results for all quantified variables in all categories are combined into a final set of models (Appendix E, Results 41).

Water Stealing

The outcome of water stealing is determined by the question asking farmers whether water stealing is occurring in the area. Some farmers responded with “maybe” or “I don’t know” and these responses, for regressions involving water stealing, have been removed. This deletes an additional 12 observations leaving only 174 observations in total. The final variable used in the regressions (water stealing) is binary, 1 for if the farmer says stealing is happening and 0 if not. Logistic regressions were used to determine whether there were differences between the WUAs with regard to water stealing (see Appendix E,

Results 42). Results for predicting water stealing with physical factors (Appendix E, Results 43), institutional factors (Appendix E, Results 44) and user factors (Appendix E, Results 45) are determined as well. Results for all quantified variables in all categories are combined into a final set of models (Appendix E, Results 46).

Fairness of the WUA

Farmers were asked whether the WUA is fair and treats farmers equally. Some farmers answered “I don’t know” and these responses have been removed, leaving two additionally deleted observations and a remaining 184 observations for any subsequent regressions involving fairness. The variable fairness of wua is a binary variable with 1 as

154 the WUA is fair and 0 as the WUA is not fair. Logistic regressions were used to determine whether there were differences between the WUAs with regard to fairness of wua (see Appendix E, Results 47). Results for predicting fairness of wua with physical factors (Appendix E, Results 48) and institutional factors (Appendix E, Results 49) are determined as well. User factors are not further analyzed with regard to fairness of wua because none of the individual factors were deemed at all significant. Results for all quantified variables in all categories are combined into a final set of models (Appendix E, Results 50).

Membership in the WUA

The variable involving membership is a simple binary variable, membership, with 1 signifying that the farmer is a member and 0 that he is not a member. When only those farmers who are eligible to be members (Jordanians who are renters or owners) are considered, farmers who cannot be members (agents or non-Jordanians) are removed, resulting in the loss of 64 additional observations and leaving only 122 in the sample set. For all subsequent regressions involving the outcome membership, only those farmers who are eligible for membership will be considered. Logistic regressions were used to determine whether there were differences between the WUAs with regard to membership both when including all farmers and when including only those farmers eligible for membership (see Appendix E, results 51 and 52). The additional variable elections was used to determine if members attend WUA elections; 1 is for those members who attend elections and 0 if not. In assessing election attendance, only WUA member farmers were included, making for a sample size of only 51 farmers. Logistic regressions were used to determine whether there is a difference between the four WUAs with regard to elections (see Appendix E, Results 53). 155

Results for predicting member with physical factors (Appendix E, Results 54), institutional factors (Appendix E, Results 55) and user factors (Appendix E, Results 56) are determined as well. Results for all quantified variables in all categories are combined into a final set of models (Appendix E, Results 57).

SUMMARY

A variety of research methods are used to elicit information that is used in both qualitative and quantitative assessments in the following chapters. The material in Chapter Six is largely a product of the final interviews conducted with donor agency staff as well as the interviews conducted with WUA Heads. In Chapter Seven, the detailed information on each of the four selected case studies originates both with the initial contextual assessment in which overarching data on the WUAs was gathered from the JVA and the WUAs, and with the deep, on-the-ground dives and personal observations made within each of the four WUAs. Much of the quantitative data in Chapters Eight through Twelve relating to the physical, institutional and user-related factors comes directly from the survey conducted among farmers in each of the four case study WUAs. Material used for the community factors and some of the other factors in other categories was gleaned from additional textual research or various interviews throughout all research stages. The following chapters thus display the results of all research methods taken in this study.

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Chapter Six: Water User Associations in Jordan

The chapter provides an account of water user associations in the Jordan Valley in terms of how they were created and developed, as well as how they operate within the present water management scheme. What is initially discussed is the origin of the idea of WUAs and why it was proposed as a solution to water management issues in the Jordan

Valley. This is followed by a discussion of the generally negative reaction to the idea of WUAs, especially among farmers but also JVA employees. The gradual way in which WUAs were implemented is thus better understood and put within this context of negative reception. Finally, the general characteristics of the WUAs in today’s Jordan Valley are offered in detail in terms of their physical presence and duties, the personal characteristics of their heads, their daily management and operations duties, their financial affairs, and their membership traits. Before launching into the specific results found within just four of the WUAs in the subsequent chapters, it is useful to have this overview of all WUAs in the valley. As a note on sourcing, some of the material in this chapter originates in interviews with those involved with the GIZ WUA project as listed in the previous chapter. In order to not repeatedly list the dates of these interviews, they are listed as follows and the rest of the chapter will simply mention the name of the source: Jochen Regner (3/29/2015,

3/30/2015); Ali al-Adwan (5/18/2015); Fadi Abu Sahyoun (5/19/2014, 3/25/2015); Ali al-

Omari (3/23/2015); Ahmed al-Bukhari (3/19/2015); Khaled al-Qsous (3/24/2015).

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THE IDEA OF WATER USER ASSOCIATIONS

Around the year 2000, the idea of privatization of the management of irrigation water in the Jordan Valley (JV) was floated but was finally rejected due to the potentially unfavorable social and political consequences. Thus, the idea of water user associations (WUAs) was considered more heavily within the Jordan Valley Authority (JVA) as a viable alternative. While a USAID project had attempted to establish WUAs and failed in years prior, there was still some thought among JVA management that this way forward was viable (Interview with Regner). The reasoning behind wanting to adopt new management in the first place, such as private sector participation, had been because of rather lackluster performance by JVA staff and worsening conditions in the water distribution systems. In specific, reasons included: water unreliability; poor water quality; deterioration of the water distribution networks; destruction of water meters and the manipulation of flow limiters by farmers; poor and inefficient systems maintenance; ill-will, competitiveness and lack of trust among farmers (leading to and as a result of illegal connections and water stealing); several years of drought that caused particularly acute water stress; negative attitudes on the part of farmers towards the JVA and its inept bureaucratic processes and failure to meet farmer needs; and bribing of JVA employees, especially at the lower level, by farmers (Interview with Regner; USAID, 2013; Hayek and Al Adwan, 2012). All of these problems signaled that government administration of the water distribution networks in the JV would not be sufficient to maintain the systems for the future. A look to farmer participation in management was thus pursued. With this situation at hand, in 2001, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmBH, the German development agency, launched a project in

158 cooperation with the JVA to create water user associations (WUAs) among farmers for the management of irrigation water. Officially, the initial goal of this participatory approach was “to introduce a sustainable participatory approach” to manage irrigation water with the goal of increasing the efficiency of irrigation water distribution in the JV (Hayek and Al Adwan, 2012, p. 5). This project also came at a time when the JVA was mandated with improving its management efficiency and cost effectiveness (Hayak and Al Adwan, 2012).

Some of the stated issues and problems that farmers were facing and that user participation might potentially resolve, according to GIZ, were: an unreliable water supply, both in quantity and quality; inability of the JVA water distribution staff to meet farmer needs; competition and water stealing among farmers that had eroded trust among farmers; mistrust between small and large farmers; and competing demands for water from other economic sectors in Jordan (Hayek and Adwan, 2012, p. 7). The initial goals, in general, were vague and not detailed by design. The lack of a defined visions at the beginning of the project has been echoed by those who participated in its inception (all GIZ-related interviews, 2015). There was a general desire for farmers to take over responsibilities from the JVA, and GIZ even harbored the ultimate aspiration of autonomy for the WUAs (as those implemented in Turkey and Spain), but there was still a felt need to let Jordan evolve in its own way and simply allow for a step-by-step process to assess at each point how to move forward (Interview with Regner).

REACTION TO THE IDEA OF WATER USER ASSOCIATIONS

In the beginning, farmers were against the idea of associations due to their bad experience with agricultural associations in the past (Interview with Regner; Hayek and Al Adwan, 2012). These associations were established in the 1950s in the JV and Jordan was 159 essentially pressured into having these kinds of associations as establishments where international aid agency money could be channeled into agriculture. The associations were designed to offer loans to farmers that should be paid back, although it seems that farmers never really intended to pay back their received loans (Interview with Regner). Eventually, it appears that the more powerful and large-scale farmers and tribal figures were allocating more money to themselves out of these agricultural associations, not just for farming ventures but for personal expenses like weddings, as well. They, too, did not pay back the loans. So in the 1990s, there were lawsuits targeting farmers who had not paid back these loans and the experience of associations ended poorly, with little interest in experiencing them once again (Interview with Regner). By the early 2000s, when the idea of water user associations started to circulate, farmers were thus not in favor of more associations. They were fearful that once again the “big fish” would end up getting the larger share of the perks to use for their own personal benefit. Abu Sahyoun recounted first going to the field to try to convince farmers of the idea and how farmers believed that these associations would again provide a way for some farmers to dominate others. Al-Adwan also stated this succinctly when he recalled that some farmers simply didn’t want yet another “sheikh” ruling over them, as they thought would be the case for whoever got to head the new associations. He additionally added that some farmers initially rejected the idea of associations because they simply liked and benefited from the system under the JVA. Under the JVA, farmers got away with a lot of illegal activities, like stealing in exchange for bribes, and some farmers didn’t want to lose these benefits. Al-Bukhari mentioned as well that farmers didn’t like the idea of having to monitor each other because this could lead to more conflicts between them. And Abu Sahyoun posited that there was simply no trust between farmers in the first place, with

160 constant stealing happening among them, so it was seen as an improbable solution that they watch over each other. These farmers preferred that the JVA continue to have all of the control and act as the arbiter between farmers. Hayek and Al Adwan (2012) pose some additional issues that farmers had in their initial opposition to the idea of WUAs. There was general insecurity on the part of farmers with there being no solid legal framework for the WUAs (discussed below). Related to this point, farmers did not trust that the JVA would actually and eventually transfer management to them. With regard to the physical status of the networks, farmers did not want to take over networks that were in such disrepair, foreseeing the amount of work and money that would be required to restore and maintain them. And Abu Sahyoun also mentioned that there was considerable suspicion on the part of farmers towards GIZ, with some farmers believing that GIZ was in cahoots with the Jews or Israelis and had intentions other than the betterment of Jordan’s well-being.

Regner and Al-Adwan additionally suggested that there was a difference in the level of acceptance of associations between the northern and southern sections of the JV. The level of tribal influence in the southern areas, according to Regner, was much stronger whereas in the north, the Jordanian tribal influence was somewhat mitigated by the presence of more Palestinians, Pakistanis, and agricultural entrepreneurs and investors who had no tribal ties in the valley. That tribal influence and “spirit” in the south, as he added, is not easily able to mesh with a western kind of democracy and the following of interest groups. So farmers in the south were less amenable to the idea of associations or anything that would get in the way of their more heavily tribal society and dominance. As Al-Adwan recounts, farmers in the south were generally less trusting of the government as well and thus they provided more resistance to the idea from the beginning.

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Not only were farmers against the idea of WUAs in the beginning, but so too were many in the JVA, especially at the lower levels. Many in the JVA did not want to have to talk to or deal with farmers and they certainly did not want to lose any of their established control and power. What is more, they did not want to lose those additional incomes that they were receiving in the form of bribes from farmers (Interview with Regner).

IMPLEMENTATION OF WATER USER ASSOCIATIONS IN THE JORDAN VALLEY

According to Regner, the first foray into the valley and implementing the idea of farmer participation was in the south of the valley, in the areas of Hisban and Kafrein. The reason why GIZ chose to focus on this area first was that the irrigation network was in the worst condition of anywhere in the valley with 40-50% of farms not connected to the network and distribution pipes destroyed in many areas, leading to additional farms not receiving water from the network. GIZ first visited farmers to talk with them, one-on-one and in discussion groups, about the problems they faced in water distribution and how these problems could be resolved. While having these discussions with farmers on how to rehabilitate the network and keep it maintained, GIZ was trying to create alliances with farmers and cooperation between farmers. Regner was able to convince the JVA to allocate some money to Hisban and Kafrein in order to rehabilitate their networks and GIZ contributed to this cause as well. During the period from 2002 to 2004, they were able to strengthen the relations between farmers, repair the network and cut down on the number of illegal connections, as recounted by Regner. The idea in starting with Hisban and Kafrein, the areas that seemed the worst off in the valley, was also to encourage farmers elsewhere that if farmer participation could improve the situation there, it could do the same elsewhere. And from there, indeed, other 162 associations were initiated although with more of a goal of controlling water distribution and organization rather than network rehabilitation, since networks were not as bad in other locations. A prime focus was also cooperation between the JVA and farmers and convincing JVA employees of the benefits of farmer participation. Abu Sahyoun would argue that efforts really started with Pump Station (PS) 28 that supplies water to an area in the north of the Jordan Valley, although his view could be skewed slightly considering that he headed-up GIZ’s efforts in the north of the valley and is very proud of his work. But it could still be said that the first official association-type entity was established at PS 28 whereas other initial work was first done in the south. He recounted that they decided to start with this pump station because it was the most problematic in the region, with the greatest amount of tension between farmers and network vandalism. As Abu Sahyoun remembered, their first steps were to research the area, its farmers, its farms, and their particular issues on the ground. This research phase lasted for some four or five months and through this stage, he stated that he found the influential farmers in the community with whom he could work and by whom he could eventually disseminate the idea of farmer participation to other farmers. During this initial stage, the goal was also to build trust between farmers and the JVA so there were many meetings held between the two sides to discuss problem areas.

GIZ terms this the “confidence building” stage and it lasted from roughly 2001 to 2003 (Hayek and Al Adwan, 2012). The JVA, along with GIZ, also made many visits to the field to get an idea of the ground reality for themselves. It was during this phase that farmers could sense, according to Abu Sahyoun, that a better awareness was forming among all involved parties. It was at this point that he thought that they needed to start getting the attention of the higher levels in the JVA. This would require forming an actual

163 water council that could be visited by JVA officials on a formal basis. At first, he tried to find a farmer representative on each lateral line in the area of PS 28, all of whom would eventually make up this council. Such representatives were found but while some representatives were attentive to their lateral issues, others were not. Thus, this idea was put aside and instead he focused on any farmers who wanted to be in the council and would help to encourage and contribute to the council.

Abu Sahyoun was able to get 25 farmers to establish a council at PS 28, from which an administrative council was decided and then a head selected. This was the first council created and marked the first step in creating WUAs. GIZ wanted to establish water councils that would be recognized by the JVA and include 15-20 elected farmers, chosen through informal discussions (Hayek and Al Adwan, 2012). These would eventually progress to water user committees that would actually elect the general assembly or administrative council instead of just deciding on one through informal discussions (Hayek and Al

Adwan, 2012). This first water council created some buzz with the JVA secretary general, the American ambassador, German officials and journalists. Other areas in the valley were then, according to Abu Sahyoun, a bit jealous and wanted councils of their own. The head of PS 28’s council and GIZ also made visits elsewhere in the valley to further promote the idea. It helped that the head of PS 28 was well-known among farmers and could thus bring further clout to the topic. GIZ initially pushed the idea of having the influential and powerful farmers to be the heads of councils because they thought that farmers would be most likely to accept and trust the idea if someone higher-up in the tribe was leading the efforts. Al-Adwan admitted that this was a risk that they took, that it could have created “big sharks” that would later dominate the circumstances to their personal benefit. But he adds that in many cases, after

164 the initial phase, the “big sheikh” stepped down from the head position or got voted-out in the first elections so there was eventually “new blood” and competition. Regner also stated that the tribal elders and powerful farmers were able to get elected as heads for the first term but many times they would use the office to try to enhance their personal statuses and thus be elected out of office in the next elections. Farmers would subsequently choose someone more practical to be the council head.

Where this pattern was different, according to Regner, was in the south of the valley where the influential tribal elder has been the head since the very beginning. But he added that this is the practical choice in that region because he is able to get things done whereas someone else would not. Abu Sahyoun actually noted the opposite occurring in PS 28, where the first head stepped down in favor of someone with even more tribal power and influence because this would allow the association to have more success and notoriety. It is noteworthy that even GIZ allowed this relationship to develop between the associations and tribal politics in the valley, justifying this use of tribal politics as beneficial and necessary despite potential negative consequences that are still playing-out today. What was also occurring during this developmental stage were trips for those participating in the water councils, committees and later user associations to other places where this type of participatory management was already in operation. Trips to the south of Jordan were taken to see how the participatory approach works in long-standing water associations there. Later on, additional trips were taken to Syria, Turkey, Egypt and Spain, countries that have also established WUAs, in order to see how they are managed and if there is some pertinent technical know-how to take back to Jordan. In these international visits the JVA top management was also able to have discussions with the corresponding

165 top management on how to implement and develop WUAs. Farmers covered their costs for these visits, according to Hayek and Al Adwan (2012). A rather large issue towards the end of the initial stage of establishment of associations in the Jordan Valley in 2004 was how they would evolve and gain a more permanent and legal status. Water councils and committees could exist without any legal changes but if they wanted bodies that had structure, authority, an organizational lay-out and collected fees, they needed to establish formal associations with a legal basis under Jordanian law. While it would have been optimal to establish the associations legally under the JVA, this would have required a new law to be enacted, something that to this day is still being attempted but will always be difficult in Jordan’s glacially-paced political environment. Besides this unlikely option, there were two other options: placing the associations under the Interior Ministry (not ideal and unsuitable) or under the Jordan Cooperative

Corporation (JCC) as per Cooperation Law 18 of 1997 and the Cooperative Bylaw Regulation 13 of 1998 (USAID, 2013). The JCC was the more promising as it had a long experience with associations and already had connections in the JV. One issue was that technically, under the JCC, an association is supposed to be profitable and this was definitely not going to be possible for water user associations in the beginning since their capabilities were going to be so limited. Thankfully, due to good personal relations between GIZ, the JVA and the JCC, the JCC allowed the establishment of associations with a postponement of this condition. To this day, as Regner remarked, there is still a question as to whether the WUAs should have been established under the JCC due to their continued limitations towards ultimate autonomy from the JVA. But as USAID (2013)

166 precisely notes, registering the WUAs with the JCC “was an expedient, resorted to in the absence of a more appropriate legal mechanism” (p. 11). With their registration under the JCC, which began for some WUAs in 2008 and for others in subsequent years, WUAs were then tasked with developing official objectives, procedures for membership, and general regulations with regard to financial and administrative issues (Hayek and Al Adwan, 2012). This was also the point at which the

WUAs were to begin experiencing “task transfer,” or the shifting of responsibilities from the JVA to the WUAs. These tasks that have been transferred from the JVA to the WUAs have included: responsibility for water distribution between the main water source (the KAC) and individual farm units; minor maintenance on the FTAs; monitoring of the water distribution network; and issuing of tickets to farmers who disobey the rules. The idea of task transfer, in general, was in-line with the overall development agency trend of the time that supported governments pulling out of the implementation sector (Interview with Regner). Considering Jordan’s heavy reliance on GIZ, the entire venture was being influenced by world-wide trends in the development world. Thus, task transfer was not simply an endeavor pursued because it was precisely what Jordan needed and was ready for but was also the mission of the development agency and its goals. One particularly important part of task transfer was that the associations were given budgets. No longer were activities to be conducted on a voluntary basis, as they had been up to this point, but rather heads of associations were to receive salaries and eventually other association employees as well were to have salaries. There were negotiations between the associations and the JVA on what this entire budget amount should be and there was no dispute from any party about the head of the association receiving a salary. This was thought to be natural and essentially basic compensation for their efforts

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(Interview with Regner). Al-Adwan added that they really had to give the heads salaries in order to get them to come to the table and agree to participate and continue to support the project, despite the risk of this later causing greed and corruption on the part of association heads.

CURRENT STATUS OF WATER USER ASSOCIATIONS IN THE JORDAN VALLEY

WUAs are not currently operating over all agricultural land in the Jordan Valley. Within any given region, some Development Areas (DAs) or parts of DAs are managed by WUAs and others are still managed solely by the JVA’s stage offices. According to Al- Qsous (Interview on 3/24/2015) and Al-Omari (Interview on 3/23/2015), who work in the WUA Unit for the JVA at present, the current goal is to cover the entire JV farm area with WUAs by 2017, with 30-33 WUAs in all. WUAs currently cover only a small percentage of land area in the Jordan Valley. For example, as reported by Abu Sahyoun (Interviews on 5/19/2014 and 3/25/2015), out of 390,000 dunums in the north of the valley, only 43,421 dunums are covered by WUAs, or about 11%. The fully functioning 13 WUAs (as of May 2015 and discussed in more depth later in this chapter) operate over only about 30% of the farm land in the Jordan Valley. From 2008 to 2015, the area covered by WUAs has not increased (Interview with Regner, 3/30/2015). With regard to the areas managed by WUAs, this WUA management can be at various stages of development. Table 6.1 gives an idea of location of the WUAs and their level of development, although this information is roughly from 2014 and these WUAs continue to be in flux. Some WUAs have experienced some level of task transfer (TT in the table below), meaning that some management tasks have been transferred to their cadre of employees. The first level of task transfer is to be given responsibility for water 168 distribution in the WUA’s area and the second level of task transfer is to be given the additional responsibility of light maintenance at the farm level, but not along the main supply lines. Additional levels of task transfer, for pump operations, full maintenance tasks and control of financial affairs, are supposed to occur in the coming years. While there are officially 19 WUAs with some form of task transfer (as presented in Table 6.1), the reality is slow to catch-up. Three other associations have recently been established but have yet to undertake any task responsibilities, and by 2017 the plan is for the total number of WUAs to reach 33.

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Table 6.1: Location and stage of development of water user association in the Jordan Valley. Water User Development Area(s) Development Stage Association Al-Adassiya 1 Process of being established Abu Obayda 2 Process of being established Al-Baqoura 3,4 Established, no TT Al-Ziraa Al-Taawania 5 Established, no TT Pump 14 6, part of 7, part of 8, Established, no TT part of 10 Tel al-Arbaeen part of 10 Established, no TT Al-Zamaliya, Pump 24 part of 11 Process being established Pump 28 part of 11, part of 12 Distribution TT Pump 33 part of 12, 13, part of Distribution/Maintenance TT 14 Wadi al-Rayan, Pump 15 Process of being established 36 Pump 41 part of 16, 17 Distribution TT Pump 50 18, 19, part of 20 Distribution/Maintenance TT Pump 55 part of 20, 21 Distribution/Maintenance TT Intake 22 22 Process of being established DA 23 23 Process of being established DA 24 24 Process of being established Pump 78 25 Established, no TT Pump 81 26 Distribution TT Pump 86/87 part of 27 Process of being established Pump 91 part of 27 Distribution/Maintenance TT Pump 95 part of 27, 28 Distribution/Maintenance TT Wadi Shaib part of 50 Established, no TT Kafrein 31 Distribution/Maintenance TT Rama 32 Distribution/Maintenance TT Mazraa-Haditha 45,46,47,48 Distribution TT Al-Safi 40,41,42 Established, no TT Fifa 43 Distribution TT Khanizeera 44 Distribution TT Source: JVA WUA Unit. TT=task transfer.

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The task transfers have still only been partially completed. As USAID (2013) has somewhat cynically remarked: “Instead of allowing WUAs to operate as independent legal entities that generate their own funds, JVA has established the TT [task transfer] Agreement system to shift partial responsibility for O&M of the irrigation infrastructure” (p. 30). WUAs are in no way autonomous. They receive a budget from the JVA and are given tasks based on what the JVA wants to relinquish from its set of tasks. What is more, since the water distribution infrastructure is still technically the property of the Jordanian State, the JVA, which represents the state in this domain, is still charged with making sure that the infrastructure is sufficiently operated and maintained. Thus, the JVA is responsible for overseeing the WUAs in these tasks and reviews the WUA budget, conducts field inspections of WUA management practices, and requires WUAs to provide receipts of their expenditures (USAID, 2013). With the WUAs’ legal basis originating in the Cooperation Law, and the fact that the JVA has the authority to transfer some tasks to private organizations under the Jordan Valley Development Law, there is little potential for the WUAs to break free of their overseer. It also means that some tasks, especially the collection of water fees, can’t be legally transferred to the WUAs, restricting their potential to a large degree (USAID, 2013). WUA heads also very much feel this restriction and voiced these opinions in interviews. One stated simply that “the contract is all in the interest of the JVA.” Others remarked that “the contract is not important and is just empty words on a page” and that

“the contract is not based on justice and equality, it is a contract of submission.” Most critically, one disdainfully quipped: “This is simply a contract of employment,” meaning that the JVA is merely employing the WUA to do its work. Many WUA heads feel that

171 they are still under the thumb of the JVA when they should have more independence and say-so in the decision-making. Every association has a separate contract with the JVA, signed every year anew, that establishes the roles of the WUA and the JVA, the rights of each party, the regulations under which the WUA operates, and the budget given to the WUA by the JVA on a yearly basis. See Appendix G for an example of one of these contracts. This budget is mainly based on the size and number of employees of the association and includes money for: the salaries of the head, engineer, ditchriders, guards and sometimes someone who conducts data entry; administrative/office costs; and light maintenance if the WUA has maintenance task transfer. For those associations that do not yet have any determined tasks, the annual budget is roughly 7000 to 9000 Jordanian dinars ($9875-12698); for associations with task transfer, the annual budget ranges from 15,825 to 42,100 Jordanian dinars ($22,326- 59,396). With specific regard to salaries, there are detailed regulations that determine the salaries of each employee depending on his position, years of experience, area served and time spent on the job (see Appendix H for these regulations). The contract between the WUA and the JVA includes additional pages of examples of the kinds of performance indicators that the WUA is expected to collect on a monthly basis regarding violations, water consumption, supply efficiency, complaints, and maintenance cases.

Each association’s head is elected by an administrative council and this administrative council is, in turn, elected by the general body of members. The number of members on any administrative council varies and is decided upon by the association. Membership is voluntary and in some areas, membership is more selective and based on certain criteria developed by the head and administrative council. The fees for membership, upfront and monthly, vary substantially from association to association.

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Some associations charge a large upfront fee while for others, it is fairly minimal. Some associations have official fees but rarely collect them. According to Jordanian law, only Jordanian owners and renters are allowed to be members, thus preventing non-Jordanian renters (largely made-up of Pakistanis and Egyptians) from being members in any WUA. The membership rate varies among associations, with some having almost 100% membership and others falling fall far short of this percentage.

Many associations have an engineer, while in others the head acts as the engineer in addition to his administrative duties as head. Associations employ 1-6 ditchriders, depending on land area covered and need, whose tasks can include monitoring the lateral distribution lines for any violations of water turns, writing reports of violations of water stealing or tampering with parts of the irrigation network, opening and closing the water supply to individual farms, attending to light maintenance within the irrigation network, and recording crop patterns for the JVA. The WUAs in the southern ghor region also employ guards in order to protect the main water source (natural springs coming from the mountains) whereas in the portion of the Jordan Valley above the Dead Sea, the water supply in the KAC is protected by guards employed through the JVA.

WATER USER ASSOCIATIONS WITH TASK TRANSFER AGREEMENTS

As noted in the methodology chapter, interviews were conducted with all heads of WUAs that have undergone task transfer and the following information pertains to these

WUAs. As seen in Table 6.2, WUAs were not all established at once, with some coming into existence as early as 2002-2003 and others as late as 2008-2009. The number of farm units within the jurisdiction of any given WUA ranges from 56 to 295 units, with land area under WUA administration ranging from 1,680 dunums to 11,850 dunums. Associations 173 are not all privy to the same source of water, especially in terms of quality. In the southern ghor, farms receive water primarily from fresh springs, which would be of fairly high quality although there are sometimes reports of its high mineral content (such as calcium) that can eventually corrode piping (Discussion with Haidar Malhas, 2/9/2014). Farms in the southern JV, on the other hand, primarily use treated wastewater, of lesser quality, but some also use fresh springs and water collected in dams. The middle JV is provided solely with treated wastewater. And the northern JV receives some treated wastewater but mainly freshwater from the KAC.

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Table 6.2: Year established, number of farm units, land area and water source of WUAs. WUA Year Number of Land Area Water Source Est. Farm Units (dunums) Southern Ghor Fifa 2008 122 3660 Wadi Fifa (spring water) Khanizeera 2008 56 1680 Wadi Khanizeera (spring water) Mazraa- 2005 395 11850 Wadi Bin Hamad/Wadi Haditha Karak (spring water) Southern JV Rama 2005 317 9990 Wadi Hisban (spring water), Kafrein Dam (runoff) Kafrein 2006 241 8907 Kafrein Dam (runoff) PS 95 2006 265 10496 KAC South (treated wastewater) PS 91 2004 254 10000 KAC South (treated wastewater) PS 81 2009 374 15735 KAC South (treated wastewater) Middle JV PS 50 2003 213 9000 ZC III (treated wastewater) PS 55 2004 291 10654 ZC III (treated wastewater) Northern JV PS 41 2004 136 5567 KAC North, ZCIII (treated wastewater) PS 33 2006 205 8413 KAC North, ZCIII (treated wastewater and freshwater) PS 28 2002 205 7921 KAC North (freshwater) Source: JVA WUA Unit data, interviews with WUA heads.

With regard to the number, membership status and origin of farmers in the different WUAs, Table 6.3 provides this data. The number of farmers within an area administered by any of the WUAs can range from 57 to 350 farmers. The percentage of these farmers who are members in the WUA varies widely. As can be seen in the table, many WUAs in

175 the southern JV have particularly low membership rates whereas in other areas, membership includes practically all farmers. It is also of note that some areas include registered farmers who are not Jordanian, especially in the southern and middle sections of the JV. The large non-Jordanian farmer populations include Pakistanis and Egyptians.

Table 6.3: Number of farmers, members and non-Jordanian presence in WUAs. WUA Number Number Non-Jordanians Present of of WUA (Yes/No) Farmers Members Southern Ghor Fifa 105 85 No Khanizeera 57 50 No Mazraa-Haditha 114 104 No Southern JV Rama 85 66 Yes (Pakistani) Kafrein 140 46 No PS 95 175 72 Yes (Pakistani and Egyptian) PS 91 130 30 Yes (Pakistani and Egyptian) PS 81 150 40 Yes (Pakistani and Egyptian) Middle JV PS 50 110 90 Yes (Pakistani) PS 55 138 110 Yes (Pakistani) Northern JV PS 41 136 129 No PS 33 350 254 No PS 28 230 220 No Source: JVA WUA Unit data, interviews with WUA heads.

Table 6.4 includes data on a few farm characteristics of the farms within these 13 WUAs. Use of greenhouses is particularly heavy in the middle JV but also in areas of the southern JV. Greenhouses are nonexistent or few in number in much of the southern ghor and northern JV. This would make sense in these areas because temperatures in the

176 southern ghor are much warmer than in the other sections of the JV, making the use of greenhouses less needed. In addition, farmers in this area typically have less capital to invest in greenhouses. In the northern JV, many farmers have citrus trees and would thus not need greenhouses. With regard to major crops within each WUA-administered area, as noted above, the northern section has the largest amount of citrus trees. Vegetables are predominant in the rest of the valley. Bananas are a more common crop in parts of the southern JV, as are date palm trees.

Table 6.4: Number of greenhouses and dunums of major crops in WUAs. WUA Number Vegetables Citrus Banana Palm of green- (dunums) (dunums) (dunums) (dunums) houses Southern Ghor Fifa 65 2738 0 0 0 Khanizeera 0 5936 0 431 110 Mazraa-Haditha 179 1335 0 0 26 Southern JV Rama 1200 2005 200 919 400 Kafrein 120 796 130 2745 37 PS 95 1245 5737 84 0 1827 PS 91 1145 5092 60 0 3032 PS 81 5425 7042 918 15 2116 Middle JV PS 50 2000 4262 1347 0 450 PS 55 8000 6660 588 0 60 Northern JV PS 41 200 3599 970 0 100 PS 33 0 1894 4879 0 325 PS 28 80 1415 5145 0 0 Source: JVA WUA Unit data, interviews with WUA heads. Excluded crops: grapes, other trees, field crops, herbs and nurseries.

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The interviews with the WUA heads also revealed information regarding their personal characteristics, opinions on the role of the WUAs, and the WUAs’ work processes and management practices. This information, as covered in the sections below, reveals the kind of stark variation among WUAs and how they are by no means of one mind or kind. It was from these sources of variation that four WUAs were chosen for a more in-depth analysis, as covered in subsequent chapters.

PERSONAL CHARACTERISTICS OF WUA HEADS

Table 6.5 offers a brief comparison of the personal traits of the WUA heads. Their ages range from 40 to 67 years old, with an average age of 54 years. Three out of the 13 heads have a bachelor’s degree, three heads have a technical diploma that puts them slightly above a high school education, and seven heads have somewhere between a middle school and high school education. Over half of the heads live in the same area as where the association and their farm are located, with four heads living in other cities that range from 20 minutes to one and half hours away. A couple of the heads also have more than one wife, with the wives living in separate cities, meaning that the head’s time and efforts are stretched even further. Most of the heads also have secondary sources of income, meaning that they do not solely depend on agriculture or their positions as WUA heads for their livelihoods. Some of them are retired army officers or retired JVA employees, meaning that they receive a retirement stipend every month. Other heads have separate businesses such as small shops, apartment/land rental offices, export/import services or another type of trading company. One head owns several large farms in the Highlands region outside of the Jordan Valley.

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Table 6.5: Personal characteristics of the WUA heads of WUAs with task transfer agreements. WUA Age Education Years Farm Crops Number Secondary Years Elected or Lives (Head Level as Area Grown of Income as Appointed in Name) Farmer (dunums) Green- Head Same houses Area as Farm Fifa 45 High 30 150 Vegetables 0 Yes 9 Appointed Yes (Mousa al- School Khutaba) Khanizeera 55 High 24 90 Vegetables 0 Yes 6 Appointed No (Ayed School Rawashedeh) Mazraa- 59 Technical 30 60 Vegetables 0 No 9 Elected Yes Haditha Diploma (Salim al- Huwaymil) Rama 47 High 35 600 Vegetables, 49 Yes 1 Elected Yes (Talal School Bananas, Farhan al- Palms Adwan) Kafrein 65 High 25 70 Bananas, 0 Yes 8 Appointed Yes (Ahmed al- School Vegetables Adwan) PS 95 54 Ninth 35 8 Vegetables 0 Yes 2 Elected No (Ahmed al- Grade Yemani)

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Table 6.5: (continued) WUA Age Education Years Farm Crops Number Secondary Years Elected or Lives (Head Level as Area Grown of Income as Appointed in Name) Farmer (dunums) Green- Head Same houses Area as Farm PS 91 59 Bachelor 35 150 Vegetables, 150 No 10 Elected Yes (Ali Degree Nursery Moustapha) PS 81 67 Middle 45 500 Vegetables 550 Yes 2 Elected Yes/No (Tawfiq al- School Satary) PS 50 55 Technical 28 60 Citrus, 43 No 2 Elected Yes (Hafiz al- Diploma Vegetables Shobaky) PS 55 47 Bachelor 14 150 Strawberries, 250 No 6 Elected Yes (Walid al- Degree Vegetables Faqir) PS 41 51 Technical 10 84 Vegetables 0 Yes 2 Elected No (Zaki Diploma Rababa) PS 33 59 High 19 33 Vegetables 0 Yes 4 Appointed Yes (Nawaf School Kraym) PS 28 40 Bachelor 21 350 Citrus 0 Yes 6 Elected No (Ashraf al- Degree Ghazawi) Source: Interviews with WUA heads.

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The range of experience in farming among the heads is from 10 to 45 years, with the average number of years of experience being 27. Most of them learned about agriculture from their fathers and through experience on the farm growing up, while some of them also received training and experience from international aid agency courses and the JVA. Their present agricultural pursuits also vary widely, with over half of the heads owning less than 100 dunums of land (8 to 90) and the rest owning more than 100 dunums of land (150 to 600). Most of them grow vegetables with a couple having citrus tree orchards. One head grows strawberries (a high-end crop), two have banana farms, and one grows date palms as well. Only five farmers use greenhouses, two of them with a moderate amount (45 and 49 houses), one with 150 houses, one with 250 houses, and one with 550 houses. These different farm statistics speak to the varying capabilities (material and financial) of heads to use farming as a profit-making industry. Lastly for their personal characteristics, the heads have varying levels of experience as association heads and leaders in general. Their years as head range from 1 year to 10 years, with the average being around 5 years. Most of them came to be in the position of head through elections, with four heads simply being appointed by the farmers without elections. Even with those who were elected, it is sometimes seen upon further investigation that the elections are somewhat of a foregone conclusion and merely window- dressing to please outside parties. Also, farmers differ in how they originally felt about being head of the association, with some claiming to have no desire for the position and simply acquiescing to what the farmers wanted. On the other hand, some heads were founding members of the association and from the beginning wanted to play big parts in the life of the association. A few heads said that they had had leadership positions before, such as being officers in the army or in administrative positions in other associations,

181 companies or the JVA. Others said that they had had no experience in a position of leadership prior to being head of the association.

MANAGEMENT AND OPERATIONS OF THE WUAS

Every association has an administrative council that usually meets every one or two months to discuss the general needs of the association, any current problems or issues, and the renewal of the association contract every spring. The number of farmers in the administrative council ranges from 5 to 15 farmers, with the average being 8 farmers, and all of the members on this council are of course members in the general council of the association and land owners of the area under association control. The general council is composed of all of the members in the association and it usually meets once a year to discuss any developments in the association, the budget and on election years, elections are held at this meeting.

During the elections, which happen for half of the associations every four years and for the other half, every two years (as per the ruling of the administrative council), the members elect farmers to the administrative council and then the administrative council elects the head. One association, Rama, handles elections in a different fashion; it has what they call bloc elections. Members are in one of four blocs, or parties (which really represent families), and every four years, a different bloc takes over control, making for a type of sharing of power among these four blocs (Interview with Rama Head; Discussion with

Mamoun al-Kharabsheh, 3/16/2014). As for term limits of the head or the administrative council, there are none; any farmer can be elected to the position of head or as a member of the administrative council an unlimited number of times. Finally, with regard to general administrative needs, all associations have their own office, computer(s) and printer. 182

In most of the associations, the administrative council chooses the ditchriders and in a few associations, the head alone chooses them. For the most part, when asked if there are any qualifications for these employees to be hired, the answer was vague and general. The ditchriders, as stated by the heads, should be from the area, have field experience and/or have some basic education. On the whole, it does not appear that qualifications are very important. One head even admitted that “favoritism” plays a part in how employees are picked and this has been observed in a few associations in which relatives or friends are chosen as ditchriders. It is also seen that ditchriders are largely from the same area as that of the association, which can be seen as a positive aspect in that they know the area and the people and the problems, or as a negative aspect in that their relations with the farmers are too close and thus not strict enough. WUA heads mentioned that there is some basic training for ditchriders on the part of the JVA and the implementing agency, GIZ, but they also largely learn what they need to know in the field. Some ditchriders are also farmers and some have no experience in farming. When asked whether the association provides any other services besides water distribution and light maintenance on the farm turnout assemblies (FTAs), WUA heads either struggled to come up with some way to make the association look good or else simply shrugged and said no, in such a way that indicated that the association is not meant to provide any other services. For those who came up with other services that the association provides, among these services were: helping farmers in times of floods, monitoring cropping patterns, clearing trees from roads, helping to put out fires in orchards, and giving to children’s charities. In general, many heads do not have any ambitions for the association beyond their current duties so it is not surprising that many had nothing to say in response to this question.

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FINANCIAL AFFAIRS OF THE WUAS

All association require an upfront membership fee from the farmer for entrance into the association and this varies widely in amount and timing. Some associations require an upfront bulk amount of 100-250 Jordanian dinars (JD), or $141-353. Other associations require a fairly minimal upfront fee of 10-35 JD ($14-49) but then also require 5 JD ($7) every month. Yet other associations require payment of 40-60 JD ($56-84) each year and still others require a fee of 100-300 JD ($141-423) for membership but allow the farmer to pay this over several years. As to how these membership fees are used, most WUA heads state that the money goes directly into the association’s bank account and represents investment shares of the members in the association. It is not spent at present but is accruing for potential future use on a project that the association deems worthy of the money. A few associations also indicated that some of the money is used for association activities, office maintenance or an auditor.

With regard to the money received from the JVA each year according to the contract between each WUA and the JVA, almost all association heads believed this to be completely insufficient, especially with regard to salaries. They believe that the salary of a ditchrider is too low for the cost of living in Jordan and that it should cover social security, which is covered for all employees of the JVA. There was also mention that the WUA does not have enough money for utility costs (electricity and water), administrative and general council meetings, maintenance, the yearly association license fee and contract fee, and the hiring of an accountant or an auditor every year. Furthermore, when asked whether their salaries as heads were enough, only four heads indicated that the salary was sufficient. The rest stated that their salaries were not in-line with the cost of living and that it did not cover all of the efforts, time and gasoline that they were expected to expend in this job.

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As a side note, WUA heads were asked for their thoughts on the recent initiative from the JVA to raise the price of irrigation water. About half of the WUA heads rejected any price rise because they see farmers as already having to pay too much for everything else (labor, fertilizers, pesticides, irrigation piping, seeds, etc.) and their produce is not reaping reasonable and fair prices in the market. A price rise would mean making farmers pay more for water without any sort of similar rise in their profits. On the other hand, the other half of the WUA heads could see that this is a necessary step and that the price of water cannot remain stagnant forever. Some did, however, say that the timing of this price rise initiative was not good and that the way it was proposed was not good, in that it should have been presented as a more gradual change. A few also mentioned that the service and quantity of water need to be better before a price rise will be accepted, even if it is presently needed.

WUA MEMBERSHIP

In order to be an association member, there are generally few requirements other than that the farmer be a Jordanian owner or renter in the region under the control of the association. Non-Jordanians are currently not allowed to be members under the Jordanian Cooperative Corporation Law under which associations were established. Eight heads believe that non-Jordanians should never be allowed to be members because they are not as “committed” and “loyal” as Jordanians to the land and their responsibilities and because they can leave at any time without consequence. Four heads thought that allowing non- Jordanians to be members was acceptable, seeing non-Jordanian participation as possibly leading to more cooperation among farmers and other potential benefits to service in the area. One head came down in the middle, saying that non-Jordanians should be indirect 185 members, in that they should participate in association meetings and activities but not be allowed to participate in elections. This was likely said in fear of non-Jordanians becoming members and then possibly voting that head out of office. Otherwise, with regard to membership qualifications, some heads also mention that the farmer needs to be a good person, not a criminal, and pay his membership dues. The ultimate decision on whether a farmer is allowed into the association is made by the administrative council and these councils display more selectivity in some associations than in others. As to whether association members receive any benefits that non-members do not receive, all heads stated that there are no special benefits for members. Some did remark that in the future, if the association invests in a project (with the shares that it has built up from membership fees), this project would be only for the benefit of members. When asked why some farmers have still not opted to be members in the association, heads were generally not sure. Some possible reasons they gave were: non-members are those farmers who are not very present in the valley most of the time; they have personal reasons and preferences for not being members; the membership fee is too high; Jordan’s history with cooperative councils does not inspire confidence; some farmers are not convinced that the association is any better than the JVA; or non-members might be suspicious of the intentions of the associations and their GIZ backers.

Two somewhat tangential questions asked were in regard to the number or percentage of farmers in the area who own more than one farm unit (more than about 35 dunums) and whether there exists any cooperative efforts between farmers and animal herders. As to the first question, some areas have as few as 5-10% of the farmers owning more than one farm unit, with another handful having 15-35% of farmers owning more than one unit and yet other areas having 60% of farmers as owners of more than one farm

186 unit. One head said that virtually all farmers in his area have more than one farm unit. The answer to this question indicates that some areas have more big landowners than others, potentially influencing the kind of production and level of cooperation seen in the area. As to the second question on cooperation with animal herders, it was generally remarked that there is no cooperation of this kind although farmers do sometimes let herders onto their lands after the harvest so that animals can feed on the remaining plant products. This question was of interest because there is a natural symbiosis between farming and animal husbandry and it would seem more sustainable and less wasteful if there was more cooperation along this avenue.

SUMMARY

This chapter has offered a more detailed account of WUAs in the Jordan Valley, both in terms of their initial development and their present features. Within their beginning stages, the prominent and influential role of GIZ has been identified in initiating and funding the project. Due to the strong resistance from farmers and JVA employees in the beginning, a slower and more adaptive approach was used by GIZ to ensure cooptation and acceptance by the involved parties. This step-by-step process meant that few end goals were determined or set in the beginning and to this day the end game is ill-defined. In addition, WUAs were established, due to expediency, under the JCC and thus remain without a more solid legal basis to this day. Partly for this reason, the WUAs continue to depend on the JVA for all of their funding for operations and salaries instead of operating independently from the JVA. With regard to the present status of the WUAs, WUAs do not exist in all areas of the Jordan Valley, with some areas remaining under the full auspices of the JVA. Not all 187

WUAs are at the same stage of task transfer and some remain without any real duties or tasks to this day. All are contracted under the JVA but the internal workings of each WUA differ slightly in terms of membership fees, term limits for heads and how elections are conducted. Membership is only open to Jordanians. These elements all play into subsequent analyses in later chapters. Finally, a more in-depth look is taken with those WUAs that have undergone task transfer, either of distribution or distribution and light maintenance. This represents the pool of WUAs from which the four case studies, discussed in the following chapter, were chosen based on their physical and geographic diversity. The final sections in this chapter also delve deeper into the personalities, skills and opinions of the WUA heads, demonstrating the diversity in this sense as well. Overall, what can be observed is that the WUAs by no means represent a cohesive and homogenous bloc; rather, they display a variety of characteristics within a number of stages of development and are still works in progress. The following chapter looks in greater depth at just four of these WUAs that are then used for final analyses of the factors and outcomes of import in this dissertation.

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Chapter Seven: The Case Studies

This chapter will provide detailed descriptions of the four WUAs chosen as case studies in which to conduct all of the proceeding analyses. These associations differ in terms of their physical, farmer and leadership characteristics. Much detail is provided on the inner-workings of the water distribution networks and the layers of management responsible for their operations with the purpose of providing sufficient background to better understand the results sections in subsequent chapters. It is due to many of these features that associations differ in their outcomes.

SELECTION OF CASE STUDIES

From the information gathered in the head interviews, the four associations at PS 33, PS 55, PS 91 and Mazraa-Haditha (MH) were chosen for more in-depth analyses and farmer surveys (see Figure 7.1 for their locations in the Jordan Valley). These associations display physical differences as well as variations in internal organization, membership and farmer characteristics as seen in Table 7.1 below. The following sections will give detailed descriptions of these four WUAs with regard to their physical parameters, operations and management framework.

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Figure 7.1: Map of four case study water user associations in the Jordan Valley.

Source: Map produced by Dr. Samer Talozi at Jordan University of Science and Technology, Irbid, Jordan.

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Table 7.1: Select characteristics of the four WUAs chosen for in-depth analysis. PS 33 PS 55 PS 91 MH Land Area* 841 1065 1000 11850 (dunums) Number of 205 291 254 395 Farm Units* Major Crops* Citrus, Vegetables Vegetables, Vegetables, vegetables palms melons, bananas Number of 0 8000 1145 179 Plastic Houses* Water Flow** Pressure and Gravity Pressure and Pressure and Gravity Gravity Gravity Number of 3, operates 2 0 4, operates 3 3, operates 2 Pumps in Use** usually usually usually Farms in Yes Yes No No Zhor** Water Source* KAC and KTD KTD KTD Wadi Bin Hamad Spring, Wadi Karak Dam President Nawaf Kraym Walid al-Faqir, Ali Mustapha, Saleem al- Information** al-Rayahna, decent land minor land Huwaymil, one minimal land holdings, holdings, elected farm unit, holdings, appointed not elected but appointed not elected basically elected appointed, also consultant for rehab company Membership** High but very High Low, very High concentrated selective within al- Rayahna family Nearest Town** Masharaa Kraymeh Karama Mazraa Farmer Jordanian Jordanian, Jordanian, Jordanian, Nationality** Egyptian Egyptian, Egyptian, Pakistani Pakistani Farmer Land Mainly owners Mainly Owners and Owners and Status** renters/investors renters renters *Source: Data provided through interviews with WUA and JVA employees in the Jordan Valley. **Source: Information gleaned from personal observation in the field, discussions with WUA ditchriders and survey data.

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PS 91 – AL-BALADNA WATER USER ASSOCIATION

The Al-Baladna WUA (referred to here as PS 91) maintains part of the area within Development Area (DA) 27, just north and to the side of the village of Karama in the south of the Jordan Valley (Figure 7.2). Its total land area is 10,258 dunums (2,535 acres) and the total number of farm units is 254 (26 of these units are not being farmed). The approximate land area used for vegetable farming is 5,502 dunums (1,360 acres) at the peak growing season (the winter season) and this number is much smaller in the summer when many vegetable farmers do not choose to grow crops. Palm trees cover approximately 3032 dunums (749 acres) of the land area under this association (Data provided by JVA and WUA employees).

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Figure 7.2: Development Area (DA) 27 and the details of the area administered by the WUA at PS 91.

Source: Ministry of Water and Irrigation base map provided by WUA at PS 91 (colors and labels added).

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With regard to the water supply, about 4,728 dunums (1,168 acres) and 119 farm units are supplied from the pressure network (the area in Figure 7.1 covered in red and pink lines) and 5,530 dunums (1,366 acres) and 135 farm units are supplied through the gravity network (the area in Figure 7.2 covered in dark and light green lines). In addition, 25 farm units next to or close to the King Abdullah Canal (KAC) are supplied separately through plastic pipes (marked with blue dots on the farm unit in Figure 7.2) that draw water directly from the canal as there is insufficient pressure in these parts of the pressure network (Discussion with engineer at PS 91, 4/27/2014). One line in the pressure system, DH24

(see Figure 7.2), draws water from a pipe directly connected to the KAC instead of from the pressure network. This change was made due to its location at the farthest point from the pump station, which resulted in very poor pressure and service that could not be remedied in any fashion other than to take it out of the system. The royal family, in the name of Princess Alia, also owns land in this area (1250 dunums of date palm trees) and these farms are operated by a Jordanian agent. Units 1022 and 1036 just to the west of DA 27, near the border with Israel, are supplied through a plastic pipe directly to the KAC. These farms are not under the purview of the WUA but under the JVA. The Princess also has two units (404 and 494) within the system under the purview of the WUA (Interview with farm agent, 11/24/2014; discussion with engineer at

PS 91, 5/4/2014). The network relying on pressured distribution has its main line (marked in red in

Figure 7.2) and 10 laterals (marked in pink) running on the eastern portion of the DA that is closer to the canal. Farm units within the pressure network are supplied with water three times a week during the winter season (from roughly September/October to April/May), each turn being for six to seven hours (generally between 5:00 a.m. and 12:00 p.m.) at 6

194 liters per second (L/s), resulting in about 151 cubic meters of water per turn. Every lateral line remains open constantly, with the left side of each lateral taking water one day and the right side the next. Lines P37 and P35 are an exception; they are open only every other day, with all units left and right of the lines taking water at the same time. These peculiarities in the system are due to certain capacity and design issues that warrant these adjustments (Discussions with engineer at PS 91, 3/15/2014, 5/1/2014, 11/2/2014).

The network relying on the gravity intake off of the KAC has its main line (marked in dark green in Figure 7.2) and 11 laterals (marked in light green) running on the western portion of the DA that is farther from the canal and lower down from the canal, thus making use of better gravitational flow. Along the gravity network, farms are supplied with water three times a week (again during the winter season), each turn being for five hours (either between 5:00 a.m.-10:00 a.m. or 10:00 a.m.-3:00 p.m.) at 9 L/s, resulting in about 162 cubic meters of water per turn. Five of these gravity lines are supplied with water one day and six lines the next in this alternating pattern. On any given turn, both the left and the right sides of the lateral line take water at the same time, either in the morning or in the afternoon. This morning-afternoon system alternates between lines such that one week a line will get morning turns and the next, afternoon turns (Discussion with engineer at PS 91, 11/2/2014).

During the summer months (May/June to August/September), fewer farms are supplied with water because some farmers do not plant crops in these months, partially due to a water shortage in the summer and higher temperatures that are not suitable for many crops. Typically, date palm farmers use water during the summer months, as date palms require higher water intake just before their ripening season in August/September (Interview with date palm farmer, 2/15/2015). For almost all farmers, water turns are

195 reduced from three turns a week to two turns as week during the summer months, which makes for some days during the week when no farm is receiving water and the WUA employees have no field work. As for the daily operations of the Al-Baladna WUA, it maintains a small building that has a large office for the president (with computer and TV), a small office for the engineer (with computer), a meeting room with tables and chairs, a kitchen, a couple of bathrooms and a few other rooms used for storage or sleeping-over if there is work to be done at night. For employees, besides the president and the engineer, there are four ditchriders. One ditchrider has been tasked with a name that literally translates from Arabic as “correspondent,” which means that he keeps the office clean and fetches tea, coffee or other small snacks and favors when called upon by the president, engineer, a ditchrider or any guest. This is not a position that has been outlined in the contract between the JVA and the WUA with regard to the ditchriders. The contract states that the association will have salaries for four ditchriders. It is the association that has converted one of these positions from the field to menial office labor. With regard to the other three ditchriders, one of them (they rotate this duty between the three of them) opens the gravity in-take every morning at 5:00 a.m.; the pump in-take is still controlled, opened and closed by JVA employees. The ditchriders open and close the lateral lines according to the water order (schedule) printed the day before that outlines which laterals receive water at what time of day. Sometimes the ditchriders open and close the laterals at the end of the day, after that day’s water distribution has ended, in preparation for the next day. At several other points in the day, usually two ditchriders drive a car around the area to perform various duties (Discussions with PS 91 ditchriders, 3/15/2014). One task is checking for violations when traveling among the farm units. Violations can

196 be of two kinds, either taking water out of turn or tampering with and damaging the FTA or any other part of the distribution network. Ditchriders also remove a pressure gauge from an FTA (rendering the FTA incapable of receiving water) if the farmer has been issued a violation that he has not paid and the JVA orders the farmer’s water to be cut off (Figure 10). The ditchriders have to return pressure gauges to FTAs when the fine has been paid and the violation resolved. Ditchriders perform light maintenance tasks, fix minor problems on the FTAs, clean out the intake points from the main canal (pictures shown later) and collect information on crop patterns within the farm units at certain points in the month. They also field calls from farmers about problems with their water flow and attempt to fix these problems directly if the solution is something simple having to do with the FTA (Discussion with engineer at PS 91, 4/27/2014; field tours with PS 91 ditchriders, April/May 2014). The JVA stage office, co-located with the WUA at PS 91, is involved in the affairs and daily activities of the WUA, so it must be taken into account. The JVA office oversees DAs 26, 27 and 28, which includes three WUAs, including the one at PS 91. Within the stage office, there are, officially, eight employees: one director, three financial affairs staff, two administrative affairs employees, one driver and one “correspondent” (same meaning as above for the WUA “correspondent”). There are also, officially, four guards for the office (who work in shifts), seven ditchriders (to maintain the networks in the DAs not under WUA control), and eight employees to operate the pressure network pumps off of the KAC for the WUAs and the areas under JVA control (Discussions with stage office employees, 5/8/2014). It is common knowledge among stage office employees (and the PS 91 WUA co-located with the stage office) that some of these employees frequently do not show up to work, with some never showing up for work. The stage office collects:

197 recorded violations from the WUA that are then sent to the directorate; farmer water fee payments; and cropping pattern information and any other requested data from the WUA on farmers, field and water conditions. The stage office also deals with changes to the water order and responds to requests from farmers to modify the water. It additionally works in the field in the areas where WUAs are not operating and monitors farmers who have license to use plastic pipes directly into the KAC (Discussions with stage office employees, 5/8/2014). The last and upper layer of management over Al-Baladna WUA is the South

Directorate located about 15 kilometers away, just south of Shunneh al-Junubiyah (South Shunneh). It has about 93 employees involved in administration, finance, operations, and maintenance, as well as secretaries, drivers and “correspondents.” (Discussion with director of south directorate, 2/17/2014) Of most importance to the WUA is the office handling operations and maintenance, as well as the office responsible for ticketing violations. According to the director of operations and maintenance (Discussion with Mamoun al-Kharabsheh, 5/6/2014), the maintenance team for the southern section of the valley (a fairly large expanse) consists of only three employees, one of whom is the driver. The JVA has only one backhoe (an essential machine for maintenance) and few other pieces of machinery to deal with only the direst maintenance needs of the main lines; other deterioration problems within the network may be sidelined. The office dedicated to handling violations receives information on the violations from the stage offices, records them in folders by hand, and then awaits the final sign-off until the farmer comes to the directorate to pay the required fee (Discussion with Khalil al-Adwan, in charge of violations at the South Directorate, 5/10/2014). Sometimes a violation is waived due to a general pardon issued by the JVA, an acceptable plea from the farmer as to why there was

198 no violation in the first place, or a personal relation/connection that the farmer has with someone in the JVA (Personal observation of the folders in which violations are recorded by hand in the South Directorate).

PS 33 WATER USER ASSOCIATION

The water user association at Pump Station 33 (PS 33) is located in the area of the town of Masha’ra in the north of the Jordan Valley. It covers a small section of Development Area (DA) 12, all of DA 13, and most of DA 14 (see Figure 7.3), with a total of 8,413 dunums (2,079 acres) under its purview. There are a total of 205 farm units of varying sizes. About 78 of the units are in the gravity network that abuts the Jordan River and is at a lower elevation (this area is commonly referred to as the zhor and has green lines covering it in Figure 7.3); the rest of the farm units are in the pressure network at a higher elevation and closer to the KAC (this area has pink lines covering it in Figure 7.3).

There are no farms in this area that legally have plastic pipes that connect directly to the KAC although they do exist, albeit in an illegal manner.

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Figure 7.3: Map of the area in DAs 12, 13 and 14 under the administration of the WUA at PS 33.

Source: Ministry of Water and Irrigation base map provided by Haidar Malhas (USAID, ISSP) (colors and labels added).

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The official count says that there are roughly 350 farmers under PS 33, which is larger than the number of farm units because many of the units are broken-up and farmed by several farmers (Data obtained from JVA North Directorate). This “fragmentation of ownership” is a common occurrence especially in the north, where land is owned more often than rented, and as mentioned earlier, inheritance laws in Jordan, based on Islamic tradition, split land from the father among all of the children. Many farm units are thus owned by a group of siblings, most often brothers, so there are farmers who own as few as 5-10 dunums (1.2-2.5 acres) because the rest of the farm unit is owned by brothers. There are also farmers who own or rent an entire farm unit, or around 35 dunums (8.6 acres). The farmers under PS 33 are almost all Jordanian owners or renters, although it is not exactly known whether the number of owners is greater than that of the renters. Due to land fragmentation, owners can own very little land or a farm unit or two. Renters also vary in the amount of land they own, with some having just one farm unit and others having as many as 23 farm units. In particular, there are two investors in the area who have very large tracks of land: Al-Naoura and Al-Ghoul (Interviews with engineers from these farms, 9/18/2014 and 10/5/2014, respectively). For example, the letter G on the map in Figure 7.3 marks all of Al-Ghoul’s farm units under PS 33. With regard to crops grown in this area, there are around 4,879 dunums (1,206 acres) of citrus trees, 1894 dunums (468 acres) of vegetables, 325 dunums (80 acres) of date palms, 60 dunums (15 acres) of grape leaves, and the remainder being various other kinds of trees, leaf crops and grain crops (Data from

JVA North Directorate). Unlike in the south, not all farm units have a pool. Many farms have fences, some such that the FTA is locked inside the fence and unreachable by the WUA or JVA employees.

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The farms under PS 33 are supplied by both freshwater from the KAC and treated wastewater from the King Talal Dam (KTD). While the supply line from the KTD is sometimes in disrepair, as it was during the Spring of 2014, on a normal basis the farms would be supplied with two turns from the KAC each week and one turn from the KTD (Discussion with engineer at PS 33, 5/21/2014). At PS 33, there are three main pumps that pump water into the pressure and gravity systems. Unlike elsewhere, both the gravity network and the pressure network are supplied by the same pumps; there is not a separate intake for the gravity network as in PS 91. According to the engineer of the WUA

(Discussion on 5/21/2014), two pumps are used the majority of the time and only when the quantity that the WUA is able to take in is over 800 cubic meters per hour does the association use all three pumps. Water turns for each farm unit are a bit more complicated than in PS 91. Not all farms receive the same pressure and hours of water in a turn. Depending on the area of the farm unit and the crops grown, the farm could receive 6, 9 or 12 L/s and for 4 to 6 hours per turn. Each week, every farm unit receives three turns of water on three separate days. In the gravity network (marked with green lines in Figure 7.3), water is supplied during one time period per day, from roughly 7:00 a.m. to 1:00 or 2:00 p.m. Three days a week, water is supplied to the northern half of the gravity network; on the other three days, it is supplied to the southern half. In the pressure network (marked with a red line for the main line and pink lines for the lateral lines in Figure 7.3), farm units also receive water three days a week. For three days of the week, lines 104-108 are supplied either in the morning (from roughly 7:00 a.m. to 1:00 p.m.) or the afternoon (1:00 p.m. to 7 p.m.). Lines 109- 111 receive water the other three working days of the week only in the afternoon shift

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(Discussion with engineer at PS 33, 5/21/2014; discussion with PS 33 ditchrider, 10/23/2014). During the summer, water turns are sometimes more limited due to water shortages. During the winter, farm units only receive two turns a week due to less water demand in the presence of winter rains (Discussion with PS 33 ditchrider, 10/23/2014). In farm units where there is more than one farmer, it is the responsibility of the farmers within the unit to come to an agreement on the how the water turns will be divided. Some agree to all take water during every turn for a shorter time period; others agree to each take only one day’s turn of water each week (Discussions with farmers in PS 33, Summer 2014). Within every farm unit, as in PS 91, there is an FTA. Unlike in PS 91, many of the FTAs in PS 33 have working meters although they are still not utilized on any consistent basis. With regard to the daily operations of the WUA at PS 33, the association engineer receives the water order every day from the JVA and the ditchriders use the water order to determine what laterals need to be opened and closed for the day. There are four ditchriders in the association: one is responsible for the gravity system; two are responsible for the pressure system during the morning and one is responsible for the pressure system in the afternoon. Ditchriders do not rotate these positions (Discussion with PS 33 ditchrider, 10/23/2014). The ditchrider responsible for the gravity network has a farm in the gravity network. One of the ditchriders for the pressure network has a farm in the pressure network.

The ditchriders’ duties begin at roughly 7:00 a.m. when the main pumps are started. They review the water order and make sure that the appropriate laterals are opened and closed according to this order. They also monitor the field for any violations of water stealing or tampering with FTAs, although this is a challenge when some FTAs are locked

203 within farm fences, as mentioned earlier. At the end of the month, they are also responsible for collecting re1adings from the working meters and conveying this information to the JVA, although it is not clear whether this task is consistently tended to or not. The ditchriders additionally clean out the intake from the KAC because, as in PS 91, trash builds-up and reduces the water pressure coming into the system. There were trash removal machines placed at the intake but, as elsewhere, they do not operate anymore so trash has to be removed by hand (Discussion with PS 33 ditchriders, 5/29/2014; field tours with PS 33 ditchriders, June 2014).

The stage office that oversees the work of the WUA (Stage Office #2) at PS 33 is located about five kilometers south along the KAC and is responsible for DAs 11-17 and 36-39, with three WUAs being contained within this area. There are nine employees who work in the office: one accountant, two for finances, one director, two monitors, one for entering information into the computer or files, one secretary and one driver. Two guards are also employed by the stage office and a host of ditchriders work in all of the DAs not under WUA control. The stage office employs the pump operators, such as the pump at PS 33 (Discussion with stage office employees, 5/21/2014). There is a JVA office co- located with the WUA office at PS 33 and it has pump operators who work in shifts and whose sole responsible is to simply turn on and turn off the main pumps. The stage office receives recorded violations from the WUA and forwards the data to the northern directorate. Farmers also pay water bills at this stage office. The state office deals with the WUA when any changes need to be made to the water order. Sometimes the stage office receives complaints directly from farmers who do not want to bring their complaint to the WUA (Discussion with stage office employees, 5/21/2014).

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At the next highest level of authority over the WUA, there is the North Directorate. There are nine WUAs under its purview, six of which have some type of task transfer. There is also land not yet under a WUA that is still the sole responsibility of the JVA. Within the directorate there are roughly 230 employees, including five engineers (three females) (Discussion with director of North Directorate, 2/18/2014). On a day-to-day basis, the directorate does not have much interaction with the WUA except for with regard to violations. A farmer must come directly to the directorate to pay his/her violations. Any owner who wishes to rent his farmland is also legally required to register his renter with the directorate, although this frequently does not happen and the renter and owner have a private, non-official agreement.

PS 55 WATER USER ASSOCIATION

The water user association at Pump Station 55 (PS 55) is located near the town of

Kraymeh, just north of the central town of Deir Alla and roughly 55 kilometers from the northwest corner of Jordan. This WUA operates within all of DA 21 and part of the gravity network in DA 20 (see Figure 7.4). The land area under its purview is roughly 10,654 dunums (2,633 acres) and there are 291 farm units, with about 190 of the farm units in the pressure network (covered in light blue lines in Figure 7.4) and the rest in the zhor, or in the gravity network in the area next to the border (covered in light green lines in Figure 7.4). This area is largely vegetable farms (6660 dunums) and farmed primarily in greenhouses, with an estimated 8000 greenhouses. There are also about 588 dunums of citrus trees, with some farmers planting palms, grape leaves and other tree varieties (Data from JVA Middle Directorate).

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Figure 7.4: Map of the area within DAs 20 and 21 under the administration of the WUA at PS 55.

Source: Ministry of Water and Irrigation base map provided by Haidar Malhas (USAID, ISSP) (colors and labels added).

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Water is supplied to PS 55 through the Zarqa Carrier III (ZCIII) underground pipeline that transfers wastewater supplied through the King Talal Dam to farms in the section of the valley above Deir Alla. PS 55 uses solely treated wastewater in both the pressure and gravity networks. While there are technically two networks, both distribute water at this time by gravity (Discussion with PS 55 water official, 9/3/2014). For the purpose of distinguishing between these two regions under PS 55, the pressure network will still be referred to as such even though it uses gravity for water distribution. The pumps are still in place in case the supply from ZCIII malfunctions and water from the

KAC must be used temporarily. As in other areas of the valley, some farmers have private wells and some rely almost entirely on them, without taking any water from the network operated by the WUA and the JVA. Within DA 21, the pressure network (with main line marked in dark blue and lateral lines in light blue in Figure 7.4) is located closest to the KAC and the gravity network (with main line in dark green and the lateral network in light green in Figure 7.4) is located within the zhor area at a lower elevation and right at the border. Every farm unit is supplied with roughly 8-10 hours of water twice a week, with this amount varying according to rainfall and other unexpected issues in the network. Farm units in the pressure network are largely equipped with flow limiters of 6 L/s, although if the unit is larger than 40 dunums, the farmer is provided with a 9 L/s flow limiter. In addition, very large farms and citrus farms are provided with 12 L/s or 15 L/s flow limiters. Farms located in the zhor have 9 L/s flow limiters due to the fact that they are farther away from the main source (roughly two kilometers) and they are all essentially on the same lateral, reducing their potential water pressure and thus justifying larger flow limiters (Discussion with PS 55 water official, 9/3/2014).

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As for the water order among all of the farms in the pressure and gravity systems, as elsewhere, there is a weekly schedule. On Saturdays and Tuesdays, water is distributed to laterals 107-112 in the pressure network, with three lines supplied in the morning/afternoon (between 7:00 a.m. and 4:00 p.m.) and three lines supplied at night (between 4:00 p.m. to 1:00 p.m.). Laterals 104-106 in the pressure network are supplied with water on Sundays and Thursdays and only in the morning/afternoon period. On

Mondays and Wednesdays, water is distributed only to the zhor, or gravity network, during the morning/afternoon period. Whenever a lateral is supplied with water, all farm units along the lateral take water at the same time, although due to differences in flow limiters, some farm units are receiving more water and some less water (Discussion with PS 55 ditchriders, 10/12/2014). Along any given lateral in the pressure network, there are anywhere from 9 to 26 farm units. The zhor area is pretty much all connected and has many more farm units supplied all at once. As with other areas in the Jordan Valley, PS

55 does not use water meters as most have become inoperable. The water user association at PS 55 has its headquarters in a small building that houses a single large room for meetings, a computer and printer for administrative tasks, a small kitchen and a bathroom. One WUA employee is responsible for managing the daily operations and unlike elsewhere, he is not an engineer; he is, though, a member of the same family as the WUA president. For maintenance, there is one employee who works part of his time for this association and part of his time with the WUA at PS 50.

There are four ditchriders who work in shifts, with two working during the morning/afternoon shift and the other two working during the night shift. They also alternate weeks working the morning/afternoon or night shift and they have days-off, meaning that at times there might only be one ditchrider working at any given time

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(Discussion with PS 55 water official, 9/3/2014). Two of the ditchriders are retired from the army, one ditchrider has no significant previous work experience, and one ditchrider is also a farmer in this area (Discussion with PS 55 ditchrider, 10/14/2014). As for transportation, they either use one vehicle, with the two ditchriders and the engineer going together to make field tours and check on any current issues. One ditchrider also has a motorbike that he can use to make field tours. And as in other WUAs, ditchriders are responsible for opening and closing lateral lines, making field tours to check for any farmers taking water out of turn or removing their flow limiters, responding to farmer complaints, and fixing any very minor FTA problems. More significant FTA problems are left to the maintenance employee and, if he does not have a solution, it is passed off to the JVA (Field tours with PS 55 ditchriders, Fall 2014). Stage Office 3 is the JVA office that oversees the work of the association at PS 55, along with all of the area in DAs 18, 19 and 20 that includes another association at PS 50.

This stage office has around 20 employees, including a director, an accountant who is present three days a week, ditchriders, facility or pump operators, and guards. Employees work from roughly 7:30 a.m. to 3:00 p.m., with one shift-time employee staying for 24 hours to guard and monitor the facilities and operations. The stage office ditchriders monitor the work of the WUA ditchriders in morning and evening shifts with two ditchriders always present. Every month the stage office collects crop pattern information from farmers and passes it on to a higher level within the JVA. For the three days that the accountant is present at the stage office, farmers can pay their water bill there or else they have to go to the directorate in Deir Alla (see below) to pay. The farmer is given 45 days to pay his water bill and, if he is unable to make the payment, his FTA is rendered inoperable by the stage office employees and he is given at 16 Jordanian dinar fine. When

209 the farmer eventually makes his water payment, the FTA is opened once again. With regard to maintenance tasks, the WUA hires a team to do light maintenance on the FTAs. For larger maintenance activities, this stage office is not the responsible party but rather leaves this work to the directorate (Discussion with stage office employees, 2/10/2015). The Middle Directorate is located in Deir Alla and covers the entire middle portion of the Jordan Valley, or DAs 18-25, 29, 53 and 54. There are four stage offices under this directorate but only the two previously-mentioned WUAs (PS 55 and PS 50), although there have been plans that more would be created in this zone. For example, WUA 78 is supposedly registered but not functioning. A WUA planned off of KAC Intake 22 was supposed to be started but, due to tribal issues between two families, it has not been able to move forward. This directorate employs around 160 people (Discussion with directorate of Middle Directorate, 2/27/2014). A large role that the directorate fulfills is taking care of all maintenance tasks for all of the DAs under its purview, including those with WUAs.

Farmers are also directed towards the directorate to pay any fines that they have been issued in the field; as mentioned above, farmers can pay their water bill here half of the week when the accountant is present. If the farmer’s crop pattern has changed or something else has affected the amount of water required, he/she can make requests to change the allotted water quantity at the directorate.

MAZRAA-HADITHA WATER USER ASSOCIATION

The Mazraa-Haditha (MH) water user association is located, as its name would suggest, in the area of the small villages of Mazraa and Haditha, just to the south of the Dead Sea and before reaching the major potash company facilities. This WUA covers the area within DAs 45, 46 and 47, which comprise about 11,850 dunums (2928 acres) of total 210 land area and 393 farm units (see Figure 7.5). New lands just outside of these DAs are being cultivated and will eventually be hooked into the same system as the current farm units. The majority of the farmland (5936 dunums) is planted with vegetables crops, with a large portion of that production being tomatoes. There are also around 431 dunums of bananas, 110 dunums of date palm trees, 49 dunums of melons, 268 dunums of grapes and 47 dunums of a variety of fruit trees (Data from JVA Southern Ghor Directorate). Most farms use drip irrigation systems although there are a few farms growing animal feed that use sprinkler systems.

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Figure 7.5: Plot areas of DAs 45, 46 and 47 that comprise the area under the administration of the WUA at Mazraa- Haditha.

Source: Arab Business Corporation base maps (colors and labels added). 212

Irrigation water for DAs 47 and 46, as well as the southwestern half of DA 45, comes from Wadi Ibn Hammad, a fresh spring source originating in the mountains to the east of the valley, and flows to farms through both gravity (marked with light green lines in Figure 7.5) and pressure systems. Water for the other half of DA 45 comes from Wadi Karak, another mountain spring, and only gravity is used to distribute its waters. Each of these three water sources has a small storage pond co-located with its collection point. Due to limited storage, when there is excess water in Wadi Ibn Hammad and farmers are unable to use it (especially in summer months when there is less agricultural activity), the nearby potash company is able to buy this water. With regard to the pressurized network (marked in blue lines in Figure 7.5), the pump house is located in DA 46 and supplies around 127 of the 393 total farm units: 22 out of 60 farm units in DA 47, 67 out of 142 farm units in DA 46, and 38 out of 191 farm units in DA 45. There are a total of three pumps but only two pumps are used most of the time. The pumps are operated 24 hours a day and seven days a week, with a rest hour from roughly 8:00 a.m. to 9:00 a.m. every day (Discussion with MH ditchrider, 12/1/2014). Water is released to each DA in turn for 24 hours, once every 48 hours (one day with water, two days without, etc.), and the flow is kept at roughly 255 L/s. While the DAs differ in land area and number of lateral lines, they are still all provided with the same 24- hour turns of water separately. At the farm gate, flow limiters of 3 L/s are installed, a much lower flow rate but for a much longer time period than elsewhere in the valley. FTAs remain open constantly because all of the farm units in each DA take water at the same time, leaving no reason for farmers to ever close the FTA. While there are a few FTAs that have meters, meters in general are not used in this area (Discussion with MH Head, 9/6/2014; discussion with MH ditchrider, 12/1/2014).

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Unlike in the water distribution networks in PS 33, PS 55 and PS 91, the network in MH is being rehabilitated with the help of funding from Gulf countries. The rehabilitation is being carried out by a private company, the Arab Business Company, and includes the replacement of the old metal piping network with high-density polyethylene pipes. About 83 kilometers of new piping is being installed and the pipes range in width from 90 to 630 millimeters, going from the most major supply lines to the small lateral lines. There are also plans to install pumps, filters, gates, valves and other structural elements throughout the network, as well as to rehabilitate the FTAs on each farm

(Interview with Arab Business Company project consultant, 9/6/2014). MH has an office consisting of two small rooms within the JVA stage office in Mazraa. Unlike other WUAs, this one has not yet taken on any tasks involving maintenance; it simply distributes water. The president plays both his title role and that of the engineer or employee specifically charged with overseeing water distribution matters.

There are four guards and four ditchriders employed by the WUA. On a daily basis, the guards are responsible for guarding the water sources and making sure that they are not jeopardized by surrounding locals and farmers. The four ditchriders are responsible for opening and closing the main valves, touring fields to check for violations, and taking note of any other farm-related information as requested by the WUA or JVA. As with other

WUAs, they work in a unique shift-like fashion, where two ditchriders act as the drivers for the other two, each working for three days and then having off three days. One ditchrider just works in the field, going around with one of the drivers to open water lines and check for any violations, for three days on and then three days off. The remaining ditchrider works in this same capacity for the three days that the aforementioned ditchrider

214 is off and he also works in the office during the time that the other ditchrider is in the field (Discussion with MH ditchrider, 12/1/2014). The above-mentioned stage office that houses the WUA office is Stage Office #1 and oversees the work within DAs 45, 46, 47, 48 (Dhirra) and 55, which is a new DA being developed at present for additional farmable lands. There are about 30 employees in this stage office, with six pump operators, six guards and two ditchriders. The pump operators work in shifts and are responsible for the daily operations at the pump house. The guards protect the office building and the cars, trucks and maintenance supplies that sit there on a daily basis. The stage office ditchriders are tasked with monitoring the field, taking note of any violations of the network, and overseeing the WUA’s activities (Discussion with stage office employees, 2/14/2015) Unlike in other stage offices in the north of the Jordan Valley, this stage office does the majority of daily work related to farmer affairs, not the directorate. Due to MH still being at a relatively nascent stage, the stage office is responsible for all of the minor and major maintenance activities on the FTAs or the lateral and main lines. There is a storage facility in the directorate is further materials or machines are need but the work is done by this stage office. Farmers also come to this stage office to pay fines, to pay their water bills, and to request extra water turns or a different allotment of water. Both the stage office ditchriders and the WUA ditchriders have the ability to fine farmers for illegal activity. If a farmer has violated a rule and has yet to pay the fine, the stage office replaces his flow limiter with a solid metal sheet to prevent water from reaching his farm. Only after he has paid the fine does the farmer again receive water from the network. The stage office employs collect information on cropping patterns and send this information on to higher levels within the JVA (Discussion with stage office employees, 2/14/2015).

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The highest level of authority for water distribution for MH is the Southern Ghor Directorate located in Safi, about 30 kilometers to the south of Mazraa-Haditha. This office oversees water management for irrigation purposes over all of the area south of the Dead Sea from Mazraa-Haditha to Fifa, an area roughly 45 kilometers south of the Dead Sea. The directorate employs about 320 people working in the various administrative, financial and maintenance departments. Four WUAs have been established under its purview, three of them responsible for water distribution and one that is still in the initial stages of being established (Discussion with director of Southern Ghor Directorate, 2/23/2014). The directorate is responsible for implementing larger policies directives and is in contact with the heads of the stage offices for these purposes. Most of the daily, on-the-ground activities are not handled by the directorate but by the stage offices and the WUAs.

SUMMARY

The uniqueness of each case, in terms of water networks and participants, was established in this chapter and this understanding will help explain the differences seen in performance among them. The networks are complex, multi-faceted operating systems with water schedules that are designed to match not only the needs of farmers but also the capabilities of the infrastructure. Distinct turns in terms of hours and pressure amounts are accounted for in theory, as has been outlined within each case study area’s network. But in reality, this determined order is susceptible to the abilities of the ditchriders who are limited in number, availability of vehicular transport and time, if they have second jobs. Their responsibilities are clear, as are those of employees in the JVA stage offices and directorates, but whether these responsibilities are attended to in reality will be seen in the results chapters in greater detail. 216

What has also been demonstrated is the diversity of farmers between and within the WUAs in terms of nationality, crops grown and socioeconomic status in the areas where there are “big fish” who own large plots of land in comparison to other farmers who own less than one full plot. These farmers are then cast within areas that have differing types of water sources (fresh river water, treated wastewater or spring water), geographical locations in the valley and administrative realms. Each WUA has its own specific concerns to take into account in how it operates and each certainly takes liberties in determining how it should manage the field and its farmers. What the following results chapters will show, though, is that despite these differences that do matter in some cases, there are other factors that matter more and across the board in all studied WUAs. Beyond simply being able to state that certain WUAs are performing better or worse than others, it is interesting that there are still commonalities among them and ways in which all are deficient. It is from these points that we can move forward in making improvements.

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Chapter Eight: Physical Factors

This chapter, as well as the following three chapters, deal in turn with the detailed analyses of individual factors within the four major categories: physical, community, institutional and user. These analyses offer specific explanations for the behavior of these individual factors but are also only stepping stones to the overall and comprehensive analyses of all factors considered together in Chapter Twelve. Due to whatever singular effect is seen within the factors in these chapters, they either will or will not be included in the overall analyses. Each of these four chapters on the four categories of potentially influential factors for WUA performance and participation will begin with an overall summary of their impacts and then address the details. Descriptive statistics of the outcome variables are recorded in Chapter Twelve. Table 8.1 outlines the major findings of this chapter involved with the effects of the physical factors on the outcomes of WUA performance and participation levels. The poor status of the infrastructure is problematic throughout the Jordan Valley and likely has led and continues to lead to poor performance of the WUAs. Water scarcity, contrary to its hypothesis, does not lead to better WUA performance but in fact the opposite appears to be the case. The predictability of the water supply has the hypothesized positive effect on WUA performance but the unequal reliability across the network is of additional note.

There are no clear conclusions reached with regard to system size and crop/farm diversity.

Larger size and more diversity could potentially have negative effects, although these claims remain statistically unproven in this work. And changes in climatic conditions are likely having a negative impact on farmers, with little that the WUA performance can do to improve this situation.

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Table 8.1: Summary of physical factors, hypotheses and conclusions reached. Factor Hypothesis Results Nature of Data Status of Where infrastructure is of higher quality and is  Hypothesis accepted. Qualitative and Infrastructure operated and maintained at a higher level,  Likely negative effect of poor descriptive. cooperative efforts will meet with more infrastructure on WUA performance. success and farmers will be more likely to be members of the WUA. Water Moderate water scarcity, versus extreme or  Hypothesis rejected. Quantitative. Scarcity minimal water scarcity, leads to more  WUA performance better where cooperation between farmers and better there is lower water scarcity. performance. If farmers have secondary water  No significant impact on WUA resources, they will be less likely to cooperate membership. in an effective manner.  No significant impact of a secondary water source.  Crop type likely an important secondary issue at work. Water A more predictable and reliable supply of  Hypothesis accepted for WUA Quantitative. Predictability water will make the WUA more successful and performance, seen as better where more farmers will be likely to join the WUA. water supply is more reliable. Performance of the WUA will be enhanced  Hypothesis rejected for WUA where water is more equally reliable across all membership; no effect observed. parts of the distribution network.  Potential for unequal reliability across the network to affect water reliability.

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Table 8.1: (continued). Factor Hypothesis Results Nature of Data System Size WUAs with responsibility for a larger area will  No clear conclusions. Qualitative and not perform as well as WUAs with smaller  Potentially negative effect of large descriptive. areas of responsibility. size for MH. Crop/Farm Where there is more diversity and complexity  No clear conclusions. Qualitative and Diversity of cropping patterns, it will be harder for a  Potentially positive effect of lower descriptive. WUA to perform well and it will be less likely crop diversity in PS 55. for farmers to join a WUA. Climate and Extreme weather-related and natural events  Hypothesis accepted. Qualitative and Natural will have a negative impact on farmers’ ability  Evidence of changing climate descriptive. Events and desire to cooperate in matters of water patterns likely negatively affects distribution. farm production with WUA unable to improve the situation. Source: Data from surveys, interviews and observational analysis.

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STATUS OF THE INFRASTRUCTURE

Hypothesis: Where infrastructure is of higher quality and is operated and maintained at a higher level, cooperative efforts will meet with more success and farmers will be more likely to be members of the WUA.

Much of the water distribution infrastructure in the Jordan Valley is old, unprotected, poorly-designed, insufficiently maintained and inefficiently operated

(Discussion with PS 91 engineer, 3/15/2014; Discussion with Haidar Malhas, 2/9/2014). While a specific date of origin cannot be placed on the Jordan Valley irrigation network, the development of the network began in the 1970s (USAID, 2013; Courcier et al., 2005). Considering that JVA employees and directors, and all WUA heads, commented that there has been no wholesale rehabilitation of the network, it can be assumed that at least a good part of the network is over three decades old. One area of exception is Mazraa-Haditha (MH), where a rehabilitation project is being completed by the Arab Business Company, a contracting firm working for the JVA. This project is replacing all of the main and lateral lines with high-density polyethylene piping, an improvement over former concrete piping that suffered from calcium build-up due to the mineral content in the spring water in that area. This project is also replacing or improving various parts of the diversion weirs, pumps, storage ponds, intakes and valves (Interview with Arab Business Company project consultant, 9/6/2014). Small-scale rehabilitation has taken place in other parts of the Jordan Valley. For example, in the southern JV area, during the initial implementation of the WUAs in that area (2002-2004), repairs were done on the network to encourage farmers to join the WUA (Interviews with Regner, 3/29/2015, 3/30/2015). In the north, the French Regional Mission for Agriculture and Water funded a project called Irrigation Optimization in the Jordan Valley (IOJoV) in 221 the late 1990s and early 2000s. This project made some technical improvements to the water distribution networks and provided assistance to farmers to help with on-farm water efficiency (Mazahreh et al., 2004). The infrastructure is likely leading to poor performance of the network, regardless of whether the WUA or the JVA is in the management position. Observing the water distribution network from beginning to end, or from the main canal (KAC) to the farmer’s water intake device (FTA), no part appears to be consistently well-maintained (Field observations, Spring and Fall 2014) other than the KAC in the very north of the Jordan

Valley, where the water first enters the KAC and is fresh from springs and the Yarmouk River (Figure 8.1).

Figure 8.1: King Abdullah Canal near the beginning of its length.

Source: Personal photograph.

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For the rest of its length, the KAC remains open-air and largely unprotected. While the JVA constructed metal fencing along both sides of its length (Figure 8.2), much of this fencing has since been stolen and sold (Figure 8.3). The KAC is thus prone to having all sorts of trash either blown or thrown into it. As one ditchrider in PS 91 noted, “the KAC is a trash bin.”

Figure 8.2: The KAC and fencing along its sides in the area of PS 55 (a) and PS 91 (b). a) b)

Source: Personal photographs.

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Figure 8.3: The KAC near PS 33 with no fencing on either side (a) and the KAC near PS 91 with no fencing on one side (b). a) b)

Source: Personal photographs.

Trash within the KAC both pollutes the water and leads to trash being sucked into pump or gravity systems, either at the intake grate or further along the lines. For water to enter the distribution systems, ditchriders constantly have to clean-out intake grates, where trash builds-up on a more-than-once-a-day basis (Figures 8.4 and 8.5). If they do not do this, the flow of water slows and farmers do not receive their set water amount according to the water schedule. If trash is not cleared from the intake grates, it can move further into the system and block main or lateral lines or FTAs. Farmers and WUA employees frequently referenced occurrences of having to remove plastic bottles and other trash from the lines.

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Figure 8.4: Trash build-up at PS 33 KAC intake point (a) and a ditchrider manually clearing-out trash from a subsequent intake point (b). a) b)

Source: Personal photographs.

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Figure 8.5: Trash floating in the KAC near PS 91 (a), trash build-up at PS 91 intake point (b) and the trash removed by ditchriders (c). a)

Source: Personal photographs.

Local Bedouins and sheep and goat herders also may contaminate the KAC’s waters. According to a ditchrider in PS 91, local herders throw trash into the KAC and bathe in the KAC. From observation it was seen that they frequently graze their livestock quite close to the canal and many times live right next to the canal. It was observed several times that they let their livestock drink from the canal (Figures 8.6). And a dead goat was observed in the canal, signaling that when an animal dies, instead of disposing of the body

226 properly, herders sometimes simply dump it into the KAC (Figure 8.7). When the ditchriders from PS 91 saw the dead goat pressed up to their intake point from the KAC, they simply pushed it off and let it continue its journey down the canal.

Figure 8.6: Sheep grazing alongside the KAC at PS 41 (a) and PS 91 (b). a) b)

Source: Personal photographs.

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Figure 8.7: The black blob is a dead goat floating in the KAC near PS 91.

Source: Personal photograph.

There are other open-air water structures within the irrigation water distribution system susceptible to trash and water theft. For example, in MH, irrigation water comes from an open-air fresh spring that has no fence around it or cover over it, although it is in a pretty secluded and remote area (Figure 8.8). The water from this spring is transferred to open-air reservoirs (Figure 8.9), which are more prone to vandalism and pollution, and subsequently allotted to farmland.

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Figure 8.8: The spring near Mazraa-Haditha that supplies water to farmland.

Source: Personal photograph.

Figure 8.9: Open-air reservoirs in the Mazraa-Haditha area.

Source: Personal photographs.

At the farmer level, the holding pools that farmers use to store and manage water that they receive from the network are also problematic (Figure 8.10). There is the risk of 229 evaporation as well as trash blowing into the pool. While most pools are lined with plastic or concrete, there are some farmers who simply dig a hole into the ground, creating the possibility that water seeps into the ground and thus is not available for use. In the south of the Jordan Valley where treated wastewater is used, the amount of suspended solids and biological contaminants is much higher, causing algae to build-up in ponds and thus clogging water filters or irrigation lines. Farmers can help address the algae issue by having fish in their ponds or by using a water filter, which may have to be cleaned frequently, as much as once every 20 minutes. Many farmers do not have a water filter, leading to larger particles flowing into the drip irrigation lines and blocking or damaging the drippers (Discussion with Haidar Malhas, 2/9/2014).

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Figure 8.10: On-farm holding pools lined with plastic (a and b), lined with concrete (c) and earthen (d). a) b)

c) d)

Source: Personal photographs.

Every year a significant amount of silt and mud develops along the KAC (Figure 8.11), a natural occurrence that requires consistent attention and removal of silt and mud.

The JVA, as the entity still responsible for the operations of the KAC, is the party in charge of removing silt and mud. Farmers report that the JVA is not prompt in removal of silt, which causes delays in getting water to farmers. Furthermore, the removed silt and mud is

231 much of the time dumped alongside the canal very close to or on top of farmers’ lands (Figure 8.12).

Figure 8.11: Along the far side of the KAC, built-up mud can be seen when the water level is low.

Source: Personal photograph.

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Figure 8.12: The removed mud from the KAC that the JVA dumps alongside the road next to the KAC.

Source: Personal photograph.

When there is flooding in the Highlands and flood waters rush into the valley in a short period of time, the canal can flood as well. For example, at one point in the area of PS 55, the water was muddied and turned a faint reddish color (Figure 8.13). At times such as this, the water is so filled with mud and debris that it cannot even be used by farmers.

WUAs and the JVA have to wait for several days in order for the KAC to be cleared of this water before it can start siphoning water once again.

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Figure 8.13: Water in the KAC in the area of PS 55 after major flooding from heavy rains.

Source: Personal photograph.

Especially in PS 91, and for those WUAs located in the southern section of the JV, a particularly troublesome and frequent problem is that the level of the KAC is too low and water cannot be siphoned into the intakes for further distribution to farmland. A ditchrider and the engineer in PS 91 (Discussions on 5/5/2014) remarked that the general design of the canal is poor in this area of the JV. They believe that the pump is not in the right place and that there should be a dam to elevate the water to a higher level to better permit gravity flow. Farmers state that many times a water turn is halted midway through the allotted time because water simply isn’t entering the network (Discussions with farmers, Spring 2014). Several farmers complained that after a halt in their water turn, they wouldn’t be compensated for the lost time at a later point.

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There are visible problems with the pumps and main and lateral lines that impede proper functioning of the water distribution network. These problems were seen most prominently in PS 91 but are present elsewhere to a lesser degree. With regard to the pumps, in visits to both PS 33 and PS 91, the main pumps were seen to have substantial leaks with water quite visibly flowing out of the apparatus and onto the surrounding ground (Figure 8.14). While over the course of an hour the overall water seepage might not amount to much, when this happens day after day for a long period of time, the loss can be substantial.

Figure 8.14: Main pumps at PS 33 (a) and PS 91 (b) with water leakages. a) b)

Source: Personal photographs.

Many lateral lines have portions above ground and unprotected from vandalism, sun damage, and flood waters that can carry damaging stones and trash (Figure 8.15).

Ideally the lateral lines are placed several feet below ground, as is now being done with the newly rehabilitated system in MH (Figure 8.16).

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Figure 8.15: A lateral line laying above ground in the area of PS 91.

Source: Personal photograph.

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Figure 8.16: New lateral lines being placed below ground in the rehabilitation of the network in Mazraa-Haditha.

Source: Personal photograph.

There are visible leakages from lines in some places (Figure 8.17); ditchriders and the engineer in PS 91 report that these lines have gone without maintenance for many months (Discussions on 5/5/2014). Some of these leakages could be due to pipes bursting as a result of them not being flushed out from time to time (discussion with Haidar Malhas,

2/9/2014).

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Figure 8.17: A pool of water has formed over where the lateral line runs in the area of PS 91, signaling that there is a leakage in the piping.

Source: Personal photograph.

Valves that connect the main and lateral lines are uncovered and unprotected in some areas as well (Figure 8.18a). Thus, they are more easily damaged or manipulated by farmers who wish to open them at times other than their water turns. These devices could be placed in locked boxes and only accessible to WUA or JVA employees (see Figure

8.18b).

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Figure 8.18: a) Uncovered lateral valves in the area of PS 91; b) a ditchrider unlocking a covered lateral valve. a) b)

Source: Personal photographs.

There are further infrastructure problems at the farm level, mostly related to the FTA and its status and maintenance. These issues are seen valley-wide and in all four surveyed WUAs. The FTAs are supposed to be covered and in a concrete box (Figure

8.19a) but the cover or the box are not always present, leading to the potential for farmers to vandalize the FTA or weather to damage it. Trash can also get blown into the FTA and potentially damage it (Figure 8.19b), with some FTAs being overgrown by plant life and thus not easily managed or monitored (Figure 8.19c). In many areas, FTAs were seen to be leaking and this was a known occurrence on the part of the JVA and the WUA according to ditchriders in PS 91 (Field tours in Spring 2014) (Figure 8.19d).

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Figure 8.19: A covered and enclosed FTA (a); an FTA filled with trash (b); an FTA overgrown by weeds (c); an FTA without a box and leaking (d). a) b)

c) d)

Source: Personal photographs.

The appropriateness and effectiveness of the original design of the network in some areas can also be questioned. For example, WUA and JVA employees as well as farmers fault the original open-air KAC design and limited slope especially in the southern section (Discussions with PS 91 WUA employees, JVA staff in the south directorate and farmers in PS 91 in Spring 2014). This has led to numerous inefficiencies in its operation from the very beginning.

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Other design issues have been mentioned. In particular, in the northern and middle section of the JV (PS 33 and PS 55), some farm units are located in the zhor. As depicted in Figure 8.20, the zhor is the area farthest west abutting the Jordan River, at a lower elevation than most of the Jordan Valley (Al Ghor in the figure) and further distanced from the main canal and pump stations. As would be expected, farmers complain of the water pressure being very weak in this area and water simply not reaching their farms due to the distance from the water sources. Much of the time, water is sent to this area simply by the force of gravity and while it would seem sufficient to let elevation loss take care of the energy costs, this is perhaps not enough. Some farmers also remarked that when it comes to maintenance issues in the zhor, they are more readily and easily ignored by the JVA and the WUA. Farmers are not allowed into the zhor area in the evening or nighttime hours because it is part of the military border zone between Jordan and Israel and under the continued monitoring of the Jordanian Army. This is an additional handicap for farmers if they should need to work longer hours or if after-hours issues arise on their farms.

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Figure 8.20: Depiction of elevation differences between the Highlands and the Jordan Valley areas of Al Ghor and Al Zhor.

Source: Venot et al. (2007). BSL=below sea level; ASL=above sea level.

In all surveyed WUAs, other infrastructure design issues have led to reduced performance levels of the network. Since their construction, these water distribution networks have had to supply more farmers than they were originally designed to supply. The number of farmers has increased dramatically over the past half century, owing to the Green Revolution region-wide (Alterman and Dziuban, 2010) and the general push to develop agriculture in the Jordan Valley in particular since the 1970s. The aforementioned land fragmentation, owing to Islamic inheritance laws that split farm units among the children when the father passes away, has also increased the number of individual farmers, thus increasing the number of water users and water demand on a system that was not designed to supply this number of farmers. In PS 33 and PS 55, for example, the WUAs and the JVA have had to start supplying more lines at once due to the limited supply of water. With only the existing networks, not enough pressure is created to adequately supply farm units.

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In PS 91, there has been an attempt to alleviate this problem of a lack of pressure in the network that is caused by an increase in farming in the area by altering its water distribution schedule. On any given lateral in PS 91, only farm units on one side of the lateral (the left or the right) are supposed to take water at any given time in order to increase pressure in the line, as compared to a case where farmers on both sides take water at the same time. Unfortunately, farmers do not always obey this distribution schedule and open their laterals even at times when it is the other side of the lateral that should be taking water. Thus, PS 91’s solution to the pressure problem is overridden and nullified by farmer infractions. And despite this attempt to remedy the pressure problem, there are farms at the end of the pressure network that no longer even receive water from the network because it simply doesn’t reach these farms. To supply these farms, the WUA and JVA have had to connect them directly to the KAC through above-ground plastic piping as a temporary solution (Figure 8.21).

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Figure 8.21: Above-ground plastic piping connecting some farms in PS 91 directly to the KAC due to pressure issues in the lateral lines.

Source: Personal photograph.

Due to these various issues with the physical infrastructure of the networks across the Jordan Valley, it is not surprising that there are farmers who believe that the water supply is problematic. It is also not surprising that farmers do not always see the WUA as an improvement over the JVA and that they are not always eager to join the WUA. In sum, no quantitative proof is offered to support or disprove the original hypothesis that better infrastructure will lead to better WUA performance. The data used is at the level of the entire Jordan Valley and speaks in general to the overall situation, not able to distinguish between the four case studies. But the poor state of the infrastructure in all areas likely negatively affects the ability of the WUAs to efficiently supply water to farmers and farmer opinion of the WUAs.

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WATER SCARCITY

Hypothesis: Moderate water scarcity, versus extreme or minimal water scarcity, leads to more cooperation between farmers and better performance. If farmers have secondary water resources, they will be less likely to cooperate in an effective manner.

Because farmers were only questioned as to whether they think the water supply is adequate or not, the moderate kind of water scarcity cannot be assessed but rather, only whether scarcity exists or not can be assessed. On the whole, water is simply not adequate in any of the four WUA case studies, with 75% of all surveyed farmers saying that the water supply is not adequate (Figure 8.22). This is to be expected, though, considering the general shortage of water in Jordan. It is not necessarily and only the WUA that is to blame but could simply be a factor of Jordan’s overall water scarcity and the dwindling allotment of water to agriculture in the Jordan Valley.

Figure 8.22: Percentage of farmers among surveyed WUAs who think that the water supply is adequate or not adequate.

Source: Survey data.

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Across all WUAs, while most farmers reported that the water supply does not adequately meet their demands, there are some differences across the four WUAs as seen in Figure 8.23. The only significant difference is between PS 91 and PS 33, with the former having a more adequate supply of water than the latter.

Figure 8.23: Percentage of farmers within each of the four WUAs who think that the water supply is adequate or not adequate.

Source: Survey data.

In assessing whether water adequacy alone has an effect on a farmer’s opinion of the WUA, the following simple regression equation is used:

Equation: opinion of wua = β0 + β1 adequacy

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The results of the ordered logistic model (all results in odds ratio format) used to predict a farmer’s view of his water adequacy on his opinion of the WUA are shown in Table 8.2 below. Water adequacy, when analyzed by itself, is a significant and positive predictor of a farmer’s general satisfaction with the WUA’s performance. For those farmers who reported that their water supply is adequate, they are 4.4 times more likely to have a higher level of satisfaction with the WUA. This result goes against the initial hypothesis that water scarcity, not water adequacy, would lead to better performance of the WUA. And while the R2 value of around 4% is low, signifying that water adequacy is explaining 4% of the variation in farmer opinion of the WUA, for one single factor it is not wholly irrelevant.

Table 8.2: Regression results of water adequacy’s effect on opinion of the WUA. Dependent Variable Independent opinion of wua Variable adequacy 4.40*** (2.22) Prob > chi2 0.0008 Pseudo R2 0.0398 No. Obs. 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

When assessing a farmer’s comparison of the WUA’s performance to that of the JVA’s in light of the farmer’s level of water scarcity, a similar and simple equation is used:

Equation: comparison of wua to jva = β0 + β1 adequacy

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) 247 adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no)

As seen with the previous results, the results of the ordered logistic regression reported in Table 8.3 shows that water adequacy is a significant and positive predictor, although to a lesser degree. For those who reported that their water supply is adequate, they are 2.3 times more likely to have a greater level of satisfaction with the WUA versus the JVA. Again, this runs contrary to the initial hypothesis although with an R2 of 2%, this factor would seem to weigh little into the issue.

Table 8.3: Regression results of water adequacy’s effect on farmer opinion of the WUA in comparison to the JVA. Dependent Variable Independent comparison of wua to jva Variable adequacy 2.33*** (0.80) Prob > chi2 0.0113 Pseudo R2 0.0176 No. Obs. 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

When looking at water adequacy’s effect on the level of water stealing in the area as observed by farmers, the following equation is used:

Equation: water stealing = β0 + β1 adequacy

The results of the logistic regression in Table 8.4 reveal that water adequacy, on its own, negatively and significantly affects whether a farmer reports that water stealing, by himself or others around him, is occurring. For farmers who reported that their water supply is adequate, they are 4.5 times less likely (the inverse of the reported odds ratio 0.22 below) to report that water stealing is occurring. In other words, for those who say that the water supply is not adequate, they are 4.5 times more likely to report that water stealing is occurring. This is a result that is again contrary to the original hypothesis, which says that with greater water scarcity, there should be more successful cooperation among farmers and thus more rule-following (less stealing). The WUA is not such a strong institution among farmers that deters stealing so indeed, without an adequate water supply, the farmer has few qualms about stealing water instead of remedying his situation through the WUA. Water adequacy is explaining roughly 8% of the variation in reported water stealing (as seen in the R2 value).

Table 8.4: Regression results of water adequacy’s effect on reporting of water stealing. Dependent Variable Independent water stealing Variable adequacy 0.22*** (0.10) Prob > chi2 0.0016 Pseudo R2 0.0758 No. Obs. 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

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Yet another measure of the WUA’s performance is seen in farmer opinion of whether the WUA treats all farmers equally and fairly and the simple equation used to assess the impact of water scarcity on this fairness is as follows:

Equation: fairness of wua = β0 + β1 adequacy

As reported in the results of the logistic regression in Table 8.5, for those farmers who report that the water supply is adequate, they are 3.15 times more likely to think that the WUA is fair. Yet again, the original hypothesis is contradicted; greater water scarcity leads to less favorable views, and presumably performance, of the WUA. And as with farmer opinion of the WUA, water adequacy is explaining only 4% of the variation in farmer opinion of the fairness of the WUA; this is minimal but again is only referring to one factor.

Table 8.5: Regression results of water adequacy’s effect on opinion of fairness of the WUA. Dependent Variable Independent fairness of wua Variable adequacy 3.15*** (1.34) Prob > chi2 0.0038 Pseudo R2 0.0355 No. Obs. 184 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

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Lastly, a similar, simple equation is used to observe the effect of water adequacy on farmer participation, or membership, in the WUA:

Equation: membership = β0 + β1 adequacy

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no)

In the results of the logistic regression listed in Table 8.6, water adequacy does not appear to significantly affect whether a farmer is a member in the WUA or not. The R2 value is very small and irrelevant. The original hypothesis that where water is scarcer, more farmers are members, cannot be supported.

Table 8.6: Regression results for water adequacy’s effect on membership in the WUA. Dependent Variable Independent membership Variable adequacy 0.78 (0.36) Prob > chi2 0.5931 Pseudo R2 0.0018 No. Obs. 122 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%

Within sideline commentary, many farmers gave their own perceptions of why the water supply is inadequate. Interestingly, they did not cite Jordan’s overall water scarcity

251 as the primary reason but rather, they pinpointed specific areas in the network or among farmers that were the culprits for their inadequate water supply. The biggest complaint was with the weakness of the water pressure, leading to their farms not receiving the scheduled quantity. Some suggested that this is because other farmers are stealing water (either by removing their flow limiters or taking water out-of-turn) on their lateral line, thus reducing the strength of the flow to their farms. Others said that the network, in general, is very poorly designed, leading to lateral lines having to support too many farmers or water being released to too many laterals at the same time, thus vastly reducing network pressure. Those located far away from the initial intake or main pumps noted that their distant location could play a role in water not reaching their farms. Within the farm unit, some mentioned problems with the distance between the FTA and the crops to be water, with the hilly terrain of their farm units leading to water not reaching some parts of the farm.

There are other reasons that farmers feel that the water supply is inadequate besides weak water pressure. Some lamented the use of smaller flow limiters (6 liters per second) that are commonly used in the FTA today as compared to 9 liter per second flow limiters used in the past. This will of course reduce the farmer’s water quantity but is also being used in concert with a general reduction in per farm water allotment according to the overall water schedule. Farmers also noted that it is difficult to get an extra water turn, thus limiting a farmer’s ability to increase his water supply at certain crucial times in the growing season. Many farmers reported that water shortages are only seasonal, meaning that they occur mostly from March to May of every year, as well as at the beginning of the growing season in September. Others farmers said that it depends on the crops that they

252 grow. For example, if they grow herbs, herbs need a lot of water and thus the farmer will inevitably feel a greater water shortage. Some farmers suspected that their water shortage is a result of some type of favoritism in water distribution in their area. Some in PS 55 said that the zhor area is not being allotted a fair share of the water, feeling that all of the water is for the area above the zhor. In MH, some farmers felt that the Potash company is prioritized over them in getting shares of the water source that they share, although this can be rationalized by considering that while farmers pay 0.008 JD per cubic meter, the Potash company pays 0.0123 JD per cubic meter. Farmers who share their units with other farmers inevitably report more water shortage because they are only getting part of a water turn. This is particularly problematic if the farmer has no holding pond; he might only get water one day a week with no way to save some for another day of watering. One farmer in PS 91 also reported that he cannot use the network water for when he has to spray pesticides on his crops because the treated wastewater is not the right quality for this process. He has to by drinking-level water for this job instead. Several farmers in PS 91 with palm trees also noted that while the water supply is adequate at present for their young trees, when the trees get bigger they anticipate greater water shortages. With regard to PS 33’s slightly higher percentage of farmers who find the water supply inadequate, this could be because there are more citrus farms in this area than in the other WUAs. Citrus trees have higher water demands, especially in the summer when they remain standing while many vegetable farms lay fallow or are less intensive in their cultivation. The summers in the Jordan Valley can also be brutally hot and dry, wreaking definite havoc on citrus trees if the water supply does not meet a minimum demand.

253

Secondary Water Resources

The other part of the hypothesis regarding water scarcity is that farmers who have secondary water resources will, by default, be less likely to think that their water supply is inadequate. Some farmers did comment that they consider their water supply “adequate” because they have this secondary source. Having this extra water also means that these farmers can be less involved or concerned with the WUA because they do not have to rely on it for all of their water needs. As one farm said, he doesn’t ever talk to the WUA or the JVA because he has water from a well. The survey revealed that many farmers have a secondary source of water outside of the official network. Results showed that 46%, 40%, 23% and 12% of farmers in PS 33, PS 55, PS 91 and MH, respectively, have secondary water sources (Table 8.7). These secondary sources of water include: a private well; a fresh spring on the property; buying water from another farmer who has a well; using water from the Jordan River if the farm is next to the river; taking water directly from the King Abdullah Canal; or using rainwater runoff that rushes into the valley in times of flooding in the winter months. It is noteworthy that many secondary sources of water that farmers use are not legal. This is the likely reason why some farmers did not admit to having a secondary source of water. Even if a farmer said he did not have a secondary source of water, sometimes this was discovered after-the-fact when talking to the WUA ditchriders or engineer who knew whether the farmer had a secondary source of water.

254

Table 8.7: Percentage of farmers surveyed in each of the four WUAs who admitted to having a secondary source of water. WUA Percentage of farmers with secondary water source PS 33 46% PS 55 40% PS 91 23% MH 12%

In PS 33, the secondary sources of water are primarily fresh springs that are located within farms. Three farmers within the gravity network, located in the zhor, use water from the Jordan River and they are allowed to do this but this water is very salty and must be mixed with the network water. Two farmers fully admitted to stealing extra water from the KAC with pipes and pumps hooked up to the canal. From the extensive water-stealing operation going on in this region, there are likely many more farmers taking water from the KAC than those who admitted as much. And one farmer uses runoff from a side valley when it is available (after rains predominantly) and one farmer has a licensed well. In PS 55, most of the secondary water sources that farmers admitted to using are fresh springs. Two other farmers have wells. And two farmers buy water from other farmers who get extra water either from a well or by stealing it from the KAC. Within PS 91, most farmers who have a secondary water source said that they have a well (most illegal). Unlike in other areas of the valley, well water in the south is usually very salty and has to either be mixed with water received through the government network or run through a desalination plant, which a few farmers do own and operate. Another few said that they bought water from another farmer who has a well, usually at the rate of 5 JD per hour. And in MH, farmers who did talk of a second source said that it was either from a

255 well or from runoff in a side valley (in times of rain), both of which need to be mixed with water from the government network in order to combat high salinity levels. While all of the aforementioned information should point towards a significant effect of a farming having a secondary source of water on his view of the adequacy of his water supply, this is not seen in the quantitative analyses. The following equation is used to assess whether having a secondary source, on its own, affects a farmer’s level of water scarcity:

Equation (1): adequacy = β0 + β1 secondary water

In Table 8.8, results of the logistic regression show that having a secondary source of water has no significant impact on a farmer’s opinion of the adequacy of his water supply. It also explains almost none of the variation (as seen in the R2 value) in water adequacy.

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Table 8.8: Regression results of effect of having a secondary water source on water adequacy. Dependent Variable Independent adequacy Variable secondary water 0.92 (0.35) Prob > chi2 0.8221 Pseudo R2 0.0002 No. Obs. 186 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

As a check to establish whether the impact of secondary water is particular to any one of the four examined WUAs, the following equations are used to assess the additional impact of including the WUAs (leaving out Mazraa-Haditha, MH, as the base line) and their interactions with having a secondary source of water:

Equation (1): adequacy = β0 + β1 secondary water + β2 ps33 + β3 ps55 + β4 ps91

Equation (2): adequacy = β0 + β1 secondary water + β2 ps33 + β3 ps55 + β4 ps91 + β5 secondary water*ps33 + β6 secondary water*ps55 + β7 secondary water*ps91

Where: adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no) secondary water: a binary variable for whether a farmer has a secondary source of water (1=yes, 0=no) ps33: a binary variable for whether a farmer is in PS 33 (1=yes, 0=no) ps55: a binary variable for whether a farmer is in PS 55 (1=yes, 0=no) ps91: a binary variable for whether a farmer is in PS 91 (1=yes, 0=no)

As reported below in Table 8.9, the results of the logistic regressions show that the WUAs and their interactions with having a secondary water source have little significant

257 effect (Equations 1 and 2). This is with the exception of PS 33’s very slight significance when interactions are included (Equation 2); farmers in PS 33 are less likely than farmers in MH (the base category) to report having an adequate water supply. But this effect is minimal so the effects of the WUAs will be left to later analyses. And again, having a secondary water source does not appear to make a difference in farmer opinion of the adequacy of the water supply.

Table 8.9: Regression results of having a secondary source of water and individual WUAs on water adequacy. Dependent Variable adequacy Independent (1) (2) Variables secondary water 1.09 3.57 (0.44) (5.28) ps33 0.49 0.27* (0.29) (0.23) ps55 1.15 1.61 (0.62) (0.94) ps91 1.69 1.98 (0.84) (1.06) secondary water*ps33 0.96 (1.65) secondary water*ps55 0.14 (0.24) secondary water*ps91 0.22 (0.36) Prob > chi2 0.1442 0.1593 Pseudo R2 0.0329 0.0507 No. Obs. 186 186 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

258

Special Crops

While not part of the original hypothesis, from observational analysis in the field and learning about what crops require more or less water and at specific times in the year, it is worthwhile to think about water adequacy in terms of what crop a farmer grows. In specific, as heard in farmer commentary, citrus trees are particularly water intensive and require water year-round, unlike vegetables that are much of the time not grown in the summer months. Also, date palm trees need more water in the summer months right before their harvest in September/October but are not as intensive in water needs for the rest of the year. The following equations are used to assess the impact of growing citrus or date palm trees, separately and then together, on a farmer’s view of his level of water scarcity:

Equation (1): adequacy = β0 + β1 citrus

Equation (2): adequacy = β0 + β1 palm

Equation (3): adequacy = β0 + β1 citrus + β2 palm

In the results of the logistic regressions in Table 8.10, growing either citrus or date palm trees does have a significant impact on a farmer’s viewpoint of the adequacy of the water supply. For farmers with citrus trees (Equation 1), they are 2.4 times less likely (the inverse of the value 0.42 in the table below) to think that the water supply is adequate than farmers who do not grow citrus trees. For farmers with date palm trees (Equation 2), they are 3.26 times more likely to think that the water supply is adequate than farmers who do

259 not grow date palm trees. When both are taken together (Equation 3), these results remain relatively stable.

Table 8.10: Regression results for the effect of growing citrus or date palm trees on water adequacy. Dependent Variable adequacy Independent (1) (2) (3) Variables citrus 0.42** 0.47* (0.19) (0.21) palm 3.26*** 2.90** (1.55) (1.40) Prob > chi2 0.0398 0.0151 0.0113 Pseudo R2 0.0203 0.0284 0.0431 No. Obs. 186 186 186 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

While growing either citrus or date palm trees is only explaining roughly 2% of the variation in farmer opinion of water adequacy (as seen in the R2 values), these results are expected from what was heard in the field. One explanation is that date palms simply need, overall, less water and are not competing with vegetable crops in the summer when they need water most. Thus, date palm farmers can be more satisfied with the water quantity.

Citrus trees, on the other hand, need water all of the time and compete with all crops, year- round, so it is no surprise that citrus farmers feel a greater water scarcity. Another explanation is possible. Date palm farmers are many times already wealthy and influential (as creating a date palm farm requires heavy up-front investment) and for this reason may be able to acquire their water needs by way of their wealth and influence. On the other

260 hand, citrus farmers are not always as well-off or influential, making it possible that their lack of water could be a result of their lack of wealth and influence. To further explore this potential impact of the wealth and influence of farmers on their views of water adequacy, an additional two equations are used that include, in addition to whether farmers grow citrus or date palm trees, indicators of wealth and influence: farm size (how many dunums they own), whether they have greenhouses, and whether they export any of their produce. The equations are as follows:

Equation: adequacy = β0 + β1 citrus + β2 palm + β3 dunums + β4 greenhouses + β5 exporting

Where: adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no) citrus: a binary variable for whether a farmer grows citrus trees (1=yes, 0=no) palm: a binary variable for whether a farmer grows date palm trees (1=yes, 0=no) dunums: an interval variable for the log of the number of dunums farmed greenhouses: a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) exporting: a binary variable for whether a farmer personally exports produce (1=yes, 0=no)

In the logistic regression results in Table 8.11, none of the wealth indicators display any significant impact but farmers who grow date palms are still much more likely (4.25 times more so) to feel that the water supply is adequate. Only slightly more of the variation is explained with the addition of these wealth indicators (up to 6% from 4% by the R2) so on the whole, perhaps a farmer’s perception of his water adequacy is not a product of this explanation.

261

Table 8.11: Regression results for the effect of crop type, farm size, greenhouses and exporting crops on water adequacy. Dependent Variable Independent adequacy Variables citrus 0.52 (0.25) palm 4.25*** (2.27) dunums 0.55 (0.23) greenhouses 1.78 (0.72) exporting 0.80 (0.44) Prob > chi2 0.0200 Pseudo R2 0.0644 No. Obs. 186 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

WATER PREDICTABILITY

Hypothesis: A more predictable and reliable supply of water will make the WUA more successful and more farmers will be likely to join the WUA. Performance of the WUA will be enhanced where water is more equally reliable across all parts of the distribution network.

Taking all of the surveyed WUAs together, only 54% of the farmers thought the water supply was reliable (Figure 8.24). Between WUAs, PS 55 appears to have the most reliable service, with 71% of farmers believing that the water supply is reliable (Figure

8.25). On the other hand, in MH, service appears to be worse with only 24% of farmers reporting that the water supply is reliable. And in fact, MH has a significantly less reliable source of water than any of the other three surveyed WUAs.

262

Figure 8.24: Percentage of farmers among surveyed WUAs who think that the water supply is reliable or not reliable.

Source: Survey data.

Figure 8.25: Percentage of farmers within each of the four WUAs who think that the water supply is reliable or not reliable.

Source: Survey data.

263

To address whether water reliability has an effect on farmer opinion of the WUA, the following simple equation is used as a start:

Equation: opinion of wua = β0 + β1 reliability

The results of the ordered logistic regression in Table 8.12 show that water reliability does have a significant and positive effect on farmer opinion of the WUA and this supports the proposed hypothesis. For farmers who report that the water supply is reliable, they are 3.39 times more likely to have a higher level of satisfaction with the WUA. But as with water adequacy, water reliability in this case is only explaining around

5% of the variation in farmer opinion of the WUA as seen in the R2 value in the table.

Table 8.12: Regression results of water reliability’s effect on farmer opinion of the WUA. Dependent Variable Independent opinion of wua Variable reliability 3.39*** (1.13) Prob > chi2 0.0002 Pseudo R2 0.0494 No. Obs. 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

264

To assess the effect of water reliability on farmer opinion of the WUA as it compares to the JVA, the following equation is used:

Equation: comparison of wua to jva = β0 + β1reliability

Within the results of the ordered logistic regressions shown in Table 8.13, water reliability also positively and significantly affects a farmer’s view of the WUA when compared to the JVA. For farmers who report that the water supply is reliable, they are 3.05 times more likely to have a higher level of comparative satisfaction with the WUA over the JVA. This result also supports the preliminary hypothesis that where the water supply is more reliable, the WUA will have more success or in this case, higher levels of farmer satisfaction.

Table 8.13: Regression results of water reliability’s effect on farmer opinion of the WUA in comparison to the JVA. Dependent Variable Independent comparison of wua to jva Variable reliability 3.05*** (0.89) Prob > chi2 0.0001 Pseudo R2 0.0413 No. Obs. 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

265

When looking at the effect of water reliability on reported water stealing, used as the indication of whether farmers are following the rules, the equation is as follows:

Equation: water stealing = β0 + β1reliability

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no)

Water reliability, observed in the results of the logistic regression in Table 8.14, has a negative and significant impact on the reporting of (and likely conducting of) water stealing and explains almost 5% of the variation in water stealing. For farmers who report that the water supply is reliable, they are 3.4 times less likely (the inverse of 0.29) to report that water stealing is occurring. Again, this supports the initial hypothesis that water reliability enhances the performance of the WUA and in this case, farmer rule-following.

Table 8.14: Regression results of water reliability’s effect on reporting of water stealing. Dependent Variable Independent water stealing Variable reliability 0.29** (0.16) Prob > chi2 0.0136 Pseudo R2 0.0461 No. Obs. 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

266

The equation for testing whether water reliability affects farmer opinion of whether the WUA treats farmers fairly is similarly constructed:

Equation: fairness of wua = β0 + β1reliability

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no)

Water reliability continues to have a large impact, showing a positive and significant effect in the results of the logistic regressions in Table 8.15 below. For those farmers who think that the water supply is reliable, they are 3.06 times more likely to find the WUA fair. In this case, water reliability explains 5% of the variation in WUA fairness, explaining more variation than was explained for the other outcomes.

Table 8.15: Regression results of water reliability’s effect on opinion of fairness of the WUA. Dependent Variable Independent fairness of wua Variable reliability 3.06*** (0.98) Prob > chi2 0.0004 Pseudo R2 0.0527 No. Obs. 184 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Finally, to test water reliability’s effect on whether farmers are members in the WUA, the equation is as follows:

267

Equation: membership = β0 + β1reliability

As with water adequacy, seen in the results in Table 8.16 of the logistic regressions, water reliability does not have a significant impact on membership in the WUA and explains none of the variation in membership among farmers. The original hypothesis that greater water predictability would lead to higher membership in the WUA is not supported.

Table 8.16: Regression results for water reliability’s effect on membership in the WUA. Dependent Variable Independent membership Variable reliability 1.47 (0.55) Prob > chi2 0.3030 Pseudo R2 0.0066 No. Obs. 122 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In all four surveyed WUAs, reasons for the unreliability of the water supply were very similar. Some farmers reported that the water would come at the beginning of the turn but didn’t last until the end of the turn, while others would say that the water comes late on every turn or that in general it doesn’t come for the full turn. A few farmers suggested that this is because ditchriders are lax in their work and are not opening the lateral lines at the correct time according to the water schedule. Other farmers put the

268 blame on matters outside of the WUA’s control, such as when there are electricity outages and the pumps cannot operate, thereby cutting off the water supply unexpectedly. Similarly, garbage being stuck in a certain part of the network, whether the main line, lateral line or FTA, can cause water to stop flowing, as can a pipe breakage anywhere along the distribution network. In addition, if a farmer is located at the end of a lateral at the far end of the network, water might take a longer time to reach his farm, thus making it unlikely that the water arrives at the beginning of the turn. These water outages can be particularly irksome because farmers are not always compensated for lost water turns.

One issue specific to MH in relation to water reliability, and that might explain its higher percentage of farmers who believe that the water supply is unreliable, is that when it rains in Jordan, usually in the mountainous region just to the east of the farming area, this causes widespread problems for this area’s water supplies and can render them unusable for anywhere from four days to two weeks. Many farmers in MH commented that this is the number one reason why their water supply is unreliable, although this is only for the winter months when Jordan receives rain. Farmers who mentioned this issue as the reason for the unreliability of the water supply usually had no other issue to note.

Equality across the network

With regard to the secondary aspect of the main hypothesis, whether water is equally reliable across all parts of the network and whether this affects the performance of or participation in the WUA, further analyses are conducted. Farmers are either in a network where water is distributed by pressure pumps or by gravity, and they are located at either the beginning, middle or the end of a lateral line. If they own multiple farm units, they might be located in both network types and/or lateral positions. 269

A first step is to test the effect of network type and lateral position on water reliability. The equations below are used. Equation (1) tests the effects of being in the pressure network or being in both networks types against being in the gravity network (the excluded category). Equation (2) tests the effects of being at the beginning of the lateral, middle of the lateral or on multiple lateral positions against being at the end of the lateral (the excluded category). Equation (3) is provided due to the stronger sentiment heard from farmers in the gravity network in PS 33 that their water supply is unreliable; this equation tests the effects of being in the gravity network, in PS 33 and their interaction.

Equation (1): reliability = β0 + β1 pressure network + β2 both networks

Equation (2): reliability = β0 + β1 beginning position + β2 middle position + β3 multiple positions

Equation (3): reliability = β0 + β1 gravity network + β2 ps33 + β3 gravity network*ps33

Where: reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) pressure network: a binary variable for whether the farm(s) is solely in the pressure network (1=yes, 0=no) gravity network: a binary variable for whether the farm(s) is solely in the gravity network (1=yes, 0=no) both networks: a binary variable for whether the farm(s) is in both the pressure and gravity networks (1=yes, 0=no) beginning position: a binary variable for whether the farm(s) is solely at the beginning of the lateral line (1=yes, 0=no) middle position: a binary variable for whether the farm(s) is solely at the middle of the lateral line (1=yes, 0=no) multiple positions: a binary variable for whether the farm(s) is at multiple lateral positions (1=yes, 0=no) ps33: a binary variable for whether a farmer is in PS 33 (1=yes, 0=no)

270

Table 8.17 lists the results of the logistic regressions. Between farmers in the gravity network and those in the pressure network (Equation 1), there is no significant difference but for those farmers in both kinds of networks, the effect is significant and indicates that they are 2.9 times less likely (the inverse of 0.34) to think that the water supply is reliable than farmers in the gravity network. Across lateral positions (Equation 2), there is little significant difference of water reliability between farmers, although the effect for farmers at multiple positions is slightly significant and indicates that they are 2.4 times less likely to think that the water supply is reliable than farmers at the end of the lateral line. Perhaps farmers within both networks, or at multiple positions, have a greater ability to compare service across the network and feel cheated in some areas as they compare to others. On the whole, though, it does not appear that there are stark differences in water reliability across the network and only a minimal portion of the variation in terms of R2 values is being explained.

271

Table 8.17: Regressions results for the effects of network type, lateral position and WUA on water reliability. Dependent Variable reliability Independent (1) (2) (3) Variables pressure network 1.56 (0.51) gravity network 1.61 (0.58) both networks 0.34** (0.18) beginning position 0.78 (0.34) middle position 1.16 (0.44) multiple positions 0.42* (0.19) ps33 2.10* (0.85) gravity network*ps33 0.03*** (0.03) Prob > chi2 0.0047 0.1206 0.0016 Pseudo R2 0.0419 0.0228 0.0596 No. Obs. 186 186 186 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Equation (3) provides more insight into PS 33’s particular situation. Indeed, for farmers solely in PS 33 and in the gravity network, they are 33 times less likely (the inverse of 0.03) to find the water supply reliable as compared to farmers in other WUAs and in the pressure network or both kinds of networks. There is a more acute water reliability issue in PS 33’s gravity system and as seen in the field, this is not surprising considering the distance between the main water intake and farms in the gravity network. In this instance,

272 taking into account network type and PS 33 accounts for 6% of the variation in water reliability as seen in the R2 value. The second step in this analysis of the effects of network type and lateral position is to test them on farmer opinion of the WUA, while simultaneously taking into account water reliability. The following equations are used:

Equation (1): opinion of wua = β0 + β1 reliability + β2 pressure network + β3 both networks

Equation (2): opinion of wua = β0 + β1 reliability + β2 beginning position + β3 middle position + β4 multiple positions

Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) pressure network: a binary variable for whether the farm(s) is solely in the pressure network (1=yes, 0=no) both networks: a binary variable for whether the farm(s) is in both the pressure and gravity networks (1=yes, 0=no) beginning position: a binary variable for whether the farm(s) is solely at the beginning of the lateral line (1=yes, 0=no) middle position: a binary variable for whether the farm(s) is solely at the middle of the lateral line (1=yes, 0=no) multiple positions: a binary variable for whether the farm(s) is at multiple lateral positions (1=yes, 0=no)

The results of the ordered logistic regressions are shown in Table 8.18. Water reliability remains a strong predictor of farmer opinion of the WUA when network type or lateral position is included. Network type here appears to have no significant impact on farmer opinion of the WUA (Equation 1) whereas being at the beginning of the lateral line has a significant and positive effect (Equation 2). For those farmers at the beginning of the 273 lateral, as compared to those at the end, they are 3.11 times more likely to have a higher level of satisfaction with the WUA. Interactions of all types were assessed and none proved significant. The result for farmers at the beginning of the lateral is somewhat to be expected because they are closer to the water source and siphon water from the lateral line first, meaning that their supply is probably better than those further down the line. But on the whole, lateral position does not explain much more of the variation in farmer opinion of the WUA than does water reliability on its own.

Table 8.18: Regression results of the effects of water reliability, network type and lateral position on farmer opinion of the WUA. Dependent Variable opinion of wua Independent (1) (2) Variable reliability 3.30*** 3.48*** (1.14) (1.20) pressure network 1.20 (0.44) both networks 1.05 (0.53) beginning position 3.11** (1.69) middle position 1.78 (0.73) multiple positions 1.20 (0.57) Prob > chi2 0.0027 0.0006 Pseudo R2 0.0503 0.0691 No. Obs. 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

274

To test the effects of network type and lateral position in conjunction with water reliability on farmer opinion of the WUA when it is compared to that of the JVA, the following equations are used:

Equation (1): comparison of wua to jva = β0 + β1 reliability + β2 pressure network + β3 both networks

Equation (2): comparison of wua to jva = β0 + β1 reliability + β2 beginning position + β3 middle position + β4 multiple positions

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) pressure network: a binary variable for whether the farm(s) is solely in the pressure network (1=yes, 0=no) both networks: a binary variable for whether the farm(s) is in both the pressure and gravity networks (1=yes, 0=no) beginning position: a binary variable for whether the farm(s) is solely at the beginning of the lateral line (1=yes, 0=no) middle position: a binary variable for whether the farm(s) is solely at the middle of the lateral line (1=yes, 0=no) multiple positions: a binary variable for whether the farm(s) is at multiple lateral positions (1=yes, 0=no)

The results of the ordered logistic regressions shown in Table 8.19 interestingly differ from those for farmer opinion of the WUA. Water reliability continues to be a strong predictor and network type is also a very significant predictor. For farmers in the pressure network or in a mix of networks, as compared to those solely in the gravity network, they are 3.15 and 3.50 times more likely, respectively, to have better views of the WUA than the JVA (Equation 1). There is no significant difference seen in lateral position (Equation 2). It would appear that for farmers in the gravity network, their situation might have been 275 more favorable under the JVA or they were expecting a better situation under the WUA and have been disappointed in the absence of change in their poor situation. Network type also raises the variation explained to 8% (from around 4% with water reliability on its own).

Table 8.19: Regression results of the effects of water reliability, network type and lateral position on farmer opinion of the WUA versus JVA. Dependent Variable comparison of wua to jva Independent (1) (2) Variables reliability 3.20*** 3.31*** (0.98) (1.00) pressure network 3.15*** (1.02) both networks 3.50*** (1.68) beginning position 1.07 (0.47) middle position 0.69 (0.25) multiple positions 1.35 (0.61) Prob > chi2 0.0000 0.0013 Pseudo R2 0.0826 0.0492 No. Obs. 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

The effects of network type and lateral position, in combination with water reliability, are finally tested on farmer membership in the WUA. The equations are as follows:

Equation (1): membership = β0 + β1 reliability + β2 pressure network + β3 both networks 276

Equation (2): membership = β0 + β1 reliability + β2 beginning position + β3 middle position + β4 multiple positions

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) pressure network: a binary variable for whether the farm(s) is solely in the pressure network (1=yes, 0=no) both networks: a binary variable for whether the farm(s) is in both the pressure and gravity networks (1=yes, 0=no) beginning position: a binary variable for whether the farm(s) is solely at the beginning of the lateral line (1=yes, 0=no) middle position: a binary variable for whether the farm(s) is solely at the middle of the lateral line (1=yes, 0=no) multiple positions: a binary variable for whether the farm(s) is at multiple lateral positions (1=yes, 0=no)

The results of the logistic regressions in Table 8.20 for Equation 1 show that farmers in the pressure network are 2.84 times more likely than farmers in the gravity network to be members in the WUA. Equation 2 shows that there is only a slightly significant and negative impact for those at the beginning of the lateral. For these farmers, they are 3.3 times less likely (the inverse of 0.30) to be members than farmers at the end of the lateral. Both of these results show that farmers closer to the main intake points are more likely to be WUA members.

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Table 8.20: Regression results of the effects of water reliability, network type and lateral position on whether farmers are members in the WUA. Dependent Variable membership Independent (1) (2) Variables reliability 1.29 1.61 (0.51) (0.64) pressure network 2.84** (1.22) both networks 1.44 (0.87) beginning position 0.30* (0.20) middle position 1.01 (0.48) multiple positions 1.68 (0.92) Prob > chi2 0.0613 0.0814 Pseudo R2 0.0455 0.0513 No. Obs. 122 122 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

The effects of network type and lateral position were also tested on reporting of water stealing and farmer opinion of the fairness of the WUA but no significant impacts were observed. Two conclusions can be made. First, water stealing is common everywhere, regardless of network position. Second, fairness of the WUA is not a product of network position.

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SYSTEM SIZE

Hypothesis: WUAs with responsibility for a larger area will not perform as well as WUAs with smaller areas of responsibility.

MH is responsible for the largest land area and number of farm units. PS 33 is responsible for the smallest are and PS 55 and PS 91 are somewhere in between (see Table 8.21). Therefore, it would be hypothesized that MH would likely perform the worst and

PS 33 the best.

Table 8.21: Land area and number of farmer units in each of the four surveyed WUAs. PS 33 PS 55 PS 91 MH Land Area 841 1065 1000 11850 (dunums) Number of 205 291 254 395 Farm Units Source: Data obtained from JVA and WUA employees.

From the basics statistics of the outcome variables (listed in Table 8.22), among the four WUAs, MH had the lowest number of “good” reviews from farmers, and fewer farmers believed that it is better than the JVA. Perhaps it could be suggested that this is due to it being the largest WUA by such a wide margin. PS 91 received the most favorable reviews on both accounts although it is not the smallest WUA. Reporting of water stealing is comparable across all four associations. For membership rates, they are highest in MH, going against the hypothesis that its large size negatively affects participation in cooperative efforts. And PS 91, not the smallest or the largest WUA, has the lowest membership rate. Overall, because the sample size of four WUAs is too small, it is impossible to statistically determine whether the hypothesis can be accepted or not. Based

279 solely on WUA size, there might be a negative effect on MH due to its much larger size but this remains unproven.

Table 8.22: Summary of outcome variable statistics between WUAs. Outcome WUA PS 33 PS 55 PS 91 MH Opinion of 63% good 75% good 84% good 44% good WUA* 31% so-so 19% so-so 11% so-so 29% so-so 6% bad 4% bad 5% bad 12% bad Opinion of 54% better 54% better 61% better 22% better WUA in 25% same thing 29% same thing 32% same thing 46% same thing comparison to 21% worse 13% worse 5% worse 17% worse JVA* Farmer 75% yes 83% yes 87% yes 81% yes reporting of 13% no 11% no 9% no 17% no water stealing* Farmer opinion 60% fair 60% fair 73% fair 59% fair of fairness of 36% not fair 40% not fair 27% not fair 41% not fair WUA* Farmer 56% member 39% member 15% member 40% member membership in 44% non- 61% non- 85% non- 60% non- WUA** member member member member

Source: Survey data. *Answers of no opinion, maybe, and don’t know not included. **Only among owners and renters eligible to be members.

CROP/FARM DIVERSITY

Hypothesis: Where there is more diversity and complexity of cropping patterns, it will be harder for a WUA to perform well and it will be less likely for farmers to join a WUA.

Farmers in MH grow the most variety of crops (see Table 8.23). 46% of farmers grow just one type of crop, 34% grow two, and 20% grow more than two. On the other hand, in PS 55, 85% of farmers grow only one type of crop (vegetables), with many fewer growing any more than one. In PS 91 a high percentage of farmers grow only one crop (77%). In PS 33, farmers are more evenly divided between growing one crop or two crops. 280

In sum, in terms of individual crop diversity from greatest to least, the order among the WUAs would be MH, PS 33, PS 91 and PS 55.

Table 8.23: Percentage of surveyed farmers in each WUA growing one, two or more than two crops. Percentage of Percentage of Percentage of farmers farmers farmers growing one growing two growing more crop crops than two crops PS 33 54% 42% 4% PS 55 85% 13% 2% PS 91* 77% 20% 4% MH 46% 34% 20% Source: Survey data. *Percentages rounded to whole numbers; all three categories do not always add up to 100%.

Table 8.24 lists the percentage of farmers growing different kinds of crops within each WUA. Both MH and PS 55 have the least diversity, with 98% and 94%, respectively, of their farmers growing at least one similar crop. Because there are more farmers growing other crops besides vegetables in MH (22% bananas, 37% melons), whereas this is not the case in PS 55, PS 55 displays the least crop diversity between farmers. In PS 33, 83% of farmers grow the same crop (citrus), 46% grow vegetables, and some grow other crops. In PS 91, only 68% of farmers grow the same crop (vegetables). In sum, in terms of crop diversity among farmers, PS 55 displays the least diversity and PS 91 the most.

Table 8.24: Percentage of farmers in the four WUAs growing different crops (farmers can be in more than one category). Citrus Vegetables Palms Bananas Grape Herbs Olives Melons Leaves PS 33 83% 46% 4% 0% 10% 2% 2% 0% PS 55 13% 94% 2% 0% 0% 0% 2% 0% PS 91 0% 68% 30% 0% 2% 21% 4% 0% MH 0% 98% 5% 22% 7% 0% 0% 37% Source: Survey data.

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On both individual and WUA-level crop diversity, PS 55 is the least diverse. Most farmers in PS 55 are only growing one type of crop and it is the same for all of them: vegetables. This means that PS 55, according to the initial hypothesis, should perform the best and have the highest rate of participation. But from the overall results (seen earlier in Table 8.22), PS 55 does not receive the most favorable reviews but also is not the worst.

With regard to membership, it is also not the WUA with the highest membership but has generally high membership. As with the factor of system size, this factor suffers from a small sampling of only four WUAs. The factor remains statistically unproven but PS 55 could benefit from the greater homogeneity among its farmers in terms of crops grown.

CLIMATE AND NATURAL EVENTS

Hypothesis: Extreme weather-related and natural events will have a negative impact on farmers’ ability and desire to cooperate in matters of water distribution.

The most important climatic factors to consider are temperature and rainfall. In their assessment of the longer term temperature trends in Jordan from 1979 to 2008, Matouq et al. (2013) found that while the mean minimum temperature appeared to increase from 1979 to 1998, it has decreased from 1999 to 2008 and is expected to continue decreasing in the following decade. They also found that the mean maximum temperature, while fluctuating in the past in different regions in Jordan, is expected to decrease in the eastern desert, increase in the central region, decrease in the southern region, and increase in the northern region. Smiatek et al. (2014) found that for the neighboring Upper Jordan River region, temperatures are expected to rise throughout the 21st century. Jararweh et

282 al. (2014), on the other hand, found no significant increase in temperatures in Jordan but they did find significant increases in humidity and dew point measurements. The dew point temperature is influenced by the increasing humidity and this can in turn have important impacts for animal husbandry and crop production. The thermal neutral zone in which an animal performs most optimally is shrinking due to the increase in dew points and increasing dew points can have a negative impact on soil erosion and water supplies, which can impact the level of crop production. In sum, while there is disagreement in the literature regarding the precise nature of future temperature and related factor trends, either increasing or decreasing temperatures could negatively impact crop production in the Jordan Valley. Farmers will become increasingly susceptible to crop failure due to unexpected and unprepared-for heat waves and frosts. These events can lead to farmers losing partial or entire crops in a matter of days. And indeed, farmers frequently commented on problems related to temperature

(Discussions with farmers in Spring and Fall, 2014). During the winter months, frosts can occur suddenly and without warning, destroying crops partially or sometimes fully because farmers do not have the ability to cover their crops. On the opposite extreme, date palm farmers need very hot temperatures in August and September when the dates are reaching the final stage of ripeness before harvest. In a recent summer, the temperature was not high enough and the date harvest was not as good. If the temperature does not reach a high enough degree, the fruits stay yellow and are too big in size (Discussion with date farmer from Karama, March 2014). Any fluctuations in the normal temperature range in the Jordan Valley can thus lead to detrimental outcomes for farmers and their normal agricultural production cycle may no longer be viable in the coming years.

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The present and potential future fluctuations in rainfall are equally worrying. Matouq et al. (2013) predict a general reduction in rainfall for the 2009-2018 decade, especially in the northern region of Jordan that includes the Jordan Valley. Similarly, Smiatek et al. (2014) expect a decrease in precipitation for the entire Jordan River area and thus decreasing discharge into the Upper Jordan River (and thus the Lower Jordan River as well). The Jordan Ministry of Water and Irrigation (MWI) (2013), while assessing there to be no change in the amount of rainfall in the 2012-2013 season as compared to the long- term average between 1937 and 2013, observes an increase in evaporation loss and a decrease in groundwater recharge compared to the long-term average. Also according to the MWI (2013), rainfall volumes for the past ten years over most of the main water basins in Jordan, taken individually, have been less than the long-term average. Black (2009) adds a seasonal aspect and predicts that there will be a general reduction in rainfall for the winter (November, December, January and February) and an increase in rainfall in the spring (April and May). These studies point to a future with less rainfall and a change in when the rainfall will occur. This could have an impact on farmers who depend on the amount and timing of rainfall. Particularly in the Jordan Valley, the major growing season is from October to April/May and rainfall is essential in those winter months. Every year, there is no way to predict whether it will be a rainy or a dry winter. The winter of 2013-2014 was particularly dry and thus an especially difficult Spring and Summer for farmers (Discussions with farmers from January-May 2014). In the following winter of 2014-2015, rain was more plentiful and farmers had fewer complaints (Discussions with farmers November 2014 to February 2015).

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Furthermore, from observational analysis in the Jordan Valley and in discussions with farmers, extreme flooding was observed to wreak particular havoc on farming in the Jordan Valley. Because of the Jordan Valley’s low elevation, when flooding occurs in the Highlands, the water rushes off of the mountains that have little vegetation or soil to absorb the water and pours into the valley at high speed, in large quantity, and in a short amount of time. This huge and quick quantity of water was seen to do significant damage to roads and water distribution networks in the Jordan Valley. Sometimes roads get covered in thick mud, rocks and other debris after such flooding events and are unpassable, leaving farmers without the ability to get their products to the market or, in some cases, to get to their farms. As mentioned in the section on infrastructure, flooding causes the water in the KAC to become muddy and unusable by farmers, leaving them without a water supply for the days after such an event. In MH in particular, farmers complained of their water supply being cut-off in times of flooding because rocks and debris from floods damage the gravity network and leave farmers without any water for days and sometimes weeks (Discussions with farmers in MH, November-December 2014). One weather-related event that is particular to the MH area is the presence of sinkholes (Figure 8.26). Because the Dead Sea has lost over one third of its surface area and continues to shrink in depth by one meter per year, the shoreline has receded at a rapid rate, leading to a drop in the water table of the surrounding area and the presence of innumerable sink holes along its shoreline (FOEME website). MH, comprising an area directly abutting the Dead Sea, is susceptible to sink holes and has lost various structures and farmland to sinkholes in previous years. Farmers also lost land to sinkholes and others will be threatened in the future as the Dead Sea continues to shrink.

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Figure 8.26: Damage from sinkholes in the area of Mazraa-Haditha.

Source: Personal photographs.

The hypothesis that extreme weather-related or natural events will have a negative impact on WUA performance cannot be statistically proven but in assessing the overall climatic changes in the Jordan Valley, it is likely that there is a negative effect. Particularly in MH, farmer satisfaction with the WUA may be low partially due to the damages that occur from flooding and the sinkholes. In all areas of the Jordan Valley, changing weather patterns are problematic. Apart from climatic variables affecting farming, there are other natural and ecological events at play. For example, as several farmers recounted in discussions during fieldwork, over the past few years farms have been hit hard by a bug called tuta absoluta. This bug critically affects tomato plants in particular and many times whole crops are lost. Farmers point fingers at the Ministry of Agriculture for not giving warning of such a threatening bug and also for not helping them to eradicate it. This is an arena in which the water user associations are not meant to operate; their milieu is that of water management, not agricultural sciences. When farmers are stricken with such large-scale and devastating

286 events to their crops, no improvements in water quantity or timing will help. Some natural events will have no remedy from the WUAs unless the WUAs take on tasks beyond water management.

SUMMARY

This chapter has found a negative effect of water scarcity and a positive effect of water predictability on WUA performance. However, other information gathered in this study and related literature suggests the great importance of physical factors. The breadth of troubling issues in the water distribution networks alone serve as a noteworthy impediment to any water management efforts in the Jordan Valley and contribute to issues of water scarcity and predictability. System size and crop/farm diversity are not statistically proven to be influential, with the small sample size of four WUAs (and thus only four observations to test), but there is potential for MH’s large system size to have a negative effect and for PS 55’s lower crop diversity to have a positive effect. Finally, from farmer commentary, related literature and personal observation in the Jordan Valley, climate change causes significant problems for agricultural production and there is little that the WUAs can do to remedy these issues. While all of these physical factors have their part to play in WUA success in the Jordan Valley, the next chapter will show how community factors can pose just as much of an obstacle to success.

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Chapter Nine: Community Factors

This chapter addresses factors related to the community that could have an impact on WUA performance and participation rates. The Jordan Valley’s tribal networks are an influential force in all aspects of life, to include the internal workings of the WUAs. Heterogeneity among farmers is also problematic for cooperative efforts. And farmers find themselves within overarching political and market environments that are not automatically conducive to the success of their agricultural pursuits. There are no statistical analyses to aid in the investigation of these factors due to the lack of farmer-level data that is offered for more of the physical, institutional and user-level factors. These community factors reach beyond the farmer-level and are thus assessed for their influence on the Jordan Valley as a whole. Table 9.1 offers a summary of the findings and the conclusions reached on the initial hypotheses.

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Table 9.1: Summary of community factors, hypotheses and conclusions reached. Factor Hypothesis Results Nature of Data Preexisting Having prior or even present experience with  Hypothesis rejected (first half), Qualitative and Community some form of communal management, or having accepted (second half). descriptive. Organizations close agreement between WUA management and  Jordan’s tribal society is likely an local customs and traditions, will be of benefit to obstacle to WUA fairness and WUA performance. At the same time, there is a democratic proceedings. potential danger that such prior or existing experience could harm newer efforts at communal management. Heterogeneity Heterogeneity in endowments or socio-economic  No clear conclusions for effect of Qualitative and of Farmers status could have a positive impact on WUA heterogeneity. descriptive. performance, although only if those wealthier  Endowment heterogeneity could parties support the WUA in additional ways even have a positive effect for though others less-wealthy might be free-riding performance in PS 55 and PS 91. on their efforts, and a negative impact on WUA  Lack of identity heterogeneity participation. On the other hand, socio-cultural may benefit PS 33 membership or identity-related heterogeneity and rate. heterogeneity in interests will negatively impact  Interests heterogeneity possibly WUA outcomes and participation rates in positive for PS 91 performance WUAs. and PS 33 membership.

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Table 9.1: (continued). Factor Hypothesis Results Nature of Data Political WUAs can only succeed and farmers join them if  Hypothesis accepted. Qualitative and Environment there is strong political support from all levels of  Political obstacles exist to WUA descriptive. and Support government for WUAs. success, including lack of attention from the government and donors.  The Arab Spring complicates more democratic efforts such as WUAs. Market Only with a strong national market for  Hypothesis accepted. Qualitative and Environment agricultural goods and marketing support for  The absence of strong national descriptive. and Support farmers will they be interested in joining WUAs marketing strategies, difficult and working collectively to manage their joint local market conditions and few water resources. agricultural extension services for farmers negatively impact their interest in the WUAs. Source: Data from surveys, interviews and observational analysis.

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PREEXISTING COMMUNITY ORGANIZATIONS

Hypothesis: Having prior or even present experience with some form of communal management, or having close agreement between WUA management and local customs and traditions, will be of benefit to WUA performance. At the same time, there is a potential danger that such prior or existing experience could harm newer efforts at communal management.

In interviews with WUA heads, most heads stated that there was no prior experience with communal management in the Jordan Valley for water management. There was mention of previous agricultural associations and women’s associations, which were for small-scale production of craft or food items. A GIZ employee involved in the initiation of the WUAs recounted how prior experience with agricultural associations had a dampening effect on arousing interest in the WUAs (Interview with Regner, 3/29/2015, 3/30/2015). Farmers believed that these types of associations only served the interests of the few and would not benefit the vast majority of farmers in any meaningful way. Thus, prior experience appears to be of little benefit to the WUAs and was in fact a hindrance. Apart from prior experience with associations, there is a type of community organization that is unofficial, affects the WUAs and speaks to the latter half of the initial hypothesis regarding the danger of existing communal bodies. Jordan’s tribal network is a powerful force throughout the country and from talking to WUA employees and farmers, there are clues that it weighs heavily into WUA activities as well. The hypothesis is not proven for the case study WUAs with statistical evidence but the following gives qualitative evidence for the Jordan Valley at large. As a simple demonstration, the names of the WUA heads and whether they are members of a major or prominent local tribe are listed in Table 9.2. Sometimes the tribe is influential in terms of land-holdings in the area, sometimes in terms of maintaining the

291 largest portion of the residents in the nearby village. Sometimes the tribe is Jordanian, sometimes Palestinian in origin. From the table, by and large, most association heads (among those interviewed) are members of a prominent local tribe, although this is not a rule. Within the four surveyed associations, the heads of PS 33, PS 55 and Mazraa-Haditha (MH) are members of a major local tribe whereas in PS 91 the president is not. This could be a reason for the more favorable reviews of the WUA in PS 91. At the same time, the

PS 91 head has built-up an unofficial “tribe” among the wealthier and more commercially- oriented date palm farmers (Discussions with farmers and ditchriders in PS 91, Spring and

Fall, 2014).

Table 9.2: Interviewed WUA Heads and their tribal status. WUA WUA Head Member of a prominent Jordan Valley tribe (yes/no) PS 28 Ashraf al-Ghazawi Yes PS 33 Nawah Khraym al-Rayahna Yes PS 41 Zaki Rababa No (tribe from Ajloun) PS 50 Hafiz al-Shobaky Yes PS 55 Walid al-Faqir Yes PS 81 Tawfiq al-Satary Yes PS 91 Ali Mustapha No PS 95 Ahmed al-Yemani No Rama Talal Farhan al-Adwan Yes Kafrein Ahmed al-Adwan Yes Mazraa-Haditha Saleem al-Huwaymil Yes Fifa Mousa al-Khutaba Yes Khanizeera Ayed al-Rawashedeh Yes Source: WUA Head interviews; discussion with Jordan Valley farm consultant (12/7/2015).

The evidence is not only in the names and tribal affiliations of the WUA heads. The influence of the tribe was observed during fieldwork and in discussions with farmers and ditchriders across the Jordan Valley. For example, in PS 33, the head of the WUA

292 hires relatives to fill all of the ditchrider positions, to include his son and his nephew. Similarly, in PS 55, the engineer is a close relative of the WUA head. Mustafa et al. (2016), on their research on WUAs in the Jordan Valley, also note that “almost all of the employees of a WUA are hired based upon their tribal and familial linkages to the WUA president” (p. 172). In the Jordan Valley environment, the WUA head likely prefers to keep WUA activities within the family and ensure his full control. The director of the northern directorate in the Jordan Valley (Discussion on 2/18/2014) pointed out that a significant weakness for the WUAs is that they do not always employ qualified people but rather employ family who may or may not be qualified. Mustafa et al. (2016) also note that farmer’s see the WUA head’s power in terms of how capable he is of negotiating better water turns for the farmer. This is with the assumption, on the part of the farmers, that the WUA head is more capable when he has more influence within higher levels of the Ministry of Water and Irrigation. Farmers are thus not necessarily against the WUA head having strong tribal connections; this can be a benefit. When the WUA head has more social and political connections, the farmer may be rewarded with a more favorable water share. As Mustafa et al. (2016) state, “one of the main benefits of joining a WUA is to gain formal patronage of the association president to improve one’s access to water both collectively and individually” (p. 171).

The king and the royal family, as the highest echelon of Jordan’s tribal network, are also part of tribal influence on the WUAs in the Jordan Valley. For example, in PS 91 there is a very large farm owned by Princess Alia (daughter of former King Hussein). Two of her farm units are within the WUA’s purview but there are two extensive areas of land just to the west of the WUA’s area and outside of its control (shown in Figure 9.1). Date palms are planted in these areas and much of the production is sold to markets in Lebanon

293 and Europe (Discussion with farm agent, 11/24/2014). These areas are supplied water through plastic pipes directly connected to the KAC (Figure 9.2), bypassing the pressure and gravity networks that all other farmers must use. As heard from PS 91 ditchriders and confirmed by the farm agent, these farms are at liberty to take water when needed from January to August from these plastic pipes.

Figure 9.1: Princess Alia’s date farm in the zhor area beyond the purview of the WUA at PS 91.

Source: Personal photograph.

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Figure 9.2: The plastic pipe that directly connects Princess Alia’s farm to the KAC.

Source: Personal photograph.

The royal family owns farms outside of the purview of any WUA or the JVA in other locations as well. Just north of PS 91 in Ghor Kibid, Princess Alia also owns farm plots for vegetables and “perseem,” which is a type of clover plant used for animal feed. The perseem is watered both by sprinklers (water intensive for the Jordan Valley and thus not used by most farmers) and rain, and is grown in order to feed horses that Princess Alia owns at a location in Kamaliyya just outside of Amman (Discussion with engineer in the Jordan Valley, Spring 2014). King Abdullah owns a large date palm farm just north of the

Dead Sea and operates five private wells, something not legally afforded to other farmers in the Jordan Valley, to provide water to these palms (Kafrein WUA Head, February 2014). There is no solid proof to state that Jordan’s tribal network directly has a negative effect on the WUAs, either in terms of the WUAs’ performance or whether farmers are

295 members in the WUA. This section has indicated the potential, though, for the influence of tribal politics to be negative within the WUAs and within the Jordan Valley at large in terms of all farmers receiving an equitable share of water resources.

HETEROGENEITY OF FARMERS

The subject of heterogeneity among farmers and its impact on the outcomes is complex. Hypotheses associated with heterogeneity are thus multifaceted:

Hypotheses: Heterogeneity in endowments or socio-economic status could have a positive impact on WUA performance, although only if those wealthier parties support the WUA in additional ways even though others less-wealthy might be free-riding on their efforts, and a negative impact on WUA participation. On the other hand, socio- cultural or identity-related heterogeneity and heterogeneity in interests will negatively impact WUA outcomes and participation rates in WUAs.

Endowments

Endowments are measured through indicator variables: the number of dunums a farmer has, whether he uses greenhouses, whether he exports his produce and his education level. In Figure 9.3, surveyed farmers within each WUA have a variety of land holdings. Within each WUA, 10-19% of farmers have large holdings, more than 150 dunums. Most farmers have 0-70 dunums, or from less than a farm unit (35 dunums) to around two farm units. Smaller groups of farmers have 71-150 dunums. Within all four WUAs, there is high heterogeneity among farmers in terms of farm size.

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Figure 9.3: Farm size among farmers in the four surveyed WUAs.

Source: Survey data.

Greenhouses, which identify farmers who have a greater capability to farm more intensively and efficiently and can make the large upfront and continuing investment for the additional labor, monitoring and capital required, also indicate endowment level. Table 9.3 shows that 85% of farmers in PS 55 have greenhouses, 20% in PS 91, 12% in MH and 2% in PS 33. PS 33 displays the least amount of heterogeneity in terms of greenhouses with almost all farmers not using greenhouses. Table 9.4 lists the number of farmers who have varying quantities of greenhouses in each of the surveyed WUAs. PS 55 has 16 farmers with 75 or more greenhouses and PA 91 has two farmers with 75 or more greenhouses. PS 55 and PS 91 could be said to display more heterogeneity in terms of greenhouses.

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Table 9.3: Percentage of farmers with greenhouses within each of the surveyed WUAs. Percentage of farmers with greenhouses PS 33 2% PS 55 85% PS 91 20% MH 12% Source: Survey data.

Table 9.4: Number of greenhouses owned by farmers in the four surveyed WUAs. Number of Greenhouses 0 1-25 26-50 50-75 75-100 100+ PS 33 51 1 0 0 0 0 PS 55 7 10 12 3 4 12 PS 91 45 6 3 0 1 1 MH 36 3 1 1 0 0 Source: Survey data.

In terms of exporting capacity, the vast majority of surveyed farmers in all WUAs are only able to sell their produce in local markets (see Table 9.5). A handful of farmers in all four surveyed associations are able to sell their produce in foreign markets, more so in PS 55 and PS 91. More heterogeneity in exporting capacity among surveyed farmers is thus seen in PS 55 and PS 91.

Table 9.5: Number of farmers selling to local or international markets in the four surveyed WUAs. Number of Number of Farmers Crops Exported Farmers Selling Selling to to Local Markets International Markets PS 33 52 4 Citrus, dates, vegetables, thyme PS 55 45 13 Vegetables, seeds PS 91 52 10 Dates, vegetables MH 41 4 Vegetables Source: Survey data.

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Lastly, in terms of education level, farmers in all four WUAs are fairly evenly distributed between the seven education levels considered (Figure 9.4). PS 55 has the largest percentage of farmers having completed high school or a higher level of education. Otherwise, little else can be implied from this variable. All WUAs are heterogeneous in farmer education levels.

Figure 9.4: Education level of farmers in the four surveyed WUAs.

Source: Survey data.

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In sum, all WUAs display some heterogeneity in endowments along all four considered variables. All WUAs are generally equal in heterogeneity in terms of land size and education level among farmers. PS 33 displays less heterogeneity in terms use of greenhouses (few farmers have them) and PS 33 and MH display less heterogeneity in terms of exporting capacity (few farmers export). PS 55 and PS 91 are more heterogeneous in terms of greenhouses and exporting capacity. The original hypothesis states therefore that PS 55 and PS 91 should perform better because of their greater heterogeneity in endowments. Table 9.6 shows the general statistics of the outcome variables (a reproduction of Table 8.22). PS 91 is stronger in terms of farmer opinion of the WUA and its level of fairness. PS 55 is also strong in these categories. But both are worse in terms of how much water stealing is occurring. There is no explicit link between heterogeneity of endowments and better performance but it could have a positive impact.

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Table 9.6: Summary of outcome variable statistics between WUAs. Outcome WUA PS 33 PS 55 PS 91 MH Opinion of 63% good 75% good 84% good 44% good WUA* 31% so-so 19% so-so 11% so-so 29% so-so 6% bad 4% bad 5% bad 12% bad Opinion of 54% better 54% better 61% better 22% better WUA in 25% same thing 29% same thing 32% same thing 46% same thing comparison to 21% worse 13% worse 5% worse 17% worse JVA* Farmer 75% yes 83% yes 87% yes 81% yes reporting of 13% no 11% no 9% no 17% no water stealing* Farmer opinion 60% fair 60% fair 73% fair 59% fair of fairness of 36% not fair 40% not fair 27% not fair 41% not fair WUA* Farmer 56% member 39% member 15% member 40% member membership in 44% non- 61% non- 85% non- 60% non- WUA** member member member member

Source: Survey data (reproduced from Table 8.22). *Answers of no opinion, maybe, and don’t know not included. **Only among owners and renters eligible to be members.

To address the latter half of the hypothesis, that with heterogeneity in endowments, the few wealthier parties might contribute more to the WUA to the benefit of all regardless of whether other poorer farmers do the same, this is not borne out in farmer commentary heard during survey implementation. Some farmers noted that the wealthier farmers are able to bribe or pay their way into or out of any situation for their own personal benefit; they are not putting forth extra money to benefit the WUA as a whole. One farm agent in one WUA noted that there is a wealthy farmer who donates money and supplies to the

WUA but this is not with the intention of improving the WUA’s operations; rather, this is a bribe to keep the WUA “in his pocket.”

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Identity

Heterogeneity in identity (measured here in nationality and land-holding status), unlike endowments, is hypothesized to have a negative impact on WUA performance. Farmers in the Jordan Valley are generally Jordanian (to include Jordanian-Palestinians), Egyptian, and Pakistani (one Iraqi farm agent was interviewed but not included in this analysis). Table 9.7 shows that among the surveyed farmers, PS 33 and MH are less heterogeneous with a larger percentage (83%) of Jordanians than PS 55 and PS 91. There are also a good portion of Egyptians in PS 33 and Pakistanis in MH. In PS 55 and PS 91, 73% and 71%, respectively, of farmers are Jordanian. PS 55 also has a large portion of Egyptians and PS 91 has a large portion of both Egyptians and Pakistanis. All WUAs display some level of heterogeneity in nationality but slightly less so in PS 33 and MH.

Table 9.7: Percentage of Jordanians, Egyptians and Pakistanis in the four surveyed WUAs. Jordanian Egyptian Pakistani PS 33 83% 17% 0% PS 55 73% 27% 0% PS 91 71% 16% 13% MH 83% 5% 10% Source: Survey data.

Farmer commentary lends weight to the potential negative effect of heterogeneity in identity, in terms of nationality, for PS 91. Several farmers in PS 91 believed that the Pakistanis are the ones causing all of the stealing problems and any other troubles that arise in the field. A couple of farmers felt resentment for Pakistanis because they see Pakistanis as able to receive all of the benefits of living and working in Jordan without having to pay the full costs as Jordanians do in terms of paying taxes or other costs for services. These farmers also felt that Jordan is not really benefiting from these Pakistanis or receiving any

302 sort of contribution from them. One farmer said that Egyptians are better because they just come, take a salary, “do their time,” and then leave; Pakistanis, on the other hand, stay forever. Not all farmers in PS 91 displayed this sort of disdain for Pakistani farmers but it was heard enough to hint that heterogeneity in this regard might lead to more tension among farmers and thus less cooperation and desire to cooperate. For the other marker of heterogeneity in identity, Table 9.8 shows the percentage of owners (including those who are both owners and renters), renters and agents in each of the four surveyed WUAs among the survey sample. Farmers in all of the WUAs are fairly dispersed among the three categories. PS 33 has the largest percentage of farmers in a single category; 54% of its farmers are owners. PS 55 has the most even split among owners, renters and agents. PS 91 and MH are relatively evenly divided among the three categories. PS 33 is only slightly less heterogeneous in terms land-holding status than the other three WUAs.

Table 9.8: Percentage of owners, renters and agents in the four surveyed WUAs. Owner Renter Agent PS 33 54% 19% 27% PS 55 33% 31% 36% PS 91 26% 45% 29% MH 43% 37% 20% Source: Survey data.

In sum, PS 33 and MH are slightly less heterogeneous by nationality and PS 33 is slightly less heterogeneous by land-holding status. According to the hypothesis, PS 33 should suffer less from any negative impact of these two types of heterogeneity. In reverting to the general statistics of the outcomes in Table 9.5, PS 33’s performance is not

303 better than the other WUAs but its membership is higher. Maybe its greater homogeneity in nationality and land-holding status induces more farmers to join the WUA.

Interests

Heterogeneity of interests, here measured by whether farmers have secondary work or a secondary income and thus more interests beyond farming, among the WUAs is displayed in Table 9.9. Table 9.10 gives supplementary data on the types of secondary work or income in which farmers in the four WUAs are engaged. In PS 33 and PS 91, just over half of farmers have secondary work or income. 35% and 39% of farmers in PS 55 and MH, respectively, have secondary work or income.

Table 9.9: Percentage of farmers in the four WUAs who have secondary work or income. Has secondary work/income PS 33 52% PS 55 35% PS 91 52% MH 39% Source: Survey data.

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Table 9.10: Types of secondary work or income for farmers in the four surveyed WUAs. Types of Secondary Work/Income PS 33 Pension from the army, pension from government, hospital employee, transportation of goods, farm equipment rental, olive factory, air- conditioning company, real estate, factory worker, agricultural consultant and trainer, lawyer, WUA maintenance. PS 55 Pension from the army, pension from government, animal husbandry, trade and export business, farm rentals, electrician. PS 91 Pension from the army, pension from company, animal husbandry, transportation of goods, shop owner, laborer, gas canister business worker, tile and brick layer, worker at sand/disc filter company, real estate, government employee, university professor, pharmacist. MH Pension from the army, pension from the government, government employee, worker at chemical/industrial company, pension from potash company, teacher, shop owner, airport policeman, university facilities employee

By the terms of the original hypothesis, the performance of PS 33 and PS 91 should be worse due to their greater heterogeneity of interests among farmers. Again referring to the statistics of the outcome variables in Table 9.5, it cannot be argued that both PS 33 and PS 91 perform better on all measures. PS 91’s performance is generally better and PS 33’s membership is higher. Maybe homogeneity in interests in PS 91 makes its performance higher; maybe homogeneity in interests in PS 33 makes its membership higher. There is no statistical proof for either explanation.

POLITICAL ENVIRONMENT AND SUPPORT

Hypothesis: WUAs can only succeed and farmers join them if there is strong political support from all levels of government for WUAs.

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In discussions with farmers in the Jordan Valley, many feel that the government no longer cares about farmers in their region. In the 1970s and amid the global green revolution, much attention and effort was put into developing the JV and making it the bread basket of the country (USAID, 2013; Courcier et al., 2005). But over the course of recent decades, farmers have felt waning interest in their affairs. The Ministry of Agriculture in Jordan, the one ministry mandated with farmer affairs, is absent from the

Jordan Valley as far as farmers can recall. Farmers in the Jordan Valley lament what they see as the great attention and favors given to both farmers and domestic water users in the Highlands while they are left to fend for themselves. Many farmers take note of how much water is siphoned from the KAC and pumped up to Amman instead of being used for agriculture in the valley, as it once was. One farmer stated that those in Amman are basically “stealing their [the farmers’] money,” in that taking KAC water has a direct impact on farmers’ pocketbooks. Another farmer noted that more attention is given to agriculture in the Highlands because ministers and other “important” people have farms in the Highlands whereas not very many of these important people have farms in the Jordan Valley. Thus, so this logic goes, the government doesn’t have to care about the Jordan Valley farming situation. Farmers in the southern ghor in particular feel slighted because they are so far-removed from the capital city of

Amman in comparison to other portions of the Jordan Valley. Farmers do not simply feel neglected but they sometimes feel like the government, the JVA or society in general is actively working against them. A citrus farmer in the north stated that “all of the area around me is putting me down, not encouraging me” (Interview with farmer on 6/9/2014). Another farmer mentioned that there is a “culture of shame” surrounding agriculture in Jordan, in that farming is considered a lowly job and not for the

306 respectable type (Interview with farmer on 5/4/2014). While not many farmers cared to mention their eastern neighbor in any positive light, one farmer did actually respect the way Israel treats its farmers and supports them, a situation a far cry from what farmers in Jordan experience (Interview with farmer in Spring 2014). Farmers also do not feel that there is support for WUAs anymore. During the initial stages of the project in the early 2000s, both GIZ and the Jordanian Government were outwardly and explicitly in support of the WUA project (Interviews with former GIZ project employees). Visits were frequently made to the JV by higher-level officials and farmers felt that this project was a going concern, one that important people wanted to see succeed. This spurred the WUAs to be active and some farmers noted that the WUA employees used to be more present in the field. Unfortunately, at present GIZ has left the JV and the WUA project, and no other aid agency is involved. Farmers see no foreign aid agency visitors anymore and now WUAs are carried solely by the JVA. High-level officials infrequently visit the JV and when they do, it is usually not to WUAs unless they have personal business with farmers under their purview (Discussions with WUA employees and farmers, Spring and Fall 2014). For example, USAID’s Institutional Support and Strengthening Program (ISSP) made efforts to help assess the progress of the WUAs a few years ago (USAID, 2013).

Now, there is little interest in USAID for these entities and it is largely focused on a hydroponic farming project in the Jordan Valley (Discussion with USAID employee,

4/14/2015). Indeed, when the US Ambassador to Jordan Alice Wells made a visit to the Jordan Valley in late February of 2015, she visited a dam, the KAC and an ecopark to learn about the valley’s general water situation. Her visit to an actual farm was to USAID’s pilot project for hydroponic farming, the Hydroponic Green Farming Initiative (Jordan Times,

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2015). When the US Secretary of Agriculture Thomas Vilsack visited Jordan in May of 2015, USAID’s hydroponic farming project was again a major focus (Embassy of the United States in Amman, Jordan, 2015). Hydroponic farming is not a bad idea; using less water for farming is a good thing, a very necessary thing for Jordan. But for the vast majority of farmers in the Jordan Valley, it is not currently a realistic prospect. The WUAs, on the other hand, are a daily concern for a large number of farmers and represent bodies that have a direct impact on their water situation. But donor agencies have lost interest. For the Jordan government’s part, a factor that could explain the lack of strong support for divesting WUAs both from the JVA and the tribal networks is the Arab Spring. Jordan was not immune to the general hostility towards the region’s authoritarian governments with the onset of various demonstrations, riots and protests in the Middle East in December 2010. While the country remains relatively peaceful and the monarchy maintains its control, protests did occur weekly in early 2011 in Amman and other cities after Friday prayers, with slogans in line with the region’s sentiments for change in government and general economic and political reforms (Tobin, 2012; Kadri and Kershner, 2011; Hybels, 2011). Protests have also occurred in response to rising fuel, electricity or food prices. For example, in November of 2012, protests broke-out in several cities in Jordan, including the capital, over a spike in fuel prices due to the government cancelling its substantial fuel subsidization (Al-Jazeera, 2012; Rudoren and Kadri, 2012; Buck, 2012). These protests were not void of some even calling for regime change.

Amid this street tension, the Jordanian monarchy is likely intent on keeping the public content and less-inclined to reject its rule, especially in light of the on-going strain from the refugee crisis. Other than continuing to subsidize goods prices, the government has also shored-up its support through the tribal networks. These tribes are the backbone

308 of the Jordanian monarchy; if they are kept satisfied, the Jordanian monarchy in turn is stronger. Neither the Jordanian government nor foreign benefactors involved in various sectors in the country are interested in rocking the boat and this traditional system of rule. As said in a recent article in The Economist (2016, 9 January): “Both the kingdom’s own people and the donor countries that prop it up are too spooked by the chaos buffeting its borders and flooding it with refugees to talk much of political reform.”

The reason why the monarchy’s concerns with the Arab Spring are relevant to WUAs in the Jordan Valley is that tribal networks have significant influence within the

WUAs and could be a detriment to their goals of effective, efficient and autonomous management in the future. If the Jordanian government supports the tribes for its own existential purposes, then it might be unlikely that it will speak against the tribes even if they are manipulating WUAs for their own interests and not for the common good. This echoes what Mustafa et al. (2016) found in terms of the behavior of people in Jordan since the start of the Arab Spring. A water official is cited in the article as saying that people have stopped paying their full water bills or vehicle registration fees, knowing that the government will not hound them for these payments out of a fear of rebellion or anger from the people. In the survey, one citrus farmer in the Jordan Valley also noted that “people stopped caring about what the government would do” after the Arab Spring because it became obvious that the government wouldn’t crack-down. This farmer stated that there is less respect for the government now, meaning that there is also less following of rules and order (Discussion with farmer, 6/18/2014). Those tribes in control of some of the WUAs could also be feeling free to flaunt authority with the knowledge that few negative consequences will be met.

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Another negative results of the lack of political support for WUAs and continued support of the local tribes is that farmers sometimes don’t want to join the WUA for these reasons. In discussions with farmers, many see the WUAs as weak, ineffective and pointless ventures that will be discarded as have so many other ventures. While some farmers actually see an advantage to the WUA having strong tribal backing, others see the domination of their WUA by a local tribe as a negative characteristic and they don’t feel that their participation will have any effect. Elections are also no guarantee that one’s vote counts. Farmers have little faith that a strong tribal leader can ever be unseated. In sum, some farmers said that they have no desire to be members in the WUA because they believe that the WUA only benefits the head and his relatives and friends. Nothing definitive can be posited to prove or disprove the original hypothesis that a lack of political support is a detriment to both WUA performance and farmer willingness to join the WUA. But there is an evident and palpable belief among many farmers in the

Jordan Valley that the government and donor agencies are disinterested in their livelihoods and in the WUAs. The Arab Spring has made it difficult for the Jordanian government to act against its tribal base, which could be impeding the work of WUAs and making farmers uninterested in becoming members. For the WUAs to succeed, gaining back this political support would appear to be necessary.

MARKET ENVIRONMENT AND SUPPORT

Hypothesis: Only with a strong national market for agricultural goods and marketing support for farmers will they be interested in joining WUAs and working collectively to manage their joint water resources.

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Jordan became a member of the World Trade Organization (WTO) in April of 2000. Among the many impacts that this membership yielded, Jordan opened itself to greater exportation of agricultural goods, fewer restrictions on the importation of goods, and decreased domestic subsidization of the agricultural sector (Malkawi, 2006). As Jordan’s WTO trade policy review (2008, WT/TPR/G/206) reported in the years after its membership in the WTO, between 2003 and 2006, exports of agricultural goods to Gulf and European markets increased by 90% and imports increased 40%. This represented a strong reversal of the previous exports-to-imports ratio of one-to-four. In theory, there should have been a positive impact for farmers; with increased demand, better selling prices would follow. The Jordan WTO report also suggests that the Jordanian government put forth other efforts to increase the productivity of the agricultural sector, such as cancelling the sales tax on agricultural inputs. Additionally, Jordan signed a number of bilateral trade agreements, most notably with the US in 2000 that eliminated tariffs and promoted free trade between the two countries. In reality, there are questions as to the positive effects of WTO membership on Jordan and how the terms were implemented, particularly in the agricultural sector. While Jordan reduced or cancelled tariffs on the importation of many agricultural goods, others managed to maintain their high tariff rates. For example, during certain months of the year, there are “seasonal tariffs” for such agricultural products as garlic, onions, peas, grapefruit, oranges, potatoes, grapes and apples; bananas also enjoy a constant higher tariff limit and compound duties (WTO, 2008, WT/TPR/S/206). For bananas in particular, their special treatment is suspect. It could simply be that bananas, as suggested by Aymen al-Hosni at the Ministry of Agriculture (Interview on 4/16/2015), along with olives and other select fruits and vegetables, are “too vulnerable” and need extra protection at this point in time.

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But it could also be that, for bananas in particular, many of the banana producers are from the south of the JV, in specific from the Al-Adwan tribe. As suggested elsewhere (Mustafa et al., 2016; Van Aken et al., 2007), this tribe has been successful in putting pressure on the government to maintain this high tariff. The government’s policy could be dictated by tribal politics more than anything else. But despite these areas of higher tariffs, in general, agricultural products in Jordan no longer enjoy such broad special protections. This leaves farmers in a more vulnerable position. While Jordan is technically now “open” to foreign markets, farmers have had considerable difficulty in reaching these foreign markets, even the ones right next door. Some farmers said that they used to be able to sell their goods to the closer foreign markets of Syria, Turkey and Iraq (Discussions with farmers, Spring 2014). But due to the ongoing conflicts in Iraq and Syria, Jordan’s borders with these countries have frequently closed and transporting goods through these countries, even if the borders are open, is an extremely risky venture. A few farmers spoke of being able to export through the only other option, Haifa, Israel, but this involves more difficulties at the border with Israel and higher expenses. The local market is also a problem for farmers. Most farmers lamented the consistently low and stagnant prices to be paid for their goods at the local market. Many are quick to blame “criminal” traders who are all “in cahoots” with each other to keep the prices low (Discussions with farmers, Spring 2014). They view the government as solely interested in collecting its taxes, letting traders do as they please as long as they collect the taxes. Many farmers said that regardless of who they sell their produce to in the local market, the price is the same. Mustafa et al. (2016) also hint that the royal family is very interested in appeasing the “powerful Palestinian commercial interests” that likely

312 dominate the local market. The director of the south directorate in the Jordan Valley (discussion in February 2014) also mentioned how farmers can be trapped by traders and be at their mercy; a trader will lend a farmer money and then the farmer can only sell to him, with the trader taking additional interest or a commission. Stories circulated among farmers reinforce their distrust of and displeasure with the local market and lack of support from the government. For example, a ditchrider and farmer in PS 41 (Discussions on 2/3/2014) believed that Israel sometimes sells potatoes to the Jordanian market, where the potatoes are relabeled by traders as Jordanian and exported to Gulf countries, supposedly because Gulf countries won’t buy Israeli produce but they will buy Jordanian produce. Farmers thus believe that their potatoes have a hard time in the market because of an influx of Israeli potatoes, with the further insult that Israeli potatoes are passed-off as Jordanian. During the winter of 2014-2015, stories cropped-up in the newspaper of certain citrus crops from the Jordan Valley, such as pomelos and mandarins, being rotten and blackened inside. When I mentioned these articles to a citrus farmer in the north of the valley (Discussion on 12/29/2014), he vehemently refuted this claim and said that certain parties were out to get Jordanian citrus farmers and ruin their potential profits. Similar stories of extreme hormone use in melons in the southern ghor region surfaced in the newspapers and farmers refuted these claims (Discussions with farmers in the southern ghor, Spring 2015). While it is impossible to know whether local market traders or others in Jordan are actively working against farmers, the claim that prices are stagnant can be assessed more fully. Data is gathered regarding market prices in the Amman/central local market for select vegetables and fruits in their harvesting seasons within the Jordan Valley only, as these vegetables are harvested at differing times from the Highlands (Discussions with

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Jordan Valley farm consultant, 12/18/2015). Data is from Jordan’s DoS (monthly data from 1998-2012) and Jordan’s Ministry of Agriculture (monthly data from 2007-2014). Figures 9.5-9.9 display monthly prices from 1998 to 2012 for tomatoes, cucumbers, eggplants, sweet peppers and string beans, popular vegetables to grow in the Jordan Valley. The prices are stacked in order to see the trend by month; comparing June to June from year to year, for example, is more accurate than comparing overall yearly prices because prices depend on the harvest time and the availability of the produce in the market.

Figure 9.5: Prices for tomatoes in their harvesting season in the Jordan Valley from 1998 to 2012.

Source: Jordan Department of Statistics data.

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Figure 9.6: Prices for cucumbers in their harvesting season in the Jordan Valley from 1998 to 2012.

Source: Jordan Department of Statistics data.

Figure 9.7: Prices for eggplants in their harvesting season in the Jordan Valley from 1998 to 2012.

Source: Jordan Department of Statistics data.

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Figure 9.8: Prices for sweet peppers in their harvesting season in the Jordan Valley from 1998 to 2012.

Source: Jordan Department of Statistics data.

Figure 9.9: Prices for string beans in their harvesting season in the Jordan Valley from 1998 to 2012.

Source: Jordan Department of Statistics data.

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These graphs reveal general ups and downs in the market from year to year. There is an overall upward trend in prices for some produce in some months but stagnation for others. For example, prices for tomatoes (Figure 9.6) in January, February and March were fairly stagnant and dropped overall from 1998 to 2012, whereas in April, May and June, tomato prices increased over these years. Cucumbers (Figure 9.7) show similar trends, with stagnation of prices from 1998 to 2012 for January, February, March and April and slightly upward trends for October through December. Eggplants (Figure 9.8) and string beans (Figure 9.9) show healthier upward trends in their selling prices across all harvesting months during these same years. And for sweet peppers (Figure 9.10), while the trend is upward for November and April, it is fairly stagnant from January to June. The market price trends for vegetables from 1998 to 2012 reveal that for some of the major harvesting and marketing months, prices have risen but for other months during the harvesting season, prices have stagnated and even dropped for some vegetables.

Farmers likely have reason to lament the less than robust market for their produce. Market prices at the central market in Amman for selected fruits (oranges, grapefruits, lemons, guava and bananas) also display interesting patterns (Figures 9.10- 9.14). Prices for oranges, grapefruit and lemon are recorded from 1998 to 2012 in their peak harvesting season within the Jordan Valley. Data for guava is from 2007 to 2014 and again focuses on its main harvesting months in the valley. And data for bananas is recorded from 2007 to 2014 with all months included as bananas can be productive year-round

(Discussions with Jordan Valley farm consultant, 12/18/2015).

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Figure 9.10: Prices for oranges in their harvesting season in the Jordan Valley from 1998 to 2012.

Source: Jordan Department of Statistics data.

Figure 9.11: Prices for lemons in their harvesting season in the Jordan Valley from 1998 to 2012.

Source: Jordan Department of Statistics data.

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Figure 9.12: Prices for grapefruit in their harvesting season in the Jordan Valley from 1998 to 2012.

Source: Jordan Department of Statistics data.

Figure 9.13: Prices for guava in its harvesting season in the Jordan Valley from 2007 to 2014.

Source: Jordan Ministry of Agriculture data.

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Figure 9.14: Monthly prices for bananas in the Jordan Valley from 2007 to 2014.

Source: Jordan Ministry of Agriculture Data.

For oranges and lemons, from 1998 to 2012 their selling prices have risen slightly overall in the autumnal months but have been fairly stagnant otherwise (Figures 9.11 and 9.12). If farmers were able to harvest their citrus in the Fall (citrus is harvested just once per year), this would be a good trend. If the harvest comes later, the trend would not be good. The selling price for grapefruit has witnessed much stronger upward trends, especially from October to December (Figure 9.13). Guava (Figure 9.14) has an erratic trend overall from 2007 to 2014, making it difficult to decide whether the trend is upward or downward. And bananas (Figure 9.15) are a unique case in that their prices have remained stagnant, something to be expected as the price is fixed and protected by the aforementioned tariff. For fruits, it would also appear that farmers are somewhat justified in their complaints about the stagnation of market prices although it is difficult to tell how significant or insignificant any of the trends are. Market prices are a bitter issue for farmers because while they see stagnant selling prices, they also experience increasing farm input prices (Discussions with farmers, Spring and Fall 2014). Farmers and WUA heads 320 mentioned that the prices of pesticides, in particular, have risen substantially over the past few years. When looking at trends in prices of other goods in Jordan, according to the consumer price indices (Table 9.11), from 2007 to 2008 there was an especially distinct rise in consumer prices but a smaller dip from 2008 to 2009. In the following years, consumer prices generally rose by 4-6% with some stronger increases in fuels and electricity, and transportation and communications, areas that could affect farmers’ inputs to their farms and production.

Table 9.11: Percentage of rise or fall from previous year in consumer price indices in Jordan. 2008 2009 2010 2011 2012 2013 All items 13.9% -0.7% 5.0% 4.4% 4.7% 5.6% Food items 18.4% 1.7% 5.0% 4.1% 4.6% 3.7% Housing rents 2.1% 1.6% 3.8% 4.8% 3.6% 4.5% Fuels and 48.4% -11.1% 6.6% 2.8% 4.1% 19.7% electricity Transportation 14.5% -10.8% 8.1% 4.8% 7.1% 9.0% and communications Source: Jordan Department of Statistics (2013).

To further illustrate the weight of input prices for farmers in comparison to their potential profits, Tables 9.12-14 list the range of costs that vegetable, palm and citrus farmers pay for various inputs compared to their potential profits (In-depth discussions with farmers on 12/10/2014, 12/17/2014 12/23/2014, 12/29/2014, 12/30/2014, 1/27/2015, 2/15/2015, 12/18/2015). There are seasonal costs, which must be paid every growing season, and there are fixed costs that are purchased on a less frequent basis. The range of prices listed is due to different farmers quoting different prices. Renters have to pay for the land every year whereas owners do not. For citrus farming, only one owner was

321 consulted, thus the absence of a range of prices or land rental cost. Depending on what a farmer spends on inputs, a healthy profit is possible but coming out even or at a loss are also potential outcomes. Because precise data on farmer income was not gathered, it is impossible to know whether farmers are being truthful in complaining about farming not being a profitable business. It can be profitable but also there is potential for it to be very difficult and offer only slight profit.

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Table 9.12: Seasonal costs, selling price and fixed costs for one farm unit of vegetables for one season. Seasonal Costs: (JD) Mulch 320-1800 Sprayed pesticides 1208-4903 Seeds 1200-7000 Nursery styrofoam 300-900 Tractor rental 200-600 Manure 350-1500 Permanent labor 600-6667 Temporary labor 640-1280 Boxes 6250-17500 Tape 420-1400 Dyanas (transport trucks) 2100-14,000 Water 240-467 Electricity 343-540 Truck rental for spraying 2400 Diesel for tractors 200-257 Land rental (for renters) 3000-7000 Total Costs (owner) 16,771-61,214 Total Costs (renter) 19,771-68,214 Potential Selling Price 14,400-75,000 Fixed Costs: (JD) Drip Irrigation Lines 875-6500 Tractor (small/large) 7000-25,000 Tractor attachments 3600-4500 Plastic for tunnels 480-2280 Metal for tunnels 750-2000 Plastic for greenhouses 10,000-18,000 Metal for greenhouses 40,000-80,000 Rope in greenhouses 8000 Digging holding pond 200-400 Plastic lining in pond 400-800 Grate filter 100-200 Disc filter 100-150 Sand filter 1000 Land price 100,000 Pick-up truck 22,000 Source: In-depth interviews with Jordan Valley farmers (12/10/2014, 12/17/2014 12/23/2014, 12/30/2014, 1/27/2015).

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Table 9.13: Seasonal costs, selling price and fixed costs for one farm unit of date palm trees for one season. Seasonal Costs: (JD) Water 300-600 Manure 1500 Pesticide spraying 900-2625 Machine maintenance 100-200 Piping maintenance 20-50 Boxes 4000 Electricity 200 Permanent laborers 4320-25,200 Temporary laborers 2240-6075 Storage room rental 4320 Land rental 26,250-70,000 Total Costs (owner) 17,900-44,770 Total Costs (renter) 44,150-114,770 Potential Selling Price 44,000-236,250 Fixed Costs: (JD) Drip irrigation lines 2000-7875 New trees 10,500-24,150 Tractor (w/attachments) 25,000 Motor/filter/piping for pond 2500 Disc filter 70 Sand filter 300 Land price 90,000-450,000 Source: In-depth interviews with Jordan Valley farmers (12/17/2014, 2/15/2015, 12/18/2015).

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Table 9.14: Seasonal costs, selling price and fixed costs for one farm unit of citrus for one season. Seasonal Costs: (JD) Pesticide spraying 510-630 Weed spraying/removal 360-1600 Irrigation line replacement 200 Permanent labor 3240 Harvesting labor 2450-5600 Electricity 300-960 Manure 1078 Boxes 4250-4590 Tape 360 Water 300 Dyanas (transport trucks) 4760-5950 Labor for branches 500 Total Costs (owner) 18,308-25,008 Potential Selling Price 84,300 Fixed Costs: (JD) Drip irrigation lines 600 New trees 3000 Digging deep holding pond 10,000 Source: In-depth interviews with Jordan Valley farmers (12/29/2015).

Labor, in particular, is not only a significant cost but, according to many farmers (Discussions with farmers, Spring 2014), the supply of laborers is not reliable. Several farmers mentioned that their laborers, particularly the Egyptian laborers, are difficult to work with because they have the upper hand. Laborers are in such high demand that they can demand higher wages. The laborers may prefer to be day laborers instead of permanent laborers, leaving farmers the task of having to find laborers with each new day instead of having permanent staff. One farmer remarked that Egyptians “leave you high and dry at times,” with a WUA employee stating more diplomatically that Egyptian laborers are more free-moving and independent and thus can do what they want.

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To compare the average income of farmers with other sectors in the economy, according to the DoS Household Expenditure and Income Survey of 2010, the average annual income of a household in Jordan was 3842.8 JD from employment (excluding income from money transfers, property, rentals and personal accounts). The monthly income from employment is thus around 320 JD. For skilled agricultural, forestry and fishery workers, according to the survey, the average annual income from employment was

2907.9 JD, or 242 JD per month. For non-skilled workers in the agricultural sector, while there is no data from the survey, from interviews with farmers it was determined that farmers pay laborers as little as 150 JD per month for day laborers and 250-300 JD per month for permanent laborers. From these income statistics, it would appear that those solely reaping an income from the agricultural sector are generally earning less than the average Jordanian. Beyond income levels, farmers complain about the lack of production capacities and strategies to aid them in exporting their goods at higher prices or selling them within higher-priced local markets. In the Jordan Valley, there are few government-run or private- run agricultural extension services that are available and affordable to the majority of farmers (Discussions with farmers, Spring and Fall 2014). For those better-off farmers, they can afford to hire independent consultants to advise them on their farm activities and marketing of their production. This includes helping farmers use newer on-farm technologies in irrigation, seed choice, and pesticides and fertilizers, as well as developing better practices of sorting, grading, packaging and storing. In a competitive price market, these practices can give a farmer a distinct advantage. Despite all of the complaints heard from farmers about the poor marketing situation, farmers are not without responsibility. According to a Ministry of Agriculture employee

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(Discussion on 2/25/2014), a couple of decades ago the Jordanian Government regulated crop patterns in the Jordan Valley; each farm unit was allowed a certain number of dunums of types of vegetables, citrus, bananas, or whatever other crop they wanted to grow. While it is unknown whether this kind of regulation was ever actually implemented, or whether it merely remained an idea on paper, it represents a way in which supply could be better regulated. Without this kind of regulation, as is the case presently, farmers can grow however much of any crop they want, whenever they want. The result is a flooding of the Jordanian market by various crops at select times because all farmers sometimes grow the same crop or crops. For example, every year, especially in the southern ghor, farmers produce entirely too many tomatoes for the market demand to handle. While market prices are low in the first place, they are that much lower because every farmer is growing tomatoes and sending them to the market at the same time. Another example (Discussions with farmers, Spring

2014) from a few years ago relates to a smaller portion of farmers who decided to grow eggplants, a crop that not many other farmers at the time were growing, and they gained a good profit. The following year, after witnessing their good profits, many more farmers decided to grow eggplants. But unlike the previous year, because eggplants flooded the market, farmers did not reap a good profit from them. Farmers may need to carry some of the responsibility in paying attention to the market and determining where a profit can be met instead of continuing to farm in the same manner year after year.

Regardless of whether farmers become more savvy in changing their crop patterns, farmers are able to grow more now and in more seasons due to more advanced irrigation techniques like drip irrigation and greenhouses. It is not surprising that farmers are continually facing lower market prices in a market that is increasingly seeing greater

327 supply. Jordan needs more markets within the region and further afield; the government has a larger role to play in this arena. Answering these foreign markets, though, would also necessitate training and certifying farmers in international standards for crop production and processing. In sum, market conditions in Jordan are not automatically conducive agricultural production. There is no way in this study to make the direct connection between a weak market environment and WUA performance or the willingness of farmers to participate. But what has been shown is the difficult situation faced by farmers and their perceived inability to overcome this situation. To speak to participation in the WUA, one farmer stated that farmers simply have an overload of work regarding their laborers, equipment, water, utilities and overseeing the crop quality and production; there is no time to participate in a WUA. As she stated: “The farmer has to be just a farmer” and other needs should be provided by others (Interview with farmer on 5/17/2014). Because WUAs are solely concerned with water at present, it is also unlikely in the short-term that they can provide any relief for these market woes. Thus, farmers might not feel that WUAs are important to their livelihoods if they cannot help with vital marketing services.

SUMMARY

While it is not possible to quantify the impact of these community factors on the performance of and participation in the WUAs, there can be no doubt that these issues weigh heavily on WUA activities and farmers’ lives. The tribal network in Jordan permeates all aspects of life and the WUAs are just as susceptible to their pressures as any other organization in the country at large, to the detriment of the WUA’s ability to treat farmers fairly and work in a democratic fashion. Farmers in the Jordan Valley and under 328 the purview of these WUAs are also a very heterogeneous bunch, making it difficult to rally them to a unifying cause. Tensions already exist between them on some levels, socioeconomically or with regard to nationality, and these tensions have not subsided with the presence of the WUAs. Finally, there is no doubt, from the perspective of farmers, that Jordan’s political and economic systems are working against them at worst and ignoring them at best. The community factors offer distinct impediments to the future success of the WUAs and overcoming these impediments is challenging, to say the least. There is the possibility, though, that the institutional factors discussed in the next chapter could help to alleviate some of these issues if they are more strongly rooted and supported in the coming years.

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Chapter Ten: Institutional Factors

The institutional aspects of the WUAs are of vital concern to their longevity and acceptance by Jordan Valley farmers and those involved in water management. Having an established and respected authority, democratic proceedings, and solid monitoring, sanctioning and conflict resolution mechanisms are the aspects assessed in this chapter. A brief review of the results found regarding the institutional factors is offered in Table 10.1. Overall, WUAs are indeed more successful where these institutions are in good use, especially the monitoring, sanctioning and conflict resolution measures. Having sufficient legal authority and fairer internal rules and regulations are still an issue with all WUAs and their absence is observed to be a significant hindrance to the needed autonomy and legitimacy to make WUAs viable entities in the long-term.

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Table 10.1: Summary of institutional factors, hypotheses and conclusions reached. Factor Hypothesis Results Nature of Data Legal The WUAs must possess legitimacy and  Hypothesis accepted, although less so Qualitative, Authority autonomy in the eyes of all stakeholders for membership. descriptive and in order to be successful and make  WUA’s lack of legal and societal quantitative. farmers willing to participate in them. autonomy and legitimacy is problematic.  WUAs more successful where farmers seek help from them, indicating their legitimacy. Collective- The WUA will perform better and farmers  Hypothesis accepted. Qualitative and choice will want to join the WUA at a higher rate  The democratic nature of internal descriptive. Arrangements when there are clear and democratic proceedings for membership terms and and Rules collective-choice arrangement, meaning elections are questionable and thus a that there are open and democratic problem for WUA success. procedures for determining membership  PS 33’s low membership fees and and electing leaders that would allow holding of elections could partially farmers to participate in the decision- explain its high membership rate. making. Monitoring Where there is clear and effective  Hypothesis accepted for WUA Quantitative. monitoring, the WUA will be more performance, rejected for membership. successful and have higher membership.  More monitoring is observed to have a beneficial effect.

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Table 10.1: (continued). Factor Hypothesis Results Nature of Data Sanctioning WUAs will perform better and incentivize  Hypothesis accepted for WUA Quantitative. more farmers to join when there are performance, rejected for membership. effective formal, or informal, sanctioning  More proper sanctioning has positive measures. results. Conflict Where there are formal, or informal,  Hypothesis accepted for WUA Quantitative. Resolution conflict resolution mechanisms to aid performance, rejected for membership. farmers with issues that arise between  A WUA is more favorably viewed when them, membership in WUAs will be a farmer can go to it for help. higher and WUAs will more effectively  A third of farmers report having no manage water resources. conflicts in the first place. Source: Data from surveys, interviews and observational analysis.

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LEGAL AUTHORITY

Hypothesis: The WUAs must possess legitimacy and autonomy in the eyes of all stakeholders in order to be successful and make farmers willing to participate in them.

The WUAs remain under the umbrella of the Jordan Cooperative Corporation (JCC). Under its regulations, the WUAs do have independent financial and administrative powers but only with regard to their particular internal rules. For water management and activities pertaining to water distribution among farmers, the WUAs are still squarely under the JVA. The WUA signs a contract with the JVA every year and these contracts explicitly establish the WUA’s powers. For the most part, there has been no legal change to the guiding principle that the JVA is the decision-maker above all others in matters of water management in the Jordan Valley. Task transfers to the WUAs have not involved transfer of authority over water resources. Water quantities and the timing of water allotments are still decided by the JVA, although it may take into account requests from the WUAs.

WUAs therefore do not have independent legal authority over water distribution but rather operate within a constricted legal space underneath the JVA. The hypothesis states that without control and authority over its domain, WUAs will not be successful and this appears to be the case in Jordan. WUAs have stagnated in their development; transfer of any responsibilities beyond water distribution and light maintenance have not occurred. There are planned further task transfers of financial responsibilities, enabling WUAs to collect water fees from farmers, and WUAs are meant to eventually take control of bulk water collection from the main water source. But again, there has been no action on the part of the JVA to hand over these tasks.

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This state of affairs potentially affects how farmers view the WUA and whether they look to the WUA for decisions and help or whether they continue to rely on the JVA. When farmers were asked whether they seek help, should a problem arise, from the WUA or the JVA, only 39% of the surveyed farmers said that they would go to the WUA (Figure 10.1). Another 32% said that they would go to the JVA, 26% would ask for help from both the WUA and the JVA, and 3% wouldn’t ask either party for help. These basic numbers, showing that most farmers do not solely go to the WUA for help but rather wholly or partially rely on the JVA, signal that most farmers do not find the WUA to be a legitimate authority.

Figure 10.1: Percentage of farmers who seek help from the WUA, the JVA, both or neither among all surveyed WUAs.

Source: Survey data.

In comparing between WUAs (Figure 10.2), PS 33 and PS 55 enjoy more legitimacy among their farmers, with 58% and 52%, respectively, of farmers saying that they would go to the WUA with their problems. Only 37% of farmers in PS 91 would go 334 to the WUA for help and only 3% of farmers in Mazraa-Haditha (MH) seek help from the WUA. In MH, 85% of farmers still go directly to the JVA for help. The differences between the WUAs are in fact significant overall and in particular, significantly fewer farmers in MH ask for help from the WUA and significantly more farmers ask for help from the JVA. Also, significantly more farmers in PS 33 ask for help from the WUA than in PS 91 and PS 55. In PS 91, significantly more farmers ask for help from both the WUA and the JVA than in any of the other WUAs.

Figure 10.2: Percentage of farmers who seek help from the WUA, the JVA, both or neither in the four surveyed WUAs.

Source: Survey data. 335

From farmer commentary in the survey, farmers in PS 33, PS 55 and PS 91 go to the JVA for requests for more water. Farmers see the JVA as the “decision-maker” and still the “director” of the WUA. In PS 33, as seen in field observations, when one farmer came to the WUA for an issue with his water quantity, the WUA actually sent the farmer to the JVA with this request. Farmers also go to the JVA for larger maintenance issues or problems with the lateral and main lines, as these are tasks still not yet transferred to the

WUAs. Some farmers remarked that they would contact both the JVA and the WUA for the same problem because they hoped that their request would be heard by more parties and thus have a better chance of being resolved. In MH, with 85% of farmers going to the JVA for help, it is no surprise that some farmers stated that they have no work relations with the WUA; all of their communication is with the JVA. Others said that they don’t know the difference between the WUA and the JVA; they thought there was only one entity, the JVA, operating in the field. The fact that the WUA does not have a separate office from the JVA could confuse farmers as well. One farmer said that he has never even seen the WUA president or ditchriders come to his lateral line; thus, he only knows to go to the JVA for help. To assess whether the WUA’s level of legitimacy has an effect on farmer opinion of the WUA, the concept of who farmers ask for help is used. The following equation is used to test the effect on farmer opinion of the WUA of whether a farmer goes to the WUA or the WUA and the JVA for help, as compared to the excluded category of going solely to the JVA for help:

Equation: opinion of wua = β0 + β1 wua help + β2 wua-jva help

Where:

336 opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no)

As seen in the results of the ordered logistic regression in Table 10.2, for those farmers who solicit help only from the WUA, they are 5.05 times more likely to have a more favorable view of the WUA in comparison to those farmers who go only to the JVA for help. Similarly, for those farmers who go to both the WUA and the JVA for help, they are 2.63 times more likely to have a more favorable view of the WUA in comparison to those farmers who go only to the JVA for help. Both of these results are highly significant in their effects on farmer opinion of the WUA but together only explain about 6% of the variation in this outcome (as seen in the R2 value).

Table 10.2: Regression results of the effect of where a farmer seeks help (WUA, JVA or both) on farmer opinion of the WUA. Dependent Variable Independent opinion of wua Variables wua help 5.05*** (2.10) wua-jva help 2.63** (1.08) Prob chi2 0.0003 Pseudo R2 0.0622 No. Obs. 182 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

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The following similar equation is used to test the effect of who the farmer goes to for help on his opinion of the WUA as it compares to the JVA:

Equation: comparison of wua to jva = β0 + β1 wua help + β2 wua-jva help

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no)

The results of the ordered logistic regression in Table 10.3 are comparable with the above results and are equally as significant and explain about the same about of variation in farmer viewpoints (7%, according to the R2 value). For farmers who go only to the WUA for help and for farmers who go to the WUA and the JVA for help, they are 4.78 and

4.19 times more likely, respectively, to have a more favorable view of the WUA in comparison to the JVA as compared to those who go only to the JVA for help. In general, in both of the results presented thus far, for farmers who view the WUA as having more legitimacy, and thus go to it for help, they have higher opinions of the WUA and thus think that the WUA is performing better. This proves the initial hypothesis correct.

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Table 10.3: Regression results of the effect of where a farmer seeks help (WUA, JVA or both) on farmer opinion of the WUA as compared to the JVA. Dependent Variable Independent comparison of wua to Variables jva wua help 4.78*** (1.70) wua-jva help 4.19*** (1.61) Prob chi2 0.0000 Pseudo R2 0.0668 No. Obs. 182 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

To assess the effect of where farmers seek help (the WUA, the JVA or both) on farmer reporting of water stealing, this equation is used:

Equation: water stealing = β0 + β1 wua help + β2 wua-jva help

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no)

Table 10.4 records the results of the logistic regression and there is nothing of significance to report and no variation explained, giving no weight to the proposed hypothesis.

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Table 10.4: Regression results of the effect of where a farmer seeks help (WUA, JVA or both) on farmer reporting of water stealing. Dependent Variable Independent water stealing Variables wua help 0.98 (0.53) wua-jva help 1.14 (0.68) Prob chi2 0.9603 Pseudo R2 0.0006 No. Obs. 171 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

For testing the effect of where farmers go to for help on farmer opinion of the fairness of the WUA, the equation is as follows:

Equation: fairness of wua = β0 + β1 wua help + β2 wua-jva help

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no)

The results of the logistic regression in Table 10.5 demonstrate some significance in effect. For those farmers who only go to the WUA for help, they are 3.28 times more likely to think that the WUA is fair versus those who only go to the JVA for help. This result is highly significant and explains roughly 5% of the variation in the outcome. It would make sense that farmers who only go to the WUA for help likely do trust the WUA to be fair and/or honest in helping them, whereas those who do not see the WUA as such 340 go only to the JVA for help. And again, the original hypothesis is somewhat borne out by proving that farmers who see the WUA as acting more fairly will see it as more successful and a viable party from which to seek help.

Table 10.5: Regression results of the effect of where a farmer seeks help (WUA, JVA or both) on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variables wua help 3.28*** (1.30) wua-jva help 1.29 (0.51) Prob chi2 0.0054 Pseudo R2 0.0455 No. Obs. 180 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Lastly, to observe the effect of where farmers seek help on membership rates, this equation is used:

Equation: membership = β0 + β1 wua help + β2 wua-jva help

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no)

From Table 10.6’s results of the logistic regression, there is no significant difference among farmers who ask for help from the WUA, the JVA or both bodies with 341 regard to their membership status. Unlike what is proposed in theory, that only when farmers feel that the WUA is a legitimate and noteworthy entity will they be members, this is not borne out in this analysis.

Table 10.6: Regression results of the effect of where a farmer seeks help (WUA, JVA or both) on farmer membership in the WUA. Dependent Variable Independent membership Variables wua help 0.99 (0.44) wua-jva help 0.63 (0.33) Prob chi2 0.5870 Pseudo R2 0.0068 No. Obs. 119 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In addition to farmer perspective on the legitimacy of the WUA, the WUA heads (in their individual interviews) also spoke negatively of the WUAs’ lack of autonomy and thus legitimacy. Some argued that the contract between the WUA and the JVA is insufficient and leaves the WUA with little chance of becoming a legitimized and authoritative entity in the Jordan Valley. These WUA heads made the following comments about these contracts: “The contract is all in the interest of the JVA,” “the contract is not important and is just empty words on a page,” “the contract is not based on justice and equality, it is a contract of submission,” and “this is simply a contract of employment [the JVA employing the WUA].” Several WUA heads also noted that the WUAs would only succeed if they are given the financial, technical, physical and administrative capacity to conduct all of the necessary activities of a fully-functioning WUA. 342

A few WUA heads resisted the idea of taking on more responsibilities. They were content with the status quo of the JVA remaining as the major power-broker. They felt that the JVA is a necessary force in the Jordan Valley and must always remain. These heads preferred receiving their budget from the JVA over attaining financial independence.

COLLECTIVE-CHOICE ARRANGEMENTS AND RULES

The associations differ in terms of their membership terms and matters surrounding elections. Membership in the WUA is voluntary, meaning that not all farmers who can be members are members. Non-Jordanians and agents cannot be members. Therefore, some farmers are automatically denied a voice in decision-making and association activities.

For member farmers, membership fees vary among the WUAs. Among those WUAs with some level of task transfer, their membership fees are listed in Table 10.7. Some WUAs request minimal and largely symbolic fees, as little as 35-40 JD, whereas others require payments of 200-250 JD.

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Table 10.7: Membership fees and use of elections in the WUAs with task transfer agreements. Membership Fees Elections or Appointment PS 28 102 JD to join Appointment then Elections PS 33 100 JD to join Elections then Appointment PS 41 220 JD to join Elections PS 50 100 JD to join Elections PS 55 200 JD to join Appointment PS 81 60 JD per year Elections PS 91 250 JD to join, 5 JD per month thereafter Elections PS 95 10 JD to join, 5 JD per month thereafter Elections Rama 25 JD to join, 5 JD per month until Rotating Blocs reaching 1000 JD Kafrein 35 JD to join, 5 JD per month thereafter Appointment Mazraa- 40 JD per year Elections Haditha Fifa 100 JD over four years Appointment Khanizeera 300 JD over six years Appointment Source: Interviews with WUA heads.

Among the surveyed WUAs, MH requires the least in terms of membership fees whereas PS 55 and PS 91 have high membership fees. In the survey, some farmers did state that they are not members because they cannot afford the membership fee. The WUA heads noted that membership fees are put into the WUA’s bank account and regarded as shares that the farmer has in the WUA. These fees are to be used at a future date for some agreed-upon project or WUA administrative needs, to be determined by members. For some farmers, this could be an additional deterrent in that they don’t know how their fees will be spent.

The other half of Table 10.7 lists whether the WUA elects or appoints its leader. For those WUAs who do so by “appointment,” farmers presumably agree on their choice of leader without the need for elections. In reality, it is not clear whether some farmers are not able to voice their opinion and are pressured into following the majority. Farmers can 344 also be pressured during elections so holding elections is no guarantee for democratic proceedings. But having elections is perhaps one step closer. The exceptional case is the WUA at Rama that uses a system of rotating leadership among four large families. Also, in all WUAs, there are no terms limits for the head, allowing for one head to dominate affairs for a long period of time. Among the surveyed WUAs, PS 33 and PS 55 appointed leaders for the latest term and PS 91 and MH elected leaders.

In sum, WUA collective-choice arrangements are potentially problematic. Membership is not open to all farmers, membership fees can be a deterrent to joining the

WUA, the democratic nature of elections or appointments is questionable, and there are no term limits for WUA heads. Among the surveyed WUAs, MH should be in the most favorable position because its membership fees are low and there are elections. From the basic statistics of the outcome variables (re-posted as Table 10.8, originally Table 8.22), perhaps MH’s higher membership rate could be explained in this way. But with only four cases, without statistical proof, it is hard to reach further conclusions on this account other than to say that WUA performance and farmer willingness to participate could be enhanced by more democratic and welcoming proceedings.

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Table 10.8: Summary of outcome variable statistics between WUAs. Outcome WUA PS 33 PS 55 PS 91 MH Opinion of 63% good 75% good 84% good 44% good WUA* 31% so-so 19% so-so 11% so-so 29% so-so 6% bad 4% bad 5% bad 12% bad Opinion of 54% better 54% better 61% better 22% better WUA in 25% same thing 29% same thing 32% same thing 46% same thing comparison to 21% worse 13% worse 5% worse 17% worse JVA* Farmer 75% yes 83% yes 87% yes 81% yes reporting of 13% no 11% no 9% no 17% no water stealing* Farmer opinion 60% fair 60% fair 73% fair 59% fair of fairness of 36% not fair 40% not fair 27% not fair 41% not fair WUA* Farmer 56% member 39% member 15% member 40% member membership in 44% non- 61% non- 85% non- 60% non- WUA** member member member member

Source: Survey data (reproduced from Table 8.22). *Answers of no opinion, maybe, and don’t know not included. **Only among owners and renters eligible to be members.

MONITORING, SANCTIONING AND CONFLICT RESOLUTION

The following sections delve into the heart of the most important institutions provided by the WUA: monitoring, sanctioning and conflict resolution mechanisms. Their hypotheses are tested and evaluated individually here and eventually together in Chapter Twelve.

Monitoring

Hypothesis: Where there is clear and effective monitoring, the WUA will be more successful and have higher membership.

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Monitoring is here measured by farmer opinion on how often the WUA ditchriders tour, or monitor, the field. In Figure 10.3, 56% of surveyed farmers said that ditchriders are always making field tours, the highest amount of effort. On the other hand, 26% and 16% said that ditchriders are only sometimes or rarely, respectively, touring the fields.

Figure 10.3: Level of monitoring reported by surveyed farmers.

Source: Survey data.

Comparing the four surveyed WUAs (Figure 10.4), PS 91 performs the best with 78% of surveyed farmers reporting that ditchriders are always monitoring the field. 67% and 50% of farmers in PS 55 and PS 33, respectively, report that ditchriders always tour the fields. While the majority of farmers in these two WUAs report that ditchriders are at least sometimes touring the fields, 19% in PS 55 and 13% in PS 33 report that ditchriders rarely do this task. MH is by far the worst performing; only 19% of farmers report that ditchriders are always touring the fields, 34% report that they are sometimes touring the fields, and 37% report that they are rarely touring the fields. The differences between the

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WUAs are significant overall and in particular, PS 33, PS 55 and PS 91 all have significantly greater levels of touring than MH. PS 91 has significantly higher levels of touring that PS 55 or PS 33.

Figure 10.4: Level of monitoring reported by surveyed farmers in the four WUAs.

Source: Survey data.

To test the impact of the level of monitoring (sometimes or always, as compared to the excluded category of rarely) on farmer opinion of the WUA, the following equation is used:

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Equation: opinion of wua = β0 + β1 touring

The results of the ordered logistic regression in Table 10.9 demonstrate the great significance of monitoring efforts and how this one factor by itself can explain almost 13% of the variation (seen in the R2 value) in farmer opinion of the WUA. For farmers who say that the ditchriders are always monitoring the field, they are 14.48 times more likely to have a more favorable view of the WUA that those who say that the ditchriders rarely monitor the field. For those farmers who say that the ditchriders are sometimes monitoring the field, even they are 4.22 times more likely to have a more favorable view of the WUA than those who say that the ditchriders rarely monitor the field. The original hypothesis that more monitoring means more WUA success is proven in this case.

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Table 10.9: Regression results of the effect of monitoring the field on farmer opinion of the WUA. Dependent Variable Independent opinion of wua Variable touring sometimes 4.22*** (2.04) always 14.48*** (6.97) Prob > chi2 0.0000 Pseudo R2 0.1265 No. Obs. 183 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

For assessing the effect of the level of monitoring on farmer opinion of the WUA as it compares to the JVA, the equation follows:

Equation: comparison of wua to jva = β0 + β1 touring

Similarly highly significant results, from the ordered logistic regression outcomes in Table 10.10 below, are seen in this case although much less of the variation (5%, as per the R2 value) is being explained. For those farmers who report that the ditchriders are always or sometimes monitoring the field, they are 5.23 and 2.97 times more likely to have a comparatively more favorable view of the WUA over the JVA than those who say that the ditchriders are never touring the fields. In sum, ditchrider touring of the field does once 350 again have a positive and significant impact on farmer opinion of the WUA, this time when it is seen in contrast to the performance of the JVA.

Table 10.10: Regression results of the effect of monitoring the field on farmer opinion of the WUA as compared to the JVA. Dependent Variable Independent comparison of wua to jva Variable touring sometimes 2.97** (1.33) always 5.23*** (2.15) Prob > chi2 0.0002 Pseudo R2 0.0470 No. Obs. 183 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In order to test the impact of the level of monitoring on whether farmers report water stealing, the following equation is used:

Equation: water stealing = β0 + β1 touring

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) touring: a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always)

Table 10.11 reports the results of the logistic regression. The level of monitoring in the field by ditchriders does not appear to have any significant effect on whether water stealing is reported or not. Very little of the variation in reported water stealing is explained. Thus, no support can be lent to the original hypothesis on this score. 351

Table 10.11: Regression results of the effect of monitoring the field on farmer reporting of water stealing. Dependent Variable Independent water stealing Variable touring sometimes 1.34 (1.08) always 0.74 (0.50 Prob > chi2 0.5717 Pseudo R2 0.0088 No. Obs. 171 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

For assessing the impact of the level of monitoring on farmer opinion of the fairness of the WUA, the equation is as follows:

Equation: fairness of wua = β0 + β1 touring

As Table 10.12 reports in its results of the logistic regression, monitoring levels do have a significant impact in this case and explain almost 14% of the variation in farmer opinion of WUA fairness, seen in the R2 value. For farmers who think that ditchriders are always touring the field, they are almost 10.75 times more likely to see the WUA as fair than farmers who rarely see ditchriders touring. Although significant to a lesser degree, 352 for farmers who think that the ditchriders are only sometimes monitoring the fields, they are still 2.61 times more likely to think that the WUA is fair than farmers who think that the ditchriders rarely monitor. These results support the initial hypothesis of better WUA performance with greater monitoring.

Table 10.12: Regression results of the effect of monitoring the field on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variable touring sometimes 2.61* (1.33) always 10.75*** (5.23) Prob > chi2 0.0000 Pseudo R2 0.1395 No. Obs. 181 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Finally, for testing the impact of the level of monitoring on membership levels, this equation is used:

Equation: membership = β0 + β1 touring

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) touring: a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always)

Within the results of the logistic regression in Table 10.13, it appears that the level at which farmers see ditchriders touring the field does not significantly impact whether a

353 farmer is a member of the WUA or not and no variation in membership rates is explained. No weight can be given to the original hypothesis.

Table 10.13: Regression results of the effect of monitoring the field on farmer membership in the WUA. Dependent Variable Independent membership Variable touring sometimes 1.80 (1.03) always 1.44 (0.75) Prob > chi2 0.5774 Pseudo R2 0.0069 No. Obs. 119 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In PS 33, PS 55 and PS 91, most farmers who said that the ditchriders “always” tour the field further stated that this means that the ditchriders come out to the field to monitor activity one or two times per water turn. During any given 5-10 hour turn on a lateral line, the ditchriders may pass by once or twice. Several farmers mentioned that a single field tour by the ditchriders would usually happen just at the beginning of the turn, after the lateral line has been opened-up. For the rest of the turn, farmers are on their own. One farmer said that the ditchriders only check the beginning of the lateral line, leaving the end of the lateral line free of monitoring. Due to MH’s 24-hour water turns, some farmers there felt that ditchriders monitor more at night than during the day. The range of reported monitoring heard from farmers was as much as three to four times per water turn and as little as once per week or month.

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Among farmers who said that ditchriders never monitor the field, some stated that ditchriders open and close the lateral lines and then go back to the office to “drink tea and sleep.” Some farmers said that ditchriders only come to the field when they receive a complaint from a farmer about the water supply, water stealing or a maintenance issue. One farmer said that ditchriders only come to the field if they are completing another task, such as collecting crop patterns from farmers.

Despite these negative viewpoints of the ditchriders’ monitoring efforts, some farmers sympathized with ditchriders. They said that it must be hard for ditchriders to do everything “at 100%” and monitor all farmers since there are so few ditchriders and such a large area to monitor. As one stated, it is a challenge when “farmers are not committed” themselves to following the rules; they make the ditchrider’s job very difficult. In PS 33, where many farmers have fenced-in their farm units with the FTA inside the locked fence, some farmers pointed out that ditchriders run into obstacles like this and cannot do their duty. Rather than forcing their way through the fence and creating a problem with a potentially influential, physically intimidating or powerful farmer, ditchriders walk away.

Sanctioning

Hypothesis: WUAs will perform better and incentivize more farmers to join when there are effective formal, or informal, sanctioning measures.

Sanctioning is measured by how often a farmer thinks that the WUA ditchriders are giving tickets to farmers when warranted. Tickets are most frequently given for taking water out-of-turn or manipulating any part of the FTA. The graph below (Figure 10.5) shows the percentage of farmers within all surveyed WUAs combined who think that the

355 ditchriders are always (42%), sometimes (46%), or rarely (4%) ticketing farmers when necessary.

Figure 10.5: Level of sanctioning as reported by surveyed farmers.

Source: Survey data.

Comparing among the surveyed WUAs (Figure 10.6), PS 33, PS 55 and PS 91 are all comparable in the percentage of farmers who think that ditchriders are always or sometimes appropriately sanctioning farmers. PS 91 has the lowest number of farmers who think that the ditchriders are rarely giving out tickets when they should, slightly better in this respect that PS 33 and PS 55. MH is by far the worst performing; only 27% of farmers think that the WUA is always giving out tickets when necessary. Overall, the differences among the four WUAs are not actually significant.

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Figure 10.6: Level of sanctioning as reported by surveyed farmers in the four WUAs.

Source: Survey data.

Using the same series of equations as with monitoring, to start, the following equation tests the effect of the level of sanctioning (whether ditchriders are sometimes or always ticketing appropriately, versus rarely, the excluded category) on farmer opinion of the WUA.

Equation: opinion of wua = β0 + β1 punishing

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Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always)

In Table 10.14, the ordered logistic regression results reveal that the sanctioning level does have a highly significant impact on farmer opinion of the WUA although less of the variation is explained (6%, as per the R2 value) than there was with monitoring. For farmers who believe that ditchriders are always ticketing farmers when appropriate, they are 17.33 times more likely to have a more favorable view of the WUA in comparison to those who rarely see the ditchriders ticketing farmers. For farmers who sometimes see ditchriders ticketing, even they are 12.76 times more likely to have a more favorable view of the WUA than farmers who see ditchriders rarely ticketing. The original hypothesis of higher sanctioning efforts leading to better WUA performance is supported here.

Table 10.14: Regression results of the effect of sanctioning on farmer opinion of the WUA. Dependent Variable Independent opinion of wua Variable punishing sometimes 12.76*** (9.18) always 17.33*** (12.63) Prob > chi2 0.0004 Pseudo R2 0.0604 No. Obs. 176 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

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To assess the effect of the level of sanctioning on farmer opinion of the WUA as it compares to the JVA, the following equation is used:

Equation: comparison of wua to jva = β0 + β1 punishing

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always)

The ordered logistic regression results in Table 10.15 reveal that sanctioning efforts again have a highly significant impact on farmer opinion of the WUA when it is compared to the JVA, although much less of the variation is explained in this case (only 3%, per the R2). For farmers who always or sometimes see the ditchriders ticketing farmers when appropriate, they are around 7.38 and 8.85 times more likely, respectively, to have a more favorable view of the WUA as compared to the JVA than those farmers who rarely see ditchriders ticketing. Again, the hypothesis is supported that when ditchriders appropriately sanction, farmers have a more positive view of the WUA.

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Table 10.15: Regression results of the effect of sanctioning on farmer opinion of the WUA as compared to the JVA. Dependent Variable Independent comparison of wua to jva Variable punishing sometimes 8.85*** (6.36) always 7.38*** (5.30) Prob > chi2 0.0088 Pseudo R2 0.0279 No. Obs. 176 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In analyzing the impact of the level of sanctioning on water stealing, the equation is as follows:

Equation: water stealing = β0 + β1 punishing

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always)

As with monitoring, and as seen in the logistic regression results in Table 10.16, whether ditchriders are ticketing farmers when necessary does not seem to significantly impact whether farmers report water stealing. Some of the variation in reported water stealing, 4% according to the R2 value, does seem to be explained but not significantly. No support it given to the original hypothesis.

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Table 10.16: Regression results of the effect of sanctioning on farmer reporting of water stealing. Dependent Variable Independent water stealing Variable punishing sometimes 3.67 (3.33) always 1.18 (1.01) Prob > chi2 0.0565 Pseudo R2 0.0444 No. Obs. 164 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

To assess the effect of the level of sanctioning on farmer opinion of the fairness of the WUA, the equation is as follows:

Equation: fairness of wua = β0 + β1 punishing

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always)

Results of the logistic regression in Table 10.17 suggest that sanctioning levels do have some significant impact on farmer assessment of the fairness of the WUA and explain about 8% of the variation in this outcome (per the R2 value). While no significant effect is observed for farmers who only sometimes see the ditchriders appropriately ticketing farmers, for farmers who believe that the ditchriders are always fulfilling this role, they are 8.67 times more likely to see the WUA as fair than those farmers who say that the 361 ditchriders rarely ticket farmers. The original hypothesis that WUA performance is improved with greater sanctioning is supported with this result.

Table 10.17: Regression results of the effect of sanctioning on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variable punishing sometimes 2.47 (1.83) always 8.67*** (6.61) Prob > chi2 0.0002 Pseudo R2 0.0777 No. Obs. 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Finally, to test the impact of the level of sanctioning on membership in the WUA, the equation used is as follows:

Equation: membership = β0 + β1 punishing

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always)

Within Table 10.18, the results of the logistic regression show that sanctioning levels have no significant impact on whether farmers are members in the WUA or not and explain none of the variation in this outcome. As with monitoring levels, sanctioning

362 efforts do little to support the original hypothesis when it comes to why farmers participate in the WUA.

Table 10.18: Regression results of the effect of sanctioning on farmer membership in the WUA. Dependent Variable Independent membership Variable punishing sometimes 1.21 (0.94) always 0.76 (0.60) Prob > chi2 0.5125 Pseudo R2 0.0087 No. Obs. 116 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Across all surveyed WUAs, farmers noted that for a first-time offense, the farmer is almost always not issued a ticket. If the same incidence of water stealing or vandalism is repeated, the farmer is then issued a ticket. In PS 33 and PS 91, several farmers were not happy with how often and, according to them, how unfairly the WUA gives out tickets. They felt that they did not deserve a ticket, either because the ditchriders did not understand the situation correctly or were being too harsh in their implementation of the rules. Some farmers see the WUA as a ticket-giving organization that only seeks to punish farmers instead of help farmers. One farmer added that when the water order or a certain regulation changes, the farmers are not informed of it and sometimes they can get a ticket because they did not know that their water turn had changed to another time or day. One farmer

363 made the claim that the ditchriders don’t know how to read or write so they can’t possibly be able to give tickets. On the other hand, in PS 55 and PS 91, several farmers thought that the ditchriders were very sympathetic to farmers’ water shortage problems and were appropriately lenient in their issuing of tickets. Farmers said that the ditchriders are “understanding,” “diplomatic,” and “tactful” with farmers when it comes to issuing tickets, and at times, due to “humane” reasons, they choose to “help” farmers and let them go without a ticket. Even a WUA employee in PS 91 stated that they choose to be “lenient” at times because “they know the farmers” and feel for their situation. Ditchriders in PS 55 also plainly stated that they want to deal with farmers with friendship, not violations, and that they want to create trust between farmers and the WUA; this cannot happen if they are always giving out tickets, according to them. The reason for 34% of farmers in MH saying that they don’t know whether the

WUA properly tickets farmers is due to some farmers thinking that the JVA is still in charge of this task. This either means that farmers in MH are simply not aware of what the WUA is doing or the WUA is not carrying out this task at all.

Conflict Resolution

Hypothesis: Where there are formal, or informal, conflict resolution mechanisms to aid farmers with issues that arise between them, membership in WUAs will be higher and WUAs will more effectively manage water resources.

The WUA’s ability to resolve conflicts among farmers is measured by whether farmers think that the WUA always helps when conflicts arise, the WUA only sometimes helps, or the WUA cannot help. Overall, 34% of surveyed farmers said that there are no

364 problems that arise between farmers (Figure 10.7). The other 66% of farmers do acknowledge that conflicts arise among farmers that relate either to matters of water distribution or sometimes other issues of a personal nature.

Figure 10.7: WUA’s ability to resolve conflicts among farmers, including whether problems exist.

Source: Survey data.

Between the surveyed WUAs (Figure 10.8), PS 55 has the most farmers, around 46%, reporting that there are no problems between farmers. In MH, 39% report that there are no problems, 30% in PS 91 and only 21% in PS 33.

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Figure 10.8: WUA’s ability to resolve conflicts among farmers, including whether problems exist, among the four surveyed WUAs.

Source: Survey data.

Figure 10.9 offers further insight into the potential causes of why problems seem to exist in some WUAs more so than others. Farmers are divided into categories of viewing neighboring farmers: as friends (and sometimes they are relatives) with whom they spend time outside of farming duties; as acquaintances and work colleagues with whom they only have surface relations; or as hostile relations. In PS 33, unlike in the other WUAs, 10% of farmers said that farmers don’t like each other and are at times on hostile terms. This is

366 the WUA that also has the lowest percentage of farmers reporting that there are no problems. In PS 55, PS 91 and MH, where larger percentages of farmers reported that there are no problems, the percentage of farmers who report that there are just surface relations among farmers is greater than those who report that relations are more along the friend/relative line. Perhaps having more work-based relationships among farmers rather than familial relationships actually helps to reduce the number of conflicts among farmers.

Figure 10.9: Nature of relations among farmers within the four surveyed WUAs.

Source: Survey data.

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Among the overall portion of farmers who acknowledge that problems arise between farmers, 41% say that the WUA can help with all issues whereas 15% say that the WUA only helps sometimes, depending on the issue at hand, and 44% say that the WUA never helps (Figure 10.10). The latter group are forced to solve these issues among themselves or go to the JVA for help.

Figure 10.10: WUA’s ability to resolve conflicts among farmers.

Source: Survey data.

Among farmers who believe that there are conflicts among them, within the four surveyed WUAs examined separately, PS 91 has the largest percentage of farmers reporting that the WUA can help and the smallest percentage reporting that the WUA does not help (Figure 10.11). PS 55 performs second-best whereas PS 33 is more problematic with 41% of farmers reporting that the WUA does not help. MH performs the worst with 88% of farmers saying that the WUA does not help. The overall differences among the

368 four WUAs are significant and in particular, MH is significantly worse. PS 91 and PS 55 are significantly better than PS 33.

Figure 10.11: WUA’s ability to resolve conflicts among farmers within the four surveyed WUAs.

Source: Survey data.

To test the effect of whether the WUA is able to provide conflict resolution services to farmers (whether the WUA can always help or sometimes help, as opposed to never helping, the excluded category) on farmer opinion of the WUA, the following equation is used: 369

Equation: opinion of wua = β0 + β1 resolving conflict

Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always)

From the results of the ordered logistic regression in Table 10.19, whether the WUA resolves conflicts among farmers has a positive and significant impact on farmer satisfaction with the WUA and explains 10% (per the R2 value) of the variation in farmer opinion of the WUA. For those farmers who believe that the WUA helps them with their conflicts, they are 7.25 times more likely to have a more favorable view of the WUA than farmers who think that the WUA never helps them with their conflicts. Less significantly, for those farmers who believe that the WUA sometimes helps them with their conflicts, they are 2.81 times more likely to have a more favorable view of the WUA than farmers who do not believe that the WUA helps them with any of their conflicts. The original hypothesis that greater conflict management provided will lead to better WUA performance is herein supported.

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Table 10.19: Regression results of the effect of the WUA’s conflict resolution abilities on farmer opinion of the WUA. Dependent Variable Independent opinion of wua Variable resolving conflict sometimes 2.81* (1.56) always 7.25*** (3.39) Prob > chi2 0.0000 Pseudo R2 0.1004 No. Obs. 126 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In order to test the impact of the conflict management abilities of the WUA on farmer opinion of the WUA as it compares to the JVA, the equation is as follows:

Equation: comparison of wua to jva = β0 + β1 resolving conflict

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always)

Table 10.20’s results of the ordered logistic regression show that the WUA’s conflict resolution capabilities do positively and significantly affect farmer opinion in this instance as well and explain 8% (per the R2 value) of the variation in this outcome. For farmers who said that the WUA can always or sometimes help them with their conflicts, they are 5.23 and 4.37 times more likely, respectively, to have a higher opinion of the WUA

371 than the JVA in comparison to farmers who do not think that the WUA can help them with their conflicts. As with the previous results, these results support the original hypothesis that the WUA’s conflict management skills have a positive impact on WUA performance.

Table 10.20: Regression results of the effect of the WUA’s conflict resolution abilities on farmer opinion of the WUA as compared to the JVA. Dependent Variable Independent comparison of wua to jva Variable resolving conflict sometimes 4.37*** (2.46) always 5.23*** (2.07) Prob > chi2 0.0000 Pseudo R2 0.0810 No. Obs. 126 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

For assessing the effect of the WUA’s conflict resolution abilities on reported water stealing, the equation is as follows:

Equation: water stealing = β0 + β1 resolving conflict

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always)

In the logistic regression results listed in Table 10.21, it is clear that whether the farmer thinks that the WUA can resolve conflicts between farmers or not has no significant 372 impact on whether farmers report that water stealing is occurring, or that essentially the WUA’s rules are not being followed. Almost none of the variation is explained either and thus, no support can be given to the original hypothesis.

Table 10.21: Regression results of the effect of the WUA’s conflict resolution abilities on farmer reporting of water stealing. Dependent Variable Independent water stealing Variable resolving conflict sometimes 0.53 (0.51) always 0.46 (0.34) Prob > chi2 0.5401 Pseudo R2 0.0168 No. Obs. 120 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

To test whether the WUA’s ability to resolve conflicts has an effect on farmer opinion of the fairness of the WUA, this is the equation:

Equation: fairness of wua = β0 + β1 resolving conflict

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always)

As Table 10.22 demonstrates in the results of the logistic regression, there is only a slight significance felt in the effect of conflict resolution abilities on farmer opinion of 373 whether the WUA is fair or not. But the effect sizes and significance are minimal, as is the amount of variation explained, so it can be concluded that there no support to give to the original hypothesis.

Table 10.22: Regression results of the effect of the WUA’s conflict resolution abilities on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variable resolving conflict sometimes 2.60* (1.53) always 1.81* (0.72) Prob > chi2 0.1504 Pseudo R2 0.0225 No. Obs. 126 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

As a final test for the subject of conflict resolution, the following equation tests whether the WUA’s ability to resolve conflicts among farmers has an effect on whether farmers join the WUA or not:

Equation: membership = β0 + β1 resolving conflict

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always)

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From the results of the logistic regression in Table 10.23, the WUA’s conflict resolution abilities have no significant impact on membership levels and explain little of the variation in membership levels. Thus, the original hypothesis is not backed-up by these results.

Table 10.23: Regression results of the effect of the WUA’s conflict resolution abilities on farmer membership in the WUA. Dependent Variable Independent membership Variable resolving conflict sometimes 1.83 (1.12) always 0.60 (0.31) Prob > chi2 0.2140 Pseudo R2 0.0269 No. Obs. 87 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

SUMMARY

After more than a decade of experience, the WUAs in the Jordan Valley are still struggling with fundamental institutional questions regarding their internal and external legitimacy and how they carry out their daily duties to ensure rule-following and mutual respect among farmers and their joint water resources. This chapter has demonstrated support for the beneficial impact of proper and effective monitoring, sanctioning and conflict resolution procedures for WUA performance. But for all WUAs, there remains the issue of not having sufficient legal authority, likely leading to the delegitimization of the WUAs in the eyes of farmers and other government bodies. WUAs also do not operate

375 in the fairest and most open manner in their internal proceedings. While most of these issues relate to the outcomes associated with WUA performance, little is observed to hint at an effect on WUA membership. It could be that a farmer’s decision to join or not to join the WUA is not affected by institutional concerns. For factors that weigh more heavily in this decision, the following chapter on user factors will provide more fruit for thought.

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Chapter Eleven: User Factors

What is seen with user factors is that for those measured quantitatively, their overall impact is minimal and where they do weigh-in most heavily is in terms of membership rates. In particular, land size, being an owner and having a higher education level appear to have positive effects on being a member in the WUA. While the quantitative aspects of user factors only lend mild support to the initial hypothesis regarding leadership and the perceived benefits of WUA membership, the qualitative aspects of these factors offer some relevant notes. The leaders in PS 55 and PS 91 demonstrate greater skills and initiative and this could be a reason for the better performance of their respective WUAs. Additionally, and for all WUAs, their lack of benefits, tangible or intangible, short-term or long-term, could be a distinct hindrance to farmers feeling a need to join the WUA or to make it a successful venture. Table 11.1 below displays the user factors, their initial hypotheses, and the general results found from the analyses conducted in this chapter. What follows is a more detailed reviews of these generalized results.

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Table 11.1: Summary of user factors, hypotheses and conclusions reached. Factor Hypothesis Results Nature of Data Leadership A skilled, committed and visionary  No clear conclusions. Qualitative and leader will be a boon for any WUA but a  WUA heads vary widely in their descriptive. good leader is not a substitute for having abilities and ambitions. a strong institution of leadership and  Likely that better leadership in PS 55 active participation of other members. and PS 91 is helping WUA performance. Level of When the users are more dependent on  Hypothesis mildly accepted for WUA Quantitative. Dependence the joint water resource and agriculture performance; mildly rejected for for their livelihoods, they are more likely membership. to actively participate in the WUA and  There is slight evidence that having the WUA is likely to perform better. secondary work or water negatively impacts WUA performance.  Having secondary work or water does not deter membership. Socioeconomic For farmers with higher socioeconomic  Hypothesis somewhat accepted. Quantitative. Status status, they are more likely to participate  Those with more land are more likely to in the WUAs. be members. Land-holding Where the majority of water users are  Hypothesis tentatively rejected for Quantitative. Status owners, or at least more permanent WUA performance, accepted for renters, participation rates in the WUA membership. will be higher and the WUA will perform  Owners are more likely than renters to better. be members but have lower opinion of WUA.

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Table 11.1: (continued). Factor Hypothesis Results Nature of Data Education Better-educated farmers are more likely  Hypothesis accepted. Quantitative. to participate in the WUA.  Better educated farmers are more likely to be members. Perceived Farmers will only join WUAs when they  No clear conclusions. Qualitative and Benefits to perceive that the benefits will be greater  Wide reporting of WUA providing no descriptive. Membership than the costs, especially when the benefits. benefits will accrue in the short-term but  Likely that lack of benefits deters regardless of whether the benefits are farmers from joining or being satisfied necessarily tangible. with WUA. Source: Data from surveys, interviews and observational analysis.

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LEADERSHIP

Table 11.2 lists some of the personal and farming characteristics of the heads of the four surveyed WUAs (gathered in interviews with the WUA heads). Education levels differ; heads in PS 55 and PS 91 have the most education. In terms of farm size, PS 55 and PS 91 heads also have the largest farms and presumably the most to lose if water management is not effective. Secondary work is also taken into account. While no WUA head admitted to having secondary work, from observational analysis and talking with ditchriders and farmers in the area, PS 91’s head does conduct some type of business deals on the side and the head in Mazraa-Haditha (MH) acts as a consultant for the company conducting rehabilitation on the water network. Based on these factors, it would appear that the head of PS 55 is the one most connected-to and dependent on the WUA’s work, with the additional bonus of being highly educated.

Table 11.2: Personal and farm characteristics of the heads of the four surveyed WUAs. Age Education Number of Secondary work dunums reported? PS 33 59 High School 33 No PS 55 47 Bachelors 150 No PS 91 59 Bachelors 150 No MH 59 Diplome 60 No Source: Interviews with WUA heads.

Beyond these descriptive elements, the views of the WUA heads on the position of being a WUA head and their compensation for this role are of import. Both of the heads 380 in PS 33 and MH admitted to no desire to be the head in the first place; rather, farmers insisted on them taking on this role and so they did. On the other hand, the heads of PS 55 and PS 91 wanted the position. PS 55’s head even put up half of the money in order for the WUA to get off the ground and running. The head in PS 91 was very interested in seeing an improvement in management of the distribution network. Because there was no major tribal leader in the area of PS 91 to take on this task, he knew that someone had to step forward and so he did. In terms of their salary, only the head of PS 55 reported that it was sufficient and made no complaints about needing more compensation. The heads of PS 33 and MH stated that the salary they receive is too low. PS 33’s head added that a “fancy” head should be elected if you don’t want to pay him very much. PS 91’s head did not definitively state his opinion on the matter but in alluding to the need for those with more qualifications to be paid more than others, he hinted that he deserves a larger salary with his educational background. Again, PS 55’s head would appear to be the most committed, from the beginning, to the WUA. He feels sufficiently compensated and willing to do the work with the present remuneration. To examine the visionary status of the heads, WUA heads were asked about whether WUAs are the solution to water management issues in the Jordan Valley. The head of PS 55 does not see the WUA as the final solution for water management because the problem, in his view, is the simple lack of water and the WUA cannot fix this. PS 91’s head also argues that the WUA is not the final solution, as it currently functions, but it could be if it acquired more capabilities. The head of MH solidly stated the opposite; for him, the WUAs are the solution “100%.” PS 33’s head also stated that the WUAs are the final solution but money matters, according to him, should always be controlled by the

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JVA. This latter position is contradictory because a final state of the WUAs envisions control of money matters. Oddly, for those heads who wanted to be the head of the WUA (PS 55 and PS 91), they are the ones who question its long-term viability. Their viewpoints on the privatization of water distribution, in the event that the WUAs fail, is also relevant. Three of the heads did not, as might be expected, reject the idea outright. The head of PS 91 stated that a private company would actually be better but only in its ideal form in which it is transparent and allows for open competition. PS 33’s head said that if WUAs fail, privatization will be the only solution but he worries about an increase in the water price. MH’s head argued that privatization would be fine but only in cooperation with the WUAs. He envisioned no replacement of WUAs with private companies but rather their joint management. On the other hand, PS 55’s head stated that WUAs are in fact private entities and they are better than a private company because they care about farmers; a company just cares about profit. In this way, PS 55’s head remained perhaps the most loyal to the use of WUAs but still, as noted earlier, he did not see management change in general as the solution to Jordan’s overall water problem. Another element of vision is whether the WUA heads have any specific initiatives for the WUA. The heads of PS 33 and MH said that they have no particular initiatives at present. On the other hand, the head of PS 55 said that he is already engaged in helping farmers to acquire export contracts with Europe, a service outside of the water distribution purview but nonetheless an initiative. The head of PS 91 discussed the WUA’s desire for a pilot project to test the use of electronic water meters. He also expressed a desire to create a maintenance center to service the southern area of the valley. Finally, how the WUA heads envision the immediate future of the WUAs is examined. Table 11.3 lists the viewpoints of the WUA heads on the future of both the

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WUA and the JVA. The heads of PS 55, PS 91 and MH see a future where the JVA is the bulk water supplier and the WUA buys water from it and sells water to farmers. MH’s head adds a caveat that this will happen only if farmers receive more marketing support. Both MH and PS 33’s heads think that the JVA needs to continue in its role of protecting the main water sources, with PS 33’s head demonstrating little desire for the WUA to acquire full autonomy, especially in financial affairs. In terms of whether the WUA should have a role outside of water distribution or even agriculture, PS 33’s head is the only one lacking enthusiasm. The heads of PS 55, PS 91 and MH, on the other hand, want to see the WUA involved with more aspects of farming such as marketing, grading and sorting, packaging and storing and cooling. Only MH’s head believes that the WUA should be involved outside of the realms of water distribution and agriculture.

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Table 11.3: Opinions of surveyed WUA heads on the future of the WUA and JVA. Head WUA and JVA after 5-10 Should WUA have Should WUA years? role beyond water have role distribution? beyond agriculture? PS 33 WUA: will be able to choose Yes it’s possible if Yes, but only if the time for water turns, there are task WUA becomes have more control over this transfers for it but for-profit. factor. this is not JVA: will maintain control necessarily desired. of finances, protect KAC and deal with large maintenance tasks. PS 55 WUA: will complete all task Yes, WUA can help No. transfers and buy bulk water with input prices and and sell it to farmers. marketing. JVA: bulk water supplier. PS 91 WUA: will control all water Yes, WUA should No. distribution after being help with post- supplied bulk water from harvest needs JVA. (grading, storage and JVA: bulk water supplier. marketing) and educational services. MH WUA: no big change unless Yes, there needs to Yes, such as with bigger markets are opened be support to transportation for up to farmers, for WUA to farmers for workers and the buy bulk water from JVA, packing/cooling selling of would need more capital. services and agricultural JVA: bulk water supplier, marketing. products. protect water sources. Source: Interviews with WUA heads.

All of these characteristics and opinions of the WUA heads could play a role in the potential success of the WUA. These elements of leadership are tabulated and totaled in Table 11.4. Heads are ranked 1-3 in terms of education and size of farm. The other elements are given a 0 or 1, where one is the more positive score for leadership vision and commitment to the WUA and the idea of WUAs (see Chapter Five for the exact

384 methodology of this scoring rubric). From the results, PS 55’s head is the most skilled, committed and visionary leader, with PS 91 close behind. In comparison, the heads of PS 33 and MH are weaker. The strength of the leadership in PS 55 and PS 91 could play into their comparatively higher scores for overall farmer satisfaction with the WUA but the subject remains open to interpretation and necessitates more evaluation.

Table 11.4: Tabulation of WUA head characteristics and opinions as an overall score.

Education level Size of farm Secondary work Desire to be head Salary sufficiency WUA as the soluti View on privatization Personal initiatives View of independence for WUA Additional activities for WUA Total points PS 33 1 1 1 0 0 1 0 0 0 0 4 PS 55 3 3 1 1 1 0 1 1 1 1 13 PS 91 3 3 0 1 0 0 0 1 1 1 10 MH 2 2 0 0 0 1 0 0 1 1 7 Source: Calculations made with the use of data from interviews with WUA heads.

An additional note relates to the latter half of the original hypothesis, which states that a strong leader is not a substitute for a strong leadership institution (for when this head is no longer head) or high commitment on the part of other member farmers. As will be seen in Chapter Twelve, where membership activities are explained in greater detail, many members do not participate in any WUA events and many only participate in yearly elections or meetings. Member farmers do not display high levels of commitment and this could be worrying, in the present or future absence of a strong leader.

385

LEVEL OF DEPENDENCE ON WATER SOURCE AND AGRICULTURE

What is examined in this case is a farmer’s access to additional sources of income and/or water. Both of these factors have been previously discussed (in the sections on heterogeneity of farmer interests and water scarcity, respectively) but not in relation to the final outcomes. Among the four WUAs, there are no significant differences with regard to secondary income. For secondary water, on the other hand, PS 33, PS 55 and PS 91 have significantly more farmers with secondary water resources than MH, and PS 33 has significantly more farmers with secondary water resources that PS 91. To assess the effect of having secondary work or a secondary water source on farmer opinion of the WUA, the following equations are used:

Equation (1): opinion of wua = β0 + β1 secondary work

Equation (2): opinion of wua = β0 + β1 secondary water

Equation (3): opinion of wua = β0 + β1 secondary work + β2 secondary water + β3 secondary work*secondary water

Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) secondary work: a binary variable for whether a farmer as work or receives an income from a source outside of his farm considered in the survey area (1=yes, 0=no) secondary water: a binary variable for whether a farmer has a secondary source of water (1=yes, 0=no)

386

From the results of the ordered logistic regressions in Table 11.5, neither having a secondary water source nor a secondary income, each by itself, has a significant impact (Equations 1 and 2). But their interaction (Equation 3) has a significant and negative effect; for those farmers with both a secondary water source and a secondary source of income, they are 5 times less likely (the inverse of 0.20) to have a more favorable view of the WUA. This conclusion somewhat supports the original hypothesis that having secondary work or water sources leads to worse performance of the WUA. It could be that farmers have secondary work or water because the WUA performs poorly and they cannot fully rely on it. But so little of the variation in farmer opinion of the WUA is explained, only 2% (according to the R2 value), that these variables mean little in this case.

Table 11.5: Regression results of the effects of secondary work and secondary water resources on farmer opinion of the WUA. Dependent Variable opinion of WUA Independent Variables (1) (2) (3) secondary work 0.74 1.14 (0.24) (0.44) secondary water 1.06 2.62 (0.38) (1.56) secondary work*secondary water 0.20** (0.15) Prob > chi2 0.3512 0.8639 0.1280 Pseudo R2 0.0031 0.0001 0.0202 No. Obs. 186 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

To assess the effect of having secondary work or water resources on farmer opinion of the WUA as it compares to that of the JVA, the following equations are used:

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Equation (1): comparison of wua to jva = β0 + β1 secondary work

Equation (2): comparison of wua to jva = β0 + β1 secondary water

Equation (3): comparison of wua to jva = β0 + β1 β1 secondary work + β2 secondary water + β3 secondary work*secondary water

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) secondary work: a binary variable for whether a farmer as work or receives an income from a source outside of his farm considered in the survey area (1=yes, 0=no) secondary water: a binary variable for whether a farmer has a secondary source of water (1=yes, 0=no)

The results of the ordered logistic regressions in Table 11.6 demonstrate that neither having a secondary water source nor a secondary income (Equations 1 and 2), or their interaction (Equation 3), has a strongly significant impact. Almost none of the variation in the outcome is explained either.

Table 11.6: Regression results of the effects of secondary work and secondary water resources on farmer opinion of the WUA as compared to the JVA. Dependent Variable comparison of wua to jva Independent Variables (1) (2) (3) secondary work 1.15 1.22 (0.33) (0.41) secondary water 1.63* 1.89 (0.52) (0.85) secondary work*secondary water 0.71 (0.46) Prob > chi2 0.6178 0.1244 0.4257 Pseudo R2 0.0007 0.0065 0.0076 No. Obs. 186 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

388

The following equations assess the impact of having secondary work or water resources on farmer reporting of water stealing:

Equation (1): water stealing = β0 + β1 secondary work

Equation (2): water stealing = β0 + β1 secondary water

Equation (3): water stealing = β0 + β1 secondary work + β2 secondary water + β3 secondary work* secondary water

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) secondary work: a binary variable for whether a farmer as work or receives an income from a source outside of his farm considered in the survey area (1=yes, 0=no) secondary water: a binary variable for whether a farmer has a secondary source of water (1=yes, 0=no)

From the results of the logistic regressions in Table 11.7, having a secondary water source does not significantly affect whether a farmer reports that water stealing is occurring (Equation 2). Even if the outcome variable signified whether the farmer himself is stealing water, we could not expect a particular result; farmers with a secondary water source might not have a need to steal water or the secondary source of water might be stolen water. With regard to having a secondary source of income (Equation 1), farmers with secondary work are 3.58 times more likely to report that water stealing is occurring. Secondary work is also explaining about 5% of the variation (as per the R2 value) in farmer reporting of water stealing. This could be because farmers with supplementary or outside sources of income are more comfortable admitting that water stealing occurs. The interaction between

389 secondary work and secondary water resources is not significant but secondary work remains significant (Equation 3).

Table 11.7: Regression results of the effects of secondary work and secondary water resources on farmer reporting of water stealing. Dependent Variable water stealing Independent Variable (1) (2) (3) secondary work 3.58** 2.89* (1.91) (1.76) secondary water 1.09 0.83 (0.56) (0.49) secondary work*secondary water 2.28 (2.93) Prob > chi2 0.0099 0.8706 0.0690 Pseudo R2 0.0504 0.0002 0.0537 No. Obs. 174 174 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

To examine the effect of secondary work and water resources on farmer opinion of the fairness of the WUA, these equations are used:

Equation (1): fairness of wua = β0 + β1 secondary work

Equation (2): fairness of wua = β0 + β1 secondary water

Equation (3): fairness of wua = β0 + β1 secondary work + β2 secondary water + β3 secondary work*secondary water

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) secondary work: a binary variable for whether a farmer as work or receives an income from a source outside of his farm considered in the survey area (1=yes, 0=no)

390 secondary water: a binary variable for whether a farmer has a secondary source of water (1=yes, 0=no)

The results of the logistic regressions in Table 11.8 show that neither having secondary work (Equation 1) nor a secondary source of water (Equation 2), or their interaction (Equation 3), have any significant impact and none of the variation in the outcome is explained.

Table 11.8: Regression results of the effects of secondary work and secondary water resources on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent Variable (1) (2) (3) secondary work 1.04 1.19 (0.33) (0.44) secondary water 1.34 1.74 (0.47) (0.87) secondary work*secondary water 0.59 (0.42) Prob > chi2 0.8960 0.3924 0.7293 Pseudo R2 0.0001 0.0031 0.0055 No. Obs. 184 184 184 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Finally, to assess the impact of having secondary work or water resources on membership in the WUA, the following equations are used:

Equation (1): membership = β0 + β1 secondary work

Equation (2): membership = β0 + β1 secondary water

Equation (3): membership = β0 + β1 secondary work + β2 secondary water + β3 secondary work*secondary water

391

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) secondary work: a binary variable for whether a farmer as work or receives an income from a source outside of his farm considered in the survey area (1=yes, 0=no) secondary water: a binary variable for whether a farmer has a secondary source of water (1=yes, 0=no)

As the results of the logistic regressions in Table 11.9 show, having a secondary water resource has no significant impact (Equation 2) and having a secondary source of income (Equation 1) has only a slightly significant and positive impact. For farmers with a secondary source of income, they are 1.8 times more likely (or 80% more likely) to be members in the WUA. The interaction of these variables (Equation 3) is insignificant. This conclusion defies the original hypothesis, in which it was assumed that those with secondary work would be less likely to be members.

Table 11.9: Regression results of the effects of secondary work and secondary water resources on farmer membership in the WUA. Dependent Variable membership Independent Variable (1) (2) (3) secondary work 1.80* 1.34 (0.69) (0.60) secondary water 1.68 0.83 (0.70) (0.62) secondary work*secondary water 2.64 (2.43) Prob > chi2 0.1214 0.2162 0.2005 Pseudo R2 0.0148 0.0095 0.0287 No. Obs. 122 122 122 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

392

SOCIOECONOMIC STATUS

Hypothesis: For farmers with higher socioeconomic status, they are more likely to participate in the WUAs.

While the hypothesis does not relate to WUA performance, the outcomes relating to performance are also assessed in any case to ensure that nothing is of significance. The equations to test for whether the indicators of socioeconomic status (land size, having greenhouses and exporting produce) have an effect on farmer opinion of the WUA are as follows:

Equation (1): opinion of wua = β0 + β1 dunums

Equation (2): opinion of wua = β0 + β1 greenhouses

Equation (3): opinion of wua = β0 + β1 exporting

Equation (4): opinion of wua = β0 + β1 dunums + β2 greenhouses + β3 exporting + β4 dunums*greenhouses + β5 dunums*exporting + β6 greenhouses*exporting

As seen in the results of the ordered logistic regressions in Table 11.10, each indicator variable alone has no significant impact on farmer opinion of the WUA and explains none of the variation in farmer opinion of the WUA (Equations 1-3). When interactions between each of these three variables are included (Equation 4), having both greenhouses and exporting abroad has an oddly strong and positive effect. For farmers

393 who both have greenhouses and export abroad, they are 12.49 times more likely to have a more favorable view of the WUA. But this result only explains a very small percentage of the variation in the outcome (as per the R2 value of 3%) and could be the result of a faulty interaction between two binomial variables.

Table 11.10: Regression results of the effects of farm size, greenhouses and exporting on farmer opinion of the WUA. Dependent Variable opinion of wua Independent (1) (2) (3) (4) Variables dunums 1.01 1.07 (0.34) (0.51) greenhouses 0.34 0.82 (0.37) (1.28) exporting 1.01 0.10 (0.46) (0.22) dunums*greenhouses 1.08 (0.98) dunums*exporting 1.61 (1.54) greenhouses*exporting 12.49** (14.40) Prob > chi2 0.9709 0.3449 0.9769 0.3097 Pseudo R2 0.0000 0.0032 0.0000 0.0253 No. Obs. 186 186 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

To test the effect of these socioeconomic indicators on farmer opinion of the WUA when it is compared to the JVA, the equations are as follows:

Equation (1): comparison of wua to jva = β0 + β1 dunums

Equation (2): comparison of wua to jva = β0 + β1 greenhouses 394

Equation (3): comparison of wua to jva = β0 + β1 exporting

Equation (4): comparison of wua to jva = β0 + β1 dunums + β2 greenhouses + β3 exporting + β4 dunums*greenhouses + β5 dunums*exporting + β6 greenhouses*exporting

In Table 11.11, the results of the ordered logistic regressions indicate that a farmer’s socioeconomic status has no strongly significant impact on farmer opinion of the WUA as it compares to the JVA. The number of dunums does exhibit some slight positive significance on farmer opinion both on its own and when interactions terms are introduced

(Equations 1 and 4). Perhaps for farmers with more land holdings, they are slightly more likely to have a more favorable view of the WUA in comparison to the JVA and this could contribute to what is found below, that they are more likely to be members in the WUA. But no variation in the outcome is explained, making it unlikely that there is relevance to be found here.

395

Table 11.11: Regression results of the effects of farm size, greenhouses and exporting on farmer opinion of the WUA as compared to the JVA. Dependent Variable comparison of wua to jva Independent (1) (2) (3) (4) Variables dunums 1.64* 1.86* (0.49) (0.79) greenhouses 0.01 0.59 (0.31) (0.77) exporting 1.13 0.56 (0.43) (1.09) dunums*greenhouses 1.16 (0.89) dunums*exporting 0.88 (0.74) greenhouses*exporting 3.22 (2.94) Prob > chi2 0.0971 0.9695 0.7402 0.4854 Pseudo R2 0.0075 0.0000 0.0003 0.0150 No. Obs. 186 186 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In examining the effect of the socioeconomic indicators on farmer reporting of water stealing, the equations are below. Whether a farmer exports his produce is excluded because all farmers who export reported water stealing, so there arises the problem of perfect prediction in the statistical analysis.

Equation (1): water stealing = β0 + β1 dunums

Equation (2: water stealing = β0 + β1 greenhouses

Equation (3): water stealing = β0 + β1 dunums + β2 greenhouses + β3 dunums*greenhouses

Where: 396 water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) dunums: an interval variable for the log of the number of dunums farmed greenhouses: a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) exporting: a binary variable for whether a farmer personally exports produce (1=yes, 0=no)

In the logistic regressions in Table 11.12, while having greenhouses has no significant impact (Equation 2), the number of dunums has a significant and positive effect on reporting of water stealing and explains 6% of the variation (as per the R2 value) (Equation 1). For those farmers with more dunums of land (the unit here is the log of the number of dunums), they are more likely to report water stealing. The significant impact remains even when the other variables are added (Equation 3). It is possible that farmers with more land represent the “big fish” who are more confident and influential and are not afraid to report water stealing. It is also possible that farmers with more land have more farm units that lose water when other farmers steal water, so they are more upset about stealing and talk more openly and critically about it. It is thirdly possible that farmers with more dunums are usually owners and renters, not agents, and thus again have more confidence in speaking-out than does an agent who is an employee of an owner or renter.

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Table 11.12: Regression results of the effects of farm size, greenhouses and exporting on farmer reporting of water stealing. Dependent Variable water stealing Independent (1) (2) (3) Variables dunums 4.42*** 4.13** (2.37) (2.71) greenhouses 0.90 0.32 (0.44) (0.62) dunums*greenhouses 1.61 (1.93) Prob > chi2 0.0035 0.8330 0.0259 Pseudo R2 0.0645 0.0003 0.0702 No. Obs. 174 174 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Looking at the effect of socioeconomic factors on farmer opinion of the fairness of the WUA, the following equations are used:

Equation (1): fairness of wua = β0 + β1 dunums

Equation (2): fairness of wua = β0 + β1 greenhouses

Equation (3): fairness of wua = β0 + β1 exporting

Equation (4): fairness of wua = β0 + β1 dunums + β2 greenhouses + β3 exporting + β4 dunums*greenhouses + β5 dunums*exporting + β6 greenhouses*exporting

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) dunums: an interval variable for the log of the number of dunums farmed greenhouses: a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) exporting: a binary variable for whether a farmer personally exports produce (1=yes, 0=no) 398

Table 11.13 shows the results of the logistic regressions and none of the socioeconomic factors display any significant impact on their own on farmer opinion of the fairness of the WUA (Equations 1-3). The interaction between whether a farmer has greenhouses and whether he exports his produce (Equation 4) does have a mildly significant and positive impact; for farmers who export and have greenhouses, they are

5.24 times more likely to say that the WUA is fair. In the interaction model (Equation 4), land size also appears to have a mild and positive impact. It could be that more well-off farmers are better treated by the WUA or it could be that because of their status, they are generally unconcerned and unaffected by the actions of the WUA so they think that it is, by default, pretty fair. But there is only slight evidence, with only 2% of the variation is explained (as per the R2 value), so these results are tenuous.

399

Table 11.13: Regression results of the effects of farm size, greenhouses and exporting on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent (1) (2) (3) (4) Variables dunums 1.20 2.15* (0.39) (1.05) greenhouses 0.88 1.65 (0.29) (2.38) exporting 0.88 1.92 (0.37) (4.12) dunums*greenhouses 0.56 (0.47) dunums*exporting 0.43 (0.38) greenhouses*exporting 5.24* (5.32) Prob > chi2 0.5711 0.6925 0.7604 0.5147 Pseudo R2 0.0014 0.0007 0.0004 0.0221 No. Obs. 184 184 184 184 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Finally, to test the effect of socioeconomic status on farmer membership, the outcome of most relevance in the hypothesis, the equations are below. A variable denoting PS 55 is included because it is the WUA with by far a greater number of greenhouses.

Equation (1): membership = β0 + β1 dunums

Equation (2): membership = β0 + β1 greenhouses

Equation (3): membership = β0 + β1 exporting

Equation (4): membership = β0 + β1 dunums + β2 greenhouses + β3 exporting + β4 dunums*greenhouses + β5 dunums*exporting + β6 greenhouses*exporting

400

Equation (5): membership = β0 + β1 dunums + β2 greenhouses + β3 exporting + β4 dunums*greenhouses + β5 dunums*exporting + β6 greenhouses*exporting + β7 ps55

In the results of the logistic regressions in Table 11.14, the only variable on its own to have a significant impact is the number of dunums farmed, by itself explaining 5% of the variation (per the R2 value) (Equation 1). Farmers with more dunums are more likely to be members in the WUA. This variable remains significant when other variables are added to the equation (Equations 4 and 5). When interactions are included between pairs of socioeconomic variables (Equation 4), farmers both with more dunums of land and greenhouses are much more likely to be members. This remains the case if the variable for PS 55 is included (Equation 5) and it is seen that farmers in PS 55 are more likely to be members (the impact of the four WUAs will be more fully covered in the next chapter). By adding in the interaction variables and PS 55, almost 12% of the variation is being explained in farmer membership in the WUAs.

401

Table 11.14: Regression results of the effects of farm size, greenhouses, exporting and WUA on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) (3) (4) (5) Variables dunums 3.45*** 3.46* 3.17* (1.54) (2.33) (2.15) greenhouses 0.72 0.006* 0.0006** (0.30) (0.02) (0.002) exporting 2.07 89.67 74.69 (1.09) (302.18) (253.38) dunums*greenhouses 9.20* 19.33* (13.08) (30.20) dunums*exporting 0.16 0.19 (0.23) (0.26) greenhouses*exporting 0.47 0.37 (0.65) (0.53) ps55 3.50* (2.63) Prob > chi2 0.0029 0.4251 0.1685 0.0143 0.0083 Pseudo R2 0.0547 0.0039 0.0117 0.0983 0.1173 No. Obs. 122 122 122 122 122 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

LAND-HOLDING STATUS

Hypothesis: Where the majority of water users are owners, or at least more permanent renters, participation rates in the WUA will be higher and the WUA will perform better.

From Chapter Nine’s description of heterogeneity among farmers with regard to identity, the WUAs are not statistically significantly different in their overall make-up of agents, renters and owners. But there are some specific differences between the WUAs: PS 55 has significantly fewer agents than MH; PS 33 has significantly fewer renters than

402

MH or PS 91; PS 33 has significantly more owners than PS 91 or PS 55; and MH has significantly more owners than PS 91. The hypothesis would posit that because there are more owners in PS 33 and MH, their performance and participation rates would be better. The following equation is used to first test the effect of land-holding status (where renters and owners are compared to agents, the excluded category) on farmer opinion of the WUA:

Equation: opinion of wua = β0 + β1 ownership

Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner)

Table 11.15 shows the results of the ordered logistic regressions and reveals that farmers who are owners are 3.7 times less likely (the inverse of 0.27) to have a more favorable view of the WUA than agents. This result is highly significant and contributes to explaining 4% of the variation in this outcome. Contrary to the initial hypothesis, where there are owners, WUA performance is not necessarily better. A caveat is here warranted in that these results cannot prove whether having more owners in an area improves WUA performance but simply that owners are giving less favorable views of the WUA.

403

Table 11.15: Regression results of the effect of ownership status on farmer opinion of the WUA. Dependent Variable Independent opinion of wua Variable ownership renter 0.70 (0.33) owner 0.27*** (0.12) Prob > chi2 0.0024 Pseudo R2 0.0430 No. Obs. 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

To test the effect of land-holding status on farmer opinion of the WUA as it compares to the JVA, the equation is as follows:

Equation: comparison of wua to jva = β0 + β1 ownership

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner)

The results of the ordered logistic regressions in Table 11.16 show again that owners, as compared to agents, are roughly 2 times less likely (the inverse of 0.51) to have a more favorable view of the WUA when it is compared to the JVA. In this case, only 2% of the variation (from the R2 value) is explained.

404

Table 11.16: Regression results of the effect of ownership status on farmer opinion of the WUA as compared to the JVA. Dependent Variable Independent comparison of wua to jva Variable ownership renter 1.20 (0.44) owner 0.51** (0.18) Prob > chi2 0.0262 Pseudo R2 0.0200 No. Obs. 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In examining the effect of land-holding status on the reporting of water stealing, the equation is as follows:

Equation: water stealing = β0 + β1 ownership

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner)

From the results of the logistic regression in Table 11.17, renters are 3.41 times more likely than agents to report that water stealing is happening (a result of only mild significance) whereas owners are not more likely than agents to report on this. On the whole, there is little to conclude from these results and only 3% of the variation is explained (from the R2 value). Renters could be more liberal in what they say about water stealing as they are not as attached to the area or do not feel as inhibited by the WUA and its rules. 405

Table 11.17: Regression results of the effect of ownership status on farmer reporting of water stealing. Dependent Variable Independent water stealing Variable ownership renter 3.41* (2.18) owner 1.83 (0.94) Prob > chi2 0.1343 Pseudo R2 0.0304 No. Obs. 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In assessing the impact of land-holding status on farmer opinion of the fairness of the WUA, the equation follows:

Equation: fairness of wua = β0 + β1 ownership

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner)

The results of the logistic regression in Table 11.18 show that there is no significant difference among agents, renters and owners in their opinion of the fairness of the WUA and little variation is explained in this outcome.

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Table 11.18: Regression results of the effect of ownership status on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variable ownership renter 1.08 (0.45) owner 0.63 (0.25) Prob > chi2 0.2807 Pseudo R2 0.0107 No. Obs. 184 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Lastly, the effect of land-holding status on membership in the WUA is tested with the equations below. PS 55 is added because of its significant interaction with land-holding status.

Equation (1): membership = β0 + β1 owner

Equation (2): membership = β0 + β1 owner + β2 ps55 + β3 owner*ps55

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) owner: a binary variable for whether a farmer is an owner (1=yes, 0=no) ps55: a binary variable for whether a farmers is in PS 55 (1=yes, 0=no)

Table 11.19’s results of the logistic regressions demonstrate, in Equation 1, that farm owners are 1.95 times more likely to be members in the WUA than renters (for membership-related equations, agents are excluded because they cannot be members in the first place). This proves the original hypothesis although the variable is only mildly 407 significant and explains only 1% of the variation in membership (as per the R2 value). Within PS 55 in specific (Equation 2), owners are actually 12 times less likely (the inverse of 0.08) to be members in the WUA and almost 7% of the variation (per the R2 value) is explained. It is not known why owners in PS 55 might have more of an aversion to membership in the WUA but this will be reassessed in the following chapter.

Table 11.19: Regression results of the effect of ownership status on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) Variable owner 1.95* 3.87*** (0.77) (1.92) ps55 4.83** (3.37) owner*ps55 0.08*** (0.08) Prob > chi2 0.0855 0.0146 Pseudo R2 0.0183 0.0651 No. Obs. 122 122 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

EDUCATION

Hypothesis: Better-educated farmers are more likely to participate in the WUA.

From Chapter Nine’s review of the heterogeneity in endowments among farmers in the four WUAs, there are no strong differences among them with regard to the breakdown of education levels among farmers. While the outcome of participation is the focus within the hypothesis, education levels are examined for their potential effects on the other four performance-related outcomes as well. 408

To test the effect of education level on farmer opinion of the WUA, the following equations are used:

Equation (1): opinion of wua = β0 + β1 education

Equation (2): opinion of wua = β0 + β1 high school

Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) education: a categorical variable for education level (0=none, 1=elementary school, 2=middle school, 3=high school, 4=diplome, 5=bachelor’s degree or higher) high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no)

The results of the ordered logistic regressions in Table 11.20 show no significant impact of education, either when breaking-down the levels of education (Equation 1) or when simply looking at those with above high school level education (Equation 2). None of the variation in farmer opinion of the WUA is explained either.

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Table 11.20: Regression results of the effects of education level on farmer opinion of the WUA. Dependent Variable opinion of wua Independent (1) (2) Variables education elementary 0.85 (0.52) middle school 1.76 (0.97) high school 1.03 (0.48) diplome 1.26 (0.80) bachelor's or 0.90 higher (0.49) high school 0.87 (0.28) Prob > chi2 0.8434 0.6639 Pseudo R2 0.0073 0.0007 No. Obs. 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In assessing whether education level affects farmer opinion of the WUA when compared to the JVA, the equations are as follows:

Equation (1): comparison of wua to jva = β0 + β1 education

Equation (2): comparison of wua to jva = β0 + β1 high school

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) education: a categorical variable for education level (0=none, 1=elementary school, 2=middle school, 3=high school, 4=diplome, 5=bachelor’s degree or higher) 410 high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no)

As with the above results, those displayed in Table 11.21 for the ordered logistic regressions relating to opinion of the WUA compared to the JVA reveal that education level is not a significant predicator of this opinion. In Equation 1, though, for farmers with only an elementary-level education, they are 2.3 times less likely (the inverse of 0.43) to have a more favorable view of the WUA when it is compared to the JVA as compared to farmers with no education. But this result explains so little of the variation that it is perhaps not relevant.

Table 11.21: Regression results of the effects of education level on farmer opinion of the WUA as compared to the JVA. Dependent Variable comparison of wua to jva Independent (1) (2) Variables education elementary 0.43* (0.24) middle school 0.86 (0.39) high school 1.14 (0.47) diplome 1.24 (0.68) bachelor's or 1.14 higher (0.55) high school 1.43 (0.40) Prob > chi2 0.5443 0.2010 Pseudo R2 0.0111 0.0045 No. Obs. 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%. 411

For testing the effect of education level on farmer reporting of water stealing, the equations are as follows:

Equation (1): water stealing = β0 + β1 education

Equation (2): water stealing = β0 + β1 high school

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) education: a categorical variable for education level (0=none, 1=elementary school, 2=middle school, 3=high school, 4=diplome, 5=bachelor’s degree or higher) high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no)

As demonstrated in the results for the logistic regressions in Table 11.22, education does not have a significant effect on farmer reporting of water stealing.

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Table 11.22: Regression results of the effects of education level on farmer reporting of water stealing. Dependent Variable water stealing Independent (1) (2) Variables education elementary 1.68 (1.48) middle school 1.39 (0.92) high school 1.68 (1.06) diplome 1.28 (0.99) bachelor's or (empty)ª higher high school 1.78 (0.83) Prob > chi2 0.9427 0.2073 Pseudo R2 0.0062 0.0120 No. Obs. 148 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%. ªThe category for bachelor’s degree or higher is empty because all of its observations reported water stealing; with perfection prediction its observations are dropped and leave on 148 observations for this equation.

To test the effect of education level on farmer opinion of the fairness of the WUA, the following equations are used:

Equation (1): fairness of wua = β0 + β1 education

Equation (2): fairness of wua = β0 + β1 high school

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no)

413 education: a categorical variable for education level (0=none, 1=elementary school, 2=middle school, 3=high school, 4=diplome, 5=bachelor’s degree or higher) high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no)

From the results of the logistic regressions in Table 11.23, there are generally no strongly significant effects of education on farmer opinion of the fairness of the WUA. But in Equation 1, farmers with a middle school level of education are 2.39 times more likely to think that the WUA is fair, as compared to farmers with no education, although this equation only explains 2% of the variation (as per the R2 value) in this outcome so it is not hugely important and is hard to interpret as well.

Table 11.23: Regression results of the effects of education level on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent (1) (2) Variables education elementary 0.70 (0.42) middle school 2.39* (1.32) high school 1.13 (0.52) diplome 1.73 (1.09) bachelor's or 0.88 higher (0.45) high school 0.90 (0.28) Prob > chi2 0.3362 0.7309 Pseudo R2 0.0241 0.0005 No. Obs. 184 184 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%. 414

Finally, for the outcome of most relevance, to test the impact of education level on farmer membership in the WUA, the equations are as follows:

Equation (1): membership = β0 + β1 education

Equation (2): membership = β0 + β1 high school

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) education: a categorical variable for education level (0=none, 1=elementary school, 2=middle school, 3=high school, 4=diplome, 5=bachelor’s degree or higher) high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no)

In Table 11.24, the results of the logistic regressions reveal some significance to education’s impact. Farmers with a bachelor’s degree or higher are 4 times more likely to be members in the WUA than farmers with no education (Equation 1), or farmers with at least a high school level of education are 2.05 times more likely to be members in the WUA (Equation 2). While only 2% of the variation (as per the R2 value) in membership is explained in either case, the original hypothesis is offered support in that those with more education are more likely to be WUA members.

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Table 11.24: Regression results of the effects of education level on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) Variables education elementary 1.60 (1.58) middle 1.75 school (1.59) high school 2.88 (2.44) diplome 2.67 (2.72) bachelor's or 4.00* higher (3.59) high school 2.05* (0.82) Prob > chi2 0.5178 0.0671 Pseudo R2 0.0261 0.0207 No. Obs. 122 122 Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

PERCEIVED BENEFITS TO AND INCENTIVES FOR MEMBERSHIP

Hypothesis: Farmers will only join WUAs when they perceive that the benefits will be greater than the costs, especially when the benefits will accrue in the short-term but regardless of whether the benefits are necessarily tangible.

When those farmers who are members of the WUA were asked what benefits they receive from being in the WUA, 73% reported that they see no extra benefit (Figure 11.1).

It would appear, when looking at the WUAs individually, that farmers in PS 33 and PS 91 are more likely to receive benefits from their WUA membership and that farmers in MH are much less likely (Figure 11.2) but this is hard to establish definitively due to the very low number of members surveyed in all associations, especially in PS 91.

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Figure 11.1: Percentage of farmers in all surveyed WUAs who do and do not see a benefit to their membership in the WUA.

Source: Survey data.

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Figure 11.2: Percentage of farmers within each of the four surveyed WUAs who do and do not see a benefit to their membership in the WUA.

Source: Survey data.

Thus it is not unexpected that farmers do not opt to be members of the WUAs when even members cannot claim to receive any benefits despite having to pay membership fees.

So far, the WUAs and the JVA have been reluctant to give WUA members an advantage when it comes to water distribution. This is because they want farmers to voluntarily join the WUA and not be coerced into so doing, and because water distribution is the most sensitive and fundamental service and they cannot deny farmers what they see as their

418 innate right. There has been consideration, though, of charging non-members more for their water and then being a member would indeed have a pay-off. But as with a general price increase for water in the Jordan Valley, the JVA is resistant to implementing such measures for fear of farmer riots and revolts. As matters stand, any real benefits to WUA membership are unlikely in the short- term with the exception of having the right to participate in elections. Indeed, the members in PS 33 and PS 55 who said that there is a benefit mentioned being able to vote as a positive note. This is not a small right; voting can determine the WUA leadership and thus water distribution activities in the field. But as has been mentioned previously, sometimes elections are a fait accompli due to the strong tribal community in place. Members can thus feel that their vote counts for little. WUA members who saw a benefit also mentioned that the WUA helps them to get their “water rights,” hinting that members are able to ensure their fair water share more than nonmembers. Other benefits included being able to know more about what was going on in the WUA and what it was doing and knowing more about the surrounding farmers. One mentioned benefit that is more tangible is that in years past, when companies and aid agencies offered aid to the area in the form of on-farm irrigation equipment, WUA member farmers were offered lowered prices on these goods. This certainly seemed to make an impression on member farmers and it was something that they could readily remember and see as a distinct benefit to their membership. Finally, member farmers noted that there have been some benefits that accrued to nonmembers as well. For example, the WUA, in general, serves to offer farmers greater influence with the JVA; farmers find it hard to approach the JVA but now that there is a WUA, the WUA can approach the JVA on behalf of the farmers.

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It would appear, overall, that there are few perceived benefits or incentives for farmers to join the WUA and in fact, it is somewhat surprising that any farmers join. Some of the WUA presidents tried to emphasize that they have plans for the accrued membership fees in the WUA bank account, such as building a juice factory or offering farmers assistance with packaging or marketing their goods. But these kinds of ideas have remained in words only, with no action to back them up or no proof that they will ever materialize. Perhaps it could be cynically stated that the only reason a farmer would join the

WUA is so that he can have more “wasta,” or influence and connections, in the area in case it can be of benefit in getting more water. This is not an intended benefit of the WUAs but is rather a by-product of the general mindset in Jordan where everyone is geared towards acquiring wasta within the tribal networks. What is more problematic than farmers not seeing a benefit from membership in the WUAs is that many surveyed farmers, especially in an area like MH, are completely unaware of the existence of the WUA. It is impossible to expect them to be eager to be members in an organization about which they know nothing. There is clearly a lack of communication between the WUA and farmers to a degree that farmers have no idea of the realm of responsibilities of the WUA and no idea of the potential benefits that they should be receiving or demanding from the WUAs.

SUMMARY

The impact of user factors on the outcomes of WUA performance and farmer participation in the WUA is statistically smaller than for the institutional factors and somewhat smaller than for the physical factors. But this is only with regard to those factors that have been quantitatively collected. There is still reason to believe that other aspects 420 such as leadership quality and the absence of benefits to membership in the WUA could be vital reasons for why WUAs are performing at a certain level or why farmers desire to join the WUA or not. The following comprehensive quantitative results chapter will address the overarching conclusions of this dissertation as well as the comparative impact of the three categories of factors that have been quantified (physical, institutional and user) to assess where the most influence is felt in the five outcome variables.

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Chapter Twelve: Outcomes and All Factors

The previous four chapters have catalogued and analyzed all of the individual factors within each of the four main categories (physical, community, institutional and user) for their impacts on the performance of and participation in the WUAs. This process has separated out those factors that can be quantified and among those quantified factors, those that have a significant effect on the outcomes. In the present chapter, those quantified and significant factors (leaving out the community factors as none are quantified for use in statistical analyses) are gathered together in comprehensive statistical analyses of their effects on the five major outcomes: opinion of the WUA, opinion of the WUA in comparison to the JVA, farmer reporting of water stealing, farmer opinion of the fairness of the WUA and membership in the WUA. Each outcome is described for its overall nature and details, gathered from the farmer surveys and field visits, are added for a fuller description. Next, each outcome is predicted by each category of factors (physical, institutional and user factors). Finally, all factors in all categories that remain significant or otherwise relevant within the individual category analyses are combined to predict the outcomes. Categories of variables and individual variables are thus revealed for their particular importance to each outcome. Table 12.1 catalogues the overall results in this chapter. The ratings of the WUAs as per the outcomes are listed as well as those categories of factors and the specific factors that are significant in their effects on the outcomes. These categories and factors are listed in order of importance as per the amount of variation they explain and their level of significance. The amount of variation in the outcomes (as per the Pseudo R2 values) explained by each category of variables and all variables is listed briefly in Table 12.2.

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Table 12.1: Summary of all outcomes, overall statistics and influential categories of factors and factors. Outcome Overall Rating Influential Categories and Factors (in order of importance) Opinion of WUA 68% Good 1. Institutional 22% So-so  Monitoring, conflict resolution and WUA ability to help 7% Bad 2. Physical  Water adequacy WUA vs. JVA 49% Better 1. Institutional 32% Same thing  Conflict resolution and WUA ability to help 14% Worse 2. Physical  Water adequacy Water Stealing 82% Happens 1. Physical 12% Doesn’t happen  Water adequacy and reliability 1. User  Land size Fairness of WUA 63% Fair 1. Institutional 36% Not fair  Monitoring and WUA ability to help 2. Physical  Water adequacy WUA Membership 68% Members 1. User 32% Not members  Land size and land-holding status 2. Physical  Network and lateral positions

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Table 12.2: Percent of variation explained in outcome variables by factor categories. Physical Institutional User All Factors* Factors* Factors* Factors Opinion of WUA 8% 30% 7% 34% Comparison of WUA to JVA 7% 15% 3% 21% Water Stealing 9% 10% 17% Fairness of WUA 7% 20% 22% Membership 8% 12% 20% *Percentages taken from models displaying the most significance while not including effects of WUAs.

OPINION OF WUA

The general opinion of farmers of the WUA is favorable; 68% report that the WUA is good and only 22% say that it is just so-so and 7% that it is bad (Figure 12.1). Another 3% did not answer the question because they had little knowledge of the WUA. Comparing between the WUAs (see Figure 12.2), PS 91 has the highest percentage of good reviews, with 84% of its surveyed farmers believing that the WUA is good. PS 55 and PS 33 receive

75% and 63% in percent of good reviews, respectively. The percentage of bad reviews for PS 91, PS 55 and PS 33 among farmers was similar and around 5%. Mazraa-Haditha (MH) obtained a smaller percentage of farmers ranking it as good, only 44%, with more than twice the percentage of farmers as in the other WUAs saying that it is bad (12%). Another 15% of farmers in MH had no opinion on the WUA, signaling their lack of awareness of the WUA in general. Overall, PS 91 and PS 55 have significantly better reviews from farmers than MH, and PS 91 also has significantly better reviews than PS 33.

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Figure 12.1: Overall opinion from all surveyed farmers regarding their WUA.

Source: Survey data.

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Figure 12.2: Opinion from surveyed farmers in PS 33, PS 55, PS 91 and MH regarding their WUA.

Source: Survey data.

Physical Factors

To test the effects of the physical factors on farmer opinion of the WUA, the following equations are used:

Equation (1): opinion of wua = β0 + β1 adequacy + β2 reliability

Equation (2): opinion of wua = β0 + β1 adequacy + β2 reliability + β3 beginning position + β4 pressure network 426

Equation (3): opinion of wua = β0 + β1 adequacy + β2 reliability + β3 beginning position + β4 secondary work + β5 secondary water + β6 secondary work*secondary water

Equation (4): opinion of wua = β0 + β1 adequacy + β2 reliability + β3 beginning position + β4 ps91

Equation (5): opinion of wua = β0 + β1 adequacy + β2 reliability + β3 beginning position + β4 ps55 + β5 ps91 + β6 reliability*ps55 + β7 adequacy*ps91

From the results of the ordered logistic regressions in Table 12.3, water adequacy and water reliability remain significant when considered together and explain about 7% of the variation (from the R2 value) in farmer opinion of the WUA (Equation 1). For farmers who think that the water supply is adequate or reliable, they are roughly 3 times more likely to have a more favorable opinion of the WUA. For farmers at the beginning of the lateral, they are roughly 2 times more likely to have a favorable opinion of the WUA (Equations

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2 and 3) and for farmers with both secondary work and a secondary water source, they are 4 times less likely (the inverse of 0.25) to have a favorable view of the WUA (Equation 3). But both of these results are only mildly significant and do not explain much more of the variation than water adequacy and water reliability.

Table 12.3: Regression results of the effects of the physical factors on farmer opinion of the WUA. Dependent Variable opinion of wua Independent Variables (1) (2) (3) adequacy 3.10** 3.05** 2.75* (1.62) (1.62) (1.46) reliability 2.66*** 2.61*** 2.75*** (0.93) (0.94) (0.99) beginning position 2.19* 2.12* (1.09) (1.08) pressure network 1.25 (0.43) secondary work 1.08 (0.44) secondary water 2.27 (1.40) secondary work*secondary water 0.25* (0.20) Prob > chi2 0.0001 0.0001 0.0002 Pseudo R2 0.0689 0.0806 0.0928 No. Obs. 186 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

In sum, water reliability and adequacy remain two important physical factors to consider when viewing farmer opinion of the WUA. Having secondary work and water resources also continue to be significant but only together. Being at the beginning of the

428 lateral line is additionally important. Overall, the physical factors explain roughly 9% of the variation in farmer opinion of the WUA.

Institutional Factors

The following equation is used to assess the effects of the institutional factors on farmer opinion of the WUA:

Equation: opinion of wua = β0 + β1 touring + β2 punishing + β3 resolving conflict + β4 wua help + β5 wua-jva help

Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) touring: a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no)

Table 12.4’s results of the ordered logistic regression reveal that the institutional factors explain a larger portion of the variation in farmer opinion of the WUA, 30% (as per the R2 value), and all display some level of significance. For farmers who think that the ditchriders are always monitoring the field, they are 8.41 times more likely to have a more favorable view of the WUA than farmers who think that the ditchriders rarely monitor the field. For farmers who believe that the ditchriders are either sometimes or always 429 appropriately ticketing farmers, they are 18.60 and 11.75 times more likely to have a more favorable view of the WUA than farmers who think that the ditchriders only rarely do this task. For farmers who think that the WUA can always resolve conflicts among farmers, they are 6.10 times more likely to have a more favorable view of the WUA than farmers who think that the WUA does not help in such conflicts. And for farmers who seek help solely from the WUA, signifying their high level of trust in the WUA, they are 4.7 times more likely to have a more favorable view of the WUA than farmers who seek help solely from the JVA. When the four WUAs are included in the regression, there is no significant impact. Overall, institutional factors strongly influence and explain farmer opinion of the WUA.

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Table 12.4: Regression results of the effects of the institutional factors on farmer opinion of the WUA. Dependent Variable Independent opinion of wua Variables touring sometimes 3.21 (2.75) always 8.41** (7.37) punishing sometimes 18.60** (25.65) always 11.75* (17.08) resolving conflicts sometimes 1.69 (1.09) always 6.10*** (3.52) wua help 4.70*** (2.80) wua-jva help 1.40 (0.88) Prob > chi2 0.0000 Pseudo R2 0.3012 No. Obs. 115 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

User Factors

For assessing the impact of user factors on farmer opinion of the WUA, the following equation is used:

Equation: opinion of wua = β0 + β1 dunums + β2 greenhouses + β3 exporting + β4 greenhouses*exporting + β5 ownership + β6 high school

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Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) dunums: an interval variable for the log of the number of dunums farmed greenhouses: a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) exporting: a binary variable for whether a farmer personally exports produce (1=yes, 0=no) ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner) high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no)

The results of the ordered logistic regression in Table 12.5 show that user factors explain about 7% of the variation in farmer opinion of the WUA. While farm size and having greenhouses are not significant on their own, farmers who have both greenhouses and export their produce are 11.44 times more likely to have a favorable opinion of the WUA. Farmers who are owners are 4.3 times less likely (the inverse of 0.23) to have more favorable views of the WUA in comparison to agents. Education no longer appears to have any significant impact when put together with other user factors. On the whole, user factors are not very important in determining farmer opinion of the WUA but perhaps being a wealthy farmer makes one more inclined to favor the WUA and being an owner less so.

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Table 12.5: Regression results of the effects of user factors on farmer opinion of the WUA. Dependent Variable opinion of wua Independent (1) Variables dunums 0.96 (0.38) greenhouses 0.93 (0.39) exporting 0.24** (0.17) greenhouses*exporting 11.44** (12.36) ownership renter 0.54 (0.27) owner 0.23*** (0.11) high school 1.00 (0.35) Prob > chi2 0.0073 Pseudo R2 0.0686 No. Obs. 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

All Factors

The final test to determine the impact of all factors in all categories on farmer opinion of the WUA involves a series of equations:

Equation (1): opinion of wua = β0 + β1 adequacy + β2 reliability + β3 touring + β4 punishing + β5 resolving conflict + β6 wua help + β7 wua-jva help + β8 exporting + β9 greenhouses + β10 ownership

Equation (2): opinion of wua = β0 + β1 adequacy + β2 reliability + β3 touring +

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β4 punishing + β5 resolving conflict + β6 wua help + β7 wua-jva help + β8 exporting + β9 greenhouses + β10 ownership + β11 ps55 + β12 ps91 + β13 mh

Equation (3): opinion of wua = β0 + β1 adequacy + β2 reliability + β3 touring + β4 punishing + β5 resolving conflict + β6 wua help + β7 wua help*ps33 + β8 wua-jva help + β9 exporting + β10 greenhouses + β11 ownership + β12 ps33

Equation (4): opinion of wua = β0 + β1 adequacy + β2 reliability + β3 touring + β4 punishing + β5 resolving conflict + β6 wua help + β7 wua-jva help + β8 exporting + β9 greenhouses + β10 greenhouses*exporting + β11 ownership

Where: opinion of wua: a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) touring: a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no) greenhouses: a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) exporting: a binary variable for whether a farmer personally exports produce (1=yes, 0=no) ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner) ps33: a binary variable for whether a farmer is in PS 33 (1=yes, 0=no) ps55: a binary variable for whether a farmer is in PS 55 (1=yes, 0=no) ps91: a binary variable for whether a farmer is in PS 91 (1=yes, 0=no) mh: a binary variable for whether a farmers is in MH (1=yes, 0=no)

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Table 12.6 displays the results of the logistic regressions using all of the equations listed above. Equation 1 reveals the most basic model and shows that all factors in the three categories together explain 34% of the variation (seen in the R2 value) in farmer opinion of the WUA. None of the user factors are significant. Only water adequacy among the two physical factors is modestly significant and remains so throughout the other models (the lack of significance for the physical factors could be due to the loss of observations when having to include the institutional factors). Several of the institutional factors remain strong in their effects in this comprehensive model. Farmers who observe ditchriders sometimes or always monitoring the fields are much more likely to have favorable views of the WUA and this stands throughout all models. Farmers who think that the WUA is always able to resolve conflicts among farmers are much more likely to have favorable views of the WUA and this remains constant throughout the rest of the models. And farmers who solely go to the WUA for help have consistently favorable views of the WUA as opposed to those who go to the JVA for help. And for farmers who solely go to the WUA for help, they are 4.6 times more likely to have a more favorable view of the WUA than farmers who solely go to the JVA for help.

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Table 12.6: Regression results of the effects of all factors on farmer opinion of the WUA. Dependent Variable opinion of wua Independent (1) (2) (3) (4) Variables adequacy 4.29* 4.52* 3.88* 3.57* (3.62) (3.97) (3.57) (3.10) reliability 1.17 1.15 1.37 1.23 (0.69) (0.68) (0.85) (0.77) touring sometimes 3.78* 4.29* 4.59* 4.45* (3.36) (3.83) (4.17) (4.11) always 9.16** 8.54** 12.64*** 11.22*** (8.31) (7.84) (11.63) (10.50) punishing sometimes 9.22* 11.24* 8.08 5.95 (13.70) (16.54) (12.34) (9.10) always 5.19 7.14 3.93 3.24 (8.17) (11.21) (6.30) (5.21) resolving conflict sometimes 1.20 0.99 1.16 1.25 (0.84) (0.72) (0.85) (0.89) always 5.56*** 5.41*** 5.77*** 5.65*** (3.39) (3.36) (3.53) (3.52) wua help 4.60** 2.99* 1.46 5.42*** (2.92) (2.18) (1.16) (3.53) wua-jva help 1.01 0.65 1.02 1.31 (0.69) (0.53) (0.72) (0.92) wua help*ps33 12.26** (14.47) exporting 2.57 3.64* 2.29 1.02 (2.10) (3.21) (1.92) (0.95) greenhouses 0.74 0.62 1.10 0.44 (0.51) (0.54) (0.83) (0.33) greenhouses*exporting 20.90* (40.28)

436

Table 12.6: (continued) ownership renter 1.08 1.31 0.84 0.92 (0.79) (1.02) (0.64) (0.69) owner 0.59 0.62 0.54 0.52 (0.43) (0.46) (0.41) (0.39) ps33 0.52 (0.38) ps55 1.01 (0.92) ps91 1.02 (0.75) mh 0.34 (0.31) Prob > chi2 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.3381 0.3462 0.3652 0.3533 No. Obs. 115 115 115 115 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

When variables for the WUAs are added (Equation 2), no significant impact is seen in them. Equation 3 reveals that for farmers in PS 33 who only go to the WUA for help, they are much more likely to have a favorable view of the WUA than farmers who only go to the WUA for help in other WUAs. This issue of being able to go to the WUA for help is especially important in PS 33. Equation 4 shows the strong and positive impact of having both greenhouses and exporting produce on farmer opinion of the WUA. Overall, every model explains roughly 34-36% of the variation in farmer opinion of the WUA.

COMPARISON BETWEEN WUA AND JVA

Another outcome variable compares present WUA performance against former JVA performance. While the overall rating of WUAs was good (in the previous outcome), 437 only 49% of farmers see the WUA as actually better than the JVA (Figure 12.3). Another 32% see the WUA as essentially the same thing as the JVA and 14% think that the WUA is worse than the JVA.

Figure 12.3: Overall opinion of farmers comparing the WUA to the JVA, whether it is better, the same thing or worse than the JVA.

Source: Survey data.

In comparing between the WUAs, 54%, 54% and 61% of farmers in PS 33, PS 55 and PS 91, respectively, say that the WUA is better than the JVA, with MH coming in with a less favorable review of only 22% of farmers believing that the WUA is superior to the JVA (Figure 12.4). The remainder of the farmers in PS 33, 21% and 25%, see the WUA as worse than or the same thing as the JVA, respectively. In PS 55 and PS 91, fewer farmers think that the WUA is necessarily worse but rather just the same, with 13% and 5% ranking the WUA as worse, respectively, and 29% and 32% ranking it as the same thing as the JVA, respectively. While PS 33, PS 55 and PS 91 rank evenly in favorable reviews, PS 55 and PS 91 see the added compliment of fewer farmers thinking they are 438 worse than the JVA in comparison to farmers in PS 33. In MH, 17% of farmers think that the WUA is worse than the JVA and 46% believe that it is the same thing. 15% of surveyed farmers in MH don’t have an opinion on this matter. Overall, PS 91 and PS 55 have significantly better reviews from farmers when compared with the JVA than does MH.

Figure 12.4: Opinion of farmers comparing their WUA’s performance to that of the JVA in PS 33, PS 55, PS 91 and MH.

Source: Survey data.

Table 12.7 lists the reasons, as heard from farmers during the survey, for why the WUA is either better or worse than the JVA, according to the farmer’s response to the 439 initial question of how the WUA ranks in comparison to the JVA. As the answers in both columns demonstrate, there are very similar reasons for either more favorably or less favorably viewing the WUA in comparison to the JVA. Farmers have viewpoints that directly contradict each other but this is the nature of farmers in the Jordan Valley who vary widely in their points of view.

Table 12.7: Farmer opinion of why WUA is either better or worse than the JVA. WUA Reasons WUA is Better Reasons WUA is Worse PS 33 Better organized, follows and JVA is fairer, has more authority, is understands ground issues more, stricter with the rules, more responds quicker, is in contact with organized, has more specialized farmers more, present in the field employees more, better at “getting farmers their water rights,” not lazy like JVA, fairer PS 55 Understands the field more, more More committed to the work, vigilant in its work and less lazy, monitors the field more, stricter with adheres more to water schedule, more the rules, less favoritism, protects respectable, more organized, farmers water rights better, has more monitors more, closer to farmer weight and authority, has more mindset, responds quicker equipment and machines PS 91 Better organized, stricter with the More capabilities, more power and rules, more committed to the work, authority, stricter in rule enforcement, responds quicker, not lazy like the JVA employees more accountable JVA, more honest, fairer, more on the side of farmers, easier to work with, more friendly with farmers, MH More organized, stricter with the JVA employees held to account in rules, follows the ground situation their work more, less corrupt, greater more closely, responds quicker, abilities in the field, supplies water available after hours, more more reliably. committed to the work, cooperates better with farmers Source: Data from farmer survey.

440

Physical Factors

To test the effect of the physical factors on farmer opinion of the WUA when it is compared to the JVA, the following equations are used:

Equation (1): comparison of wua to jva = β0 + β1 adequacy + β2 reliability

Equation (2): comparison of wua to jva = β0 + β1 adequacy + β2 reliability + β3 beginning position + β4 pressure network

Equation (3): comparison of wua to jva = β0 + β1 adequacy + β2 reliability + β3 pressure network + β4 ps33 + β5 ps55 + β6 ps91

Equation (4): comparison of wua to jva = β0 + β1 adequacy + β2 reliability + β3 pressure network + β4 ps33 + β5 ps91 + β6 reliability*ps33

The results of the ordered logistic regressions are recorded in Table 12.8. Equation

1, which only includes water adequacy and water reliability, reveals that water reliability is the more influential factor in this case. Farmers who think that the water supply is reliable are 2.67 times more likely to have a more favorable view of the WUA as it compares to the JVA. This simple equation only explains 4% of the variation (per the R2 441 value). Equation 2 reveals that being in the pressure network is highly significant and remains so throughout all remaining equations, adding slightly to the overall variation explained (up to 7%). For farmers in the pressure network, they are 2.5 times more likely to have a more favorable view of the WUA as it compares to the JVA. When the WUAs are added in Equation 3, little changes with the other variables, very little extra variation is explained but farmers in PS 91 do appear to have more favorable views of the WUA as it compares to the JVA than farmers in MH. Equation 4 further demonstrates that farmers in PS 33 who think that the water supply is reliable are much more favorably inclined to the

WUA as it compares to the JVA than farmers who think the water supply is reliable in any other WUA. Again, PS 33 has some particular factors that make water reliability all the more important there. While the final model explains roughly 10% of the variation in farmer opinion of the WUA as compared to the JVA, it can be said overall that the physical factors themselves are still only explaining about 7% of the variation.

442

Table 12.8: Regression results for the effects of physical factors on farmer opinion of the WUA as compared to the JVA. Dependent Variable comparison of wua to jva Independent (1) (2) (3) (4) Variables adequacy 1.62 1.93* 1.82* 1.98* (0.59) (0.73) (0.70) (0.77) reliability 2.67*** 2.21*** 2.01** 1.31 (0.82) (0.70) (0.67) (0.48) beginning 1.18 position (0.45) pressure 2.50*** 2.93*** 2.85*** network (0.76) (1.01) (0.92) ps33 1.05 0.42* (0.50) (0.21) ps55 1.19 (0.59) ps91 2.50** 2.38*** (1.10) (0.87) reliability*ps33 5.68*** (4.00) Prob > chi2 0.0002 0.0000 0.0000 0.0000 Pseudo R2 0.0461 0.0722 0.0891 0.1062 No. Obs. 186 186 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Institutional Factors

To examine the effect of the institutional factors on farmer opinion of the WUA as it compares to the JVA, the equations below are used. The second equation is necessary due to the problematic nature of the variable for sanctioning. Five of its observations are completely determined and the standard errors are questionable; thus Equation 2 is the focus herein but Equation 1 is given simply as the baseline.

443

Equation 1: comparison of wua to jva = β0 + β1 touring + β2 punishing + β3 resolving conflict + β4 wua help + β5 wua-jva help

Equation 2: comparison of wua to jva = β0 + β1 touring + β2 resolving conflict + β3 wua help + β4 wua-jva help

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) touring: a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no)

The results for the ordered logistic regressions are seen in Table 12.9 and again, the results for Equation 2 are of main note. Roughly 15% of the variation (per the R2 value) in farmer opinion of the WUA as it compares to the JVA is explained by the institutional factors. Some explained variation was lost with the necessity of removing the variable for sanctioning from the equation. Otherwise, while monitoring does not appear to be significant, conflict resolution skills and willingness or ability to help farmers are. For farmers who think that the WUA sometimes or always is able to resolve conflicts among farmers, as compared to those farmers who think the WUA is never able to resolve such conflicts, they are 3.20 and 4.37 times more likely, respectively, to have a favorable view of the WUA when it is compared to the JVA. And for those farmers who go solely to the WUA for help, they are 3.65 times more likely to have a more favorable view of the WUA as compared to the JVA than farmers who solely go to the JVA for help. As with farmer 444 opinion of the WUA, when the WUA is compared to the JVA, institutional factors display high significance and explain a greater portion of the variation in this outcome than the physical factors.

Table 12.9: Regression results of the effects of institutional factors on farmer opinion of the WUA as compared to the JVA. Dependent Variable comparison of wua to jva Independent (1) (2) Variables touring sometimes 1.09 2.38 (0.77) (1.57) always 1.00 2.16 (0.70) (1.32) punishing sometimes 1.40e+07 (1.34e+10) always 1.36e+07 (1.30e+10) resolving conflict sometimes 4.02** 3.20** (2.60) (1.93) always 4.33*** 4.37*** (2.07) (2.05) wua help 3.71*** 3.65*** (1.91) (1.82) wua-jva help 2.57* 2.63* (1.45) (1.45) Prob > chi2 0.0000 0.0000 Pseudo R2 0.1886 0.1461 No. Obs. 115 115 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

445

User Factors

To test the effects of the user factors on farmer opinion of the WUA as it compares to the JVA, the following equations are used:

Equation (1): comparison of wua to jva = β0 + β1 dunums + β2 greenhouses + β3 exporting + β4 ownership + β5 high school

Equation (2): comparison of wua to jva = β0 + β1 dunums + β2 greenhouses + β3 exporting + β4 ownership + β5 high school + β6 ps33 + β7 ps55 + β8 ps91

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) dunums: an interval variable for the log of the number of dunums farmed greenhouses: a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) exporting: a binary variable for whether a farmer personally exports produce (1=yes, 0=no) ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner) high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no) ps33: a binary variable for whether a farmers is in PS 33 (1=yes, 0=no) ps55: a binary variable for whether a farmers is in PS 55 (1=yes, 0=no) ps91: a binary variable for whether a farmers is in PS 91 (1=yes, 0=no)

Table 12.10 records the results of the ordered logistic regressions. Equation 1, which only includes the user factors before adding in the effects of the WUAs, reveals that they are only explaining about 3% of the variation in farmer opinion of the WUA as it compares to the JVA. Farmers who are owners, as opposed to agents, are less likely to have a favorable view of the WUA in comparison to the WUA and those with a high school education or above are more likely to have a more favorable view of the WUA in comparison to the JVA. None of the socioeconomic user factors are of note. And even the effects of being an owner or having a higher education subside once the WUAs are added 446 into the equations (Equations 2). Overall, user factors do not appear to be very significant in predicting this outcome.

Table 12.10: Regression results of the effects of user factors on farmer opinion of the WUA as compared to the JVA. Dependent Variable comparison of wua to jva Independent (1) (2) Variables dunums 1.39 1.58 (0.48) (0.55) greenhouses 0.92 0.58 (0.30) (0.27) exporting 0.85 0.80 (0.38) (0.36) ownership renter 1.16 1.30 (0.42) (0.50) owner 0.48** 0.56* (0.18) (0.21) high school 1.58* 1.41 (0.47) (0.44) ps33 2.45** (1.07) ps55 4.28*** (2.48) ps91 3.84*** (1.65) Prob > chi2 0.0819 0.0078 Pseudo R2 0.0307 0.0613 No. Obs. 186 186 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

447

All Factors

Finally, to test the impact of all factors in all categories on farmer opinion of the WUA as it compares to the JVA, these equations are used:

Equation (1): comparison of wua to jva = β0 + β1 adequacy + β2 reliability + β3 touring + β4 punishing + β5 resolving conflict + β6 wua help + β7 wua-jva help + β8 ownership

Equation (2): comparison of wua to jva = β0 + β1 adequacy + β2 reliability + β3 touring + β4 resolving conflict + β5 wua help + β6 wua-jva help + β7 ownership

Equation (3): comparison of wua to jva = β0 + β1 reliability + β2 touring + β3 resolving conflict + β4 wua help + β5 wua-jva help + β6 ownership

Equation (4): comparison of wua to jva = β0 + β1 adequacy + β2 reliability + β3 reliability*ps33 + β4 touring + β5 resolving conflict + β6 wua help + β7 wua-jva help + β8 ownership + β9 ps33

Equation (5): comparison of wua to jva = β0 + β1 adequacy + β2 reliability + β3 reliability*ps33 + β4 resolving conflict + β5 wua help + β6 wua-jva help + β7 ps33

Where: comparison of wua to jva: a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) touring: a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no) 448 ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner) ps33: a binary variable for whether a farmers is in PS 33 (1=yes, 0=no)

The results of the ordered logistic regressions are recorded in Table 12.11. As occurred earlier, the variable for sanctioning is problematic (Equation 1) so the first model of relevance is Equation 2, which leaves out the variable for sanctioning. In Equation 2, water adequacy is highly significant, with farmers who think that the water supply is adequate being 6.45 times more likely to think more favorably of the WUA in comparison to the JVA. Even when water adequacy is removed (Equation 3), water reliability is not significant. For the institutional factors, there are highly significant and positive effects on farmer opinion of the WUA when it is compared to the JVA for farmers who think that the WUA always resolves conflicts among farmers and for those farmers who go solely to the WUA for help. These factors remain significant and strong in the subsequent equation.

Finally, in Equation 4, for farmers in PS 33 who think that the water supply is reliable, they are much more likely than farmers in other WUAs who think that the water supply is reliable to have a more favorable view of the WUA when it is compared to the JVA. Overall, all of the factors explain roughly 21% of the variation in this outcome, as seen in Equation 2’s R2 before the influence of any of the WUAs is taken into account (as done in

Equation 4).

449

Table 12.11: Regression results of the effects of all factors on farmer opinion of the JVA as compared to the JVA. Dependent Variable comparison of wua to jva Independent (1) (2) (3) (4) Variables adequacy 5.17*** 6.45*** 7.46*** (3.45) (4.35) (5.10) reliability 1.52 1.26 1.81 0.54 (0.74) (0.60) (0.82) (0.32) reliability*ps33 11.06*** (10.56) touring sometimes 1.29 2.63 2.39 2.43 (0.96) (1.77) (1.60) (1.69) always 0.92 1.76 1.76 2.00 (0.67) (1.11) (1.11) (1.29) punishing sometimes 1.73e+07 (2.42e+10) always 1.65e+07 (2.31e+10) resolving conflict sometimes 2.88* 2.40 3.52** 2.77* (2.00) (1.57) (2.15) (1.91) always 3.64*** 3.63*** 4.15*** 3.21** (1.76) (1.74) (1.96) (1.57) wua help 3.65** 3.71** 3.06** 4.12*** (2.01) (2.01) (1.57) (2.32) wua-jva help 2.47* 2.48* 2.72* 2.31 (1.46) (1.45) (1.52) (1.35) ownership renter 1.43 1.51 1.31 1.38 (0.80) (0.85) (0.72) (0.81) owner 0.64 0.50 0.57 0.50 (0.37) (0.28) (0.32) (0.30) ps33 0.30* (0.23) Prob > chi2 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.2405 0.2116 0.1716 0.2419 No. Obs. 115 115 115 115 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

450

WATER STEALING

From the survey results, water stealing is an obvious problem everywhere. 82% of farmers admitted that water stealing is occurring in their area (Figure 12.5), with many admitting that it is they who are stealing as well, not just the farmers around them.

Figure 12.5: Percentage of surveyed farmers who say that water stealing is happening among farmers.

Source: Survey data.

Between the four surveyed WUAs, there are no significant differences (see Figure 12.6).

451

Figure 12.6: Reporting of water stealing in all four WUAs.

Source: Survey data.

Water is stolen by several methods: taking water out-of-turn on the lateral, removing the flow limiter in the FTA, illegally attaching a pipe to a lateral or main line, and siphoning water directly from the King Abdullah Canal (KAC). Farms closer to the

KAC are more likely to partake in the last form of water stealing. While essentially all strategies of water stealing exist in all four of the surveyed WUAs, each WUA displays varying tendencies for the different strategies, as heard from farmers during and outside of the survey period. In PS 33, more farmers appear to steal 452 directly from the KAC. Farmers said that stealing water from the KAC happens day and night, around-the-clock, and that “farmers live off of stolen KAC water.” Even for farms two to five kilometers away from the KAC, farmers said that stealing from the canal is possible and done all the time, although it requires money that not all farmers have. As one farmer lamented: “If I was close to the KAC, I would steal from it.” Stealing directly from the KAC is not possible for all farmers because it does take some finesse and can require bribing of JVA employees responsible for guarding the KAC as well as installing new piping networks. A few farmers in PS 33 took the time to explain and point-out the vast KAC stealing operations that occur in the area and the network of piping that is used for this purpose. Farmers near the KAC can steal more readily by sinking a pipe into the KAC and siphoning off water with a pump. One farmer has a particularly large pump that he uses for this purpose to not only supply himself but to supply other farmers, at a cost. If a farm is located some distance from the KAC, pipes have to be run along the edges of the farms between that farm and the KAC, necessitating the agreement of those farmers who reside in between and possibly also paying them off for the placement of piping on their land. This is in addition to the price tag attached to the stolen water from the farmer who is siphoning it off from the KAC. As one farmer put it, “it’s becoming like a mafia here.” At some farms along the canal, groups of young men and boys can be seen sitting near the canal to protect a pipe that is siphoning water from the KAC.

Much of the time, JVA and WUA employees turn a blind eye to water stealing from the KAC. Farmers remarked that stealing water from the KAC is not really a hidden affair but rather the pipes taking water out of the KAC are readily visible. Indeed, in the middle of the day these pipes were seen and in some areas, due to certain indentions in the road

453 and missing concrete along the KAC, it is evident that water stealing occurs even though it is not occurring at present. Figure 12.7 depicts evidence of illegally siphoning water from the KAC. In the upper left photo, piping is laid under the road and concrete is poured on top to create a permanent fixture to siphon water from the KAC. In the upper right photo, an indention remains in the road from where a pipe was laid across the road to reach the KAC and illegally siphon water from it. The lower left photo shows where the concrete lining the KAC is broken in order to fit a pipe through and to the KAC. And the lower right photo shows a plastic pipe resting under a concrete block on the right side of the road, ready to be dragged across the road and used to steal water from the KAC. Further evidence of water stealing in this format is when a farmer does not have a water turn and his lateral line is not receiving water that day, and yet water is still streaming into his holding pond.

454

Figure 12.7: Evidence of illegal siphoning of water from the KAC in the area of PS 33.

Source: Personal photographs.

Water stealing in PS 33 occurs not only along the KAC. Farmers also said that sometimes farmers take water out-of-turn, meaning that they open their FTAs at a time when it is not their turn, and others remove their flow limiters in order to increase the flow and volume of water that is siphoned onto their farms. WUAs have attempted to decrease the amount of flow limiter removal by placing a simple wire clasp, acting as a lock, on the section of the FTA that houses the flow limiter (Figure 12.8). In this case, if a farmer does

455 remove the flow limiter, it will be more readily noticeable and perhaps a farmer will be more deterred to remove it in the first place.

Figure 12.8: The small wire clasp to “lock” the FTA’s flow limiter.

Source: Personal photograph.

Some general commentary on water stealing was also heard in PS 33. One farmer said: “Water stealing is part of the Jordanian mentality,” calling attention to the general societal attitude on stealing, corruption and lack of law enforcement in these kinds of affairs. Another farmer said that it is simply impossible to succeed as a farmer here unless you are willing to do whatever it takes, whether it’s stealing or bribing or whatever. Yet another farmer stated that you have to lie, cheat, steal and pay bribes here to get your water. As a farmer with a secondary water source said, it’s just “natural” that those without a secondary water source have to steal water.

456

In PS 55, the focus of water stealing is geared more towards farmers removing the flow limiters from their FTAs. As one farmer stated, “if farmers don’t play with the FTA, they don’t get their water.” Another farmer fully admitted to having removed his flow limiter and he said that he has even gotten tickets for his water stealing ($25 the first time, then $50, $75 and $100). But he doesn’t care about the tickets and sees the water as more important and valuable than any fines he incurs. This sentiment was echoed by another farmer who believes that the chance of him getting a ticket for stealing water does not deter him from continuing to steal water. According to one farmer, some new meters and flow limiters were installed in certain areas but he laughingly brushed this off, saying that farmers have already removed these things so it is not a problem. Some farmers in PS 55 said that stealing from the KAC happens in this area as well, especially at night and especially among those farmers located next to the KAC. Other farmers said that there is some stealing that goes on directly from the main line through illegal connections. Some farmers added that there is a seasonality to stealing, that it mainly happens in the Spring months of March, April and May and in the Fall months of September, October and November. Many fields lay fallow in the summer and in the winter there is rain and a cooler climate, decreasing water demand. In general, farmers in PS 55 remarked that “the majority of farmers steal water,” that there is “100% water stealing” here, and that “everyone does it.” As in PS 33, stealing is seen as part of the mentality and “something natural.” One farmer said that farmers are watching their crops dying and have no choice but to steal water. Another one said that it is understandable for those without a well to steal water. An observation by yet another farmer made it clear that farmers do not think about how stealing water may affect the water supply of their neighbors; they are only focused on their own water shortage. A last

457 but vital point is that farmers pointed out how easy it is for farmers to get away with stealing water. All they have to do is wait for the WUA ditchriders to pass by and leave and then they can steal water for hours with no one monitoring their actions. PS 91, according to surveyed farmers, witnesses a variety of water stealing practices. As in PS 55, farmers remarked that water stealing is seasonal, largely occurring in the Spring months of March, April and May and the Fall months of September, October and November. It is easier for farmers here to open their FTA out-of-turn due to most laterals operating such that one side of the lateral takes water at any given time, with the other side able to free-ride and steal water at the same time. Several farmers readily admitted to doing this on a regular basis. Farmers also remove the flow limiter from the FTA. Due to the more frequent ability of the WUA to give farmers extra water turns in this area, neighboring farmers can open their FTAs when a farmer has acquired one of these extra turns and thereby steal water that is not for them. One farmer also suggested that farmers make deals between each other to take water on each other’s turns. A couple of farmers said that farmers sometimes open their lateral line during a time that is not their turn and they have the key to do this (a metal bar in the shape of a T that is used by ditchriders to open the lateral line). Another farmer said that sometimes the main pump is opened illegally at night. And yet another farmer said that he has seen other farmers have illegal connections on the lateral and main lines, with these apparatuses sometimes three meters below ground so that the WUA and JVA cannot seem them.

A unique aspect of PS 91 and other WUAs in the south of the valley is that there are farms with plastic pipes directly and legally-placed into the KAC. But this additionally serves as a way for farmers take more water than their allotted turn either for themselves or to sell to other farmers. A few farmers who buy this water from another farmer said that

458 it typically costs 5 JD per hour. Another farmer was not happy with this system and stated angrily that “the KAC is not their pool” and that they shouldn’t be able to treat it as such. As in PS 33 and PS 55, there are also farmers not granted a plastic pipe to the canal who put one into the KAC anyway to steal water, especially on Fridays (Discussion with PS 91 ditchrider, 9/27/2014). In general commentary within PS 91, farmers said that no one thinks about their neighbors when they steal water. It is just pure personal greed. In fact, one farmer recounted a time when a farmer put a rock in the lateral line just below his FTA intake in order to block the flow from continuing further down the line, to give himself better water pressure and thus an increased volume of water. Another farmer stated that he is completely willing to pay tickets for water stealing in order to get more water, saying that “water is more important than tickets.” One farmer said that because the WUA doesn’t supply farmers with enough water, it is their right to take more. And another farmer said that animal herders sometimes take water from the FTAs at the end of the lateral lines so it is not just farmers who steal water. This farmer also said that when animal herders do this, it reduces the pressure in the lateral line and thus reduces the flow into the FTAs along the rest of the line. Finally, in MH, removal of flow limiters was a frequently-admitted occurrence.

There is also reported tampering with the lateral lines in order to get water out of turn or increase the flow in the lateral line. One farmer reported that another farmer once put a rock in the lateral line in order to block the flow of water to the rest of the line. There was again a mention of the seasonality of water stealing, in that it occurs most frequently at the beginning of the growing season in the Fall. There is more reported water stealing from

459 those farms that receive water from Wadi Karak Dam as opposed to the Wadi Bin Hamad spring due to the reportedly more unreliable nature of the former water source. Farmers in MH offered similar general commentary to those farmers in the other three WUAs. They felt that water stealing is understandable simply because “they need it.” As one farmer put it, farmers don’t steal “because they want to” but rather “because they are in great need of more water.” In times of great need, one farmer said plainly, “I would be the first to steal water.” Because everyone else steals water, farmers feel that they have to as well. Put simply, from yet another farmer: “If a farmer can steal, he will.”

Physical Factors

To assess whether the physical factors affect farmer reporting of water stealing, the following equation is used:

Equation: water stealing = β0 + β1 adequacy + β2 reliability

The results of the logistic regression are recorded in Table 12.12. Taking into account water adequacy and water reliability together, water adequacy is highly significant and water reliability mildly significant. For those farmers who report that the water supply is adequate, they are 3.4 times less likely (the inverse of 0.29) to say that water stealing is happening. For farmers who think that the water supply is reliable, they are 2.3 times less 460 likely (the inverse of 0.44) to say that water stealing is happening. This shows that where water is adequate and reliable, less water stealing and more rule-following is occurring. Overall, the physical factors are explaining 9% of the variation (as per the R2 value) in reported water stealing.

Table 12.12: Regression results of the effects of physical factors on farmer reporting of water stealing.

Dependent Variable Independent water stealing Variables adequacy 0.29*** (0.14) reliability 0.44* (0.25) Prob > chi2 0.0022 Pseudo R2 0.0929 No. Obs. 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Institutional Factors

For the outcome of whether farmers think that water stealing is occurring in the area, none of the institutional factors are significant predictors.

User Factors

In examining the effects of the user factors on reported water stealing, the following equation is used:

Equation: water stealing = β0 + β1 dunums + β2 ownership + β3 high school

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Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) dunums: an interval variable for the log of the number of dunums farmed ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner) high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no)

Within the results of the logistic regression in table 12.13, the user factors together are explaining about 10% of the variation (per the R2 value) of farmer reporting of water stealing. Land size is highly significant; for farmers with more dunums, they are more likely to report water stealing. Of lesser significance is land-holding status. Both owners and renters are more likely than agents to report water stealing. Education has no significant impact.

Table 12.13: Regression results for the effects of user factors on farmer reporting of water stealing. Dependent Variable Independent water stealing Variables dunums 4.74*** (2.75) ownership renter 3.39* (2.21) owner 2.53* (1.44) high school 1.33 (0.65) Prob > chi2 0.0089 Pseudo R2 0.1026 No. Obs. 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%. 462

All Factors

In a final assessment of the effects of all factors on farmer reporting of water stealing, the following series of equations are used:

Equation (1): water stealing = β0 + β1 adequacy + β2 reliability + β3 dunums + β4 ownership

Equation (2): water stealing = β0 + β1 reliability + β2 dunums + β3 ownership + β4 mh

Equation (3): water stealing = β0 + β1 adequacy + β2 reliability + β3 dunums + β4 ownership + β5 mh

Equation (4): water stealing = β0 + β1 adequacy + β2 reliability + β3 dunums + β4 ownership + β5 mh + β5 secondary work

Where: water stealing: a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) dunums: an interval variable for the log of the number of dunums farmed ownership: a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner) mh: a binary variable for whether a farmer is in MH (1=yes, 0=no) secondary work: a binary variable for whether a farmer as work or receives an income from a source outside of his farm considered in the survey area (1=yes, 0=no)

From the results of the logistic regressions in Table 12.14, several factors still remain significant in their effects on farmer reporting of water stealing. Across all models including water adequacy (Equations 1, 3 and 4), this variable maintains high significance. For farmers who report that the water supply is adequate, they are 3.3 times less likely to 463 report that water stealing is happening. Water reliability does gain significance when water adequacy is excluded (Equation 2) but the amount of variation explained decreases from 17% to 15% so this is likely not as good a model. Farmers with more dunums of land are more likely to report water stealing and this remains highly significant throughout all equations. When WUAs are added to the models, MH appears to be significant. In Equation 3, farmers in MH are 2.7 times less likely to report water stealing than farmers in the other three WUAs. Adding in secondary work (Equation 4) raises the explanatory power to 21% and is mildly significant; those farmers with secondary work 2.93 times more likely to report water stealing. Overall, fewer factors are of significance in their effects on water stealing but some physical and user factors do have strong effects.

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Table 12.14: Regression results of the effects of all factors on farmer reporting of water stealing. Dependent Variable water stealing Independent (1) (2) (3) (4) Variables adequacy 0.30** 0.32** 0.31** (0.16) (0.17) (0.17) reliability 0.53 0.24** 0.38* 0.36* (0.32) (0.15) (0.25) (0.24) dunums 5.22*** 5.20*** 5.35*** 4.32** (3.21) (3.16) (3.32) (2.68) ownership renter 2.54 3.27* 2.83* 2.40 (1.73) (2.21) (1.96) (1.71) owner 2.21 2.15 2.27 1.59 (1.32) (1.26) (1.37) (1.02) mh 0.34* 0.37* 0.35* (0.22) (0.25) (0.24) secondary 2.93* work (1.75) Prob > chi2 0.0004 0.0012 0.0004 0.0002 Pseudo R2 0.1707 0.1518 0.1866 0.2134 No. Obs. 174 174 174 174 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

FAIRNESS OF THE WUA

Another outcome to be analyzed in WUA performance is the level of fairness of the WUA in its treatment of farmers. Among all surveyed WUAs, 63% of surveyed farmers think that the WUA is fair, 36% think that it is not fair and 1% reported that they don’t know (Figure 12.9). Between the associations (Figure 12.10), the percentage of farmers who think that the association is fair is comparable between PS 33, P55 and MH,

465 around 60%. In PS 91, more farmers find the WUA fair (73%). But none of these differences are significant.

Figure 12.9: Percentage of farmers in all surveyed WUAs who believe the WUA is fair, not fair or don’t have an answer.

Source: Survey data.

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Figure 12.10: Views of farmers in the four surveyed WUAs with regard to the fairness of the WUA.

Source: Survey data.

Farmers who did not find the WUA fair provided a variety of reasons for this opinion. The three main factors leading to unfairness in all surveyed WUAs were: the use of power, influence and wealth; the use of family connections and friends; and bribing. In PS 33, many farmers said that the bigger, more powerful and influential farmers get favors and the smaller, weaker and poor farmers do not. One farmer said: “Here, the powerful eat the weak.” It is not necessarily that the powerful farmer is paying anyone off

467 but rather that his general position and clout in society, in the farming community or in Jordan at large, is what provides him with special treatment. For example, in one section of the gravity system, two farmers complained about their neighbor farmer who was allowed to have more than one FTA whereas they were not. They argued that their plot sizes are the same and thus deserve whatever the other farmer has, but they felt that their neighbor’s influence, wealth and connections got him the extra FTAs. It is also believed that one investor farmer who owns a very large portion of the land under this WUA essentially has the WUA “in his hands.” This influence is kept in subtle but important and meaningful ways, such as providing for the WUA office’s refreshments supply and having one of his employees as the WUA secretary. Other farmers in PS 33 reported that bribing is the most prevalent form of favoritism and that the WUA favors those farmers who give bribes, monetary or in kind. As one farmer put it simply, “if you pay [extra], you get water.” According to another farmer, ditchriders do their field tours routinely but mainly for the sake of collecting these kinds of bribes, not for the sake of monitoring duties. One farmer who refuses to pay-off the ditchriders believed that he got less water for this very reason. To him, the WUA is basically selling the water to farmers. Farmers in PS 33 also said that the relationship or friendship between the farmer and the WUA matters. If the farmer is on friendly terms with the WUA employees and head or is from the same family as them, then he doesn’t get ticketed for stealing water or removing his flow limiter. Another piece of evidence in support of this claim is that the administrative council is largely made up of members of the same family as the WUA head. One farmer even suggested that if you are in the administrative council, you are not in the water order because you can get water whenever you want and never get a ticket. This

468 might be exaggeration but there is likely something amiss when so many of the head’s family members are not only in the administrative council, but also when they are the ditchriders and engineer of the WUA. In PS 55, favoritism is widely due to farmers being close friends and relatives of WUA employees, especially with regard to ticketing. Some farmers mentioned that if a farmer is simple friends with the WUA, then things will work out better for him and no violations will be on his record. As one put it, “there are degrees in their [the WUA’s] work,” meaning that depending on the farmer’s relationship with the WUA, his situation will be better or worse. Another farmer mentioned that relatives of the WUA head get special treatment. And one farm more colorfully explained that the WUA is like the Baladia, or the local village government: it looks out for those who vote for it. Farmers also claimed that “wasta,” or influence, and a farmer’s general position in society matter when it comes to his ability to influence water distribution and whether or not he is fined. Some farmers mentioned that there are basically no tickets for those with money and that there are “whales,” or big fish, in the area who take more than their fair share of the water quantity. In some of these comments it was presumed that those with money also have the ability to bribe ditchriders and thus yield more favorable situations for themselves. On the whole, claims of bribery were not as great in PS 55 as they were in

PS 33 but a few farmers made mention of this. One farmer gave a bit more detail in how the bribing of the ditchrider would benefit a farmer, saying that the ditchrider might open a lateral line during a time in which it did not have a turn in exchange for a pay-off. Several farmers in the zhor area of PS 55 said that farmers “up above,” or not in the zhor, are favored by the WUA. They believed that water flows more freely in the upper regions and at their expense. One farmer also believed quite strongly that water is only for

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“up above” because that is where the WUA head and all of his friends have farms and they are stealing all of the water. Within PS 91, farmers emphasized that the relationship between the farmer and the WUA employees matters and can determine how much water the farmer gets and whether or not he is issued any tickets. Several farmers remarked on the importance of the relationship between the ditchriders and the farmer especially with regard to whether the ditchriders choose to give the farmer a ticket or not. A farmer who, by observation, got fairly cozy treatment by the ditchriders, in that they let her off without a ticket when she deserved one, described the ditchriders as “honey,” or very sweet and kind. Another farmer stated that being on the “good side” of the WUA engineer can serve a farmer very well. One farmer even believed that one lateral line is open 24 hours a day [not likely but it could be opened for longer than its turn] because the WUA employees indirectly own and are renting out land on this line.

A few farmers mentioned that favoritism on the part of the WUA at PS 91 is based on race, with farmers using the word “racism” to describe actions of the ditchriders. Some farmers believed that Pakistanis are more favored. One farmer believed that the ditchriders on the whole favor the Pakistanis and are really easy with them. Another farmer was convinced that the ditchriders are even partners with the Pakistani farmers, giving them special advantage and then profiting from it. Other farmers thought that water stealing happens mainly with the Pakistanis and the WUA knows about it but chooses to turn a blind eye to it. As elsewhere, money and influence were thought to be at the base of favoritism among farmers by a few individuals in PS 91. One farmer said that those with power and more weight can scare the WUA employees, sometimes get violent in from of them, and

470 make WUA employees fearful if they are not treated well. Farmers mentioned that water here is for the “big guys, not the average farmers,” and that the ministers and “big fish” can get more water. In the same vein, one farmer stated: "Here the big fish are eating the small fish." While reports of bribing of WUA employees were fewer in PS 91 than in PS 33, it was still noted. One farmer mentioned that there are a lot of “below the table” dealings between farmers and ditchriders, adding that if you have money, you essentially have no problem and can buy your way out of problems. Unlike in the other WUAs, farmers also distinguished between ditchriders when it came to whether they acted fairly or not. One particular ditchrider was mentioned as being not good and not treating farmers well or fairly, whereas the other two ditchriders were thought to be good and respectable in their dealings with farmers. And one farmer remarked that there is some favoritism but he felt that this was perfectly natural. He said that it is like having kids: “You love them [your kids] all but you love some more than the others just a little bit.” He took it in stride that sometimes there is just more of an understanding and connection between the ditchriders and some farmers and it is natural that the ditchriders would not be as harsh with them. In MH, many farmers also stated that favoritism occurs for friends and family and that while “the brother benefits, the other [not the brother] gets hurt.” A farmers stated that

“there is no treating of everyone on the same level,” and that family and friends can get away with more in the field without getting tickets. Another farmer claimed that the head and his relations get to have bigger flow limiters. As in PS 33, the fact that one of the farmers in MH’s area is also a ditchrider makes for a conflict of interest. This ditchrider’s interest in keeping this piece of information “hush-hush” is evidence that even he knows it is not right.

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Many farmers in MH talked about how bribing is the essential route for favoritism. One stated: “If there is money, there is water.” Another stated that “the WUA is all about money and corruption.” One farmer was convinced that some farmers have paid-off ditchriders and acquired a bigger flow limiter in return. It was also heard that if a farmer can pay a little extra on the side or give a ditchrider a bag of vegetables, he can get more water from the network. A farmer posed the question simply: “Will the ditchrider be nice to the guy who gives him the box of vegetables or the guy who does not?” Several farmers mentioned that this is nothing unusual in Jordan and that “it happens with us Arabs.” It was even witnessed first-hand how easily and naturally this happens. While traveling around the field with a ditchrider, he would frequently and subtly request vegetables or cigarettes from various farmers and farmers would readily agree. While a tomato or cucumber here and there would just be a nice snack, this ditchrider was gathering boxes and large bags of produce to take back to his home.

Farmers in MH also mentioned that one’s power and influence matter in the field. One farmer felt that it is very much the “big guys” against the “small guys” here. As one stated: “The big guys get everything and the little guys die.” Another similarly commented that “the big eat the small” in this area while yet another lamented seeing all the water and good stuff going to the ministers and “big people” in the area with nothing for the small farmers. Lastly, farmers in MH brought up the subject of “racism,” as in PS 91 but of a different variety. One farmer noted that there is distinct tension between the people of the ghor, or the valley, and those from Karak, a bigger city in the Highlands from where many investor farmers in the area originate. This farmer stated that there are more tickets for those not from the ghor and that it is essentially a matter of being a relative of the WUA

472 head or one of “his people.” A couple of other farmers mentioned that if a farmer is a member of a powerful local tribe, he is not likely to get a ticket. One farmer stated that it is exactly these tribal relations that make any chance for “democratic” proceedings in the WUA very unlikely. One Pakistani farmer felt that he has less power and can do very little to better his situation because he is not Jordanian.

Physical Factors

To test the impact of the physical factors on farmer opinion of the fairness of the WUA, the following equations are used:

Equation (1): fairness of wua = β0 + β1 adequacy + β2 reliability

Equation (2): fairness of wua = β0 + β1 adequacy + β2 reliability + β3 beginning position + β4 pressure network

Equation (3): fairness of wua = β0 + β1 adequacy + β2 reliability + β3 secondary work + β4 secondary water + β5 secondary work*secondary water

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) beginning position: a binary variable for whether the farm(s) is solely at the beginning of the lateral line (1=yes, 0=no) pressure network: a binary variable for whether the farm(s) is solely in the pressure network (1=yes, 0=no) secondary work: a binary variable for whether a farmer as work or receives an income from a source outside of his farm considered in the survey area (1=yes, 0=no) secondary water: a binary variable for whether a farmer has a secondary source of water (1=yes, 0=no)

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Table 12.15 includes the results of the logistic regressions and within the three models, the physical factors are explaining only around 7% of the variation in farmer opinion of the fairness of the WUA. Water reliability is of great significance; for farmers who think that the water supply is reliable, they are 2.5 times more likely to think that the WUA is fair. Water adequacy is less significant but still mildly so, with farmers who think that the water supply is adequate being 2.3 times more likely to think that the WUA is fair. The effects of lateral or network position (Equation 2) and secondary work or water resources (Equation 3) are not significant and add nothing to the variation explained. Thus, it is only water reliability and adequacy, of the physical factors, that affect farmer perception of WUA fairness.

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Table 12.15: Regression results of the effects of physical factors on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent Variables (1) (2) (3) adequacy 2.30* 2.33* 2.28* (1.03) (1.05) (1.03) reliability 2.54*** 2.55*** 2.53*** (0.85) (0.88) (0.86) beginning position 0.78 (0.32) pressure system 0.94 (0.31) secondary work 1.26 (0.49) secondary water 1.47 (0.77) secondary work*secondary water 0.72 (0.53) Prob > chi2 0.0003 0.0023 0.0044 Pseudo R2 0.0688 0.0704 0.0722 No. Obs. 184 184 184 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Institutional Factors

In examining the impact of the institutional factors on farmer opinion of the fairness of the WUA, the equations below are used. The second equation, which does not include the variable for sanctioning, is preferred; when the sanctioning variable is included, as has happened in earlier regressions, there is perfect prediction and collinearity so it is best if it is dropped.

Equation (1): fairness of wua = β0 + β1 touring + β2 punishing + β3 resolving conflict + β4 wua help + β5 wua-jva help

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Equation (2): fairness of wua = β0 + β1 touring + β2 resolving conflict + β3 wua help + β4 wua-jva help

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) touring: a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always) punishing: a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always) resolving conflict: a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no)

In the results of the logistic regressions in Table 12.16, specifically in Equation 2, the level of monitoring and where a farmer seeks help are significant in effect. For farmers who sometimes and always see the ditchriders monitoring the fields, they are 4.11 and 9.95 times more likely to think that the WUA is fair. And for farmers who solely go to the WUA for help, as opposed to those who solely go to the JVA for help, they are 4.06 times more likely to think that the WUA is fair. Overall, the institutional variables are explaining about 20% of the variation (as per the R2 value) in whether farmers view the WUA as fair.

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Table 12.16: Regression results of the effects of institutional factors on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent (1) (2) Variables touring sometimes 2.35 4.11* (2.04) (3.32) always 4.28* 9.95*** (3.62) (7.62) punishing sometimes 0.41* (0.20) always (omitted) resolving conflict sometimes 2.03 1.73 (1.49) (1.19) always 1.15 1.26 (0.61) (0.65) wua help 4.69*** 4.06** (2.84) (2.34) wua-jva help 1.14 1.05 (0.69) (0.62) Prob > chi2 0.0005 0.0000 Pseudo R2 0.1808 0.1963 No. Obs. 110 115 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

User Factors

Factors related to the water users did not appear to greatly weigh-in on farmer view of the fairness of the WUA in previous individual analyses of these factors so no results are necessary here.

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All Factors

Finally, to test the effect of all factors on farmer opinion of the fairness of the WUA, the series of equations is listed below. Equations 4 and 5 are the same as Equations 1 and 3 but use more observations; those observations dropped for the sanctioning and conflict resolution variables (that are not used in any of these models) are added back within Equations 4 and 5 to see if the results change.

Equation (1): fairness of wua = β0 + β1 adequacy + β2 reliability + β3 touring + β4 wua help + β5 wua-jva help

Equation (2): fairness of wua = β0 + β1 reliability + β2 touring + β3 wua help + β4 wua-jva help

Equation (3): fairness of wua = β0 + β1 adequacy + β2 reliability + β3 touring + β4 wua help + β5 wua-jva help + β6 mh

Equation (4): fairness of wua = β0 + β1 adequacy + β2 reliability + β3 touring + β4 wua help + β5 wua-jva help

Equation (5): fairness of wua = β0 + β1 adequacy + β2 reliability + β3 touring + β4 wua help + β5 wua-jva help + β6 mh

Where: fairness of wua: a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no) reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) touring: a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always) wua help: a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) wua-jva help: a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no) mh: a binary variable for whether a farmer is in MH (1=yes, 0=no)

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The results of the logistic regressions are recorded in Table 12.17. In the first and most basic model (Equation 1), water adequacy has maintained some mild significance and this is retained throughout other models (Equations 3-5). For farmers who think that the water supply is adequate, they are roughly 3 times more likely to think that the WUA is fair. Water reliability does not gain significance even when water reliability is excluded from the analysis (Equation 2). The level of ditchrider monitoring is a highly significant factor, especially for those who think that the ditchriders are always monitoring the fields. And for farmers who only seek help from the WUA, as opposed to those who only go to the JVA for help, they are significantly more likely to think that the WUA is fair. All of these observations hold true for Equations 4 and 5 when more observations are used. Among the WUAs, MH is observed to have a very significant impact; farmers in MH are 5.96 times more likely to think that the WUA is fair and this addition does not change the general significance and sign of the other variables. Overall, without the addition of MH, all included factors explain about 20% of the variation in farmer opinion of the fairness of the WUA.

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Table 12.17: Regression results of the effects of all factors on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent (1) (2) (3) (4) (5) Variables adequacy 3.05* 3.31* 3.20** 2.92** (1.97) (2.14) (1.61) (1.46) reliability 1.23 1.65 1.43 1.28 1.47 (0.61) (0.76) (0.73) (0.52) (0.61) touring sometimes 4.13* 4.33* 3.86* 2.92* 2.85* (3.36) (3.52) (3.21) (1.63) (1.65) always 9.53*** 10.14*** 12.05*** 8.94*** 11.13*** (7.27) (7.72) (9.74) (4.84) (6.41) wua help 4.17** 3.75** 8.61*** 1.95 3.66** (2.51) (2.20) (6.15) (0.93) (2.05) wua-jva help 0.93 1.12 1.77 0.68 1.27 (0.56) (0.65) (1.22) (0.32) (0.70) mh 5.96** 3.83** (5.14) (2.34) Prob > chi2 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.2208 0.1997 0.2513 0.1907 0.2135 No. Obs. 115 115 115 177 177 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

MEMBERSHIP IN WUAS

A final outcome examined is the rate of membership among farmers in the four case study WUAs. Membership in a WUA is voluntary. It is only available to Jordanians, meaning that Pakistani and Egyptian farmers are not allowed to be members. Additionally, farm agents who were interviewed, who simply operate the farmer on behalf of the owner or renter, are not allowed to be WUA members. Figure 12.11 shows that overall, from data acquired from the JVA and WUA, 68% of farmers within the four surveyed WUAs are

480 members. Comparing between WUAs, the lowest rate of membership (23%) is in PS 91 (see Figure 12.12). PS 33, PS 55 and MH all have higher membership rates at 73%, 80% and 91%, respectively.

Figure 12.11: Overall membership among farmers within the four surveyed WUAs according to data from the JVA and WUAs.

Source: Data obtained from JVA and WUA employees.

481

Figure 12.12: Membership rates within the four WUAs according to data from the JVA and WUAs.

Source: Data obtained from JVA and WUA employees.

Among only those farmers who were surveyed in each of the four WUAs, only 26% are members of their WUA (Figure 12.13). Between associations, the lowest rate of membership among surveyed farmers (9%) is again in PS 91, with PS 33, PS 55 and MH all having significantly higher membership rates among surveyed farmers at 44%, 27% and 27%, respectively (Figure 12.14).

482

Figure 12.13: Percentage of WUA members and non-members among all surveyed farmers.

Source: Survey data.

483

Figure 12.14: Membership rates among surveyed farmers in PS 33, PS 55, PS 91 and MH.

Source: Survey data.

Among those surveyed farmers who are eligible to be members (Jordanian renters and owners), only 38% overall are members in a WUA (Figure 12.15). When comparing between the WUAs (Figure 12.16), 56% of eligible surveyed farmers in PS 33 are members and around 40% of eligible surveyed farmers in PS 55 and MH are members. Only 15% of eligible surveyed farmers in PS 91 are members, a percentage significantly lower than that of the other three WUAs.

484

Figure 12.15: Membership rates among surveyed farmers who are eligible to be members in a WUA.

Source: Survey data.

485

Figure 12.16: Membership rates among surveyed farmers within each of the four WUAs who are eligible to be members in the WUA.

Source: Survey data.

From what farmers said during the survey, major reasons heard as to why some are not members in the WUA are the following: the farmer has no extra time; the farmer has no extra money to afford the membership fee; one of the farmer’s relatives is in the association so he feels that he hears about it and has a voice in it through that relative; the farmer is already a member in another WUA and is not allowed to be a member in two WUAs; the farmer is new to the area and has not heard much about the WUA yet; the

486 farmer has no real reason but just doesn’t feel like it and sees no point or benefit to membership; and the WUA never asked the farmer to join or how to become a member. In some cases, the farmer did not feel welcomed to become a member. In PS 55, one farmer was not on good terms with some of the WUA employees for personal reasons and another farmer felt unwelcomed because he wouldn’t vote for those currently in the WUA administrative council. One farmer in PS 91 asked to become a member but the

WUA said no. Another farmer in PS 91 was working under the false belief that only owners could be members. And in MH, some farmers felt that the WUA was not very encouraging with regard to membership and thus didn’t feel that they were desired as members. Another farmer thought that WUA membership was only for big farmers. Additional reasons heard from farmers as to why they are not members include: the farmer is only temporarily on this farm and will move to another area soon; the farmer feels that there are already too many farmers in the WUA so there is no need for his membership; the farmer is already friends with the WUA head so he can already get his voice heard through direct contact; the farmer believes that the WUA only benefits the head and his friends; the farmer hates the WUA and wants the JVA to return; the farmer doesn’t want to be a part of anything and just wants to be left alone; and the farmer is a former member who withdrew his membership because the WUA wasn’t doing anything useful according to him. For farmers who are members in the WUA, they were asked why they are members.

Some of the main reasons they gave are the following: the WUA asked the farmer to join; the farmer wanted to be able to get his full water share and rights; the farmer wanted to have a voice and a say in decisions and participate in a larger group; the farmer wanted to benefit from current and future projects that would be conducted in the area and yield

487 potential perks for members only; and the farmer can’t think of a real reason. One farmer in MH was unsure of whether he was a member and the WUA had to be consulted on this point. Other stated reasons for being members include: the farmer wanted to be able to vote in WUA elections; everyone else was becoming a member and donors were excited about it so the farmer decided to join in; the farmer wanted to help make the situation in the field better and stop the water stealing; the farmer wanted to get to know other farmers; and the farmer simply took his father’s place in the WUA when the father was no longer capable of being a member.

Member Activities

Of those surveyed farmers who are members in the WUA, 57% said that they participate in elections, with 43% stating that they attend the yearly meeting of the WUA that sometimes occurs at the same time as the elections. Only a little over half of members attend elections, an activity occurring once every 2-4 years and serving as the most basic of member functions. Among the four surveyed WUAs, member farmers are significantly more likely to attend elections in PS 55 than in PS 33. A few farmers mentioned that elections are just something “for show” so there isn’t even a real vote. In observing elections held at PS 33 in May 2014, this comment seems legitimate. Less than half of the WUA members were present, there were no elections but simple agreement to keep the current administrative council and head, and few farmers listened to the proceedings and the financial and administrative reviews. By the end of the meeting, a few heated arguments had erupted over some key issues like membership fees, what would be done with the money accumulated thus far by the WUA from the

488 membership fees, and term limits for the WUA head. But the meeting ended in general chaos with absolutely no resolution on any of these issues. As for other WUA activities, only a handful of member farmers, mostly in PS 33, reported that they attended workshops, fieldtrips and lectures hosted by the WUA. Two surveyed farmers also hold office in their WUA administrative councils. Within PS 33, PS 91 and MH, 22%, 20% and 27%, respectively, of surveyed member farmers noted that they don’t participate in any WUA activities. From commentary by member farmers in PS 33, one said that he only participates in elections because the WUA doesn’t ask for any other type of participation. Another farmer said that he didn’t even participate in the last elections because he was never told when they were being held. And another member farmer who used to be on the monitoring committee for two years said that he eventually quit because in those two years, the committee never held a meeting and did nothing. One other member farmer said that the administrative council does nothing except elect the head, so even being in the WUA administrative council might not signify a high amount of participation. In PS 55, one member farmer said that elections are really just about appointing someone so there isn’t really a vote anyway and thus very little real participation. It’s all about who has the most relatives backing him up. At the same time, another member farmer thought that this was just fine and a working and reasonable system. In PS 91, one member farmer said that he only visits the WUA and participates in a meeting when the Secretary General of the JVA visits or if there is a specific meeting relating to date palm trees (the crop he grows). Another member farmer said that one way to participate in the WUA is sometimes just to “eat zirb,” which is a traditional Jordanian dish of meat that has been cooked underground. He meant that a way to participate is to

489 simply come together with fellow farmers and have a meal. As he said: “We have good relations with someone when we eat with him. We see each other, understand each other, and then help each other.” In MH, one member farmer said that his father was a founding member and the first head of the WUA but he was voted out of office because the current president had more family contacts and relations in the area. He recounted what his father used to say about the current head: “Even if King Abdullah ran against him in the WUA elections, the king couldn’t win!” That is because of the importance of tight family relations in the valley.

Another member farmer had the simple complaint that despite his being a relatively big farmer in the area, according to him, no one bothers to tell him about WUA meetings so he can’t participate even if he wants to.

Physical Factors

In order to assess the effects of the physical factors on farmer membership in the WUA, the following equations are used:

Equation (1): membership = β0 + β1 adequacy + β2 reliability

Equation (2): membership = β0 + β1 adequacy + β2 reliability + β3 beginning position + β4 pressure network

Equation (3): membership = β0 + β1 beginning position + β2 pressure network + β3 ps33 + β4 ps55 + β5 mh

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) adequacy: a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no)

490 reliability: a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) beginning position: a binary variable for whether the farm(s) is solely at the beginning of the lateral line (1=yes, 0=no) pressure network: a binary variable for whether the farm(s) is solely in the pressure network (1=yes, 0=no) ps33: a binary variable for whether a farmer is in PS 33 (1=yes, 0=no) ps55: a binary variable for whether a farmer is in PS 55 (1=yes, 0=no) mh: a binary variable for whether a farmer is in MH (1=yes, 0=no)

The results in Table 12.18 of the logistic regressions demonstrate that neither water adequacy nor reliability hold any significant weight and explain none of the variation in farmer membership in the WUAs (Equation 1). But being at the beginning of the lateral and being in the pressure network are significant (Equation 2) and explain 8% of the variation in farmer membership (as per the R2 value). For farmers at the beginning of the lateral, as opposed to being at the end, they are 4 times less likely (the inverse of 0.25) to be members and for those in the pressure network, they are 2.66 times more likely to be members. When variables for the WUAs are added, PS 33 and MH are significant and demonstrate that farmers in these associations are more likely to be members than farmers in PS 91. From the descriptive statistics of the survey sample, this is already evident. The WUAs are included to show that even their inclusion does not negate the effect of the significance of network and lateral position.

491

Table 12.18: Regression results of the effects of physical factors on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) (3) Variables adequacy 0.67 0.89 (0.32) (0.46) reliability 1.61 1.23 (0.63) (0.52) beginning position 0.25** 0.27** (0.15) (0.17) pressure network 2.66** 2.24* (1.09) (1.02) ps33 4.83*** (3.04) ps55 2.34 (1.54) mh 3.29* (2.15) Prob > chi2 0.4102 0.0092 0.0009 Pseudo R2 0.0110 0.0834 0.1285 No. Obs. 122 122 122 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

Institutional Factors

For the outcomes of whether farmers are members in the WUA or not, none of the institutional factors are significant predictors in either case.

User Factors

To assess the importance of user factors in determining whether farmers are members in the WUA, the following equations are used: 492

Equation (1): membership = β0 + β1 dunums + β2 greenhouses + β3 dunums*greenhouses + β4 owner + β5 high school

Equation (2): membership = β0 + β1 dunums + β2 greenhouses + β3 owner + β4 high school

Equation (3): membership = β0 + β1 dunums + β2 greenhouses + β3 dunums*greenhouses + β4 owner + β5 high school + β6 ps33 + β7 ps55 + β8 mh

Equation (4): membership = β0 + β1 dunums + β2 greenhouses + β3 dunums*greenhouses + β4 owner + β5 high school + β6 ps91

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) dunums: an interval variable for the log of the number of dunums farmed greenhouses: a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) owner: a binary variable for whether a farmer is an owner (1=yes, 0=no) high school: a binary variable for whether a farmer has a high school education or more (1=yes, 0=no) ps33: a binary variable for whether a farmer is in PS 33 (1=yes, 0=no) ps55: a binary variable for whether a farmer is in PS 55 (1=yes, 0=no) ps91: a binary variable for whether a farmer is in PS 91 (1=yes, 0=no) mh: a binary variable for whether a farmer is in MH (1=yes, 0=no)

From the results of the logistic regressions in Table 12.19, farmers with more dunums of land are significantly more likely to be members in the WUA (Equation 1). This effect is enhanced in Equation 2 when the insignificant variables in Equation 1 are removed. Being an owner is also significant; farmers who are owners are over 2 times more likely than renters to be members in the WUA. These two effects, of land size and land-holding status, continue to be significant and with similar effect sizes when variables for the WUAs are included (Equations 3 and 4). From Equation 3 or 4, farmers in PS 91 are much less likely to be members in the WUA and again this is already evident from the

493 descriptive statistics of the survey sample. Without the inclusion of the WUAs, the user factors are accounting for about 10-12% of the variation (R2 value) in WUA membership.

Table 12.19: Regression results of the effects of user factors on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) (3) (4) Variables dunums 3.04** 5.12*** 5.30*** 4.61*** (1.69) (2.58) (3.34) (2.78) greenhouses 0.02* 0.47* 0.01* 0.01* (0.05) (0.22) (0.02) (0.02) dunums*greenhouses 5.39 7.39 7.16 (6.78) (10.86) (10.03) owner 2.36** 2.38** 2.08* 2.13* (1.01) (1.00) (0.99) (0.98) high school 1.47 1.38 1.52 (0.64) (0.66) (0.71) ps33 10.24*** (7.19) ps55 9.73*** (8.54) mh 4.33** (3.12) ps91 0.13*** (0.08) Prob > chi2 0.0016 0.0009 0.0000 0.0000 Pseudo R2 0.1204 0.1016 0.2249 0.2108 No. Obs. 122 122 122 122 Source: Survey data analyzed in Stata with logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

All Factors

Finally, in taking into account all of the previously significant physical and user factors to determine farmer membership in the WUAs, the following equations are used:

494

Equation (1): membership = β0 + β1 beginning position + β2 pressure network + β3 dunums + β4 greenhouses + β5 owner

Equation (2): membership = β0 + β1 beginning position + β2 pressure network + β3 dunums + β4 greenhouses + β5 owner + β6 ps33 + β7 ps55 + β8 mh

Equation (3): membership = β0 + β1 beginning position + β2 pressure network + β3 dunums + β4 greenhouses + β5 owner + β6 ps91

Where: membership: a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) beginning position: a binary variable for whether the farm(s) is solely at the beginning of the lateral line (1=yes, 0=no) pressure network: a binary variable for whether the farm(s) is solely in the pressure network (1=yes, 0=no) dunums: an interval variable for the log of the number of dunums farmed greenhouses: a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) owner: a binary variable for whether a farmer is an owner (1=yes, 0=no) ps33: a binary variable for whether a farmer is in PS 33 (1=yes, 0=no) ps55: a binary variable for whether a farmer is in PS 55 (1=yes, 0=no) ps91: a binary variable for whether a farmer is in PS 91 (1=yes, 0=no) mh: a binary variable for whether a farmer is in MH (1=yes, 0=no)

The results of the logistic regressions are listed in Table 12.20. In Equation 1, before the WUAs are added, the relevant physical and user factors are explaining 20% of the variation in farmer membership. Lateral position and network type are highly significant in their effects. For farmers at the beginning of the lateral line, as opposed to the end, they are 3.7 times less likely (the inverse of 0.27) to be members in the WUA. For farmers in the pressure network, they are 4.66 times more likely to be members in the WUA. Additionally, owners are over 3 times more likely to be members than renters and this is a highly significant effect. And for farmers with more dunums, they are much more likely to be members in the WUA. When variables related to the WUAs are added in 495

Equations 2 and 3, the significance of these physical and user factors remain constant and their effect sizes remain relatively stable. As noted before, and due to the original descriptive statistics of the survey data, it is not surprising that farmers in PS 91 are much less likely to be members in the WUA.

Table 12.20: Regression results of the effects of all factors on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) (3) Variables beginning position 0.27** 0.34* 0.36* (0.18) (0.23) (0.24) pressure network 4.66*** 3.26** 3.68*** (2.20) (1.79) (1.81) dunums 6.56*** 9.11*** 8.80*** (3.79) (5.55) (5.35) greenhouses 0.47* 0.48 0.37* (0.25) (0.36) (0.20) owner 3.75*** 3.02** 3.27** (1.81) (1.55) (1.64) ps33 6.60*** (4.84) ps55 3.79* (3.25) mh 3.90* (2.83) ps91 0.21*** (0.13) Prob > chi2 0.0000 0.0000 0.0000 Pseudo R2 0.2041 0.2541 0.2495 No. Obs. 122 122 122 Source: Survey data analyzed in Stata with ordered logistic regression. Results as odds ratios with standard errors in parentheses. *Significant at 10%, **significant at 5%, ***significant at 1%.

496

SUMMARY

The results presented in this chapter demonstrate the dominance of institutional factors in determining farmer opinion of the WUA in general terms and in terms of the WUA’s fairness. In particular, those factors relating to the WUA’s monitoring efforts, its ability to resolve conflicts among farmers, and its legitimacy as seen in the eyes of the farmers are of importance. The physical factor of the adequacy of the water supply is also significant for these outcomes but to a lesser extent. Physical and user factors play more into farmer reporting of water stealing, and thus the level of rule-breaking, and whether farmers are members in the WUA. Both water adequacy and reliability, along with farm size, help to explain the rates of water stealing whereas network and lateral position, as well as land size and land-holding status, help to explain membership in the WUA. All of these factors that have remained significant in these final and most comprehensive analyses can prove or disprove the original hypotheses and better explain the status of WUAs in Jordan. Institutional factors do matter a great deal in the performance of the WUA whereas factors more closely related to the farmers or their location in the farm area matter more for membership in the WUA. The following chapter will discuss in more detail the meaning of these findings and what kinds of policy implications they hold for Jordan and the trend of WUAs at large.

497

Chapter Thirteen: Conclusions

Water user associations in the Jordan Valley face challenges in their present and potential development. The preceding results chapters have used quantitative and qualitative assessments of potential influential factors to identify where difficulties and significant hurdles exist to collective management among farmers. This chapter describes the specific challenges and policy recommendations where warranted, and categorizes them at the levels of the surrounding community, the internal WUA institutions, the water users and the physical environment. While many of the challenges drawn from the results relate to WUA performance, there are some issues related to the lack of inclusiveness in WUA membership that will be addressed as well. Some limitations to this current research are discussed, providing opportunities for future study.

THE COMMUNITY

The general social, political and economic contexts in which WUAs exist serve as significant impediments to their operations and to that of farmers. Existing tribal organizations have a heavy and negative influence over the associations. Because most of the WUA heads are from a major local tribe in their particular part of the Jordan Valley, this affects who they choose to be WUA employees and how they run daily operations. Relatives are frequently hired, some of whom may not necessarily be the persons best qualified for the job. Farmers in all four case study WUAs frequently reported that the association can be unfair because it favors family and friends of the WUA employees when it comes to who gets a ticket or who is favored with more water. The symptoms of this tribal dominance in management of farming and irrigation water are present at the very top

498 levels of the Jordanian monarchy. Unlike most farms, those of the royal family work in an independent and special-status fashion, free from any constraints of water quotas imposed on other farmers. In some ways, GIZ’s embrace of the tribal communities as their basis of support to initiate and implement the WUAs was understandable. There must be some modicum of working within the existing institutions at the ground level for any project to succeed. At the same time, somewhere along the WUAs’ development path, the tribal connection has been allowed to remain and become even stronger. If all farmers were of the same tribe within each WUA and there was an indication that all farmers felt comfortable having a tribal head be the WUA head, the system would not be worrisome, albeit a little unorthodox for western standards. But farmers did state that they don’t feel that the WUA takes them into consideration because they are not part of the same tribe. A future study could explore this issue further by including the Kafrein WUA, which is known for almost all of its farmers being from the Al-Adwan tribe. But for much of the Jordan Valley and within most of the WUAs, there are many family and tribal connections present. The lack of political support for farmers and agriculture in the Jordan Valley is another problematic issue for farmers that negatively impacts their views of the viability or use of the associations. Farmers in the Jordan Valley don’t believe that the national government cares about them as opposed to the Highlands, where wealthier and more influential people have farms. Farmers likewise fixate on the government’s pumping of water out of the Jordan Valley and up to the Highlands for domestic use, water that they view as theirs. And while there was more significant donor support and activity in the Jordan Valley in years past, this level of attention has largely dried up, leaving the JVA to flounder on its own with the WUAs. GIZ in particular only provides “soft support” to the

499

WUAs at present in the form of workshops and discussion groups (Al-Omari, Interview on 3/23/2015). Previous waves of international pressure for institutional reform in 2002 and again in 2006 might be on the wane (Regner, Interview on 3/30/2015). Added to the weak political support for agricultural production, farmers and the WUAs in the Jordan Valley, there is little support in the marketing realm either. This again weakens any notion that the WUAs alone can make any positive improvement on farmer lives and livelihoods. For example, Jordan’s membership in the World Trade Organization since 2000 has meant fewer protections for agricultural goods and thus a tougher market in which to sell agricultural produce. The conflicts in Syria and Iraq have limited the potential to export agricultural produce to or through those bigger markets. Emblematic of these changes and current events, farmers face continually low and stagnant market prices for their goods in the central market. These difficulties come in combination with high input costs for farmers, both in terms of their seasonal crop needs and fixed cost investments, and a lack of extensions services to aid farmers in production, packaging, storage and export of their goods. Due to the absence of crop pattern regulations, there are frequent imbalances between supply and demand in the markets, with too much of the same crop being grown by too many farmers, which hinders farmers seeking good prices at the market.

In light of the weak political and market circumstances, farmers sense that they are unimportant to the government and its policy makers. Why should anyone care? Jordan is dealing with much larger issues than farmer preferences, such as repercussions of the Arab Spring, Syrian refugees and trying to keep the country’s other development sectors running. But the Jordan Valley needs to be recognized as a potentially significant pillar for the country’s security and stability as its only long-term and sustainable agricultural

500 production option. As discussed by Talozi et al. (2015), the Jordan Valley’s climate, use of only surface waters and treated wastewater, and lower energy consumption make it a more viable option for profitable future farming than the Highlands’ agriculture, which uses Jordan’s shrinking groundwater supplies, consumes a larger amount of energy in pumping groundwater, and is not suitable for farming year-round. It would behoove the Jordanian government to facilitate greater participation of private companies in the Jordan

Valley to provide agricultural extension services and marketing support to farmers. The Ministry of Agriculture should take a more active role in its duties to keep farmers abreast of impending weather changes and crop infestations, better techniques for cropping and irrigation, and appropriate crop pattern strategies. With regard to the WUAs in particular, the current absence of a law or by-law to allow them to operate independently from the JVA is a huge political impediment and is largely responsible for their current stagnation and ineffectiveness. Within the contracts between the JVA and the WUAs, associations receive a budget and are tasked with management responsibilities by the JVA. No matter what the WUAs attempt to do under the present system to develop more skills or administrative and technical capabilities, they will remain in service to the JVA, not directly to farmers. A new law to give WUAs an independent status, allowing them to buy bulk water from the JVA and sell it to farmers, is one way forward. Farmers will then directly pay their service provider, the WUA, for water and tickets. This would create an accountability link between farmers and the

WUAs. Otherwise, the WUA endeavor has simply created a new layer of bureaucracy and expense.

501

THE INSTITUTIONS

Characteristics of the internal dynamics and rules-in-use in the WUAs prove to be barriers to better operations. The quantitative results indicate that higher levels of monitoring and sanctioning are strong elements why farmers have more favorable views of the WUAs, in terms of their general satisfaction with the WUA and whether they think it is fair in its treatment of farmers. While some farmers benefit from a less well-monitored network, if they choose to steal water, most farmers in the field connected more monitoring with better service. But unfortunately, even for those farmers who reported that the ditchriders “always” monitor the field, or 56% of surveyed farmers, that only meant once or twice per water turn, perhaps only at the beginning of the water turn when the lateral valve is opened-up. Some farmers noted that the ditchriders open the lateral line and then go back to the office to drink tea and sleep for the rest of the water turn. While many farmers connected proper ticketing to better opinion of and service from the WUA, others appreciated that ditchriders understand their tough situation and thus didn’t ticket them much. The fact that some farmers prefer to steal water and pay a ticket rather than not get the water also shows that ticket amounts are not high enough to act as deterrents. When the JVA later annuls tickets or doesn’t demand their payment, this further undermines the value of even having tickets. Due to the significance of monitoring and sanctioning efforts for higher levels of satisfaction with the WUA among farmers, and evidence that they are not robustly implemented in the field, these are elements worthy of improvement. More oversight by an outside party, or at least on the part of the JVA, would be useful to ensure that ditchriders take sufficient tours of the field and give tickets when warranted. Greater incentives to ditchriders, in the form of higher salaries, social security provision or gas money, would

502 be beneficial but less feasible in light of already tight budgets. Technical solutions could be found to keep the FTAs and other parts of the system more secure and less vulnerable to manipulation; without strict enforcement of punishments for those who damage new devices, this would be a useless step. Ditchriders should ideally not be farmers in the area as they cannot be expected to adequately monitor and ticket themselves. For improved sanctioning, increased monitoring can lead to increased opportunities to see unlawful behavior and thus ticketing. The JVA or any other higher level of management ought not to be able to annul tickets, as this works against the WUA’s mandate to ticket. Considering the minimal and non-deterrent fees currently paid for any transgression ($25 for the first offense, $50 for the second, $75 for the third and $100 for the fourth), the fees could be raised and enforced strictly. Another issue at the level of the inner-workings of the WUA is that not all farmers are eligible for membership. Non-Jordanians and farm agents are not allowed to be members. Membership fees can also be very high, ranging from a minimal amount of 10- 25 Jordanian dinars in some WUAs to as much as 200-250 Jordanian dinars in others. No WUA was observed to have used these membership fees for any significant purpose up to the present so farmers are at a loss as to why they should pay. The regulations against certain farmers being members and the high membership fees for everyone represent a significant deterrence to comprehensive membership. There are thus a large number of farmers who labor daily in the fields but who do not even have the option of having a voice in WUA elections and meetings. In PS 91, a more particular form of selective membership is occurring, whereby the head and current members allow only farmers they deem to be more potentially active members to join. The reality is that members in PS 91 are usually

503 larger land-owners, many of whom own date palm farms. There is no indication to an outsider that they are necessarily “better” members than other farmers could be. Membership should be offered to all farmers, regardless of citizenship or ownership status. If the farmer is present in the field most days of the week, he deserves to have a say in how the WUA is run and operates. Regulations to limit membership in the administrative council to only Jordanians could stand, but there is no reason that farmers of any creed should be kept from participating in meetings, elections and workshop opportunities. Due to the results pointing to more well-to-do and owner farmers being members, poorer and less well-off farmers should be encouraged to be members by the WUA heads and engineers. There is evident heterogeneity among farmers along a number of lines and to exclude any category might be to the detriment of cooperative efforts. And while it might be preferable to make membership mandatory, the Jordan Cooperative Corporation regulations that guide WUA affairs demand that membership be completely voluntary. Any change in this regulation will require further legislative action that is not likely in the near term. With regard to the membership fee, the WUAs should not be allowed to have such high fees, especially considering that the money is usually put into a bank as shares in the WUA and not used. WUA heads state that these fees will be used to invest in future projects. However, in their current status, the WUAs are not mandated to establish such special projects anyway. Furthermore, due to the fees not producing any special benefits to member farmers over non-member farmers, farmers cannot see any advantage to having to pay to be a member. Much of the time, farmers cannot see a benefit to being a member in the WUA regardless of whether there is a fee to join. The problem may be in how “benefits” are defined and imagined. Farmers might have to be convinced that better

504 service is one benefit a WUA could deliver, so to make this a reality, their membership is needed. By being a member, they can voice concerns and push for better service. The expectations for membership likely need to be lowered. Another issue within the WUA is that elections are not used in all associations. An “appointment” is meant to signify that there is mutual agreement among all member farmers on who should be in the administrative council and who should be the head. It would be hasty to disregard the possibility that farmers, in fact, do agree on these points. But in talking to farmers, there were indications that not everyone feels that the WUA acts in their interests. Some farmers were explicit in stating that they do not like the head or that he does not act in their interests. Thus, with elections, there would at least be a system that recognizes their dissent and, if enough farmers hold similar views, they could make a big difference. There is no reason why elections cannot be held in all WUAs, regardless of whether farmers outwardly say they all agree or not. Some farmers seemed unwilling to voice any dissent to other farmers or the WUA, even though they were not happy about events in their area. This was especially true for small-holder and poorer farmers and farmers not close family or friends with the WUA employees. Even among member farmers, when 43% of them reported in the survey that they don’t participate in elections, this signals that many see elections, as they are performed at present, as pointless or illegitimate. Elections should thus entail a secret ballot instead of a raising of hands or any other open ballot system, ensuring that farmers can express their true opinions. The fairness of the WUA in its daily activities is negatively affected by three forces: money; power and influence; and family and friends. The ditchriders and the WUAs are susceptible to bribes in the form of hard cash, bartered goods or other favors. As with

505 monitoring and sanctioning, more future oversight would help. The JVA may not be the best overseer because of its poor track record with taking bribes. While the WUA head and engineer can work to deter taking bribes among the ditchriders, perhaps again a measure like a wage increase for ditchriders, even if minimal, would be more effective and symbolically powerful. To combat the use on the part of some farmers of their power and influence, there is no easy remedy. WUAs need to gain more legitimacy in the eyes of farmers and the JVA to the point that the WUA is the ultimate arbiter and its decisions mean something.

Some farmers go around the WUA to higher levels in the JVA or the MWI. This option should not exist and officials in the MWI and the JVA should not allow these channels to exist. In terms of favoritism among family and friends, while combatting these bonds is somewhat futile, farmers can be taught that the WUA is a service-provider and can function outside of these bonds. The WUA is not centered on making farmers better friends and a closer family; it is centered on mutual respect between water users, paying the same rate for water and receiving the same service for their payment. Cutting off the royal family farms from special treatment could set a good example and demonstrate the government’s seriousness about cracking-down on favoritism among family members. Finally, within the institutions of the WUA, the institution of the WUA head could be in danger. First, the WUA leadership is lacking in some areas and this could be a reason for poorer WUA performance. Leaders who are motivated, committed, qualified and not easily swayed by the local tribes can improve WUA efficiency and fairness. Some WUA heads, though, were judged to be lacking in administrative and farming capabilities, limited in their level of investment in the WUA’s future and the future of agriculture in the Jordan Valley, and were perceived to be without vision for the WUA’s future. Even if a current

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WUA head is strong and sufficiently capable to be a good leader, this does not guarantee that the office of the WUA is strong and capable, no matter who is sitting in its seat. There are no easy methods to find good leaders and then make them WUA heads. Perhaps with a few regulations there could be more of a guarantee that a farmer who is motivated to head a WUA for the right reasons, to help farmers and the WUA, becomes the head. First, the salary for the WUA head could be withdrawn with the hope that only farmers who are seriously interested in supporting other farmers and making water distribution more efficient and fair are put in the position, versus the current situation where farmers vie for an easy salary. In the first years of implementation of the WUAs, salaries were not offered and there were still farmers who volunteered for the position. Salaries were offered once the WUAs took on tasks that some might argue represent real responsibilities. However, a salary is not as necessary considering that the head position is largely involved with overall guidance and decision-making, unlike the engineer who is involved in the daily operations. The head should also have a farm that he works on a regular basis. Having term limits for the head position would help, ensuring that no farmer is either burdened by the duties of the head or able to dominate the WUA’s affairs for a long period of time. Another option is to install a system such as exists in the WUA at Rama, where farmers form different blocs and are elected in rotation, giving each group of farmers a chance to lead.

THE WATER USERS

Farmers are a heterogeneous group in terms of their education level, nationality, and land-ownership status, and whether they have secondary work or income. They differ in the amount of land they farm, whether they use greenhouses and how many, and whether 507 they are able to export crops. These disparities result in different capability sets among farmers as well as differing levels of ambition for their farms and production. Some farms are more commercial in nature, using advanced technology and aligning with international standards to market produce to more elite or export markets. Other farmers operate at a small-scale, using rudimentary irrigation with lower yields and much lower profit margins. Some farmers have a casual attitude towards their farm as it is a secondary career or even a hobby. Cooperation among farmers in the Jordan Valley is challenging when their situations and agricultural production goals differ so markedly. There is no statistical or hard evidence in this research to make a causal claim between heterogeneity among farmers and lower levels of WUA performance. From general observation, heterogeneity, or more specifically a lack of efforts to overcome this heterogeneity and make farmers feel more unified, has led to a situation in which farmers are concerned for the most part with their individual farm and water supply. Farmers acting alone without concern for the larger farm commons has led to a high rate of water stealing. Among surveyed farmers, 82% reported that water stealing is occurring around them. Water stealing occurs among all types of farmers, within all levels of the water distribution system, and in all parts of the Jordan Valley. Farmers steal water regardless of its detrimental effects to surrounding farmers and regardless of the damage done to the water network. Few if any farmers expressed shame in stealing water.

WUAs were intended to remedy the problems of trust and ill-will among farmers, yet this goal has not been fulfilled. One element is the exclusion of farmers within the WUA. As stated before, non-Jordanians and agents cannot be members. The statistics in this study indicate that large land holders and owners are more likely to be members among

508 those eligible for membership. For the WUA to have any positive impact on the level of cooperation and collaboration among farmers, all farmers ought to be actively included. Only with personal interaction among farmers could some form of mutual understanding and respect develop. The WUAs should also make a concerted effort to fully inform farmers about the water order, how it is determined, and why farmers are only allotted a particular share. Water distribution should be transparent, with no doubts about why one farmer gets a certain amount at a certain time of day and for a certain number of hours, whereas another gets a different amount at a different time of the week. Technical devices, such as more secure FTAs and water meters, can help to make sure that farmers are allotted more accurate water quantities and make sure that they do not steal water. But considering how farmers have broken or manipulated past devices meant to ensure such compliance, devices cannot be the sole way forward.

THE PHYSICAL STRUCTURES

Regardless of the importance of the larger community, the WUA institutions, and the water users, the deficiencies of the physical infrastructure in the irrigation networks in the Jordan Valley remain an obstacle to any form of management, whether the government agency or WUAs. Issues of water reliability and adequacy are, in part, a direct result of poor infrastructure. The best management and farmer obedience to rules cannot overcome a network shortfall. Main and lateral lines, lateral valves and the FTAs are all in need of attention. The rehabilitation project in Mazraa-Haditha is a positive step forward, but has been possible only through donations from aid agencies, in particular from Gulf countries. The government should seek support from donors for this purpose.

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Stealing from and polluting of the King Abdullah Canal is an obstacle to better water management, both for the JVA and for the WUAs. There is little chance of changing the canal to a closed system at this point in time. Putting up more fencing is futile. Monitoring is important but so is enforced ticketing of those who steal from the KAC. At present, farmers who steal from the KAC do so with the knowledge that the JVA is unlikely to crack-down on their illegal operations or enforce mandated punishments. With regard to the trash that pollutes the KAC, a potential strategy is to implement a community-wide campaign similar to the Keep America Beautiful campaign in the 1950s. With more awareness and options for disposing of trash, less might end up in the KAC.

LIMITATIONS AND FUTURE STEPS

This study seeks to unearth those elements of Jordan’s experience with WUAs that facilitate or hinder their performance. The field study methodology led to assumptions and data limits that could be improved. The choice of the Institutional Analysis and Development (IAD) Framework, rather than another of the several frameworks provided in the literature, reflected its simplicity and coverage of potential influential factors. The IAD framework includes the surrounding community, the physical world, the internal workings of the WUAs, and water users, and those categories are broad enough to accommodate new factors that arise through fieldwork. For example, while neither the framework nor the literature explicitly suggest that tribal formations in Jordan would pose such a distinct hindrance to collective management, this element fit perfectly within the community category and within the factor of pre-existing community organization. On the other hand, some important factors could have been excluded if they did not fit into any of the four categories, the literature did not suggest their relevance, or they were not 510 discernable in the field. For example, there could have been further social stratifications among farmers in the Jordan Valley that were not referenced in the literature and were not observed in the fieldwork, but could have had an impact on the assessed outcomes. Perhaps there are additional ramifications of globalization that have not been assessed but could be impactful for WUA performance. The inclusion of only four WUAs in the Jordan Valley constricts a more thorough analysis of WUAs. If farmers from all of the 13 fully functional WUAs had been included in the survey and statistical analyses, results might have enabled stronger inference for why a set of WUAs out-performs other WUAs. Additional observations would have allowed for inclusion in the statistical analyses of WUA-level characteristics, such as whether the WUA uses elections or appointment, whether its head is a member of a major local tribe, and other physical characteristics of the network or water supply. Statistical analyses could have been more robust if farm areas not under the administration of a WUA could have been evaluated; such an approach would have allowed for a with-and-without study that could yield causal claims. Additional time in the field would be invaluable. There are now areas where WUAs have just been established but they have not acquired any tasks. If these WUAs along with the others could be tracked over time, leading to a before-and-after study in some locations, this would again aid in making more causal claims about the effect of factors on WUA performance. Even just with the four WUAs included in this study, it would be valuable to return and look at these same WUAs after a few years to observe any changes that have occurred on the ground and in the opinion of the farmers. The chosen performance measures affect the conclusions of the research. Data would have been useful on: how much water is ordered for the WUA; how much actually

511 arrives; what percentage of the water makes it to farm; and how much each farm takes. This, of course, would require that the network be in better condition and that all farms use meters. Clear counts of how many tickets for water stealing and network vandalism within each WUA would also be useful. While these numbers are technically supposed to be recorded by the WUAs and the JVA, there is no accurate and reliable methodology employed for this purpose. Without numerical counts, this study relied on farmer opinion of performance outcomes. Reliance on farmer opinion is not invalid, as farmers are the final customers, but it would have been better to confirm their opinions with hard data.

There remains disagreement in the literature and in practice over the end goal of user participation in management (Ghazouani et al., 2012; Mukherji et al., 2010; Vermillion, 1991; Groenfeldt, 1988). Should user-based networks value higher levels of operational and administrative efficiency, government budget savings, higher levels of on- farm production, or social development and empowerment? These are all valid performance outcomes to consider. Taking results from one study and applying them to another will depend on what kinds of outcomes have been used. It is not wrong for Jordan to consider different outcomes than those pursued in Turkey or Mexico, for example, but care should be taken in applying lessons learned. For this case of Jordan, the literature helped to guide the research in terms of outcomes but did not determine them, working under the assumption that Jordan’s farmers work within a unique set of circumstances. WUA goals can change and evolve throughout the implementation process. In Jordan, while there might have been an indication in the beginning that the government wanted to save money through the use of WUAs, this goal was quickly sidelined, as the reality of the long implementation process set in without an opportunity to release the WUAs from the

512 government’s budget. Thus, a reduction in the financial burden for the government was not considered as an outcome variable in this study. One special element within the experience of WUAs in Jordan is the heavy participation of a donor agency, GIZ, in their creation and growth for over 10 years. Donors have assisted in the development of WUAs elsewhere, so this is not something entirely unique to Jordan. But through discussions with those involved in GIZ’s implementation of WUAs in Jordan, it is evident that GIZ crafted its project according to the specific circumstances in Jordan. This research on Jordan’s WUAs could have been improved with an even more in-depth examination of the donor effect on WUAs, its influence in determining the needed performance outcomes, and its involvement in the internal organization and regulations within the WUAs. As the literature suggests, user-based management has yet to prove itself as better than government-led management. To review, Mukherji et al. (2010) posit in their exposition of the assumptions surrounding IMT that there is no guarantee that because self- governed systems worked in previous time periods, that they will work now with more modern and larger systems. There is no solid proof that the government agency is not capable of managing the networks in an economically and financially viable way. There is no proof that water users will be the most suited to managing these networks. For Jordan, only more time will reveal whether its WUAs will reap greater fruits on the ground. It is interesting that groups of farmers, engineers and government managers from

Yemen, the Palestinian Territories and Iraq have already visited Jordan to study and learn from its experience with WUAs (Discussion with Ali al-Omari at JVA WUA Unit, 1/30/2014). It behooves Jordan to be honest in its reporting of the strengths and limits of farmer self-management. If Jordan is an example to others, if aid agencies advertise

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Jordan’s experience as a model by which others can learn, there is a need to finish what has been started. WUAs in the Jordan Valley represent neither an ideal outcome nor a failure. They require serious effort and attention, with new direction, energy and leadership that can withstand the temptations of tribal loyalties, monetary rewards and singular and selfish mindsets. If the political will exists to more honestly assess the WUA experiment, then both Jordanian offices and donor agencies ought to seek valid, meaningful and reliable performance measures. On the other hand, there is no shame in reverting back to government management if the WUAs cannot prove themselves to be better, as the goal ought to be to provide water fairly, efficiently and effectively to farmers. Inclusionary concepts work in the abstract but do not always lead to higher performance, inclusiveness and fairness. Ideals need to be better balanced with reality when livelihoods are at stake.

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Appendix A – Contextual Assessment Interviews

The persons listed below from universities, donor agencies and national-level ministries were part of initial meetings to assess the situation of the water user associations in the Jordan Valley. These meeting were not official in nature or organized by any strict interview questionnaire. The parties below were questioned for their thoughts at a broad and general scale in order to aid in constructing the subsequent research stages.

Jordan University  Emad al-Karablieh  Amer Salman Jordan University of Science and Technology  Samer Talozi Jordan Valley Authority  Khalil al-Absi, Director of the Planning and Regional Water Unit  Qais Owais, JVA Director of the Jordan Valley  Mash-hour Harb, JVA Director of the Southern Ghor  Ghassan al-Obaydat, Director of the Northern Directorate  Muhammed al-Fahili, Director of the Middle Directorate  Mahmoud al-Gammaz, Director of the Southern Directorate  Ali al-Omari, Director of the WUA Unit in Deir Alla  Mousa Huwarat, Director of the Control Center in Deir Alla  Ma’moun al-Kharabshed, Engineer in the Southern Directorate  Anees Shahadat, Employee in middle JVA Stage Office  Anwar al-Adwan, Employee in the WUA Unit  Khalid Abu Husayyan, Employee in northern JVA Stage Office United States Agency for International Development, Institutional Support and Strengthening Program  Haidar Malhas  Nayef Seder  Akram Rabadi Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ)  Nour Habjouka, Project Coordinator  Johannes Stork, Manager of WUA Project Ministry of Agriculture, Jordan Valley  Mohammed al-Sarayra, Director of the Southern Ghor

515  Abd al-Karim al-Shihab, Director of the Jordan Valley in Deir Alla  Issa Rabie, Employee in the Southern Ghor

A substantial number of persons connected with the WUAs in the Jordan Valley also contributed to my initial contextual assessment by way of more in-depth interviews and conversations on how the WUAs operate in the field.

WUA at PS 50/55  Suleiman Abu Fawaris, Engineer WUA at PS 41  Zaki Rababa, Head  Abu Laura, Ditchrider WUA at PS 81  Tawfiq al-Satary, Head WUA at PS 91  Ali Mustapah, Head  Shafiq Habash., Engineer  Ra’if, Khaldoun, Mahmoud and Hossam, Ditchriders WUA at Rama  Talal Farhan al-Adwan, Head WUA at PS 14  Abdullah al-Huorani, Head WUA at Kafrein  Ahmed al-Adwan WUA at PS 33  Nawaf Kraym, Head  Mohammed al-Rayadha, Engineer WUA at PS 5  Ra’if Ayoub, Head WUA at PS 3/4  Ali al-Rashid al-Hussein, Head WUA at Mazraa-Haditha  Salim al-Huwaymil, Head

516 One informal conversation was conducted with the private sector employee listed below who provided insight into the specific arena of drip irrigation technology and farmer needs in this arena.

National Drip Irrigation Company  Ayman al-Idreesi, Engineer

517 Appendix B – WUA Head Interview Questions

The following is a translation of the interview protocol that was conducted in Arabic with each of the WUA heads of WUAs with some sort of task transfer, either for distribution or both distribution and maintenance. Information was elicited about their personal attributes, characteristics of the WUAs daily operations and their opinions about the future of the WUA. Each head was given a letter directly before the interview that outlined my research objectives and the voluntary nature of their participation.

The interview protocol is as follows:

Date:______Name of President:______Telephone Number:______Name of the Association:______The number of the related Stage Office:______Is it co-located with WUA?: Yes No

(Review the information on the association from the JVA before conducting interview).

Personal Information

How old are you?______

What is your education level? Elementary School - High School - Bachelors’ Degree - Masters Degree - Doctorate - Other In what subject was your university degree if you went to university?______

In what city do you live?______

Do you own a farm in this area or in different area? Yes No Where:______How many dunums:______Crops:______Do you use greenhouses? Yes No If yes, how many?______

How many years have you been a farmer?______

Have you had any special training in agriculture or administration? ______518

Do you have any other work besides farming and being the president of the association? ______

How many years have you been president?_____ Is his your first, second or more term in office?

How many days each week do you work in the association office?______

How did you get this position? Did you want to be president of the association or did other farmers nominate you to the position? If you wanted this position, why did you want it? ______

Did you have any previous experience in a leadership position? ______Do you want to nominate yourself to be president in the coming elections? Yes No

Management of the Association

How many people are in the administrative council? Are they elected from the association members or are they appointed by the president? Are they all members in the association and farmers in the area? Yes No ______

What are the matters that the administrative council settles?______

What are the matters that the general council settles?______

How often are presidential elections held?______

In the elections, every member has one vote or if he has a larger farm area, this means that he has a larger vote?______

Is it possible for any member to nominate himself for the position of president? Yes No If you want to nominate yourself for the position of president, what do you have to do?______519

Is there a limit to how many terms a person can be president of the association? Yes No What is the limit?______

Does the association have: A private office or an office within the JVA? Computer? Yes No Printer? Yes No -- Copier? Yes No

Daily Activities of the Association

Do you think that the distributional efficiency now is better than when the association was established? Yes No If yes, how do you know this and how do you measure this?______

How were the employees chosen (ditchriders, engineer, maintenance person, accountant)? Who chose them:______Why? Any qualifications?______In what city do the employees live?______

Was there any training for you or for the employees with regard to management of the association, water distribution, or maintenance of the irrigation network? ______

Does the association provide any services apart from water distribution and network maintenance for farmers?______

History of the Association

Were there any cooperative efforts among farmers in this area before the establishment of the association? Yes No If yes, what were they?______

520 Was there any type of community organization in this area before the establishment of the association? Yes No If yes, what was this?______

Was there any rehabilitation in the irrigation network in this area since the start of the association? Yes No What area – type of rehabilitation – what year:______

In each stage of task transfer, who was the initiator of this step? The JVA or an international agency or the association?______

Financial Matters

Is there an initial fee to be an association member? Yes No What is it:______

Is there a monthly or yearly fee to continue being an association member? Yes No What is it:______

How do you spend the money that is obtained from membership fees? ______

Is there someone in the association that records the revenue and expenses of the association? Yes No How:______

In your opinion, is the current budget (the amount from the JVA per year) enough for the things that the association does? Yes No If no, why not?______

In your opinion, is the president’s salary enough? Too high or too low or just right? Why?______

What is your opinion on the latest initiative to raise the price of water for irrigation in the Jordan Valley? Is this necessary or not appropriate? Is this a good thing but the timing is

521 bad? Under what conditions would you be able to accept a rise in the price of irrigation water? ______

Members

Are there any qualifications or conditions for becoming a member in the association? Yes No If yes, what are these conditions?______

Are there any special benefits provided solely to association members? ______

Why are some farmers not members in the association? Do you have any idea why? They don’t want to be members or they don’t have the capability to be members? ______

(If there are non-Jordanian farmers in the area) What is your opinion on allowing non- Jordanians to be members in the association? Are they already a part of the association in an indirect manner through the owners? ______

How many farmers or what is the percentage of farmers in this area who farm more than one unit of land?______Are they members in the association? Yes No Some of them

Is there any communication or relationship between farmers in the association and herders? Yes No In your opinion, should there be a connection or cooperation between them? ______

522 Relationship with the JVA

Are the roles of the JVA and the association clear and distinct from each other? Or is there some interference between the two?______

How is the relationship between the JVA and the association? Is there 100% agreement in all matters or are there some differences in some things? If yes, in what areas are there differences? ______

Does the JVA interfere in the internal affairs of the association? Yes No If yes, in what affairs:______

Do you have any problem with any condition or responsibility or duty for the association or the JVA in the contract between you two? Do you have any problem with anything else in the contract? ______

Future Outlook

In general, do you think that the associations are the final solution to the problems of water management in the Jordan Valley? Will the associations succeed in the long term and why? ______

As president of the association, do you have any special initiatives right now? ______

After five or ten years, how will the association be different? ______

523 In the future, will the association be able to manage its financial affairs on her own and without the help of the JVA? ______

In the future, will the association has a role in helping farmers with marketing, improving the quantity and quality of their production, guaranteeing loans or any other agricultural activities? ______

In the future, should the association have a role in matters not related to water or agriculture? Yes No Like what:______

What should be the role of the JVA in the future? ______

Do you see any place for the private sector in any part of water management in the Jordan Valley in the future? ______

At present, the costs of distributing water and maintaining the water network are higher than the revenues from water fees and the government is paying the different between the two and is thus the biggest loser in this situation. Do you have any idea as to how the gap between costs and revenues can be narrowed? ______

524 Appendix C – Farmer Survey Questions

The following is a translation of the survey protocol that was conducted with farmers in Arabic in the four case study WUAs. Farmers answered these questions anonymously and their identities remain so in this dissertation. Before each survey was conducted, farmers were told that their participation is voluntary, that they can choose to not answer any question or participate, and that their identities will not be revealed.

The fifty questions asked of each farmer are as follows:

1. Date 2. Lateral line number 3. Farm unit number(s) 4. Placement on the lateral: beginning – middle – end

Farmer 5. Owner or renter 6. (if renter) How many times have you moved from a farm to another farm in the past 10 years? 7. (if renter) Do you want to move to another farm in the near future? 8. How old are you? 9. What is your education level? 10. What is your nationality? (Jordanian – Egyptian – Pakistani) 11. Gender (male – female) 12. How many days during the week are you on the farm? 13. How many years have you been a farmer? 14. Where did you learn how to farm? (ex. your family – other farmers – computer) 15. Do you have an income from anything else? (secondary work or another farm elsewhere)

The Farm 16. How many dunums do you have? 17. What is the crop pattern? (vegetables – citrus – palms – bananas) 18. How many greenhouses do you have? 19. What type of irrigation do you use? (drip – sprinklers – basin) 20. What is your water turn? (days of the week – time of turn – number of hours per turn) 21. Do you have a holding pool? Yes – no 22. What type of pool is it? Earthen – plastic-covered – concrete-lined 23. Do you use a filter from the holding pool to the farm? Yes – no 24. What type of filter is it? Grate – disc – sand 25. Do you have a secondary source of water? (natural spring – well – pipe to the KAC) 26. Do you sell the produce in the local or international market? 525 Water Management 27. Do you trust water distribution here? Are you able to rely on your water turn, does the water come at the appropriate time and for the demanded hours according to the water order? (yes – no)

28. Is the amount of water enough for your farm? (yes – no) 29. If not, what is the percentage of water shortage?

30. Do you think water distribution between farmers is fair and neutral? Does the WUA treat farmers in the same way or does it favor some farmers over others with regard to water distribution and violations?

Your opinion on the ditchriders: 31. Do they punish farmers when there are violations? (always – sometimes – rarely) 32. Do they open and close the lateral and main values at the appropriate time? (always – sometimes – rarely) 33. Do they do regular field tours? (always – sometimes – rarely) 34. When the FTA has a problem, do they fix it quickly and in a good manner? (always – sometimes – rarely)

35. When you have a water problem on your farmer, do you request help from the WUA or from the JVA or another office? 36. Is the WUA able to help you with the problem? 37. When there is a problem or conflict between farmers, is the WUA able to solve the conflict and help the famers? 38. How are relations between farmers here? Are you all close and friends or do you not know each other very well? Is there just hi and goodbye or are you all friends and you know each other well? 39. Does water stealing happen here about farmers? 40. If yes, is this something that happens always or sometimes or rarely? 41. Does vandalism or playing with the FTAs and the lateral or main lines happen here among farmers? 42. If yes, is this something that happens always or sometimes or rarely? 43. In general, the association is: good – bad – in the middle. 44. In general, the association is: better or worse or the same thing as the JVA? 45. If it is better or worse, why is this the case? 46. Are you a member in the WUA? 47. Why or why not? 48. (for members) What are the benefits to membership in the WUA? 49. (for members) What are the activities for the WUA that you participate in throughout the year? 50. In your opinion, how can the WUA be better? How can the WUA better solve the problems that arise here? 526 Appendix D – Variable Key

The following table displays all of the variables and variable names used in the quantitative analyses for this dissertation. Variables that were deemed irrelevant or unusable are also herein included.

Variable Description

DEPENDENT opinion of wua a categorical variable for farmer satisfaction with the WUA (1=bad, 2=so-so, 3=good) comparison of wua to jva a categorical variable for farmer satisfaction with the WUA when compared to the JVA (1=worse, 2=same, 3=better) water stealing a binary variable for whether a farmer reports that water stealing is happening in the area (1=yes, 0=no) fairness of wua a binary variable for whether a farmer thinks that the WUA is fair in its treatment between farmers (1=yes, 0=no) membership a binary variable for whether a farmer is a member in the WUA (1=yes, 0=no) (Secondary outcome) elections a binary variable for whether a WUA member attends elections (1=yes, 0=no)

INDEPENDENT

General: ps33 a binary variable for whether a farmers is in PS 33 (1=yes, 0=no) ps55 a binary variable for whether a farmers is in PS 55 (1=yes, 0=no) ps91 a binary variable for whether a farmers is in PS 91 (1=yes, 0=no) mh a binary variable for whether a farmers is in MH (1=yes, 0=no) Physical: adequacy a binary variable for whether a farmer thinks that the water supply is adequate (1=yes, 0=no)

527 reliability a binary variable for whether a farmer thinks that the water supply is reliable (1=yes, 0=no) citrus a binary variable for whether a farmer grows citrus trees (1=yes, 0=no) palm a binary variable for whether a farmer grows date palm trees (1=yes, 0=no) pressure network a binary variable for whether the farm(s) is solely in the pressure network (1=yes, 0=no) gravity network a binary variable for whether the farm(s) is solely in the gravity network (1=yes, 0=no) both networks a binary variable for whether the farm(s) is in both the pressure and gravity networks (1=yes, 0=no) beginning position a binary variable for whether the farm(s) is solely at the beginning of the lateral line (1=yes, 0=no) middle position a binary variable for whether the farm(s) is solely at the middle of the lateral line (1=yes, 0=no) end position a binary variable for whether the farm(s) is solely at the end of the lateral line (1=yes, 0=no) multiple positions a binary variable for whether the farm(s) is at multiple lateral positions (1=yes, 0=no) Institutional: touring a categorical variable of the farmers viewpoint of how often the ditchriders tour/monitor the field (1=rarely, 2=sometimes, 3=always) punishing a categorical variable of the farmers viewpoint of how often the ditchriders are giving out tickets to farmers when warranted (1=rarely, 2=sometimes, 3=always) resolving conflict a categorical variable of the farmers viewpoint of how often the WUA is able to be used to resolve conflicts between farmers (1=never, 2=sometimes, 3=always) wua help a binary variable for whether a farmer seeks help solely from the WUA (1=yes, 0=no) jva help a binary variable for whether a farmer seeks help solely from the JVA (1=yes, 0=no) wua-jva help a binary variable for whether the farmer seeks help from both the WUA and the JVA (1=yes, 0=no) User: secondary water a binary variable for whether a farmer has a secondary source of water (1=yes, 0=no) secondary work a binary variable for whether a farmer as work or receives an income from a source outside of his farm considered in the survey area (1=yes, 0=no)

528 dunums an interval variable for the log of the number of dunums farmed greenhouses a binary variable for whether a farmer uses greenhouses (1=yes, 0=no) exporting a binary variable for whether a farmer personally exports produce (1=yes, 0=no) ownership a categorical variable for the farmer’s ownership status (1=agent, 2=renter, 3=owner) owner a binary variable for whether a farmer is an owner (1=yes, 0=no) education a categorical variable for education level (0=none, 1=elementary school, 2=middle school, 3=high school, 4=diplome, 5=bachelors degree or higher) high school a binary variable for whether a farmer has a high school education or more (1=yes, 0=no)

Additional variables collected but not used in quantitative or qualitative analyses:

Variable Description age an interval variable for the age of the farmer in years (17-91) has moved a binary variable for whether farmer has moved in the past 10 years (1=yes, 0=no) wants to move a binary variable for whether farmer wants to move soon (1=yes, 0=no) time on farm an interval variable for the number of days per week the farmer is on farm (1-7) years farming an interval variable for the number of years the farmer has been farming (1-70) learning to farm a categorical variable for how a farmer learned to farm (1=learned from father/family, 2=learned from experience/working with other farmers, 3=learned from school, self-study or the internet) type of irrigation a categorical variable for type of irrigation used on the farm (1=drip irrigation, 2=basin irrigation, 3=surface irrigation) farm pond a binary variable for whether the farmer has a farm pond (1=yes, 0=no)

529 type of pond a categorical variable for type of farm pond (1=lined with plastic sheeting, 2=lined with concrete, 3=earthen) filter a binary variable for whether the farmer uses a water filter type of filter a categorical variable for type of water filter in use (1=grate, 2=disc, 3=sand) opening and closing a categorical variable for the farmer’s opinion of how often ditchriders properly open and close the lateral lines (1=rarely, 2=sometimes, 3=always) maintaining a categorical variable for the farmer’s opinion of how often the ditchriders conduct their obligatory maintenance duties (1=rarely, 2=sometimes, 3=always) vandalism a binary variable for the farmer’s opinion of whether vandalism in the network occurs in the area (1=yes, 0=no) ticket count an interval variable for how many tickets the WUA/JVA issued a farmer from 2010-2014 (0-10) [data collected only for PS 91]

530 Appendix E – Supplementary Stata Results

The Stata results listed in this appendix are documented in Chapter Five in the methodology for determining the effects of the various factors on the outcome variables. These results are referred to in the text and are here shown as proof that certain tests and checks were run.

Results 1: Logistic regressions of adequate as a function of wua33, wua55, wua91 and wuamh

Logistic regression Number of obs = 186 LR chi2(3) = 6.80 Prob > chi2 = 0.0786 Log likelihood = -100.64219 Pseudo R2 = 0.0327

------adequate | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.6820973 .5731849 -1.19 0.234 -1.805519 .4413244 wua55 | .1670541 .5264009 0.32 0.751 -.8646727 1.198781 wua91 | .539575 .4938329 1.09 0.275 -.4283196 1.50747 _cons | -1.178655 .4043038 -2.92 0.004 -1.971076 -.3862342 ------

Logistic regression Number of obs = 186 LR chi2(3) = 6.80 Prob > chi2 = 0.0786 Log likelihood = -100.64219 Pseudo R2 = 0.0327

Logistic regression Number of obs = 186 LR chi2(3) = 6.80 Prob > chi2 = 0.0786 Log likelihood = -100.64219 Pseudo R2 = 0.0327

Logistic regression Number of obs = 186 LR chi2(3) = 6.80 Prob > chi2 = 0.0786 Log likelihood = -100.64219 Pseudo R2 = 0.0327

Results 2: Ordered logistic regressions of catop, catcom, member, steal1 and fair1 as a function of adequate

Ordered logistic regression Number of obs = 186 LR chi2(1) = 11.19 Prob > chi2 = 0.0008 Log likelihood = -135.06259 Pseudo R2 = 0.0398

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.396983 2.220394 2.93 0.003 1.634233 11.8303 ------+------/cut1 | -2.451251 .3038109 -3.04671 -1.855793 /cut2 | -.6203345 .1769255 -.9671021 -.273567 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 6.41 Prob > chi2 = 0.0113 Log likelihood = -179.30704 Pseudo R2 = 0.0176

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.32513 .7965917 2.46 0.014 1.188023 4.550611 ------+------/cut1 | -1.64238 .2212493 -2.07602 -1.208739 /cut2 | .1276049 .169412 -.2044366 .4596463 ------

Logistic regression Number of obs = 122 LR chi2(1) = 0.29 Prob > chi2 = 0.5931 Log likelihood = -80.69453 Pseudo R2 = 0.0018

Logistic regression Number of obs = 174 LR chi2(1) = 10.01 Prob > chi2 = 0.0016 Log likelihood = -61.036534 Pseudo R2 = 0.0758

Logistic regression Number of obs = 184 LR chi2(1) = 8.38 532 Prob > chi2 = 0.0038 Log likelihood = -114.04761 Pseudo R2 = 0.0355

Results 3: Logistic regression of adequate as a function of secwater, wua33, wua55, wua91, wua33sw, wua55sw and wua91sw

Logistic regression Number of obs = 186 LR chi2(1) = 0.05 Prob > chi2 = 0.8221 Log likelihood = -104.01615 Pseudo R2 = 0.0002

Logistic regression Number of obs = 186 LR chi2(7) = 10.55 Prob > chi2 = 0.1593 Log likelihood = -98.764332 Pseudo R2 = 0.0507

------adequate | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwater | 3.571429 5.276605 0.86 0.389 .1973489 64.63225 wua33 | .2747253 .2333255 -1.52 0.128 .0519959 1.451537 wua55 | 1.607143 .9425701 0.81 0.419 .5091373 5.073107 wua91 | 1.984127 1.062131 1.28 0.201 .6948796 5.665384 wua33sw | .9578947 1.651903 -0.02 0.980 .0326143 28.13372 wua55sw | .1435897 .2382995 -1.17 0.242 .0055524 3.713345 wua91sw | .224 .3644854 -0.92 0.358 .00923 5.436194 _cons | .28 .119733 -2.98 0.003 .1211069 .6473619 ------

Results 4: Logistic regressions of reliable as a function of wua33, wua55, wua91 and wuamh

LOGISTIC REGRESSIONS

Logistic regression Number of obs = 186 LR chi2(3) = 20.98 Prob > chi2 = 0.0001 Log likelihood = -117.35877 Pseudo R2 = 0.0820

------reliable | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | 1.410456 .4913477 2.87 0.004 .4474327 2.37348 wua55 | 2.190256 .5264009 4.16 0.000 1.158529 3.221982 wua91 | 1.58412 .4891003 3.24 0.001 .625501 2.542739 _cons | -1.178655 .4043038 -2.92 0.004 -1.971076 -.386234 ------533

Logistic regression Number of obs = 186 LR chi2(3) = 20.98 Prob > chi2 = 0.0001 Log likelihood = -117.35877 Pseudo R2 = 0.0820

Logistic regression Number of obs = 186 LR chi2(3) = 20.98 Prob > chi2 = 0.0001 Log likelihood = -117.35877 Pseudo R2 = 0.0820

Logistic regression Number of obs = 186 LR chi2(3) = 20.98 Prob > chi2 = 0.0001 Log likelihood = -117.35877 Pseudo R2 = 0.0820

Results 5: Logistic regressions of catop, catcom, member, steal1 and fair1 as a function of reliable

Ordered logistic regression Number of obs = 186 LR chi2(1) = 13.91 Prob > chi2 = 0.0002 Log likelihood = -133.70401 Pseudo R2 = 0.0494

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 3.385037 1.134224 3.64 0.000 1.755301 6.527928 ------+------/cut1 | -2.14824 .3232592 -2.781817 -1.514664 /cut2 | -.2844981 .2217578 -.7191354 .1501391 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 15.06 Prob > chi2 = 0.0001 Log likelihood = -174.98337 Pseudo R2 = 0.0413 534

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 3.052332 .8921451 3.82 0.000 1.721234 5.41282 ------+------/cut1 | -1.307539 .2451244 -1.787974 -.8271042 /cut2 | .5308482 .2188737 .1018636 .9598327 ------

Logistic regression Number of obs = 122 LR chi2(1) = 1.06 Prob > chi2 = 0.3030 Log likelihood = -80.306948 Pseudo R2 = 0.0066

Logistic regression Number of obs = 174 LR chi2(1) = 6.09 Prob > chi2 = 0.0136 Log likelihood = -62.99885 Pseudo R2 = 0.0461

Logistic regression Number of obs = 184 LR chi2(1) = 12.47 Prob > chi2 = 0.0004 Log likelihood = -112.00677 Pseudo R2 = 0.0527

Results 6: Logistic regressions of reliable as a function of sys1 and sys3, and pos1, pos2 and pos4

Logistic regression Number of obs = 186 LR chi2(2) = 10.72 Prob > chi2 = 0.0047 Log likelihood = -122.48762 Pseudo R2 = 0.0419

------reliable | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------sys1 | 1.563025 .5123423 1.36 0.173 .822151 2.971532 sys3 | .3431373 .1753555 -2.09 0.036 .1260298 .9342488 _cons | 1.133333 .2838883 0.50 0.617 .6936501 1.851718 ------

Logistic regression Number of obs = 186 535 LR chi2(3) = 5.82 Prob > chi2 = 0.1206 Log likelihood = -124.9368 Pseudo R2 = 0.0228

------reliable | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .7840909 .3434254 -0.56 0.579 .3323128 1.85006 pos2 | 1.161616 .4353318 0.40 0.689 .5572667 2.421376 pos4 | .4181818 .1903139 -1.92 0.055 .1713897 1.020342 _cons | 1.434783 .3897259 1.33 0.184 .8425131 2.443405 ------

Results 7: Ordered logistic regressions of catop and catom as functions of sys1 and sys3, and pos1, pos2 and pos4

Ordered logistic regression Number of obs = 186 LR chi2(3) = 14.16 Prob > chi2 = 0.0027 Log likelihood = -133.57594 Pseudo R2 = 0.0503

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 3.303468 1.137425 3.47 0.001 1.682251 6.48708 sys1 | 1.197032 .4356102 0.49 0.621 .586606 2.442673 sys3 | 1.04792 .5347051 0.09 0.927 .3854778 2.848767 ------+------/cut1 | -2.063504 .3896141 -2.827134 -1.299875 /cut2 | -.1985298 .3130497 -.8120959 .4150363 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 19.44 Prob > chi2 = 0.0006 Log likelihood = -130.94051 Pseudo R2 = 0.0691

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 3.480708 1.204159 3.61 0.000 1.76682 6.857135 pos1 | 3.110006 1.691973 2.09 0.037 1.07071 9.033384 pos2 | 1.780941 .7295832 1.41 0.159 .7978933 3.975155 pos4 | 1.198539 .5679565 0.38 0.702 .4734671 3.033992 ------+------/cut1 | -1.772 .4013589 -2.558649 -.9853514 /cut2 | .1361912 .339234 -.5286953 .8010777 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 30.13 Prob > chi2 = 0.0000 Log likelihood = -167.4466 Pseudo R2 = 0.0826

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 3.196509 .9844996 3.77 0.000 1.747884 5.845738 sys1 | 3.15422 1.015467 3.57 0.000 1.678251 5.928257 sys3 | 3.500723 1.677703 2.61 0.009 1.368434 8.955535 ------+------/cut1 | -.6511055 .2974342 -1.234066 -.0681452 /cut2 | 1.314544 .3071959 .7124516 1.916637 536 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 17.96 Prob > chi2 = 0.0013 Log likelihood = -173.53532 Pseudo R2 = 0.0492

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 3.307929 .9951951 3.98 0.000 1.834303 5.965425 pos1 | 1.073696 .4699061 0.16 0.871 .4553557 2.531699 pos2 | .6869683 .2462857 -1.05 0.295 .3402291 1.387081 pos4 | 1.345429 .6135451 0.65 0.515 .5504199 3.288723 ------+------/cut1 | -1.35891 .3426633 -2.030518 -.6873022 /cut2 | .5013375 .3248227 -.1353033 1.137978 ------

Results 8: Logistic regressions of catop and catcom as functions of reliable, sys1, sys3, pos1, pos2, pos4, relsys1, relsys3, relpos1, relpos2, relpos4, sys1pos1, sys1pos2, sys1pos4, sys3pos1, sys3pos2 and sys3pos4

Ordered logistic regression Number of obs = 186 LR chi2(5) = 15.05 Prob > chi2 = 0.0102 Log likelihood = -133.13357 Pseudo R2 = 0.0535

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.140079 .556036 2.05 0.040 .0502686 2.22989 sys1 | .0490257 .4906828 0.10 0.920 -.9126948 1.010746 sys3 | .1963547 .5970229 0.33 0.742 -.9737887 1.366498 relsys1 | .2720931 .7327497 0.37 0.710 -1.16407 1.708256 relsys3 | -.7647472 1.115601 -0.69 0.493 -2.951285 1.42179 ------+------/cut1 | -2.089664 .4311573 -2.934717 -1.244612 /cut2 | -.2211073 .3635332 -.9336193 .4914046 ------

Ordered logistic regression Number of obs = 186 LR chi2(7) = 21.37 Prob > chi2 = 0.0033 Log likelihood = -129.97273 Pseudo R2 = 0.0760

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.745478 .5872885 2.97 0.003 .5944133 2.896542 pos1 | 1.463779 .7048863 2.08 0.038 .0822277 2.845331 pos2 | 1.117205 .5878462 1.90 0.057 -.0349522 2.269362 pos4 | .3427667 .5892812 0.58 0.561 -.8122032 1.497737 relpos1 | -.7228394 1.111049 -0.65 0.515 -2.900456 1.454778 relpos2 | -1.077 .8237214 -1.31 0.191 -2.691465 .5374638 relpos4 | -.1598216 1.062648 -0.15 0.880 -2.242574 1.922931 ------+------/cut1 | -1.548618 .4501102 -2.430817 -.666418 /cut2 | .387276 .410783 -.4178439 1.192396 ------

Ordered logistic regression Number of obs = 186 LR chi2(12) = 26.21 537 Prob > chi2 = 0.0100 Log likelihood = -127.55363 Pseudo R2 = 0.0932

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.316808 .3612324 3.65 0.000 .6088059 2.024811 sys1 | .1045791 .5936417 0.18 0.860 -1.058937 1.268095 sys3 | .6370408 1.325797 0.48 0.631 -1.961473 3.235554 pos1 | 1.235583 .9016204 1.37 0.171 -.5315605 3.002727 pos2 | .2293602 .6274336 0.37 0.715 -1.000387 1.459107 pos4 | 1.520276 1.20939 1.26 0.209 -.8500842 3.890637 sys1pos1 | .0645531 1.185194 0.05 0.957 -2.258384 2.38749 sys1pos2 | .453672 .8477837 0.54 0.593 -1.207953 2.115298 sys1pos4 | -1.489029 1.417657 -1.05 0.294 -4.267587 1.289528 sys3pos1 | -1.395931 2.023144 -0.69 0.490 -5.361221 2.569359 sys3pos2 | 15.88615 2100.195 0.01 0.994 -4100.421 4132.193 sys3pos4 | -2.123592 1.814868 -1.17 0.242 -5.680669 1.433485 ------+------/cut1 | -1.68054 .5049593 -2.670242 -.6908381 /cut2 | .2688064 .4639988 -.6406146 1.178227 ------Note: 4 observations completely determined. Standard errors questionable.

Ordered logistic regression Number of obs = 186 LR chi2(5) = 30.41 Prob > chi2 = 0.0000 Log likelihood = -167.30796 Pseudo R2 = 0.0833

------catcom | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.310001 .4760023 2.75 0.006 .3770537 2.242949 sys1 | 1.254077 .4775614 2.63 0.009 .3180734 2.19008 sys3 | 1.414569 .5700268 2.48 0.013 .2973367 2.531801 relsys1 | -.1935865 .6381219 -0.30 0.762 -1.444282 1.057109 relsys3 | -.5455261 1.066219 -0.51 0.609 -2.635276 1.544224 ------+------/cut1 | -.5802685 .3445306 -1.255536 .094999 /cut2 | 1.39514 .3661544 .67749 2.112789 ------

Ordered logistic regression Number of obs = 186 LR chi2(7) = 19.68 Prob > chi2 = 0.0063 Log likelihood = -172.67554 Pseudo R2 = 0.0539

------catcom | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.793635 .5630412 3.19 0.001 .690095 2.897176 pos1 | .5874727 .6386997 0.92 0.358 -.6643557 1.839301 pos2 | .0721619 .5378885 0.13 0.893 -.9820801 1.126404 pos4 | .6366146 .5812185 1.10 0.273 -.5025526 1.775782 relpos1 | -.9766028 .8800459 -1.11 0.267 -2.701461 .7482553 relpos2 | -.8240907 .7310687 -1.13 0.260 -2.256959 .6087776 relpos4 | -.6787849 .9721415 -0.70 0.485 -2.584147 1.226577 ------+------/cut1 | -1.049804 .4159638 -1.865078 -.2345301 /cut2 | .8209653 .4113317 .01477 1.627161 ------

Ordered logistic regression Number of obs = 186 LR chi2(12) = 38.88 Prob > chi2 = 0.0001 Log likelihood = -163.07566 Pseudo R2 = 0.1065 538

------catcom | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.285917 .3211537 4.00 0.000 .6564669 1.915366 sys1 | 1.026076 .5674408 1.81 0.071 -.0860879 2.138239 sys3 | 14.90871 610.6478 0.02 0.981 -1181.939 1211.756 pos1 | -.0948226 .6349604 -0.15 0.881 -1.339322 1.149677 pos2 | -.6082107 .5647424 -1.08 0.281 -1.715085 .498664 pos4 | .9718121 .9035487 1.08 0.282 -.7991108 2.742735 sys1pos1 | .57384 .9346195 0.61 0.539 -1.25798 2.40566 sys1pos2 | .3853078 .7633713 0.50 0.614 -1.110872 1.881488 sys1pos4 | -.9748702 1.178043 -0.83 0.408 -3.283792 1.334051 sys3pos1 | -13.36055 610.6494 -0.02 0.983 -1210.211 1183.49 sys3pos2 | -13.74928 610.6485 -0.02 0.982 -1210.598 1183.1 sys3pos4 | -14.89479 610.6486 -0.02 0.981 -1211.744 1181.954 ------+------/cut1 | -.7684111 .4382506 -1.627366 .0905443 /cut2 | 1.257074 .4436102 .3876144 2.126534 ------Note: 1 observation completely determined. Standard errors questionable.

Results 9: Logistic regressions of member as a function of reliable, sys1, sys3, pos1, pos2, pos4, relsys1, relsys3, relpos1, relpos2, relpos4, sys1pos1, sys1pos2, sys1pos4, sys3pos1, sys3pos2 and sys3pos4

Logistic regression Number of obs = 122 LR chi2(3) = 7.36 Prob > chi2 = 0.0613 Log likelihood = -77.158586 Pseudo R2 = 0.0455

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.292601 .514381 0.64 0.519 .5925639 2.819641 sys1 | 2.838517 1.221789 2.42 0.015 1.220975 6.59897 sys3 | 1.441136 .8746497 0.60 0.547 .4386283 4.734929 _cons | .3028291 .1179619 -3.07 0.002 .1411324 .6497832 ------

Logistic regression Number of obs = 122 LR chi2(5) = 12.99 Prob > chi2 = 0.0235 Log likelihood = -74.341658 Pseudo R2 = 0.0804

Logistic regression Number of obs = 122 LR chi2(4) = 8.29 Prob > chi2 = 0.0814 Log likelihood = -76.690317 Pseudo R2 = 0.0513

539 ------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.605963 .6395172 1.19 0.234 .7358256 3.505064 pos1 | .3041039 .1977845 -1.83 0.067 .0849989 1.088004 pos2 | 1.00729 .4818941 0.02 0.988 .3943974 2.572615 pos4 | 1.677611 .9177693 0.95 0.344 .5741495 4.90182 _cons | .5195571 .2120869 -1.60 0.109 .2334355 1.156378 ------

Logistic regression Number of obs = 122 LR chi2(7) = 14.81 Prob > chi2 = 0.0385 Log likelihood = -73.430411 Pseudo R2 = 0.0916

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 2.4 1.706458 1.23 0.218 .5956433 9.670217 pos1 | .4363636 .4080041 -0.89 0.375 .069818 2.727281 pos2 | 2.4 1.752712 1.20 0.231 .5735635 10.04248 pos4 | 1.309091 .9627908 0.37 0.714 .3096996 5.533488 relpos1 | .5092593 .6654707 -0.52 0.606 .0393222 6.595384 relpos2 | .2222222 .2162785 -1.55 0.122 .0329877 1.497002 relpos4 | 4.583333 6.367037 1.10 0.273 .3011039 69.76643 _cons | .4166667 .2217878 -1.64 0.100 .1467919 1.182702 ------

Logistic regression Number of obs = 122 LR chi2(12) = 24.77 Prob > chi2 = 0.0159 Log likelihood = -68.451243 Pseudo R2 = 0.1532

Results 10: Logistic regressions of steal1 and fair1 as a function of reliable, sys1, sys3, pos1, pos2, pos4, relsys1, relsys3, relpos1, relpos2, and relpos4 note: sys3 != 0 predicts success perfectly sys3 dropped and 24 obs not used

Logistic regression Number of obs = 150 LR chi2(2) = 4.54 Prob > chi2 = 0.1033 Log likelihood = -60.262019 Pseudo R2 = 0.0363

------steal1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] 540 ------+------reliable | -1.061243 .5414013 -1.96 0.050 -2.12237 -.0001162 sys1 | .2825253 .4730371 0.60 0.550 -.6446103 1.209661 sys3 | 0 (omitted) _cons | 2.306898 .5188484 4.45 0.000 1.289974 3.323822 ------note: sys3 != 0 predicts success perfectly sys3 dropped and 24 obs not used

Logistic regression Number of obs = 150 LR chi2(3) = 5.00 Prob > chi2 = 0.1717 Log likelihood = -60.032134 Pseudo R2 = 0.0400

Logistic regression Number of obs = 144 LR chi2(3) = 5.76 Prob > chi2 = 0.1237 Log likelihood = -58.677871 Pseudo R2 = 0.0468

------steal1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | -1.031065 .5424651 -1.90 0.057 -2.094277 .0321468 pos1 | .5217924 .7247696 0.72 0.472 -.89873 1.942315 pos2 | -.2655341 .5167463 -0.51 0.607 -1.278338 .74727 pos4 | 0 (omitted) _cons | 2.417494 .5627981 4.30 0.000 1.31443 3.520558 ------note: pos4 != 0 predicts success perfectly pos4 dropped and 30 obs not used note: relpos4 omitted because of collinearity

Logistic regression Number of obs = 144 LR chi2(5) = 6.63 Prob > chi2 = 0.2497 Log likelihood = -58.245088 Pseudo R2 = 0.0538

------steal1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | -1.812378 1.110019 -1.63 0.103 -3.987976 .3632192 pos1 | -.3364719 1.454058 -0.23 0.817 -3.186374 2.51343 pos2 | -1.147402 1.196224 -0.96 0.337 -3.491958 1.197154 pos4 | 0 (omitted) relpos1 | 1.119231 1.692772 0.66 0.508 -2.19854 4.437003 relpos2 | 1.131654 1.333234 0.85 0.396 -1.481437 3.744744 relpos4 | 0 (omitted) _cons | 3.044522 1.023532 2.97 0.003 1.038435 5.050609 ------

541

Logistic regression Number of obs = 184 LR chi2(3) = 13.63 Prob > chi2 = 0.0035 Log likelihood = -111.4242 Pseudo R2 = 0.0576

------fair1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.197872 .334387 3.58 0.000 .542486 1.853259 sys1 | .0142823 .3554409 0.04 0.968 -.6823691 .7109338 sys3 | .5226763 .5254658 0.99 0.320 -.5072178 1.55257 _cons | -.0461155 .313269 -0.15 0.883 -.6601115 .5678805 ------

Logistic regression Number of obs = 184 LR chi2(4) = 12.86 Prob > chi2 = 0.0120 Log likelihood = -111.81201 Pseudo R2 = 0.0544

------fair1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.115782 .3279065 3.40 0.001 .4730973 1.758467 pos1 | -.0877055 .4696441 -0.19 0.852 -1.008191 .8327801 pos2 | .1726323 .4083124 0.42 0.672 -.6276454 .97291 pos4 | .1221298 .4834765 0.25 0.801 -.8254666 1.069726 _cons | .0124165 .3405219 0.04 0.971 -.6549941 .6798272 ------

Logistic regression Number of obs = 184 LR chi2(5) = 15.38 Prob > chi2 = 0.0088 Log likelihood = -110.54847 Pseudo R2 = 0.0651

Logistic regression Number of obs = 184 LR chi2(7) = 14.26 Prob > chi2 = 0.0467 Log likelihood = -111.10956 Pseudo R2 = 0.0603

------fair1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.494508 .6038642 2.47 0.013 .3109559 2.67806 pos1 | -.0689929 .6612878 -0.10 0.917 -1.365093 1.227107 pos2 | .6241543 .6048744 1.03 0.302 -.5613777 1.809686 pos4 | .3829923 .6207685 0.62 0.537 -.8336917 1.599676 relpos1 | .0095694 .9695612 0.01 0.992 -1.890736 1.909874 relpos2 | -.8377285 .8249394 -1.02 0.310 -2.45458 .779123 relpos4 | -.5965665 1.005543 -0.59 0.553 -2.567394 1.374262 _cons | -.1823216 .4281744 -0.43 0.670 -1.021528 .6568849 ------

542 Results 11: ANOVA of help and wua, Logistic regressions of help1, help2 and help3 as a function of wua33, wua55, wua91 and wuamh

Analysis of Variance

Number of obs = 186 R-squared = 0.0568 Root MSE = .857211 Adj R-squared = 0.0413

Source | Partial SS df MS F Prob>F ------+------Model | 8.0546788 3 2.6848929 3.65 0.0136 | wua | 8.0546788 3 2.6848929 3.65 0.0136 | Residual | 133.73564 182 .73481123 ------+------Total | 141.79032 185 .76643418

Logistic regression Number of obs = 186 LR chi2(3) = 36.68 Prob > chi2 = 0.0000 Log likelihood = -107.08037 Pseudo R2 = 0.1462

------help1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | 3.806662 1.053134 3.61 0.000 1.742558 5.870767 wua55 | 3.630039 1.058106 3.43 0.001 1.556189 5.703889 wua91 | 2.936892 1.053031 2.79 0.005 .8729888 5.000795 _cons | -3.496508 1.015038 -3.44 0.001 -5.485946 -1.507069 ------

Logistic regression Number of obs = 186 LR chi2(3) = 36.68 Prob > chi2 = 0.0000 Log likelihood = -107.08037 Pseudo R2 = 0.1462

Logistic regression Number of obs = 186 LR chi2(3) = 36.68 Prob > chi2 = 0.0000 Log likelihood = -107.08037 Pseudo R2 = 0.1462

Logistic regression Number of obs = 186 LR chi2(3) = 36.68 Prob > chi2 = 0.0000 Log likelihood = -107.08037 Pseudo R2 = 0.1462 543

Logistic regression Number of obs = 186 LR chi2(3) = 59.96 Prob > chi2 = 0.0000 Log likelihood = -82.956621 Pseudo R2 = 0.2654

------help2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -3.321833 .60733 -5.47 0.000 -4.512178 -2.131489 wua55 | -3.010621 .6025398 -5.00 0.000 -4.191577 -1.829665 wua91 | -3.683149 .6310097 -5.84 0.000 -4.919905 -2.446393 _cons | 1.757858 .4842342 3.63 0.000 .8087763 2.70694 ------

Logistic regression Number of obs = 186 LR chi2(3) = 59.96 Prob > chi2 = 0.0000 Log likelihood = -82.956621 Pseudo R2 = 0.2654

Logistic regression Number of obs = 186 LR chi2(3) = 59.96 Prob > chi2 = 0.0000 Log likelihood = -82.956621 Pseudo R2 = 0.2654

Logistic regression Number of obs = 186 LR chi2(3) = 59.96 Prob > chi2 = 0.0000 Log likelihood = -82.956621 Pseudo R2 = 0.2654

544 Logistic regression Number of obs = 186 LR chi2(3) = 18.23 Prob > chi2 = 0.0004 Log likelihood = -101.09731 Pseudo R2 = 0.0827

------help3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .6992262 .6313736 1.11 0.268 -.5382434 1.936696 wua55 | .76214 .6417981 1.19 0.235 -.4957611 2.020041 wua91 | 1.978535 .5967283 3.32 0.001 .8089694 3.148101 _cons | -2.014903 .5322906 -3.79 0.000 -3.058173 -.9716325 ------

Logistic regression Number of obs = 186 LR chi2(3) = 18.23 Prob > chi2 = 0.0004 Log likelihood = -101.09731 Pseudo R2 = 0.0827

Logistic regression Number of obs = 186 LR chi2(3) = 18.23 Prob > chi2 = 0.0004 Log likelihood = -101.09731 Pseudo R2 = 0.0827

Logistic regression Number of obs = 186 LR chi2(3) = 18.23 Prob > chi2 = 0.0004 Log likelihood = -101.09731 Pseudo R2 = 0.0827

Results 12: Ordered logistic and logistic regressions of catop, catcom, steal1, fair1 and member as a function of help1 and help3

Ordered logistic regression Number of obs = 182 LR chi2(2) = 16.57 Prob > chi2 = 0.0003 Log likelihood = -124.91066 Pseudo R2 = 0.0622

545 ------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------help1 | 5.053524 2.097338 3.90 0.000 2.240395 11.39893 help3 | 2.632681 1.081008 2.36 0.018 1.177295 5.887232 ------+------/cut1 | -2.082101 .3721235 -2.811449 -1.352752 /cut2 | -.0670877 .2734089 -.6029592 .4687839 ------

Ordered logistic regression Number of obs = 182 LR chi2(2) = 23.67 Prob > chi2 = 0.0000 Log likelihood = -165.41257 Pseudo R2 = 0.0668

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------help1 | 4.778741 1.700066 4.40 0.000 2.379542 9.596957 help3 | 4.188758 1.609576 3.73 0.000 1.972431 8.895467 ------+------/cut1 | -.9925137 .2713238 -1.524299 -.4607289 /cut2 | .9061568 .2683457 .3802089 1.432105 ------

Ordered logistic regression Number of obs = 171 LR chi2(2) = 0.08 Prob > chi2 = 0.9603 Log likelihood = -65.593024 Pseudo R2 = 0.0006

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------help1 | .9806763 .5307885 -0.04 0.971 .3394807 2.832933 help3 | 1.141304 .6785581 0.22 0.824 .3558958 3.659992 ------+------/cut1 | -1.882731 .4057047 -2.677898 -1.087565 ------

Logistic regression Number of obs = 180 LR chi2(2) = 10.43 Prob > chi2 = 0.0054 Log likelihood = -109.35572 Pseudo R2 = 0.0455

Logistic regression Number of obs = 119 LR chi2(2) = 1.07 Prob > chi2 = 0.5870 Log likelihood = -78.382304 Pseudo R2 = 0.0068

Results 13: Ordered logistic regression of tour as a function of wua33, wua55, wua91 and wuamh

LOGISTIC REGRESSIONS

Ordered logistic regression Number of obs = 183 LR chi2(3) = 29.87 Prob > chi2 = 0.0000 Log likelihood = -160.07363 Pseudo R2 = 0.0853

------tour | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | 1.148174 .4241592 2.71 0.007 .3168372 1.979511 wua55 | 1.611815 .4657983 3.46 0.001 .6988675 2.524763 wua91 | 2.407783 .4729477 5.09 0.000 1.480822 3.334743 ------+------/cut1 | -.5033346 .3368438 -1.163536 .1568672 /cut2 | 1.087397 .3496952 .4020067 1.772787 ------

Ordered logistic regression Number of obs = 183 LR chi2(3) = 29.87 Prob > chi2 = 0.0000 Log likelihood = -160.07363 Pseudo R2 = 0.0853

------tour | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -1.259609 .4167538 -3.02 0.003 -2.076431 -.4427864 wua55 | -.7959674 .4542578 -1.75 0.080 -1.686296 .0943616 wuamh | -2.407783 .4729477 -5.09 0.000 -3.334743 -1.480822 ------+------/cut1 | -2.911117 .3790979 -3.654136 -2.168099 /cut2 | -1.320386 .3235216 -1.954477 -.6862954 ------

Ordered logistic regression Number of obs = 183 LR chi2(3) = 29.87 Prob > chi2 = 0.0000 Log likelihood = -160.07363 Pseudo R2 = 0.0853

------tour | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.4636414 .4119487 -1.13 0.260 -1.271046 .3437633 wua91 | .7959674 .4542578 1.75 0.080 -.0943616 1.686296 wuamh | -1.611815 .4657983 -3.46 0.001 -2.524763 -.6988675 ------+------/cut1 | -2.11515 .366024 -2.832544 -1.397756 /cut2 | -.5244187 .3201299 -1.151862 .1030245 ------

Ordered logistic regression Number of obs = 183 LR chi2(3) = 29.87 Prob > chi2 = 0.0000 Log likelihood = -160.07363 Pseudo R2 = 0.0853

------tour | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua55 | .4636414 .4119487 1.13 0.260 -.3437633 1.271046 wua91 | 1.259609 .4167538 3.02 0.003 .4427864 2.076431 547 wuamh | -1.148174 .4241592 -2.71 0.007 -1.979511 -.3168372 ------+------/cut1 | -1.651509 .3060119 -2.251281 -1.051736 /cut2 | -.0607773 .2651751 -.5805109 .4589563 ------

Results 14: Ordered logistic and logistic regressions of catop, catcom, steal1, fair1 and member as a function of tour

Ordered logistic regression Number of obs = 183 LR chi2(2) = 35.03 Prob > chi2 = 0.0000 Log likelihood = -120.96676 Pseudo R2 = 0.1265

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 4.2157 2.039397 2.97 0.003 1.633392 10.8805 3 | 14.4787 6.971896 5.55 0.000 5.634465 37.20546 ------+------/cut1 | -1.136786 .4104171 -1.941189 -.3323834 /cut2 | .9308233 .4011041 .1446737 1.716973 ------

Ordered logistic regression Number of obs = 183 LR chi2(2) = 16.97 Prob > chi2 = 0.0002 Log likelihood = -172.05338 Pseudo R2 = 0.0470

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.969857 1.328345 2.43 0.015 1.235987 7.13604 3 | 5.230501 2.154735 4.02 0.000 2.332845 11.72737 ------+------/cut1 | -.6834071 .3590286 -1.38709 .020276 /cut2 | 1.205037 .3713614 .4771817 1.932891 ------

Logistic regression Number of obs = 171 LR chi2(2) = 1.12 Prob > chi2 = 0.5717 Log likelihood = -63.135013 Pseudo R2 = 0.0088

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 1.343749 1.08191 0.37 0.714 .2773186 6.511144 3 | .7410714 .5017862 -0.44 0.658 .1965639 2.793936 | _cons | 8 4.898979 3.40 0.001 2.409005 26.56699 ------

Logistic regression Number of obs = 181 LR chi2(2) = 32.63 Prob > chi2 = 0.0000 Log likelihood = -100.65486 Pseudo R2 = 0.1395

------548 fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.608696 1.33055 1.88 0.060 .9600042 7.088816 3 | 10.75 5.229275 4.88 0.000 4.143301 27.89141 | _cons | .4 .167332 -2.19 0.028 .1761882 .9081198 ------

Logistic regression Number of obs = 119 LR chi2(2) = 1.10 Prob > chi2 = 0.5774 Log likelihood = -78.845506 Pseudo R2 = 0.0069

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 1.804511 1.028184 1.04 0.300 .590687 5.512668 3 | 1.443609 .7547461 0.70 0.483 .518114 4.022294 | _cons | .4375 .198259 -1.82 0.068 .1799884 1.063436 ------

Results 15: ANOVA of punish and wua, Ordered logistic regressions of punish as a function of wua33, wua55, wua91 and wuamh

ANALYSIS OF VARIANCE

Number of obs = 176 R-squared = 0.0102 Root MSE = .591141 Adj R-squared = -0.0071

Source | Partial SS df MS F Prob>F ------+------Model | .6165404 3 .20551347 0.59 0.6236 | wua | .6165404 3 .20551347 0.59 0.6236 | Residual | 60.105051 172 .34944797 ------+------Total | 60.721591 175 .34698052

LOGISTIC REGRESSIONS

Ordered logistic regression Number of obs = 176 LR chi2(3) = 1.95 Prob > chi2 = 0.5820 Log likelihood = -150.27673 Pseudo R2 = 0.0065

------punish | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.1860871 .4763267 -0.39 0.696 -1.11967 .7474961 wua55 | .3758383 .4902577 0.77 0.443 -.5850492 1.336726 wua91 | .0954426 .4666676 0.20 0.838 -.8192092 1.010094 ------+------/cut1 | -2.868777 .4949699 -3.8389 -1.898654 /cut2 | .2087442 .3896258 -.5549084 .9723968 ------

Ordered logistic regression Number of obs = 176 LR chi2(3) = 1.95 549 Prob > chi2 = 0.5820 Log likelihood = -150.27673 Pseudo R2 = 0.0065

------punish | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.2815296 .3792883 -0.74 0.458 -1.024921 .4618619 wua55 | .2803957 .3963642 0.71 0.479 -.4964639 1.057255 wuamh | -.0954426 .4666676 -0.20 0.838 -1.010094 .8192092 ------+------/cut1 | -2.96422 .4057047 -3.759386 -2.169053 /cut2 | .1133016 .261463 -.3991565 .6257597 ------

Ordered logistic regression Number of obs = 176 LR chi2(3) = 1.95 Prob > chi2 = 0.5820 Log likelihood = -150.27673 Pseudo R2 = 0.0065

------punish | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.5619254 .4082204 -1.38 0.169 -1.362023 .2381719 wua91 | -.2803957 .3963642 -0.71 0.479 -1.057255 .4964639 wuamh | -.3758383 .4902577 -0.77 0.443 -1.336726 .5850492 ------+------/cut1 | -3.244615 .4397785 -4.106565 -2.382665 /cut2 | -.1670941 .3003153 -.7557013 .4215131 ------

Ordered logistic regression Number of obs = 176 LR chi2(3) = 1.95 Prob > chi2 = 0.5820 Log likelihood = -150.27673 Pseudo R2 = 0.0065

------punish | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua55 | .5619254 .4082204 1.38 0.169 -.2381719 1.362023 wua91 | .2815296 .3792883 0.74 0.458 -.4618619 1.024921 wuamh | .1860871 .4763267 0.39 0.696 -.7474961 1.11967 ------+------/cut1 | -2.68269 .4057189 -3.477884 -1.887496 /cut2 | .3948313 .2802894 -.1545258 .9441884 ------

Results 16: Ordered logistic and logistic regressions of catop, catcom, steal1, fair1 and member as a function of punish

Ordered logistic regression Number of obs = 176 LR chi2(2) = 15.58 Prob > chi2 = 0.0004 Log likelihood = -121.17456 Pseudo R2 = 0.0604

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------punish | 2 | 12.76311 9.176837 3.54 0.000 3.118399 52.23736 3 | 17.33344 12.63124 3.91 0.000 4.155241 72.30582 ------+------/cut1 | -.3673807 .6517029 -1.644695 .9099336 /cut2 | 1.565606 .683694 .2255902 2.905621 ------550

Ordered logistic regression Number of obs = 176 LR chi2(2) = 9.47 Prob > chi2 = 0.0088 Log likelihood = -165.03087 Pseudo R2 = 0.0279

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------punish | 2 | 8.845925 6.358154 3.03 0.002 2.16236 36.18748 3 | 7.384856 5.298482 2.79 0.005 1.809766 30.13433 ------+------/cut1 | .0240141 .6730909 -1.29522 1.343248 /cut2 | 1.853131 .6944865 .4919631 3.2143 ------

Logistic regression Number of obs = 164 LR chi2(2) = 5.75 Prob > chi2 = 0.0565 Log likelihood = -61.775015 Pseudo R2 = 0.0444

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------punish | 2 | 3.666667 3.325387 1.43 0.152 .6198641 21.68934 3 | 1.183673 1.012388 0.20 0.844 .2214138 6.327893 | _cons | 3.5 2.806243 1.56 0.118 .7270905 16.84797 ------

Logistic regression Number of obs = 174 LR chi2(2) = 17.32 Prob > chi2 = 0.0002 Log likelihood = -102.77498 Pseudo R2 = 0.0777

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------punish | 2 | 2.473684 1.830514 1.22 0.221 .5800421 10.54943 3 | 8.666667 6.611998 2.83 0.005 1.942902 38.65923 | _cons | .5 .3535534 -0.98 0.327 .1250488 1.999219 ------

Logistic regression Number of obs = 116 LR chi2(2) = 1.34 Prob > chi2 = 0.5125 Log likelihood = -75.812892 Pseudo R2 = 0.0087

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------punish | 2 | 1.212121 .9430451 0.25 0.805 .2638158 5.56918 3 | .7619048 .6020517 -0.34 0.731 .1619151 3.585206 | _cons | .6 .438178 -0.70 0.484 .1433909 2.51062 ------

551 Results 17: ANOVA of conflict and wua, Ordered logistic regressions of conflict as a function of wua33, wua55, wua91 and wuamh

ANALYSIS OF VARIANCE Number of obs = 126 R-squared = 0.1602 Root MSE = .858216 Adj R-squared = 0.1395

Source | Partial SS df MS F Prob>F ------+------Model | 17.134921 3 5.7116402 7.75 0.0001 | wua | 17.134921 3 5.7116402 7.75 0.0001 | Residual | 89.857143 122 .73653396 ------+------Total | 106.99206 125 .85593651

Ordered logistic regression Number of obs = 126 LR chi2(3) = 23.80 Prob > chi2 = 0.0000 Log likelihood = -115.69447 Pseudo R2 = 0.0933

------conflict | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | 1.837728 .6871993 2.67 0.007 .4908422 3.184614 wua55 | 2.593572 .7431449 3.49 0.000 1.137034 4.050109 wua91 | 2.818405 .7104855 3.97 0.000 1.425879 4.210931 ------+------/cut1 | 1.710414 .6253663 .4847183 2.936109 /cut2 | 2.424283 .6406846 1.168564 3.680002 ------

Ordered logistic regression Number of obs = 126 LR chi2(3) = 23.80 Prob > chi2 = 0.0000 Log likelihood = -115.69447 Pseudo R2 = 0.0933

------conflict | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.9806765 .4303253 -2.28 0.023 -1.824099 -.1372545 wua55 | -.2248331 .5088186 -0.44 0.659 -1.222099 .7724331 wuamh | -2.818405 .7104855 -3.97 0.000 -4.210931 -1.425879 ------+------/cut1 | -1.107991 .3414935 -1.777306 -.4386759 /cut2 | -.3941214 .3269724 -1.034975 .2467327 ------

Ordered logistic regression Number of obs = 126 LR chi2(3) = 23.80 Prob > chi2 = 0.0000 Log likelihood = -115.69447 Pseudo R2 = 0.0933

------conflict | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.7558435 .4832984 -1.56 0.118 -1.703091 .191404 wua91 | .2248331 .5088186 0.44 0.659 -.7724331 1.222099 wuamh | -2.593572 .7431449 -3.49 0.000 -4.050109 -1.137034 ------+------/cut1 | -.8831578 .4047941 -1.67654 -.089776 /cut2 | -.1692884 .3960104 -.9454545 .6068778 ------552

Ordered logistic regression Number of obs = 126 LR chi2(3) = 23.80 Prob > chi2 = 0.0000 Log likelihood = -115.69447 Pseudo R2 = 0.0933

------conflict | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua55 | .7558435 .4832984 1.56 0.118 -.191404 1.703091 wua91 | .9806765 .4303253 2.28 0.023 .1372545 1.824099 wuamh | -1.837728 .6871993 -2.67 0.007 -3.184614 -.4908422 ------+------/cut1 | -.1273143 .2879999 -.6917838 .4371552 /cut2 | .5865551 .2934268 .0114491 1.161661 ------

Results 18: Ordered logistic and logistic regressions of catop, catcom, steal1, fair1 and member as a function of conflict

Ordered logistic regression Number of obs = 126 LR chi2(2) = 21.34 Prob > chi2 = 0.0000 Log likelihood = -95.58765 Pseudo R2 = 0.1004

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------conflict | 2 | 2.814524 1.558504 1.87 0.062 .9507445 8.331939 3 | 7.251055 3.388349 4.24 0.000 2.901636 18.12006 ------+------/cut1 | -1.672272 .3461296 -2.350674 -.9938706 /cut2 | .2462462 .2707045 -.2843249 .7768173 ------

Ordered logistic regression Number of obs = 126 LR chi2(2) = 20.42 Prob > chi2 = 0.0000 Log likelihood = -115.8411 Pseudo R2 = 0.0810

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------conflict | 2 | 4.368693 2.463701 2.61 0.009 1.446508 13.19417 3 | 5.22771 2.071256 4.17 0.000 2.404692 11.36485 ------+------/cut1 | -.8131382 .2833237 -1.368442 -.257834 /cut2 | .8131382 .2833237 .257834 1.368442 ------

Logistic regression Number of obs = 120 LR chi2(2) = 1.23 Prob > chi2 = 0.5401 Log likelihood = -36.149291 Pseudo R2 = 0.0168

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------conflict | 2 | .53125 .5076121 -0.66 0.508 .0816529 3.45642 3 | .4583333 .3379134 -1.06 0.290 .1080488 1.94421 553 | _cons | 16 9.521905 4.66 0.000 4.983721 51.36724 ------

Logistic regression Number of obs = 126 LR chi2(2) = 3.79 Prob > chi2 = 0.1504 Log likelihood = -82.304848 Pseudo R2 = 0.0225

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------conflict | 2 | 2.6 1.528491 1.63 0.104 .8214249 8.229601 3 | 1.805556 .7183815 1.49 0.138 .8278294 3.938047 | _cons | 1.076923 .2933026 0.27 0.786 .6314775 1.836587 ------

Logistic regression Number of obs = 87 LR chi2(2) = 3.08 Prob > chi2 = 0.2140 Log likelihood = -55.685145 Pseudo R2 = 0.0269

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------conflict | 2 | 1.828571 1.121543 0.98 0.325 .5495824 6.084025 3 | .6 .3065942 -1.00 0.317 .2203918 1.633454 | _cons | .625 .2057127 -1.43 0.153 .3278803 1.191365 ------

Results 19: Logistic regressions of secwork and secwater as a function of wua33, wua55, wua91 and wuamh

Logistic regression Number of obs = 186 LR chi2(3) = 4.29 Prob > chi2 = 0.2322 Log likelihood = -126.09349 Pseudo R2 = 0.0167

------secwork | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .3133498 .4430974 0.71 0.479 -.5551051 1.181805 wua55 | -.4567584 .4682929 -0.98 0.329 -1.374596 .4610788 wua91 | .2727564 .4382346 0.62 0.534 -.5861676 1.13168 _cons | -.2363888 .3453958 -0.68 0.494 -.9133521 .4405745 ------

Logistic regression Number of obs = 186 LR chi2(3) = 4.29 Prob > chi2 = 0.2322 Log likelihood = -126.09349 Pseudo R2 = 0.0167

Logistic regression Number of obs = 186 LR chi2(3) = 4.29 Prob > chi2 = 0.2322 Log likelihood = -126.09349 Pseudo R2 = 0.0167

Logistic regression Number of obs = 186 LR chi2(3) = 4.29 Prob > chi2 = 0.2322 Log likelihood = -126.09349 Pseudo R2 = 0.0167

LOGISTIC REGRESSIONS FOR SECWATER

Logistic regression Number of obs = 186 LR chi2(3) = 20.15 Prob > chi2 = 0.0002 Log likelihood = -102.85974 Pseudo R2 = 0.0892

------secwater | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | 2.618435 .7801472 3.36 0.001 1.089375 4.147496 wua55 | 2.177879 .7926106 2.75 0.006 .6243908 3.731367 wua91 | 1.599866 .7949726 2.01 0.044 .0417482 3.157983 _cons | -2.772586 .7288681 -3.80 0.000 -4.201141 -1.344031 ------

Logistic regression Number of obs = 186 LR chi2(3) = 20.15 Prob > chi2 = 0.0002 Log likelihood = -102.85974 Pseudo R2 = 0.0892

Logistic regression Number of obs = 186 LR chi2(3) = 20.15 Prob > chi2 = 0.0002 Log likelihood = -102.85974 Pseudo R2 = 0.0892

Logistic regression Number of obs = 186 LR chi2(3) = 20.15 Prob > chi2 = 0.0002 Log likelihood = -102.85974 Pseudo R2 = 0.0892

Results 20: Ordered logistic regressions of catop and catcom as functions of secwork, secwater, secworkwat, wua33 and secwater33

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.87 Prob > chi2 = 0.3512 Log likelihood = -140.22352 Pseudo R2 = 0.0031

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | .7415246 .2378303 -0.93 0.351 .3954745 1.390377 ------+------/cut1 | -2.817602 .3389894 -3.482009 -2.153196 /cut2 | -1.032204 .2232335 -1.469734 -.5946745 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.03 Prob > chi2 = 0.8639 Log likelihood = -140.64332 Pseudo R2 = 0.0001

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwater | 1.062777 .3781689 0.17 0.864 .5291254 2.134647 ------+------/cut1 | -2.657029 .3144098 -3.273261 -2.040797 /cut2 | -.87653 .1900136 -1.24895 -.5041101 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 5.68 Prob > chi2 = 0.1280 Log likelihood = -137.81564 Pseudo R2 = 0.0202

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | 1.143545 .4375332 0.35 0.726 .540224 2.420653 secwater | 2.622749 1.557339 1.62 0.104 .8190857 8.398161 secworkwat | .1981368 .1519958 -2.11 0.035 .0440541 .8911353 556 ------+------/cut1 | -2.628405 .3542132 -3.32265 -1.93416 /cut2 | -.8167787 .248741 -1.304302 -.3292553 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.25 Prob > chi2 = 0.6178 Log likelihood = -182.38941 Pseudo R2 = 0.0007

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | 1.151777 .3264148 0.50 0.618 .6609015 2.007242 ------+------/cut1 | -1.755947 .2435643 -2.233324 -1.27857 /cut2 | -.0232843 .1930103 -.4015775 .3550088 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 2.36 Prob > chi2 = 0.1244 Log likelihood = -181.33343 Pseudo R2 = 0.0065

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwater | 1.625854 .5200407 1.52 0.129 .8685983 3.043297 ------+------/cut1 | -1.694365 .224827 -2.135018 -1.253713 /cut2 | .0516623 .1717052 -.2848737 .3881983 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 2.79 Prob > chi2 = 0.4257 Log likelihood = -181.12063 Pseudo R2 = 0.0076

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | 1.223794 .4095478 0.60 0.546 .6351118 2.358123 secwater | 1.894632 .849117 1.43 0.154 .7871217 4.560451 secworkwat | .7142514 .45836 -0.52 0.600 .2030511 2.512446 ------+------/cut1 | -1.612369 .2617166 -2.125324 -1.099414 /cut2 | .1366712 .2216009 -.2976586 .571001 ------

Ordered logistic regression Number of obs = 186 LR chi2(2) = 1.75 Prob > chi2 = 0.4169 Log likelihood = -139.78323 Pseudo R2 = 0.0062

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwater | 1.184918 .435414 0.46 0.644 .5766375 2.434858 wua33 | .6276828 .2211012 -1.32 0.186 .3147056 1.251918 ------+------/cut1 | -2.77893 .3305142 -3.426726 -2.131134 /cut2 | -.987885 .2109297 -1.4013 -.5744705 ------

Ordered logistic regression Number of obs = 186 557 LR chi2(2) = 2.75 Prob > chi2 = 0.2532 Log likelihood = -181.14023 Pseudo R2 = 0.0075

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwater | 1.687519 .550258 1.60 0.109 .890621 3.197454 wua33 | .8162078 .266123 -0.62 0.533 .4307925 1.546441 ------+------/cut1 | -1.739798 .236991 -2.204291 -1.275304 /cut2 | .0093534 .1848893 -.3530229 .3717297 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 6.96 Prob > chi2 = 0.1383 Log likelihood = -137.1803 Pseudo R2 = 0.0247

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | 1.145371 .4393821 0.35 0.723 .5400225 2.429295 secwater | 2.814397 1.68643 1.73 0.084 .8696254 9.108326 secworkwat | .2083019 .1604148 -2.04 0.042 .0460444 .9423444 wua33 | .6651694 .2386753 -1.14 0.256 .3292343 1.343877 ------+------/cut1 | -2.73257 .368062 -3.453958 -2.011181 /cut2 | -.912769 .2649365 -1.432035 -.393503 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 3.19 Prob > chi2 = 0.5258 Log likelihood = -180.91652 Pseudo R2 = 0.0088

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | 1.239145 .4155712 0.64 0.523 .6421773 2.391052 secwater | 1.949497 .8795811 1.48 0.139 .8051451 4.720313 secworkwat | .7267375 .4674085 -0.50 0.620 .2060245 2.563518 wua33 | .8107913 .2655906 -0.64 0.522 .4266594 1.540767 ------+------/cut1 | -1.654541 .2701228 -2.183972 -1.12511 /cut2 | .098062 .229701 -.3521436 .5482676 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 1.75 Prob > chi2 = 0.6257 Log likelihood = -139.7827 Pseudo R2 = 0.0062

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwater | 1.197051 .5782615 0.37 0.710 .4644309 3.085347 wua33 | .6328389 .2739203 -1.06 0.290 .2709293 1.47819 secwater33 | .9760368 .7270534 -0.03 0.974 .2266747 4.202709 ------+------/cut1 | -2.776799 .3368563 -3.437025 -2.116573 /cut2 | -.9857772 .2205371 -1.418022 -.5535325 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 4.34 558 Prob > chi2 = 0.2272 Log likelihood = -180.34516 Pseudo R2 = 0.0119

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwater | 1.270479 .4961379 0.61 0.540 .5909661 2.731318 wua33 | .6089665 .243261 -1.24 0.214 .2783351 1.332352 secwater33 | 2.397738 1.673666 1.25 0.210 .6104461 9.417947 ------+------/cut1 | -1.812033 .24592 -2.294028 -1.330039 /cut2 | -.0515679 .1919155 -.4277153 .3245794 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 7.00 Prob > chi2 = 0.2206 Log likelihood = -137.15787 Pseudo R2 = 0.0249

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | .1360189 .3839119 0.35 0.723 -.6164346 .8884724 secwater | .9734924 .6621217 1.47 0.141 -.3242423 2.271227 wua33 | -.4596146 .4332752 -1.06 0.289 -1.308818 .3895893 secworkwat | -1.581623 .7723334 -2.05 0.041 -3.095369 -.0678773 secwater33 | .1626553 .7676305 0.21 0.832 -1.341873 1.667183 ------+------/cut1 | -2.746008 .3738778 -3.478795 -2.013221 /cut2 | -.9258918 .2726346 -1.460246 -.3915377 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 7.00 Prob > chi2 = 0.2206 Log likelihood = -137.15787 Pseudo R2 = 0.0249

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | 1.145704 .4398492 0.35 0.723 .5398658 2.431413 secwater | 2.647173 1.752751 1.47 0.141 .723075 9.691285 wua33 | .631527 .273625 -1.06 0.289 .2701391 1.476374 secworkwat | .2056411 .1588235 -2.05 0.041 .0452583 .9343751 secwater33 | 1.176631 .9032179 0.21 0.832 .2613558 5.297227 ------+------/cut1 | -2.746008 .3738778 -3.478795 -2.013221 /cut2 | -.9258918 .2726346 -1.460246 -.3915377 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 4.94 Prob > chi2 = 0.4234 Log likelihood = -180.04462 Pseudo R2 = 0.0135

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | 1.262415 .4251794 0.69 0.489 .6524069 2.442786 secwater | 1.52073 .7370343 0.86 0.387 .5881802 3.931823 wua33 | .5975048 .2389261 -1.29 0.198 .2728782 1.30832 secworkwat | .6531944 .4244152 -0.66 0.512 .182798 2.334068 secwater33 | 2.516887 1.771589 1.31 0.190 .63346 10.00019 ------+------/cut1 | -1.723084 .276673 -2.265354 -1.180815 /cut2 | .0420545 .2342162 -.4170008 .5011098 559 ------

Results 21: Logistic regressions of member as a function of secwork, secwater and secworkwat

Logistic regression Number of obs = 122 LR chi2(1) = 2.40 Prob > chi2 = 0.1214 Log likelihood = -79.637835 Pseudo R2 = 0.0148

Logistic regression Number of obs = 122 LR chi2(1) = 1.53 Prob > chi2 = 0.2162 Log likelihood = -80.072727 Pseudo R2 = 0.0095

Logistic regression Number of obs = 122 LR chi2(3) = 4.64 Prob > chi2 = 0.2005 Log likelihood = -78.519414 Pseudo R2 = 0.0287

Results 22: Logistic regressions of steal1 and fair1 as functions of secwork, secwater, secworkwat, wua33 and secwork33

Logistic regression Number of obs = 174 LR chi2(1) = 0.03 Prob > chi2 = 0.8706 Log likelihood = -66.029572 Pseudo R2 = 0.0002

Logistic regression Number of obs = 174 LR chi2(1) = 6.65 Prob > chi2 = 0.0099 Log likelihood = -62.716064 Pseudo R2 = 0.0504 560

Logistic regression Number of obs = 174 LR chi2(3) = 7.09 Prob > chi2 = 0.0690 Log likelihood = -62.49585 Pseudo R2 = 0.0537

Logistic regression Number of obs = 184 LR chi2(1) = 0.02 Prob > chi2 = 0.8960 Log likelihood = -118.2315 Pseudo R2 = 0.0001

Logistic regression Number of obs = 184 LR chi2(1) = 0.73 Prob > chi2 = 0.3924 Log likelihood = -117.87422 Pseudo R2 = 0.0031

Logistic regression Number of obs = 184 LR chi2(3) = 1.30 Prob > chi2 = 0.7293 Log likelihood = -117.59044 Pseudo R2 = 0.0055

Logistic regression Number of obs = 184 LR chi2(2) = 0.46 Prob > chi2 = 0.7961 Log likelihood = -118.01197 Pseudo R2 = 0.0019

561 ------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------secwork | 1.054917 .3306402 0.17 0.865 .5707264 1.949882 wua33 | .794774 .2744079 -0.67 0.506 .4039738 1.56363 _cons | 1.998907 .4552313 3.04 0.002 1.279204 3.123527 ------

Logistic regression Number of obs = 184 LR chi2(3) = 0.51 Prob > chi2 = 0.9162 Log likelihood = -117.98393 Pseudo R2 = 0.0022

Results 23: Ordered logistic regressions of catop and catcom as functions of dunums_log, greenhouse0or1, market2, dungreen, markgreen and dunmark

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.00 Prob > chi2 = 0.9709 Log likelihood = -140.65736 Pseudo R2 = 0.0000

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.012215 .3373426 0.04 0.971 .5267343 1.945155 ------+------/cut1 | -2.653371 .643482 -3.914572 -1.392169 /cut2 | -.8730321 .5927806 -2.034861 .2887965 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.89 Prob > chi2 = 0.3449 Log likelihood = -140.21192 Pseudo R2 = 0.0032

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------greenhouse_~1 | .3417904 .3670999 0.93 0.352 -.3777121 1.061293 ------+------/cut1 | -2.585477 .311457 -3.195921 -1.975032 /cut2 | -.8002424 .1874854 -1.167707 -.4327777 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.00 Prob > chi2 = 0.9769 Log likelihood = -140.6576 Pseudo R2 = 0.0000

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------market2 | 1.013099 .4554933 0.03 0.977 .4197092 2.445431 ------+------/cut1 | -2.672215 .3058057 -3.271583 -2.072847 562 /cut2 | -.8918791 .1748089 -1.234498 -.5492601 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 7.12 Prob > chi2 = 0.3097 Log likelihood = -137.09705 Pseudo R2 = 0.0253

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.074609 .5068296 0.15 0.879 .4263722 2.708395 greenhouse_~1 | .8182462 1.280882 -0.13 0.898 .0380552 17.59359 market2 | .0956081 .2157505 -1.04 0.298 .0011473 7.967588 dungreen | 1.077729 .9806183 0.08 0.934 .1811369 6.412275 dunmark | 1.612269 1.537665 0.50 0.617 .2486697 10.45326 markgreen | 12.48651 14.40486 2.19 0.029 1.301564 119.7888 ------+------/cut1 | -2.620502 .8203402 -4.228339 -1.012665 /cut2 | -.7883842 .7796764 -2.316522 .7397534 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 2.75 Prob > chi2 = 0.0971 Log likelihood = -181.13744 Pseudo R2 = 0.0075

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.639966 .4940821 1.64 0.101 .9086328 2.959929 ------+------/cut1 | -.9862497 .5423041 -2.049146 .0766468 /cut2 | .7637248 .5354142 -.2856678 1.813117 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.00 Prob > chi2 = 0.9695 Log likelihood = -182.51319 Pseudo R2 = 0.0000

------catcom | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------greenhouse_~1 | .0117865 .3088048 0.04 0.970 -.5934599 .6170328 ------+------/cut1 | -1.813629 .2298848 -2.264195 -1.363064 /cut2 | -.082617 .1724597 -.4206319 .2553979 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.11 Prob > chi2 = 0.7402 Log likelihood = -182.45893 Pseudo R2 = 0.0003

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------market2 | 1.134125 .4317043 0.33 0.741 .5378375 2.3915 ------+------/cut1 | -1.796919 .219757 -2.227634 -1.366203 /cut2 | -.0651958 .1596189 -.378043 .2476514 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 5.47 563 Prob > chi2 = 0.4854 Log likelihood = -179.78022 Pseudo R2 = 0.0150

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.858147 .7890093 1.46 0.145 .808428 4.270892 greenhouse_~1 | .5905002 .7687894 -0.40 0.686 .0460273 7.575724 market2 | .5616648 1.091874 -0.30 0.767 .0124378 25.3635 dungreen | 1.16266 .8878012 0.20 0.844 .2603033 5.19309 dunmark | .884128 .737787 -0.15 0.883 .172268 4.537594 markgreen | 3.224074 2.943624 1.28 0.200 .5385753 19.30028 ------+------/cut1 | -.8909879 .7057606 -2.274253 .4922775 /cut2 | .8801272 .7023625 -.4964779 2.256732 ------

Results 24: Logistic regressions of member as a function of dunums_log, greenhouse_0or1, market2, dungreen, dunmark, markgreen, wua55 and dungreen55

Logistic regression Number of obs = 122 LR chi2(1) = 8.84 Prob > chi2 = 0.0029 Log likelihood = -76.417306 Pseudo R2 = 0.0547

Logistic regression Number of obs = 122 LR chi2(1) = 0.64 Prob > chi2 = 0.4251 Log likelihood = -80.519179 Pseudo R2 = 0.0039

Logistic regression Number of obs = 122 LR chi2(1) = 1.90 Prob > chi2 = 0.1685 Log likelihood = -79.889423 Pseudo R2 = 0.0117

Logistic regression Number of obs = 122 LR chi2(6) = 15.90 Prob > chi2 = 0.0143 Log likelihood = -72.888071 Pseudo R2 = 0.0983

------564 member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 3.462014 2.327381 1.85 0.065 .9270393 12.92884 greenhouse_~1 | .0062006 .0171577 -1.84 0.066 .0000274 1.405395 market2 | 89.66566 302.1849 1.33 0.182 .1213296 66265.19 dungreen | 9.197222 13.0848 1.56 0.119 .5657965 149.5041 dunmark | .1638699 .2281761 -1.30 0.194 .0106971 2.510332 markgreen | .4664488 .6529938 -0.54 0.586 .0300044 7.251421 _cons | .0875298 .095924 -2.22 0.026 .010217 .7498743 ------

Logistic regression Number of obs = 122 LR chi2(7) = 18.96 Prob > chi2 = 0.0083 Log likelihood = -71.355364 Pseudo R2 = 0.1173

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 3.169503 2.150561 1.70 0.089 .8383716 11.98245 greenhouse_~1 | .0006997 .0022641 -2.25 0.025 1.23e-06 .3975324 market2 | 74.68854 253.376 1.27 0.204 .0967406 57663.26 dungreen | 19.32677 30.20259 1.90 0.058 .9035624 413.3904 dunmark | .1870912 .2632167 -1.19 0.233 .0118715 2.948494 markgreen | .3730956 .5311991 -0.69 0.489 .0229041 6.07754 wua55 | 3.496067 2.631726 1.66 0.096 .7995077 15.28751 _cons | .0943389 .1040031 -2.14 0.032 .0108715 .8186361 ------

Logistic regression Number of obs = 122 LR chi2(8) = 20.13 Prob > chi2 = 0.0099 Log likelihood = -70.774675 Pseudo R2 = 0.1245

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 3.303498 2.235361 1.77 0.077 .8769924 12.44378 greenhouse_~1 | .000399 .0014007 -2.23 0.026 4.10e-07 .3882147 market2 | 70.97174 242.9047 1.25 0.213 .0866566 58125.84 dungreen | 20.54302 33.89043 1.83 0.067 .8098636 521.0948 dunmark | .185126 .2632021 -1.19 0.235 .0114097 3.003719 markgreen | .3765639 .5456988 -0.67 0.500 .0219946 6.44704 wua55 | 1.547444 1.606858 0.42 0.674 .2021767 11.84401 dungreen55 | 2.075723 1.422555 1.07 0.287 .5417705 7.952861 _cons | .0920986 .1011082 -2.17 0.030 .0107098 .7919979 ------

Results 25: Logistic regressions of steal1 and fair1 as functions of dunums_log, greenhouse_0or1, market2, dungreen, dunmark and markgreen

Logistic regression Number of obs = 174 LR chi2(1) = 8.53 Prob > chi2 = 0.0035 Log likelihood = -61.779899 Pseudo R2 = 0.0645

Logistic regression Number of obs = 174 565 LR chi2(1) = 0.04 Prob > chi2 = 0.8330 Log likelihood = -66.020619 Pseudo R2 = 0.0003

. logit steal1 market2, or note: market2 != 0 predicts success perfectly market2 dropped and 27 obs not used

Iteration 0: log likelihood = -62.051439 Iteration 1: log likelihood = -62.051439

Logistic regression Number of obs = 147 LR chi2(0) = 0.00 Prob > chi2 = . Log likelihood = -62.051439 Pseudo R2 = 0.0000

. logit steal1 dunums_log greenhouse_0or1 market2 dungreen dunmark markgreen, or note: market2 != 0 predicts success perfectly market2 dropped and 27 obs not used note: dunmark omitted because of collinearity note: markgreen omitted because of collinearity Iteration 0: log likelihood = -62.051439 Iteration 1: log likelihood = -59.255744 Iteration 2: log likelihood = -59.086649 Iteration 3: log likelihood = -59.086464 Iteration 4: log likelihood = -59.086464

Logistic regression Number of obs = 147 LR chi2(3) = 5.93 Prob > chi2 = 0.1151 Log likelihood = -59.086464 Pseudo R2 = 0.0478

Logistic regression Number of obs = 174 LR chi2(3) = 9.27 Prob > chi2 = 0.0259 Log likelihood = -61.406157 Pseudo R2 = 0.0702

Logistic regression Number of obs = 184 LR chi2(1) = 0.32 Prob > chi2 = 0.5711 Log likelihood = -118.07963 Pseudo R2 = 0.0014

Logistic regression Number of obs = 184 LR chi2(1) = 0.16 Prob > chi2 = 0.6925 Log likelihood = -118.16183 Pseudo R2 = 0.0007

Logistic regression Number of obs = 184 LR chi2(1) = 0.09 Prob > chi2 = 0.7604 Log likelihood = -118.19353 Pseudo R2 = 0.0004

Logistic regression Number of obs = 184 LR chi2(6) = 5.23 Prob > chi2 = 0.5147 Log likelihood = -115.62498 Pseudo R2 = 0.0221

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 2.151588 1.050815 1.57 0.117 .8261152 5.603736 greenhouse_~1 | 1.645633 2.378233 0.34 0.730 .0968712 27.95574 market2 | 1.918746 4.115521 0.30 0.761 .0286599 128.4578 dungreen | .5598971 .465794 -0.70 0.486 .1096403 2.859213 dunmark | .4251984 .3849198 -0.94 0.345 .0721147 2.507031 markgreen | 5.242882 5.318262 1.63 0.102 .7180096 38.28334 _cons | .6468966 .507937 -0.55 0.579 .1388288 3.014326 ------

Results 26: ANOVA of wua and owner, Logistic regressions of own1, own2, and own3 as functions of wua33, wua55, wua91 and wuamh 567 anova owner wua

Number of obs = 186 R-squared = 0.0294 Root MSE = .80461 Adj R-squared = 0.0134

Source | Partial SS df MS F Prob>F ------+------Model | 3.5716554 3 1.1905518 1.84 0.1417 | wua | 3.5716554 3 1.1905518 1.84 0.1417 | Residual | 117.82619 182 .64739667 ------+------Total | 121.39785 185 .65620459

Logistic regression Number of obs = 186 LR chi2(3) = 3.21 Prob > chi2 = 0.3601 Log likelihood = -107.64778 Pseudo R2 = 0.0147

------own1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .5419152 .5478368 0.99 0.323 -.5318253 1.615656 wua55 | .9457369 .5471413 1.73 0.084 -.1266403 2.018114 wua91 | .5596147 .5422615 1.03 0.302 -.5031982 1.622428 _cons | -1.540444 .4498676 -3.42 0.001 -2.422168 -.6587198 ------

Logistic regression Number of obs = 186 LR chi2(3) = 3.21 Prob > chi2 = 0.3601 Log likelihood = -107.64778 Pseudo R2 = 0.0147

------own1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.0176996 .4352138 -0.04 0.968 -.8707029 .8353037 wua55 | .3861221 .4343379 0.89 0.374 -.4651645 1.237409 wuamh | -.5596147 .5422615 -1.03 0.302 -1.622428 .5031982 _cons | -.9808293 .302765 -3.24 0.001 -1.574238 -.3874207 ------

Logistic regression Number of obs = 186 LR chi2(3) = 3.21 Prob > chi2 = 0.3601 Log likelihood = -107.64778 Pseudo R2 = 0.0147

------own1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.4038217 .441279 -0.92 0.360 -1.268713 .4610691 wua91 | -.3861221 .4343379 -0.89 0.374 -1.237409 .4651645 wuamh | -.9457369 .5471413 -1.73 0.084 -2.018114 .1266403 _cons | -.5947071 .3114205 -1.91 0.056 -1.20508 .015666 ------

Logistic regression Number of obs = 186 LR chi2(3) = 3.21 Prob > chi2 = 0.3601 Log likelihood = -107.64778 Pseudo R2 = 0.0147

------own1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] 568 ------+------wua55 | .4038217 .441279 0.92 0.360 -.4610691 1.268713 wua91 | .0176996 .4352138 0.04 0.968 -.8353037 .8707029 wuamh | -.5419152 .5478368 -0.99 0.323 -1.615656 .5318253 _cons | -.9985288 .3126409 -3.19 0.001 -1.611294 -.3857638 ------

Logistic regression Number of obs = 186 LR chi2(3) = 9.05 Prob > chi2 = 0.0287 Log likelihood = -113.86856 Pseudo R2 = 0.0382

------own2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.9555114 .4983489 -1.92 0.055 -1.932257 .0212345 wua55 | -.3153568 .4777329 -0.66 0.509 -1.251696 .6209826 wua91 | .2972515 .4448319 0.67 0.504 -.5746031 1.169106 _cons | -.4795731 .3529053 -1.36 0.174 -1.171255 .2121085 ------

Logistic regression Number of obs = 186 LR chi2(3) = 9.05 Prob > chi2 = 0.0287 Log likelihood = -113.86856 Pseudo R2 = 0.0382

------own2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -1.252763 .4440077 -2.82 0.005 -2.123002 -.3825238 wua55 | -.6126083 .4207374 -1.46 0.145 -1.437238 .2120219 wuamh | -.2972515 .4448319 -0.67 0.504 -1.169106 .5746031 _cons | -.1823216 .2708013 -0.67 0.501 -.7130823 .3484392 ------

Logistic regression Number of obs = 186 LR chi2(3) = 9.05 Prob > chi2 = 0.0287 Log likelihood = -113.86856 Pseudo R2 = 0.0382

------own2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.6401547 .4769656 -1.34 0.180 -1.57499 .2946807 wua91 | .6126083 .4207374 1.46 0.145 -.2120219 1.437238 wuamh | .3153568 .4777329 0.66 0.509 -.6209826 1.251696 _cons | -.7949299 .3220041 -2.47 0.014 -1.426046 -.1638134 ------

Logistic regression Number of obs = 186 LR chi2(3) = 9.05 Prob > chi2 = 0.0287 Log likelihood = -113.86856 Pseudo R2 = 0.0382

------own2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua55 | .6401547 .4769656 1.34 0.180 -.2946807 1.57499 wua91 | 1.252763 .4440077 2.82 0.005 .3825238 2.123002 wuamh | .9555114 .4983489 1.92 0.055 -.0212345 1.932257 _cons | -1.435085 .3518658 -4.08 0.000 -2.124729 -.7454403 ------

Logistic regression Number of obs = 186 LR chi2(3) = 9.00 Prob > chi2 = 0.0293 569 Log likelihood = -120.09135 Pseudo R2 = 0.0361

------own3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .3905395 .4434853 0.88 0.379 -.4786757 1.259755 wua55 | -.4567584 .4682929 -0.98 0.329 -1.374596 .4610788 wua91 | -.7444404 .4593092 -1.62 0.105 -1.64467 .155789 _cons | -.2363888 .3453958 -0.68 0.494 -.9133521 .4405745 ------

Logistic regression Number of obs = 186 LR chi2(3) = 9.00 Prob > chi2 = 0.0293 Log likelihood = -120.09135 Pseudo R2 = 0.0361

------own3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | 1.13498 .411154 2.76 0.006 .3291329 1.940827 wua55 | .287682 .4377975 0.66 0.511 -.5703853 1.145749 wuamh | .7444404 .4593092 1.62 0.105 -.155789 1.64467 _cons | -.9808292 .302765 -3.24 0.001 -1.574238 -.3874207 ------

Logistic regression Number of obs = 186 LR chi2(3) = 9.00 Prob > chi2 = 0.0293 Log likelihood = -120.09135 Pseudo R2 = 0.0361

------own3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .8472979 .4211662 2.01 0.044 .0218273 1.672768 wua91 | -.287682 .4377975 -0.66 0.511 -1.145749 .5703853 wuamh | .4567584 .4682929 0.98 0.329 -.4610788 1.374596 _cons | -.6931472 .3162278 -2.19 0.028 -1.312942 -.0733521 ------

Logistic regression Number of obs = 186 LR chi2(3) = 9.00 Prob > chi2 = 0.0293 Log likelihood = -120.09135 Pseudo R2 = 0.0361

------own3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua55 | -.8472979 .4211662 -2.01 0.044 -1.672768 -.0218273 wua91 | -1.13498 .411154 -2.76 0.006 -1.940827 -.3291329 wuamh | -.3905395 .4434853 -0.88 0.379 -1.259755 .4786757 _cons | .1541507 .2781743 0.55 0.579 -.391061 .6993623 ------

Results 27: Ordered logistic and logistic regressions of catop, catcom, steal1 and fair1 as functions of owner

Ordered logistic regression Number of obs = 186 LR chi2(2) = 12.10 Prob > chi2 = 0.0024 Log likelihood = -134.60794 Pseudo R2 = 0.0430

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------570 owner | 2 | .702716 .333166 -0.74 0.457 .2774694 1.779691 3 | .2703435 .1172439 -3.02 0.003 .1155481 .6325125 ------+------/cut1 | -3.433169 .4553199 -4.32558 -2.540759 /cut2 | -1.578899 .3652742 -2.294823 -.8629743 ------

Ordered logistic regression Number of obs = 186 LR chi2(2) = 7.28 Prob > chi2 = 0.0262 Log likelihood = -178.87238 Pseudo R2 = 0.0200

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------owner | 2 | 1.202259 .4364385 0.51 0.612 .5901997 2.449048 3 | .5071382 .1775321 -1.94 0.052 .2553578 1.007172 ------+------/cut1 | -2.048935 .3140281 -2.664419 -1.433452 /cut2 | -.2660348 .265219 -.7858544 .2537849 ------

Logistic regression Number of obs = 174 LR chi2(2) = 4.01 Prob > chi2 = 0.1343 Log likelihood = -64.035415 Pseudo R2 = 0.0304

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------owner | 2 | 3.407143 2.177776 1.92 0.055 .9734637 11.92507 3 | 1.828571 .943831 1.17 0.242 .6649022 5.02882 | _cons | 3.888889 1.453438 3.63 0.000 1.869375 8.090115 ------

Logistic regression Number of obs = 184 LR chi2(2) = 2.54 Prob > chi2 = 0.2807 Log likelihood = -116.96949 Pseudo R2 = 0.0107

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------owner | 2 | 1.078431 .4503161 0.18 0.857 .4757292 2.444698 3 | .6323529 .2470774 -1.17 0.241 .2940168 1.360025 | _cons | 2.266667 .7025878 2.64 0.008 1.234653 4.161314 ------

Results 28: Logistic regression of member as a function of own3, wua33, wua55, wua91, wuamh, ownwua33, ownwua55, ownwua91 and ownwuamh

Logistic regression Number of obs = 122 LR chi2(1) = 0.32 Prob > chi2 = 0.5724 Log likelihood = -80.677979 Pseudo R2 = 0.0020

Logistic regression Number of obs = 122 LR chi2(3) = 7.17 Prob > chi2 = 0.0666 Log likelihood = -77.251573 Pseudo R2 = 0.0444

Logistic regression Number of obs = 122 LR chi2(3) = 12.18 Prob > chi2 = 0.0068 Log likelihood = -74.745805 Pseudo R2 = 0.0754

Logistic regression Number of obs = 122 LR chi2(3) = 10.82 Prob > chi2 = 0.0127 Log likelihood = -75.427133 Pseudo R2 = 0.0669

Logistic regression Number of obs = 122 LR chi2(3) = 3.46 Prob > chi2 = 0.3257 Log likelihood = -79.106265 Pseudo R2 = 0.0214

572 Results 29: ANOVA of education and wua, Multinomial logistic regressions of education as a function of wua33, wua55, wua91 and wuamh anova education wua

Number of obs = 197 R-squared = 0.0208 Root MSE = 1.65379 Adj R-squared = 0.0056

Source | Partial SS df MS F Prob>F ------+------Model | 11.237411 3 3.7458036 1.37 0.2534 | wua | 11.237411 3 3.7458036 1.37 0.2534 | Residual | 527.85904 193 2.7350209 ------+------Total | 539.09645 196 2.7504921

Multinomial logistic regression Number of obs = 197 LR chi2(18) = 28.94 Prob > chi2 = 0.0491 Log likelihood = -334.32097 Pseudo R2 = 0.0415

------education | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------1 | wua33 | -.5879094 .6313644 -0.93 0.352 -1.825361 .6495421 wua55 | -.8755878 .6635456 -1.32 0.187 -2.176113 .4249377 wua91 | -.0487868 .6048904 -0.08 0.936 -1.23435 1.136777 _cons | -.1052658 .4594743 -0.23 0.819 -1.005819 .7952872 ------+------2 | wua33 | -.1335539 .7108714 -0.19 0.851 -1.526836 1.259728 wua55 | -.6933469 .7826364 -0.89 0.376 -2.227286 .8405922 wua91 | -1.252616 .9334695 -1.34 0.180 -3.082183 .5769504 _cons | -.6930129 .5477188 -1.27 0.206 -1.766522 .3804962 ------+------3 | wua33 | -.3522704 .631371 -0.56 0.577 -1.589735 .885194 wua55 | -.9399674 .6982178 -1.35 0.178 -2.308449 .4285144 wua91 | .2923141 .602591 0.49 0.628 -.8887425 1.473371 _cons | -.2231213 .4743567 -0.47 0.638 -1.152843 .7066007 ------+------4 | (base outcome) ------+------5 | wua33 | 13.7265 604.953 0.02 0.982 -1171.96 1199.413 wua55 | 14.28623 604.9529 0.02 0.981 -1171.4 1199.972 wua91 | 14.67123 604.9529 0.02 0.981 -1171.015 1200.357 _cons | -15.11281 604.9527 -0.02 0.980 -1200.798 1170.573 ------+------6 | wua33 | -.4701522 .6422612 -0.73 0.464 -1.728961 .7886566 wua55 | -.3523134 .6313538 -0.56 0.577 -1.589744 .8851172 wua91 | -1.317162 .7935503 -1.66 0.097 -2.872492 .2381677 _cons | -.222995 .4743401 -0.47 0.638 -1.152684 .7066945 ------+------7 | wua33 | -14.86573 1336.432 -0.01 0.991 -2634.225 2604.494 wua55 | -.4701448 1.470483 -0.32 0.749 -3.352239 2.411949 wua91 | -.3366047 1.473556 -0.23 0.819 -3.224722 2.551512 _cons | -2.30237 1.048746 -2.20 0.028 -4.357875 -.2468655 ------573

Multinomial logistic regression Number of obs = 197 LR chi2(18) = 28.94 Prob > chi2 = 0.0491 Log likelihood = -334.32097 Pseudo R2 = 0.0415

------education | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------1 | wua33 | -.5391226 .5850472 -0.92 0.357 -1.685794 .6075489 wua55 | -.826801 .6196386 -1.33 0.182 -2.04127 .3876683 wuamh | .0487868 .6048904 0.08 0.936 -1.136777 1.23435 _cons | -.1540526 .3934155 -0.39 0.695 -.9251329 .6170276 ------+------2 | wua33 | 1.119062 .8813125 1.27 0.204 -.6082784 2.846403 wua55 | .5592693 .940156 0.59 0.552 -1.283403 2.401941 wuamh | 1.252616 .9334695 1.34 0.180 -.5769504 3.082183 _cons | -1.945629 .7558898 -2.57 0.010 -3.427146 -.4641123 ------+------3 | wua33 | -.6445845 .5583159 -1.15 0.248 -1.738863 .4496946 wua55 | -1.232281 .6329261 -1.95 0.052 -2.472794 .0082309 wuamh | -.2923141 .602591 -0.49 0.628 -1.473371 .8887425 _cons | .0691928 .3716202 0.19 0.852 -.6591694 .797555 ------+------4 | (base outcome) ------+------5 | wua33 | -.9447276 .7035921 -1.34 0.179 -2.323743 .4342876 wua55 | -.3850041 .6228047 -0.62 0.536 -1.605679 .8356707 wuamh | -14.67123 604.9529 -0.02 0.981 -1200.357 1171.015 _cons | -.4415782 .4272439 -1.03 0.301 -1.278961 .3958046 ------+------6 | wua33 | .8470101 .7695613 1.10 0.271 -.6613023 2.355323 wua55 | .964849 .7604819 1.27 0.205 -.5256682 2.455366 wuamh | 1.317162 .7935503 1.66 0.097 -.2381677 2.872492 _cons | -1.540157 .6361789 -2.42 0.015 -2.787045 -.2932695 ------+------7 | wua33 | -14.52913 1336.432 -0.01 0.991 -2633.888 2604.83 wua55 | -.1335401 1.460805 -0.09 0.927 -2.996665 2.729585 wuamh | .3366047 1.473556 0.23 0.819 -2.551512 3.224722 _cons | -2.638975 1.035132 -2.55 0.011 -4.667797 -.610153 ------

Multinomial logistic regression Number of obs = 197 LR chi2(18) = 28.94 Prob > chi2 = 0.0491 Log likelihood = -334.32097 Pseudo R2 = 0.0415

------education | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------1 | wua33 | .2876784 .6455081 0.45 0.656 -.9774942 1.552851 wua91 | .826801 .6196386 1.33 0.182 -.3876683 2.04127 wuamh | .8755878 .6635456 1.32 0.187 -.4249377 2.176113 _cons | -.9808536 .4787235 -2.05 0.040 -1.919135 -.0425727 ------+------2 | wua33 | .559793 .7196291 0.78 0.437 -.8506542 1.97024 wua91 | -.5592693 .940156 -0.59 0.552 -2.401941 1.283403 wuamh | .6933469 .7826364 0.89 0.376 -.8405922 2.227286 574 _cons | -1.38636 .5590383 -2.48 0.013 -2.482055 -.2906648 ------+------3 | wua33 | .587697 .6603854 0.89 0.374 -.7066347 1.882029 wua91 | 1.232281 .6329261 1.95 0.052 -.0082309 2.472794 wuamh | .9399674 .6982178 1.35 0.178 -.4285144 2.308449 _cons | -1.163089 .5123416 -2.27 0.023 -2.16726 -.1589177 ------+------4 | (base outcome) ------+------5 | wua33 | -.5597235 .7196199 -0.78 0.437 -1.970153 .8507055 wua91 | .3850041 .6228047 0.62 0.536 -.8356707 1.605679 wuamh | -14.28623 604.9529 -0.02 0.981 -1199.972 1171.4 _cons | -.8265823 .4531537 -1.82 0.068 -1.714747 .0615827 ------+------6 | wua33 | -.1178388 .6009243 -0.20 0.845 -1.295629 1.059951 wua91 | -.964849 .7604819 -1.27 0.205 -2.455366 .5256682 wuamh | .3523134 .6313538 0.56 0.577 -.8851172 1.589744 _cons | -.5753083 .4166642 -1.38 0.167 -1.391955 .2413385 ------+------7 | wua33 | -14.39559 1336.432 -0.01 0.991 -2633.755 2604.964 wua91 | .1335401 1.460805 0.09 0.927 -2.729585 2.996665 wuamh | .4701448 1.470483 0.32 0.749 -2.411949 3.352239 _cons | -2.772515 1.030753 -2.69 0.007 -4.792754 -.7522762 ------

Multinomial logistic regression Number of obs = 197 LR chi2(18) = 28.94 Prob > chi2 = 0.0491 Log likelihood = -334.32097 Pseudo R2 = 0.0415

------education | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------1 | wua55 | -.2876784 .6455081 -0.45 0.656 -1.552851 .9774942 wua91 | .5391226 .5850472 0.92 0.357 -.6075489 1.685794 wuamh | .5879094 .6313644 0.93 0.352 -.6495421 1.825361 _cons | -.6931752 .4330178 -1.60 0.109 -1.541875 .1555242 ------+------2 | wua55 | -.559793 .7196291 -0.78 0.437 -1.97024 .8506542 wua91 | -1.119062 .8813125 -1.27 0.204 -2.846403 .6082784 wuamh | .1335539 .7108714 0.19 0.851 -1.259728 1.526836 _cons | -.8265668 .453147 -1.82 0.068 -1.714719 .061585 ------+------3 | wua55 | -.587697 .6603854 -0.89 0.374 -1.882029 .7066347 wua91 | .6445845 .5583159 1.15 0.248 -.4496946 1.738863 wuamh | .3522704 .631371 0.56 0.577 -.885194 1.589735 _cons | -.5753917 .4166714 -1.38 0.167 -1.392053 .2412693 ------+------4 | (base outcome) ------+------5 | wua55 | .5597235 .7196199 0.78 0.437 -.8507055 1.970153 wua91 | .9447276 .7035921 1.34 0.179 -.4342876 2.323743 wuamh | -13.7265 604.953 -0.02 0.982 -1199.413 1171.96 _cons | -1.386306 .559021 -2.48 0.013 -2.481967 -.2906448 ------+------6 | wua55 | .1178388 .6009243 0.20 0.845 -1.059951 1.295629 wua91 | -.8470101 .7695613 -1.10 0.271 -2.355323 .6613023 575 wuamh | .4701522 .6422612 0.73 0.464 -.7886566 1.728961 _cons | -.6931472 .4330138 -1.60 0.109 -1.541839 .1555443 ------+------7 | wua55 | 14.39559 1336.432 0.01 0.991 -2604.964 2633.755 wua91 | 14.52913 1336.432 0.01 0.991 -2604.83 2633.888 wuamh | 14.86573 1336.432 0.01 0.991 -2604.494 2634.225 _cons | -17.1681 1336.432 -0.01 0.990 -2636.527 2602.19 ------

Results 30: Ordered logistic and logistic regressions of catop, catcom, steal1, fair1 and member as functions of education and edu8

Ordered logistic regression Number of obs = 186 LR chi2(5) = 2.04 Prob > chi2 = 0.8434 Log likelihood = -139.63754 Pseudo R2 = 0.0073

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------education | 2 | .8542728 .5231449 -0.26 0.797 .2572365 2.837009 3 | 1.759702 .9744494 1.02 0.307 .5944006 5.209535 4 | 1.027442 .4780181 0.06 0.954 .4127955 2.557286 5 | 1.264523 .795336 0.37 0.709 .3685991 4.3381 6 | .9027168 .4864126 -0.19 0.849 .3139764 2.59541 ------+------/cut1 | -2.590908 .4375504 -3.448491 -1.733325 /cut2 | -.7996921 .3595638 -1.504424 -.0949599 ------

Ordered logistic regression Number of obs = 186 LR chi2(1) = 0.19 Prob > chi2 = 0.6639 Log likelihood = -140.56363 Pseudo R2 = 0.0007

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------edu8 | .8698168 .2795349 -0.43 0.664 .4633139 1.632978 ------+------/cut1 | -2.750308 .347823 -3.432028 -2.068588 /cut2 | -.9688906 .2385707 -1.436481 -.5013006 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 4.04 Prob > chi2 = 0.5443 Log likelihood = -180.4962 Pseudo R2 = 0.0111

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------education | 2 | .4308243 .2396683 -1.51 0.130 .1448023 1.281814 3 | .8638344 .3853923 -0.33 0.743 .3603083 2.071032 4 | 1.138783 .4737941 0.31 0.755 .5038442 2.573866 5 | 1.244181 .6818043 0.40 0.690 .42504 3.641978 6 | 1.138783 .5537639 0.27 0.789 .4390577 2.953661 ------+------/cut1 | -1.871559 .3515905 -2.560664 -1.182454 /cut2 | -.1092608 .3142185 -.7251176 .5065961 ------576

Ordered logistic regression Number of obs = 186 LR chi2(1) = 1.64 Prob > chi2 = 0.2010 Log likelihood = -181.69634 Pseudo R2 = 0.0045

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------edu8 | 1.433736 .4046335 1.28 0.202 .8245943 2.49286 ------+------/cut1 | -1.641873 .2498554 -2.131581 -1.152165 /cut2 | .1001429 .2061274 -.3038594 .5041453 ------

. logit steal1 i.education, or note: 6.education != 0 predicts success perfectly 6.education dropped and 26 obs not used

Iteration 0: log likelihood = -62.212962 Iteration 1: log likelihood = -61.832965 Iteration 2: log likelihood = -61.829015 Iteration 3: log likelihood = -61.829015

Logistic regression Number of obs = 148 LR chi2(4) = 0.77 Prob > chi2 = 0.9427 Log likelihood = -61.829015 Pseudo R2 = 0.0062

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------education | 2 | 1.68 1.481923 0.59 0.556 .298172 9.465677 3 | 1.392 .9245536 0.50 0.619 .3786858 5.116812 4 | 1.68 1.058724 0.82 0.410 .4885267 5.777372 5 | 1.28 .9935472 0.32 0.750 .279576 5.860303 6 | 1 (empty) | _cons | 4.166666 1.894192 3.14 0.002 1.709336 10.15664 ------

Logistic regression Number of obs = 174 LR chi2(1) = 1.59 Prob > chi2 = 0.2073 Log likelihood = -65.247636 Pseudo R2 = 0.0120

. logit fair1 i.education, or

Iteration 0: log likelihood = -118.24004 Iteration 1: log likelihood = -115.41616 Iteration 2: log likelihood = -115.38841 Iteration 3: log likelihood = -115.3884 Iteration 4: log likelihood = -115.3884

Logistic regression Number of obs = 184 LR chi2(5) = 5.70 Prob > chi2 = 0.3362 577 Log likelihood = -115.3884 Pseudo R2 = 0.0241

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------education | 2 | .6964286 .4182361 -0.60 0.547 .214631 2.259751 3 | 2.387755 1.317462 1.58 0.115 .8097165 7.041199 4 | 1.134921 .5205819 0.28 0.783 .4618745 2.788734 5 | 1.733333 1.090721 0.87 0.382 .5049557 5.949917 6 | .8769841 .4529001 -0.25 0.799 .3187179 2.413109 | _cons | 1.615385 .5700777 1.36 0.174 .8088769 3.226038 ------

Logistic regression Number of obs = 184 LR chi2(1) = 0.12 Prob > chi2 = 0.7309 Log likelihood = -118.18087 Pseudo R2 = 0.0005

Logistic regression Number of obs = 122 LR chi2(5) = 4.22 Prob > chi2 = 0.5178 Log likelihood = -78.725747 Pseudo R2 = 0.0261

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------education | 2 | 1.6 1.579873 0.48 0.634 .2310074 11.0819 3 | 1.75 1.594669 0.61 0.539 .2933524 10.43966 4 | 2.88 2.444704 1.25 0.213 .5455637 15.20336 5 | 2.666667 2.721655 0.96 0.337 .360757 19.71164 6 | 4 3.592922 1.54 0.123 .6878357 23.26137 | _cons | .25 .1976424 -1.75 0.080 .0530892 1.177264 ------

Logistic regression Number of obs = 122 LR chi2(1) = 3.35 Prob > chi2 = 0.0671 Log likelihood = -79.161073 Pseudo R2 = 0.0207

Results 31: ANOVA with wua and catop, Ordered logistic regressions of catop as a function of wua33, wua55, wua91 and wuamh in pairs of three

ANOVA

Number of obs = 186 R-squared = 0.0521 578 Root MSE = .588859 Adj R-squared = 0.0365

Source | Partial SS df MS F Prob>F ------+------Model | 3.4712252 3 1.1570751 3.34 0.0206 | wua | 3.4712252 3 1.1570751 3.34 0.0206 | Residual | 63.10942 182 .34675505 ------+------Total | 66.580645 185 .35989538

Ordered logistic regression Number of obs = 186 LR chi2(3) = 11.58 Prob > chi2 = 0.0090 Log likelihood = -134.86854 Pseudo R2 = 0.0412

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .4635007 .4341481 1.07 0.286 -.3874138 1.314415 wua55 | 1.124606 .487571 2.31 0.021 .1689846 2.080228 wua91 | 1.475299 .4935429 2.99 0.003 .5079729 2.442626 ------+------/cut1 | -1.977354 .4020415 -2.765341 -1.189368 /cut2 | -.1262498 .3342152 -.7812996 .5287999 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 11.58 Prob > chi2 = 0.0090 Log likelihood = -134.86854 Pseudo R2 = 0.0412

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.6611055 .4546657 -1.45 0.146 -1.552234 .2300229 wua91 | .350693 .5101187 0.69 0.492 -.6491213 1.350507 wuamh | -1.124606 .487571 -2.31 0.021 -2.080228 -.1689846 ------+------/cut1 | -3.101961 .4458695 -3.975849 -2.228072 /cut2 | -1.250856 .3571826 -1.950921 -.5507911 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 11.58 Prob > chi2 = 0.0090 Log likelihood = -134.86854 Pseudo R2 = 0.0412

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -1.011799 .4608443 -2.20 0.028 -1.915037 -.1085604 wua55 | -.350693 .5101187 -0.69 0.492 -1.350507 .6491213 wuamh | -1.475299 .4935429 -2.99 0.003 -2.442626 -.5079729 ------+------/cut1 | -3.452654 .4548013 -4.344048 -2.561259 /cut2 | -1.601549 .3646073 -2.316166 -.8869318 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 11.58 Prob > chi2 = 0.0090 Log likelihood = -134.86854 Pseudo R2 = 0.0412

579 ------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua55 | .6611055 .4546657 1.45 0.146 -.2300229 1.552234 wua91 | 1.011799 .4608443 2.20 0.028 .1085604 1.915037 wuamh | -.4635007 .4341481 -1.07 0.286 -1.314415 .3874138 ------+------/cut1 | -2.440855 .3768409 -3.17945 -1.702261 /cut2 | -.5897505 .2829587 -1.144339 -.0351616 ------

Results 32: Ordered logistic regressions of catop as a function of adequate, reliable, wua33, wua55, wua91 and wuamh

Ordered logistic regression Number of obs = 186 LR chi2(5) = 25.07 Prob > chi2 = 0.0001 Log likelihood = -128.12109 Pseudo R2 = 0.0891

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.9442 1.578504 2.01 0.044 1.029447 8.420359 reliable | 2.258982 .8464947 2.17 0.030 1.083795 4.708455 wua33 | 1.331138 .6176463 0.62 0.538 .5361261 3.305061 wua55 | 2.021724 1.077991 1.32 0.187 .7109807 5.748913 wua91 | 3.070628 1.592036 2.16 0.030 1.111483 8.483042 ------+------/cut1 | -1.703694 .4133309 -2.513807 -.8935799 /cut2 | .2355383 .3567406 -.4636605 .934737 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 25.07 Prob > chi2 = 0.0001 Log likelihood = -128.12109 Pseudo R2 = 0.0891

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.9442 1.578504 2.01 0.044 1.029447 8.420359 reliable | 2.258982 .8464947 2.17 0.030 1.083795 4.708455 wua55 | 1.518793 .7162884 0.89 0.376 .6026394 3.827715 wua91 | 2.306768 1.101473 1.75 0.080 .9048128 5.880974 wuamh | .7512367 .3485727 -0.62 0.538 .3025663 1.865233 ------+------/cut1 | -1.989728 .4088295 -2.791019 -1.188437 /cut2 | -.0504963 .3388314 -.7145936 .6136011 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 25.07 Prob > chi2 = 0.0001 Log likelihood = -128.12109 Pseudo R2 = 0.0891

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.9442 1.578504 2.01 0.044 1.029447 8.420359 reliable | 2.258982 .8464947 2.17 0.030 1.083795 4.708455 wua33 | .4335069 .2069978 -1.75 0.080 .1700399 1.105201 wua55 | .6584072 .3506267 -0.78 0.433 .2318449 1.869784 wuamh | .3256663 .1688489 -2.16 0.030 .1178822 .8996989 ------+------580 /cut1 | -2.825576 .4876658 -3.781383 -1.869768 /cut2 | -.8863439 .4119177 -1.693688 -.0790001 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 25.07 Prob > chi2 = 0.0001 Log likelihood = -128.12109 Pseudo R2 = 0.0891

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.9442 1.578504 2.01 0.044 1.029447 8.420359 reliable | 2.258982 .8464947 2.17 0.030 1.083795 4.708455 wua33 | .6584176 .3105209 -0.89 0.376 .2612525 1.659367 wua91 | 1.518817 .8088274 0.78 0.433 .5348211 4.313228 wuamh | .4946275 .2637373 -1.32 0.187 .1739459 1.406508 ------+------/cut1 | -2.407644 .5033616 -3.394215 -1.421073 /cut2 | -.4684121 .4367575 -1.324441 .3876168 ------

Results 33: Ordered logistic regressions of catop as a function of adequate, reliable, wua33, wua55, wua91, wuamh, pos1, secwork, secwater, secworkwat, adeq33, rel33, adeq55, rel55, adeq91, adeq91, adeqmh, and relmh.

Ordered logistic regression Number of obs = 186 LR chi2(5) = 22.97 Prob > chi2 = 0.0003 Log likelihood = -129.17372 Pseudo R2 = 0.0816

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.428282 2.037908 2.07 0.038 1.069273 10.9917 reliable | 1.811975 .7642997 1.41 0.159 .7927045 4.141838 wua33 | .4465658 .2121544 -1.70 0.090 .1759936 1.133115 rel33 | 3.461842 2.639395 1.63 0.103 .7768373 15.42711 adeq33 | .6197482 .8262181 -0.36 0.720 .0454396 8.452706 ------+------/cut1 | -2.376363 .3750063 -3.111361 -1.641364 /cut2 | -.4472548 .2761826 -.9885628 .0940532 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 28.11 Prob > chi2 = 0.0000 Log likelihood = -126.60193 Pseudo R2 = 0.0999

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 1.910273 1.073209 1.15 0.249 .6351592 5.745243 reliable | 4.103725 1.729378 3.35 0.001 1.796667 9.373218 wua55 | 3.918103 3.213415 1.67 0.096 .7851755 19.55172 rel55 | .1069974 .1060487 -2.25 0.024 .0153362 .7464964 adeq55 | 3442533 3.53e+09 0.01 0.988 0 . ------+------/cut1 | -1.992133 .3317042 -2.642261 -1.342005 /cut2 | -.0634606 .240855 -.5355278 .4086066 ------Note: 12 observations completely determined. Standard errors questionable.

581 Ordered logistic regression Number of obs = 186 LR chi2(5) = 27.17 Prob > chi2 = 0.0001 Log likelihood = -127.07383 Pseudo R2 = 0.0966

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 6.180743 4.784125 2.35 0.019 1.355748 28.17749 reliable | 2.796243 1.098189 2.62 0.009 1.29502 6.037728 wua91 | 3.318512 1.933702 2.06 0.040 1.059124 10.39776 rel91 | 1.033988 .9708086 0.04 0.972 .1641816 6.511881 adeq91 | .1151051 .1365812 -1.82 0.068 .011248 1.17791 ------+------/cut1 | -1.869018 .3390119 -2.533469 -1.204567 /cut2 | .0702458 .2575477 -.4345383 .57503 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 22.30 Prob > chi2 = 0.0005 Log likelihood = -129.50937 Pseudo R2 = 0.0793

Ordered logistic regression Number of obs = 186 LR chi2(4) = 22.85 Prob > chi2 = 0.0001 Log likelihood = -129.23511 Pseudo R2 = 0.0812

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.144417 1.676588 2.15 0.032 1.105818 8.941217 reliable | 1.842838 .7726281 1.46 0.145 .8102381 4.191427 wua33 | .4382761 .2070906 -1.75 0.081 .1735981 1.106498 rel33 | 3.299329 2.474931 1.59 0.112 .7584244 14.35287 ------+------/cut1 | -2.382717 .3747455 -3.117204 -1.648229 /cut2 | -.4525006 .2756909 -.9928448 .0878436 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 24.12 Prob > chi2 = 0.0001 Log likelihood = -128.59982 Pseudo R2 = 0.0857

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.084365 1.626053 2.14 0.033 1.097536 8.667875 reliable | 3.756155 1.560737 3.18 0.001 1.663631 8.480667 wua55 | 4.224295 3.441636 1.77 0.077 .855578 20.85686 rel55 | .1452061 .1409696 -1.99 0.047 .021658 .9735359 ------+------582 /cut1 | -1.95715 .330392 -2.604706 -1.309593 /cut2 | -.0313681 .2406681 -.503069 .4403328 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 25.96 Prob > chi2 = 0.0002 Log likelihood = -127.67971 Pseudo R2 = 0.0923

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.302111 1.762798 2.24 0.025 1.159809 9.401492 reliable | 3.61896 1.733453 2.69 0.007 1.415354 9.253427 wua55 | 3.621573 3.027478 1.54 0.124 .7036061 18.64082 wuamh | .663488 .3167433 -0.86 0.390 .2603017 1.691177 rel55 | .1490674 .1491283 -1.90 0.057 .0209813 1.059089 relmh | .5379471 .5568252 -0.60 0.549 .0707403 4.090838 ------+------/cut1 | -2.12342 .3854462 -2.87888 -1.367959 /cut2 | -.182125 .3032472 -.7764787 .4122286 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 23.70 Prob > chi2 = 0.0001 Log likelihood = -128.80939 Pseudo R2 = 0.0842

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.915897 1.54978 2.01 0.044 1.028879 8.263805 reliable | 2.976254 1.158611 2.80 0.005 1.387746 6.383076 wua91 | 2.837393 1.625558 1.82 0.069 .9231269 8.721226 rel91 | .5773504 .4870723 -0.65 0.515 .1104936 3.016768 ------+------/cut1 | -1.896497 .3380753 -2.559112 -1.233881 /cut2 | .0294955 .2550222 -.4703389 .5293299 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 29.96 Prob > chi2 = 0.0000 Log likelihood = -125.67696 Pseudo R2 = 0.1065

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.000914 1.607879 2.05 0.040 1.049984 8.576781 reliable | 4.655958 2.356547 3.04 0.002 1.726575 12.55546 wua55 | 5.911685 4.890028 2.15 0.032 1.168461 29.90944 wua91 | 3.720997 2.174831 2.25 0.025 1.183459 11.69945 rel55 | .1174029 .1193527 -2.11 0.035 .0160081 .8610271 rel91 | .3672654 .3328375 -1.11 0.269 .0621686 2.169646 ------+------/cut1 | -1.681566 .346975 -2.361625 -1.001508 /cut2 | .305906 .2806402 -.2441386 .8559506 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 28.88 Prob > chi2 = 0.0001 Log likelihood = -126.22021 Pseudo R2 = 0.1026

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] 583 ------+------adequate | 2.898136 1.557439 1.98 0.048 1.010855 8.309002 reliable | 3.342191 1.466334 2.75 0.006 1.414426 7.897371 wua33 | 1.181458 .5594201 0.35 0.725 .4670619 2.98856 wua55 | 5.874263 5.034914 2.07 0.039 1.094905 31.51594 wua91 | 2.790283 1.467342 1.95 0.051 .995456 7.82122 rel55 | .164557 .1612979 -1.84 0.066 .0240976 1.123723 ------+------/cut1 | -1.67278 .4163526 -2.488816 -.8567435 /cut2 | .2975139 .3612504 -.4105239 1.005552 ------

Ordered logistic regression Number of obs = 186 LR chi2(5) = 28.75 Prob > chi2 = 0.0000 Log likelihood = -126.2821 Pseudo R2 = 0.1022

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.827459 1.505346 1.95 0.051 .9958974 8.027457 reliable | 3.498154 1.468188 2.98 0.003 1.536691 7.963268 wua55 | 5.397182 4.440883 2.05 0.040 1.075934 27.07376 wua91 | 2.527311 1.126171 2.08 0.037 1.055268 6.052775 rel55 | .157855 .1536177 -1.90 0.058 .0234372 1.06319 ------+------/cut1 | -1.757456 .3416813 -2.427139 -1.087773 /cut2 | .2117406 .2673512 -.312258 .7357393 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 24.94 Prob > chi2 = 0.0001 Log likelihood = -128.18707 Pseudo R2 = 0.0887

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.733476 1.453218 1.89 0.059 .9642395 7.749001 reliable | 2.749577 .971331 2.86 0.004 1.375833 5.49498 pos1 | 1.889147 .965913 1.24 0.213 .6935058 5.14614 wua91 | 1.990844 .8632634 1.59 0.112 .8510247 4.657282 ------+------/cut1 | -1.869412 .3368812 -2.529687 -1.209137 /cut2 | .061131 .2525548 -.4338672 .5561293 ------

Ordered logistic regression Number of obs = 186 LR chi2(9) = 36.58 Prob > chi2 = 0.0000 Log likelihood = -122.36976 Pseudo R2 = 0.1300

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 6.920563 5.466322 2.45 0.014 1.471654 32.54447 reliable | 2.40815 1.599826 1.32 0.186 .6549344 8.854604 pos1 | 2.013481 1.066415 1.32 0.186 .7130489 5.685591 wua33 | .8733697 .4677971 -0.25 0.800 .3056855 2.495293 wua55 | 6.035939 5.24522 2.07 0.039 1.099138 33.14648 wua91 | 4.048549 2.449245 2.31 0.021 1.236942 13.25103 rel33 | 2.767185 2.53514 1.11 0.267 .4594255 16.66715 rel55 | .197858 .2198073 -1.46 0.145 .0224246 1.745754 adeq91 | .114837 .131277 -1.89 0.058 .0122186 1.079296 ------+------584 /cut1 | -1.615715 .4322749 -2.462958 -.7684719 /cut2 | .4166808 .3791025 -.3263465 1.159708 ------

Ordered logistic regression Number of obs = 186 LR chi2(7) = 35.29 Prob > chi2 = 0.0000 Log likelihood = -123.01167 Pseudo R2 = 0.1255

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 6.544333 5.117043 2.40 0.016 1.413537 30.29868 reliable | 4.353799 1.954445 3.28 0.001 1.806173 10.49488 pos1 | 2.018664 1.06714 1.33 0.184 .7162875 5.689061 wua55 | 6.06596 5.043161 2.17 0.030 1.189109 30.94408 wua91 | 3.449359 1.819808 2.35 0.019 1.226486 9.700946 rel55 | .1101529 .1103074 -2.20 0.028 .0154739 .7841352 adeq91 | .0860447 .0975399 -2.16 0.030 .0093284 .7936748 ------+------/cut1 | -1.591826 .3497101 -2.277245 -.9064066 /cut2 | .4204392 .2857199 -.1395615 .9804398 ------

Ordered logistic regression Number of obs = 186 LR chi2(10) = 37.76 Prob > chi2 = 0.0000 Log likelihood = -121.77829 Pseudo R2 = 0.1342

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 6.022063 4.717053 2.29 0.022 1.297189 27.9568 reliable | 4.453301 2.095785 3.17 0.002 1.770496 11.20132 pos1 | 1.849808 .9968547 1.14 0.254 .6433057 5.319074 secwork | 1.084828 .4617584 0.19 0.848 .4710274 2.49848 secwater | 1.399065 .9155995 0.51 0.608 .3879544 5.045398 secworkwat | .357963 .2961151 -1.24 0.214 .0707465 1.811221 wua55 | 5.658303 4.994477 1.96 0.050 1.003106 31.91726 wua91 | 3.483235 1.855421 2.34 0.019 1.226229 9.894502 rel55 | .1178078 .1263686 -1.99 0.046 .0143919 .964341 adeq91 | .0843743 .0963646 -2.16 0.030 .0089959 .7913611 ------+------/cut1 | -1.670146 .4056225 -2.465152 -.8751409 /cut2 | .3663501 .3473547 -.3144526 1.047153 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 19.64 Prob > chi2 = 0.0002 Log likelihood = -130.83691 Pseudo R2 = 0.0698

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.129084 1.642608 2.17 0.030 1.118356 8.754962 reliable | 2.556223 .9114634 2.63 0.008 1.270834 5.141722 wua55 | 1.242341 .5273063 0.51 0.609 .5406948 2.854497 ------+------/cut1 | -2.060223 .3285351 -2.70414 -1.416306 /cut2 | -.1679701 .2332473 -.6251264 .2891863 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 23.28 Prob > chi2 = 0.0000 585 Log likelihood = -129.01926 Pseudo R2 = 0.0827

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.822813 1.492943 1.96 0.050 1.00114 7.959202 reliable | 2.673389 .9359972 2.81 0.005 1.345992 5.309844 wua91 | 2.232647 .9461679 1.90 0.058 .9729525 5.123287 ------+------/cut1 | -1.939215 .33265 -2.591197 -1.287233 /cut2 | -.0198094 .2440622 -.4981624 .4585437 ------rdered logistic regression Number of obs = 186 LR chi2(3) = 20.22 Prob > chi2 = 0.0002 Log likelihood = -130.55017 Pseudo R2 = 0.0719

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.911004 1.540438 2.02 0.043 1.031825 8.212578 reliable | 2.719435 .9513563 2.86 0.004 1.369928 5.398332 wua33 | .7193487 .257263 -0.92 0.357 .3568795 1.449965 ------+------/cut1 | -2.200165 .3489008 -2.883998 -1.516332 /cut2 | -.3010988 .2534756 -.7979018 .1957042 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 21.86 Prob > chi2 = 0.0001 Log likelihood = -129.72974 Pseudo R2 = 0.0777

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.311383 1.75135 2.26 0.024 1.174409 9.336833 reliable | 2.222543 .8172978 2.17 0.030 1.081028 4.569443 wuamh | .5182693 .2143337 -1.59 0.112 .2304281 1.16567 ------+------/cut1 | -2.327655 .3639614 -3.041006 -1.614303 /cut2 | -.4136841 .2675725 -.9381165 .1107483 ------

Results 34: Ordered logistic and logistic regressions of catop as a function of tour, punish, conflict, help1, help3, wua33, wua55, wua91, wuamh

Ordered logistic regression Number of obs = 115 LR chi2(8) = 55.74 Prob > chi2 = 0.0000 Log likelihood = -64.651932 Pseudo R2 = 0.3012

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 3.214748 2.748358 1.37 0.172 .601778 17.17345 3 | 8.408979 7.369092 2.43 0.015 1.509401 46.84701 | punish | 2 | 18.60022 25.65034 2.12 0.034 1.246469 277.5584 3 | 11.75234 17.07633 1.70 0.090 .6812645 202.737 | 586 conflict | 2 | 1.692047 1.087166 0.82 0.413 .4802887 5.961046 3 | 6.097918 3.524817 3.13 0.002 1.964067 18.93245 | help1 | 4.698862 2.803708 2.59 0.010 1.459148 15.13164 help3 | 1.399684 .8753216 0.54 0.591 .4108787 4.768114 ------+------/cut1 | 1.948658 1.176856 -.3579372 4.255252 /cut2 | 5.000517 1.319648 2.414055 7.586979 ------

Ordered logistic regression Number of obs = 115 LR chi2(11) = 56.44 Prob > chi2 = 0.0000 Log likelihood = -64.301508 Pseudo R2 = 0.3050

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 3.395268 2.919395 1.42 0.155 .6294699 18.31357 3 | 7.427431 6.702504 2.22 0.026 1.266822 43.54735 | punish | 2 | 24.49252 35.24728 2.22 0.026 1.459037 411.1504 3 | 16.88795 25.7311 1.86 0.064 .8524215 334.5794 | conflict | 2 | 1.578645 1.032793 0.70 0.485 .4379301 5.690681 3 | 5.507365 3.310408 2.84 0.005 1.695498 17.88918 | help1 | 3.652281 2.528393 1.87 0.061 .9403644 14.18509 help3 | 1.062748 .7694051 0.08 0.933 .2571464 4.392184 wua33 | 1.698141 1.424692 0.63 0.528 .3279746 8.7924 wua55 | 1.942423 1.840182 0.70 0.483 .3033515 12.43774 wua91 | 2.151641 2.049015 0.80 0.421 .3327878 13.91144 ------+------/cut1 | 2.469271 1.375216 -.2261021 5.164644 /cut2 | 5.575756 1.548462 2.540826 8.610687 ------

Ordered logistic regression Number of obs = 115 LR chi2(11) = 56.44 Prob > chi2 = 0.0000 Log likelihood = -64.301508 Pseudo R2 = 0.3050

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 3.395268 2.919395 1.42 0.155 .6294699 18.31357 3 | 7.427431 6.702504 2.22 0.026 1.266822 43.54735 | punish | 2 | 24.49252 35.24728 2.22 0.026 1.459037 411.1504 3 | 16.88795 25.7311 1.86 0.064 .8524215 334.5794 | conflict | 2 | 1.578645 1.032793 0.70 0.485 .4379301 5.690681 3 | 5.507365 3.310408 2.84 0.005 1.695498 17.88918 | help1 | 3.652281 2.528393 1.87 0.061 .9403644 14.18509 help3 | 1.062748 .7694051 0.08 0.933 .2571464 4.392184 wua33 | .7892309 .5314389 -0.35 0.725 .2108805 2.953736 wua55 | .9027638 .7225419 -0.13 0.898 .1880619 4.333585 wuamh | .4647616 .4425942 -0.80 0.421 .0718833 3.004918 587 ------+------/cut1 | 1.70304 1.357521 -.9576517 4.363732 /cut2 | 4.809526 1.48599 1.897039 7.722012 ------

Ordered logistic regression Number of obs = 115 LR chi2(11) = 56.44 Prob > chi2 = 0.0000 Log likelihood = -64.301508 Pseudo R2 = 0.3050

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 3.395268 2.919395 1.42 0.155 .6294699 18.31357 3 | 7.427431 6.702504 2.22 0.026 1.266822 43.54735 | punish | 2 | 24.49252 35.24728 2.22 0.026 1.459037 411.1504 3 | 16.88795 25.7311 1.86 0.064 .8524215 334.5794 | conflict | 2 | 1.578645 1.032793 0.70 0.485 .4379301 5.690681 3 | 5.507365 3.310408 2.84 0.005 1.695498 17.88918 | help1 | 3.652281 2.528393 1.87 0.061 .9403644 14.18509 help3 | 1.062748 .7694051 0.08 0.933 .2571464 4.392184 wua33 | .8742385 .622502 -0.19 0.850 .2165366 3.529624 wua91 | 1.10771 .8865736 0.13 0.898 .2307558 5.317397 wuamh | .5148208 .4877228 -0.70 0.483 .0804004 3.296506 ------+------/cut1 | 1.805335 1.193622 -.5341214 4.14479 /cut2 | 4.91182 1.331757 2.301625 7.522015 ------

Ordered logistic regression Number of obs = 115 LR chi2(11) = 56.44 Prob > chi2 = 0.0000 Log likelihood = -64.301508 Pseudo R2 = 0.3050

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 3.395268 2.919395 1.42 0.155 .6294699 18.31357 3 | 7.427431 6.702504 2.22 0.026 1.266822 43.54735 | punish | 2 | 24.49252 35.24728 2.22 0.026 1.459037 411.1504 3 | 16.88795 25.7311 1.86 0.064 .8524215 334.5794 | conflict | 2 | 1.578645 1.032793 0.70 0.485 .4379301 5.690681 3 | 5.507365 3.310408 2.84 0.005 1.695498 17.88918 | help1 | 3.652281 2.528393 1.87 0.061 .9403644 14.18509 help3 | 1.062748 .7694051 0.08 0.933 .2571464 4.392184 wua55 | 1.143853 .8144809 0.19 0.850 .2833163 4.618156 wua91 | 1.267056 .8531889 0.35 0.725 .3385543 4.742022 wuamh | .5888792 .4940527 -0.63 0.528 .1137346 3.049017 ------+------/cut1 | 1.939737 1.283801 -.5764676 4.455941 /cut2 | 5.046222 1.423471 2.25627 7.836174 ------

588 Results 35: Ordered logistic regressions of catop as a function of dunums_log, greenhouse_0or1, market2, markgreen, owner, edu8, wua33, wua55, wua91, wuamh, ownermh, edu8mh

Ordered logistic regression Number of obs = 186 LR chi2(6) = 13.69 Prob > chi2 = 0.0333 Log likelihood = -133.81468 Pseudo R2 = 0.0487

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | .7924034 .3060966 -0.60 0.547 .3716522 1.689491 greenhouse_0~1 | 1.456006 .5780499 0.95 0.344 .6686935 3.170292 market2 | .7787771 .4214446 -0.46 0.644 .2696343 2.24932 | owner | 2 | .664062 .3192856 -0.85 0.395 .2587886 1.70401 3 | .2441906 .1110593 -3.10 0.002 .1001377 .5954704 | edu8 | 1.074946 .3699055 0.21 0.834 .547615 2.110077 ------+------/cut1 | -3.807934 .8102723 -5.396039 -2.21983 /cut2 | -1.936377 .7517173 -3.409716 -.4630384 ------

Ordered logistic regression Number of obs = 186 LR chi2(7) = 19.28 Prob > chi2 = 0.0073 Log likelihood = -131.01566 Pseudo R2 = 0.0686

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | .9601484 .3800125 -0.10 0.918 .4420236 2.085601 greenhouse_0~1 | .9290978 .3916255 -0.17 0.861 .4066961 2.122525 market2 | .2446322 .1744389 -1.97 0.048 .0604715 .9896385 markgreen | 11.44325 12.35532 2.26 0.024 1.378809 94.97179 | owner | 2 | .540709 .272413 -1.22 0.222 .2014291 1.45146 3 | .2312098 .1078992 -3.14 0.002 .0926348 .5770831 | edu8 | 1.004573 .3490087 0.01 0.990 .50846 1.98475 ------+------/cut1 | -3.767096 .8154544 -5.365357 -2.168834 /cut2 | -1.846483 .7565382 -3.329271 -.3636955 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 19.27 Prob > chi2 = 0.0037 Log likelihood = -131.02094 Pseudo R2 = 0.0685

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------greenhouse_0~1 | .9258345 .3889227 -0.18 0.854 .4064081 2.109135 market2 | .236385 .1489607 -2.29 0.022 .0687426 .8128569 markgreen | 11.68466 12.38968 2.32 0.020 1.462366 93.36332 | owner | 2 | .5392815 .2713195 -1.23 0.220 .2011708 1.44566 3 | .2327997 .1074848 -3.16 0.002 .0941843 .5754219 589 | edu8 | .9970229 .3384942 -0.01 0.993 .5125286 1.939511 ------+------/cut1 | -3.703375 .5281077 -4.738447 -2.668303 /cut2 | -1.783142 .4373741 -2.64038 -.9259045 ------

Ordered logistic regression Number of obs = 186 LR chi2(10) = 27.83 Prob > chi2 = 0.0019 Log likelihood = -126.74316 Pseudo R2 = 0.0989

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.046895 .4202179 0.11 0.909 .4766892 2.299168 greenhouse_0~1 | .6525602 .3815187 -0.73 0.465 .2074753 2.05246 market2 | .1863277 .1422186 -2.20 0.028 .0417426 .831716 markgreen | 14.87171 16.52586 2.43 0.015 1.684541 131.2926 | owner | 2 | .5481707 .2854075 -1.15 0.248 .1975759 1.520889 3 | .2711189 .1310378 -2.70 0.007 .1051368 .6991407 | edu8 | .9164023 .3330472 -0.24 0.810 .4495053 1.868261 wua33 | 1.732054 .8116222 1.17 0.241 .6913491 4.339358 wua55 | 2.887302 1.929015 1.59 0.112 .7794748 10.69504 wua91 | 4.300049 2.252045 2.79 0.005 1.540556 12.00243 ------+------/cut1 | -2.986075 .9077906 -4.765312 -1.206838 /cut2 | -1.007923 .8582273 -2.690018 .6741712 ------

Ordered logistic regression Number of obs = 186 LR chi2(10) = 27.83 Prob > chi2 = 0.0019 Log likelihood = -126.74316 Pseudo R2 = 0.0989

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.046895 .4202179 0.11 0.909 .4766892 2.299168 greenhouse_0~1 | .6525602 .3815187 -0.73 0.465 .2074753 2.05246 market2 | .1863277 .1422186 -2.20 0.028 .0417426 .831716 markgreen | 14.87171 16.52586 2.43 0.015 1.684541 131.2926 | owner | 2 | .5481707 .2854075 -1.15 0.248 .1975759 1.520889 3 | .2711189 .1310378 -2.70 0.007 .1051368 .6991407 | edu8 | .9164023 .3330472 -0.24 0.810 .4495053 1.868261 wua33 | .4027986 .1999419 -1.83 0.067 .1522534 1.065636 wua55 | .6714579 .4354894 -0.61 0.539 .1883443 2.393784 wuamh | .2325555 .1217952 -2.79 0.005 .0833164 .6491161 ------+------/cut1 | -4.444702 .9096373 -6.227558 -2.661845 /cut2 | -2.46655 .8439967 -4.120753 -.8123466 ------

Ordered logistic regression Number of obs = 186 LR chi2(10) = 27.83 Prob > chi2 = 0.0019 Log likelihood = -126.74316 Pseudo R2 = 0.0989

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] 590 ------+------dunums_log | 1.046895 .4202179 0.11 0.909 .4766892 2.299168 greenhouse_0~1 | .6525602 .3815187 -0.73 0.465 .2074753 2.05246 market2 | .1863277 .1422186 -2.20 0.028 .0417426 .831716 markgreen | 14.87171 16.52586 2.43 0.015 1.684541 131.2926 | owner | 2 | .5481707 .2854075 -1.15 0.248 .1975759 1.520889 3 | .2711189 .1310378 -2.70 0.007 .1051368 .6991407 | edu8 | .9164023 .3330472 -0.24 0.810 .4495053 1.868261 wua33 | .5998866 .3968417 -0.77 0.440 .1640498 2.193626 wua91 | 1.489297 .9659174 0.61 0.539 .4177486 5.309424 wuamh | .3463441 .2313935 -1.59 0.112 .0935013 1.282915 ------+------/cut1 | -4.046398 .9737689 -5.95495 -2.137846 /cut2 | -2.068246 .913901 -3.859459 -.2770328 ------

Ordered logistic regression Number of obs = 186 LR chi2(10) = 27.83 Prob > chi2 = 0.0019 Log likelihood = -126.74316 Pseudo R2 = 0.0989

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.046895 .4202179 0.11 0.909 .4766892 2.299168 greenhouse_0~1 | .6525602 .3815187 -0.73 0.465 .2074753 2.05246 market2 | .1863277 .1422186 -2.20 0.028 .0417426 .831716 markgreen | 14.87171 16.52586 2.43 0.015 1.684541 131.2926 | owner | 2 | .5481707 .2854075 -1.15 0.248 .1975759 1.520889 3 | .2711189 .1310378 -2.70 0.007 .1051368 .6991407 | edu8 | .9164023 .3330472 -0.24 0.810 .4495053 1.868261 wua55 | 1.666982 1.102755 0.77 0.440 .4558662 6.095709 wua91 | 2.48263 1.232332 1.83 0.067 .9384067 6.567996 wuamh | .5773492 .2705397 -1.17 0.241 .2304489 1.446447 ------+------/cut1 | -3.535383 .8424756 -5.186605 -1.884161 /cut2 | -1.557231 .7804163 -3.086819 -.0276435 ------

Ordered logistic regression Number of obs = 186 LR chi2(8) = 24.90 Prob > chi2 = 0.0016 Log likelihood = -128.21043 Pseudo R2 = 0.0885

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | .9551142 .3807701 -0.12 0.908 .4372315 2.086407 greenhouse_0~1 | 1.011116 .4305463 0.03 0.979 .4388829 2.329451 market2 | .1942234 .1452775 -2.19 0.028 .0448343 .8413822 markgreen | 14.22524 15.70581 2.40 0.016 1.634047 123.8382 | owner | 2 | .4745543 .2431028 -1.46 0.146 .1738744 1.295198 3 | .2412654 .1146851 -2.99 0.003 .0950337 .6125089 | edu8 | 1.03869 .3664761 0.11 0.914 .5201885 2.074013 wua91 | 2.647352 1.144995 2.25 0.024 1.134129 6.179611 ------+------/cut1 | -3.565432 .8320548 -5.196229 -1.934634 591 /cut2 | -1.61066 .7720946 -3.123937 -.0973822 ------

Ordered logistic regression Number of obs = 186 LR chi2(8) = 24.32 Prob > chi2 = 0.0020 Log likelihood = -128.4966 Pseudo R2 = 0.0865

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.079226 .4290953 0.19 0.848 .4950837 2.35259 greenhouse_0~1 | .7967903 .3453109 -0.52 0.600 .3407631 1.863097 market2 | .2126718 .1570839 -2.10 0.036 .0500028 .9045349 markgreen | 12.87749 14.11238 2.33 0.020 1.503153 110.3213 | owner | 2 | .6035227 .3064913 -0.99 0.320 .22306 1.632922 3 | .2560269 .1208456 -2.89 0.004 .1015116 .645737 | edu8 | .898492 .3205633 -0.30 0.764 .4465017 1.808028 wuamh | .3887348 .1620536 -2.27 0.023 .1717154 .8800302 ------+------/cut1 | -3.826852 .8148514 -5.423932 -2.229773 /cut2 | -1.870364 .7504101 -3.34114 -.3995868 ------

Ordered logistic regression Number of obs = 186 LR chi2(12) = 29.23 Prob > chi2 = 0.0036 Log likelihood = -126.04331 Pseudo R2 = 0.1039

------catop | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | .0347033 .4057422 0.09 0.932 -.7605368 .8299433 greenhouse_0~1 | -.5288424 .5980288 -0.88 0.377 -1.700957 .6432726 market2 | -1.842905 .7732731 -2.38 0.017 -3.358492 -.3273175 markgreen | 2.890014 1.131136 2.55 0.011 .6730292 5.107 | owner | 2 | -.7237017 .5426411 -1.33 0.182 -1.787259 .3398554 3 | -1.590883 .5541591 -2.87 0.004 -2.677015 -.5047514 | edu8 | -.1306905 .419922 -0.31 0.756 -.9537225 .6923414 wua33 | -.8991762 .5008737 -1.80 0.073 -1.880871 .0825182 wua55 | -.3443227 .6574796 -0.52 0.600 -1.632959 .9443136 wuamh | -2.994076 1.385164 -2.16 0.031 -5.708947 -.2792037 ownermh | .673335 .5882806 1.14 0.252 -.4796738 1.826344 edu8mh | -.0745303 .8747854 -0.09 0.932 -1.789078 1.640017 ------+------/cut1 | -4.703339 .9558852 -6.576839 -2.829838 /cut2 | -2.717476 .8913516 -4.464493 -.9704593 ------

Ordered logistic regression Number of obs = 186 LR chi2(12) = 29.23 Prob > chi2 = 0.0036 Log likelihood = -126.04331 Pseudo R2 = 0.1039

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.035312 .4200699 0.09 0.932 .4674155 2.293189 greenhouse_0~1 | .5892867 .3524105 -0.88 0.377 .1825087 1.902697 market2 | .1583568 .122453 -2.38 0.017 .0347877 .7208549 592 markgreen | 17.99357 20.35317 2.55 0.011 1.960166 165.174 | owner | 2 | .4849538 .2631559 -1.33 0.182 .1674185 1.404744 3 | .2037456 .1129075 -2.87 0.004 .0687681 .6036556 | edu8 | .8774893 .368477 -0.31 0.756 .3853041 1.998389 wua33 | .4069047 .2038079 -1.80 0.073 .1524573 1.086018 wua55 | .7087002 .4659559 -0.52 0.600 .1953507 2.571048 wuamh | .0500829 .069373 -2.16 0.031 .0033162 .7563858 ownermh | 1.960766 1.15348 1.14 0.252 .6189853 6.211136 edu8mh | .9281793 .8119577 -0.09 0.932 .1671142 5.15526 ------+------/cut1 | -4.703339 .9558852 -6.576839 -2.829838 /cut2 | -2.717476 .8913516 -4.464493 -.9704593 ------

Results 36: Ordered logistic regressions of catop as a function of adequate, reliable, tour, punish, conflict, help1, help3, market2, greenhouse_0or1, owner, wua, wua33, wua55, wua91, wuamh, rel33, help1_33, help1_55, help1_91, help1_mh

Ordered logistic regression Number of obs = 115 LR chi2(14) = 62.57 Prob > chi2 = 0.0000 Log likelihood = -61.238954 Pseudo R2 = 0.3381

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.288757 3.619622 1.73 0.084 .8202287 22.42476 reliable | 1.16623 .686727 0.26 0.794 .3677561 3.698356 | tour | 2 | 3.781893 3.363268 1.50 0.135 .6618034 21.61173 3 | 9.158985 8.309797 2.44 0.015 1.54727 54.21613 | punish | 2 | 9.21977 13.70357 1.49 0.135 .5006774 169.7783 3 | 5.194662 8.171557 1.05 0.295 .2379903 113.3849 | conflict | 2 | 1.195726 .8391629 0.25 0.799 .3021738 4.731585 3 | 5.562239 3.386968 2.82 0.005 1.686299 18.34698 | help1 | 4.598228 2.916208 2.41 0.016 1.32665 15.93767 help3 | 1.009137 .6880986 0.01 0.989 .2651811 3.840233 market2 | 2.571785 2.100326 1.16 0.247 .5188899 12.74659 greenhouse_0~1 | .7350793 .5131126 -0.44 0.659 .1871389 2.887383 | owner | 2 | 1.082575 .7928039 0.11 0.914 .2576893 4.547993 3 | .5924953 .4348995 -0.71 0.476 .1405696 2.497344 ------+------/cut1 | 1.2183 1.485728 -1.693674 4.130274 /cut2 | 4.351185 1.583775 1.247043 7.455326 ------

Ordered logistic regression Number of obs = 115 LR chi2(17) = 64.06 Prob > chi2 = 0.0000 Log likelihood = -60.49374 Pseudo R2 = 0.3462

------593 catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.519613 3.965866 1.72 0.086 .8094496 25.23555 reliable | 1.149017 .6846607 0.23 0.816 .3573749 3.694273 | tour | 2 | 4.286146 3.825711 1.63 0.103 .7452553 24.65068 3 | 8.543749 7.835571 2.34 0.019 1.415799 51.55791 | punish | 2 | 11.23727 16.53883 1.64 0.100 .6278794 201.1154 3 | 7.141874 11.20736 1.25 0.210 .3296609 154.7237 | conflict | 2 | .9912592 .7182724 -0.01 0.990 .2395529 4.101786 3 | 5.413909 3.355994 2.72 0.006 1.606442 18.24555 | help1 | 2.988698 2.18415 1.50 0.134 .7135448 12.51823 help3 | .6481574 .5340248 -0.53 0.599 .1289333 3.258336 market2 | 3.63916 3.213444 1.46 0.143 .6447254 20.54128 greenhouse_0~1 | .6180269 .5368457 -0.55 0.580 .1126201 3.391556 | owner | 2 | 1.311229 1.02036 0.35 0.728 .2852977 6.026407 3 | .6173828 .4572635 -0.65 0.515 .1445818 2.636304 | wua | 1 | 1.007914 .9152959 0.01 0.993 .1699964 5.975955 2 | 1.021407 .752 0.03 0.977 .2412744 4.324011 3 | .3448695 .3139294 -1.17 0.242 .0579187 2.05348 ------+------/cut1 | .9732161 1.509199 -1.984759 3.931191 /cut2 | 4.181127 1.614013 1.01772 7.344535 ------

Ordered logistic regression Number of obs = 115 LR chi2(15) = 65.38 Prob > chi2 = 0.0000 Log likelihood = -59.832376 Pseudo R2 = 0.3533

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.570968 3.09889 1.47 0.142 .651797 19.56409 reliable | 1.232581 .7656488 0.34 0.736 .3648129 4.16448 | tour | 2 | 4.454154 4.105185 1.62 0.105 .7315647 27.11925 3 | 11.21502 10.50339 2.58 0.010 1.788999 70.3056 | punish | 2 | 5.952621 9.098412 1.17 0.243 .297626 119.0544 3 | 3.244871 5.212285 0.73 0.464 .1392835 75.59538 | conflict | 2 | 1.24743 .8861106 0.31 0.756 .3100033 5.019567 3 | 5.652805 3.519628 2.78 0.005 1.668311 19.15363 | help1 | 5.420748 3.528237 2.60 0.009 1.513673 19.41272 help3 | 1.314749 .9154189 0.39 0.694 .3358752 5.146453 market2 | 1.017157 .9510917 0.02 0.985 .1627322 6.357744 greenhouse_0~1 | .4352884 .3280818 -1.10 0.270 .0993612 1.906941 markgreen | 20.89832 40.28085 1.58 0.115 .4780216 913.6404 | owner | 2 | .9232882 .6863829 -0.11 0.915 .2150524 3.963968 594 3 | .5151439 .3862922 -0.88 0.376 .1184775 2.239861 ------+------/cut1 | .8358078 1.497573 -2.099382 3.770998 /cut2 | 4.028741 1.596443 .8997698 7.157713 ------

Ordered logistic regression Number of obs = 115 LR chi2(17) = 68.98 Prob > chi2 = 0.0000 Log likelihood = -58.034858 Pseudo R2 = 0.3728

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.57549 4.021458 1.73 0.084 .8171565 25.61945 reliable | .4288478 .3633626 -1.00 0.318 .0814855 2.256971 wua33 | .461299 .3794951 -0.94 0.347 .0919873 2.313328 rel33 | 8.530342 10.20425 1.79 0.073 .817964 88.96081 | tour | 2 | 4.432348 4.063581 1.62 0.104 .734939 26.73107 3 | 15.01765 14.35652 2.83 0.005 2.306074 97.79816 | punish | 2 | 4.997198 7.705746 1.04 0.297 .2433113 102.6339 3 | 2.475948 4.035997 0.56 0.578 .1014411 60.43231 | conflict | 2 | 1.407453 1.016258 0.47 0.636 .341838 5.794923 3 | 5.551229 3.561046 2.67 0.008 1.578897 19.51752 | help1 | 6.328184 4.365051 2.67 0.007 1.637336 24.45797 help3 | 1.216091 .841467 0.28 0.777 .313316 4.720086 market2 | .7714195 .7482736 -0.27 0.789 .1152467 5.163599 greenhouse_0~1 | .5976528 .4592311 -0.67 0.503 .1325541 2.694664 markgreen | 23.10682 43.87681 1.65 0.098 .5590112 955.1243 | owner | 2 | .7043058 .5530075 -0.45 0.655 .151152 3.281773 3 | .4437881 .3461538 -1.04 0.298 .0962144 2.046969 ------+------/cut1 | .1976505 1.586074 -2.910998 3.306299 /cut2 | 3.508766 1.665419 .244604 6.772928 ------

Ordered logistic regression Number of obs = 115 LR chi2(18) = 69.10 Prob > chi2 = 0.0000 Log likelihood = -57.970937 Pseudo R2 = 0.3734

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.543415 4.011735 1.71 0.086 .8049899 25.64333 reliable | .4215464 .3592348 -1.01 0.311 .0793351 2.239882 wua33 | .5203893 .4625761 -0.73 0.462 .0911364 2.971425 wua91 | 1.347415 1.125467 0.36 0.721 .2621268 6.926145 rel33 | 8.737714 10.47629 1.81 0.071 .833354 91.6149 | tour | 2 | 4.351968 3.986759 1.61 0.108 .7226293 26.20933 3 | 13.85105 13.55467 2.69 0.007 2.034676 94.29099 | punish | 2 | 5.284665 8.163452 1.08 0.281 .2559348 109.1203 3 | 2.725741 4.499648 0.61 0.544 .1072316 69.28616 595 | conflict | 2 | 1.410348 1.016421 0.48 0.633 .3434595 5.791315 3 | 5.431831 3.495949 2.63 0.009 1.538542 19.17711 | help1 | 5.963642 4.242257 2.51 0.012 1.47913 24.04455 help3 | 1.055283 .8458429 0.07 0.946 .2193331 5.077313 market2 | .8269479 .8306811 -0.19 0.850 .1154606 5.922736 greenhouse_0~1 | .6639248 .5462569 -0.50 0.619 .132366 3.330131 markgreen | 20.32406 39.28588 1.56 0.119 .459896 898.1755 | owner | 2 | .6857113 .5407872 -0.48 0.632 .1461638 3.216938 3 | .4434606 .3469585 -1.04 0.299 .095694 2.055064 ------+------/cut1 | .2803761 1.603643 -2.862706 3.423458 /cut2 | 3.596103 1.685453 .292677 6.89953 ------

Ordered logistic regression Number of obs = 115 LR chi2(18) = 70.03 Prob > chi2 = 0.0000 Log likelihood = -57.505833 Pseudo R2 = 0.3785

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.540874 4.065848 1.69 0.091 .785205 26.26007 reliable | .4135585 .3508271 -1.04 0.298 .0784244 2.180833 wua33 | .3389187 .299823 -1.22 0.221 .0598527 1.919143 wuamh | .3727384 .361263 -1.02 0.309 .055771 2.491151 rel33 | 8.893751 10.64498 1.83 0.068 .8516814 92.8737 | tour | 2 | 4.924048 4.515216 1.74 0.082 .8161952 29.70644 3 | 13.60604 13.02949 2.73 0.006 2.082569 88.89228 | punish | 2 | 6.244704 9.622785 1.19 0.235 .3046855 127.9888 3 | 3.437152 5.659011 0.75 0.453 .1363829 86.62389 | conflict | 2 | 1.239677 .9071455 0.29 0.769 .2954157 5.202157 3 | 5.171529 3.349088 2.54 0.011 1.453383 18.4017 | help1 | 4.646196 3.531278 2.02 0.043 1.047498 20.60828 help3 | .8505957 .6766774 -0.20 0.839 .1788808 4.044665 market2 | 1.036716 1.097717 0.03 0.973 .1301285 8.259371 greenhouse_0~1 | .4897877 .3877366 -0.90 0.367 .1037913 2.311293 markgreen | 18.20525 33.63699 1.57 0.116 .4869355 680.6471 | owner | 2 | .8108124 .6539051 -0.26 0.795 .1668946 3.939114 3 | .476963 .3737251 -0.94 0.345 .1026891 2.215363 ------+------/cut1 | -.1128191 1.603181 -3.254996 3.029358 /cut2 | 3.275168 1.673231 -.0043046 6.55464 ------

Ordered logistic regression Number of obs = 115 LR chi2(14) = 62.57 Prob > chi2 = 0.0000 Log likelihood = -61.238954 Pseudo R2 = 0.3381

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] 596 ------+------adequate | 4.288757 3.619622 1.73 0.084 .8202287 22.42476 reliable | 1.16623 .686727 0.26 0.794 .3677561 3.698356 | tour | 2 | 3.781893 3.363268 1.50 0.135 .6618034 21.61173 3 | 9.158985 8.309797 2.44 0.015 1.54727 54.21613 | punish | 2 | 9.21977 13.70357 1.49 0.135 .5006774 169.7783 3 | 5.194662 8.171557 1.05 0.295 .2379903 113.3849 | conflict | 2 | 1.195726 .8391629 0.25 0.799 .3021738 4.731585 3 | 5.562239 3.386968 2.82 0.005 1.686299 18.34698 | help1 | 4.598228 2.916208 2.41 0.016 1.32665 15.93767 help3 | 1.009137 .6880986 0.01 0.989 .2651811 3.840233 market2 | 2.571785 2.100326 1.16 0.247 .5188899 12.74659 greenhouse_0~1 | .7350793 .5131126 -0.44 0.659 .1871389 2.887383 | owner | 2 | 1.082575 .7928039 0.11 0.914 .2576893 4.547993 3 | .5924953 .4348995 -0.71 0.476 .1405696 2.497344 ------+------/cut1 | 1.2183 1.485728 -1.693674 4.130274 /cut2 | 4.351185 1.583775 1.247043 7.455326 ------

Ordered logistic regression Number of obs = 115 LR chi2(17) = 64.06 Prob > chi2 = 0.0000 Log likelihood = -60.49374 Pseudo R2 = 0.3462

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.519613 3.965866 1.72 0.086 .8094496 25.23555 reliable | 1.149017 .6846607 0.23 0.816 .3573749 3.694273 | tour | 2 | 4.286146 3.825711 1.63 0.103 .7452553 24.65068 3 | 8.543749 7.835571 2.34 0.019 1.415799 51.55791 | punish | 2 | 11.23727 16.53883 1.64 0.100 .6278794 201.1154 3 | 7.141874 11.20736 1.25 0.210 .3296609 154.7237 | conflict | 2 | .9912592 .7182724 -0.01 0.990 .2395529 4.101786 3 | 5.413909 3.355994 2.72 0.006 1.606442 18.24555 | help1 | 2.988698 2.18415 1.50 0.134 .7135448 12.51823 help3 | .6481574 .5340248 -0.53 0.599 .1289333 3.258336 market2 | 3.63916 3.213444 1.46 0.143 .6447254 20.54128 greenhouse_0~1 | .6180269 .5368457 -0.55 0.580 .1126201 3.391556 | owner | 2 | 1.311229 1.02036 0.35 0.728 .2852977 6.026407 3 | .6173828 .4572635 -0.65 0.515 .1445818 2.636304 | wua33 | 2.899648 2.639505 1.17 0.242 .4869782 17.26557 wua55 | 2.922596 3.367361 0.93 0.352 .3055134 27.95808 wua91 | 2.961721 3.092138 1.04 0.298 .382702 22.92069 ------+------/cut1 | 2.037805 1.616835 -1.131133 5.206744 597 /cut2 | 5.245717 1.758741 1.798648 8.692785 ------

Ordered logistic regression Number of obs = 115 LR chi2(17) = 64.06 Prob > chi2 = 0.0000 Log likelihood = -60.49374 Pseudo R2 = 0.3462

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.519613 3.965866 1.72 0.086 .8094496 25.23555 reliable | 1.149017 .6846607 0.23 0.816 .3573749 3.694273 | tour | 2 | 4.286146 3.825711 1.63 0.103 .7452553 24.65068 3 | 8.543749 7.835571 2.34 0.019 1.415799 51.55791 | punish | 2 | 11.23727 16.53883 1.64 0.100 .6278794 201.1154 3 | 7.141874 11.20736 1.25 0.210 .3296609 154.7237 | conflict | 2 | .9912592 .7182724 -0.01 0.990 .2395529 4.101786 3 | 5.413909 3.355994 2.72 0.006 1.606442 18.24555 | help1 | 2.988698 2.18415 1.50 0.134 .7135448 12.51823 help3 | .6481574 .5340248 -0.53 0.599 .1289333 3.258336 market2 | 3.63916 3.213444 1.46 0.143 .6447254 20.54128 greenhouse_0~1 | .6180269 .5368457 -0.55 0.580 .1126201 3.391556 | owner | 2 | 1.311229 1.02036 0.35 0.728 .2852977 6.026407 3 | .6173828 .4572635 -0.65 0.515 .1445818 2.636304 | wua33 | .9790413 .7208085 -0.03 0.977 .2312667 4.144659 wua55 | .9867896 .9783208 -0.01 0.989 .1413605 6.888441 wuamh | .3376415 .3525093 -1.04 0.298 .0436287 2.612999 ------+------/cut1 | .9520346 1.58908 -2.162505 4.066574 /cut2 | 4.159946 1.682881 .8615589 7.458333 ------

Ordered logistic regression Number of obs = 115 LR chi2(17) = 64.06 Prob > chi2 = 0.0000 Log likelihood = -60.49374 Pseudo R2 = 0.3462

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.519613 3.965866 1.72 0.086 .8094496 25.23555 reliable | 1.149017 .6846607 0.23 0.816 .3573749 3.694273 | tour | 2 | 4.286146 3.825711 1.63 0.103 .7452553 24.65068 3 | 8.543749 7.835571 2.34 0.019 1.415799 51.55791 | punish | 2 | 11.23727 16.53883 1.64 0.100 .6278794 201.1154 3 | 7.141874 11.20736 1.25 0.210 .3296609 154.7237 | conflict | 2 | .9912592 .7182724 -0.01 0.990 .2395529 4.101786 3 | 5.413909 3.355994 2.72 0.006 1.606442 18.24555 | 598 help1 | 2.988698 2.18415 1.50 0.134 .7135448 12.51823 help3 | .6481574 .5340248 -0.53 0.599 .1289333 3.258336 market2 | 3.63916 3.213444 1.46 0.143 .6447254 20.54128 greenhouse_0~1 | .6180269 .5368457 -0.55 0.580 .1126201 3.391556 | owner | 2 | 1.311229 1.02036 0.35 0.728 .2852977 6.026407 3 | .6173828 .4572635 -0.65 0.515 .1445818 2.636304 | wua33 | .9921479 .9009784 -0.01 0.993 .1673373 5.882476 wua91 | 1.013387 1.00469 0.01 0.989 .1451707 7.07411 wuamh | .3421616 .3942322 -0.93 0.352 .0357678 3.273179 ------+------/cut1 | .9653331 1.562152 -2.096429 4.027095 /cut2 | 4.173244 1.646394 .9463719 7.400117 ------

Ordered logistic regression Number of obs = 115 LR chi2(17) = 64.06 Prob > chi2 = 0.0000 Log likelihood = -60.49374 Pseudo R2 = 0.3462

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.519613 3.965866 1.72 0.086 .8094496 25.23555 reliable | 1.149017 .6846607 0.23 0.816 .3573749 3.694273 | tour | 2 | 4.286146 3.825711 1.63 0.103 .7452553 24.65068 3 | 8.543749 7.835571 2.34 0.019 1.415799 51.55791 | punish | 2 | 11.23727 16.53883 1.64 0.100 .6278794 201.1154 3 | 7.141874 11.20736 1.25 0.210 .3296609 154.7237 | conflict | 2 | .9912592 .7182724 -0.01 0.990 .2395529 4.101786 3 | 5.413909 3.355994 2.72 0.006 1.606442 18.24555 | help1 | 2.988698 2.18415 1.50 0.134 .7135448 12.51823 help3 | .6481574 .5340248 -0.53 0.599 .1289333 3.258336 market2 | 3.63916 3.213444 1.46 0.143 .6447254 20.54128 greenhouse_0~1 | .6180269 .5368457 -0.55 0.580 .1126201 3.391556 | owner | 2 | 1.311229 1.02036 0.35 0.728 .2852977 6.026407 3 | .6173828 .4572635 -0.65 0.515 .1445818 2.636304 | wua55 | 1.007914 .9152959 0.01 0.993 .1699964 5.975955 wua91 | 1.021407 .752 0.03 0.977 .2412744 4.324011 wuamh | .3448695 .3139294 -1.17 0.242 .0579187 2.05348 ------+------/cut1 | .9732161 1.509199 -1.984759 3.931191 /cut2 | 4.181127 1.614013 1.01772 7.344535 ------

Ordered logistic regression Number of obs = 115 LR chi2(16) = 67.58 Prob > chi2 = 0.0000 Log likelihood = -58.731297 Pseudo R2 = 0.3652

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------599 adequate | 3.875549 3.568385 1.47 0.141 .6376688 23.55435 reliable | 1.374901 .8474455 0.52 0.605 .4107878 4.601773 | tour | 2 | 4.588843 4.166842 1.68 0.093 .7740699 27.20359 3 | 12.63783 11.62525 2.76 0.006 2.082917 76.67841 | punish | 2 | 8.083413 12.34024 1.37 0.171 .4056394 161.0829 3 | 3.932808 6.301774 0.85 0.393 .1701263 90.91468 | conflict | 2 | 1.161582 .8512757 0.20 0.838 .2762098 4.884954 3 | 5.774647 3.532483 2.87 0.004 1.741109 19.15247 | help1 | 1.462043 1.161527 0.48 0.633 .3081195 6.937469 help3 | 1.015535 .7209028 0.02 0.983 .2526088 4.082644 market2 | 2.288155 1.922335 0.99 0.324 .4409303 11.8741 greenhouse_0~1 | 1.104736 .8252586 0.13 0.894 .2555023 4.776636 | owner | 2 | .8395167 .6409865 -0.23 0.819 .1879842 3.74919 3 | .5396358 .4118271 -0.81 0.419 .1209205 2.40825 | wua33 | .5235463 .3826769 -0.89 0.376 .1249638 2.19344 help1_33 | 12.26228 14.46804 2.12 0.034 1.214097 123.8479 ------+------/cut1 | .9279996 1.516132 -2.043564 3.899564 /cut2 | 4.184608 1.610417 1.028249 7.340967 ------

Ordered logistic regression Number of obs = 115 LR chi2(16) = 66.81 Prob > chi2 = 0.0000 Log likelihood = -59.117616 Pseudo R2 = 0.3610

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.763415 3.294418 1.51 0.130 .6767952 20.92699 reliable | 1.519848 .929885 0.68 0.494 .4581539 5.041837 | tour | 2 | 4.324809 3.873887 1.63 0.102 .7473349 25.02757 3 | 8.838837 7.872519 2.45 0.014 1.542597 50.64516 | punish | 2 | 12.96889 20.32312 1.64 0.102 .6011913 279.7646 3 | 8.916545 14.73179 1.32 0.185 .3498268 227.2689 | conflict | 2 | 1.222229 .8691229 0.28 0.778 .3032957 4.925372 3 | 5.07785 3.102085 2.66 0.008 1.533474 16.81448 | help1 | 8.780133 6.539386 2.92 0.004 2.039537 37.79814 help3 | 1.333496 .9541373 0.40 0.688 .3280529 5.420505 market2 | 2.6281 2.091857 1.21 0.225 .552232 12.50726 greenhouse_0~1 | .7546386 .6633396 -0.32 0.749 .1347469 4.226288 | owner | 2 | 1.184985 .9148882 0.22 0.826 .2609325 5.38143 3 | .6700055 .5104711 -0.53 0.599 .1505066 2.982642 | wua55 | 4.95177 5.939039 1.33 0.182 .4718986 51.96036 help1_55 | .0586067 .0825997 -2.01 0.044 .0037006 .9281602 ------+------600 /cut1 | 2.024461 1.634665 -1.179423 5.228346 /cut2 | 5.299334 1.771518 1.827222 8.771446 ------

Ordered logistic regression Number of obs = 115 LR chi2(16) = 64.30 Prob > chi2 = 0.0000 Log likelihood = -60.374118 Pseudo R2 = 0.3475

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.367264 3.787847 1.70 0.089 .7978827 23.90451 reliable | 1.133927 .6745052 0.21 0.833 .3533915 3.638429 | tour | 2 | 4.425959 4.041484 1.63 0.103 .7391768 26.50126 3 | 11.21729 10.8994 2.49 0.013 1.670357 75.32983 | punish | 2 | 9.091905 13.67792 1.47 0.142 .4765431 173.4633 3 | 4.367217 7.014313 0.92 0.359 .1875277 101.7054 | conflict | 2 | 1.031746 .7524729 0.04 0.966 .2470444 4.308945 3 | 5.663187 3.473416 2.83 0.005 1.702125 18.84215 | help1 | 5.917569 4.091974 2.57 0.010 1.525953 22.94804 help3 | .666375 .5390907 -0.50 0.616 .1364914 3.25336 market2 | 2.98275 2.569945 1.27 0.205 .5510862 16.14411 greenhouse_0~1 | .7025364 .5012449 -0.49 0.621 .173522 2.844351 | owner | 2 | .945003 .7042281 -0.08 0.939 .2193346 4.071545 3 | .533567 .392028 -0.85 0.393 .1264111 2.252127 | wua91 | 2.101029 1.754699 0.89 0.374 .4088282 10.7975 help1_91 | .178747 .234278 -1.31 0.189 .0136961 2.332821 ------+------/cut1 | 1.138825 1.498235 -1.797661 4.075311 /cut2 | 4.329208 1.595037 1.202993 7.455424 ------

Ordered logistic regression Number of obs = 115 LR chi2(15) = 64.06 Prob > chi2 = 0.0000 Log likelihood = -60.494155 Pseudo R2 = 0.3462

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 4.534835 3.943754 1.74 0.082 .8247219 24.93535 reliable | 1.146941 .6787697 0.23 0.817 .3595774 3.658387 | tour | 2 | 4.287425 3.811227 1.64 0.102 .7508215 24.48254 3 | 8.583845 7.71606 2.39 0.017 1.47412 49.984 | punish | 2 | 11.2049 16.42206 1.65 0.099 .633688 198.1257 3 | 7.110731 11.09748 1.26 0.209 .3337896 151.4801 | conflict | 2 | .9892187 .7127503 -0.02 0.988 .2409827 4.06068 3 | 5.427105 3.322142 2.76 0.006 1.634988 18.01448 | 601 help1 | 2.990154 2.184519 1.50 0.134 .7142177 12.51862 help3 | .6523221 .5097581 -0.55 0.585 .1410228 3.017414 market2 | 3.62952 3.172837 1.47 0.140 .654259 20.13486 greenhouse_0~1 | .6194696 .4388524 -0.68 0.499 .1545258 2.483355 | owner | 2 | 1.314595 .998167 0.36 0.719 .2968095 5.822454 3 | .6164189 .4524482 -0.66 0.510 .1462509 2.598085 | wuamh | .3429613 .3019928 -1.22 0.224 .0610554 1.926487 help1_mh | 1 (omitted) ------+------/cut1 | .9673135 1.46058 -1.89537 3.829997 /cut2 | 4.174824 1.560709 1.11589 7.233757 ------

Ordered logistic regression Number of obs = 115 LR chi2(18) = 69.06 Prob > chi2 = 0.0000 Log likelihood = -57.991277 Pseudo R2 = 0.3732

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.691696 3.420546 1.41 0.159 .6005431 22.69383 reliable | 1.538992 .9639514 0.69 0.491 .4509041 5.252772 | tour | 2 | 5.141416 4.73808 1.78 0.076 .8446108 31.29744 3 | 12.33725 11.69841 2.65 0.008 1.923501 79.13051 | punish | 2 | 11.6998 18.91727 1.52 0.128 .4919047 278.2762 3 | 6.553996 11.20335 1.10 0.271 .229863 186.8716 | conflict | 2 | 1.130566 .8502579 0.16 0.870 .2589034 4.936895 3 | 5.315936 3.271204 2.72 0.007 1.591424 17.75716 | help1 | 2.425112 2.58762 0.83 0.406 .2995649 19.63237 help3 | 1.23264 .9053378 0.28 0.776 .2921819 5.200185 market2 | 2.521273 2.073103 1.12 0.261 .5031805 12.63327 greenhouse_0~1 | .8055428 .7056695 -0.25 0.805 .1446843 4.484933 | owner | 2 | .9936082 .7879511 -0.01 0.994 .2099885 4.701484 3 | .5934112 .4590305 -0.67 0.500 .1302905 2.702706 | wua33 | .626785 .4735412 -0.62 0.536 .1425703 2.75555 wua55 | 4.114763 5.182309 1.12 0.261 .3485821 48.57184 help1_33 | 7.826331 10.60944 1.52 0.129 .5491134 111.5461 help1_55 | .2028213 .3294627 -0.98 0.326 .0084028 4.895587 ------+------/cut1 | 1.694028 1.687661 -1.613726 5.001782 /cut2 | 5.031625 1.81714 1.470096 8.593153 ------

Ordered logistic regression Number of obs = 115 LR chi2(16) = 65.87 Prob > chi2 = 0.0000 Log likelihood = -59.588679 Pseudo R2 = 0.3560

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 5.17201 4.426797 1.92 0.055 .9662815 27.68312 602 reliable | .4648685 .3664805 -0.97 0.331 .0991478 2.179603 wua33 | .4978493 .4057655 -0.86 0.392 .1007714 2.459566 rel33 | 7.179935 8.267855 1.71 0.087 .751524 68.59589 | tour | 2 | 3.550709 3.128222 1.44 0.150 .6315316 19.96342 3 | 11.34674 10.3661 2.66 0.008 1.893367 67.99979 | punish | 2 | 8.515048 12.78309 1.43 0.154 .44909 161.451 3 | 4.520551 7.170837 0.95 0.342 .2018142 101.2584 | conflict | 2 | 1.320312 .9448591 0.39 0.698 .3247345 5.36815 3 | 5.292558 3.313768 2.66 0.008 1.55136 18.05588 | help1 | 5.179029 3.452655 2.47 0.014 1.402122 19.12982 help3 | .9544277 .65253 -0.07 0.946 .2499122 3.64501 market2 | 2.247062 1.864021 0.98 0.329 .4420911 11.42137 greenhouse_0~1 | .9976266 .7223888 -0.00 0.997 .2413274 4.124101 | owner | 2 | .8721509 .6606704 -0.18 0.857 .1976017 3.849396 3 | .5385154 .403981 -0.83 0.409 .1237792 2.342872 ------+------/cut1 | .7282617 1.544802 -2.299494 3.756017 /cut2 | 3.973244 1.629876 .7787466 7.167742 ------

Ordered logistic regression Number of obs = 115 LR chi2(13) = 59.19 Prob > chi2 = 0.0000 Log likelihood = -62.929597 Pseudo R2 = 0.3198

------catop | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.663976 .9183129 0.92 0.356 .5641402 4.908028 | tour | 2 | 3.203746 2.880274 1.30 0.195 .5500468 18.66021 3 | 7.810974 7.089667 2.26 0.024 1.318585 46.27029 | punish | 2 | 12.85107 19.48241 1.68 0.092 .6584234 250.8264 3 | 8.37576 13.24221 1.34 0.179 .3777996 185.6893 | conflict | 2 | 1.623191 1.073138 0.73 0.464 .4442383 5.930935 3 | 6.402513 3.902294 3.05 0.002 1.938868 21.14232 | help1 | 4.436688 2.765615 2.39 0.017 1.307559 15.05416 help3 | 1.24484 .8100293 0.34 0.736 .3477193 4.456547 market2 | 2.614176 2.124845 1.18 0.237 .5314526 12.85894 greenhouse_0~1 | .8110272 .5583011 -0.30 0.761 .210416 3.126022 | owner | 2 | 1.123014 .8109588 0.16 0.872 .272715 4.624459 3 | .7388257 .5304433 -0.42 0.673 .1808914 3.017631 ------+------/cut1 | 1.733493 1.486068 -1.179147 4.646133 /cut2 | 4.833495 1.598267 1.70095 7.966041 ------

603 Results 37: ANOVA with wua and catcom, Ordered logistic regressions of catcom as a function of wua33, wua55, wua91 and wuamh

ANOVA

Number of obs = 186 R-squared = 0.0610 Root MSE = .703414 Adj R-squared = 0.0455

Source | Partial SS df MS F Prob>F ------+------Model | 5.8459161 3 1.9486387 3.94 0.0094 | wua | 5.8459161 3 1.9486387 3.94 0.0094 | Residual | 90.051933 182 .49479084 ------+------Total | 95.897849 185 .51836675

Ordered logistic regression Number of obs = 186 LR chi2(3) = 11.16 Prob > chi2 = 0.0109 Log likelihood = -176.93199 Pseudo R2 = 0.0306

------catcom | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .7634814 .4150368 1.84 0.066 -.0499757 1.576939 wua55 | 1.079972 .4290601 2.52 0.012 .2390297 1.920914 wua91 | 1.311702 .4140065 3.17 0.002 .5002637 2.123139 ------+------/cut1 | -1.041888 .3274225 -1.683624 -.4001516 /cut2 | .7743334 .3209131 .1453554 1.403311 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 11.16 Prob > chi2 = 0.0109 Log likelihood = -176.93199 Pseudo R2 = 0.0306

------catcom | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.3164906 .4030983 -0.79 0.432 -1.106549 .4735676 wua91 | .2317295 .3987466 0.58 0.561 -.5497995 1.013258 wuamh | -1.079972 .4290601 -2.52 0.012 -1.920914 -.2390297 ------+------/cut1 | -2.12186 .3433744 -2.794861 -1.448859 /cut2 | -.3056386 .2956456 -.8850934 .2738161 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 11.16 Prob > chi2 = 0.0109 Log likelihood = -176.93199 Pseudo R2 = 0.0306

------catcom | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.5482201 .3860839 -1.42 0.156 -1.304931 .2084904 wua55 | -.2317295 .3987466 -0.58 0.561 -1.013258 .5497995 wuamh | -1.311702 .4140065 -3.17 0.002 -2.123139 -.5002637 ------+------/cut1 | -2.353589 .3270431 -2.994582 -1.712597 /cut2 | -.5373681 .2706937 -1.067918 -.0068183 604 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 11.16 Prob > chi2 = 0.0109 Log likelihood = -176.93199 Pseudo R2 = 0.0306

------catcom | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua55 | .3164906 .4030983 0.79 0.432 -.4735676 1.106549 wua91 | .5482201 .3860839 1.42 0.156 -.2084904 1.304931 wuamh | -.7634814 .4150368 -1.84 0.066 -1.576939 .0499757 ------+------/cut1 | -1.805369 .320833 -2.43419 -1.176548 /cut2 | .010852 .2795718 -.5370986 .5588025 ------

Results 38: Ordered logistic regressions of catcom as a function of adequate, reliable, wua33, wua55, wua91, pos1, sys1, rel33, rel55, rel91, relmh

Ordered logistic regression Number of obs = 186 LR chi2(5) = 22.47 Prob > chi2 = 0.0004 Log likelihood = -171.27925 Pseudo R2 = 0.0616

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 1.663913 .6258981 1.35 0.176 .796054 3.477915 reliable | 2.199562 .7198281 2.41 0.016 1.15817 4.177344 wua33 | 1.785532 .7822874 1.32 0.186 .7565446 4.214062 wua55 | 2.066543 .9540035 1.57 0.116 .8361685 5.107343 wua91 | 2.766158 1.200865 2.34 0.019 1.181264 6.477493 ------+------/cut1 | -.781972 .3440181 -1.456235 -.1077088 /cut2 | 1.115863 .3467424 .4362598 1.795465 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 24.31 Prob > chi2 = 0.0001 Log likelihood = -170.35904 Pseudo R2 = 0.0666

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 1.790076 .6763067 1.54 0.123 .8536498 3.75373 reliable | 1.641757 .5858275 1.39 0.165 .8157832 3.30402 wua33 | .3802135 .1796816 -2.05 0.041 .1505796 .9600393 rel33 | 6.250257 4.305227 2.66 0.008 1.620255 24.11084 ------+------/cut1 | -1.55824 .2901808 -2.126984 -.989496 /cut2 | .3555371 .2522799 -.1389223 .8499966 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 18.90 Prob > chi2 = 0.0008 Log likelihood = -173.06516 Pseudo R2 = 0.0518

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------605 adequate | 1.620233 .5942323 1.32 0.188 .789574 3.324772 reliable | 3.27129 1.154104 3.36 0.001 1.638389 6.531622 wua55 | 2.117016 1.244441 1.28 0.202 .6689044 6.700143 rel55 | .3597259 .2623967 -1.40 0.161 .0861143 1.502685 ------+------/cut1 | -1.172159 .2581029 -1.678032 -.6662871 /cut2 | .6961027 .2413741 .2230181 1.169187 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 20.89 Prob > chi2 = 0.0003 Log likelihood = -172.07075 Pseudo R2 = 0.0572

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 1.58555 .5888034 1.24 0.215 .7657396 3.283059 reliable | 3.182405 1.13091 3.26 0.001 1.585877 6.386184 wua91 | 2.524175 1.21879 1.92 0.055 .9797596 6.503086 rel91 | .4971713 .3255483 -1.07 0.286 .1377648 1.794212 ------+------/cut1 | -1.063644 .2681099 -1.58913 -.5381585 /cut2 | .8248396 .2591962 .3168243 1.332855 ------

Ordered logistic regression Number of obs = 186 LR chi2(4) = 24.39 Prob > chi2 = 0.0001 Log likelihood = -170.31921 Pseudo R2 = 0.0668

Ordered logistic regression Number of obs = 186 LR chi2(6) = 32.53 Prob > chi2 = 0.0000 Log likelihood = -166.25089 Pseudo R2 = 0.0891

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 1.821351 .6983356 1.56 0.118 .8590708 3.861519 reliable | 2.008079 .667162 2.10 0.036 1.047077 3.851085 sys1 | 2.934557 1.014062 3.12 0.002 1.490741 5.776739 wua33 | 1.052317 .4982439 0.11 0.914 .4160308 2.661753 wua55 | 1.193209 .5919812 0.36 0.722 .4512463 3.155144 wua91 | 2.49789 1.101665 2.08 0.038 1.052352 5.929056 ------+------/cut1 | -.6393822 .3533796 -1.331994 .0532292 /cut2 | 1.343787 .3626228 .6330592 2.054515 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 38.78 Prob > chi2 = 0.0000 Log likelihood = -163.12251 Pseudo R2 = 0.1062 606

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 1.982515 .7671578 1.77 0.077 .9286109 4.23252 reliable | 1.312625 .4828021 0.74 0.460 .6383472 2.699136 sys1 | 2.849208 .9240827 3.23 0.001 1.508878 5.380146 wua33 | .4217686 .2070049 -1.76 0.079 .1611769 1.103687 rel33 | 5.68476 3.99621 2.47 0.013 1.433322 22.54657 wua91 | 2.37776 .8666023 2.38 0.017 1.163959 4.857338 ------+------/cut1 | -.9704013 .331285 -1.619708 -.3210945 /cut2 | 1.072472 .3265763 .4323942 1.71255 ------

Results 39: Ordered logistic and logistic regressions of catcom as a function of tour, punish, conflict, help1, help3, wua33, wua55, wua91, wuamh

Ordered logistic regression Number of obs = 115 LR chi2(8) = 42.94 Prob > chi2 = 0.0000 Log likelihood = -92.36239 Pseudo R2 = 0.1886

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 1.08526 .7743464 0.11 0.909 .2680349 4.394167 3 | 1.002278 .6979691 0.00 0.997 .2559919 3.924192 | punish | 2 | 1.40e+07 1.34e+10 0.02 0.986 0 . 3 | 1.36e+07 1.30e+10 0.02 0.986 0 . | conflict | 2 | 4.019262 2.597058 2.15 0.031 1.132766 14.26109 3 | 4.328392 2.067862 3.07 0.002 1.696955 11.04035 | help1 | 3.713433 1.913688 2.55 0.011 1.352433 10.19613 help3 | 2.5719 1.45275 1.67 0.094 .8500585 7.781428 ------+------/cut1 | 16.09882 956.0067 -1857.64 1889.837 /cut2 | 17.97122 956.0067 -1855.768 1891.71 ------Note: 5 observations completely determined. Standard errors questionable.

Ordered logistic regression Number of obs = 115 LR chi2(11) = 46.17 Prob > chi2 = 0.0000 Log likelihood = -90.744274 Pseudo R2 = 0.2028

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 1.166822 .8523057 0.21 0.833 .278769 4.883881 3 | .7338489 .537415 -0.42 0.673 .1746834 3.082916 | punish | 2 | 1.79e+07 1.61e+10 0.02 0.985 0 . 3 | 2.04e+07 1.84e+10 0.02 0.985 0 . | conflict | 2 | 4.015277 2.674806 2.09 0.037 1.088131 14.81664 607 3 | 3.899352 1.91346 2.77 0.006 1.490381 10.20205 | help1 | 2.868709 1.645901 1.84 0.066 .9317851 8.83196 help3 | 1.661497 1.043592 0.81 0.419 .4851288 5.690389 wua33 | 1.900382 1.424093 0.86 0.392 .437496 8.25482 wua55 | 2.433966 1.922263 1.13 0.260 .517684 11.44364 wua91 | 3.94409 3.231143 1.67 0.094 .7917926 19.64636 ------+------/cut1 | 16.74704 903.2411 -1753.573 1787.067 /cut2 | 18.6782 903.2412 -1751.642 1788.998 ------Note: 5 observations completely determined. Standard errors questionable.

Ordered logistic regression Number of obs = 115 LR chi2(11) = 46.17 Prob > chi2 = 0.0000 Log likelihood = -90.744274 Pseudo R2 = 0.2028

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 1.166822 .8523057 0.21 0.833 .278769 4.883881 3 | .7338489 .537415 -0.42 0.673 .1746834 3.082916 | punish | 2 | 1.79e+07 1.61e+10 0.02 0.985 0 . 3 | 2.04e+07 1.84e+10 0.02 0.985 0 . | conflict | 2 | 4.015277 2.674806 2.09 0.037 1.088131 14.81664 3 | 3.899352 1.91346 2.77 0.006 1.490381 10.20205 | help1 | 2.868709 1.645901 1.84 0.066 .9317851 8.83196 help3 | 1.661497 1.043592 0.81 0.419 .4851288 5.690389 wua33 | .4818302 .2742562 -1.28 0.200 .1579039 1.470264 wua55 | .6171172 .3857538 -0.77 0.440 .181255 2.101093 wuamh | .2535439 .2077124 -1.67 0.094 .0509 1.262957 ------+------/cut1 | 15.37482 903.241 -1754.945 1785.695 /cut2 | 17.30598 903.2411 -1753.014 1787.626 ------Note: 5 observations completely determined. Standard errors questionable.

Ordered logistic regression Number of obs = 115 LR chi2(11) = 46.17 Prob > chi2 = 0.0000 Log likelihood = -90.744274 Pseudo R2 = 0.2028

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 1.166822 .8523057 0.21 0.833 .278769 4.883881 3 | .7338489 .537415 -0.42 0.673 .1746834 3.082916 | punish | 2 | 1.79e+07 1.61e+10 0.02 0.985 0 . 3 | 2.04e+07 1.84e+10 0.02 0.985 0 . | conflict | 2 | 4.015277 2.674806 2.09 0.037 1.088131 14.81664 3 | 3.899352 1.91346 2.77 0.006 1.490381 10.20205 | help1 | 2.868709 1.645901 1.84 0.066 .9317851 8.83196 help3 | 1.661497 1.043592 0.81 0.419 .4851288 5.690389 608 wua33 | .7807758 .451591 -0.43 0.669 .2513055 2.425776 wua91 | 1.620438 1.012919 0.77 0.440 .4759428 5.517088 wuamh | .4108521 .3244769 -1.13 0.260 .0873848 1.93168 ------+------/cut1 | 15.85752 903.2408 -1754.462 1786.177 /cut2 | 17.78868 903.2409 -1752.531 1788.108 ------Note: 5 observations completely determined. Standard errors questionable.

Ordered logistic regression Number of obs = 115 LR chi2(11) = 46.17 Prob > chi2 = 0.0000 Log likelihood = -90.744274 Pseudo R2 = 0.2028

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 1.166822 .8523057 0.21 0.833 .278769 4.883881 3 | .7338489 .537415 -0.42 0.673 .1746834 3.082916 | punish | 2 | 1.79e+07 1.61e+10 0.02 0.985 0 . 3 | 2.04e+07 1.84e+10 0.02 0.985 0 . | conflict | 2 | 4.015277 2.674806 2.09 0.037 1.088131 14.81664 3 | 3.899352 1.91346 2.77 0.006 1.490381 10.20205 | help1 | 2.868709 1.645901 1.84 0.066 .9317851 8.83196 help3 | 1.661497 1.043592 0.81 0.419 .4851288 5.690389 wua55 | 1.280777 .7407857 0.43 0.669 .4122392 3.979221 wua91 | 2.07542 1.181323 1.28 0.200 .68015 6.332967 wuamh | .5262101 .3943272 -0.86 0.392 .1211413 2.285735 ------+------/cut1 | 16.10499 903.241 -1754.215 1786.425 /cut2 | 18.03615 903.2411 -1752.284 1788.356 ------Note: 5 observations completely determined. Standard errors questionable.

Ordered logistic regression Number of obs = 115 LR chi2(6) = 33.26 Prob > chi2 = 0.0000 Log likelihood = -97.201078 Pseudo R2 = 0.1461

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.383164 1.566731 1.32 0.187 .6569916 8.64466 3 | 2.163556 1.315545 1.27 0.204 .657048 7.124249 | conflict | 2 | 3.20036 1.928806 1.93 0.054 .9821848 10.42808 3 | 4.365825 2.04888 3.14 0.002 1.740195 10.95304 | help1 | 3.650692 1.817248 2.60 0.009 1.37614 9.684737 help3 | 2.625166 1.453295 1.74 0.081 .8870138 7.769322 ------+------/cut1 | .4848332 .5186132 -.5316299 1.501296 /cut2 | 2.246651 .5691073 1.131221 3.36208 ------

Ordered logistic regression Number of obs = 115 LR chi2(7) = 35.12 Prob > chi2 = 0.0000 609 Log likelihood = -96.273245 Pseudo R2 = 0.1542

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.454624 1.627882 1.35 0.176 .669078 9.005199 3 | 1.878272 1.169564 1.01 0.311 .5542825 6.364812 | conflict | 2 | 3.481986 2.109148 2.06 0.039 1.062252 11.4137 3 | 4.338608 2.060766 3.09 0.002 1.710189 11.00669 | help1 | 3.472588 1.737878 2.49 0.013 1.30216 9.260665 help3 | 1.957945 1.164667 1.13 0.259 .6101975 6.282468 wua91 | 1.951944 .9678519 1.35 0.177 .7385972 5.158544 ------+------/cut1 | .4987382 .5222061 -.5247668 1.522243 /cut2 | 2.285131 .5748992 1.158349 3.411912 ------

Ordered logistic regression Number of obs = 115 LR chi2(7) = 33.58 Prob > chi2 = 0.0000 Log likelihood = -97.040458 Pseudo R2 = 0.1475

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.553191 1.713561 1.40 0.163 .6851786 9.513995 3 | 2.14342 1.306884 1.25 0.211 .6488036 7.081108 | conflict | 2 | 2.990282 1.834411 1.79 0.074 .8985374 9.951488 3 | 4.221402 1.992728 3.05 0.002 1.67357 10.64804 | help1 | 3.218762 1.752485 2.15 0.032 1.107246 9.356933 help3 | 2.372691 1.37757 1.49 0.137 .7603938 7.403612 wuamh | .6790562 .4640231 -0.57 0.571 .1779302 2.591563 ------+------/cut1 | .335485 .5806443 -.8025569 1.473527 /cut2 | 2.105445 .6196996 .8908566 3.320034 ------

Results 40: Ordered logistic regressions of catcom as a function of dunums_log, greenhouse_0or1, market2, markgreen, owner, edu8, wua33, wua55, wua91, wuamh, owner33, owner55, owner91, ownermh, edu8_33, edu8_55, edu8_91, edu8_mh

Ordered logistic regression Number of obs = 186 LR chi2(7) = 12.50 Prob > chi2 = 0.0852 Log likelihood = -176.26321 Pseudo R2 = 0.0342

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.495312 .5202306 1.16 0.248 .7561228 2.957137 greenhouse_0~1 | .7582713 .2786012 -0.75 0.451 .369045 1.558009 market2 | .5035222 .3213598 -1.08 0.282 .1441324 1.759039 markgreen | 2.576892 2.154071 1.13 0.257 .5006811 13.26268 | 610 owner | 2 | 1.078331 .4019087 0.20 0.840 .5193952 2.238755 3 | .4844913 .1773419 -1.98 0.048 .2364365 .9927902 | edu8 | 1.53278 .4569568 1.43 0.152 .8545102 2.749429 ------+------/cut1 | -1.329249 .6433162 -2.590125 -.068372 /cut2 | .4935394 .6336971 -.7484841 1.735563 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 11.22 Prob > chi2 = 0.0819 Log likelihood = -176.90521 Pseudo R2 = 0.0307

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.390805 .4761214 0.96 0.335 .7110011 2.720586 greenhouse_0~1 | .9163303 .3023773 -0.26 0.791 .4799173 1.749596 market2 | .850637 .3797239 -0.36 0.717 .3546242 2.040423 | owner | 2 | 1.155165 .4234518 0.39 0.694 .5631403 2.369578 3 | .4806353 .1756632 -2.00 0.045 .2348104 .9838159 | edu8 | 1.576044 .4679775 1.53 0.126 .8806826 2.820441 ------+------/cut1 | -1.359681 .6440848 -2.622064 -.0972978 /cut2 | .4514995 .6332845 -.7897153 1.692714 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 10.20 Prob > chi2 = 0.0170 Log likelihood = -177.41539 Pseudo R2 = 0.0279

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------owner | 2 | 1.157579 .423062 0.40 0.689 .5655366 2.369411 3 | .4554309 .1630492 -2.20 0.028 .2257788 .918675 | edu8 | 1.640245 .4776807 1.70 0.089 .9268665 2.902686 ------+------/cut1 | -1.865491 .3310215 -2.514281 -1.216701 /cut2 | -.0622669 .2909864 -.6325898 .5080559 ------

Ordered logistic regression Number of obs = 186 LR chi2(9) = 22.38 Prob > chi2 = 0.0078 Log likelihood = -171.32527 Pseudo R2 = 0.0613

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.582474 .5530839 1.31 0.189 .7976945 3.139327 greenhouse_0~1 | .5772996 .2722309 -1.17 0.244 .2290918 1.454766 market2 | .7987974 .364806 -0.49 0.623 .3263608 1.955129 | owner | 2 | 1.302345 .5005817 0.69 0.492 .6131262 2.766317 3 | .5635341 .212953 -1.52 0.129 .2686959 1.181896 | 611 edu8 | 1.406465 .4357253 1.10 0.271 .766347 2.581266 wua33 | 2.452353 1.071642 2.05 0.040 1.041407 5.774915 wua55 | 4.277087 2.477209 2.51 0.012 1.374511 13.30908 wua91 | 3.841775 1.654873 3.12 0.002 1.651469 8.937031 ------+------/cut1 | -.3297095 .7364257 -1.773077 1.113658 /cut2 | 1.565122 .7425704 .1097112 3.020534 ------

Ordered logistic regression Number of obs = 186 LR chi2(9) = 22.38 Prob > chi2 = 0.0078 Log likelihood = -171.32527 Pseudo R2 = 0.0613

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.582474 .5530839 1.31 0.189 .7976945 3.139327 greenhouse_0~1 | .5772996 .2722309 -1.17 0.244 .2290918 1.454766 market2 | .7987974 .364806 -0.49 0.623 .3263608 1.955129 | owner | 2 | 1.302345 .5005817 0.69 0.492 .6131262 2.766317 3 | .5635341 .212953 -1.52 0.129 .2686959 1.181896 | edu8 | 1.406465 .4357253 1.10 0.271 .766347 2.581266 wua33 | .6383387 .2657177 -1.08 0.281 .2823099 1.443365 wua55 | 1.11331 .5664621 0.21 0.833 .4106931 3.017971 wuamh | .2602964 .1121246 -3.12 0.002 .111894 .6055214 ------+------/cut1 | -1.675644 .706622 -3.060598 -.2906904 /cut2 | .219188 .6906368 -1.134435 1.572811 ------

Ordered logistic regression Number of obs = 186 LR chi2(9) = 22.38 Prob > chi2 = 0.0078 Log likelihood = -171.32527 Pseudo R2 = 0.0613

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.582474 .5530839 1.31 0.189 .7976945 3.139327 greenhouse_0~1 | .5772996 .2722309 -1.17 0.244 .2290918 1.454766 market2 | .7987974 .364806 -0.49 0.623 .3263608 1.955129 | owner | 2 | 1.302345 .5005817 0.69 0.492 .6131262 2.766317 3 | .5635341 .212953 -1.52 0.129 .2686959 1.181896 | edu8 | 1.406465 .4357253 1.10 0.271 .766347 2.581266 wua33 | .57337 .3270721 -0.98 0.330 .1874464 1.753851 wua91 | .8982222 .4570234 -0.21 0.833 .3313484 2.434908 wuamh | .233804 .1354149 -2.51 0.012 .0751367 .7275315 ------+------/cut1 | -1.782982 .7662742 -3.284852 -.2811119 /cut2 | .1118502 .7496839 -1.357503 1.581204 ------

Ordered logistic regression Number of obs = 186 LR chi2(9) = 22.38 Prob > chi2 = 0.0078 Log likelihood = -171.32527 Pseudo R2 = 0.0613

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] 612 ------+------dunums_log | 1.582474 .5530839 1.31 0.189 .7976945 3.139327 greenhouse_0~1 | .5772996 .2722309 -1.17 0.244 .2290918 1.454766 market2 | .7987974 .364806 -0.49 0.623 .3263608 1.955129 | owner | 2 | 1.302345 .5005817 0.69 0.492 .6131262 2.766317 3 | .5635341 .212953 -1.52 0.129 .2686959 1.181896 | edu8 | 1.406465 .4357253 1.10 0.271 .766347 2.581266 wua55 | 1.744075 .9948866 0.98 0.330 .5701738 5.334858 wua91 | 1.566566 .652106 1.08 0.281 .6928255 3.542206 wuamh | .4077716 .1781902 -2.05 0.040 .1731627 .9602394 ------+------/cut1 | -1.226758 .672929 -2.545674 .092159 /cut2 | .6680743 .6632489 -.6318696 1.968018 ------

Ordered logistic regression Number of obs = 186 LR chi2(11) = 26.47 Prob > chi2 = 0.0055 Log likelihood = -169.27715 Pseudo R2 = 0.0725

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.44938 .514694 1.05 0.296 .7226187 2.907068 greenhouse_0~1 | .5561445 .2613191 -1.25 0.212 .2214257 1.396841 market2 | .8011215 .3666127 -0.48 0.628 .3267141 1.964395 | owner | 2 | 1.622604 .6500453 1.21 0.227 .739955 3.558113 3 | .9084087 .4082667 -0.21 0.831 .3764655 2.191984 | edu8 | 1.170485 .4232534 0.44 0.663 .5761917 2.377742 wua33 | 10.39478 10.63715 2.29 0.022 1.398834 77.2439 wua55 | 4.922498 2.874461 2.73 0.006 1.567227 15.46106 wua91 | 4.051666 1.745008 3.25 0.001 1.741929 9.424031 owner33 | .453296 .1886346 -1.90 0.057 .2005224 1.02471 edu8_33 | 1.886904 1.299817 0.92 0.357 .4890903 7.279651 ------+------/cut1 | -.3127107 .744712 -1.772319 1.146898 /cut2 | 1.616263 .7502331 .1458331 3.086693 ------

Ordered logistic regression Number of obs = 186 LR chi2(11) = 23.53 Prob > chi2 = 0.0149 Log likelihood = -170.75118 Pseudo R2 = 0.0644

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.587127 .5565145 1.32 0.188 .7982592 3.15558 greenhouse_0~1 | .5876812 .2779867 -1.12 0.261 .2325434 1.485182 market2 | .7881853 .3635724 -0.52 0.606 .3191449 1.946564 | owner | 2 | 1.193296 .4906179 0.43 0.667 .5330661 2.671257 3 | .4911687 .2187211 -1.60 0.110 .2052033 1.175647 | edu8 | 1.272742 .4504393 0.68 0.496 .6360477 2.546779 wua33 | 2.488869 1.091189 2.08 0.038 1.05393 5.877496 wua55 | 1.74968 1.860371 0.53 0.599 .2177244 14.06081 wua91 | 3.82877 1.65308 3.11 0.002 1.642673 8.924162 owner55 | 1.333566 .555084 0.69 0.489 .589807 3.01522 613 edu8_55 | 1.692611 1.214755 0.73 0.463 .4146352 6.909527 ------+------/cut1 | -.4568641 .7537483 -1.934183 1.020455 /cut2 | 1.446021 .7581049 -.0398376 2.931879 ------

Ordered logistic regression Number of obs = 186 LR chi2(11) = 22.54 Prob > chi2 = 0.0205 Log likelihood = -171.24574 Pseudo R2 = 0.0617

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.589467 .556708 1.32 0.186 .8000547 3.157791 greenhouse_0~1 | .5824993 .2750358 -1.14 0.252 .2308808 1.469613 market2 | .7897732 .3638002 -0.51 0.608 .3201886 1.948045 | owner | 2 | 1.343636 .5359523 0.74 0.459 .6148266 2.936369 3 | .6023694 .2599679 -1.17 0.240 .2585265 1.403527 | edu8 | 1.446333 .5323271 1.00 0.316 .7030416 2.97547 wua33 | 2.441761 1.068257 2.04 0.041 1.035875 5.755713 wua55 | 4.261186 2.478735 2.49 0.013 1.362655 13.32524 wua91 | 5.39986 5.308856 1.72 0.086 .7861879 37.08845 owner91 | .8654107 .3740153 -0.33 0.738 .3709778 2.018816 edu8_91 | .9091502 .6165321 -0.14 0.888 .2406583 3.434554 ------+------/cut1 | -.2729381 .7512503 -1.745362 1.199485 /cut2 | 1.621898 .7575008 .137224 3.106573 ------

Ordered logistic regression Number of obs = 186 LR chi2(10) = 25.62 Prob > chi2 = 0.0043 Log likelihood = -169.70548 Pseudo R2 = 0.0702

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 1.465062 .5207704 1.07 0.283 .7299422 2.940518 greenhouse_0~1 | .5531124 .2608876 -1.26 0.209 .2194449 1.394123 market2 | .8063299 .3683933 -0.47 0.638 .3293204 1.974272 | owner | 2 | 1.602778 .641779 1.18 0.239 .7312029 3.513247 3 | .8657629 .3869472 -0.32 0.747 .3605453 2.078921 | edu8 | 1.389075 .4319613 1.06 0.291 .755139 2.555196 wua33 | 11.96337 12.08973 2.46 0.014 1.650675 86.70529 wua55 | 4.690224 2.730245 2.65 0.008 1.49864 14.67878 wua91 | 4.003749 1.725863 3.22 0.001 1.720071 9.319386 owner33 | .488462 .1978604 -1.77 0.077 .2208189 1.080502 ------+------/cut1 | -.242696 .7422934 -1.697564 1.212172 /cut2 | 1.677811 .7490169 .2097651 3.145857 ------

Ordered logistic regression Number of obs = 186 LR chi2(3) = 10.20 Prob > chi2 = 0.0170 Log likelihood = -177.41539 Pseudo R2 = 0.0279

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] 614 ------+------owner | 2 | 1.157579 .423062 0.40 0.689 .5655366 2.369411 3 | .4554309 .1630492 -2.20 0.028 .2257788 .918675 | edu8 | 1.640245 .4776807 1.70 0.089 .9268665 2.902686 ------+------/cut1 | -1.865491 .3310215 -2.514281 -1.216701 /cut2 | -.0622669 .2909864 -.6325898 .5080559 ------

Ordered logistic regression Number of obs = 186 LR chi2(6) = 19.31 Prob > chi2 = 0.0037 Log likelihood = -172.86031 Pseudo R2 = 0.0529

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------owner | 2 | 1.216212 .4599448 0.52 0.605 .5795667 2.552202 3 | .4947814 .1817226 -1.92 0.055 .2408715 1.016346 | edu8 | 1.537069 .4640711 1.42 0.154 .8505488 2.777715 wua33 | 2.355356 1.009005 2.00 0.046 1.017215 5.453817 wua55 | 2.601678 1.16233 2.14 0.032 1.08385 6.245076 wua91 | 3.428263 1.446218 2.92 0.003 1.499662 7.83709 ------+------/cut1 | -1.089671 .4265029 -1.925601 -.2537407 /cut2 | .7841319 .4201053 -.0392594 1.607523 ------

Ordered logistic regression Number of obs = 186 LR chi2(7) = 22.79 Prob > chi2 = 0.0019 Log likelihood = -171.11905 Pseudo R2 = 0.0624

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------owner | 2 | 1.503953 .5922443 1.04 0.300 .6950849 3.254096 3 | .7791567 .3431213 -0.57 0.571 .3286827 1.847025 | edu8 | 1.491165 .4536323 1.31 0.189 .8214453 2.706902 wua33 | 12.17967 12.27614 2.48 0.013 1.689242 87.81712 wua55 | 2.790988 1.253716 2.28 0.022 1.157169 6.731612 wua91 | 3.574863 1.509412 3.02 0.003 1.562633 8.178275 owner33 | .4809063 .1923141 -1.83 0.067 .2196168 1.053066 ------+------/cut1 | -.859238 .4423338 -1.726196 .0077204 /cut2 | 1.043124 .4423783 .1760783 1.910169 ------

Results 41: Ordered logistic regressions of catcom as a function of adequate, reliable, tour, punish, conflict, help1, help3, owner, wua33, rel33, owner33, wua91, conflict91, sys1

Ordered logistic regression Number of obs = 115 LR chi2(12) = 54.74 Prob > chi2 = 0.0000 Log likelihood = -86.460379 Pseudo R2 = 0.2405

615 ------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 5.171302 3.44501 2.47 0.014 1.401354 19.08324 reliable | 1.519768 .743032 0.86 0.392 .582928 3.962228 | tour | 2 | 1.294994 .964526 0.35 0.729 .3008037 5.575094 3 | .9224864 .6728821 -0.11 0.912 .2208384 3.853411 | punish | 2 | 1.73e+07 2.42e+10 0.01 0.990 0 . 3 | 1.65e+07 2.31e+10 0.01 0.991 0 . | conflict | 2 | 2.880026 2.003128 1.52 0.128 .7368266 11.25713 3 | 3.637246 1.758654 2.67 0.008 1.409958 9.38295 | help1 | 3.651901 2.006833 2.36 0.018 1.243819 10.72212 help3 | 2.472351 1.464508 1.53 0.126 .7742785 7.894471 | owner | 2 | 1.429461 .8038977 0.64 0.525 .4747609 4.303973 3 | .6390868 .3732202 -0.77 0.443 .2034542 2.007488 ------+------/cut1 | 16.5206 1399.4 -2726.254 2759.295 /cut2 | 18.56367 1399.401 -2724.211 2761.338 ------Note: 5 observations completely determined. Standard errors questionable.

Ordered logistic regression Number of obs = 115 LR chi2(11) = 47.63 Prob > chi2 = 0.0000 Log likelihood = -90.016567 Pseudo R2 = 0.2092

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 2.157127 1.000304 1.66 0.097 .8692728 5.352978 | tour | 2 | 1.032403 .7695737 0.04 0.966 .2395228 4.449916 3 | .7992131 .5871928 -0.31 0.760 .1893534 3.373277 | punish | 2 | 2.69e+07 3.68e+10 0.01 0.990 0 . 3 | 2.60e+07 3.55e+10 0.01 0.990 0 . | conflict | 2 | 4.159286 2.708539 2.19 0.029 1.160684 14.90472 3 | 4.180642 2.003426 2.99 0.003 1.634311 10.69428 | help1 | 3.044759 1.610354 2.11 0.035 1.079836 8.585152 help3 | 2.69006 1.537844 1.73 0.083 .8773041 8.248477 | owner | 2 | 1.242592 .6813695 0.40 0.692 .4242055 3.639829 3 | .7436014 .424583 -0.52 0.604 .2428396 2.276989 ------+------/cut1 | 16.84655 1368.191 -2664.759 2698.452 /cut2 | 18.81319 1368.191 -2662.793 2700.419 ------Note: 5 observations completely determined. Standard errors questionable.

Ordered logistic regression Number of obs = 115 LR chi2(10) = 48.18 616 Prob > chi2 = 0.0000 Log likelihood = -89.740041 Pseudo R2 = 0.2116

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 6.448265 4.352741 2.76 0.006 1.717356 24.2117 reliable | 1.264521 .604363 0.49 0.623 .495569 3.22662 | tour | 2 | 2.633963 1.773446 1.44 0.150 .7038761 9.856511 3 | 1.759803 1.105428 0.90 0.368 .5137811 6.027676 | conflict | 2 | 2.397235 1.574274 1.33 0.183 .6617943 8.683568 3 | 3.631255 1.735474 2.70 0.007 1.42313 9.265498 | help1 | 3.709806 2.005502 2.43 0.015 1.285867 10.70302 help3 | 2.481978 1.448951 1.56 0.119 .7904527 7.793275 | owner | 2 | 1.513038 .8487578 0.74 0.460 .5039154 4.542993 3 | .4972512 .2842964 -1.22 0.222 .1621486 1.52489 ------+------/cut1 | .425779 .7228263 -.9909345 1.842492 /cut2 | 2.393812 .7639543 .8964893 3.891135 ------Ordered logistic regression Number of obs = 115 LR chi2(12) = 55.08 Prob > chi2 = 0.0000 Log likelihood = -86.293538 Pseudo R2 = 0.2419

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 7.459045 5.103528 2.94 0.003 1.951127 28.5155 reliable | .5365335 .3179459 -1.05 0.293 .1679504 1.714008 rel33 | 11.05509 10.56315 2.51 0.012 1.699176 71.92603 | tour | 2 | 2.433966 1.691948 1.28 0.201 .623175 9.506462 3 | 2.004406 1.285118 1.08 0.278 .5704807 7.042561 | conflict | 2 | 2.767376 1.911986 1.47 0.141 .714449 10.71927 3 | 3.211492 1.56962 2.39 0.017 1.2322 8.370137 | help1 | 4.124847 2.323173 2.52 0.012 1.367726 12.43989 help3 | 2.3116 1.347931 1.44 0.151 .7371627 7.248729 | owner | 2 | 1.382364 .8108896 0.55 0.581 .4378356 4.364496 3 | .5015283 .2960531 -1.17 0.242 .1576992 1.595002 | wua33 | .3043017 .2279875 -1.59 0.112 .0700763 1.32141 ------+------/cut1 | -.0132196 .7823196 -1.546538 1.520099 /cut2 | 2.076686 .8099436 .4892252 3.664146 ------

Ordered logistic regression Number of obs = 115 LR chi2(13) = 55.08 Prob > chi2 = 0.0000 Log likelihood = -86.290979 Pseudo R2 = 0.2419

------617 catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 7.457602 5.101299 2.94 0.003 1.951386 28.50068 reliable | .5407718 .3256495 -1.02 0.307 .1661214 1.760364 rel33 | 10.77696 10.9799 2.33 0.020 1.46307 79.38293 | tour | 2 | 2.438152 1.69544 1.28 0.200 .6239549 9.527271 3 | 2.010271 1.290505 1.09 0.277 .5712436 7.074375 | conflict | 2 | 2.769517 1.913198 1.47 0.140 .7151369 10.72553 3 | 3.233292 1.609832 2.36 0.018 1.218535 8.579298 | help1 | 4.106539 2.325549 2.49 0.013 1.353434 12.4599 help3 | 2.299835 1.35086 1.42 0.156 .7273177 7.272256 | owner | 2 | 1.408212 .9024427 0.53 0.593 .4010343 4.944866 3 | .5183698 .3881865 -0.88 0.380 .1194562 2.24942 | owner33 | .958428 .5687616 -0.07 0.943 .2995225 3.066829 wua33 | .3395007 .5780736 -0.63 0.526 .0120638 9.55424 ------+------/cut1 | .0116419 .8555124 -1.665132 1.688415 /cut2 | 2.101945 .883182 .37094 3.83295 ------

Ordered logistic regression Number of obs = 115 LR chi2(12) = 49.33 Prob > chi2 = 0.0000 Log likelihood = -89.165936 Pseudo R2 = 0.2167

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 6.938965 4.769845 2.82 0.005 1.80376 26.69382 reliable | 1.226973 .5858387 0.43 0.668 .4812986 3.127918 | tour | 2 | 2.636664 1.777214 1.44 0.150 .7035774 9.880928 3 | 1.86456 1.16026 1.00 0.317 .55068 6.313257 | conflict | 2 | 2.380307 1.617387 1.28 0.202 .6284239 9.015984 3 | 3.911662 1.893614 2.82 0.005 1.514605 10.10237 | help1 | 3.442162 1.871397 2.27 0.023 1.185931 9.990868 help3 | 2.323325 1.362423 1.44 0.151 .7361333 7.332695 | owner | 2 | 1.997138 1.229107 1.12 0.261 .5977913 6.672164 3 | .7531626 .5432432 -0.39 0.694 .1832027 3.096319 | owner33 | .5984425 .3218841 -0.95 0.340 .2085388 1.717347 wua33 | 3.908956 5.050628 1.06 0.291 .3106363 49.18914 ------+------/cut1 | .7493024 .776244 -.7721079 2.270713 /cut2 | 2.735642 .8236294 1.121358 4.349926 ------Ordered logistic regression Number of obs = 115 LR chi2(13) = 57.03 Prob > chi2 = 0.0000 Log likelihood = -85.318125 Pseudo R2 = 0.2505

------618 catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 7.389633 5.050934 2.93 0.003 1.935588 28.21194 reliable | .5441391 .3293966 -1.01 0.315 .1661238 1.782329 rel33 | 11.53485 10.99878 2.56 0.010 1.779792 74.75747 | tour | 2 | 2.353966 1.63646 1.23 0.218 .6026308 9.194947 3 | 1.608993 1.064805 0.72 0.472 .4397878 5.886608 | conflict | 2 | 2.761987 1.885965 1.49 0.137 .7244299 10.53045 3 | 3.204007 1.586174 2.35 0.019 1.214221 8.45452 | help1 | 3.548671 2.043785 2.20 0.028 1.147713 10.97231 help3 | 1.609183 1.036569 0.74 0.460 .4552988 5.687408 | owner | 2 | 1.211347 .7222576 0.32 0.748 .3764831 3.897551 3 | .4677386 .2793599 -1.27 0.203 .1450834 1.507957 | wua33 | .4185246 .3248463 -1.12 0.262 .0914206 1.916012 wua91 | 2.292218 1.369833 1.39 0.165 .7105204 7.394949 ------+------/cut1 | -.0495913 .7848417 -1.587853 1.48867 /cut2 | 2.079467 .8146191 .4828429 3.676091 ------

Ordered logistic regression Number of obs = 115 LR chi2(14) = 57.03 Prob > chi2 = 0.0000 Log likelihood = -85.318113 Pseudo R2 = 0.2505

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 7.390753 5.056795 2.92 0.003 1.933266 28.25437 reliable | .5440967 .329472 -1.01 0.315 .1660505 1.782839 rel33 | 11.53122 11.02034 2.56 0.011 1.771679 75.0525 | tour | 2 | 2.353178 1.64384 1.23 0.221 .598464 9.252765 3 | 1.607988 1.08383 0.70 0.481 .4290898 6.025833 | conflict | 2 | 2.763297 1.905759 1.47 0.141 .7151225 10.67762 3 | 3.209249 1.917252 1.95 0.051 .995138 10.3496 | conflict91 | .9974814 .5144428 -0.00 0.996 .3629984 2.740974 help1 | 3.548507 2.0439 2.20 0.028 1.147528 10.97307 help3 | 1.609424 1.037876 0.74 0.461 .4547293 5.696238 | owner | 2 | 1.211429 .722482 0.32 0.748 .3764013 3.898922 3 | .4676815 .2795729 -1.27 0.204 .1449155 1.509334 | wua33 | .4187086 .3271523 -1.11 0.265 .0905393 1.936363 wua91 | 2.304893 2.941899 0.65 0.513 .1888868 28.12547 ------+------/cut1 | -.0493355 .7865851 -1.591014 1.492343 /cut2 | 2.0798 .8174616 .4776047 3.681995 ------

Ordered logistic regression Number of obs = 115 LR chi2(8) = 49.56 Prob > chi2 = 0.0000 619 Log likelihood = -89.053359 Pseudo R2 = 0.2177

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 5.73585 3.778883 2.65 0.008 1.576923 20.86339 reliable | .6277979 .3403372 -0.86 0.390 .2169565 1.816632 rel33 | 13.3353 12.29647 2.81 0.005 2.18832 81.26337 | conflict | 2 | 3.249176 2.252522 1.70 0.089 .8349676 12.64378 3 | 3.567458 1.681215 2.70 0.007 1.416505 8.984621 | help1 | 4.776387 2.550275 2.93 0.003 1.677313 13.60144 help3 | 2.725008 1.500116 1.82 0.069 .9263638 8.015929 wua33 | .2249425 .1560538 -2.15 0.032 .0577498 .8761779 ------+------/cut1 | -.2808456 .4757686 -1.213335 .6516437 /cut2 | 1.697821 .5139699 .6904585 2.705184 ------

Ordered logistic regression Number of obs = 115 LR chi2(13) = 55.66 Prob > chi2 = 0.0000 Log likelihood = -86.001052 Pseudo R2 = 0.2445

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 7.512494 5.18165 2.92 0.003 1.94392 29.03287 reliable | .5404044 .3210921 -1.04 0.300 .16864 1.731718 rel33 | 9.711827 9.388809 2.35 0.019 1.460194 64.59387 | tour | 2 | 2.514672 1.747894 1.33 0.185 .6439165 9.820488 3 | 2.125444 1.367774 1.17 0.241 .6021178 7.502707 | conflict | 2 | 2.487459 1.756232 1.29 0.197 .6234169 9.925068 3 | 3.060523 1.511936 2.26 0.024 1.162229 8.059343 | help1 | 3.949002 2.24073 2.42 0.015 1.298677 12.00808 help3 | 2.271295 1.327306 1.40 0.160 .7225132 7.140049 | owner | 2 | 1.576304 .9643174 0.74 0.457 .4752372 5.228411 3 | .6032189 .3852735 -0.79 0.429 .1725107 2.109279 | wua33 | .2995086 .2243992 -1.61 0.108 .0689713 1.30062 sys1 | 1.455989 .7140805 0.77 0.444 .5567905 3.807364 ------+------/cut1 | .237428 .8511531 -1.430801 1.905657 /cut2 | 2.337904 .8821415 .6089384 4.066869 ------

Ordered logistic regression Number of obs = 115 LR chi2(9) = 50.44 Prob > chi2 = 0.0000 Log likelihood = -88.61267 Pseudo R2 = 0.2215

------catcom | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 5.799507 3.843111 2.65 0.008 1.582458 21.25444 reliable | .5966902 .3273411 -0.94 0.347 .203603 1.748693 rel33 | 11.8099 10.97723 2.66 0.008 1.910114 73.01849 620 | conflict | 2 | 2.899342 2.033232 1.52 0.129 .7334563 11.46106 3 | 3.366786 1.602515 2.55 0.011 1.324532 8.557922 | help1 | 4.734463 2.546587 2.89 0.004 1.649772 13.58681 help3 | 2.702648 1.492629 1.80 0.072 .9155556 7.978007 wua33 | .2168558 .1504478 -2.20 0.028 .0556716 .8447109 sys1 | 1.517888 .6751121 0.94 0.348 .6348196 3.629353 ------+------/cut1 | -.2012204 .4837976 -1.149446 .7470055 /cut2 | 1.795449 .5254499 .7655866 2.825312 ------

Results 42: Logistic regressions of steal1 as a function of wua33, wua55, wua91 and wuamh.

Logistic regression Number of obs = 174 LR chi2(3) = 1.00 Prob > chi2 = 0.8021 Log likelihood = -65.544668 Pseudo R2 = 0.0075

------steal1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.0051151 .635777 -0.01 0.994 -1.251215 1.240985 wua55 | .2787134 .6802509 0.41 0.682 -1.054554 1.611981 wua91 | .5389964 .6756831 0.80 0.425 -.7853181 1.863311 _cons | 1.722767 .4855042 3.55 0.000 .7711959 2.674337 ------

Logistic regression Number of obs = 174 LR chi2(3) = 1.00 Prob > chi2 = 0.8021 Log likelihood = -65.544668 Pseudo R2 = 0.0075

Logistic regression Number of obs = 174 LR chi2(3) = 1.00 Prob > chi2 = 0.8021 Log likelihood = -65.544668 Pseudo R2 = 0.0075

Logistic regression Number of obs = 174 LR chi2(3) = 1.00 Prob > chi2 = 0.8021 Log likelihood = -65.544668 Pseudo R2 = 0.0075 621

Results 43: Logistic regressions of steal1 as a function of adequate, reliable, wua33, wua55, wua91, wuamh, rel33, rel55, rel91, relmh

Logistic regression Number of obs = 174 LR chi2(5) = 16.40 Prob > chi2 = 0.0058 Log likelihood = -57.841568 Pseudo R2 = 0.1242

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2498412 .1301077 -2.66 0.008 .0900308 .6933254 reliable | .3171226 .2001643 -1.82 0.069 .0920361 1.092688 wua33 | 1.34848 .9800835 0.41 0.681 .3244784 5.604064 wua55 | 2.792526 2.2 1.30 0.192 .5962191 13.07942 wua91 | 3.762139 2.924178 1.70 0.088 .8200227 17.26012 _cons | 12.20597 7.212622 4.23 0.000 3.833445 38.86469 ------

Logistic regression Number of obs = 174 LR chi2(5) = 16.40 Prob > chi2 = 0.0058 Log likelihood = -57.841568 Pseudo R2 = 0.1242

Logistic regression Number of obs = 174 LR chi2(5) = 16.40 Prob > chi2 = 0.0058 Log likelihood = -57.841568 Pseudo R2 = 0.1242

Logistic regression Number of obs = 174 LR chi2(5) = 16.40 Prob > chi2 = 0.0058 622 Log likelihood = -57.841568 Pseudo R2 = 0.1242

Logistic regression Number of obs = 174 LR chi2(4) = 15.21 Prob > chi2 = 0.0043 Log likelihood = -58.436945 Pseudo R2 = 0.1152

Logistic regression Number of obs = 174 LR chi2(6) = 16.79 Prob > chi2 = 0.0101 Log likelihood = -57.649842 Pseudo R2 = 0.1271

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2425245 .12703 -2.70 0.007 .0868784 .6770171 reliable | .2623431 .1868416 -1.88 0.060 .0649593 1.059493 wua33 | 1.447332 1.078993 0.50 0.620 .3357326 6.239399 wua55 | 3.095434 2.513729 1.39 0.164 .6302036 15.20415 wua91 | 1.984586 2.370058 0.57 0.566 .1910457 20.61592 rel91 | 2.422353 3.323827 0.64 0.519 .1645367 35.66252 _cons | 13.30395 8.257068 4.17 0.000 3.941703 44.90317 ------

Logistic regression Number of obs = 174 LR chi2(3) = 14.37 Prob > chi2 = 0.0024 Log likelihood = -58.859075 Pseudo R2 = 0.1088

Results 44: Logistic regressions of steal1 as a function of tour, punish, conflict, help1, help3

Logistic regression Number of obs = 94 LR chi2(6) = 2.46 623 Prob > chi2 = 0.8724 Log likelihood = -30.623068 Pseudo R2 = 0.0387

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 1 | 1 (empty) 2 | .7965715 .6286599 -0.29 0.773 .1696096 3.741098 3 | 1 (omitted) | punish | 1 | 1 (empty) 2 | 1.616493 1.180282 0.66 0.511 .3864282 6.762055 3 | 1 (omitted) | conflict | 2 | .4613247 .4972526 -0.72 0.473 .0557845 3.815045 3 | .4079789 .3632993 -1.01 0.314 .0712287 2.336794 | help1 | 1.95806 1.702134 0.77 0.440 .3563521 10.75901 help3 | 2.421903 2.208899 0.97 0.332 .4053382 14.47091 _cons | 7.558253 7.022897 2.18 0.029 1.223238 46.70161 ------

Results 45: Logistic regression of steal1 as a function of dunums_log, owner, edu8, wua33, wua55, wua91 and wuamh

Logistic regression Number of obs = 174 LR chi2(4) = 13.56 Prob > chi2 = 0.0089 Log likelihood = -59.26425 Pseudo R2 = 0.1026

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 4.743915 2.751297 2.68 0.007 1.522198 14.78436 | owner | 2 | 3.388102 2.214538 1.87 0.062 .9410061 12.19889 3 | 2.527092 1.436608 1.63 0.103 .8293306 7.700417 | edu8 | 1.332894 .6526845 0.59 0.557 .5104859 3.480227 _cons | .2444436 .2468337 -1.40 0.163 .0337796 1.768899 ------

Logistic regression Number of obs = 174 LR chi2(7) = 14.38 Prob > chi2 = 0.0448 Log likelihood = -58.852351 Pseudo R2 = 0.1089

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 4.744193 2.759328 2.68 0.007 1.517346 14.83338 | owner | 2 | 3.646719 2.469308 1.91 0.056 .9672236 13.74921 3 | 2.753393 1.622946 1.72 0.086 .867242 8.741706 | edu8 | 1.248236 .6289706 0.44 0.660 .4649294 3.351248 wua33 | 1.373688 .9535812 0.46 0.647 .3523744 5.355152 wua55 | 1.52549 1.13062 0.57 0.569 .3568941 6.520475 wua91 | 1.915159 1.38115 0.90 0.368 .4659576 7.871608 624 _cons | .1657433 .1888444 -1.58 0.115 .0177662 1.546241 ------

Logistic regression Number of obs = 174 LR chi2(7) = 14.38 Prob > chi2 = 0.0448 Log likelihood = -58.852351 Pseudo R2 = 0.1089

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 4.744193 2.759328 2.68 0.007 1.517346 14.83338 | owner | 2 | 3.646719 2.469308 1.91 0.056 .9672236 13.74921 3 | 2.753393 1.622946 1.72 0.086 .867242 8.741706 | edu8 | 1.248236 .6289706 0.44 0.660 .4649294 3.351248 wua33 | .7172708 .491912 -0.48 0.628 .1870337 2.75072 wua55 | .7965341 .5598068 -0.32 0.746 .2008987 3.158142 wuamh | .5221498 .3765572 -0.90 0.368 .1270388 2.146118 _cons | .3174249 .3459294 -1.05 0.292 .0374975 2.687075 ------

Logistic regression Number of obs = 174 LR chi2(7) = 14.38 Prob > chi2 = 0.0448 Log likelihood = -58.852351 Pseudo R2 = 0.1089

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 4.744193 2.759328 2.68 0.007 1.517346 14.83338 | owner | 2 | 3.646719 2.469308 1.91 0.056 .9672236 13.74921 3 | 2.753393 1.622946 1.72 0.086 .867242 8.741706 | edu8 | 1.248236 .6289706 0.44 0.660 .4649294 3.351248 wua33 | .9004897 .6207044 -0.15 0.879 .2332107 3.477034 wua91 | 1.255439 .8823266 0.32 0.746 .3166419 4.977632 wuamh | .6555272 .4858454 -0.57 0.569 .1533631 2.801952 _cons | .2528398 .284841 -1.22 0.222 .0277913 2.300282 ------

Logistic regression Number of obs = 174 LR chi2(7) = 14.38 Prob > chi2 = 0.0448 Log likelihood = -58.852351 Pseudo R2 = 0.1089

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 4.744193 2.759328 2.68 0.007 1.517346 14.83338 | owner | 2 | 3.646719 2.469308 1.91 0.056 .9672236 13.74921 3 | 2.753393 1.622946 1.72 0.086 .867242 8.741706 | edu8 | 1.248236 .6289706 0.44 0.660 .4649294 3.351248 wua55 | 1.110507 .7654684 0.15 0.879 .2876014 4.287967 wua91 | 1.394174 .9561392 0.48 0.628 .3635412 5.346629 wuamh | .7279674 .5053375 -0.46 0.647 .1867361 2.837891 _cons | .2276796 .2394261 -1.41 0.159 .0289873 1.788303 ------

625

Results 46: Logistic regressions of steal1 as a function of adequate, reliable, dunums_log, owner, wua33, wua55, wua91, wuamh, owner33, owner55, owner91, ownermh, sys1, pos1, secwater, secwork, secworkwat, dunsecwork, ownsecwork, own2

Logistic regression Number of obs = 174 LR chi2(5) = 22.54 Prob > chi2 = 0.0004 Log likelihood = -54.771918 Pseudo R2 = 0.1707

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .3013324 .1618213 -2.23 0.026 .1051803 .8632913 reliable | .5275172 .3210231 -1.05 0.293 .1600419 1.738759 dunums_log | 5.220095 3.213703 2.68 0.007 1.561867 17.44668 | owner | 2 | 2.542562 1.728087 1.37 0.170 .6710278 9.633907 3 | 2.213363 1.320521 1.33 0.183 .6874085 7.126734 | _cons | .6477084 .753681 -0.37 0.709 .0662082 6.336472 ------

Logistic regression Number of obs = 174 LR chi2(8) = 25.94 Prob > chi2 = 0.0011 Log likelihood = -53.07108 Pseudo R2 = 0.1964

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2751905 .1547148 -2.30 0.022 .0914281 .8282994 reliable | .3714868 .2501071 -1.47 0.141 .0992804 1.390026 dunums_log | 4.937005 3.074041 2.56 0.010 1.457003 16.72888 | owner | 2 | 2.740027 1.93096 1.43 0.153 .6884832 10.90476 3 | 2.576632 1.611901 1.51 0.130 .7560561 8.781138 | wua33 | 1.754657 1.354078 0.73 0.466 .386648 7.962855 wua55 | 3.389509 2.858105 1.45 0.148 .649211 17.69652 wua91 | 3.809008 3.077888 1.66 0.098 .7816176 18.5622 _cons | .3766251 .4802774 -0.77 0.444 .0309347 4.585354 ------

Logistic regression Number of obs = 174 LR chi2(8) = 25.94 Prob > chi2 = 0.0011 Log likelihood = -53.07108 Pseudo R2 = 0.1964

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2751905 .1547148 -2.30 0.022 .0914281 .8282994 reliable | .3714868 .2501071 -1.47 0.141 .0992804 1.390026 dunums_log | 4.937005 3.074041 2.56 0.010 1.457003 16.72888 | owner | 2 | 2.740027 1.93096 1.43 0.153 .6884832 10.90476 3 | 2.576632 1.611901 1.51 0.130 .7560561 8.781138 | 626 wua33 | .4606598 .3380694 -1.06 0.291 .1093201 1.941158 wua55 | .8898666 .6548426 -0.16 0.874 .2103466 3.764561 wuamh | .2625355 .2121431 -1.66 0.098 .0538729 1.279398 _cons | 1.434568 1.881793 0.28 0.783 .1096879 18.76219 ------

Logistic regression Number of obs = 174 LR chi2(8) = 25.94 Prob > chi2 = 0.0011 Log likelihood = -53.07108 Pseudo R2 = 0.1964

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2751905 .1547148 -2.30 0.022 .0914281 .8282994 reliable | .3714868 .2501071 -1.47 0.141 .0992804 1.390026 dunums_log | 4.937005 3.074041 2.56 0.010 1.457003 16.72888 | owner | 2 | 2.740027 1.93096 1.43 0.153 .6884832 10.90476 3 | 2.576632 1.611901 1.51 0.130 .7560561 8.781138 | wua33 | .5176729 .3768651 -0.90 0.366 .1242745 2.156398 wua91 | 1.123764 .826965 0.16 0.874 .2656352 4.754059 wuamh | .295028 .2487738 -1.45 0.148 .0565083 1.540331 _cons | 1.276574 1.693963 0.18 0.854 .0947405 17.2011 ------

Logistic regression Number of obs = 174 LR chi2(8) = 25.94 Prob > chi2 = 0.0011 Log likelihood = -53.07108 Pseudo R2 = 0.1964

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2751905 .1547148 -2.30 0.022 .0914281 .8282994 reliable | .3714868 .2501071 -1.47 0.141 .0992804 1.390026 dunums_log | 4.937005 3.074041 2.56 0.010 1.457003 16.72888 | owner | 2 | 2.740027 1.93096 1.43 0.153 .6884832 10.90476 3 | 2.576632 1.611901 1.51 0.130 .7560561 8.781138 | wua55 | 1.931722 1.40629 0.90 0.366 .4637364 8.046702 wua91 | 2.170799 1.593108 1.06 0.291 .5151565 9.147452 wuamh | .5699119 .4398039 -0.73 0.466 .1255831 2.586332 _cons | .6608479 .820244 -0.33 0.739 .0580217 7.526835 ------

Logistic regression Number of obs = 174 LR chi2(6) = 23.74 Prob > chi2 = 0.0006 Log likelihood = -54.171044 Pseudo R2 = 0.1798

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2750405 .1496472 -2.37 0.018 .0946815 .7989658 reliable | .524418 .319181 -1.06 0.289 .1590756 1.728827 dunums_log | 5.101262 3.174321 2.62 0.009 1.506632 17.27221 | owner | 2 | 2.38513 1.636854 1.27 0.205 .6213755 9.155244 3 | 2.375631 1.450461 1.42 0.156 .7179116 7.861169 | 627 wua91 | 1.888194 1.129668 1.06 0.288 .5845073 6.099626 _cons | .5795568 .6916604 -0.46 0.648 .0558787 6.010982 ------

Logistic regression Number of obs = 174 LR chi2(6) = 24.64 Prob > chi2 = 0.0004 Log likelihood = -53.721945 Pseudo R2 = 0.1866

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .3166629 .1708437 -2.13 0.033 .1099924 .911657 reliable | .3784972 .2527216 -1.46 0.146 .1022627 1.400902 dunums_log | 5.352411 3.317577 2.71 0.007 1.588366 18.03634 | owner | 2 | 2.83018 1.964548 1.50 0.134 .7260373 11.03238 3 | 2.271372 1.368975 1.36 0.173 .6970468 7.40141 | wuamh | .3659368 .2495654 -1.47 0.140 .0961379 1.392892 _cons | .8992556 1.082512 -0.09 0.930 .0849607 9.51806 ------

Logistic regression Number of obs = 174 LR chi2(7) = 24.37 Prob > chi2 = 0.0010 Log likelihood = -53.85678 Pseudo R2 = 0.1845

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2788546 .1545692 -2.30 0.021 .0940928 .8264167 reliable | .5622028 .3422892 -0.95 0.344 .1704712 1.854108 dunums_log | 5.315173 3.297244 2.69 0.007 1.575717 17.92902 | owner | 2 | 2.077698 1.469355 1.03 0.301 .5195291 8.309121 3 | 1.529576 1.075382 0.60 0.546 .3855905 6.067584 | wua33 | .1399126 .2020325 -1.36 0.173 .0082552 2.371287 owner33 | 2.108191 1.316522 1.19 0.232 .6199434 7.169155 _cons | .8226632 .9983757 -0.16 0.872 .0762452 8.876292 ------

Logistic regression Number of obs = 174 LR chi2(7) = 24.44 Prob > chi2 = 0.0010 Log likelihood = -53.821978 Pseudo R2 = 0.1850

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2841263 .1559601 -2.29 0.022 .0968896 .8331927 reliable | .5243054 .3282143 -1.03 0.302 .1537212 1.788278 dunums_log | 4.869584 2.960633 2.60 0.009 1.479021 16.0328 | owner | 2 | 3.334784 2.344079 1.71 0.087 .8408974 13.2249 3 | 3.393297 2.331615 1.78 0.075 .8825538 13.04676 | wua55 | 7.105023 10.63795 1.31 0.190 .3776673 133.6662 owner55 | .4560241 .3007878 -1.19 0.234 .1251832 1.66123 _cons | .5287699 .6111214 -0.55 0.581 .0548905 5.093729 ------

628 Logistic regression Number of obs = 174 LR chi2(7) = 26.22 Prob > chi2 = 0.0005 Log likelihood = -52.932358 Pseudo R2 = 0.1985

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2580869 .1410211 -2.48 0.013 .0884426 .7531308 reliable | .4704545 .2902127 -1.22 0.222 .1404205 1.576176 dunums_log | 5.055275 3.185207 2.57 0.010 1.470358 17.38066 | owner | 2 | 1.536672 1.164996 0.57 0.571 .3477446 6.790505 3 | 1.444824 1.009878 0.53 0.599 .3671616 5.685555 | wua91 | .2183304 .3331948 -1.00 0.319 .0109671 4.346461 owner91 | 3.506197 3.098817 1.42 0.156 .6202043 19.82156 _cons | .8983188 1.096873 -0.09 0.930 .082052 9.834942 ------

Logistic regression Number of obs = 174 LR chi2(7) = 25.81 Prob > chi2 = 0.0005 Log likelihood = -53.135985 Pseudo R2 = 0.1954

Logistic regression Number of obs = 174 LR chi2(5) = 20.05 Prob > chi2 = 0.0012 Log likelihood = -56.01567 Pseudo R2 = 0.1518

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | .2421671 .1494808 -2.30 0.022 .072227 .8119523 dunums_log | 5.203034 3.1555 2.72 0.007 1.584988 17.07998 | owner | 2 | 3.270194 2.211822 1.75 0.080 .8686735 12.31092 3 | 2.147337 1.255027 1.31 0.191 .6829817 6.75136 | wuamh | .3362843 .2247295 -1.63 0.103 .0907553 1.246066 _cons | .8259614 .9648161 -0.16 0.870 .0836875 8.151906 ------

Logistic regression Number of obs = 174 LR chi2(8) = 25.59 Prob > chi2 = 0.0012 Log likelihood = -53.24642 Pseudo R2 = 0.1938

629 ------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .3049937 .1669943 -2.17 0.030 .1042868 .8919741 reliable | .4098243 .2771835 -1.32 0.187 .1088654 1.542785 sys1 | .9240303 .5362074 -0.14 0.892 .2963062 2.881587 pos1 | 1.94133 1.390854 0.93 0.354 .4767179 7.905649 dunums_log | 5.816481 3.693862 2.77 0.006 1.675287 20.19442 | owner | 2 | 2.752179 1.947031 1.43 0.152 .6878498 11.01184 3 | 2.220527 1.415428 1.25 0.211 .6366132 7.745271 | wuamh | .3949017 .2917614 -1.26 0.209 .092812 1.68025 _cons | .7057713 .9742586 -0.25 0.801 .0471684 10.56032 ------

Logistic regression Number of obs = 174 LR chi2(7) = 24.88 Prob > chi2 = 0.0008 Log likelihood = -53.602207 Pseudo R2 = 0.1884

Logistic regression Number of obs = 174 LR chi2(7) = 28.18 Prob > chi2 = 0.0002 Log likelihood = -51.950466 Pseudo R2 = 0.2134

Logistic regression Number of obs = 174 LR chi2(9) = 28.52 Prob > chi2 = 0.0008 Log likelihood = -51.784315 Pseudo R2 = 0.2159

Logistic regression Number of obs = 174 LR chi2(8) = 30.69 Prob > chi2 = 0.0002 Log likelihood = -50.699524 Pseudo R2 = 0.2323

Logistic regression Number of obs = 174 LR chi2(6) = 29.26 Prob > chi2 = 0.0001 Log likelihood = -51.412117 Pseudo R2 = 0.2215

------steal1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | .2658244 .1456447 -2.42 0.016 .0908286 .777977 reliable | .2981981 .2040051 -1.77 0.077 .0780146 1.139814 secwork | 92.16038 195.7291 2.13 0.033 1.434737 5919.924 dunsecwork | .1245456 .1526926 -1.70 0.089 .0112657 1.37689 dunums_log | 7.591613 5.565576 2.76 0.006 1.804263 31.94246 wuamh | .3676305 .2555593 -1.44 0.150 .0941231 1.435908 _cons | .8539536 .9786625 -0.14 0.890 .0903495 8.071288 ------

Logistic regression Number of obs = 174 LR chi2(8) = 28.20 Prob > chi2 = 0.0004 Log likelihood = -51.943961 Pseudo R2 = 0.2135

Logistic regression Number of obs = 174 LR chi2(4) = 20.56 Prob > chi2 = 0.0004 Log likelihood = -55.76287 Pseudo R2 = 0.1557

Logistic regression Number of obs = 174 LR chi2(4) = 20.75 Prob > chi2 = 0.0004 Log likelihood = -55.665459 Pseudo R2 = 0.1571

Results 47: Logistic regressions of fair1 as a function of wua33, wua55, wua91 and wuamh

Logistic regression Number of obs = 184 LR chi2(3) = 1.80 Prob > chi2 = 0.6143 Log likelihood = -117.3385 Pseudo R2 = 0.0076

------fair1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -.1165876 .4622527 -0.25 0.801 -1.022586 .789411 wua55 | -.1071446 .4725735 -0.23 0.821 -1.033372 .8190824 wua91 | .3746934 .4695259 0.80 0.425 -.5455604 1.294947 _cons | .6061358 .3588703 1.69 0.091 -.097237 1.309509 ------

Logistic regression Number of obs = 184 LR chi2(3) = 1.80 Prob > chi2 = 0.6143 Log likelihood = -117.3385 Pseudo R2 = 0.0076

Logistic regression Number of obs = 184 LR chi2(3) = 1.80 Prob > chi2 = 0.6143 Log likelihood = -117.3385 Pseudo R2 = 0.0076

Logistic regression Number of obs = 184 LR chi2(3) = 1.80 Prob > chi2 = 0.6143 Log likelihood = -117.3385 Pseudo R2 = 0.0076

Results 48: Logistic regressions of fair1 as a function of adequate, reliable, wua33, wua55, wua91, wuamh, rel33, rel55, rel91, relmh

Logistic regression Number of obs = 184 LR chi2(5) = 19.08 Prob > chi2 = 0.0019 Log likelihood = -108.70195 Pseudo R2 = 0.0807

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.105325 .9552363 1.64 0.101 .865184 5.123062 reliable | 3.047919 1.113827 3.05 0.002 1.48916 6.238287 wua33 | .6354558 .3195317 -0.90 0.367 .2371747 1.70256 wua55 | .4879515 .2593337 -1.35 0.177 .1721808 1.382829 wua91 | .9443959 .4742562 -0.11 0.909 .3529378 2.527028 _cons | 1.276818 .4849121 0.64 0.520 .6065384 2.687818 ------

Logistic regression Number of obs = 184 LR chi2(5) = 19.08 Prob > chi2 = 0.0019 Log likelihood = -108.70195 Pseudo R2 = 0.0807

Logistic regression Number of obs = 184 LR chi2(5) = 19.08 Prob > chi2 = 0.0019 Log likelihood = -108.70195 Pseudo R2 = 0.0807

Logistic regression Number of obs = 184 LR chi2(5) = 19.08 Prob > chi2 = 0.0019 Log likelihood = -108.70195 Pseudo R2 = 0.0807

Logistic regression Number of obs = 184 LR chi2(5) = 19.08 Prob > chi2 = 0.0019 Log likelihood = -108.70195 Pseudo R2 = 0.0807

Logistic regression Number of obs = 184 LR chi2(4) = 17.05 Prob > chi2 = 0.0019 Log likelihood = -109.71702 Pseudo R2 = 0.0721

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.294094 1.036545 1.84 0.066 .9462612 5.56175 reliable | 2.194592 .870124 1.98 0.047 1.008938 4.773565 wua33 | .6452028 .3320559 -0.85 0.395 .2353003 1.769172 634 rel33 | 1.723507 1.263872 0.74 0.458 .4094629 7.254564 _cons | 1.117026 .2961557 0.42 0.676 .6643321 1.878197 ------

Logistic regression Number of obs = 184 LR chi2(4) = 17.97 Prob > chi2 = 0.0012 Log likelihood = -109.25297 Pseudo R2 = 0.0760

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.269385 1.014904 1.83 0.067 .944578 5.452285 reliable | 2.858237 1.133335 2.65 0.008 1.313965 6.217458 wua55 | .6228451 .3970534 -0.74 0.458 .1785473 2.172736 rel55 | .9481617 .7615636 -0.07 0.947 .1964252 4.576859 _cons | 1.069593 .2631514 0.27 0.785 .6603857 1.732364 ------

Logistic regression Number of obs = 184 LR chi2(4) = 17.54 Prob > chi2 = 0.0015 Log likelihood = -109.46956 Pseudo R2 = 0.0742

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.148046 .9684142 1.70 0.090 .8877532 5.197506 reliable | 2.28828 .8742795 2.17 0.030 1.082161 4.838674 wua91 | 1.171237 .5902731 0.31 0.754 .4361755 3.145056 rel91 | 1.544298 1.16983 0.57 0.566 .3498907 6.816003 _cons | .9600158 .2545958 -0.15 0.878 .5708738 1.614421 ------

Logistic regression Number of obs = 184 LR chi2(4) = 17.50 Prob > chi2 = 0.0015 Log likelihood = -109.49043 Pseudo R2 = 0.0740

Results 49: Logistic regression of fair1 as a function of tour, punish, conflict, help1, help3, wua33, wua55, wua91, wuamh note: 1.punish != 0 predicts failure perfectly 1.punish dropped and 5 obs not used note: 3.punish omitted because of collinearity Logistic regression Number of obs = 110 LR chi2(7) = 25.87 Prob > chi2 = 0.0005 Log likelihood = -58.590775 Pseudo R2 = 0.1808

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------635 tour | 2 | 2.345685 2.040352 0.98 0.327 .4264494 12.90244 3 | 4.278647 3.61941 1.72 0.086 .8151819 22.45734 | punish | 1 | 1 (empty) 2 | .4108112 .2041467 -1.79 0.073 .1551136 1.088014 3 | 1 (omitted) | conflict | 2 | 2.026417 1.491989 0.96 0.337 .4786464 8.579119 3 | 1.1511 .605762 0.27 0.789 .4103664 3.2289 | help1 | 4.688628 2.843864 2.55 0.011 1.428084 15.39352 help3 | 1.13674 .694086 0.21 0.834 .343497 3.761831 _cons | .4259413 .3651447 -1.00 0.319 .0793678 2.28589 ------note: 1.punish != 0 predicts failure perfectly 1.punish dropped and 5 obs not used note: 3.punish omitted because of collinearity Logistic regression Number of obs = 110 LR chi2(10) = 28.91 Prob > chi2 = 0.0013 Log likelihood = -57.071158 Pseudo R2 = 0.2021

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.43376 2.124181 1.02 0.308 .4398967 13.46495 3 | 5.412969 4.787083 1.91 0.056 .9564361 30.6348 | punish | 1 | 1 (empty) 2 | .4439666 .2303515 -1.57 0.118 .1605852 1.227425 3 | 1 (omitted) | conflict | 2 | 2.602986 2.021035 1.23 0.218 .5682947 11.92257 3 | 1.208499 .6784486 0.34 0.736 .4021456 3.631691 | help1 | 8.398446 5.923911 3.02 0.003 2.107644 33.46576 help3 | 1.831937 1.286925 0.86 0.389 .4623238 7.258966 | wua | 1 | 1.610953 1.224337 0.63 0.530 .3632139 7.145013 2 | 1.234591 .7846021 0.33 0.740 .3552799 4.290177 3 | 4.785358 4.445843 1.69 0.092 .7746484 29.56135 | _cons | .1555784 .1650733 -1.75 0.080 .0194446 1.244799 ------note: 1.punish != 0 predicts failure perfectly 1.punish dropped and 5 obs not used note: 3.punish omitted because of collinearity Logistic regression Number of obs = 110 LR chi2(10) = 28.91 Prob > chi2 = 0.0013 Log likelihood = -57.071158 Pseudo R2 = 0.2021

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------636 tour | 2 | 2.43376 2.124181 1.02 0.308 .4398967 13.46495 3 | 5.412969 4.787083 1.91 0.056 .9564361 30.6348 | punish | 1 | 1 (empty) 2 | .4439666 .2303515 -1.57 0.118 .1605852 1.227425 3 | 1 (omitted) | conflict | 2 | 2.602986 2.021035 1.23 0.218 .5682947 11.92257 3 | 1.208499 .6784486 0.34 0.736 .4021456 3.631691 | help1 | 8.398446 5.923911 3.02 0.003 2.107644 33.46576 help3 | 1.831937 1.286925 0.86 0.389 .4623238 7.258966 wua33 | .2089708 .1941446 -1.69 0.092 .0338279 1.290908 wua55 | .336642 .3378446 -1.08 0.278 .0470897 2.406637 wua91 | .2579933 .2505912 -1.39 0.163 .038444 1.731362 _cons | .7444983 .6919617 -0.32 0.751 .1204283 4.602555 ------note: 1.punish != 0 predicts failure perfectly 1.punish dropped and 5 obs not used note: 3.punish omitted because of collinearity Logistic regression Number of obs = 110 LR chi2(10) = 28.91 Prob > chi2 = 0.0013 Log likelihood = -57.071158 Pseudo R2 = 0.2021

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.43376 2.124181 1.02 0.308 .4398967 13.46495 3 | 5.412969 4.787083 1.91 0.056 .9564361 30.6348 | punish | 1 | 1 (empty) 2 | .4439666 .2303515 -1.57 0.118 .1605852 1.227425 3 | 1 (omitted) | conflict | 2 | 2.602986 2.021035 1.23 0.218 .5682947 11.92257 3 | 1.208499 .6784486 0.34 0.736 .4021456 3.631691 | help1 | 8.398446 5.923911 3.02 0.003 2.107644 33.46576 help3 | 1.831937 1.286925 0.86 0.389 .4623238 7.258966 wua33 | .8099852 .5147586 -0.33 0.740 .2330906 2.814682 wua55 | 1.304848 .955437 0.36 0.716 .3106648 5.480593 wuamh | 3.876069 3.76486 1.39 0.163 .5775799 26.01183 _cons | .1920756 .2208116 -1.44 0.151 .0201801 1.828185 ------note: 1.punish != 0 predicts failure perfectly 1.punish dropped and 5 obs not used note: 3.punish omitted because of collinearity Logistic regression Number of obs = 110 LR chi2(10) = 28.91 Prob > chi2 = 0.0013 Log likelihood = -57.071158 Pseudo R2 = 0.2021

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------637 tour | 2 | 2.43376 2.124181 1.02 0.308 .4398967 13.46495 3 | 5.412969 4.787083 1.91 0.056 .9564361 30.6348 | punish | 1 | 1 (empty) 2 | .4439666 .2303515 -1.57 0.118 .1605852 1.227425 3 | 1 (omitted) | conflict | 2 | 2.602986 2.021035 1.23 0.218 .5682947 11.92257 3 | 1.208499 .6784486 0.34 0.736 .4021456 3.631691 | help1 | 8.398446 5.923911 3.02 0.003 2.107644 33.46576 help3 | 1.831937 1.286925 0.86 0.389 .4623238 7.258966 wua33 | .6207507 .4717757 -0.63 0.530 .1399578 2.753198 wua91 | .766373 .5611544 -0.36 0.716 .182462 3.218903 wuamh | 2.970515 2.981126 1.08 0.278 .4155176 21.23606 _cons | .2506294 .2715996 -1.28 0.202 .0299647 2.096301 ------note: 1.punish != 0 predicts failure perfectly 1.punish dropped and 5 obs not used note: 3.punish omitted because of collinearity Logistic regression Number of obs = 110 LR chi2(10) = 28.91 Prob > chi2 = 0.0013 Log likelihood = -57.071158 Pseudo R2 = 0.2021

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.43376 2.124181 1.02 0.308 .4398967 13.46495 3 | 5.412969 4.787083 1.91 0.056 .9564361 30.6348 | punish | 1 | 1 (empty) 2 | .4439666 .2303515 -1.57 0.118 .1605852 1.227425 3 | 1 (omitted) | conflict | 2 | 2.602986 2.021035 1.23 0.218 .5682947 11.92257 3 | 1.208499 .6784486 0.34 0.736 .4021456 3.631691 | help1 | 8.398446 5.923911 3.02 0.003 2.107644 33.46576 help3 | 1.831937 1.286925 0.86 0.389 .4623238 7.258966 wua55 | 1.610953 1.224337 0.63 0.530 .3632139 7.145013 wua91 | 1.234591 .7846021 0.33 0.740 .3552799 4.290177 wuamh | 4.785358 4.445843 1.69 0.092 .7746484 29.56135 _cons | .1555784 .1650733 -1.75 0.080 .0194446 1.244799 ------

Logistic regression Number of obs = 115 LR chi2(6) = 30.04 Prob > chi2 = 0.0000 Log likelihood = -61.491693 Pseudo R2 = 0.1963

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 4.112979 3.318465 1.75 0.080 .8460262 19.99536 3 | 9.948428 7.620529 3.00 0.003 2.216824 44.6455 | 638 conflict | 2 | 1.728313 1.18686 0.80 0.426 .449872 6.639815 3 | 1.25855 .6516502 0.44 0.657 .4561803 3.472197 | help1 | 4.064552 2.341676 2.43 0.015 1.314066 12.57211 help3 | 1.053482 .6241125 0.09 0.930 .3298761 3.364364 _cons | .1333383 .0960443 -2.80 0.005 .032496 .547117 ------

Results 50: Logistic regressions of fair1 as a function of adequate, reliable, help1, help3, wua33, wua55, wua91, wuamh

Logistic regression Number of obs = 180 LR chi2(4) = 21.32 Prob > chi2 = 0.0003 Log likelihood = -103.912 Pseudo R2 = 0.0930

Logistic regression Number of obs = 180 LR chi2(7) = 27.65 Prob > chi2 = 0.0003 Log likelihood = -100.74844 Pseudo R2 = 0.1207

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.598628 1.239674 2.00 0.045 1.020185 6.619257 reliable | 2.02745 .7909286 1.81 0.070 .9438171 4.355243 help1 | 4.241677 2.126285 2.88 0.004 1.587976 11.33004 help3 | 1.293625 .6391033 0.52 0.602 .4912244 3.406721 wua33 | .3775038 .2223843 -1.65 0.098 .1189832 1.197724 wua55 | .274609 .1649943 -2.15 0.031 .0845832 .8915491 wua91 | .6635029 .3971229 -0.69 0.493 .2052946 2.144411 _cons | 1.226571 .4721121 0.53 0.596 .5768482 2.608096 ------

Logistic regression Number of obs = 180 LR chi2(7) = 27.65 Prob > chi2 = 0.0003 Log likelihood = -100.74844 Pseudo R2 = 0.1207

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.598628 1.239674 2.00 0.045 1.020185 6.619257 reliable | 2.02745 .7909286 1.81 0.070 .9438171 4.355243 help1 | 4.241677 2.126285 2.88 0.004 1.587976 11.33004 help3 | 1.293625 .6391033 0.52 0.602 .4912244 3.406721 wua33 | .5689557 .2807567 -1.14 0.253 .2162942 1.496622 wua55 | .4138776 .2068572 -1.77 0.078 .1553957 1.102313 wuamh | 1.507152 .9020681 0.69 0.493 .4663285 4.871048 _cons | .8138331 .4338563 -0.39 0.699 .2862581 2.313731 ------

Logistic regression Number of obs = 180 639 LR chi2(7) = 27.65 Prob > chi2 = 0.0003 Log likelihood = -100.74844 Pseudo R2 = 0.1207

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.598628 1.239674 2.00 0.045 1.020185 6.619257 reliable | 2.02745 .7909286 1.81 0.070 .9438171 4.355243 help1 | 4.241677 2.126285 2.88 0.004 1.587976 11.33004 help3 | 1.293625 .6391033 0.52 0.602 .4912244 3.406721 wua33 | 1.374696 .6646255 0.66 0.510 .5329362 3.545992 wua91 | 2.416173 1.20761 1.77 0.078 .9071835 6.435184 wuamh | 3.641541 2.18796 2.15 0.031 1.121643 11.82268 _cons | .3368273 .179595 -2.04 0.041 .1184541 .9577776 ------

Logistic regression Number of obs = 180 LR chi2(7) = 27.65 Prob > chi2 = 0.0003 Log likelihood = -100.74844 Pseudo R2 = 0.1207

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 2.598628 1.239674 2.00 0.045 1.020185 6.619257 reliable | 2.02745 .7909286 1.81 0.070 .9438171 4.355243 help1 | 4.241677 2.126285 2.88 0.004 1.587976 11.33004 help3 | 1.293625 .6391033 0.52 0.602 .4912244 3.406721 wua55 | .7274338 .3516932 -0.66 0.510 .2820085 1.876397 wua91 | 1.757606 .8673076 1.14 0.253 .6681715 4.623332 wuamh | 2.64898 1.560492 1.65 0.098 .8349166 8.404547 _cons | .463035 .2348117 -1.52 0.129 .1713789 1.251038 ------

Logistic regression Number of obs = 180 LR chi2(5) = 24.57 Prob > chi2 = 0.0002 Log likelihood = -102.28736 Pseudo R2 = 0.1072

Logistic regression Number of obs = 180 LR chi2(3) = 21.30 Prob > chi2 = 0.0001 Log likelihood = -103.92393 Pseudo R2 = 0.0929

Logistic regression Number of obs = 180 640 LR chi2(4) = 24.56 Prob > chi2 = 0.0001 Log likelihood = -102.29453 Pseudo R2 = 0.1072

Logistic regression Number of obs = 115 LR chi2(6) = 33.79 Prob > chi2 = 0.0000 Log likelihood = -59.618809 Pseudo R2 = 0.2208

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.046657 1.966198 1.73 0.084 .8599833 10.79337 reliable | 1.225444 .6064924 0.41 0.681 .4645362 3.232713 | tour | 2 | 4.127203 3.359058 1.74 0.082 .8372941 20.34388 3 | 9.528981 7.266471 2.96 0.003 2.137714 42.47597 | help1 | 4.165798 2.507983 2.37 0.018 1.280088 13.55679 help3 | .9335695 .5610929 -0.11 0.909 .2874474 3.03204 _cons | .1224648 .0897019 -2.87 0.004 .0291427 .5146263 ------

Logistic regression Number of obs = 115 LR chi2(9) = 38.57 Prob > chi2 = 0.0000 Log likelihood = -57.225096 Pseudo R2 = 0.2521

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.416676 2.255579 1.86 0.063 .9368461 12.46061 reliable | 1.387011 .7172547 0.63 0.527 .5033898 3.82169 | tour | 2 | 3.891788 3.285329 1.61 0.107 .7440308 20.35671 3 | 12.71959 10.51193 3.08 0.002 2.517734 64.2594 | help1 | 8.589865 6.136247 3.01 0.003 2.117985 34.83773 help3 | 1.862705 1.315929 0.88 0.379 .4664482 7.438491 wua33 | .1771789 .1611726 -1.90 0.057 .0297926 1.053698 wua55 | .1764648 .1761687 -1.74 0.082 .0249393 1.248623 wua91 | .1455069 .1398747 -2.01 0.045 .0221121 .9574961 _cons | .2753149 .2297319 -1.55 0.122 .0536487 1.412865 ------

Logistic regression Number of obs = 115 LR chi2(9) = 38.57 Prob > chi2 = 0.0000 Log likelihood = -57.225096 Pseudo R2 = 0.2521

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------641 adequate | 3.416676 2.255579 1.86 0.063 .9368461 12.46061 reliable | 1.387011 .7172547 0.63 0.527 .5033898 3.82169 | tour | 2 | 3.891788 3.285329 1.61 0.107 .7440308 20.35671 3 | 12.71959 10.51193 3.08 0.002 2.517734 64.2594 | help1 | 8.589865 6.136247 3.01 0.003 2.117985 34.83773 help3 | 1.862705 1.315929 0.88 0.379 .4664482 7.438491 wua33 | 1.217667 .7559614 0.32 0.751 .3606443 4.111289 wua55 | 1.212759 .8843435 0.26 0.791 .2904546 5.063729 wuamh | 6.872526 6.606506 2.01 0.045 1.044391 45.22409 _cons | .0400602 .0418689 -3.08 0.002 .0051651 .3107039 ------

Logistic regression Number of obs = 115 LR chi2(9) = 38.57 Prob > chi2 = 0.0000 Log likelihood = -57.225096 Pseudo R2 = 0.2521

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.416676 2.255579 1.86 0.063 .9368461 12.46061 reliable | 1.387011 .7172547 0.63 0.527 .5033898 3.82169 | tour | 2 | 3.891788 3.285329 1.61 0.107 .7440308 20.35671 3 | 12.71959 10.51193 3.08 0.002 2.517734 64.2594 | help1 | 8.589865 6.136247 3.01 0.003 2.117985 34.83773 help3 | 1.862705 1.315929 0.88 0.379 .4664482 7.438491 wua33 | 1.004047 .7171916 0.01 0.995 .2475941 4.071627 wua91 | .8245664 .6012737 -0.26 0.791 .1974829 3.442878 wuamh | 5.666854 5.657346 1.74 0.082 .8008822 40.09733 _cons | .0485834 .049625 -2.96 0.003 .006562 .3596985 ------

Logistic regression Number of obs = 115 LR chi2(9) = 38.57 Prob > chi2 = 0.0000 Log likelihood = -57.225096 Pseudo R2 = 0.2521

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------adequate | 3.416676 2.255579 1.86 0.063 .9368461 12.46061 reliable | 1.387011 .7172547 0.63 0.527 .5033898 3.82169 | tour | 2 | 3.891788 3.285329 1.61 0.107 .7440308 20.35671 3 | 12.71959 10.51193 3.08 0.002 2.517734 64.2594 | help1 | 8.589865 6.136247 3.01 0.003 2.117985 34.83773 help3 | 1.862705 1.315929 0.88 0.379 .4664482 7.438491 wua55 | .9959691 .7114213 -0.01 0.995 .2456021 4.038868 wua91 | .8212426 .5098501 -0.32 0.751 .2432327 2.772815 wuamh | 5.644011 5.134132 1.90 0.057 .9490385 33.56541 _cons | .04878 .0482607 -3.05 0.002 .0070162 .3391421 ------

Logistic regression Number of obs = 115 LR chi2(7) = 38.46 Prob > chi2 = 0.0000 Log likelihood = -57.284626 Pseudo R2 = 0.2513

Logistic regression Number of obs = 115 LR chi2(5) = 30.56 Prob > chi2 = 0.0000 Log likelihood = -61.233328 Pseudo R2 = 0.1997

------fair1 | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------reliable | 1.648402 .7564713 1.09 0.276 .6705583 4.052189 | tour | 2 | 4.331127 3.515959 1.81 0.071 .8822781 21.26162 3 | 10.14004 7.72429 3.04 0.002 2.278384 45.12863 | help1 | 3.74783 2.198746 2.25 0.024 1.186872 11.83466 help3 | 1.119517 .6493122 0.19 0.846 .3592025 3.489167 _cons | .1199769 .0893499 -2.85 0.004 .0278731 .5164278 ------

Results 51: Logistic regressions of member as a function of wua33, wua55, wua91 and wuamh (including those eligible and ineligible for membership)

Logistic regression Number of obs = 186 LR chi2(3) = 18.31 Prob > chi2 = 0.0004 Log likelihood = -100.10075 Pseudo R2 = 0.0838

------member | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .6436671 .4686445 1.37 0.170 -.2748593 1.562194 wua55 | -.0253178 .4998397 -0.05 0.960 -1.004986 .95435 wua91 | -1.427116 .6013873 -2.37 0.018 -2.605814 -.2484189 _cons | -.8754687 .3763863 -2.33 0.020 -1.613172 -.1377651 ------

Logistic regression Number of obs = 186 LR chi2(3) = 18.31 Prob > chi2 = 0.0004 Log likelihood = -100.10075 Pseudo R2 = 0.0838

Logistic regression Number of obs = 186 LR chi2(3) = 18.31 Prob > chi2 = 0.0004 Log likelihood = -100.10075 Pseudo R2 = 0.0838

Logistic regression Number of obs = 186 LR chi2(3) = 18.31 Prob > chi2 = 0.0004 Log likelihood = -100.10075 Pseudo R2 = 0.0838

Results 52: Logistic regressions of member as a function of wua33, wua55, wua91 and wuamh (excluding those ineligible for membership)

Logistic regression Number of obs = 122 LR chi2(3) = 12.97 Prob > chi2 = 0.0047 Log likelihood = -74.351994 Pseudo R2 = 0.0802

------member | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | .6286087 .5283622 1.19 0.234 -.4069623 1.66418 wua55 | -.029853 .5624938 -0.05 0.958 -1.132321 1.072615 wua91 | -1.317301 .634335 -2.08 0.038 -2.560575 -.0740276 _cons | -.4054651 .4082483 -0.99 0.321 -1.205617 .3946868 ------

Logistic regression Number of obs = 122 LR chi2(3) = 12.97 Prob > chi2 = 0.0047 Log likelihood = -74.351994 Pseudo R2 = 0.0802

Logistic regression Number of obs = 122 LR chi2(3) = 12.97 Prob > chi2 = 0.0047 644 Log likelihood = -74.351994 Pseudo R2 = 0.0802

Logistic regression Number of obs = 122 LR chi2(3) = 12.97 Prob > chi2 = 0.0047 Log likelihood = -74.351994 Pseudo R2 = 0.0802

Results 53: Logistic regressions of elections as a function of wua33, wua55, wua91 and wuamh (including only those farmers who are members of the WUA)

Logistic regression Number of obs = 51 LR chi2(3) = 8.84 Prob > chi2 = 0.0315 Log likelihood = -30.449444 Pseudo R2 = 0.1267

------elections | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------wua33 | -1.289131 .8116219 -1.59 0.112 -2.87988 .3016191 wua55 | .8574502 1.033005 0.83 0.407 -1.167203 2.882103 wua91 | -1.252763 1.144344 -1.09 0.274 -3.495637 .9901106 _cons | .8472979 .6900656 1.23 0.220 -.5052058 2.199802 ------

Logistic regression Number of obs = 51 LR chi2(3) = 8.84 Prob > chi2 = 0.0315 Log likelihood = -30.449444 Pseudo R2 = 0.1267

Logistic regression Number of obs = 51 LR chi2(3) = 8.84 Prob > chi2 = 0.0315 Log likelihood = -30.449444 Pseudo R2 = 0.1267

Logistic regression Number of obs = 51 LR chi2(3) = 8.84 Prob > chi2 = 0.0315 Log likelihood = -30.449444 Pseudo R2 = 0.1267

Results 54: Logistic regressions for member as a function of adequate, reliable, pos1, sys1 wua33, wua55, wua91, wuamh, rel33, rel55, rel91, relmh, pos1sys1

Logistic regression Number of obs = 122 LR chi2(3) = 13.40 Prob > chi2 = 0.0039 Log likelihood = -74.138374 Pseudo R2 = 0.0829

Logistic regression Number of obs = 122 LR chi2(5) = 20.77 Prob > chi2 = 0.0009 Log likelihood = -70.450599 Pseudo R2 = 0.1285

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .2735459 .1707059 -2.08 0.038 .080508 .9294395 sys1 | 2.241381 1.015595 1.78 0.075 .9222034 5.447593 wua33 | 1.467361 .8653406 0.65 0.516 .4619143 4.661358 wua55 | .710085 .4366774 -0.56 0.578 .212741 2.370115 wua91 | .3038381 .1986398 -1.82 0.068 .0843633 1.094287 _cons | .6253374 .264763 -1.11 0.268 .2727233 1.433859 ------

Logistic regression Number of obs = 122 LR chi2(5) = 20.77 Prob > chi2 = 0.0009 Log likelihood = -70.450599 Pseudo R2 = 0.1285

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .2735459 .1707059 -2.08 0.038 .080508 .9294395 sys1 | 2.241381 1.015595 1.78 0.075 .9222034 5.447593 wua33 | 4.829418 3.039805 2.50 0.012 1.406432 16.5833 646 wua55 | 2.337051 1.535736 1.29 0.196 .6446464 8.47256 wuamh | 3.291227 2.151701 1.82 0.068 .9138375 11.8535 _cons | .1900013 .1008611 -3.13 0.002 .0671276 .5377893 ------

Logistic regression Number of obs = 122 LR chi2(5) = 20.77 Prob > chi2 = 0.0009 Log likelihood = -70.450599 Pseudo R2 = 0.1285

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .2735459 .1707059 -2.08 0.038 .080508 .9294395 sys1 | 2.241381 1.015595 1.78 0.075 .9222034 5.447593 wua33 | 2.066458 1.106314 1.36 0.175 .7236379 5.901087 wua91 | .4278898 .2811774 -1.29 0.196 .1180281 1.551238 wuamh | 1.408282 .8660443 0.56 0.578 .4219204 4.700552 _cons | .4440427 .2238928 -1.61 0.107 .1652862 1.192925 ------

Logistic regression Number of obs = 122 LR chi2(5) = 20.77 Prob > chi2 = 0.0009 Log likelihood = -70.450599 Pseudo R2 = 0.1285

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .2735459 .1707059 -2.08 0.038 .080508 .9294395 sys1 | 2.241381 1.015595 1.78 0.075 .9222034 5.447593 wua55 | .4839198 .2590747 -1.36 0.175 .1694603 1.381907 wua91 | .2070643 .1303335 -2.50 0.012 .0603016 .7110192 wuamh | .6814956 .4018955 -0.65 0.516 .2145297 2.164904 _cons | .9175958 .4382813 -0.18 0.857 .3598178 2.340023 ------

Logistic regression Number of obs = 122 LR chi2(7) = 22.64 Prob > chi2 = 0.0020 Log likelihood = -69.516002 Pseudo R2 = 0.1401

------member | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | -2.236446 1.109456 -2.02 0.044 -4.410941 -.0619523 sys1 | 1.096501 .5901562 1.86 0.063 -.0601836 2.253186 wua33 | 1.634289 .8585239 1.90 0.057 -.0483865 3.316965 wua55 | .7291481 .6813032 1.07 0.285 -.6061816 2.064478 wuamh | 1.200349 .674767 1.78 0.075 -.1221695 2.522868 pos1_33 | 1.672407 1.405693 1.19 0.234 -1.0827 4.427514 sys1_33 | -.5651329 .9458808 -0.60 0.550 -2.419025 1.288759 _cons | -1.666943 .56223 -2.96 0.003 -2.768894 -.5649928 ------

note: pos1_mh != 0 predicts failure perfectly pos1_mh dropped and 2 obs not used

Logistic regression Number of obs = 120 LR chi2(6) = 21.83 Prob > chi2 = 0.0013 Log likelihood = -68.964169 Pseudo R2 = 0.1367

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] 647 ------+------pos1 | .3463825 .2247712 -1.63 0.102 .0970962 1.23569 sys1 | 3.109724 1.5739 2.24 0.025 1.153214 8.385593 wua33 | 4.344728 2.752867 2.32 0.020 1.254966 15.04157 wua55 | 2.141154 1.422304 1.15 0.252 .5823989 7.871825 wuamh | 5.070989 3.617667 2.28 0.023 1.252684 20.52786 pos1_mh | 1 (omitted) sys1_mh | .1965162 .2749949 -1.16 0.245 .0126552 3.051597 _cons | .1613456 .0893869 -3.29 0.001 .0544733 .4778928 ------

Results 55: Logistic regressions of member as a function of tour, punish, conflict, help1, help3

Logistic regression Number of obs = 78 LR chi2(8) = 5.25 Prob > chi2 = 0.7302 Log likelihood = -49.343301 Pseudo R2 = 0.0505

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------tour | 2 | 2.502774 2.459824 0.93 0.351 .364609 17.17971 3 | 2.730703 2.70011 1.02 0.310 .3931959 18.96443 | punish | 2 | .981256 1.164437 -0.02 0.987 .095869 10.04353 3 | .6303716 .8091913 -0.36 0.719 .0509251 7.802998 | conflict | 2 | 1.581201 1.069842 0.68 0.498 .4198199 5.955401 3 | .5711574 .3373751 -0.95 0.343 .1794578 1.817814 | help1 | .4393155 .3083819 -1.17 0.241 .1109857 1.738945 help3 | .4060543 .3196847 -1.14 0.252 .0867835 1.899902 _cons | .7025208 .6645505 -0.37 0.709 .1100181 4.48595 ------

Results 56: Logistic regressions of member as a function of dunums_log, greenhouse_0or1, dungreen, own3, edu8, wua33, wua55, wua91, wuamh, and own3_91

Logistic regression Number of obs = 122 LR chi2(5) = 19.46 Prob > chi2 = 0.0016 Log likelihood = -71.107967 Pseudo R2 = 0.1204

Logistic regression Number of obs = 122 LR chi2(8) = 36.36 648 Prob > chi2 = 0.0000 Log likelihood = -62.657782 Pseudo R2 = 0.2249

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 5.299632 3.343509 2.64 0.008 1.538959 18.25006 greenhouse_0~1 | .0067838 .0212395 -1.59 0.111 .0000147 3.137091 dungreen | 7.388562 10.85733 1.36 0.174 .4147037 131.6382 own3 | 2.0806 .9915842 1.54 0.124 .8175568 5.294916 edu8 | 1.375197 .6594754 0.66 0.506 .5372441 3.520128 wua33 | 2.367138 1.452638 1.40 0.160 .7109976 7.880958 wua55 | 2.249267 2.027237 0.90 0.368 .384471 13.15887 wua91 | .2311584 .1666471 -2.03 0.042 .0562678 .9496407 _cons | .0211356 .0258904 -3.15 0.002 .0019157 .2331879 ------

Logistic regression Number of obs = 122 LR chi2(8) = 36.36 Prob > chi2 = 0.0000 Log likelihood = -62.657782 Pseudo R2 = 0.2249

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 5.299632 3.343509 2.64 0.008 1.538959 18.25006 greenhouse_0~1 | .0067838 .0212395 -1.59 0.111 .0000147 3.137091 dungreen | 7.388562 10.85733 1.36 0.174 .4147037 131.6382 own3 | 2.0806 .9915842 1.54 0.124 .8175568 5.294916 edu8 | 1.375197 .6594754 0.66 0.506 .5372441 3.520128 wua33 | 10.24033 7.192341 3.31 0.001 2.585048 40.56571 wua55 | 9.730417 8.536978 2.59 0.010 1.743131 54.31663 wuamh | 4.326038 3.118735 2.03 0.042 1.05303 17.77215 _cons | .0048857 .0065665 -3.96 0.000 .0003507 .0680727 ------

Logistic regression Number of obs = 122 LR chi2(8) = 36.36 Prob > chi2 = 0.0000 Log likelihood = -62.657782 Pseudo R2 = 0.2249

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 5.299632 3.343509 2.64 0.008 1.538959 18.25006 greenhouse_0~1 | .0067838 .0212395 -1.59 0.111 .0000147 3.137091 dungreen | 7.388562 10.85733 1.36 0.174 .4147037 131.6382 own3 | 2.0806 .9915842 1.54 0.124 .8175568 5.294916 edu8 | 1.375197 .6594754 0.66 0.506 .5372441 3.520128 wua33 | 1.052404 .9599067 0.06 0.955 .1761141 6.288843 wua91 | .1027705 .0901657 -2.59 0.010 .0184106 .5736803 wuamh | .4445892 .4007028 -0.90 0.368 .0759944 2.600976 _cons | .0475396 .0658379 -2.20 0.028 .0031494 .7176118 ------

Logistic regression Number of obs = 122 LR chi2(8) = 36.36 Prob > chi2 = 0.0000 Log likelihood = -62.657782 Pseudo R2 = 0.2249

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 5.299632 3.343509 2.64 0.008 1.538959 18.25006 greenhouse_0~1 | .0067838 .0212395 -1.59 0.111 .0000147 3.137091 dungreen | 7.388562 10.85733 1.36 0.174 .4147037 131.6382 649 own3 | 2.0806 .9915842 1.54 0.124 .8175568 5.294916 edu8 | 1.375197 .6594754 0.66 0.506 .5372441 3.520128 wua55 | .9502055 .8666907 -0.06 0.955 .1590118 5.678137 wua91 | .0976531 .0685871 -3.31 0.001 .0246514 .3868399 wuamh | .4224511 .2592449 -1.40 0.160 .1268881 1.406475 _cons | .0500309 .0543411 -2.76 0.006 .0059526 .4205055 ------

Logistic regression Number of obs = 122 LR chi2(4) = 17.42 Prob > chi2 = 0.0016 Log likelihood = -72.125925 Pseudo R2 = 0.1078

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 4.595859 2.356432 2.97 0.003 1.682406 12.55459 greenhouse_0~1 | .463952 .2224798 -1.60 0.109 .181257 1.187549 own3 | 2.285317 .9631224 1.96 0.050 1.000499 5.220069 edu8 | 1.541907 .6694146 1.00 0.319 .6584326 3.610812 _cons | .0254722 .023818 -3.93 0.000 .0040752 .1592169 ------

Logistic regression Number of obs = 122 LR chi2(3) = 16.42 Prob > chi2 = 0.0009 Log likelihood = -72.628092 Pseudo R2 = 0.1016

Logistic regression Number of obs = 122 LR chi2(6) = 33.66 Prob > chi2 = 0.0000 Log likelihood = -64.005232 Pseudo R2 = 0.2082

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 9.185305 5.317391 3.83 0.000 2.953462 28.56642 greenhouse_0~1 | .3838425 .2836881 -1.30 0.195 .0901679 1.634008 own3 | 2.002519 .9306154 1.49 0.135 .8053861 4.979079 wua33 | 2.718405 1.680504 1.62 0.106 .8092927 9.13109 wua55 | 1.775102 1.433899 0.71 0.477 .3644484 8.645903 wua91 | .2344862 .1660947 -2.05 0.041 .0585035 .9398375 _cons | .0102604 .0121649 -3.86 0.000 .0010045 .1048008 ------

Logistic regression Number of obs = 122 LR chi2(6) = 33.66 Prob > chi2 = 0.0000 Log likelihood = -64.005232 Pseudo R2 = 0.2082

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 9.185305 5.317391 3.83 0.000 2.953462 28.56642 greenhouse_0~1 | .3838425 .2836881 -1.30 0.195 .0901679 1.634008 own3 | 2.002519 .9306154 1.49 0.135 .8053861 4.979079 wua33 | 11.59303 8.122951 3.50 0.000 2.936166 45.77338 650 wua55 | 7.570175 6.227413 2.46 0.014 1.509686 37.95992 wuamh | 4.264643 3.020794 2.05 0.041 1.064014 17.093 _cons | .0024059 .0030624 -4.74 0.000 .0001985 .0291561 ------

Logistic regression Number of obs = 122 LR chi2(6) = 33.66 Prob > chi2 = 0.0000 Log likelihood = -64.005232 Pseudo R2 = 0.2082

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 9.185305 5.317391 3.83 0.000 2.953462 28.56642 greenhouse_0~1 | .3838425 .2836881 -1.30 0.195 .0901679 1.634008 own3 | 2.002519 .9306154 1.49 0.135 .8053861 4.979079 wua33 | 1.531408 1.272413 0.51 0.608 .3005015 7.804322 wua91 | .1320973 .1086665 -2.46 0.014 .0263436 .6623896 wuamh | .563348 .4550636 -0.71 0.477 .1156617 2.743873 _cons | .0182132 .0217842 -3.35 0.001 .001747 .1898795 ------

Logistic regression Number of obs = 122 LR chi2(6) = 33.66 Prob > chi2 = 0.0000 Log likelihood = -64.005232 Pseudo R2 = 0.2082

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 9.185305 5.317391 3.83 0.000 2.953462 28.56642 greenhouse_0~1 | .3838425 .2836881 -1.30 0.195 .0901679 1.634008 own3 | 2.002519 .9306154 1.49 0.135 .8053861 4.979079 wua55 | .6529938 .5425579 -0.51 0.608 .1281341 3.32777 wua91 | .0862588 .0604394 -3.50 0.000 .0218468 .3405802 wuamh | .3678628 .2274109 -1.62 0.106 .109516 1.235647 _cons | .0278918 .0289727 -3.45 0.001 .0036416 .2136307 ------

Logistic regression Number of obs = 122 LR chi2(6) = 34.09 Prob > chi2 = 0.0000 Log likelihood = -63.793019 Pseudo R2 = 0.2108

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 4.613808 2.776048 2.54 0.011 1.418751 15.00419 greenhouse_0~1 | .0085464 .0235725 -1.73 0.084 .0000384 1.903515 dungreen | 7.16195 10.03379 1.41 0.160 .4597379 111.5712 own3 | 2.13285 .9774142 1.65 0.098 .8687264 5.236458 edu8 | 1.51762 .7090507 0.89 0.372 .6073951 3.791882 wua91 | .1301694 .0783664 -3.39 0.001 .0399997 .4236048 _cons | .0434332 .0460519 -2.96 0.003 .0054362 .3470121 ------

Logistic regression Number of obs = 122 LR chi2(8) = 34.11 Prob > chi2 = 0.0000 Log likelihood = -63.78031 Pseudo R2 = 0.2110

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------dunums_log | 4.537643 2.980414 2.30 0.021 1.252404 16.44055 greenhouse_0~1 | .0088611 .0244289 -1.71 0.086 .0000399 1.968519 651 dungreen | 7.029026 9.840003 1.39 0.164 .4521575 109.2699 own3 | 2.065379 1.041168 1.44 0.150 .7689621 5.547465 edu8 | 1.509229 .7074333 0.88 0.380 .6022327 3.782212 wua91 | .0955184 .261962 -0.86 0.392 .0004422 20.63152 own3_91 | 1.202088 1.462526 0.15 0.880 .1107434 13.04833 dun91 | 1.110622 1.395432 0.08 0.933 .0946413 13.03322 _cons | .0457096 .0530762 -2.66 0.008 .0046949 .4450299 ------

Results 57: Logistic regressions of member as a function of pos1, sys1, dunums_log, greenhouse_0or1, own3, wua55, green55, wua33, wua91, wuamh

Logistic regression Number of obs = 122 LR chi2(5) = 32.99 Prob > chi2 = 0.0000 Log likelihood = -64.340001 Pseudo R2 = 0.2041

Logistic regression Number of obs = 122 LR chi2(7) = 33.63 Prob > chi2 = 0.0000 Log likelihood = -64.022565 Pseudo R2 = 0.2080

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .2694048 .1744469 -2.03 0.043 .0757232 .9584766 sys1 | 4.342154 2.1111 3.02 0.003 1.67441 11.26026 dunums_log | 7.241272 4.339507 3.30 0.001 2.237236 23.43786 greenhouse_0~1 | .2753657 .2451484 -1.45 0.147 .0480967 1.576539 wua55 | .7892801 .8432523 -0.22 0.825 .0972351 6.40677 green55 | 2.604837 3.720798 0.67 0.503 .1584558 42.82061 own3 | 3.836929 1.864715 2.77 0.006 1.480154 9.946277 _cons | .006709 .0082278 -4.08 0.000 .0006064 .0742251 ------

Logistic regression Number of obs = 122 LR chi2(8) = 41.07 Prob > chi2 = 0.0000 Log likelihood = -60.299917 Pseudo R2 = 0.2541

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .3430214 .2297737 -1.60 0.110 .0922873 1.274972 sys1 | 3.257697 1.785124 2.16 0.031 1.112953 9.535526 dunums_log | 9.110443 5.547256 3.63 0.000 2.762178 30.04882 greenhouse_0~1 | .48278 .3572121 -0.98 0.325 .1132243 2.058538 own3 | 3.018375 1.546745 2.16 0.031 1.105557 8.240724 wua33 | 1.691294 1.171684 0.76 0.448 .4350399 6.575205 wua55 | .9710491 .8378652 -0.03 0.973 .1789724 5.268615 wua91 | .2563303 .185789 -1.88 0.060 .0619222 1.061093 _cons | .0065813 .0084523 -3.91 0.000 .000531 .0815649 ------652

Logistic regression Number of obs = 122 LR chi2(8) = 41.07 Prob > chi2 = 0.0000 Log likelihood = -60.299917 Pseudo R2 = 0.2541

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .3430214 .2297737 -1.60 0.110 .0922873 1.274972 sys1 | 3.257697 1.785124 2.16 0.031 1.112953 9.535526 dunums_log | 9.110443 5.547256 3.63 0.000 2.762178 30.04882 greenhouse_0~1 | .48278 .3572121 -0.98 0.325 .1132243 2.058538 own3 | 3.018375 1.546745 2.16 0.031 1.105557 8.240724 wua33 | 6.598105 4.838842 2.57 0.010 1.567382 27.77561 wua55 | 3.788273 3.245093 1.55 0.120 .7067877 20.30456 wuamh | 3.901216 2.827614 1.88 0.060 .9424241 16.1493 _cons | .001687 .0023772 -4.53 0.000 .0001066 .0267022 ------

Logistic regression Number of obs = 122 LR chi2(8) = 41.07 Prob > chi2 = 0.0000 Log likelihood = -60.299917 Pseudo R2 = 0.2541

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .3430214 .2297737 -1.60 0.110 .0922873 1.274972 sys1 | 3.257697 1.785124 2.16 0.031 1.112953 9.535526 dunums_log | 9.110443 5.547256 3.63 0.000 2.762178 30.04882 greenhouse_0~1 | .48278 .3572121 -0.98 0.325 .1132243 2.058538 own3 | 3.018375 1.546745 2.16 0.031 1.105557 8.240724 wua33 | 1.741719 1.46365 0.66 0.509 .3354845 9.042395 wua91 | .2639725 .2261229 -1.55 0.120 .04925 1.414852 wuamh | 1.029814 .8885702 0.03 0.973 .1898032 5.587455 _cons | .0063908 .0090659 -3.56 0.000 .0003963 .1030526 ------

Logistic regression Number of obs = 122 LR chi2(8) = 41.07 Prob > chi2 = 0.0000 Log likelihood = -60.299917 Pseudo R2 = 0.2541

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------pos1 | .3430214 .2297737 -1.60 0.110 .0922873 1.274972 sys1 | 3.257697 1.785124 2.16 0.031 1.112953 9.535526 dunums_log | 9.110443 5.547256 3.63 0.000 2.762178 30.04882 greenhouse_0~1 | .48278 .3572121 -0.98 0.325 .1132243 2.058538 own3 | 3.018375 1.546745 2.16 0.031 1.105557 8.240724 wua55 | .5741456 .482482 -0.66 0.509 .1105902 2.980763 wua91 | .1515587 .1111483 -2.57 0.010 .0360028 .6380065 wuamh | .5912632 .4096116 -0.76 0.448 .1520865 2.29864 _cons | .011131 .0142356 -3.52 0.000 .0009076 .1365049 ------

Logistic regression Number of obs = 122 LR chi2(6) = 40.33 Prob > chi2 = 0.0000 Log likelihood = -60.66991 Pseudo R2 = 0.2495

------member | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------653 pos1 | .3611502 .2394059 -1.54 0.124 .098498 1.324183 sys1 | 3.6843 1.814911 2.65 0.008 1.402967 9.675259 dunums_log | 8.804929 5.352452 3.58 0.000 2.674771 28.98445 greenhouse_0~1 | .37223 .2007528 -1.83 0.067 .1293414 1.071237 own3 | 3.273441 1.642572 2.36 0.018 1.224284 8.752393 wua91 | .2119808 .1303592 -2.52 0.012 .0635103 .7075372 _cons | .0081137 .0100495 -3.89 0.000 .000716 .0919389 ------

654 Appendix F – OLS Regression Models

The results listed in this appendix are the OLS regressions conducted on the logistic regressions within the results chapters (Chapter Eight through Twelve). These OLS regressions serve as a check on the effect sign and positive or negative nature that were found in the logistic regressions. The table numbers herein correspond to the table numbers to which they correspond in the results chapters.

Table 8.4A: OLS regression results of water adequacy’s effect on reporting of water stealing. Dependent Variable Independent water stealing Variable adequacy -0.20*** (0.06) Prob > F 0.0006 R2 0.0656 Adj. R2 0.0601 No. Obs. 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.5A: OLS regression results of water adequacy’s effect on opinion of fairness of the WUA. Dependent Variable Independent fairness of wua Variable adequacy 0.22*** (0.08) Prob > F 0.0052 R2 0.0420 Adj. R2 0.0368 No. Obs. 184 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.6A: OLS regression results for water adequacy’s effect on membership in the WUA. Dependent Variable Independent membership Variable adequacy -0.06 (0.11) Prob > F 0.5989

655 R2 0.0023 Adj. R2 -0.0060 No. Obs. 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.9A: OLS regression results of having a secondary source of water and individual WUAs on water adequacy. Dependent Variable adequacy Independent (1) (2) Variables secondary water 0.02 0.28 (0.07) (0.31) ps33 -0.11 -0.15 (0.10) (0.11) ps55 0.03 0.09 (0.10) (0.11) ps91 0.11 0.14 (0.09) (0.10) ps33*secondary water -0.14 (0.34) ps55*secondary water -0.40 (0.34) ps91*secondary water -0.33 (0.34) Prob > F 0.1629 0.2161 R2 0.0352 0.0515 Adj. R2 0.0139 0.0142 No. Obs. 186 186 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.10A: Regression results for the effect of growing citrus or date palm trees on water adequacy. Dependent Variable adequacy Independent (1) (2) (3) Variables citrus -0.14** -0.12* (0.07) (0.07) palms 0.26*** 0.24** (0.10) (0.10) Prob > F 0.0486 0.0097 0.0089

656 R2 0.0210 0.0358 0.0502 Adj. R2 0.0156 0.0306 0.0399 No. Obs. 186 186 186 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.11A: OLS regression results for the effect of crop type, farm size, greenhouses and exporting crops on water adequacy. Dependent Variable Independent adequacy Variables citrus -0.10 (0.07) palms 0.31*** (0.10) dunums -0.11* (0.07) greenhouses 0.11* (0.07) exporting -0.05 (0.10) Prob > F 0.0146 R2 0.0751 Adj. R2 0.0494 No. Obs. 186 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.14A: OLS regression results of water reliability’s effect on reporting of water stealing. Dependent Variable Independent water stealing Variable reliability -0.12** (0.05) Prob > F 0.0164 R2 0.0330 Adj. R2 0.0274 No. Obs. 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.15A: OLS regression results of water reliability’s effect on opinion of fairness of the WUA. Dependent Variable

657 Independent fairness of wua Variable reliability 0.25*** (0.07) Prob > F 0.0004 R2 0.0676 Adj. R2 0.0624 No. Obs. 184 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.16A: OLS regression results for water reliability’s effect on membership in the WUA. Dependent Variable Independent membership Variable reliability 0.09 (0.09) Prob > F 0.3072 R2 0.0087 Adj. R2 0.0004 No. Obs. 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.17A: OLS regressions results for the effects of network type, lateral position and WUA on water reliability. Dependent Variable reliability Independent (1) (2) (3) Variables pressure network 0.11 (0.08) gravity network 0.12 (0.09) both networks -0.25** (0.11) beginning position -0.06 (0.11) middle position 0.04 (0.09) multiple positions -0.21** (0.11) ps33 0.18*

658 (0.09) gravity network*ps33 -0.71*** (0.18) Prob > F 0.0047 0.1217 0.0025 R2 0.0569 0.0313 0.0756 Adj. R2 0.0466 0.0153 0.0604 No. Obs. 186 186 186 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 8.20A: OLS regression results of the effects of water reliability, network type and lateral position on whether farmers are members in the WUA. Dependent Variable membership Independent (1) (2) Variables reliability 0.06 0.10 (0.09) (0.09) pressure network 0.24*** (0.10) both networks 0.08 (0.13) beginning position -0.23* (0.13) middle position 0.002 (0.11) multiple positions 0.12 (0.13) Prob > F 0.0636 0.1047 R2 0.0596 0.0629 Adj. R2 0.0356 0.0309 No. Obs. 122 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.4A: OLS regression results of the effect of where a farmer seeks help (WUA, JVA or both) on farmer reporting of water stealing. Dependent Variable Independent water stealing Variables wua help -0.002 (0.06) wua-jva help 0.01 (0.07)

659 Prob > F 0.9615 R2 0.0005 Adj. R2 -0.0114 No. Obs. 171 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.5A: OLS regression results of the effect of where a farmer seeks help (WUA, JVA or both) on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variables wua help 0.25*** (0.08) wua-jva help 0.06 (0.09) Prob > F 0.0060 R2 0.0562 Adj. R2 0.0455 No. Obs. 180 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.6A: OLS regression results of the effect of where a farmer seeks help (WUA, JVA or both) on farmer membership in the WUA. Dependent Variable Independent membership Variables wua help -0.002 (0.11) wua-jva help -0.11 (0.12) Prob > F 0.6004 R2 0.0088 Adj. R2 -0.0083 No. Obs. 119 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.11A: OLS regression results of the effect of monitoring the field on farmer reporting of water stealing. Dependent Variable Independent water stealing Variable touring

660 sometimes 0.03 (0.08) always -0.03 (0.07) Prob > F 0.5898 R2 0.0063 Adj. R2 -0.0056 No. Obs. 171 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.12A: OLS regression results of the effect of monitoring the field on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variable touring sometimes 0.22** (0.10) always 0.53*** (0.09) Prob > F 0.0000 R2 0.1798 Adj. R2 0.1706 No. Obs. 181 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.13A: OLS regression results of the effect of monitoring the field on farmer membership in the WUA. Dependent Variable Independent membership Variable touring sometimes 0.14 (0.13) always 0.08 (0.12) Prob > F 0.5884 R2 0.0091 Adj. R2 -0.0080 No. Obs. 119 *Significant at 10%, **significant at 5%, ***significant at 1%.

661 Table 10.16A: OLS regression results of the effect of sanctioning on farmer reporting of water stealing. Dependent Variable Independent water stealing Variable punishing sometimes 0.15 (0.12) always 0.03 (0.12) Prob > F 0.0613 R2 0.0341 Adj. R2 0.0221 No. Obs. 164 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.17A: OLS regression results of the effect of sanctioning on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variable punishing sometimes 0.22 (0.16) always 0.48*** (0.16) Prob > F 0.0002 R2 0.0973 Adj. R2 0.0868 No. Obs. 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.18A: OLS regression results of the effect of sanctioning on farmer membership in the WUA. Dependent Variable Independent membership Variable punishing sometimes 0.05 (0.18) always -0.06 (0.19)

662 Prob > F 0.5213 R2 0.0115 Adj. R2 -0.0060 No. Obs. 116 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.21A: OLS regression results of the effect of the WUA’s conflict resolution abilities on farmer reporting of water stealing. Dependent Variable Independent water stealing Variable resolving conflict sometimes -0.05 (0.08) always -0.06 (0.06) Prob > F 0.5596 R2 0.0099 Adj. R2 -0.0071 No. Obs. 120 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.22A: OLS regression results of the effect of the WUA’s conflict resolution abilities on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variable resolving conflict sometimes 0.22* (0.13) always 0.14* (0.09) Prob > F 0.1557 R2 0.0298 Adj. R2 0.0140 No. Obs. 126 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 10.23A: OLS regression results of the effect of the WUA’s conflict resolution abilities on farmer membership in the WUA. Dependent Variable

663 Independent membership Variable resolving conflict sometimes 0.15 (0.15) always -0.11 (0.11) Prob > F 0.2181 R2 0.0356 Adj. R2 0.0126 No. Obs. 87 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.7A: OLS regression results of the effects of secondary work and secondary water resources on farmer reporting of water stealing. Dependent Variable water stealing Independent Variables (1) (2) (3) secondary work 0.13*** 0.11* (0.05) (0.06) secondary water 0.009 -0.03 (0.06) (0.08) secondary work*secondary water 0.06 (0.11) Prob > F 0.0119 0.8720 0.0862 R2 0.0362 0.0002 0.0379 Adj. R2 0.0306 -0.0057 0.0209 No. Obs. 174 174 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.8A: OLS regression results of the effects of secondary work and secondary water resources on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent Variables (1) (2) (3) secondary work 0.009 0.04 (0.07) (0.08) secondary water 0.07 0.12 (0.08) (0.11) secondary work*secondary water -0.11 (0.16) Prob > F 0.8967 0.3985 0.7418

664 R2 0.0001 0.0039 0.0069 Adj. R2 -0.0054 -0.0016 -0.0097 No. Obs. 184 184 184 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.9A: OLS regression results of the effects of secondary work and secondary water resources on farmer membership in the WUA. Dependent Variable membership Independent Variables (1) (2) (3) secondary work 0.14* 0.07 (0.09) (0.10) secondary water 0.12 -0.04 (0.10) (0.16) secondary work*secondary water 0.23 (0.21) Prob > F 0.1250 0.2160 0.1971 R2 0.0195 0.0127 0.0387 Adj. R2 0.0113 0.0045 0.0143 No. Obs. 122 122 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.12A: OLS regression results of the effects of farm size, greenhouses and exporting on farmer reporting of water stealing. Dependent Variable water stealing Independent (1) (2) (3) Variables dunums 0.14*** 0.13** (0.05) (0.06) greenhouses -0.01 -0.19 (0.06) (0.22) dunums*greenhouses 0.09 (0.12) Prob > F 0.0049 0.8333 0.0315 R2 0.0451 0.0003 0.0505 Adj. R2 0.0395 -0.0056 0.0338 No. Obs. 174 174 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.13A: OLS regression results of the effects of farm size, greenhouses and exporting on farmer opinion of the fairness of the WUA.

665 Dependent Variable fairness of wua Independent (1) (2) (3) (4) Variables dunums 0.04 0.16* (0.07) (0.11) greenhouses -0.03 0.09 (0.08) (0.33) exporting -0.03 0.12 (0.10) (0.50) dunums*greenhouses -0.12 (0.19) dunums*exporting -0.18 (0.21) exporting*greenhouses 0.37* (0.23) Prob > F 0.5746 0.6936 0.7609 0.5307 R2 0.0017 0.0009 0.0005 0.0281 Adj. R2 -0.0038 -0.0046 -0.0050 -0.0048 No. Obs. 184 184 184 184 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.14A: OLS regression results of the effects of farm size, greenhouses, exporting and WUA on farmer membership in the WUA. Dependent Variable membership Independent Variables (1) (2) (3) (4) (5) dunums 0.27*** 0.25* 0.23* (0.09) (0.14) (0.14) greenhouses -0.08 -0.58* -0.83* (0.10) (0.38) (0.41) exporting 0.18 0.68 0.59 (0.13) (0.67) (0.67) dunums*greenhouses 0.24 0.31 (0.22) (0.22) dunums*exporting -0.25 -0.20 (0.28) (0.28) exporting*greenhouses -0.15 -0.19 (0.30) (0.30) ps55 0.21* (0.14) Prob > F 0.0033 0.4321 0.1650 0.0398 0.0306

666 R2 0.0695 0.0052 0.0160 0.1068 0.1243 Adj. R2 0.0618 -0.0031 0.0078 0.0602 0.0705 No. Obs. 122 122 122 122 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.17A: OLS regressions results of the effect of ownership status on farmer reporting of water stealing. Dependent Variable Independent water stealing Variable ownership renter 0.13** (0.07) owner 0.08 (0.06) Prob > F 0.1321 R2 0.0234 Adj. R2 0.0120 No. Obs. 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.18A: OLS regressions results of the effect of ownership status on farmer opinion of the fairness of the WUA. Dependent Variable Independent fairness of wua Variable ownership renter 0.02 (0.09) owner -0.10 (0.09) Prob > F 0.2818 R2 0.0139 Adj. R2 0.0030 No. Obs. 184 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.19A: OLS regressions results of the effect of ownership status on farmer membership in the WUA. Dependent Variable membership

667 Independent (1) (2) Variables owner 0.15* 0.29*** (0.09) (0.10) ps55 0.34** (0.15) owner*ps55 -0.56*** (0.20) Prob > F 0.0895 0.0167 R2 0.0238 0.0827 Adj. R2 0.0157 0.0594 No. Obs. 122 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.22A: OLS regression results of the effects of education level on farmer reporting of water stealing. Dependent Variable water stealing Independent (1) (2) Variables education elementary 0.07 (0.10) middle school 0.05 (0.08) high school 0.07 (0.08) diplome 0.04 (0.10) bachelors or 0.19** higher (0.09) high school 0.06 (0.05) Prob > F 0.3835 0.2094 R2 0.0306 0.0091 Adj. R2 0.0018 0.0034 No. Obs. 174 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.23A: OLS regression results of the effects of education level on farmer opinion of the fairness of the WUA. Dependent Variable

668 fairness of wua Independent (1) (2) Variables education elementary -0.09 (0.14) middle school 0.18* (0.12) high school 0.03 (0.11) diplome 0.12 (0.14) bachelors or -0.03 higher (0.12) high school -0.02 (0.07) Prob > F 0.3630 0.7327 R2 0.0299 0.0006 Adj. R2 0.0027 -0.0048 No. Obs. 184 184 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 11.24A: OLS regression results of the effects of education level on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) Variables education elementary 0.09 (0.20) middle school 0.10 (0.19) high school 0.22 (0.17) diplome 0.20 (0.22) bachelors or 0.30* higher (0.19) high school 0.16* (0.09) Prob > F 0.5465 0.0709

669 R2 0.0336 0.0269 Adj. R2 -0.0080 0.0188 No. Obs. 122 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.12A: OLS regression results of the effects of physical factors on farmer reporting of water stealing. Dependent Variable Independent water stealing Variables adequacy -0.17*** (0.06) reliability -0.07 (0.05) Prob > F 0.0011 R2 0.0761 Adj. R2 0.0653 No. Obs. 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.13A: OLS regression results for the effects of user factors on farmer reporting of water stealing. Dependent Variable Independent water stealing Variables dunums 0.15*** (0.05) ownership renter 0.13** (0.07) owner 0.10* (0.06) high school 0.03 (0.05) Prob > F 0.0111 R2 0.0738 Adj. R2 0.0519 No. Obs. 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.14A: OLS regression results of the effects of all factors on farmer reporting of water stealing.

670 Dependent Variable water stealing Independent (1) (2) (3) (4) Variables adequacy -0.15*** -0.15*** -0.15*** (0.06) (0.06) (0.06) reliability -.05 -0.12** -0.07 -0.07 (0.05) (0.05) (0.06) (0.05) dunums 0.14*** 0.14*** 0.14*** 0.12** (0.05) (0.05) (0.05) (0.05) ownership renter 0.10* 0.12* 0.11* 0.09 (0.06) (0.07) (0.06) (0.06) owner 0.08 0.08 0.08 0.06 (0.06) (0.06) (0.06) (0.06) mh -0.10* -0.08 -0.08 (0.07) (0.06) (0.06) secondary 0.09* work (0.05) Prob > F 0.0003 0.0024 0.0004 0.0002 R2 0.1269 0.1032 0.1359 0.1518 Adj. R2 0.1010 0.0765 0.1048 0.1161 No. Obs. 174 174 174 174 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.15A: OLS regression results of the effects of physical factors on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent Variables (1) (2) (3) adequacy 0.15* 0.15* 0.15* (0.08) (0.08) (0.08) reliability 0.21*** 0.21*** 0.21*** (0.07) (0.07) (0.07) beginning position -0.05 (0.09) pressure network -0.01 (0.07) secondary work 0.05 (0.08) secondary water 0.08 (0.10)

671 secondary work*secondary water -0.06 (0.15) Prob > F 0.0003 0.0027 0.0052 R2 0.0847 0.0865 0.0886 Adj. R2 0.0746 0.0661 0.0630 No. Obs. 184 184 184 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.16A: OLS regression results of the effects of institutional factors on farmer opinion of the fairness of the WUA. Dependent Variable fairness of wua Independent (1) (2) Variables touring sometimes 0.18 0.28* (0.16) (0.15) always 0.31** 0.47*** (0.16) (0.14) punishing sometimes 0.24 (0.24) always 0.40* (0.25) resolving conflict sometimes 0.13 0.11 (0.12) (0.12) always 0.03 0.04 (0.10) (0.10) wua help 0.28*** 0.27** (0.11) (0.11) wua-jva help 0.02 0.01 (0.12) (0.12) Prob > F 0.0000 0.0000 R2 0.2740 0.2433 Adj. R2 0.2192 0.2013 No. Obs. 115 115 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.17A: OLS regression results of the effects of all factors on farmer opinion of the fairness of the WUA. Dependent Variable

672 fairness of wua Independent (1) (2) (3) (4) (5) Variables adequacy 0.19* 0.20* 0.18** 0.17** (0.10) (0.10) (0.08) (0.08) reliability 0.05 0.10 0.07 0.06 0.08 (0.09) (0.09) (0.09) (0.07) (0.07) touring sometimes 0.27* 0.28* 0.23* 0.24** 0.22** (0.14) (0.15) (0.14) (0.11) (0.11) always 0.44*** 0.46*** 0.44*** 0.46*** 0.48*** (0.13) (0.13) (0.13) (0.10) (0.10) wua help 0.26** 0.25** 0.37*** 0.11 0.22** (0.11) (0.11) (0.12) (0.09) (0.10) wua-jva help -0.01 0.02 0.09 -0.07 0.04 (0.12) (0.12) (0.13) (0.09) (0.10) mh 0.31** 0.26** (0.15) (0.11) Prob > F 0.0000 0.0000 0.0000 0.0000 0.0000 R2 0.2685 0.2468 0.2964 0.2287 0.2551 Adj. R2 0.2279 0.2122 0.2503 0.2015 0.2242 No. Obs. 115 115 115 177 177 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.18A: OLS regression results of the effects of physical factors on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) (3) Variables adequacy -0.09 -0.03 (0.11) (0.11) reliability 0.11 0.05 (0.09) (0.09) beginning position -0.26** -0.22** (0.11) (0.11) pressure network 0.22** 0.16* (0.09) (0.09) ps33 0.30*** (0.12) ps55 0.13 (0.13)

673 mh 0.20* (0.12) Prob > F 0.4180 0.0121 0.0017 R2 0.0146 0.1030 0.1512 Adj. R2 -0.0020 0.0724 0.1146 No. Obs. 122 122 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.19A: OLS regression results of the effects of user factors on farmer membership in the WUA. Dependent Variable membership Independent (1) (2) (3) (4) Variables dunums 0.25** 0.34*** 0.32*** 0.30*** (0.11) (0.09) (0.11) (0.11) greenhouses -0.49 -0.15* -0.45 -0.48 (0.35) (0.09) (0.38) (0.33) dunums*greenhouses 0.19 0.16 0.17 (0.19) (0.19) (0.18) owner 0.19** 0.19** 0.12* 0.14* (0.09) (0.09) (0.08) (0.08) high school 0.08 0.06 0.07 (0.09) (0.09) (0.08) ps33 0.43*** (0.12) ps55 0.33** (0.14) mh 0.24** (0.12) ps91 -0.34*** (0.09) Prob > F 0.0029 0.0010 0.0001 0.0000 R2 0.1424 0.1278 0.2507 0.2339 Adj. R2 0.1055 0.1056 0.1976 0.1939 No. Obs. 122 122 122 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

Table 12.20A: OLS regression results of the effects of all factors on farmer membership in the WUA. Dependent Variable membership

674 Independent (1) (2) (3) Variables beginning position -0.21** -0.15* -0.14 (0.10) (0.10) (0.10) pressure network 0.27*** 0.19** 0.21*** (0.08) (0.09) (0.08) dunums 0.32*** 0.37*** 0.36*** (0.09) (0.09) (0.09) greenhouses -0.13 -0.09 -0.16* (0.09) (0.12) (0.09) owner 0.24*** 0.17** 0.19** (0.08) (0.08) (0.08) ps33 0.33*** (0.12) ps55 0.19 (0.14) mh 0.21* (0.12) ps91 -0.24*** (0.10) Prob > F 0.0000 0.0000 0.0000 R2 0.2328 0.2809 0.2728 Adj. R2 0.1997 0.2300 0.2349 No. Obs. 122 122 122 *Significant at 10%, **significant at 5%, ***significant at 1%.

675 Appendix G – WUA Contract Example

The following is an example of a contract that is signed every year between a water user association (WUA) and the Jordan Valley Authority. The contains are generally the same for all WUAs but do differ in terms of what tasks are transferred depending on the development level of the WUA. There are also slight differences in the salaries for the head or water official, depending on education level and other concerns as outlined in Appendix F. The particular contract herein displayed is the one between the WUA at PS 91 and the JVA in 2010.

What follows is a translation of the contract that was originally produced in Arabic. This version was provided by Akram Rabadi of the United States Agency for International Development’s Institutional Support and Strengthening Program.

In the Name of Allah Most Gracious Most Merciful Irrigation water distribution tasks transfer agreement between the Jordan Valley Authority and pump 91 “Baladna” Water Users Association

First Party: Ministry of Water and Irrigation / Jordan Valley Authority represented by the Secretary General of Jordan Valley Authority, hereinafter referred to as Authority;

Second Party: the pump 91 “Baladna” Association, represented by the Chair of the Association and hereinafter referred to as the Association

Association mandate area: The area that is irrigated from King Abdullah Canal by pump 91 including the Development Area 21.

Introduction

Management and development of water resources is one of Jordan Valley Authority’s main tasks within its mandate area demonstrated in the Jordan Valley Development Law. Enhancing the efficiency of irrigation water utilization is a main goal for the Authority, and is included in the water sector strategy, irrigation water policy and the Authority’s strategic plan issued by the Ministry of Water and adopted by the Jordanian Government.

These strategies and policies also stipulate that farmers should participate in the irrigation water management as an option for improving irrigation water management and to raise the efficiency of use through water users associations.

The Authority adopts this option based on the Jordan Valley Development Law that allows the Authority to pass over to any private sector authority the management, 676 operation and leasing of any of the completed projects or those in the process of being completed. Accordingly, the Authority is legally allowed to pass over some or all of the tasks of water distribution to water user societies. Therefore the Authority and Pump 91 Water Users Association have agreed to handle the task of the retail irrigation water distribution, on behalf of the Authority, for the purpose of increasing of fair water distribution between farmers, lowering of costs, and benefiting from the expertise and capacities of farmers in distributing irrigation water, as well as the following:

First Article

The agreement introduction and the annexes listed below are considered an indivisible part of this agreement and are considered collectively as one unit. The annexes include:  The Association’s registration document and the annual reports  Water distribution cadre details  The Authority’s technical report of the irrigation network concerning the Pump 91 Station  The Association performance evaluation standards as agreed on between the two parties

Second Article

First party’s commitments:

1. The first party commits to paying an annual sum of twenty-three thousand seven hundred JD (23700) to cover the cost of water distribution as per the tasks carried out by the Association and agreed upon by both parties. The second party submits all monthly due financial claims (1975 JD) organized and audited properly. 2. The first party commits to the rehabilitation of the area that is irrigated by the pump 91 according to a set time frame, as per related attached annex and as per agreement between both parties. 3. The first party commits to carrying out all possible maintenance procedures concerning water distribution management within the pump 91 irrigation area as per attached technical report. 4. The first party commits to coordinating with the second party in preparing the maintenance and rehabilitation programs for the main lines and the pump. 5. The first party commits to keeping its cadre for a transitional period to accompany the Association’ cadre for a maximum period of three months after signing the agreement. 6. The first party commits to preparing and amending of the daily irrigation schedules in coordination with the second party.

677 7. The first party commits to coordinating with the second party in preparation of seasonal water budgets.

Third Article

The tasks and responsibilities of the second party: the responsibilities of the Water Users Association include the following:

 Participating alongside with the Authority in the management of irrigation water before the water resource (Pump). This participation includes: 1. Preparation of water budgets and irrigation schedules 2. Protection and control of water resources and facilities and informing the Authority of any violations.  Management of irrigation water distribution after the pump according to the following responsibilities: 1. Collection of water shares allocated to the pump area at the valves and main meters and their distribution according to the irrigation schedule. 2. Field supervision and violation and infringement control and presenting necessary reports in written form of any violations to the Authority and relevant parties. 3. Identifying problems related to the distribution of water within the working area of the Association and following up farmers’ complaints beyond the mandate area of the Association. 4. Collecting information of cropping patterns on a monthly basis (current agriculture and its surface area) in collaboration with the Authority. 5. Supervising and following up on farm units’ consumption. 6. Supervising and following up on water distribution from the pump to the irrigation network according to the irrigation water schedule. 7. Preparing monthly and any other reports regarding the progress of work according to the performance indicators, to be included with the monthly claim. 8. Appointment of qualified staff to implement the tasks of the Association. 9. Chair of the Association and his staff are committed to working hours according to irrigation schedules described in the annex on the distribution staff analysis.

Fourth Article

General regulations:

678  The duration of the agreement is one Gregorian year commencing on the date it was signed, and renewable upon both parties’ consent. In the case that either party should wish not to renew the agreement, the other party should be notified in writing two months prior to the ending of the agreement.  Regarding the differences or disputes that may generate between the parties during the contract period, the Ministry of Water, the Jordanian courts and the administrative governor are the powers of arbitration and governance between the two parties  If the second party fails to commit to their part of the obligation according to the provisions of the agreement, the first party has the right to carry out legal, administrative and financial procedures or terminate the agreement after notifying the second party in writing a month prior to ending of agreement.  If the first party fails to commit to their part of the obligations according to provisions of the agreement, the second party has the right to terminate the agreement after notifying the first party in writing a month prior to ending of agreement.  Any modification to the agreement is done with the consent of both parties.  The Association, as a respectable and moral body, is allowed to provide badges and special uniforms to distinguish its distribution management cadre.

First Party Second Party Secretary General of Chair of Pump 91 Jordan Valley Authority Water Users Association Engineer Mousa Al-Jamaani Mr. Ali Mustafa

Date: Saturday, 6/3/2010

Water Distribution Cadre / Pump 91 Water Users Association

The Association has proposed a water distribution concept based on the participation in carrying the responsibilities by appointing a cadre from outside the Association to assume responsibility for water distribution and under the supervision of the Management Committee of the Association, which holds the title of board of directors. The water cadre distribution is as follows:

The Association board of Directors carries out the following tasks:

 Preparation, approval and supervision of annual and seasonal plans of the Association in collaboration with the technical staff.

679  Follow-up the procedures of elimination of violation cases within the Authority and the relevant authorities (Administrator, the specialized courts).  Follow-up periodic reports of the Association including the achievements and performance evaluation.  Expansion of the farmers’ membership quota in the Association.  The appointment of the Association staff including the definition of qualifications and salaries, increases, penalties and incentives.

The Chair of Association or his delegate(s) in the board of directors, working part time and carries out the following tasks:

 Participation with the first party in the preparation of water budgets.  Monitoring the performance of distribution staff and performance assessment.  Follow up the implementation of the Agreement with the Authority.  Handling the farmers’ complaints.  Resolving water related disputes and conflicts among farmers and between farmers and the Authority.  Follow up on violations of water and reduction thereof.  Follow-up the preparation of performance indicators

Water Official or technical director (with a qualification of agricultural or irrigation engineering) and works during all times of distribution, carrying out the following tasks:

 Receipt of water allocated for the irrigation pump according to the schedule agreed upon between both parties.  To supervise the distribution of water on the main lines in coordination and participation with the ditch riders.  Field control and follow-up, including the follow-up of ditch riders.  Preparing of daily, weekly and monthly progress reports, including reports of violations and performance indicators.  Follow-up the filling out of operators’ forms within the pumping station and carrying out the necessary measures in coordination with the stage official.  Reporting of required maintenance cases in writing at the levels of the distribution network, farms’ turn-outs and the pumping station, and follow-up thereof with the first party.  Follow-up of an optimal operating of pumping stations (flow, pressure, main valves, excess consumption)

680

Ditchrider (graduate of tenth grade) performing the following tasks:

 Opening and closing water lines on the sub-main line  Monitoring and following up water distribution on the water line and agricultural units levels  Reporting of violations regarding illegal water use or vandalism of irrigation water distribution establishments  Reporting to the water official of any cases regarding maintenance or other matters related to water distribution  Preparation of water consumptions of agricultural units  Collecting information regarding cropping patterns on a regular (monthly) basis  Handling farmers’ complaints and working on solving them on the field or in cooperation with the water official or the Authority if needed  Coordination and cooperation with the water official and other ditch riders  Documenting field information regarding water distribution and handing it over to the water official  Registering readings of flow, pressure for certain chosen agricultural units by the first party

Treasurer/ data entry clerk: should be competent in using the computer and shall perform all administrative and financial assignments as follows:

 Entering all data and information concerning the work of the Association into the computer  Preparing field reports, minutes of meetings of the Association cadre and official correspondence  Preparing Association’s cadre budgets, salaries and various financial activities  Documentation and archiving  Entering the weekly and monthly water consumptions of agricultural units and the actual hours given to the farmers into the computer, in coordination with the water official according to the forms given by the first party

Association cadre distribution according to tasks

Responsibilities Cadre/Duration Participation before the pump: Participation in preparing water budgets Chair of Association, water official / Beginning of season 681 Participation in preparing irrigation Water official / seasonal schedules Participation in preparing the maintenance Water official / daily program Authority transfer: Implementation of the irrigation schedule Chair of Association, water official / daily / water distribution Ditchrider / daily Field supervision / violation reporting Water official, ditchrider / daily Collection of cropping pattern data Ditchrider / monthly Preparation of main meters’ readings Ditchrider / daily Collection of data on agricultural units’ Ditchrider / monthly water consumption Documentation, archiving and preparing Data entry clerk (treasurer) / daily reports Preparing of annual budget and financial Accountant (treasurer) activities Coordination and collaboration with Chair of Association, water official, donors projects association management committee

Association cadre distribution task costs

Job Number Monthly Number of Total Annual salary / JD work days Sum / JD Chair of 1 400 15 / month 2400 Association Water official, 1 550 30 / month 6600 including transportation Ditchrider, 3 350 30 / month 12600 including transportation Data entry clerk Yearly sum 600 Administrational expenses and supplies and management committee 1000 sessions allowances Maintenance workers’ wages 500 Total twenty-three thousand seven hundred Jordan Dinar 23700

Risks and hazards analysis

Item Responsible party Necessary procedure Main water resources Jordan Valley The availability of additional water scarcity Authority resources

682

Involvement of farmers in the preparation of water budgets

The future availability of information transparency Providing a qualified Water Users Setting standards for the selection technical cadre by the Association of qualified staff according to their Association Jordan Valley responsibilities Authority Training and following-up the performance of the Association cadre Technical status of the Jordan Valley Preparing a periodic schedule for irrigation network Authority the maintenance of the irrigation project, operating it according to the original design and committing thereto when signing the contract Increasing the farmers Water Users Increasing the representation of representation quota in Association farmers to 80% during the first year the Association of the Agreement Difficulty of Jordan Valley Developing a mechanism to enable monitoring and Authority the distribution cadre to control and following up the monitor the irrigation turn-outs on a distribution of water for permanent basis some irrigation turn- outs located within the area of the agricultural units Unclear violation Jordan Valley Coordinating with the Association control and reporting Authority to establish clear foundations for mechanisms control and report of violations, similar to the role of the security and protection unit of the water canal Repeated power cut- Jordan Valley To inform the Association offs for the pumps Authority beforehand about the dates of during water electricity cut-offs and to distribution compensate farmers for the water loss in coordination with the Association

683 Weak immediate Jordan Valley Providing the area with a qualified response in conducting Authority maintenance cadre with alongside maintenance tasks with the necessary equipment Low water level of the Jordan Valley Develop a mechanism to maintain canal Authority constant levels through the Directorate of Control Effect of water quality Jordan Valley Improvement and development of on the distribution Authority treatment methods for sediments network and plankton in the irrigation water Successful cooperation Jordan Valley Determining a mechanism agreed between the two Authority upon by both parties to achieve Associations coordination and communication between both societies in water management

Performance indicators of the Water Users Association work for the year 2010/2011

First indicator: penalties

Month Number of Number of Percentage Percentage Change percentage vandalizing penalties for of of penalties Increase Decrease penalties illegal use vandalizing for illegal of water penalties to use of water the total to the total penalties penalties 1 2 3 4 5 6 7 8 9 10 11 12

684 Second indicator: water consumption

Month Water Actual water Number of Total of Percentage Change percentage quantity consumption exceeded exceeded of Increase Decrease according quantity in units in quantity exceeding to the m3 consumption in m3 rate to the irrigation irrigation order in order m3 quantity 1 2 3 4 5 6 7 8 9 10 11 12

Note: To consider a unit exceeding the water consumption is when the proportion of consumption exceeds 10% of the specified irrigation order.

Third indicator: fairness of distribution

Month Number of Percentage Number of Percentage Number of Percentage units whose relative to units whose relative to units whose relative to consumption total units consumption irrigation consumption irrigation of water is of water is order of water is order identical to less than the exceeding the the irrigation irrigation irrigation ±5% order order 1 2 3 4 5 6 7 8 9 10

685 11 12

Fourth indicator: complaints

Month Number of Type of complaints Change percentage complaints Maintenance Quantity Pressure Increase Decrease shortage lack 1 2 3 4 5 6 7 8 9 10 11 12

Fifth indicator: number of maintenance cases

Month Type of maintenance Change percentage Flow meter Flow Valve Pressure Increase Decrease limiting regulator device 1 2 3 4 5 6 7 8 9 10 11 12

686 Appendix H – Determination of WUA Salaries

The following is a translation by the author of a document acquired from Ali al-Omari of the Jordan Valley Authority’s Water User Association Unit in the Jordan Valley entitled “Water User Water User Associations in the Jordan Valley: The Basis for Calculating the Salaries of Technical Personnel and Administrative Staff” (2011). This document is used by the JVA to determine the salary of all employees within the WUAs and these salary amounts are then made official within the contracts signed between each WUA and the JVA.

The text of this document is as follows and outlines the salary conditions for each type of WUA employee:

President:

The salary cap for the president of the association is set at 600 Jordanian dinars per month and the lowest salary can be 225 Jordanian dinars. The salary for the president is determined based on two factors that each potentially can be 300 Jordanian dinars:

1. The number of farm units under the association, such that with more units, the salary increases as per the table below. Number of Farm Units Monthly Amount (JD) 0 -> 100 75 101 -> 200 150 201 -> 300 225 > 300 300

2. Whether the president works in the association full-time (30 days a month) or part-time (15 days a month). If the president is full-time, he earns 300 Jordanian dinars per month and if he is part-time, he earns 150 Jordanian dinars per month.

The following table demonstrates the possible salary calculations for the association president:

Number of Farm Amount Earned Amount Earned Units if Full Time (JD) if Part Time (JD) 0 - > 100 375 225 101 -> 200 450 300 201 -> 300 525 375 > 300 600 450

687 Water Official:

The salary of the water official ranges from 385 to 700 Jordanian dinars per month. It is also determined by a series of factors: education level (20%, potential 140 JD), years of experience (20%, potential 140 JD), number of farm units (30%, potential 210 JD) and tasks (30%, potential 210 JD).

1. Education Level

Education Level Percentage Earned Amount Earned (JD) Bachelor’s Degree 20% 140 Intermediate Diploma 15% 105 High School or Below 10% 75

2. Years of Experience

Years of Experience Percentage Earned Amount Earned (JD) More than 15 Years 20% 140 Less than 15 Years 15% 105

3. Number of Farm Units

Number of Farm Units Percentage Earned Amount Earned (JD) More than 400 30% 210 Less than 400 15% 105

4. Tasks

Nature of Tasks Percentage Earned Amount Earned (JD) Distribution, Maintenance 30% 210 and Administrative Work Partial Tasks 15% 105

The water official is also given a monthly transportation allowance determined by the number of farm units in the association, as per the table below:

Number of Farm Units Amount of Transportation Allowance (JD) 0 -> 100 100 101 -> 200 150 201 -> 300 200 > 300 250

688

Ditchriders:

The salary for a ditchrider is set at 250 Jordanian dinar, with each ditchrider theoretically covering 60 to 70 farm units. The monthly transportation allowance for a ditchrider is 100 Jordanian dinars a month.

Data Entry and Secretary:

The person in charge of data entry and who acts as the secretary of the association is given 750 Jordanian dinars per year. This employee exists in the case where the water official does not undertake data entry.

Administrative Expenses:

These expenses include stationary (paper), rent, and electrical, water and other maintenance activities. For associations with over 200 farm units, the yearly allotment is 750 Jordanian dinars and for associations with under 200 farm units, the yearly allotment is 500 Jordanian dinars.

689 Glossary of Abbreviations

DoS Department of Statistics (Jordan) ESCWA Economic and Social Commission for Western Asia FAO Food and Agriculture Organization FTA Farm Turnout Assembly GCC Gulf Cooperation Council GDP Gross Domestic Product GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit ISSP Institutional Support and Strengthening Program JCC Jordan Cooperative Corporation JD Jordanian Dinar JV Jordan Valley JVA Jordan Valley Authority KAC King Abdullah Canal MCM Million Cubic Meters MH Mazraa-Haditha MWI Ministry of Water and Irrigation NPR National Public Radio OLS Ordinary Least Squares PS Pump Station SES Socioeconomic Status SO Stage Office UAE United Arab Emirates UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees USAID United States Agency for International Development USGS United States Geological Survey WAJ Water Authority of Jordan WTO World Trade Organization WUA Water User Association WWAP World Water Assessment Programme

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