Tadele Mamo, 2011. Impact of Productive Safety Net Program on Asset Accumulation and Sustainable Land Management

IMPACT OF PRODUCTIVE SAFETY NET PROGRAM ON ASSET ACCUMULATION AND SUSTAINABLE LAND MANAGEMENT PRACTICES IN THE CENTRAL RIFT VALLEY: THE CASE OF ADAMITULU JIDO KOMBOLCHA AND MESKAN DISTRICTS

M. Sc. Thesis

TADELE MAMO

October 2011 Haramaya University

IMPACT OF PRODUCTIVE SAFETY NET PROGRAM ON ASSET ACCUMULATION AND SUSTAINABLE LAND MANAGEMENT PRACTICES IN THE CENTRAL RIFT VALLEY: THE CASE OF ADAMITULU JIDO KOMBOLCHA AND MESKAN DISTRICTS

A Thesis Submitted to the School of Agricultural Economics and Agribusiness Management, School of Graduate Studies,

HARAMAYA UNIVERSITY

In Partial Fulfilment of the Requirements for the Degree of MASTER OF SCIENCE IN AGRICULTURE (AGRICULTURAL ECONOMICS)

By Tadele Mamo

October 2011 HARAMAYA UNIVERSITY

SCHOOL OF GRADUATE STUDIES HARAMAYA UNIVERSITY

As thesis research advisor, I hereby certify that I have read and evaluated this thesis prepared, under my guidance, by Tadele Mamo entitled “IMPACT OF PRODUCTIVE SAFETY NET PROGRAM ON ASSET ACCUMULATION AND SUSTAINABLE LAND MANAGEMENT PRACTICES IN THE CENTRAL RIFT VALLEY: THE CASE OF ADAMITULU JIDO KOMBOLCHA AND MESKAN DISTRICTS”. I recommend that it be submitted as fulfilling the thesis requirement.

Chilot Yirga (PhD) ______Major Advisor Signature Date

As members of the Examining Board of the Final M. Sc. Open Defence, we certify that we have read and evaluated the thesis prepared by Tadele Mamo. We recommend that it be accepted as fulfilling the thesis requirement for the degree of Master of Science in Agriculture (Agricultural Economics).

______Chair Person Signature Date ______Internal Examiner Signature Date ______External Examiner Signature Date

DEDICATION

I dedicate this thesis manuscript to all of my family members.

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STATEMENT OF AUTHOR

First, I declare that this thesis work is my bonafide work and that all sources of materials used for this thesis have been properly acknowledged. This thesis has been submitted in partial fulfilment of the requirements for M.Sc. degree at the Haramaya University and is deposited at the University Library to be made available to borrowers under rules of the Library. I seriously declare that this thesis is not submitted to any other institution anywhere for the award of any academic degree, diploma or certificate.

Brief quotations from this thesis are allowed without special permission provided that accurate acknowledgement of source is being made. Requests for permission for extended quotation or reproduction of this manuscript in whole or in part may be granted by the head of the department of Agricultural Economics or the Dean of the School of Graduate Studies when in his or her judgment the proposed use of the material is in the interest of scholarship. In all other instances, however, permissions must be obtained from the author.

Name: Tadele Mamo Signature: ______Place: Haramaya University, Haramaya Date of Submission: ______

BIOGRAPHICAL SKETCH

The author was born on August 18, 1983 in Abichuna Nye’a district of North Shewa Zone. He attended his elementary, junior and high schools in Gara Botora Elementary School, Mendida Junior Secondary School and Debre Berihan Hailemariam Mamo Comprehensive Secondary School, respectively.

Then he joined Haramaya University (the then Alemaya University) in 2001 and graduated with B.Sc. degree in Agribusiness Management in July 2005. After his graduation, he was employed in the ministry of agriculture, Ardaita ATVET College as a junior instructor of agricultural economics where he worked for two semesters until he joined the Ethiopian Institute of Agricultural Research (EIAR). He has been working in EIAR, Holetta Research Center, in project planning, monitoring and evaluation department as a junior researcher for three years until he got the chance to rejoin Haramaya University in October 2008 to pursue his M.Sc. study in Agricultural Economics.

ACKNOWLEDGMENTS

I have got helpful assistances from many persons and institutions at various levels. It is impossible to acknowledge all the individuals and institutions that supported me in conducting this research since they are so many to list by their names here. Nevertheless, I would like to take the chance to acknowledge some of them who have helped me in a special way.

First of all, my appreciation and gratitude goes to my research advisor, Dr. Chilot Yirga, for his invaluable advices and guidance. I greatly acknowledge him for his allocating his golden and busy time for my research work. Without his encouragement, stimulation and professional support the thesis work would have not been completed. I would like to thank Mr. Mistiru Tesfaye, W/ro Yewbdar Tadesse and W/ro Genet Ayele for hosting me throughout my study time. I have really no words to express their support in all aspects. Mr. Dagne Getachew, Mr. Wudneh Getahun, Mr. Yohannis Gojam, Mr. Zenebe Admasu, W/ro Tenagne H/silassie, Mr. Kasaye Negash and Mr. Alemu Tolemariam should be acknowledged for their special support.

I am very glad to acknowledge the sample farmers for their willingness and patience in responding me to my questionnaire at the expense of their invaluable time. If they had not extended their cooperation, it would have been impossible to complete this thesis. I am also indebted to extend my acknowledgement to Adamitulu Jidokombolcha and Meskan districts agriculture and rural development office with a special thank to food security departments for their support and guidance in provision of basic information. I would like to thank all staff members of Holeta agricultural research center for their encouragements and support.

I would like to thank Wageningen University which funded this research through ILCE (Improving livelihoods and resource management in the central rift valley of Ethiopia). I also thank Dr. Aad Kessler from land degradation and development group and Dr. Huib Hengsdijk from plant research international of Wageningen University for their comments.

Last but not least; I would like to express my heartfelt appreciation and gratitude to all my family for their support and encouragement. Above all, I praise God the almighty and Saint Marry, for allowing me to make my dreams come true after a very difficult journey and a burdensome effort.

LIST OF ACRONYMS

ACF Action Contre la Faim BoARD Bureau of Agriculture and Rural Development CFSTF Community Food Security Task Force DPPC Disaster Prevention and Preparedness Commission DS Direct Support EDRP Emergency Drought Recovery Program EGS Employment Generation Scheme ESAFW Environmental and Social Arrangement Framework FAO Food and Agriculture Organization FDRE Federal Democratic Republic of Ethiopia FFSCB Federal Food Security Coordination Bureau FFSP Federal Food Security Project FSTF Food Security Task Force GTZ German Technical Cooperation IDB International Development Business IDS International Development Studies KFSTF Kebele Food Security Task Force MoARD Ministry of Agriculture and Rural Development NGO Non-Governmental Organization OFSP Other Food Security Programs PASDEP Plan for Accelerated and Sustained Development to End poverty PIM Programme Implementation Manual PSNP Productive Safety Net Programme PW Public Works SC_UK Save the Children (United Kingdom) SWC Soil and Water Conservation SLM Sustainable Land Management SNTG Safety Net Targeting Guideline UNDP United Nations Development Programme USAID United States Agency for International Development WFSTF Woreda Food Security Task Force

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TABLE OF CONTENTS

STATEMENT OF AUTHOR iv

BIOGRAPHICAL SKETCH v

ACKNOWLEDGMENTS vi

LIST OF ACRONYMS vii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF TABLES IN THE APPENDIX xiii

ABSTRACT xiv

1. INTRODUCTION 1 1.1. Background 1 1.2. Statements of the Problem 4 1.3. Research Questions 4 1.4. Objectives of the Study 5 1.5. Significance of the Study 5 1.6. Scope and Limitations of the Study 6 1.7. Hypotheses to be tested 6 1.8. Organization of the Thesis 7

2. LITERATURE REVIEW 8 2.1. Concepts of Food Security and Productive Safety Net Programs 8 2.1.1. Definitions and overview of food security 8 2.1.2. Food security strategy of Ethiopia 9 2.1.3. Productive safety net program 10 2.1.4. Other food security programs 12 2.1.5. Concept of asset and asset accumulation in the context of farmers 12 2.2. Land Degradation and Sustainable Land Management Measures 13 2.2.1. Land degradation problems in Ethiopia 13 2.2.2. Sustainable land management 14 2.2.3. Previous approaches used to promote sustainable land management in Ethiopia 15

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TABLE OF CONTENTS (CONTINUED)

2.2.4. Sustainable land management technologies 15 2.4. Impact Assessment Concepts and Approaches 17 2.4.1. Concept of impact assessment 17 2.4.2. Impact assessment approaches 18 2.5. Empirical Studies on SLM Practices and Impact of Productive Safety Net Program 22 2.5.1. Studies on sustainable land management practices 22 2.5.2. Studies on the impact of productive safety net program on asset accumulation and sustainable land management practices 23 2.5.3. Studies on the application of propensity score matching method 24 2.5.4. Studies on the application of Heckman’s two-step model 25

3. RESEARCH METHODOLOGY 26 3.1. Description of the Productive Safety Net Program in the Study Area 26 3.2. Description of the Study Area 27 3.3. Sources of Data, Methods of Data Collection and Sampling Technique 29 3.3.1. Sources and Methods of data collection 29 3.3.2. Sampling technique 29 3.4. Methods of Data Analysis 30 3.4.1. Descriptive statistics 30 3. 4.2. Econometric models 31 3.5. Definitions and Choices of Variables 42 3.5.1. Choice and descriptions of variables included in PSM model 42 3.5.2. Choices and descriptions of variables included in Heckman’s two step model 45

4. RESULTS AND DISCUSSION 52 4.1. Descriptions of Sample Households' Characteristics 52 4.1.1. Households’ demographic background and farm characteristics 52 4.1.2. Plot level characteristics of the households 54 4.1.3. Perceptions of households 55 4.1.4. Access to extension and credit services 58 4.1.5. Farming system and crops grown 60

TABLE OF CONTENTS (CONTINUED)

4.1.6. Household income 63 4.1.7. Use of sustainable land management practices 64 4.2. Econometric Estimation Results 67 4.2.1. Propensity score matching model result 67 4.2.2. Heckman’s two-step model result 77

5. SUMMARY, CONCLUSIONS AND RECOMMENDATION 86 5.1. Summary 86 5.2. Conclusions 88 5.3. Recommendation 88

6. REFERENCES 90

7. APPENDICES 98

LIST OF TABLES

Table Page

Table 1. Distribution of sample households by kebeles ...... 30 Table 2. Variables related to asset accumulation that included in PSM...... 43 Table 3. Variables related to SLM that included in propensity score matching model ...... 44 Table 4. Variables included in Heckman’s two-step model for SLM activities...... 50 Table 5. Descriptive statistics of household characteristics (Continuous variables) ...... 53 Table 6. Descriptive statistics of sample households (Dummy variables) ...... 53 Table 7. Farmer perception of plot characteristics, CRV of Ethiopia, 2010 ...... 54 Table 8. Perception of sample households on land degradation and land tenure, 2010 ...... 56 Table 9. Symptoms of land degradation problems in the study area, 2010 ...... 56 Table 10. Consequences of land degradation problems in CRV of Ethiopia, 2010 ...... 57 Table 11. Farmers’ perception to profitability of SLM in CRV of Ethiopia, 2010 ...... 58 Table 12. Access to development agent and training of sample households in 2009 ...... 59 Table 13. Credit access and types received by sample households in the last five years ..... 59 Table 14. Crops grown and percent of area allocated for each crop in 2009 in CRV ...... 61 Table 15. Livestock holding and change in livestock holding of households (TLU) ...... 62 Table 16. Mean values of assets of sample households (ETB) ...... 63 Table 17. Mean income of PSNP and non PSNP households (ETB), 2009 ...... 64 Table 18. Plots under selected agronomic practices in the study area, 2009 ...... 65 Table 19. Fertility improvement and physical SWC practices in the study area in 2009 .... 66 Table 20. Logit results of household program participation ...... 69 Table 21. Distribution of estimated propensity scores ...... 71 Table 22. Matching performance of different estimators ...... 73 Table 23. Propensity score and covariate balance ...... 74 Table 24. Chi-square test for the joint significance of variables ...... 75 Table 25. Average treatment effects on the treated (ATT)...... 76 Table 26. Factors affecting chance of participation and intensity of using soil bund (m/ha)80 Table 27. Factors affecting chance of participation and intensity of using chemical fertilizer (Kg/ha) ...... 83 Table 28. Factors affecting likelihoods of participation and intensity of manure (tons/ha) . 85

LIST OF FIGURES

Figure Page

Figure 1. Map of Central Rift Valley of Ethiopia and its woredas’ ...... 28 Figure 2. Region of common support condition ...... 38 Figure 3. Kernel density of propensity score distribution ...... 70 Figure 4. Kernel density of propensity scores of participant households ...... 71 Figure 5. Kernel density of propensity scores of non participant households ...... 72

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LIST OF TABLES IN THE APPENDIX

Appendix Table Page

1. Multicollinearity test for continuous variables included in PSM model…………………..99 2. Contingency coefficient for discrete variables included in PSM model...... 99 3. Multicollinearity test for continuous variables included in Heckman two-steps model….100 4. Contingency coefficient for discrete variables included in the Heckman two step model.100 5. Conversion factors used to estimate tropical livestock units (TLU)……………………...101 6. Logit results of program participation based on SWC outcome indicators...... 102 7. Logit results of program participation based on asset accumulation outcome indicators...103 8. Matching performance of different estimators for SWC outcome indicators and asset accumulation outcome variables...... 104 9. Propensity score and covariate balance test for SWC outcome variables………………..105 10. Propensity score and covariate balance test for asset accumulation…………………….106 11. Survey questionnaire…………………………………………………………………….107

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IMPACT OF PRODUCTIVE SAFETY NET PROGRAM ON ASSET ACCUMULATION AND SUSTAINABLE LAND MANAGEMENT PRACTICES IN THE CENTRAL RIFT VALLEY: THE CASE OF ADAMITULU JIDO KOMBOLCHA AND MESKAN DISTRICTS

ABSTRACT

The main objective of this study was to assess the impact of productive safety net program (PSNP) on asset accumulation and sustainable land management (SLM) practices and to identify factors affecting SLM practices in Adamitulu Jido Kombolcha and Meskan districts of Central Rift Valley (CRV) of Ethiopia. Both primary and secondary data were used for this study. The primary data were collected through structured questionnaire from 95 PSNP and 91 non PSNP sample households of both districts operating 557 farming plots. Propensity score matching (PSM) was used to assess the impact of PSNP on asset accumulation and SLM practices whereas Heckman’s two-step model was used to identify factors affecting the likelihood of participation in SLM practices in the first step and the intensity of use in the second step. The PSM estimation results show that participation in PSNP had not brought any significant impact on both asset accumulation and SLM practices. The Heckman’s two-step estimation results show that the likelihood of participating in soil bund was positively affected by steepness of slope whereas the intensity of use was positively influenced by plot distance, steepness of slope and participation in other food security program (OFSP), and negatively influenced by livestock ownership and participation in PSNP. The result also shows that perceived profitability of using chemical fertilizer and being Meskan district influenced the likelihood of participating in chemical fertilizer positively while off-farm income had a significant negative effect on it. Plot size, livestock ownership and being Meskan district had a positive impact on the intensity of chemical fertilizer use whereas age and education of the household head, land holding, distance of a plot and soil fertility status had a negative influence on it. Plot size and perceived profitability of using manure had a significant positive impact on the likelihood of participating in manure use. Plot size and perceived profitability of using manure had a significant positive impact on the intensity of using manure whereas land holding, plot distance, off-farm income and being Meskan district had a negative effect on it. It can be recommended that policy makers should be careful in designing and implementing huge social programs such as PSNP in the way that it contributes in asset accumulation and SLM practices. Farm and plot level characteristics should be incorporated in designing physical soil and water conservation activities and soil fertility improving practices besides household demographic background, socio-economic and institutional factors to bring a positive effect in the study area.

Key words: PSM, Heckman’s two-step, Meskan, Adamitulu Jido Kombolcha, PSNP, impact.

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1. INTRODUCTION

1.1. Background

Ethiopia’s economy is highly dependent on agriculture and related activities. Agriculture alone contributes 42% to the total gross domestic product (GDP), provides livelihood to about 80% of the population, constitutes more than 80% of the nation’s total exports, and provides most of the foreign exchange earnings to the economy (United States Department of States, 2011). Agriculture is also a main source of raw materials for industries. However, in spite of its great significance in the Ethiopian economy, the performance of the agriculture sector until recently has been dismal. Growth in agricultural production has stagnated over the last five decades (Takele, 2004). Per capita food production has been declining and the pace of food production was lagged behind the population growth over the last three decades. FAO (2005) projected cereals production for 2009 to be 10 million metric tonnes whereas the requirement is projected to be 11.3 million metric tonnes indicating a gap of 1.3 million metric tonnes. The same source indicated that the projected cereals production for 2015 is 11.3 million metric tonnes while the requirement is 13.1 million metric tonnes leaving a projected gap of 1.8 million metric tonnes.

Factors contributing for the poor performance of agricultural sector include, among others, frequent drought, extreme fluctuations of rains, low levels of agricultural technology generation and utilization, population growth and land degradation in the form of soil erosion, loss of soil fertility, salinization and moisture stress (Bekele and Holden, 2000). Besides, the basic structure of the agricultural sector itself has contributed to its low production and productivity. Ethiopian agriculture is dominated by resource poor peasant farmers who primarily produce for subsistence. The smallholder farmers, who are also providing almost all the agricultural output in the country, commonly employ traditional production technologies and use limited modern inputs. Agricultural production is basically based on traditional farming practices where the use of improved agricultural technologies and natural resource management are very limited (Million and Belay, 2004; Setotaw, 2006)

The collapse of agriculture to feed the growing population and depletion of natural resource base forced the country to be food insecure and hence dependent on food aids for the last four decades (Gilligan et al., 2008). It is reported that Ethiopia takes the first rank in food aid recipient countries in Africa and listed in those countries of largest aid recipients in the world for the past two decades (Little, 2008). Although emergency food aid has been contributing a lot to alleviate starvation, it had its own problems. For instance, uncertainty of the food aid, poor timing of provision and insufficient quantity of aid for the individual beneficiary was among the main defects of food aids in the past and creating dependency syndrome on the recipients (Andersson et al., 2009).

In an attempt to break the cycle of annual appeal of food aid and achieve an acceptable level of food security at macro (national) and micro (household) level, the government of Ethiopia developed a food security strategy (FSS) in November 1996 (FDRE, 1996). The FSS highlighted government plans to address the causes and effects of food insecurity in Ethiopia. Based on the FSS, the government designed regional food security programs and projects in 2002 (FDRE, 2002a). Subsequently, to address the long term problem of food insecurity and to change the previous system of annual emergency appeals, the Ethiopian government together with an association of donors like the World Bank, United States Agency for International Development (USAID), Canadian International Development Agency (CIDA), and several European donors initiated a new social protection program known as the Productive Safety Net Program (PSNP) in 2005.

The prime aims of the productive safety net program are to reduce household vulnerability; improve household and community resilience to shocks and stresses and to break the cycle of dependence on food aid through two main components – public works (PW) and direct support. The aim of PW component of PSNP is to mitigate the impacts of climatic and food insecurity risks on chronically food-insecure households by creating employment opportunity to “able-bodied” labourers. It is the most important element of the PSNP and creates a labour market for unskilled labour, primarily by involving them in labour-intensive, community- based activities like soil and water conservation, feeder roads, social infrastructures such as primary schools and health posts, water supply projects, and small scale irrigation. Direct support is a small portion of PSNP and delivers assistance to members of the community who cannot participate in PW but food insecure and require assistance (Andersson et al., 2009).

The productive safety net program is expected to enhance sustainable land management by supporting farm households in constructing soil and water conservation practices and increasing farmers' investment capacities in sustainable land management (SLM) and other public works to ensure the long term food security of the country.

Although it is widely believed that incentives are important to adopt technologies like SLM practices at the farm level, experiences have revealed that farmers may not invest in appropriate technologies. They may reject or discontinue many useful technologies and adopt other technologies instead for various reasons (Guerin, 1999; McDonald and Brown, 2000; Soule et al., 2000).

Past studies in Ethiopia have shown that food for work programs (which had similar aims with PSNP except that PSNP focuses continuously on selected households over several years and it will eventually be phased out) had a positive influence on the probability of fertilizer adoption (Sosina and Holden, 2007). However, other studies have indicated that it also had negative impacts on agricultural intensification (Barrett et al., 2004), short-term soil conservation measures (Berhanu and Swinton 2003), informal risk sharing (Dercon and Krishnan, 2004), and growth of livestock holdings (Gilligan and Hoddinott, 2007). A study conducted by Andersson et al. (2009) evaluated the impacts of the Ethiopian PSNP on rural households' holdings of livestock and forest assets including trees. This study found that program participation had a positive effect on number of trees planted but did not have effect on livestock holdings.

The research findings conducted so far have focused on the direct impacts of the PSNP, especially on asset accumulation and improving food security status of the participating households. In other words, the impact of PSNP on investment in sustainable land management was not conducted in the country in a sufficient level though the public component of the PSNP is thought to bring a positive effect on the participating households. This is especially true in the case of central rift valley (CRV) of Ethiopia where there was no sufficient study carried out to evaluate the impact of PSNP so far. Therefore, this thesis aimed to assess the impact of PSNP on asset accumulation and on SLM practices, and assessing factors affecting participation SLM practices and intensity of using these practices in the CRV of Ethiopia. 3

1.2. Statements of the Problem

Food insecurity can be said to be the identification of Ethiopia in terms of recurrent food crisis and famines, and responses to food security have conventionally been dominated by emergency food-based interventions. However, the past decades of large scale food aid deliveries have done little to prevent households' asset depletion because of ignorance of incorporating these aids with natural resource management (Devereux et al., 2006). Recognizing this, the government of Ethiopia changed the emergency food based assistance to multi-year PSNP in 2005 that provide transfers to food insecure households with the aim of breaking dependency on food aid in the long term. These transfers are expected to be used partly to meet immediate consumption needs, but also partly invested in farming and enterprise activities (Devereux et al., 2006).

Central rift valley of Ethiopia is one of the chronically food insecure areas where PSNP has actively been implemented together with other food security programs (OFSP) to change the life of households. One of the approaches adopted for enabling the farming households of the area to protect asset depletion at household level and invest in SLM practices to increase productivity of their land is the PSNP. Even though several attempts have been made to evaluate the general impact of PSNP countrywide, there are limited empirical evidences whether or not the program efforts has the intended effect on asset accumulation (asset protection from depletion) and investment in SLM by program participants. Therefore, this study tried to assess the impact of productive safety net program on asset formation (accumulation) through analyzing the change in asset holding and investment in SLM practices over the intervention periods of PSNP in the CRV of Ethiopia.

1.3. Research Questions

The research tried to answer the following key questions:

1. Did participation in PSNP help the participants in increasing asset accumulation?

2. Did participation in PSNP help the participants in applying SLM practices?

3. What factors affect SLM practices in the study area?

1.4. Objectives of the Study

The overall objective of this study is to assess the impact of productive safety net program on farm households' asset accumulation and sustainable land management practices. Under this general objective, the following specific objectives were set:

i. To assess the impact of participation in PSNP on asset accumulation. ii. To assess the impact of participation in PSNP on sustainable land management practices. iii. To identify factors affecting participation in sustainable land management practices and the intensity of use.

1.5. Significance of the Study

As indicated earlier, asset accumulation, natural resource management and environmental protection are undoubtedly a major means of poverty reduction. Therefore, the current study has produced, at least in the study area, relevant information regarding the role of PSNP on sustainable land management. Identifying the role of PSNP on sustainable land management helps to predict to what extent SLM in the area can be increased with the application of PSNP.

The result of the study benefits farmers, rural and agriculture development offices and NGOs that operate in the field of SLM by revealing the existing situation in the study area. It also enhances understanding of the role of PSNP that contributes to asset accumulation at the household level and see the role of PSNP on SLM. The empirical information that was produced through this study serves as a basic document for future reference and existing knowledge improvement. Extension agents, policy makers, and researchers concerned with food security and sustainable land management can use the result of the study. Farming households also benefited from the result of this paper by receiving better services from woreda agricultural offices and NGO that are informed the existing situation through this paper.

1.6. Scope and Limitations of the Study

Central rift valley is severely affected by human activities. The rapidly growing population has led to an increased claim on natural resources. A large part of the area is heavily deforested for charcoal production and agricultural activities. The agricultural area has increased considerably, while changing agricultural practices have increased further the pressure on the natural resources (Scholten, 2007). Most of the area is included in PSNP and other food security programs (OFSP) since 2005. Nonetheless, this research covered, without lose of quality, only two districts (Adamitulu Jido Kombolcha district of Oromia regional state and Meskan district of Southern Nations, Nationalities and Peoples (SNNPs) regional state simply because of budgetary and time constraints. Besides, the study used a sample of 186 farm households selected from four participating kebeles of which 109 sample households from Meskan and 77 sample households form Adamitulu Jido Kombolcha (ATJK) district, respectively.

Several limitations were faced during conducting this study. First, baseline data before the implementation of the program was not readily available. So, important data before the intervention of PSNP were collected from farmers through recall which might be subjected to recall biases and inexact answers. Second, the study did not assess the overall impact of the program, rather, the research focused only on its impact on asset accumulation or protection at household level and farmers' participation in sustainable land management practices. Finally, pre-intervention data on the application of chemical fertilizer, manure and compost as well as investment in physical soil and water conservation practices were not available because of the difficulty in recalling by the respondents.

1.7. Hypotheses to be tested

The study attempted to test the following hypotheses:

1. Participation in PSNP helps the participating households to accumulate assets and to invest in SLM practices; and 2. Several biophysical and socio-economic factors affecting SLM practices.

1.8. Organization of the Thesis

This thesis is organized into five chapters. Following this introduction, relevant literatures are reviewed and presented in chapter two. Chapter three describes the methodology which contains description of the program, description of the study area, data sources and data types, and the analytical tools. Chapter four describes the results and discussion of the study using both descriptive statistics and econometric models. Finally, chapter five presents the summary, conclusions and policy implications of the study.

2. LITERATURE REVIEW

This chapter presents key concepts, theoretical explanations and research findings related to this study. The chapter is classified into five sections. The first section discusses concepts of food security and productive safety net program. The second section describes land degradation and sustainable land management measures. The third section presents the linkage of productive safety net program, other food security programs and sustainable land management. The fourth section talks about concepts and approaches of impact assessment. The final section presents empirical studies on investment in sustainable land management, impact of productive safety net program, application of propensity score matching method and application of Heckman’s two steps model.

2.1. Concepts of Food Security and Productive Safety Net Programs

2.1.1. Definitions and overview of food security

Maxwell (1996) and Ehui et al. (2002) defined food security as physical, social and economic access by all people at all times to sufficient, safe and nutritious food which meets the dietary needs for an active and healthy life. This definition shows that food security can be ensured if and only if three conditions are fulfilled. First, sufficient food shall be available though domestic production and/or import. Second, people must have adequate resources to get the appropriate food. Third, food must be used in combination with adequate water, sanitation and health to meet nutritional needs. Similarly, Thomson and Metz (1997) defined food security as assuring to all human beings the physical and economic access to the basic foods they need. This definition comprises three closely related concepts: availability, stability and access. According to Haddad (1997) food security is achieved when people at all times have access to sufficient food for a healthy and productive life and has three main components: food availability, food access and food utilization.

Based on level of analysis, food security can be seen either at national level or at the household level. However, the household level of food security is probably the most

8 important for analyst, in so far as the household is the basic economic unit which determines the level of consumption by the individual. In most analysis, there is a presumption that income comes to the household as a whole, resource allocation decisions are made at the household level and household consumption is divided amongst its members in some relation to the needs of the individuals. The households are identified as food secure if their entitlements, or demand for food is greater than their needs, defined as the aggregation of individual requirements (FAO, 1997). Vulnerability is also seen as referring to factors placing people at risk of becoming food insecure or reducing ability to cope. Vulnerability is a function of exposure to risks/shocks and of resilience to risks/shocks are events that threaten people’s food access, availability and utilization and hence their food security status (FAO, 2004).

Chronic, cyclical and transitory food insecurity has been endemic in Ethiopia for several decades. The main causes of transitory food insecurity in Ethiopia are drought and war. Seasonality is a major cause of cyclical food insecurity. Structural factors contributing to chronic food insecurity include poverty (as both cause and consequence), the fragile natural resource base, weak institutions (notably markets and land tenure) and unhelpful or inconsistent government policies (Devereux, 2000). According to FAO (2006) food insecurity in Ethiopia is characterized by a chronic form affecting between 6 and 13 million people every year.

2.1.2. Food security strategy of Ethiopia

Ethiopia's Food Security Strategy (FSS), issued in November 1996, highlighted in the government plan to address causality and effect of food insecurity in Ethiopia (FDRE, 1996). The regional food security programs and projects were subsequently designed on the basis of this strategy. The revised food security strategy of the country was developed in 2002 which updated the original 1996 FSS by sharpening the strategic element to address food insecurity based on lessons learned to date (FDRE, 2002a).

Unlike the first strategy, the revised document targeted mainly to the chronically food insecure and moisture deficit pastoral areas. A clearer focus on environmental rehabilitation

9 as a measure to reverse the level of degradation as well as a source of income generation for food insecure households through focusing on biological measures like planting trees on the farm land marks a deviation from the 1996 strategy. Water harvesting and the introduction of high value crops, livestock and agro-forestry development further inform its content. In recognition that the pursuit of food security is a long-term and multi-sector challenge, institutional strengthening and capacity building is included as a central element of the strategy. As in the past, however, the overall objective of the FSS is to ensure food security at the household level (FDRE, 2002a). This strategy is mainly assisted by Agricultural Development Led Industrialization (ADLI) which focuses on creating the conditions for national food self-sufficiency FDRE, 2002b).

In line with the revised food security strategy, food security program (FSP) was designed in 2004 to improve the food security status of some fifteen million rural Ethiopians within five years starting from 2005. The FSP was designed with two core objectives. The first objective was to help five million chronically food insecure people attain food security while the second was to significantly improve the food security of up to ten million additional food insecure people within five years. The program had three main components namely, resettlement, productive safety nets and other food security (OFSP). The resettlement program aimed at enabling about 440,000 chronically food insecure households to attain food security within three years through voluntary resettlement program (FDRE, 2004). The other two components (OFSP and PSNP) are presented in detail in the following sub-sections.

2.1.3. Productive safety net program

The Productive safety net program (PSNP) aims to reduce the number of people who rely on annual humanitarian appeals, by providing predictable and timely cash and food (DFID, 2007). It aims to shift away from a focus on short-term food needs met through emergency relief to addressing the underlying causes of household food-insecurity. The PSNP, started in 2005, has been supporting 7.2 million Ethiopians who are vulnerable to shocks such as droughts and floods. The Program tries to reduce the vulnerability of households that do not have enough to eat even when the weather and harvest is good (FAO, 2006).

The PSNP has special features such as: types of transfers, specific objectives, basic principles, basic components, and targeting principles. The type of transfer may be cash only, both cash and food or food only based on specific situation of the safety net areas. The specific objectives of the cash and food transfers provided through the PSNP are: (1) to smooth household consumption – to bridge production deficits in chronically food insecure farming households that are not self-sufficient, even in good rainfall years; (2) to protect household assets – to prevent poor households from falling further towards destitution, vulnerability to future shocks and chronic dependence on external assistance; and (3) to create community assets – by linking the delivery of transfers to activities that are productivity-enhancing, in order to promote sustainable developmental outcomes (FDRE, 2006).

The PSNP are based on two crucial basic principles. (1) Predictability – A safety net delayed is a safety net denied. Consequently, resource flows must be predictable (2) avoiding dependency – This can be achieved by requiring able-bodied beneficiaries to provide labour in exchange for program transfers (FDRE, 2006).

The PSNP has two components. The first component popularly known as public works is aimed at the provision of counter-cyclical employment on rural infrastructure projects such as road construction and maintenance, small-scale irrigation and reforestation. The second component referred as direct support is aimed at provision of direct unconditional transfers of cash or food to vulnerable households with no able-bodied members who can participate in public works projects (ibid).

Graduation is another important issue that should be defined in relation to PSNP implementation. PSNP beneficiaries are expected to be resilient from chronic food insecurity and graduated from the PSNP within three to five years (ibid). Based on the PSNP program implementation manual (PIM) and discussions with federal food security coordination office and PSNP implementers in the four regions, there are at least three definitions of graduation in common use without a uniform understanding of which applies in which situation: The first is graduation from the PSNP program, which requires households to achieve food security for one year only according to the PIM rules. The second is graduation into food security, which implies a more sustainable transition away from chronic food insecurity. The third is graduation out of poverty, which is a more substantial objective which goes beyond food

11 security considerations. Measuring graduation and setting graduation target are based on graduation out of poverty (Slater et al., 2006)

2.1.4. Other food security programs

The graduation of food insecure households from poverty is achieved by complementing the implementation of PSNP with a wide range of other food security programs (OFSP). It is OFSP that enables poor households to create assets at household levels and achieve food security (FDRE, 2006). These OFSP vary from area to area depending on the appropriateness of the area for specific program. However, they generally focus on providing money at free of interest rate either in kind (giving purchased cows, oxen, sheep, and beehives) or in cash for similar purposes which is repayable within two to five years. The OFSP in most cases include: access to improved seeds, irrigation and water-harvesting schemes, soil and water conservation, credit, the provision of livestock or of chicks, crop production extension services (Gilligan et al., 2008 ).

According to FDRE (2006) revised program implementation manual, the linkage between PSNP and OFSP is clearly indicated that many PSNP participants also benefit from other OFSP. To achieve maximum impact, woredas must integrate PSNP interventions with other food security programs and broader woreda development interventions. To improve the rate and probability of graduation for a household, participation in the PSNP will make a chronically food insecure household eligible on priority bases to participate in the OFSP.

2.1.5. Concept of asset and asset accumulation in the context of farmers

Before rushing into describing the role of PSNP in asset accumulation, it is necessary to conceptualize the term asset and asset accumulation in the context of rural farming households. Therefore, the following paragraphs briefly describe these two terms.

In accounting the term asset is resource owned excluding liability. In the farming household context, asset includes all livestock owned, productive assets, household assets, and consumer

12 durable assets that belong to the household. Although there is a variation in owning of these assets in Ethiopia, they can be listed as follows:

Livestock assets: include cattle, sheep and goats, poultry and equines. Productive assets: include all asset used to produce crop and livestock like ploughing equipments, water pump, sickle, spade, beehives, cart, pick axes and axes. Household assets: include stove, and other cooking materials. Consumer durable assets: include telephone, radio, bed, home, bicycle, etc.

Household asset accumulation means increasing the real value of all types of assets of the household over a specified reference period. The specified period is usually the period of time for which a program or an intervention that is expected to bring asset accumulation is implemented.

2.2. Land Degradation and Sustainable Land Management Measures

2.2.1. Land degradation problems in Ethiopia

Land degradation can be defined as the loss of land productivity through one or more processes, such as reduced soil biological diversity and activity, the loss of soil structure, soil removal due to wind and water erosion, acidification, salinization, water-logging, soil nutrient mining, and pollution (World Bank, 2006). Similarly, Mahmud and Pender (2005) conceived land degradation as the loss of land productivity through various processes such as wind and soil erosion, salinization, water logging, depletion of soil nutrients and soil contamination. Soil erosion and soil nutrient loss are widely regarded as the most important problems among different forms of land degradation in the northern highlands of Ethiopia whereas soil moisture stress, which is worsened by soil erosion and depletion of soil organic matter, is a common land degradation problem in most of the semi-arid areas (Fitsum et al., 1999).

2.2.2. Sustainable land management

Many authors define sustainable land management (SLM) in several ways. For instance, Hurni (1996) defined SLM as a system of technologies and/or planning that aims to integrate ecological with socio-economic and political principles in the management of land for agriculture and other purposes to achieve intra-and intergenerational equity. According to World Bank (2006) SLM is knowledge-based procedure that helps integrate land, water, biodiversity, and environmental management (including inputs and output externalities) to meet rising food and fibre demands while sustaining ecosystem services and livelihoods.

World Overview of Conservation Approaches and Technologies (WOCAT) also defined SLM as the use of renewable land resources, including soils, water, animals and plants, for agricultural and other purposes to meet changing human needs, while simultaneously ensuring the long-term productive potential of these resources and the maintenance of their environmental functions (WOCAT, 2005).

In this connection, Gete et al. (2006) conceptualized SLM in Ethiopian context as the use of renewable land resources, for agricultural and other purposes to meet community needs, while simultaneously ensuring the long-term productive potential of these resources and the maintenance of their environmental functions through systematic use of indigenous and scientific knowledge/technologies, proper participation of communities on the decision making process (planning, implementation and management), and appropriate policy environments to ensure the successful implementation of the above processes.

The above conceptualization, therefore, focuses on three development components of SLM namely, the use of different technologies/practices and integration among them to solve ecological and socio-economic constraints; the need for participatory land management planning to meet community needs and use of the renewable natural resources in a sustainable way without compromising their environmental functions; and the need for an appropriate policy environment to undertake the above major tasks on an equitable basis (Gete et al., 2006).

2.2.3. Previous approaches used to promote sustainable land management in Ethiopia

In Ethiopia, a number of approaches to promote SLM have been tested or applied by different actors. The most notable ones are: community empowerment programme used by Swedish International Development Agency (SIDA)-Wollo programme, community driven development, used by the World Bank to implement its food security project; participatory land use planning, used by German Technical Cooperation (GTZ) to implement its food security project; Local Level Participatory Planning Approach (LLPPA), used by World Food Program (WFP) to implement its Managing Environmental Resources to Enable Transitions (MERET) to more sustainable livelihood project; and Community Based Participatory Watershed Development (CBPWD), which is a recently developed approach by MoARD combining the different experiences in the country (Gete et al., 2006). These approaches have been implemented, some of them for a fairly long time and have accumulated ample experiences. Despite internal differences, almost all of them have one goal in common; the need for improving participation of communities in the decision making processes.

2.2.4. Sustainable land management technologies

A multitude of sustainable land management (SLM) practices or technologies are available throughout the world. Gete et al. (2006) classified the SLM practices or technologies that have been applied in Ethiopia into two broad categories: indigenous and introduced, with different degrees of acceptability, area coverage and benefits. Each category includes five main practices: (1) physical soil and water conservation measures (dominant in the country); (2) biological soil conservation measures; (3) soil fertility improvement measures; (4) agricultural water management measures and (5) grassland management measures and forestry and agro-forestry measures.

Under indigenous SLM technology category, the following are listed:

Physical soil and water conservation measures: consist of stone terracing, 'dinber', boy or 'fesses' (in farm ditch to divert or drain excess runoff), 'gulenta' (traditional ditches used to collect runoff from small ditches and act as a waterway, simple check dams (stones and

15 brush) and 'tekebkebo' (diversion ditches to cut-off the excess runoff from upland areas which are traditionally used by farmers; Biological soil conservation measures: include below terrace plantation, live fence around homesteads and farm lands, plantation along traditional waterways and diversion ditches, scattered trees like acacia and croton on farm lands, traditional agro-forestry, small-scale woodlots and traditional nurseries; Agricultural water management measures: like runoff diversion for floods, moisture conservation using crop residues, mulching, small-scale irrigation, and drainage ditches; Soil fertility improvement measures: contain farmyard manure, fallowing, use of special crops such as lupine inter-cropping, crop rotation and relay cropping; and Grassland management measures and forestry and agro-forestry measures: these include zero grazing, hay making and temporary blocking of grasslands.

Under introduced practices of SLM the following are mentioned:

Physical soil and water conservation measures: consist of stone and stone-faced terracing, fanyajuu, soil bund, cut-off drains, waterways and check dams (stone, concrete, gabion, brush, etc.); Biological soil conservation measures: such as bund stabilization (on, below and above bund plantation), hedge rows, grass strips, live fence around homesteads and farm lands, gully stabilization or re-vegetation, area enclosure and enrichment plantation, nursery development, agro-forestry including trees on farmlands, small woodlots (communal and individual); Soil fertility improvement measures: include compost making and application, intercropping, crop rotation alley cropping, conservation tillage, green manuring, improved agronomic practices and fertilizer application; Agricultural water management measures: like moisture harvesting using different structures, run-off harvesting, rainwater harvesting, mulching, small-scale irrigation with all modern technologies and drainages by the use of broad based moulds (BBM). Grassland management measures and forestry and agro-forestry measures: consist of improved forage plantation (grass, shrubs and legumes), improved zero grazing (stall feeding), and improved hay making and temporary blocking (padocking), and silage making.

2.4. Impact Assessment Concepts and Approaches

2.4.1. Concept of impact assessment

According to Omoto (2003) the term impact refers to the wide and long-term economic, social and environmental effects of an intervention resulting in anticipated or unanticipated, and desired or undesired outcome, at the individual or the organizational level that involve changes in both cognition and behaviour.

An impact evaluation is the process of examining the extent to which a program has caused desired changes on the participants. It is concerned with sorting out the net impact of an intervention on the participants that can be attributable solely to that specific intervention. It is the act of assessing outcomes in the short, medium or long term change due to an intervention (Baker, 2000). According to Khandker et al. (2010) impact evaluation is the act of studying whether the changes in well-being are indeed due to the intervention and not to other factors.

Identifying what would have happened without the intervention of a specific program is the key task to assess the impact of the intervention. Impact assessment involves an analysis of cause and effect to identify effects that can be attributed to interventions (Ezemenari et al., 1999). In most cases, any development program or intervention is not conducted solely. Rather, other programs are also conducted on a society. Hence, other factors or events might have contributed for the observed impact. Therefore, impact evaluation should estimate the counterfactual or enable to answer the question “what would have happened if the intervention had not taken place?” However, two situations cannot be observed for the same individual. An individual can be either participated or not participated in a program. Thus, missing data is the fundamental problem in any social program evaluation (Bryson et al., 2002 and Ravallion, 2005).

Assessing the net impact that can be attributed to a specific program or intervention is possible only if the counterfactual is correctly determined. This is possible by introducing groups known as comparison or control groups that do not participate in a program but more or less have similar characteristics with participating groups known as treatment groups

17 except exclusion from the program (Ezemenari et al., 1999). However, obtaining the control group is difficult and needs a great care for two reasons. First, the treatment groups may be chosen purposively on the basis of certain characteristics. If these characteristics are observable, it is possible to find the control group that have the same characteristics. However, if they are unobservable, the selection bias can be removed only by a randomized approach. Second, the control group may be benefited from the spill-over effects of the same intervention or from other interventions that have a similar effect. In these cases, it is necessary to correctly account for the differences that could arise from the non-random placement of the program and/or from the voluntary nature of participation in program (self- selection) to generate unbiased estimates of program impact (Gilligan et al., 2008).

2.4.2. Impact assessment approaches

According to Baker (2000), there are two main approaches in impact assessment. These are randomized (experimental) designs and quasi-experimental (non-randomized) designs.

Experimental (randomized) methods: Experimental (randomization) method is an approach in which both participants and non participants of a program are randomly selected before the implementation of the program. That is, by randomly allocating the intervention among eligible beneficiaries, the assignment process itself creates comparable treatment and control groups that are statistically equivalent to one another, given appropriate sample sizes (Baker, 2000). This method ensures that a mean difference in conditions of the treatment and the control groups after the intervention can be attributed to the intervention (Ezemenari et al., 1999).

The experimental method has both advantages and limitations. The main advantage of this method is its capability of removing selection bias, which arises when participation in the program by individuals is related to their unobservable or unmeasured characteristics, which in turn determine the program outcome and its simplicity in interpreting results. However, it has at least six problems. First, it may be unethical to randomly assign eligible members as a control group and exclude them from benefits or services for the purposes of the study. Second, it can be difficult to provide benefits to one group and exclude another politically. Third, it is difficult to obtain control groups for programs implemented in large scale that

involve all groups. Fourth, individuals in control groups may change certain identifying characteristics during the experiment that could invalidate or contaminate the results. Fifth, it may be difficult to ensure that assignment is truly random. Finally, experimental designs are not time and cost effective, especially in the collection of new data (Baker, 2000).

Quasi-experimental (non-experimental) method: Non-experimental method is applied if a program placement is deliberately located. Non-experimental method is a single cross- sectional survey done after the program is implemented (Jalan and Ravallion, 2003). According to Bryson et al. (2002) non-experimental method is divided into two as: before and after estimator and cross-sectional estimator. The essential idea of the before and after approach is to compare the outcome of interest variable for a group of individuals after participating in a program with outcome of the same variable for the same group or a broadly equivalent group before participating in the program and to analyze the difference between the two outcomes as the estimate of mean treatment effect on the participants whereas the cross- sectional estimator employs non-participants (control groups) to derive the counterfactual for participants in which case it becomes quasi-experimental method.

Quasi-experimental design involves matching treatment groups with a comparable control group of individuals who did not participate in the program. This approach simulates randomization but need not take place prior to the intervention (Kerr et al., 2000). Quasi- experimental methods can be used when constructing treatment and control groups though experimental design is not possible. A quasi-experimental method is the only alternative in two cases; in the absence of baseline data and when randomizations are not feasible options (Jalan and Ravallion, 2003).

Since the treatment and comparison groups are usually selected after the intervention by using non random methods, it is necessary to apply statistical controls to address differences between the treatment and comparison groups. In addition, sophisticated matching techniques should be used to construct a comparison group that is more or less similar to the treatment group (Gilligan et al., 2008).

Quasi-experimental design has advantages in that it can draw on existing data sources; it is quicker and cheaper to implement; and it can be performed after a program has been implemented if sufficient data exist. However, it has some limitations, too. First, the reliability 19 of the results is often reduced as the methodology is less robust statistically. Second, the methods can be statistically complex. Finally, there is a problem of selection bias that yields inaccurate results (Baker, 2000). These limitations impose methodological challenge in non- experimental evaluation methods and hence affect the reliability of results when generating a comparison groups (Foster, 2003). To avoid or reduce these problems, different econometric approaches have been developed of which some are discussed as follows:

Double difference or difference-in-differences (DID) methods: this method enables evaluators to compare a treatment and comparison group before and after a program by identifying potential participants and collecting data from them. However, only a random sub- sample of these individuals is actually allowed to participate in a certain project. The identified participants who do not actually participate in the project form the counterfactual. This method can reduce the potential selection bias and the impact of other factors exogenous to the program on observable characteristics by analyzing the difference in outcome of treatment groups relative to the difference in outcome of control groups. It looks at the difference in indicators for the two groups at the end of the program relative to the difference in indicators at the beginning (Jalan and Ravallion, 1999 and Baker, 2000).

Instrumental variables or statistical control: in this method, one uses one or more variables that affect participation but not outcomes given participation. It is used to identify the exogenous variation in impact only due to the program, recognizing that the program is purposively placed rather than randomized. The instrumental variables are used to predict program participation first and then analyze how the outcome indicator varies with the predicted values (Baker, 2000).

Reflexive comparisons: this is one of the quasi-experimental methods in which a baseline survey of treatment groups is conducted prior to the intervention and a follow-up survey is done subsequently to measure the impact through the change in impact indicators before and after the intervention (Baker, 2000). Here, the treatment groups are compared to themselves before and after the intervention and serves as both treatment and comparison group. This method is useful in analyzing of full-coverage interventions such as nationwide policies and programs in which the entire population participates and there is no scope for a control group. However, care should be taken in applying this method as it may not be able to distinguish

20 between the program and other external effects and hence compromising the reliability of results.

Propensity Score Matching (PSM): the idea of PSM is to find a comparison group that is similar to the treatment group in all respects except the exclusion from the program. It is useful to evaluators with time constraints and do not have baseline data but use a single cross- sectional data (Ravallion, 2005). The inherent problem in practice is usually how to define “similar”. Matching may be done on many characteristics and it is not clear whether a match has to be similar in all these characteristics, and (if not) what weight should be given to each characteristic (Caliendo and Kopeinig, 2005).

The method of PSM balances the observed covariates between a participant and a control (comparison) group based on similarity of their predicted probabilities of receiving the treatment (propensity scores) and can justifiably claim to be the observational analog of a randomized experiment (Rosenbaum and Rubin, 1983). The PSM summarizes the pre- treatment characteristics of each subject into a single index variable and then using the propensity score (PS) to match similar individuals. By doing this, it solves the difficulties of matching the treated and the control subjects when there is a multidimensional vector of characteristics. It forms the probability of assignment to treatment conditional on pre- treatment variables (Rosenbaum and Rubin, 1983).

The reliability of matching estimates is based on several factors. First, participants and controls groups should have the same distribution of observed and unobserved characteristics. Second, the same questionnaire is administered to both groups. Third, treated and control groups should be selected from the same economic environment. Otherwise, the difference in mean impact of the two groups is biased estimate of the mean impact of the program (Jalan and Ravallion, 1999).

Like other methods, the PSM also has its own limitations. First, PSM is non-parametric. Hence, any functional form assumptions regarding the average differences in the outcome are not made. Second, PSM method cannot address the bias created by unobservable characteristics that might affect the outcomes (Ravallion, 2005). Third, PSM requires large amounts of data to maximize efficiency (Bernard et al., 2010). Finally, one cannot be entirely

21 sure that he/she has actually included all relevant covariates in the first stage of the matching model and effectively satisfied the conditional independence assumption. Despite these limitations, PSM is the best method to impact evaluators with time constraint and working in the absence of baseline data in that it can be applied with a single cross-section data.

2.5. Empirical Studies on SLM Practices and Impact of Productive Safety Net Program

2.5.1. Studies on sustainable land management practices

Most of the empirical evidences on factors affecting investment in sustainable land management decisions have been carried out in the Ethiopian highlands. These studies found that land tenure issues, agricultural extension and credit programs, households' endowments of physical and human capital, access to markets, roads and off-farm opportunities, public projects and institutional support, population pressure, profitability, risk and discount rate are the main factors affecting SLM technology adoptions positively or negatively (Bekele and Holden, 1998; Pender et al., 2001; Deininger et al., 2003; Aklilu, 2006).

Agricultural extension and credit services are areas where different researches were carried on to investigate their impact on SLM technology adoption in the country. Studies showed that rural households in a highly drought prone areas were not constrained by availability of credit. Instead farmers were reluctant to take the available credit due to high chances of crop failure and subsequent indebtedness problems (Yared et al., 2000; Boetekees, 2002). Credit availability could be a binding constraint only when farmers are willing to take the credit but availability is limited which is not observed in literatures.

Empirical evidences show that access to formal credit has a positive impact on use of fertilizer and other purchased inputs but limited impact on most of other land management investments and practices (Pender et al., 2001; Croppenstedt et al., 2003 and Pender and Berhanu, 2004). Pender et al. (2001) found that the impact of credit on land management depends on the source and terms of credit and type of technology promoted. Their study showed that credit access from the Bureau of Agriculture (BoA) was negatively associated with use of fallowing, manure, and compost but positively associated with tree planting. Credit from the Relief

Society of Tigray (REST) was linked to more use of compost, soil bunds, tree planting and live fences, while credit from the Amhara Credit and Saving Institution (ACSI) was associated with less use of fallowing and tree planting, but with more investment in soil bunds and live fences. These relationships probably reflect the impacts of technical assistance that was associated with the credit, at least in the case of credit from the BoA and REST, since these organizations were also providing technical assistance and the analysis of Pender et al. (2001) did not control for access to technical assistance.

The impact of agricultural extension on SLM practices appears to be context dependent. Pender et al. (2003) and Pender and Berhanu (2004) found statistically insignificant impact of contact with agricultural extension agents on farmers’ land investments, annual land management practices, and use of inputs while Deininger et al. (2003) found that access to agricultural extension (within the woreda) was positively associated with farmers’ investments in planting trees and especially in building terraces.

Aklilu (2006) conducted a study to identify determinants of adoption and continued use of soil and water conservation (SWC) technologies in Beressa watershed of North Shewa. He found that different demographic, economic, institutional, farm and plot level factors affected the adoption and continued use of SWC technologies.

2.5.2. Studies on the impact of productive safety net program on asset accumulation and sustainable land management practices

Previous studies in Ethiopia have shown that food for work programs (which had similar aim with PSNP except that PSNP focuses continuously on selected households over several years and it will eventually be phased out) had a positive influence on the probability of fertilizer adoption (Sosina and Holden, 2007). However, other studies have indicated that it may have negative impact on agricultural intensification (Barrett et al., 2004), short-term soil conservation measures (Berhanu and Swinton, 2003), informal risk sharing (Dercon and Krishnan, 2004) and growth of livestock holdings (Gilligan and Hoddinott, 2007).

Different studies have been carried out on different issues regarding PSNP in Ethiopia. To list a few: Gilligan et al. (2008) conducted a study to analyze the Ethiopia’s PSNP and its linkage

23 after one and half a year since the implementation of the program. According to their study, the program had a significant positive impact on participants’ food security; borrowing for productive purposes; use of improved technologies and creating nonfarm own businesses compared to the control groups when the PSNP was only complemented with other food security program (OFSP). However, when the participants received only PSNP transfers of ETB 90 per month or more per individual without access to OFSP, their study shows that the program reduces the likelihood of households’ very low caloric intake and improves mean calorie availability. They also found no evidence for the disincentive effects on the reduction of labour supply to wage employment or private transfers. However, their study shows that relative to the control group, participants did not experience faster asset growth even when the PSNP was complemented with OFSP.

Another study conducted by Andersson et al. (2009) evaluated the impacts of the Ethiopian PSNP on rural households' holdings of livestock and forest assets including trees. This study found that program participation had a positive effect on number of trees planted.

2.5.3. Studies on the application of propensity score matching method

Propensity score matching is becoming a popular technique to assess the impact of interventions and being widely applied by different scholars abroad and in Ethiopia. Yibeltal (2008), Gilligan et al. (2008) and Andersson et al. (2009) were among the writers who applied the PSM technique in Ethiopia.

Yibeltal (2008) used the PSM technique to assess the impact of integrated food security program on household food poverty in Ibnat –Belessa districts of Amhara region. He found that the program had a significant positive impact on the participating households.

Gilligan et al. (2008) employed the PSM techniques to evaluate the impact of Ethiopia’s PSNP and its linkages in the national level. Similarly, Andersson et al. (2009) also used the PSM methods to analyze the impact of PSNP on households’ livestock and eucalyptus trees holding in Amhara region of Ethiopia.

2.5.4. Studies on the application of Heckman’s two-step model

The Heckman’s two-step selection model is widely applied to determine the probability of participation in different programs or technologies in the first step and the intensity of participation in the second step. Only a few studies that applied this model are presented here.

Aklilu (2006) employed the Heckman two-step model to determine factors influencing the probability of participation in soil and water conservation (SWC) in the first step and the continuity of adoption in the second step.

Chilot (2007) applied Heckman’s two-steps model to assess inorganic fertilizer adoption in the central highlands of Ethiopia. He used the model to analyze factors determine the likelihood of inorganic fertilizer use in the first step and the intensity of fertilizer use (in Kg/ha) in the second step. His result shows that education of household head, off-farm income, livestock, plot size, distance of plots, severity of land degradation, access to credit and to extension services affected the probability of fertilizer use positively and significantly while number of plots owned, legume application and manure application had a negative effect on it. In the second step, he found that education of household head, livestock, tenure, access to credit and to extension services, and interaction of SWC with district influenced the intensity of using inorganic fertilizer use positively and significantly whereas number of plots owned, agro-ecology and manure application had a negative effect on the intensity of use.

Sosina and Holden (2007) applied the Heckman’s two step model to assess the impact of food for work (FFW) on chemical fertilizer use in Tigray region of Ethiopia. They found that FFW had a positive impact on the probability of fertilizer use in the first step at 5% significant level though it was insignificant in the second step (the intensity equation).

3. RESEARCH METHODOLOGY

This chapter describes the overall methodology of the research. It is divided into four sections. In the first section, the productive safety net program in the study area is briefly described. The second section describes the location of the study area. The third section provides information on the sources and methods of data collection while the final section discusses the methods of data analysis.

3.1. Description of the Productive Safety Net Program in the Study Area

The Productive safety net program (PSNP) was launched since 2006 in both Meskan and Adamitulu Jido Kombolcha (ATJK) districts. Since then, the PSNP has been implemented in 20 food insecure rural kebeles of the 44 total kebeles in Meskan and 22 food insecure rural kebeles of the 43 total kebeles in ATJK districts. The program has two components in both ATJK and Meskan districts; direct support and public work component. The payment mode is in cash in ATJK district while households received both cash and wheat in Meskan district. The program has started in 2006 in Drama and 2007 in Beressa kebeles of Meskan. The amount of payment was ETB 5 in the starting time of the program and has increased to ETB 8 per day per individual since 2008 in both districts. The selection criteria of beneficiaries in both districts as confirmed by both sample households and the respective districts food security task forces shows that a community selection based on asset ranking (specially ownership of oxen) and other livestock and almost no bias for selection.

The public work PSNP households participate in labor intensive works such as fencing and construction of schools, construction of feeder roads, and hillside terracing in erosion prone areas on communal lands in both districts. The working schedule in both districts is from January to May of each year. The participants work for six days per month for four hours per day in both districts and receive 15 kilogram of wheat or ETB 48 per month which means ETB 8 per day for six days per month. However, the payment is not only for participating individuals in the household rather multiplied by the number of family members. That is, a participating household receives 15Kg of wheat or a cash equivalent to

26 the price of 15Kg of wheat multiplied by the number of family member irrespective of the number of persons who participate in the actual PW activities.

The PSNP is supplemented by other food security programs (OFSP) in both districts. Cash credit is usually offered to PSNP households of ATJK district while dairy cows purchased from Borena or cash to purchase cows are given to Meskan district PSNP households.

3.2. Description of the Study Area

Central rift valley (CRV) of Ethiopia covers the administrative regions of South Nations, Nationalities, and Peoples Regional (SNNPR) state and Oromia regional state. The total area of CRV is approximately 10,000Km2 (Mengistu, 2008). According to Hengsdijk and Jansen (2006), the CRV includes 11 woredas (Sodo, Meskan, Mareko and Selti of Guragie Zone of SNNPR; Dugda Bora, Ziway Dugda, Degeluna Tiyo, Adami Tulu Jido Kombolcha, Tijo, Bekoji and Munessa of Oromia regional state). The study was conducted in Adamitulu Jido kombolcha district of Oromia regional state and Meskan district of SNNPR state.

The CRV area is characterized by semi-arid and unreliable rainfall pattern with high evapotranspiration. Data from Adami tulu meteorological station (1985-2003) showed that the mean annual rainfall was 670 mm while the potential evapotranspiration was 1700 mm. The long term mean maximum and minimum temperature were 26.70C and 13.90C, respectively.

Adamitulu Jido kombolcha woreda which covers an area of 1,487.57 Km2 is located in east Shewa Zone of the Oromia regional state. It shares borders in the east with Ziway Dugda, in the west with Lanfero, Selti and Mareko woradas of SNNPR state, in the north with Dugda Bora, and in the south with Arsi Negale woredas. The second study location, Meskan woreda, which covers an area of 597 Km2 is located in Gurage Zone of SNNPR state, shares borders in the east with Mareko, in the west with Kokir Gedbano Gutazer, Ezhana Welene and Gumer woradas, in the north with Kokir and Sodo, and in the south with Selti woredas.

According to CSA (2008), the total population of Adami Tulu Jido Kombolcha woreda in 2007 was 142,861 of which 14.6% and 85.4% reside in urban and rural areas, respectively. 27

The same source indicated that the total population of Meskan woreda in 2007 was 159,884 of which 8.3% and 91.7% reside in urban and rural areas, respectively. Out of the total population, the proportion of male is 50.3 % and 49% in ATJK and Meskan woredas, respectively while the remaining is female (CSA, 2008). The crude population density is estimated at 101 people per km2 in ATJK and 400 in Meskan (Hengsdijk and Jansen, 2006; Scholten, 2007; Mengistu, 2008).

The livelihood of both woredas of the study location is dependent on agriculture. However, the study area is known for its low production of crops and livestock due to land degradation. Farming in central rift valley is basically dominated by rain-fed mixed crop-livestock system (OESPO, 2003). About 45% of the total land of ATJK is arable. The remaining 30 %, 7 %, and 18 %, of the land is grazing or woodlands, marginal land, and other purposes, respectively (OESPO, 2003). Crop production, livestock and forest products are the principal sources of income for the farmers. Maize, wheat, barley, haricot beans, faba beans and linseeds are the major crops grown in the area. Farmers also keep a significant number of livestock (cattle, sheep, goats and equine) for various purposes in addition to income generation.

Figure 1. Map of Central Rift Valley of Ethiopia and its woredas’

3.3. Sources of Data, Methods of Data Collection and Sampling Technique

3.3.1. Sources and Methods of data collection

Both primary and secondary data were collected from the study area. Primary data were collected from sample of 186 households (95 PSNP participants and 91 non participants) while secondary data were collected from bureau of agriculture (food security offices).

The primary data were gathered from sample households using structured questionnaire. The household survey focused on household characteristics (family size, education, sex and age category, etc), land holding (own, rented and leased), availability of labour, land management practices used by farmers, participation in food security programs, access to credit services, livestock and non livestock asset ownership before and after the intervention, non-farm employment and other socioeconomic factors.

Checklist and structured questionnaire was used to collect the primary data. The questionnaire was pre-tested before the actual conduct of the interview using households identified for the purpose. Experienced enumerators were recruited based on their proficiency in the local language and then trained on data collection techniques and on the content of the questionnaire.

Secondary data were collected from different offices of governmental and non- governmental sources located in the study area. Moreover, regular and statistical reports of the MoARD, CSA, MoFED, Disaster Prevention and Preparedness Commission, and PSNP coordinators were consulted. Price data of different assets and crops before and after intervention was also collected from the respective Woredas’ market survey offices.

3.3.2. Sampling technique

A multistage sampling technique was used to determine the sampling households. First, two representative woredas were purposely selected based on their participation in the 29 productive safety net program and based on accessibility. Second, PSNP participating and non-participating kebeles were identified in both woredas and two PSNP participating kebeles were randomly selected from each woreda. Third, the PSNP participant and non participant households were identified from the households list available at each kebele. Finally, representative samples were selected from four kebeles based on probability proportional to sample size. Systematic random sampling was used to select a specific household in each kebele. Following this procedure, 186 sample households (95 PSNP participants and 91 non-participants) were selected from both woredas as shown in Table 1.

Table 1. Distribution of sample households by kebeles

Woredas Kebeles Total number of HH in the study area Sample HH PSNP HH Non PSNP HH PSNP HH Non PSNP HH Meskan Beresa 288 96 32 31 Drama 258 83 23 23 ATJK Weyiso 370 40 16 16 Worja 575 51 24 21 Total 1491 270 95 91

Source: Own summary, 2010

3.4. Methods of Data Analysis

Both descriptive and inferential statistical and econometric tools were used to analyze the empirical data. These tools are outlined and discussed in the following section.

3.4.1. Descriptive statistics

Descriptive statistics such as mean, standard deviation, percentages, t-values and chi square are used to summarize, interpret and conclude the results.

3. 4.2. Econometric models

Propensity score matching model was used to address the first two objectives (to assess the impact of participation in PSNP on asset accumulation and to assess the impact of the PSNP on sustainable land management practices) while Heckman’s two step model was used to address the third objective (to identify factors affecting sustainable land management practices).

3.4.2.1. Propensity score matching (PSM) method

Participation in PSNP is nonrandomized and lacks baseline survey. That is, households who are eligible to the selection are purposively selected based on their asset holding and exposure to shocks and problem of food security. In addition, the baseline survey was not conducted prior to the intervention of the PSNP in the study area. Therefore, propensity score matching, which is usually used to assess the impact of a program in this case, was used to address the first two objectives.

According to Caliendo and Kopeinig (2005), the implementation of PSM involves five steps. These are: PSM estimation; choosing matching algorithm, checking for overlap (common support); matching quality (effect) estimation and sensitivity analysis.

A. Propensity score estimation procedure: Estimation of propensity score is the first step in PSM technique. Rosenbaum and Rubin (1983) revealed that matching can be performed conditioning only on P(X) rather than on X, where P(X) = Prob(D=1|X) is the probability of participating in the program conditional on X. According to these authors, if outcomes without the intervention are independent of participation given X, then they are also independent of participation given P(X) which reduces a multidimensional matching problem to a single dimensional problem.

Estimating the propensity score involves decision on two choices; what model to be used for the estimation and what variables should be included in this model. Regarding the decision of

31 choosing the type of model to be used, for the binary treatment case, where we estimate the probability of participation versus non-participation, both logit and probit models often yield similar results. Therefore, it is not a critical problem. However, due to the complexity of estimation procedure of probit model than the logit model, logit is widely used (Caliendo and Kopeinig, 2005). To capture this advantage, the logit model was used for estimating the propensity score in this study.

Regarding the choice of what variables should be included in the model, a matching strategy should be built on the conditional independence assumption (CIA) that requires the outcome variables must be independent of treatment conditional on the propensity score. Therefore, implementing matching method is based on choosing a set of variables X (covariates) that reasonably satisfy this condition (Caliendo and Kopeinig 2005). Basically, economic theories, better knowledge of previous researches and information on institutional settings are important guides to select appropriate covariates (Sianesi, 2004; Smith and Todd, 2005).

According to Gujarati (2004), in estimating the logit model, the dependent variable is participation which takes a value of 1 if the household participated in a program and 0 otherwise. The logit model is mathematically formulated as follows:

ezi Pi  (1) 1 ezi

Where, Pi is the probability of participation in the productive safety net program,

zi   0  ixi  ui (2)

Where, i = 1, 2, 3, - --, n

 0 = intercept

i =regression coefficients to be estimated

ui = a disturbance term, and

xi =pre-intervention characteristics.

The probability that a household belongs to the non participant group is:

1 1 Pi  (3) 1 ezi

Then the odds ratio can be written as:

zi Pi 1 e zi  zi  e (4) 1 Pi 1 e

Pi The left hand side of equation (4), is simply the odds ratio in favour of participating 1 Pi in PSNP. It is the ratio of the probability that the household would participate in the PSNP to the probability that he/she would not participate in the PSNP. Finally, by taking the natural log of equation (4) the log of odds ratio can be written as:

n   0 jXji  n Pi   j1  (5) Li  Ln( )  Ln e  Zi   0   jXji 1 Pi     j1

Where, Li is log of the odds ratio in favour of participation in the PSNP, which is not only linear in Xji but also linear in the parameters.

According to matching theory (Rosenbaum and Robin, 1983; Bryson et al., 2002; Jalan and Ravallion, 2003), the propensity score generated through the logit model should include predictor variables that influence the selection procedure or participation in the program and the outcome of interest. Based on findings of previous empirical studies on PSNP and the eligibility criteria for participation in the PSNP and own field observation, relevant pre- intervention covariates (explanatory variables) were identified and included in the logit model for this study. To minimize the problem of unobservable characteristics in evaluation of the impact of the PSNP, we included as many explanatory variables as possible in this study.

The effect of household’s participation in the PSNP on a given outcome (Y) is specified as:

Ti  Yi(Di 1) Yi(Di  0) (6)

Where Ti is treatment effect (effect due to participation in PSNP), Yi is the outcome on household i , Di is whether household has got the treatment or not (i.e., whether a household participated in the PSNP or not).

Nonetheless, since Yi (Di  1) and Yi (Di  0) cannot be observed for the same household simultaneously, estimating individual treatment effect is impossible and one has to shift to estimating the average treatment effects of the population than the individual one. The most commonly used average treatment effect estimation is the ‘average treatment effect on the treated (TATT ) which is specified as:

TATT  ET D 1 E[Y(1) D 1]  E[Y(0) D 1] (7)

Since the counterfactual mean for those being treated, E[Y(0) D 1] is not observed, there is a need to choose a proper substitute for it to estimate ATT. Though it might be thought that using the mean outcome of the untreated individuals, E[Y(0) D  0] as a substitute to the counterfactual mean for those being treated, is possible, it is not a good idea especially in non-experimental studies. This is because it is likely that components which determine the treatment decision also determine the outcome variable of interest.

In our particular case, variables that determine household’s participation in the PSNP could also affect household’s asset accumulation and investment in SLM. Therefore, the outcomes of individuals from treatment and comparison group would differ even in the absence of treatment leading to a self-selection bias. However, by rearranging and subtracting from both sides of equation 7, ATT can be specified as:

E[Y(1) D 1]  E[Y(0) | D  0] TATT  E[Y(0) D 1]  E[Y(0) D  0] (8)

In equation 8, both terms in the left hand side are observables and ATT can be identified if no self-selection bias. That is, if and only if E[Y(0) D 1]  E[Y(0) D  0]  0 . However, this condition can be ensured only in a randomized experiments (i.e., when there is no self-

34 selection bias). Therefore, some identified assumptions must be introduced for non- experimental studies to solve the selection problem. Basically there are two strong assumptions to solve the selection problem. These are: Conditional independence assumption and common support condition.

Conditional Independence Assumption (CIA): The CIA is given as:

Y 0Y1  D / X,X, (9)

Where ⊥ indicates independence X -is a set of observable characteristics

Y 0 -non-participants and

Y 1 -participants

Given a set of observable covariates ( X ) which are not affected by treatment (in our case, participation in the PSNP), potential outcomes (asset accumulation and level of investment in SLM activities) are independent of treatment assignment (independent of how the households were selected in PSNP).

The implication of CIA assumption is that the selection is solely based on observable characteristics ( ) and variables that influence treatment assignment (participation in PSNP) and potential outcomes (asset accumulation and investment in SLM practices) are simultaneously observed (Bryson et al., 2002; Caliendo and Kopeinig, 2005). Hence, after adjusting for observable differences, the mean of the potential outcome is similar for D = 1 and D = 0. Therefore, E(Y 0 / D 1, X)  E(Y 0 / D  0, X) .

Common support: Imposing a common support condition ensures that any combination of characteristics observed in the treatment group can also be observed among the control group (Bryson et al., 2002). The detail of this assumption is presented latter because the common support condition is one of the five steps of the implementation of PSM.

Based on the above two assumptions, the PSM estimator of ATT can be written as:

TATT  E[Y1 Y 0 / D  0, P(x)]  E[Y1 / D 1, P(x)]  E(Y 0 / D  0, P(x)] (10)

Where P(x) is the propensity score computed on the covariates X . The above equation shows that the PSM estimator is the mean difference in outcomes over the common support, appropriately weighted by the propensity score distribution of participants.

B. Matching estimators: After the estimation of propensity score, the second step in PSM is choosing among different matching estimators. In theory, several matching estimators (matching algorithm) of PSM are available. However, only the most commonly applied are discussed bellow.

Nearest neighbour matching (NNM): This is the most straightforward matching estimator. The individual from the control group is chosen as a matching partner for a participant individual that is closest in terms of propensity score (Caliendo and Kopeinig, 2005). NNM can be done with or without replacement. In the case of with replacement, an untreated individual can serve more than once as a match, whereas it is considered only once in the case of without replacement. NNM with replacement increases the average quality of matching and decreases precision of estimation while the reverse is true in the case of NN without replacement (Caliendo and Kopeinig, 2005). Nearest neighbour with replacement is preferred to without when there are big differences between treated and untreated groups to reduce the risk of bad matching.

Caliper and radius matching: used to overcome the drawbacks of NN matching risk of bad matches when the closest neighbour is far away. Caliper matching imposes a tolerance level on the maximum propensity score distance (caliper) so that bad matches are avoided and hence the matching quality rises. In caliper matching individual from the comparison group is chosen as a matching partner for a treated individual that lies within the caliper (propensity range) and is closest in terms of propensity score (Caliendo and Kopeinig, 2005). However, caliper matching has a drawback of inability of choosing a reasonable tolerate level in advance (Smith and Todd, 2005).

Radius matching: is suggested by Dehejia and Wahba (2002) as an alternative to solve the drawback of caliper matching. In radius matching, the principle is to use not only the

36 nearest neighbor within each caliper but all of the comparison members within the caliper. The advantage of this method is that it uses only as many comparison units as available within the caliper and therefore allows for usage of extra units when good matches are not available. Hence, it shares the attractive feature of oversampling problem and avoids the risk of bad matches.

Stratification and interval matching: this approach partitions the common support of the propensity score into a set of intervals (strata) and to calculate the impact within each interval by taking the mean difference in outcomes between treated and control observations (Caliendo and Kopeinig, 2005). The basic question in this method is ‘how much strata should be used in empirical analysis?’ The answer to this question as noted by Cochrane and Chambers (1965) is using five strata can reduces 95% of biases.

Kernel and local linear matching: kernel matching (KM) and local linear matching (LLM) are non-parametric matching estimators that use weighted averages of all individuals in the control group to construct the counterfactual outcome and have the potential of overcoming the problems of only a few observations from the comparison group are used to construct the counterfactual outcome of a treated individual that other estimator have in common (Caliendo and Kopeinig, 2005). These methods use more information and hence advantageous in lowering variance. However, they also have a drawback of the probability of using observations having bad match which leads to the importance of imposing the common support condition (Caliendo and Kopeinig, 2005).

Weighting on propensity score: Given several matching estimators algorithm, which approach is selected is the basic question. According to Caliendo and Kopeinig (2005) there is no the best fit algorithm fit to all cases. Rather the choice depends on the data in hand.

C. Region of common support and overlap condition: Imposing of common support is the third important step in PSM because average treatment effect on treated and on population is only defined in the common support region (Caliendo and Kopeinig, 2005). The common support region is the area within the minimum and maximum propensity scores of treated and comparison groups, respectively and it is done by cutting off those

observations whose propensity scores are smaller than the minimum and greater than the maximum of treated and comparison groups, respectively (Caliendo and Kopeinig, 2005).

Density of comparision househods density of treatement households

0 Region of common support of propensity score 1

Figure 2. Region of common support condition

Source: Ravallion, 2005

D. Testing the matching quality (effect analysis): The fourth important step in PSM is checking for matching quality whether the matching procedure can balance the distribution of different variables or not since our conditioning is on propensity score rather than on all variables in both treated and comparison groups (Caliendo and Kopeinig, 2005). While there are different procedures available to check, the basic aim of all of them is to compare before and after matching and if there still exists any difference after conditioning on propensity score. If the differences exist, there is an indication of incomplete (unsuccessful) matching and suggests remedial for actions (Caliendo and Kopeinig, 2005). There are several indicators to check for matching quality. These are: standardized bias, t-Test, joint significance and Pseudo-R2, and stratification test.

Standardized bias (SB): as noted by Rosenbaum and Rubin (1985) the SB is an appropriate indicator which enables to assess the distance in marginal distributions of the

X-variables. Though SB is a common method used, it has a drawback if there is no a clear indication for the success of the matching procedure.

T-test: it is an approach preferred when there is a concern with significance of results. Two-sample t-test is employed to check if there is significant difference between the covariate means of treated and control group and suggested by Rosenbaum and Rubin (1985) as the covariates must be balanced after matching and there should be no significant difference between the two groups. However, this test has a limitation of showing clearly visible bias reduction before and after matching.

Joint significance and pseudo-R2: The Pseudo-R2 shows how best the regressors explain the probability of participation and it should be fairly low since there should not be significant difference in the distribution of both groups after matching (Caliendo and Kopeinig, 2005).

Stratification test: this approach is dividing observations into strata based on the estimated propensity score to show that there is no statistically significant difference in the mean of the estimated propensity score of both treated and comparison groups as used by (Dehejia and Wahba, 1999, 2002)

E. Sensitivity analysis: The final step in the implementation of PSM is checking the sensitivity of the estimated results (Caliendo and Kopeining, 2005). Matching method is based on the CIA, which states that the evaluator should observe all variables that are simultaneously influencing the participation decision and outcome variables. However, this assumption is basically non-testable since the data are uninformative about the distribution of the untreated outcome for treated groups and vice versa (Becker and Caliendo, 2007). The estimation of treatment effects with matching estimators is based on the selection on observables assumption. However, a hidden bias might arise if there are unobserved variables which affect assignment into treatment and the outcome variable simultaneously which nullify the CIA. This results in biased estimates of ATTs (Rosenbaum, 2002). Since matching estimators are not robust against hidden biases, it is important to test the robustness of results to departures from the identifying assumption. However, it is impossible to estimate the magnitude of selection bias with non- experimental data. Therefore, this problem can be addressed by sensitivity analysis 39

(Caliendo and Kopening, 2005). To check the sensitivity of the estimated ATT with respect to deviation from the CIA, it is suggested that the use of Rosenbaum bounding approach is appropriate (Rosenbaum, 2002).

3.4.2.2. Heckman two-step model

The third objective, identifying factors affecting investment in SLM was estimated using Heckman’s two-step model. Here the participation in PSNP and OFSP were used as explanatory variables together with other variables that are hypothesized to affect investment in sustainable land management.

Investment in SLM is not applied in all the plots of sample households. That is, there are plots that take zero values. In these cases, the relationship between SLM practices and factors affecting them cannot be explained in a standard multiple regressions model using OLS because it results in biased and inconsistent estimates (Amemiya, 1984). Therefore, econometric models that enable censored estimation are appropriate. Tobit and Heckman’s two-step models are commonly used for this purpose (ibid). Heckman’s two-step model is preferred to the Tobit model because it can remove selectivity bias that Tobit model lacks.

Heckman (1979) has developed a two-step estimation model that corrects for sample selectivity bias. In this model, the first step ‘the likelihood of participation in SLM practices’ attempts to capture factors affecting participation decision. The selectivity term called ‘inverse Mills ratio’ (which is added to the second step outcome equation that explains factors affecting the intensity of SLM) is constructed from the first equation. The inverse mill’s ratio is a variable for controlling bias due to sample selection. The second step involves including the mills ratio to the intensity equation and estimating the second equation. If the coefficient of the Mill’s ratio is significant, the hypothesis that an unobserved selection process governs the intensity equation is confirmed. Moreover, with the inclusion of extra term, the coefficient in the second-step selectivity corrected equation is unbiased (Zaman, 2001).

Specification of the Heckman two-step model, which is written in terms of the probability of participation in SLM practices in the first equation and the intensity of use in the second equation, is:

The participation equation/the binary probit equation

Y1i  X 1i1 U1i U 1i ~ N (0, 1) (11)

yˆ 1i =1 if Y 1i > 0

yˆ 1i =0 if Y 1i ≤ 0

Where is the latent dependent variable which is not observed.

X 1i is vectors that are assumed to affect the probability of sample households’ participation in

SLM practices,  1 is vectors of unknown parameter in the selection equation, and U 1i are residuals that are independently and normally distributed with 0 mean and constant variance.

According to Green (2000), the marginal effect for a binary independent variable, say d, is:

Marginal effect= Prob[Y 1| X (d ),d 1]  Prob[Y 1| X (d ), d  0] (12)

Where X (d ) , denotes the means of all the other variables in the model.

The intensity equation

Y 2i = X 2i 2 + U 2i U 2i ~ N (0, 1) (13)

is observed if and only if = 1. The variance of is normalized to one because only

, not Y 1i is observed. The error terms U 1i and are assumed to be bivariate, normally distributed with correlation coefficient  .  1 and  2 are the parameter vectors.

is regressed on explanatory variables, X 2i , and the vector of inverse mills ratio ( i ) from the selection equation.

Where, is the observed dependent variable, X 2i is factors assumed to affect equation, is vector of unknown parameter in the equation, and is residuals in the equation that are independently and normally distributed with mean 0 and constant variance.

i = f (XB) (14) 1 F(XB) ƒ(Xβ) is density function and 1- F (Xβ) is distribution function.

3.5. Definitions and Choices of Variables

3.5.1. Choice and descriptions of variables included in PSM model

This sub section describes explanatory variables and outcome variables included in the propensity score matching model based on theories, empirical evidences and eligibility criteria of PSNP. Accordingly, several variables including household characteristics, institutional and socio-economic factors are hypothesized to determine participation in PSNP and its impact on asset accumulation and SLM practices.

3.5.1.1. Choice and definitions of explanatory variables included in PSM models

Although the purpose of estimating propensity score is not to assess the effect of covariates on the propensity score but on the outcome variable, the choice of covariates to be included in the propensity score estimation is important as omitting important variables can increase the bias in the resulting estimation Heckman et al. (1997). Anderson et al. (2009) noted that there is no a general rule on which variables should be included as covariates. However, economic theory and empirical studies are used as a guide to know which observables (explanatory variables) affect both participation and the outcomes of interest (Bryson et al., 2002).

A. Choice and definitions of explanatory variables related to asset accumulation

Explanatory variables composed of different demographic, socioeconomic and institutional that affect participation in PSNP and asset accumulation (outcome variable) were identified and presented below.

Table 2. Variables related to asset accumulation that included in PSM

Variable name Description Variable Type Dependent Participation in PSNP (1=Yes; 0=No) Dummy Covariates FAMILYSZ Number of family members Continuous (number) Continuous HHSEX Sex of household head Dummy (1=male;0=Female) Dummy HHAGE Age of household head in years (maximum grade attended) Continuous HHEDUC Education of head in school years Continuous FMEMBEDUC Education of household member (maximum grade attended) Continuous LABFORCE Adult labour force of a household of age 15-64 years Continuous LAND Size of land operated in hectares Continuous LIVESTOCK Livestock holdings in tropical livestock unit Continuous IRONSHEET Ownership of iron corrugated roof home Dummy FOODSECPROB Number of months faced food security problem before 2006) Continuous DAVISTFREQ Frequency of DA visits in number of days/year Continuous CREDIT Access to credit if needed (1=yes; 0=No) Dummy DISTRDUMMY The district in consideration (1=if Meskan; 0=otherwise) Dummy Source: Own definition

B. Choice and definitions of explanatory variables related to SLM practices In addition to explanatory variables related to asset accumulation explained above, different plot and farm level variables that simultaneously affect participation in the program and the outcome variables of sustainable land management practices are described below.

Table 3. Variables related to SLM that included in propensity score matching model

Variable name Description Variable Type Dependent Participation in PSNP (1=Yes; 0=No) Dummy Covariates FAMILYSZ Number of family members Continuous (number) Continuous HHSEX Sex of household head Dummy (1=male;0=Female) Dummy HHAGE Age of household head in years (maximum grade attended) Continuous HHEDUC Education of head in school years Continuous FMEMBEDUC Education of household member (maximum grade attended) Continuous LABFORCE Adult labour force of a household of age 15-64 years Continuous LAND Size of land operated in hectares Continuous LIVESTOCK Livestock holdings in tropical livestock unit Continuous IRONSHEET Ownership of iron corrugated roof home Dummy FOODSECPROB Number of months faced food security problem before 2006) Continuous SZPLOT Average size of plot operated in hectares Continuous DISPLOT Average plot distance from homestead Continuous SLOPLOT* Percentage of steep and very steep slope plots Continuous FERTPLOT** Percentage of infertile and medium fertile plots Continuous DAVISTFREQ Frequency of DA visits in number of days/year Continuous CREDIT Access to credit if needed (1=yes; 0=No) Dummy DISTRDUMMY The district in consideration (1=if Meskan; 0=otherwise) Dummy Source: Own definition *relevant to physical SWC (soil bund, stone bund and cut-off drain) **relevant to fertility improvement practices (chemical fertilizer, manure and compost use)

3.5.1.2. Choice, measurement and indicators of the outcome variables

A. Outcome variables for asset accumulation: Change in livestock: is a continuous variable measured in tropical livestock unit (TLU) which takes the change in values of livestock from the years 2006 to 2010. Change in Values of non-livestock assets: is a dependent variable measured in Ethiopian Birr (ETB) by taking a constant price of 2006. It is a continuous variable that measures the 44 changes in value of non-livestock assets (households’ consumer, durable and productive goods) from the year 2006 to 2010.

B. Sustainable land management outcome variables: Sustainable land management practices are very wide and cover physical measures (soil bunds, stone bunds, check dams, cut-off drains, etc.); fertility improvement practices (application of chemical and natural fertilizer and agronomic practices such as crop rotation and fallowing) and biological measures (tree plantation). Only a few of these practices which are widely used and expected as outcomes of PSNP in the study area are considered in this paper. Specifically, soil bund, stone bund, cut-off drains (boyi), chemical fertilizer and natural fertilizer (manure and compost) applications were SLM practices considered as outcome variables in this study.

Soil and water conservation (SWC) activities: include: soil bund, stone bund and cut-off drain (continuous outcome variables measured in m/ha).

Fertility improvement practices: Chemical fertilizer (Kg/ha), manure (ton/ha) and compost (ton/ha).

3.5.2. Choices and descriptions of variables included in Heckman’s two step model

As explained earlier, SLM practices are very wide and difficult to look into each of them, hence, only a few of these practices which are widely used in the study area are considered in here. Specifically, soil bund, chemical fertilizer and manure applications are the three SLM practices that are considered and used as dependent variables. Participation in these selected three SLM practices and intensity of using them are affected by different factors. A host of household characteristics, socio-economic, institutional and farm level characteristics hypothesized to affect these practices are listed and discussed as follows:

Dependent variables for participation

Participation in soil bund constriction (Y1A): is a dummy variable takes a value of 1 if a household participated in soil bund constriction on his/her plot and 0 otherwise.

Participation in chemical fertilizer use (Y2A): is a dummy variable takes a value of 1 if a household participated in chemical fertilizer (Urea and/or DAP) application on his/her plot and 0 otherwise.

Participation in manure use (Y3A): is a dummy variable takes a value of 1 if a household participated in manure (Urea and/or DAP) application on his/her plot and 0 otherwise.

Dependent variables for intensity of use

Intensity of soil bund constriction (Y1B): is a continuous variable that is measured in meter/hectare m/ha).

Intensity of use of chemical fertilizer (Y2B): is a continuous variable that is measured in kilo gram/hectare (Kg/ha).

Intensity of use of manure (Y3B): is a continuous variable that is measured in ton/hectare (Kg/ha).

Explanatory variables Age of household head (HHAGE): is a continuous variable (measured in years) and related to experience. The effect of age of farmers on soil bund construction may be negative (Berhanu and Swinton, 2003; Wogayehu and Drake, 2003; Habtamu, 2006) or positive (Aklilu, 2006). Similarly, it may have a negative effect (Maiangwa et al., 2007) or positive effect (Bekele and Holden, 1998) on fertilizer use. Therefore, it is hypothesized here to have an undecided influence on soil bund and both chemical and manure fertilizer use in priori.

Education of household head (HHEDUC): is a continuous variable measured in maximum grade attended. Empirical evidences show that education is positively associated with soil bund construction (Holden and Hailu, 2002; Croppenstedt et al., 2003; Pender et al., 2003). Studies also show the presence of positive relationship between chemical fertilizer use (Dereje et al., 2001; Chilot, 2007; Maiangwa et al., 2007) and manure use (Dereje et al., 2001). Therefore, education of the household head is hypothesized to positively affect all the three practices (soil bund, chemical fertilizer and manure).

Labour force (LABFORCE): is a continuous variable measured in number of family member of age 15-64 years. In principle, labour force is a key input in agriculture in general and in SWC practices in particular. However, studies show the inverse relationship between 46 family size (labour force) and SLM practices. For instance, Wogayehu and Drake (2003), Aklilu (2006) and Fikru (2009) found a negative relationship between family size and SWC activities while Bekele and Holden (1998) and Maiangwa et al. (2007) the negative relationship between family size and chemical fertilizer use. The authors noted that the inverse relationship as large family size spent more time in short term food security activities. Hence, it is hypothesized to have undecided sign priori on all the three practices in this study.

Land holding (LAND): is a continuous variable of a total land (in hectare) operated by a farmer. Larger land hold (farm size) is suitable for SWC practices and for ploughing and hence has positive relationship with SWC practices Holden and Hailu, 2002; Mahmud, 2004; Aklilu, 2006) and fertilizer application (Holden and Hailu, 2002; Maiangwa et al. 2007). On the other hands, more land may reduce the need to conserve land or there may be a limited resource to cover large farms with SLM technologies. Hence a negative influence (Croppenstedt et al. 2003; Pender and Berhanu, 2004). Therefore, in this study it is hypothesized to have an undecided sign on soil bund, chemical fertilizer and manure in priori.

Plot size (SZPLOT): is a continuous variable measured in hectare. Larger plot area is convenient for bund construction and hence positively related with bund construction (Berhanu and Swinton, 2003). Larger plot size is also convenient for farm activities than fragmented land. As a result, using fertilizer on larger plots is suitable (Chilot, 2007). Hence, it is hypothesized in this study that plot size has a positive relationship with soil bund, chemical fertilizer and manure use.

Distance of plots from homestead (DISPLOT): is a continuous variable measured in a minute of walk from homesteads. Near plots get supervision and attention from family (Berhanu and Swinton, 2003; Wogayehu and Drake, 2003). However, this inverse relationship does not hold true for all kinds of SLM practices. For instance, Chilot (2007) found a positive relationship between plot distance and chemical fertilizer adoption. Hence, it might have negative or positive sign depending on the type of SLM technologies.

Slope of plots (SLOPLOT): steepness of a plot initiates farmers to invest in SWC practices to protect their plots. Hence, there is a positive relationship between steepness of plots and in soil bund construction. Empirical evidences ensure this fact (Pender and Kerr, 1998; Aklilu 2006). Therefore, it is hypothesized to positively influence soil bund. 47

Fertility status of plots (FERTPLOT): plots with fertile soils that are expected to give high return will have higher marginal productivity loss. In this regard, soil fertility status has positive relationship with physical SLM practices (Berhanu and Swinton, 2003; Wogayehu and Drake, 2003). On the other hand, SWC covers fertile areas of a plot reducing cultivable areas which makes farmers reluctant to use SWC practices (Aklilu, 2006). Fertile lad needs less additional purchased fertilizer. Therefore, it is hypothesized that soil fertility status has undecided effect on soil bund construction and negative relation with chemical fertilizer use.

Perceived profitability of technologies (PERPROF): is a dummy variable that takes a value of 1 if a household perceives a SLM technology is profitable and 0 otherwise. Aklilu (2006) found a positive relationship between perceived profitability of bund and its use. Hence, it is hypothesized to have a positive relationship with all the three variables.

Development agents visit frequency (DAVISTFREQ): is continuous variable measured in number of days per year. Visit of development agent (access to extension services) has a positive influence on SLM activities because farmers who have access to extension services get training that enhance their knowledge. Studies show that access to extension services has a positive effect both on fertilizer use (Chilot, 2007; Maiangwa et al., 2007) and on soil bund construction (Abebaw et al., 2011). Hence, it is hypothesized to have a positive sign on soil bund construction and chemical fertilizer and manure use in this study.

Access to credit (CREDIT): is dummy variable that takes a value of 1 if a household has an access to credit services and 0 otherwise. It helps to solve liquidity constraints of the household to purchase fertilizer and has a positive effect (Croppenstedt et al., 2003; Pender and Berhanu, 2004; Chilot, 2007; Maiangwa et al. 2007). Therefore, this study hypothesizes positive association between credit access and chemical fertilizer use.

Livestock ownership (LS): is a continuous variable measured in TLU. Theoretically, livestock can support SLM in two ways. First, livestock is used as a source of cash to purchase inputs such as fertilizer. Second, they provide farmyard manure and compost for fertilizer. In this regard, livestock ownership has positive impact on fertilizer use and manure application (Croppendstedt et al., 2003; Fitsum, 2003; Pender and Berhanu, 2004; Chilot, 2007). On the other hands, more specialization into livestock away from cropping may reduce 48 focuses given to crop production and investment in SLM which leads to negative relationship between livestock and physical SLM practices. Most empirical results ensure this inverse relationship (Holden and Hailu, 2002; Aklilu, 2006; Abebaw et al., 2011) while some research results, for example (Fikru, 2009) shows a positive relationship between livestock and physical SLM practices. Therefore, livestock ownership has a positive relationship with fertilizer use and indecisive sign on soil bund construction in priori.

Off-farm income (OFFARMINC): is a continuous variable measured in ETB that obtained from off-farm activities (such as petty trades, carpentry, etc.). Empirical findings show a mixed effect of the effect of off-farm income on SLM practices. For example, Chilot (2007) reported a positive relationship between off-income and chemical fertilizer use while Maiangwa et al. (2007) found a negative relationship. Similarly, Fikru (2009) and (Abebaw et al. (2011) found a positive relationship between off-farm income and bund construction while Aklilu (2006) and reported a negative relationship between them. Hence, the effect of off- farm income on soil bund, chemical fertilizer and manure use is undecided in priori.

Participation in productive safety net programs (PARTPSNP): is a dummy variable taking a value of 1 if a household participated in PSNP and 0 otherwise. A wide controversial issue on labour-intensive development programs is that these programs may have a crowding out effect on on-farm activities including investment in SLM. On the other hand, if carefully designed, they can enhance on-farm investment by reducing liquidity problems that rural households usually face. PSNP was designed in such a way that participants perform PSNP activities during slack time when farm activities are not performed (FDRE, 2006). Hence, it is expected to positively influence investment in SLM activities in this study.

Participation in other food security programs (PARTOFSP): is a dummy variable takes a value of 1 if a household is participated in OFSP and 0 otherwise. One of the components of OFSP is SWC activities. Hence, it is expected to positively influence SLM practices.

Location Dummy (DISTRDUMMY): sustainable land management practices depend on the topography of area in consideration. In areas with steep slope, SWC practices such as soil and stone bunds are common than areas with flat features. The application of natural fertilizer also depends on the availability of livestock that provide manure. In our case, Meskan district has

49 more areas with steep slopes than ATJK district. On the other hand, households in ATJK district owns more land suitable for livestock rearing compared to Meskan.

Table 4. Variables included in Heckman’s two-step model for SLM activities

Variables Descriptions and units Dependent variables

Y1A=PSB Participation in soil bund (1=yes; 0=No)

Y1B=ISB Intensity of construction of soil bund (m/ha)

Y2A=PCHF Participation in chemical fertilizers use (1=yes; 0=No)

Y2B=ICHF Intensity of chemical fertilizers use (Kg/ha)

Y3A=PMA Participation in manure use (1=yes; 0=No) Expected

Y3B=IMA Intensity of manure use (ton/ha) signs on

Explanatory variables Y1 Y2 Y3 HHAGE Age of household head in years (cont.) +/- +/- +/- HHEDUC Education of head in school years (cont.) + + + LABFORCE Adult labour force of a HH of age 15-64 years (cont.) +/- +/- +/- LAND Size of land operated in hectares (cont.) +/- +/- +/- SZPLOT Size of plot operated in hectares (cont.) + + + DISPLOT Plot distance from homestead - +/- - SLOPLOT Slope of plots (1=flat;2=Medium;3=steep; 4=very steep) + NA NA FERTPLOT Fertility status of plots (1=low, 2=medium, 3=fertile) NA - +/- PERPROF Perceived profitability (1=if perceived; 0=otherwise) + + + DAVISTFREQ Frequency of DA visits in number of days/year (cont.) + + + CREDIT Access to credit if needed (1=yes; 0=No) NA + - LIVESTOCK Livestock holdings in tropical livestock unit (cont.) +/- + + OFFARMINC Non-farm income in ETB (cont.) +/- +/- +/- PARTPSNP Participation in PSNP (1=if participated; 0=otherwise) +/- + +/- PARTOFSP Participation in PSNP (1=if participated; 0=otherwise) + + + DISTRDUMMY The district in consideration (1=if Meskan; 0=otherwise) - + - Source: Own summary NA means not applicable.

Parameter estimates of any regression model are seriously affected if some basic econometric assumptions are failed. Hence, there is a need of performing different tests before proceeding to the estimation itself. Testing the existence of multicollinearity among explanatory variables is very important since it seriously affects the parameter estimates. The Variance Inflation Factor (VIF) technique is widely used to detect the problem of multicollinearity among the continuous variables while contingency coefficient is used for testing multicollinearity among discrete variables (Gujarati, 2004). VIF is defined as; 1 VIF(Xi)  (15) 2 1 Ri 2 Where Ri is the squared multiple correlation coefficient between Xi and other explanatory variables. If the value of VIF is greater than 10, it is an indication for the existence of multicollinearity.

Contingency coefficients test is used for dummy variables using the following formula.

 2 C  (16) n   2

2 Where C is contingency coefficient,  is the chi-square value and n=total sample size. For dummy variables, if the value of contingency coefficient is greater than 0.75, it is an indication of the existence of the multicollinearity problem among them.

The presence of hetroscedasticity (when variances of all observations are not the same) which leads to consistent but inefficient parameter estimates should be checked for. According to White (1980), biases in estimated standard errors may lead to invalid inferences if hetroscedasticity problem exists. The Breusch- Pagen test (hottest) in STATA is used to test for hetroscedasticity. In the present study, robust standard errors were used to correct the problem of hetroscedasticity problem. STATA software version 9 was used to analyze the data. Specifically, propensity scores matching algorithm (psmatch2) developed by Leuven and Sianesi (2003) was used to assess the impact of PSNP. Similarly, Heckman’s two-steps selection model was used to assess factor affecting investment in SLM.

4. RESULTS AND DISCUSSION

This chapter presents the main results and discussions. It is divided into two sub-sections. The first sub section provides the characteristics of sample households while the second subsection discusses econometric estimation results.

4.1. Descriptions of Sample Households' Characteristics

4.1.1. Households’ demographic background and farm characteristics

Table 5 presents the result of the pre-intervention socio-economic characteristics of participant and non participant households. The result shows that statistically there was a significant difference between the two groups in terms of family size, number of plots owned and number of food insecure months before the intervention of the PSNP. Compared to non participants, PSNP participant households had larger family size. Similarly, compared to non participants, PSNP participants encountered food security problem for greater number of months prior to the PSNP intervention.

According to Table 6, non participating households had significantly higher percentage of male headed households as compared to PSNP participating households (  2=2.842). In terms of types of house owned, both groups showed the same percentage (13%) for corrugated iron roofed house while maximum percentage of both groups had grass roofed house.

Table 5. Descriptive statistics of household characteristics (Continuous variables)

Pre-intervention Total PSNP Non PSNP Difference T-Value variables (N=186) (N=95) (N=91) in Means Mean (STD) Mean (STD) Mean (STD) Mean (STD) FamiSize 5.01 (2.135) 5.40 (2.034) 4.59 (2.17) 0.807 (0.31) 2.616** HeadAge 39.91 (14.106) 39.89 (11.67) 39.92 (16.33) -0.028(2.08) -0.014 EducHead 2.35 (3.381) 2.02 (3.125) 2.70 (3.613) -0.682 (0.5) -1.379 MaxEducHhld 4.76 (3.088) 4.47 (2.985) 5.07 (3.179) -0.592 (0.45) -1.310 LabourForce 2.39 (1.076) 2.51 (1.040) 2.26 (1.104) 0.242 (0.16) 1.536 Landholdg 0.99 (0.762) 1.04 (0.730) 0.93 (0.796) 0.11 (0.112) 1.029 Plot size 0.329 (0.343) 0.33(0.346) 0.32 (0.339) 0.009 (0.029) 0.320 No of plots 3.94(1.923) 4.07(1.941) 3.78(1.895) 0.29 (0.163) 1.802* Distance of plots 11.43 (13.517) 12 (14.927) 11 (11.707) 0.59 (1.149) 0.510 TLU 3.80 (4.321) 3.59 (3.591) 4.02 (4.981) -0.43 (0.63) -0.674 NMntsFIS06 3.2 (2.714) 4.03 (2.050) 3.10 (2.241) 0.933 (0.32) 2.964*** Source: Own survey data, 2010 *,** and *** means significant at 5% and 1% probability levels, respectively STDD for mean difference=

Table 6. Descriptive statistics of sample households (Dummy variables)

Pre-intervention Category Participant Nonparticipant Total (N=95) (N=90) (N=186) variables N % N % N % HeadSex Male 66 69.5 73 80.2 149 74.7 2.842* Female 29 30.5 18 19.8 47 25.3 CorrRoof Owned Iron 13 13.7 12 13.2 25 86.6 0.010 roofed home Did not own 82 86.3 79 86.8 161 13.4 Source: Own Survey, 2010 *means significant at 10% probability level.

4.1.2. Plot level characteristics of the households

Physical plot characteristics such as slope, soil depth, and soil fertility status are the most crucial factors that influence sustainable land management practices. Table 7 presents the most important plot level characteristics perceived by the households in the study area. From the total of 557 plots operated by the total of 186 sample households in both districts, the majority of plots are flat slope followed by medium slope in terms of slope category. However, compared to Meskan district, more plots are flat and medium slope in Adami Tulu Jido Kombolcha (ATJK).

Table 7. Farmer perception of plot characteristics, CRV of Ethiopia, 2010

Items Meskan (N=364) ATJK (N=193) Both districts (N=557) PSNP Non PSNP PSNP Non PSNP PSNP Non PSNP (N=185) (N=179) (N=113) (N=80) (N=298) (N=259) Slope plots (%) Flat 40.5 39.1 60.2 61.3 48.0 45.9 Medium 19.5 26.8 28.3 25.0 22.8 26.3 Steep 12.4 15.6 4.4 7.5 9.4 13.1 Very steep 27.6 18.4 7.1 6.3 19.8 14.7 Soil fertility plots (%) Infertile 34.1 36.3 8.0 2.5 24.2 25.9 Medium 54.6 55.9 90.3 90.0 68.1 66.4 fertile 11.4 7.8 1.8 7.5 7.7 7.7 Soil depth plots (%) Shallow 48.6 50.8 16.8 7.5 36.6 37.5 Medium 42.7 47.5 79.6 91.3 56.7 61.0 Deep 8.6 1.7 3.5 1.3 6.7 1.5 Source: Own survey data, 2010

As shown in Table 7, more plots have medium fertility status at ATJK as compared to Meskan district where more plots are categorized under infertile status. However, there is small proportion of fertile plots in both districts though it is slightly higher at Meskan. In

54 terms of soil depth, majority of plots at Meskan have shallow depth while that of ATJK have medium depth. There are small percent of deep soil in both districts.

4.1.3. Perceptions of households

In addition to plot and farm level characteristics described earlier, households’ perceptions to land degradation problems, perception to tenure security and perception towards the profitability of using different SLM technologies play a crucial role in households’ decision of using SLM practices. Households’ perceptions to soil erosion, soil fertility decline and land tenure security along with their perception to profitability of sustainable land management practices are influential factors in the decision process to use SLM practices.

4.1.3.1. Households’ perception to land degradation problems and tenure security

As shown in Table 8, The perception of households’ to land degradation problems is very high since more than 90% of them perceived both soil erosion and soil fertility decline problems. In terms of households’ perception to have right to inherit lands to once family, almost all PSNP participants and more than 90% of non participants perceived that they have a right to inherit land to their family. This suggests that the land certificate offered by the government might build their confidence on the land ownership.

Households in the study area use different symptoms for recognizing land degradation problems. A combination of factors including decrease production, gully formation, decrease soil depth and change in soil colour are used as symptoms for soil erosion while decrease in production and change in soil colour are used as symptoms for soil fertility decline in the study area (Table 9).

Table 8. Perception of sample households on land degradation and land tenure, 2010

Item Perception PSNP Non PSNP Number % Number % Erosion problem on Perceive 95 100.0 87 96.7 farm lands Did not perceive 0 0.0 3 3.3 Soil fertility decline Perceive 88 92.6 83 91.2 on farm lands Did not perceive 7 7.7 8 8.8 Right to inherit Perceive 94 98.9 84 92.3 lands to once family Did not perceive 1 1.1 7 7.7 Source: Own survey, 2010

Table 9. Symptoms of land degradation problems in the study area, 2010

Degradation Symptoms of the land degradation PSNP Non PSNP Problem problem No. % No. % Soil erosion Decrease production 2 2.1 1 1.1 Gully formation 5 5.3 6 6.9 Decrease production, gully formation and 11 11.6 14 16.1 decrease soil depth Decrease production and gully formation 6 6.3 8 9.2 Decrease production, gully formation, 66 69.5 56 64.4 decrease soil depth and soil colour change Decrease production, gully formation and 5 5.3 2 2.3 soil colour change Soil fertility Decrease production 32 36.4 31 37.8 decline Soil colour change 1 1.1 1 1.2 Decrease production and soil colour change 55 62.5 50 61.0 Source: Own Survey, 2010

Households in the study area have already understood that the prevailing land degradation problems have resulted in different effects. Table 10 shows that more than 70% of households recognized a combination of consequences consisting of loss of soil fertility, fertilizer, seed

56 and crop lands as a result of soil erosion problem. Moreover, loss of productivity as a consequence of soil fertility decline is reported by 84% and 69% of PSNP participant and non participant households, respectively.

Table 10. Consequences of land degradation problems in CRV of Ethiopia, 2010

Problem Consequences PSNP (95) Non PSNP (N=88) Number % Number % Soil Loss of soil fertility 10 10.5 13 14.9 erosion Loss of soil fertility, fertilizer and seed 2 2.1 2 2.3 Loss of soil fertility, fertilizer, seed 67 70.5 66 75.9 and crop land Loss of soil fertility and seeds 4 4.2 3 3.4 Loss of soil fertility, seeds and crop land 8 8.4 2 2.3 Loss of soil fertility and crop land 4 4.2 1 1.1 Soil Less productivity 73 83.9 59 68.6 fertility Poverty and famine 8 9.2 16 18.6 decline Less production and causes marginal land 6 6.9 11 12.8 Source: Own survey, 2010

4.1.3.2. Farmers’ perceptions to profitability of land management practices

Table 11 presents farmers’ perception to different SLM technologies. The perception of sample households’ to profitability of short term (fertilizer application water flows management), medium term (soil and stone bunds) and long term (tree planting) SLM technologies revealed that more than 65% of PSNP and 70% of non PSNP sample households perceived that all technologies are profitable except boyi (cut-off drains).

Table 11. Farmers’ perception to profitability of SLM in CRV of Ethiopia, 2010

Category SLM practicesPSNP households Non PSNP households yes % no % yes % no % Boyi 23 24.2 72 75.8 51 56.7 39 43.3 Short term Fertilizer 87 91.6 8 8.4 88 97.8 2 2.2 Manure 68 71.6 27 28.4 74 82.2 16 17.8 Compost 66 69.5 29 30.5 72 80.0 18 20.0 Medium term Soil bunds 74 77.9 21 22.1 70 77.8 20 22.2 Stone bunds 64 67.4 31 32.6 66 73.3 74 82.2 Long term Agro-forestry 81 85.3 14 14.7 80 88.9 10 11.1 Source: Own survey, 2010

4.1.4. Access to extension and credit services

Institutional factors such as access to extension services through visit of development agents; access to advices and training on SLM technologies and access to credit are important determinants of farmers’ decision to use SLM technologies. Table 12 shows that only 9.5% of PSNP participant and 14.3% of non participant sample households were not visited by development agents (DAs) at all in the year 2009. The majority of both sample households were visited by DAs 1-10 days per annum.

Access to training is low in the study area. About 38% of PSNP participants and 42% of non participants did not receive any type of training in the year 2009. Most of the households received training for a maximum of 5 days per annum (Table 12).

Table 12. Access to development agent and training of sample households in 2009

Visit and PSNP (N=95) Non PSNP (N=91) Total (N=186) training Number % Number % Number % DA visits not visited at all 9 9.5 13 14.3 22 11.8 1-10 days 38 40.0 50 56.0 88 47.3 11-20 days 34 35.8 28 30.8 62 33.3 21-30 days 13 13.7 0 0.0 13 7.0 31-40 days 1 1.1 0 0.0 1 0.5 Training not trained at all 36 37.9 38 41.8 74 39.8 1-5 days training 31 32.6 41 45.1 72 38.7 6-10 days training 23 24.2 12 13.2 35 18.8 11-15 days training 5 5.3 0 0.0 5 2.7 Source: Own survey, 2010

Table 13 shows credit access and types of credit received for the past five years. It shows that about 58% of the sample households did not receive any kind of credit for the past five years while the rest received credit mainly to purchase livestock followed by fertilizer and seed.

Table 13. Credit access and types received by sample households in the last five years

PSNP (N=95) Non PSNP (N=91) Total (N=186) Credit Status No. % No. % No. % Credit Not received 49 51.6 59 64.8 108 58.1 access Received 46 48.4 32 35.2 78 41.9 Types Fertilizer credit 5 10.6 7 20.6 12 6.5 of Livestock credit 32 68.1 16 47.1 48 25.8 credit Fertilizer, improved seed and 3 6.4 3 8.8 6 3.2 money for livestock purchase Money for trading 1 2.1 2 5.9 3 1.6 Fertilizer and seed credit 6 12.8 6 17.6 12 6.5 Source: Own survey, 2010

4.1.5. Farming system and crops grown

The study area is characterized by mixed farming. Both livestock and crop production are exercised in both districts. Cereal crops are the major crops produced in both districts followed by pulses. Livestock farming is also common in the study area. Cattle, sheep, goats and poultry are the major livestock produced in the area.

4.1.5.1. Crop production and trees holding

Cereal crops are the main crops produced in the study area. More than 77% of the total plots were allocated for cereal crops in the study area in 2009. Compared to their Meskan counterparts, ATJK households allocated more land to cereals in 2009 cropping season (Table 14). Maize and teff were the first and the second most commonly grown cereals in both districts. Pulses are also grown in the study area which covered about 15% of cultivated land in the same year. Haricot beans is the most commonly grown pulse crop in both districts covering more than 90% of pulses and about 14% of all cultivated land in 2009 cropping season. Chat and enset are also grown in some parts of Meskan district food insecure areas. However, enset is a popular root crop in the 20 food secured kebeles of Meskan district. Meskan households are allocating some plots for eucalyptus trees. Several kinds of trees are grown in both districts. On average, PSNP participant and non participant households owned 228 and 252 different kinds of trees with standard deviation of 469 and 533 trees, respectively in 2009.

Table 14. Crops grown and percent of area allocated for each crop in 2009 in CRV

Types of Meskan (N=364) ATJK (N=193) Both districts (N=557) Crop PSNP Non PSNP All PSNP Non PSNP All PSNP Non PSNP All grown (N=185) (N=179) (N=113) (N=80) (N=298) (N=259) (557) % % % % % % % % % Cereals (%) 71.9 72.5 72.4 85.4 80.5 83.1 78.8 76.7 77.8 Maize 39.3 41.4 40.4 53.8 57.3 55.6 46.6 49.4 48.0 Teff 24.7 18.0 21.4 5.1 3.7 4.4 14.9 10.9 12.9 Wheat 2.9 7.0 5.0 12.8 11.0 11.9 7.9 9.0 8.5 Sorghum 5.0 5.7 5.4 4.3 0.0 2.2 4.7 2.9 3.8 Barely 0.0 0.4 0.2 9.4 8.5 9.0 4.7 4.5 4.6 Pulses (%) 19.7 17.6 18.8 10.3 13.4 11.9 15.1 15.5 15.4 Haricot bean 16.3 16.4 16.4 10.3 13.4 11.9 13.3 14.9 14.2 Faba bean 1.3 0.4 0.9 0.0 0.0 0.0 0.7 0.2 0.5 Field Peas 1.7 0.8 1.3 0.0 0.0 0.0 0.9 0.4 0.7 Lentil 0.4 0.0 0.2 0.0 0.0 0.0 0.2 0.0 0.1 Vegetables 2.1 2.4 2.3 0.0 0.0 0.0 1.1 1.2 1.2 Pepper 0.4 1.2 0.8 0.0 0.0 0.0 0.2 0.6 0.4 cucumber 0.4 1.2 0.8 0.0 0.0 0.0 0.2 0.6 0.4 Cabbage 1.3 0.0 0.7 0.0 0.0 0.0 0.7 0.0 0.4 Others 6.2 7.3 6.8 4.3 6.1 5.3 5.4 6.8 6.2 Chat 3.3 5.3 4.3 0.0 0.0 0.0 1.7 2.7 2.2 Enset 0.4 0.4 0.4 0.0 0.0 0.0 0.2 0.2 0.2 Eucalyptus 1.3 1.2 1.3 0.0 0.0 0.0 0.7 0.6 0.7 Fallow 0.4 0.4 0.4 0.9 2.4 1.7 0.7 1.4 1.1 Grazing land 0.8 0.0 0.4 3.4 3.7 3.6 2.1 1.9 2.0 Source: Own Survey, 2010

4.1.5.2. Livestock production and other asset holding

Cattle, sheep, goats and poultry are the main livestock reared by sample households in both districts. Few equines (mostly donkeys) are also reared in the study area. Table 15 shows mean livestock holding before and after the intervention of PSNP. It also reveals the mean changes of livestock holding for the intervention period. The decline in livestock asset holding was observed in both PSNP participants and non participants over the specified period though the magnitude of decline is less for PSNP participants compared to non participants.

Table 15. Livestock holding and change in livestock holding of households (TLU)

Livestock (TLU) PSNP Non PSNP T-value P-value Mean SD Mean SD Livestock holding, 2006 3.587 3.591 4.015 4.981 -0.674 0.501 Livestock holding, 2010 3.286 2.843 3.281 3.829 0.10 0.992 Change in Livestock, 2005-2010 -0.301 3.231 -0.734 4.067 0.806 0.422 Source: Own survey, 2010

Other asset holding of the sample household consists of productive assets (include all asset used to produce crop and livestock like ploughing equipments, water pump, sickle, spade, beehives, cart, pick axes and axes, etc.), household assets (stove, and other cooking materials)and household consumer durable goods (which include telephone, radio, bed, home, bicycle, etc.). These assets are valued at constant price of 2006 to remove the impact of inflation for comparison. Table 16 shows that there was no significant difference in the values of asset holding between PSNP participants and non participants. Similarly, there was no significant difference in values of change in assets over the specified period between the two groups.

Table 16. Mean values of assets of sample households (ETB)

Value of assets (ETB) PSNP NPSNP T-value P-value Mean SD Mean SD Value of productive and household 5861 3545 5734 3290 0.253 0.780 consumer durable assets, 2006 Value of productive and household 7159 4144 7652 3917 -0.832 0.406 consumer durable assets, 2010 Change in values of assets, 2006-2010 1299 2976 1918 4029 -1.20 0.233 Source: Own Survey, 2010

4.1.6. Household income

The sources of income for sample households come from both farm and nonfarm activities. Farm income consists of both incomes from sales of livestock and livestock products and from sales of crops. Non-farm income sources are mainly from petty trade and daily works. However, PSNP sample households derive income from public works participation which increases their non farm income sources.

Table 17 shows the mean annual income generated from livestock sales, crop sales and nonfarm income of the sample households in 2009. It shows that the average annual income earned from non-farm and total annual income of non PSNP participants were significantly higher than PSNP households at 1% and 5% level of significance, respectively. However, PSNP households earned about ETB 2195 annual income from PSNP public works that reduced the income differences which resulted in insignificant income difference between the two groups. This suggests that participation of the poor households in such social welfares helps to reduce the income disparities among the rural households.

Table 17. Mean income of PSNP and non PSNP households (ETB), 2009

Income source PSNP Non PSNP Mean SD Mean SD T- Value P-Value Income from livestock 747.72 1146.82 816.22 1415.86 -0.363 0.717 Income from crop 764.29 1080.33 1062.02 2622.22 -1.02 0.309 Total farm income 1512.01 1614.34 1878.24 3066.94 -1.025 0.307 Nonfarm income 880.75 1537.83 1644.2 2385.13 -2.605*** 0.01 Total Income w/t PSNP 3904.76 3974.51 5400.66 6103.18 -1.989** 0.048 Income from PSNP 2194.94 862.6 - - Total income including PSNP 6099.70 4345.35 5400.66 6103.18 0.304 0.371 Source: Own survey, 2010 ** and *** means significant at 5% and 1% probability levels, respectively.

4.1.7. Use of sustainable land management practices

Sustainable land management practices in the study area are classified into three categories namely, agronomic, fertility improvement, and physical soil and water conservation practices.

4.1.7.1. Agronomic practices

Agronomic practices cover a wide range of cultivation practices which include proper ploughing, appropriate sowing date, crop rotation, intercropping, fallowing, use of improved seeds and agro-forestry. However, this study focused only on the use of improved seed, crop rotation, intercropping and tree planting on the farm land (agro-forestry) since farmers are usually experienced and expected to exercise proper ploughing and follow appropriate sowing dates.

According to Table 18, more than 80 % of the total plots of the sample households were covered by local seed in 2009. More than 20 % of the total plots were intercropped in the specified year. However, intercropping is rare at ATJK as compared to Meskan district. This

64 might be due to smaller land holding in Meskan compared to ATJK households. Agro-forestry practice is also common in the study area.

Different crop rotation system was also practiced in the study area. However, the appropriate rotation system pulse-cereals and vegetable-cereals were applied only on about 24% and 8% of plots, respectively as compared to about 60% cereals after cereals system in 2009. Appropriate crop rotation was limited by the suitability of plots only for cereals. Households grow cereals after cereals on plots where pulses cannot be grown at all or not productive as cereals. Meskan households are replacing some of their cereal plots with chat. Table 18 shows that 5 plots covered by cereals in 2008 were replaced in chat at Meskan district in 2009. Some 8% of plots were under fallowing in 2008 were covered in cereals in 2009. However, fallowing is less common at Meskan than at ATJK district implying that the small land holding at Meskan makes difficult to practice fallowing.

Table 18. Plots under selected agronomic practices in the study area, 2009

Agronomic Responses of PSNP (N=298) non PSNP (N=259) Total (N=557) practices households Number % Number % Number % Seed type* Local 246 82.6 208 80.3 454 81.45 Improved 52 17.4 51 19.7 103 18.55 Intercropping** Intercropped plots 56 18.8 62 23.9 118 21.35 Not intercropped 242 81.2 197 76.1 439 78.65 Agro-forestry Plots planted trees 56 18.8 92 35.5 148 27.15 Plots with no trees 242 81.2 167 64.5 409 72.85 Crop rotation Pulses-cereals 26 22.6 23 25.0 49 23.8 Pulse-pulses 2 1.7 0 0.0 2 0.85 Cereal-cereals 77 67.0 48 52.2 125 59.6 Cereals-Chat** 1 0.9 4 4.3 5 2.6 vegetable-Cereals 2 1.7 8 8.7 10 5.2 Fallow-Cereals 7 6.1 9 9.8 16 7.95 Source: Own Survey, 2010 *improved seed used in the study area was usually for maize crop. **some households are replacing cereals with chat at Meskan district

4.1.7.2. Fertility improvement and soil and water conservation practices

Both chemical and natural fertilizers were applied to improve soil fertility in the study area in 2009. However, the level of application of both chemical and natural fertilizer was quite low. Table 19 shows that the average application of chemical fertilizer of PSNP and non PSNP households was only 26 Kg/ha and 24 Kg/ha, respectively in 2009. Similarly, the application of natural fertilizer (manure and compost) in ton per hectare during the specified year was not greater than 3 tons each for both sample households. The results show that there was no significant difference between PSNP participants and non participants in the means of application of both chemical and natural fertilizers during the same year.

Table 19 shows that the mean length of soil bund, stone bund and cut-off drains in meters per hectare on farm plots in 2009. It shows that both PSNP participant and non participant households practiced soil bund than stone bund. The result reveals that there was a significant difference (at 10% level of significance) between the two groups in terms of stone bund construction in meter per hectare. Productive safety net program participant households were better in practicing stone bund on their farm land than non participants.

Table 19. Fertility improvement and physical SWC practices in the study area in 2009

SLM practices Types of PSNP Non PSNP T- Value Mean SD Mean SD Fertility Chem. fertilizer (Kg/ha) 26.13 72.507 24.13 67.754 0.335 improvement Manure (tons/ha) 2.26 6.640 1.96 4.859 0.600 practices Compost (tons/ha) 2.52 7.941 2.16 7.108 0.550 Physical SWC Soil bund (m/ha) 45.95 136.60 48.52 169.330 -0.197 practices Stone bund (m/ha) 13.87 59.14 7.05 28.492 1.693* Cut-off drain (m/ha) 18.23 79.84 13.28 76.005 0.747 Source: Own survey, 2010 *means there is a mean significant difference at 10% probability level.

4.2. Econometric Estimation Results

This section describes the econometric analysis. It is divided into two sub-sections. The first subsection explains the entire process to arrive at the impact of the program using propensity score matching model which includes estimation of propensity scores, matching methods used, common support region and balancing test. The second subsection presents the result of Heckman’s two-step model to identify factors affecting participation and intensity of using selected sustainable land management practices.

4.2.1. Propensity score matching model result

Propensity score matching (PSM) was applied to deal with the first two objectives (assessing the impact of PSNP on asset accumulation, and on SLM practices) of the study. As specified earlier, the covariates for asset accumulation and for SLM practices are different because those of SLM practices includes additional covariates of plot level characteristics in addition to demographic and socio economic variables. Hence, the matching process was performed for asset accumulation (both livestock and non livestock assets), physical soil and water conservation practices (soil bund, stone bund and cut-off drains) and for fertility improvement practices (chemical fertilizer, manure and compost). However, to keep the paper manageable, only propensity score result of fertility improvement practices was presented and discussed here annexing the rest two for readers on Appendix 6 and 7 for SWC and asset accumulation outcome indicators, respectively.

4.2.1.1. Estimation of propensity scores

This part presents the results of the logistic regression model employed to estimate propensity scores for matching treatment household with control households. As specified earlier, the dependent variable in this model is binary indicating whether the household was a participant in the PSNP which takes a value of 1 and 0 otherwise. STATA 9.0 computing software using the propensity scores matching algorithm, psmatch2 was used for the estimation purpose.

Before performing the econometric estimation itself, different econometric assumptions were tested using appropriate techniques. First, the presence of strong multicollinearity among continuous explanatory variables was tested using variance inflation factors (VIF) (Appendix 1 and Appendix 2). Second, contingence coefficient (C) was used to check the existence of multicollinerty between discrete variables (Appendix 3 and Appendix 4). There was no any continuous or discrete explanatory variable dropped from the estimated model since no serious problem of multicollinearity were detected from both the VIF and contingency coefficient results. Third, the presence of hetroscedasticity problem was tested using Breusch-Pagen test. The hypothesis for the existence of hetroscedasticity was not rejected as (p=3.47). Hence, robust standard errors were estimated to tackle heteroscedasticity problem in the data.

Table 19 shows the program participation estimation results of the logitic model. The pseudo-R2 value of the estimated model result is 0.1509 which is fairly low. This low pseudo- R2 value indicates that the allocation of the program has been fairly random (Pradhan and Rawlings, 2002). The result, therefore, suggests that treatment households do not have diverse characteristics over all and hence obtaining a good match between treatment and control households becomes easier.

As shown in Table 20 (and Appendix 6 and 7), the estimated coefficient results indicate that participation in the PSNP was significantly influenced by five explanatory variables. Family size, frequency of development agents (DA) and number of months that the household encountered food security problem prior to the intervention of PSNP were found to have positive and significant influence on participation in PSNP at 10%, 5% and 5% level of significances, respectively. Such strong positive relationship between large family size and participation in PSNP might be due to the fact that large family size is associated with higher food demand and has higher chance of being food insecure compared to small family size. Households who are frequently contacted by DA had higher chance of being included in the program. This might be because DA may understand problems of these households and considering them during selection as DA involved in selecting PSNP participants together with the community. Similarly, the positive relationship between the number of months encountered food security problem prior to the intervention of PSNP is in line with the PSNP targeting criteria. On the other hand, sex of the household head and education of family member were found to have 68

negative and significant effect on the program participation at 5% and 1% level of significances, respectively. This suggests that female headed households have higher chance to be included in the program than male headed households. The possible explanation for this relationship might be because female headed households have higher chance of being food insecure than male headed households due to several gender related factors. Similarly, the inverse relationship between higher level of family member education and participation in PSNP might be because households who have educated family members are more likely to get additional income and hence food secured. As a result, their probability of inclusion in the program is low.

Table 20. Logit results of household program participation Variables Coefficients Robust Std. Error z-values P-value CONSTANT -1.342938 1.529685 -0.88 0.380 FAMILYSZ 0.1927937 0.1053515 1.83* 0.067 HHSEX -1.134707 0.4391149 -2.58** 0.010 HHAGE 0.0064874 0.014792 0.44 0.661 HHEDUC 0.1022803 0.0811033 1.26 0.207 FMEMBEDUC -0.2231322 0.0860134 -2.59*** 0.009 LABFORCE 0.1866003 0.2062546 0.90 0.366 LAND 0.1101514 0.3321505 0.33 0.740 AVERAGEPLOTSZ 1.009621 0.7592989 1.33 0.184 AVERAGEPLOTDIST 0.0205105 0.0212728 0.96 0.335 INFERTILEPLOT (%) -0.0091583 0.0109938 -0.83 0.405 LIVESTOK -0.055842 0.0529762 -1.05 0.292 IRONROOFEHOME 0.190427 0.550964 0.35 0.730 DACONTACTFREQ .05486 0.0225363 2.43** 0.015 CREDITACCESS 0.2833035 0.3577873 0.79 0.428 FOODSECPROB 0.2010637 0.0887422 2.27** 0.023 DISTRICTDUMMY 0.5007054 0.659126 0.76 0.447 Sample size (N) = 186 Prob> χ 2 = 0.0012 Log likelihood =-108.82884 Pseudo-R2 = 0.1509 LRχ 2 (14) = 38.67 Source: Own estimation result ***,**and * means significant at 1% 5% and 10% probability levels, respectively.

The distribution of the propensity score for each household included in the treated and control groups was computed based on the above participation model to identify the existence of a common support. Figure 3 depicts the distribution of the household with respect to the estimated propensity scores. The figure shows that most of the treatment households were found in the middle and partly in the right side near to middle while most of control households are found in the left side of the distribution. It also reveals that there is wide area

in which the propensity score of both the treatment and the control groups are similar.

1.5

Density

.5 0

0 .2 .4 .6 .8 1 psmatch2: Propensity Score pscore before matching all households treated households control households

Figure 3. Kernel density of propensity score distribution

4.2.1.2. Matching program and non program households

There are four important tasks that must be carried out before conducting the matching work itself. First, estimating the predicted values of program participation (propensity score) for all the sample households of both program and control groups (which was done in the previous section) is a primary activity. Second, imposing a common support condition on the propensity score distributions of household with and without the program is another important task. Third, discarding observations whose predicted propensity scores fall outside the range of the common support region is the next work. Fourth, conducting a sensitivity analysis to check the robustness of the estimation (whether the hidden bias affects the estimated average treatment on treated or not) is the final task.

As shown in Table 21, the estimated propensity scores vary between 0.139 and 0.96 (mean = 0.61) for PSNP households and between 0.036 and 0.927 (mean = 0.41) for non PSNP participant (control) households. The common support region would therefore, lie between 0.139 and 0.927 which means households whose estimated propensity scores are less than 0.139 and larger than 0.927 are not considered for the matching purpose. As a result of this restriction, 11 households (2 PSNP and 9 non PSNP) were discarded.

Table 21. Distribution of estimated propensity scores

Groups Observation Mean Std. Dev. Minimum Maximum

Total households 186 0.51 0.501 0.0364 0.960 PSNP households 95 0.61 0.180 0.139 0.960 Control households 91 0.41 0.213 0.036 0.927 Source: Own estimation result.

1.5

Density

.5 0

0 .2 .4 .6 .8 1 psmatch2: Propensity Score

Treated households Treated households in common support

Figure 4. Kernel density of propensity scores of participant households

1.5

Density

.5 0

0 .2 .4 .6 .8 1 psmatch2: Propensity Score

Control households Control households in common support

Figure 5. Kernel density of propensity scores of non participant households

Figure 4 and 5 shows the distribution of estimated propensity scores before and after the imposition of the common support condition for participant and non-participant households, respectively. As depicted in these Figures, most of the participant households have propensity score around 0.7 while majority of the nonparticipant households have propensity score around 0.3.

4.2.1.3. Choice of matching algorithm

Different alternatives of matching estimators were conducted to match the treatment program and control households fall in the common support region. The decision on the final choice of an appropriate matching estimator was based on three different criteria as suggested by Dehejia and Wahba (2002). First, equal means test (referred to as the balancing test) which suggests that a matching estimator which balances all explanatory variables (i.e., results in insignificant mean differences between the two groups) after matching is preferred. Second, looking into pseudo-R2 value, the smallest value is preferable. Third, a matching estimator that results in the largest number of matched sample size is preferred. To sum up, a matching estimator that balances all explanatory variables, with lowest pseudo-R2 value and produces a large matched sample size is preferable. Table 22 presents the estimated results of tests of matching quality based on the three performance criteria. Looking into the result of the

matching quality, nearest neighbour matching (NN) of neighbourhood 4 was found to be the best for the data we have at hand. Appendix 8 also shows that NN 4 was found to be the best for SWC and asset accumulation outcome indicators variables. Hence, the estimation results and discussion for this study are the direct outcomes of the NN matching algorithm with a neighbour of 4.

Table 22. Matching performance of different estimators

Matching Estimator Performance Criteria Balancing test* Pseudo-R2 Matched sample size Caliper 0.1 16 0.020 123 0.25 14 0.119 140 0.5 14 0.203 156 Kernel Matching Band width of 0.1 11 0.235 164 Band width of 0.25 11 0.235 164 Band width of 0.5 11 0.235 164 Nearest Neighbour matching 1 neighbour 11 0.235 164 2 neighbour 16 0.034 175 3 neighbour 16 0.028 175 4 neighbour 16 0.022 175 5 neighbour 16 0.024 175 Source: Own estimation result. * Number of explanatory variables with no statistically significant mean differences between the matched groups of program and non-program households

2.2.1.4. Testing the balance of propensity score and covariates

Once the best performing matching algorithm is chosen, the next task is to check the balancing of propensity score and covariate using different procedures by applying the selected matching algorithm(NN (4) matching in our case). It should be clear that the main

73 intention of estimating propensity score is not to get a precise prediction of selection into treatment. Rather, to balance the distributions of relevant variables in both groups.

Table 23. Propensity score and covariate balance Variable Sample mean %bias %reduction T-test treated control |bias| T p>|t| pscore Unmatched 0.60776 0.41403 98.2 6.69 *** 0.000 Matched 0.60048 0.59799 1.3 98.7 0.10 0.922 FAMILYSZ Unmatched 5.4 4.6333 36.4 2.49** 0.014 Matched 5.3548 5.2688 4.1 88.8 0.29 0.776 HHSEX Unmatched 0.69474 0.81111 27.1 -1.84* 0.068 Matched 0.69892 0.73118 -7.5 72.3 -0.49 0.628 HHAGE Unmatched 39.895 39.7 1.4 0.09 0.925 Matched 39.925 40.04 -0.8 40.6 -0.06 0.951 HHEDUC Unmatched 2.0211 2.7333 -21.1 -1.43 0.153 Matched 2.0645 2.207 -4.2 80.0 -0.30 0.763 FMEMBEDUC Unmatched 4.4737 5.1222 -21.1 -1.44 0.152 Matched 4.4946 4.2796 7.0 66.8 0.48 0.629 LABFORCE Unmatched 2.5053 2.2778 21.2 1.44 0.150 Matched 2.5161 2.371 13.5 36.2 0.87 0.386 LAND Unmatched 1.0445 .93973 13.7 0.94 0.351 Matched 1.0236 1.0696 -6.0 56.1 -0.38 0.701 AVERAGEPLOTSZ Unmatched 0.43783 0.40072 9.2 0.62 0.535 Matched 0.44192 0.49308 -12.7 -37.9 -0.77 0.441 AVERAGEPLOTDISTUnmatched 11.767 11.626 1.3 0.09 0.929 Matched 11.928 10.575 12.5 -854.2 0.89 0.376 INFERTILEPLOT (%) Unmatched 93.698 95.383 -10.3 -0.70 0.486 Matched 94.386 93.687 4.3 58.5 0.28 0.781 LIVESTOK Unmatched 3.5871 4.0597 -10.9 -0.74 0.459 Matched 3.6628 4.1869 -12.1 -10.9 -0.87 0.383 IRONROOFEHOME Unmatched 0.13684 0.13333 1.0 0.07 0.945 Matched 0.12903 0.07527 15.6 -1432.3 1.21 0.228 DACONTACTFREQ Unmatched 12.305 8.2111 51.3 3.47*** 0.001 Matched 11.903 11.866 0.5 99.1 0.03 0.974 CREDITACCESS Unmatched 0.48421 0.35556 26.1 1.78* 0.077 Matched 0.47312 0.46774 1.1 95.8 0.07 0.942 FOODSECPROB Unmatched 4.0316 3.0333 47.4 3.22*** 0.002 Matched 3.9892 3.9489 1.9 96.0 0.14 0.892 DISTRICTDUMMY Unmatched 0.57895 0.58889 -2.0 -0.14 0.892 Matched 0.58065 0.61022 -6.0 -197.4 -0.41 0.683 Source: Own estimation result ***and * means significant at the 1%, and 10% probability levels, respectively.

The balancing powers of the estimations are ensured by different testing methods. Reduction in the mean standardized bias between the matched and unmatched households, equality of means using t-test and chi-square test for joint significance of the variables used are employed here. The fifth and sixth columns of Table 23 show the standardized bias before and after matching, and the total bias reduction obtained by the matching procedure, respectively. The standardized difference in covariates before matching is in the range of 1% and 51.3% in absolute value whereas the remaining standardized difference of covariates for almost all covariates lies between 0.5% and 15.6% after matching. Appendix 9 and 10 also show similar results. This is fairly below the critical level of 20% suggested by Rosenbaum and Rubin (1985). Therefore, the process of matching creates a high degree of covariate balance between the treatment and control samples that are ready to use in the estimation procedure. Similarly, T-values also reveal that all covariates became insignificant after matching while five of them were significant before matching.

As indicated in Table 24, the values of pseudo-R2 are very low. This low pseudo-R2 value and the insignificant likelihood ratio tests support the hypothesis that both groups have the same distribution in the covariates after matching. These results indicate that the matching procedure is able to balance the characteristics in the treated and the matched comparison groups. Hence, these results can be used to assess the impact of PSNP among groups of households having similar observed characteristics. This enables us to compare observed outcomes for treatments with those of a control groups sharing a common support.

Table 24. Chi-square test for the joint significance of variables

Categories of outcomes Sample Pseudo R2 LR chi2 p>chi2 Fertility improving practices Unmatched 0.154 39.50 0.002 Matched 0.022 5.77 0.995 Physical SWC practices Unmatched 0.146 37.70 0.003 Matched 0.033 8.43 0.957 Asset accumulation Unmatched 0.137 35.28 0.001 Matched 0.019 4.83 0.988 Source: Own estimation result

All of the above tests suggest that the matching algorithm we have chosen is relatively the best for the data at hand. Therefore, we can proceed to estimating the average treatment effect on the treated (ATT) for the sample households.

4.2.1.5. Treatment effect on the treated

The estimation result presented in Table 25 provides a supportive evidence for the effect of the program on households’ asset accumulation and on SLM practice (soil and stone bunds uses (m/ha), cut-off drains (m/ha), chemical fertilizers (Kg/ha) and manure and compost (tons/ha) on farm lands).

As shown in Table 25, the PSM estimation result shows that participation in PSNP had not brought a significant impact on both asset accumulation and on SLM technologies in the study area. The insignificant impact of PSNP on asset accumulation obtained in this study might be because households in the study area use the PSNP transfer mainly for consumption smoothing purpose than asset accumulation. Andersson et al. (2009) and Gilligan et al. (2008) also found insignificant impact of PSNP on asset holdings.

Table 25. Average treatment effects on the treated (ATT)

Categories Outcome Variables Treated Control Difference S.E1. T-value Physical Soil bunds (m/ha) 40.585 51.981 -11.396 16.926 -0.67 SWC Stone bunds (m/ha) 12.870 7.295 5.575 4.598 1.21 practices cut-off drains (m/ha) 25.932 30.014 -4.082 17.506 -0.23 Fertility Chem. fertilizer (Kg) 25.39 34.31 -8.92 7.009 -1.27 improving Manure (tons/ha) 3.20 3.24 -0.04 0.888 -0.05 practices Compost (tons/ha) 3.50 3.68 -0.174 1.45 -0.12 Asset Change in TLU(06-10) -0.380 0.498 -0.878 0.571 -1.54 Accumulation Change in Value of 1256.087 1552.622 -296.535 666.766 -0.44 Assets (06-10) (ETB) Source: Own estimation result. 1The bootstrapped S.E. is obtained after 100 replications.

Participation in PSNP had not brought significant impact on any of SLM practices in the study area. The insignificant effect of PSNP on SLM practices in the study area might be due to the fact that these outcomes are not directly expected from the primary objectives of PSNP. Moreover, the obtained insignificant effect might be because the public work components of PSNP were not conducted on private lands. Rather, these activities were done on communal lands and hillside areas in the study area.

The sensitivity analysis on the estimated average treatment effects were not conducted because there is no any outcome indicator with significant impact of the productive safety net program in our case. Hence, there is no need of conducting sensitivity analysis.

4.2.2. Heckman’s two-step model result

In the previous section, the impact of the PSNP on the treatment households was estimated. In this section, the main objective (as specified in the third objective of the study) is to examine different factors (including participation in PSNP and OSFP) that influence sustainable land management practices in the study area. Heckman’s two-step model was used to identify factors affecting participation in selected SLM practices (participation in soil bund, chemical fertilizer use and manure) in the first step and the intensity of use in the second step.

The dependent variable for the selection equation is binary indicating whether or not a household is participated in soil bund, chemical fertilizer use and manure use while the dependent variable of the outcome equation is the amount of use of these SLM practices.

Factors affecting participation and intensity of soil bund practices

Soil bund is the widely practiced SWC practice in the study area. Hence it was selected and discussed here as an example. Tables 26 presents the Heckman’s two-step model coefficient estimates (for the selection and outcome equations) and the marginal effects for the selection equations of soil bund. The likelihood function was significant (Wald χ2=790.57 with P<0.0000) showing strong explanatory powers. Similarly, the coefficients of the Mill’s ratio was significant (P<0.000) indicating the presence of self-selection and hence justifying the appropriateness of using Heckman’s two-step model.

As shown in Table 26 the likelihood of participation in soil bund practice was influenced by the slope level of plots while the intensity of constructing the soil bund was influenced by distance of plots, slope level, livestock ownership and participation in social programs (both PSNP and OFSP).

Contrary to our expectation, distance of farm plot (DISPLOT) from homestead measured in minutes of walk had a positive and significant impact on the intensity of using soil bunds at 5% significance level. The positive relationship between distance of plots and intensity of using soil bund might be due to the fact that plots near to homestead are usually more fertile than plots situated far from residence since their probability of receiving natural fertilizer (which is too heavy to apply in far plots) is high. Hence, using these fertile soils for soil bund construction is not economical.

As expected, plots with steep slope and very steep slope influenced the likelihood of using soil bund positively and significantly at 1% level of significance. The marginal effect of steep slope and very steep slope plot variables are 0.034 and 0.009, respectively suggesting that the likelihoods of constructing soil bund on plots having steep slope and very steep slope is higher by 3.4 % and 0.9%, respectively compared to plots having flat slope. Similarly, steep slope and very steep slope category had positive and significant impact on the intensity of constructing soil bund at 1% level of significance. Hence, raising the perception level of farmers towards the slope level of their plots would have a positive effect on both increasing the number of farmers participating in soil bund construction and the intensity of constructing soil bund.

Livestock ownership (LS) is another variable that had negative and significant effect on the intensity of using soil bund at 10% level of significance. The inverse relationship might be explained in terms of specialization. As a farmer specialized more in livestock production he/she gives less focus to crop production and hence investing less in soil bund. Studies conducted by Holden and Hailu (2002) and Aklilu (2006) support this finding.

Participation in productive safety net program (PSNP) had a significant negative impact whereas participation in other food security program (OFSP) positively and significantly 78 influenced the intensity of using soil bund at 5% levels of significant. The negative relationship between the former might be due to the fact that public work components of PSNP in the study area mostly focuses on protecting communal lands such as hillside terracing and participating in construction of roads, school and health centres. Hence, there might be time scarcity to construct soil bund on farm plots. As expected, participation in OFSP had a positive and significant effect on the intensity of using soil bund suggesting that arranging such program for rural farmers is useful for conserving soil and reducing erosion which in turn results in increment of crop productivity.

Table 26. Factors affecting chance of participation and intensity of using soil bund (m/ha)

Probability of participating in soil bund Intensity of use Variables Coefficient p-level Marg. Eff Coefficient p-level Constant 3.272*** 0.000 HHAGE 0.000 0.995 0.000 0.000 0.984 HHEDUC -0.006 0.655 0.002 -0.006 0.469 LABFORCE -0.001 0.957 0.000 -0.001 0.973 LAND 0.022 0.728 0.021 0.022 0.441 SZPLOT 0.069 0.720 0.077 0.069 0.324 DISPLOT 0.004 0.235 0.003 0.004** 0.018 1 SLOPLOT MEDIUMSLOPE 0.074 0.598 0.012 0.074 0.522 STEEPSLOPE 0.655*** 0.000 0.034 0.655*** 0.000 VERYSTEEPSLOPE 0.711*** 0.000 0.009 0.711*** 0.000 PERPROF 0.0687 0.392 0.031 0.069 0.131 DAVISTFREQ 0.000 0.977 0.000 0.000 0.953 LIVESTOCK -0.0161 0.404 0.0152 -0.016* 0.059 OFFARMINC 0.000 0.741 0.000 0.000 0.486 PARTPSNP -0.107 0.208 0.062 -0.001** 0.015 PARTOFSP 0.119 0.190 -0.076 0.119** 0.017 2 DISTRDUMMY 0.088 0.551 0.087 0.614 0.196 Total observation 557 Wald χ 2=790.57 Prob> χ 2=0.0000 Censored 443 Uncensored 114 Mills lamda Z=4.03*** P <0.000 Source: Own estimation result ***, **and * means significant at 1%, 5% and 10% probability levels, respectively. 1comparasion category is plots with flat slopes. 2a dummy variable, 1 indicates Meskan district and 0 indicates ATJK district.

Factors affecting participation and intensity of use of chemical fertilizer

Chemical fertilizer use (urea and DAP) was presented and described in this section as an example of soil fertility improvement practice. The dependent variable in the selection equation here is whether a household is participated in the use of chemical fertilizer or not. In the outcome equation, the dependent variable is the amount of use of chemical fertilizers (in Kg/ha).

As shown in Tables 27, the likelihood function of the Heckman’s two-step model was significant (Wald χ 2 =2899.89 with P<0.0000) showing strong explanatory powers though the coefficient of the Mill’s lamda was not significant (P<0.58) indicating that there is no evidence for the presence of self-selection.

Table 27 shows the parameter estimates of Heckman’s two-step model for the likelihoods of participation and intensity of using chemical fertilizer (Kg/ha). The likelihood of using chemical fertilizer was affected by three explanatory variables namely, perceived profitability of chemical fertilizer, off-farm income of a household and the location dummy. The intensity of using chemical fertilizer was influenced by age and education level of household head, land holding, plot size, plot distance, fertility status of plots, livestock ownership and the location dummy.

As expected, perceived profitability of a household to chemical fertilizer use had a positive and significant effect on the likelihood of participating in chemical fertilizer use at 1% level of significance. The result shows that, other things being equal, a household who perceived chemical fertilizer use is profitable, his/her likelihood of using chemical fertilizer on his/her land would be higher by 98.3% than a similar household who do not perceive chemical fertilizer use as unprofitable. Hence, raising the perception level of farmers towards the profitability of using chemical fertilizer would increase the number of farmers using it.

Contrary to our expectation, off-farm income had a negative impact on the likelihood of using chemical fertilizer at 5% level of significance. However, the marginal effect shows that, keeping other things equal, a unit increase in off-farm income decreases the chance of using chemical fertilizer only by only 0.002% which is negligible. 81

Another important variable that influenced both the likelihood of participation and intensity of using chemical fertilizer was location dummy. The results show that the chance of participating in chemical fertilizer use of Mekan district households is higher by 8.6% compared to those of Adamitulu Jido Kombolcha district. Being Meskan district had a positive impact on the intensity of chemical fertilizer use. This might be because Meskan district households owned less land size compared to Adamitulu Jido Kombolcha. As a result, they exercise intensive farming as compared to ATJK.

Age and education level of the household head affected the intensity of fertilizer use negatively and significantly at 1% and 5% level of significance, respectively. The result shows that experienced and educated households did not use chemical fertilizer in a larger amount. This might be because these farmers use natural fertilizers like manure and compost which are relatively cheaper than using chemical fertilizer.

Land holding had a significant negative effect while plot size had a significant positive effect on the intensity of using chemical fertilizer both at 1% probability level. The inverse relationship with the former might be due to lack of capital to cover all plots with purchased fertilizer which is expensive while the positive relationship with the later is due to suitability of using larger plots for farming purpose compared to fragmented plots. Chilot (2007) also found a positive relationship between plot size and the likelihood of using inorganic fertilizer.

Distance of the plots from homesteads and fertility status of plots (plots with medium fertility status) had negative impact on the intensity of fertilizer use at 5% and 10% level of significance, respectively. The former relationship is as expected while the latter is not.

In line with our expectation, livestock ownership (in TLU) had a positive and significant impact on the intensity of using chemical fertilizer at 1% level of significance. The result suggests that increasing heard size results in intensity of chemical fertilizer use as livestock are used to generate farm income which in turn helps to purchase inputs such as fertilizer. Therefore, institutional interventions targeted at improving heard size such as improving veterinary services and arranging credit services for livestock production will have a positive effect on the level of investing in chemical fertilizer in the study area.

Table 27. Factors affecting chance of participation and intensity of using chemical fertilizer (Kg/ha)

Probability of using chemical fertilizer Intensity of use Variables Coefficient p-level Marg. Eff Coefficient p-level

HHAGE 0.000 0.993 0.000 -0.023*** 0.000 HHEDUC 0.002 0.642 0.000 -0.049** 0.036 LABFORCE -0.000 0.975 -0.001 0.041 0.377 LAND 0.000 0.987 -0.014 -0.538*** 0.000 SZPLOT 0.021 0.627 0.045 0.925*** 0.001 DISPLOT 0.000 0.387 0.001 -0.015** 0.021 1 FERTILITY STATUS INFERTILEPLOTS 0.016 0.725 0.008 -0.308 0.208 MEDIUMFERTPLTS 0.016 0.725 0.006 -0.395* 0.071 PERPROF 0.976*** 0.000 0.983 0.249 0.306 DAVISTFREQ -0.001 0.555 -0.001 0.002 0.859 CREDIT -0.001 0.655 -0.006 0.074 0.616 LIVESTOCK -0.001 0.903 0.004 0.174*** 0.000 OFFARMINC -0.001** 0.023 -0.00002 -0.000 0.106 PARTPSNP -0.013 0.516 -0.014 -0.032 0.844 PARTOFSP -0.018 0.431 -0.021 -0.098 0.619 DISTRDUMMY 0.076** 0.048 0.086 0.409* 0.083 Total observation 557 Censored= 433 Uncensored =124 Mills lamda Z=-0.55 P <0.580 Wald χ 2=2899.89 Prob> χ 2= 0.0000 Source: Own estimation result ***, **and * means significant at 1%, 5% and 10% probability levels, respectively. 1comparasion category is plots which fertility status is fertile. 2a dummy variable, 1 indicates Meskan district and 0 indicates ATJK district.

Factors affecting manure application in the study area

As shown in Table 28, the likelihood of participation in manure use was influenced by plot size and perceived profitability of using manure while the intensity of using was influenced by land holding, plot size, plot distance, perceived profitability of using manure, off-farm income and location dummy.

Plot size influenced both the likelihood and the intensity of use of manure at 10% and 1% probability levels, respectively. The result suggests that larger plots are suitable for farming practices than fragmented plots. The marginal effect shows that increasing a plot size by one hectare will raise the probability of applying manure by one ton, ceteris paribus. However, the total land holding hand a negative impact on the intensity of using manure at 1% probability level. This is because the larger the size of land, the more difficult to cover it with manure which is heavy, difficult to transport and apply on the farm plots.

Plot distance from homestead influenced the intensity of manure use negatively and significantly as expected at 1% probability level. This is because transporting manure to plots situated at far distance is difficult suggesting that introducing the use of animal cart to transport such heavy inputs would have a positive effect on the level of manure use.

Perceived profitability of using manure influenced both the likelihood and the intensity of using manure at 5% and 1% probability levels, respectively. The marginal effect value shows that the probability of participating in manure use of households who perceive using manure as profitable is higher by 2.1% compared to those who do not perceive using it as profitable. Hence, raising the perception level of households towards understanding of manure application profitability through different training programs would have a potential positive effect on raising the number of farmers using manure and level of use.

Off-farm income had a negative impact on the intensity of using manure at 10% probability level suggesting that having off-farm income might build the potential of farmers to use purchased chemical fertilizer than manure.

The variable district (location) dummy had a significant negative impact on the intensity of using manure at 5% level of significance. The result shows that Meskan district households use less manure compared to ATJK households.

Table 28. Factors affecting likelihoods of participation and intensity of manure (tons/ha)

Probability of participating in manure use Intensity of use Variables Coefficient p-level Marg. Eff. Coefficient p-level Constant -0.155 0.760 HHAGE -0.000 0.957 0.003 -0.007 0.222 HHEDUC 0.005 0.744 0.011 -0.013 0.599 LABFORCE 0.002 0.937 0.004 -0.003 0.950 LAND -0.146 0.167 0.059 -0.441*** 0.001 SZPLOT 0.376* 0.053 0.324 1.499*** 0.000 DISPLOT -0.014 0.156 0.008 -0.046*** 0.000 1 FERTILITY STATUS INFERTILEPLOTS 0.153 0.351 0.096 0.122 0.666 MEDIUMFERTPLTS 0.221 0.138 0.136 0.179 0.506 PERPROF 0.596** 0.020 0.021 1.312*** 0.000 DAVISTFREQ -0.001 0.872 0.001 -0.004 0.616 CREDIT -0.059 0.564 0.021 -0.170 0.291 LIVESTOCK 0.004 0.817 0.001 0.005 0.871 OFFARMINC -0.000 0.241 0.000 -0.001* 0.051 PARTPSNP -0.011 0.912 0.007 -0.039 0.825 PARTOFSP -0.003 0.981 0.020 -0.049 0.818 2 DISTRDUMMY -0.168 0.503 0.169 -0.737** 0.010 Total observation 557 Censored= 411 Uncensored=146 Mills lamda Z=2.36** P <0.018 Wald χ 2= 246.14 Prob> χ 2= 0.0000 Source: Own estimation result ***, **and * means significant at 1%, 5% and 10% probability levels, respectively. 1comparasion category is plots which fertility status is fertile. 2a dummy variable, 1indicates Meskan district and 0 indicates ATJK district.

5. SUMMARY, CONCLUSIONS AND RECOMMENDATION

This unit is divided into three subsections. The first summarizes the major findings of the research. The next subsection presents the conclusions of the results while the third subsection forwards the recommendation based on the results obtained.

5.1. Summary

In this study the impact of PSNP on asset accumulation and sustainable land management (SLM) practices in the central rift valley (CRV) of Ethiopia has been evaluated using cross sectional data collected from Adamitulu Jido Kombolcha and Meskan districts. In addition, the study has also identified factors affecting participation and intensity of using SLM practices at farm level. The primary data for this study were collected from 95 PSNP and 91 non PSNP households using a structured questionnaire. The research questions were “what would the level of asset accumulation and SLM practices have been if the PSNP had not been implemented?” and “what factors affecting participation and intensity of using sustainable land management?” Answering the first question requires the data drawn from the same households with and without the PSNP which is practically impossible to obtain such households with both states simultaneously due to the missing counter-factual data problem.

For a randomized experiment, the impact of a program can be evaluated simply by estimating the mean difference between the participants and controlling groups. However, for non experimental design, the simple with-and-without comparison of means for the treated and control groups would make the biased estimates because the program placement creates a selection effect. Hence, the study has applied a propensity score matching (PSM) technique which is widely applied to evaluate non experimental social programs. The PSM is used to create a comparable pair of treatment-control households in a non-randomly placed program with the absence of baseline data. This technique can adjust for selection bias and for estimating the counterfactual effect though it cannot totally solve the problem of section bias.

Participation in the PSNP program was influenced by a combination of household demographic, institutional and economic factors. Before proceeding to calculate the treatment

86 effects on the treated, the resulting matches passed through different processes of matching quality tests such as t-tests, reduction in standardized bias, and chi-square tests. In addition, the computed parametric standard error was bootstrapped to capture all sources of error in the estimates. Obtaining a reliable estimate of a program needs to adequately control for such confounding factors. Propensity score matching has produced 93 program households to be matched with 82 non-program households after discarding households whose propensity score is outside of the common support region. Hence, matched comparison was conducted on these households who share common pre-intervention characteristics except participating in PSNP. The impact estimation result shows that the productive safety net program had not brought any significant effect on both asset accumulations (changes in livestock and non livestock assets) and on sustainable land management practices in the study area.

Answering the question “what factors are affecting participation in SLM” requires data drawn from faming households having different socio-economic, institutional, farm and plot level characteristics. Heckman’s two-step model, which has an ability of removing selectivity bias, was used to identify factors influencing the likelihood of participation in the first step and intensity of using SLM practices in the second step.

The results of Heckman’s two step model show that both SWC practices and fertility improvement practices were influenced by several factors. Specifically, the results show that the likelihood of participation in soil bund practice was influenced by the slope level of plots while the intensity of constructing the soil bund was influenced by distance of plots, steepness of a slope, livestock ownership and participation in social programs (both PSNP and OFSP). It also shows that the likelihood of using chemical fertilizer was affected by perceived profitability of chemical fertilizer use, off-farm income and the location dummy while the intensity of using chemical fertilizer was influenced by age and education level of household head, land holding, plot size, plot distance, fertility status of plots, livestock ownership and the location dummy. Similarly, the likelihood of participation in manure use was influenced by plot size and perceived profitability of using manure while the intensity of using it was influenced by land holding, plot size, plot distance, perceived profitability of using manure, off-farm income and location dummy.

5.2. Conclusions

In conclusion, the productive safety net program had not brought significant positive effect on the participants’ in terms of asset accumulation in the study area though it solved the immediate food shortage problems. Similarly, it had not brought a significant positive effect on SLM practices. The insignificant impact of PSNP on asset accumulation obtained in this study might be because households in the study area use the PSNP income mainly for consumption smoothing purpose than asset accumulation. Moreover, the insignificant effect of PSNP on SLM practices in the study area might be due to the fact that these outcomes are not directly expected from the primary objectives of PSNP. In addition, the public work components of PSNP were not conducted on private lands. Rather, these activities were done on communal lands and hillside areas in the study area.

The likelihoods of participation in SLM practices and intensity of using them were influenced by different household demographic background, institutional and socio-economic factors as well as farm and plot level characteristics. Hence, considering these factors is very important when implementing SLM practices at farm level.

5.3. Recommendation

The empirical results reported in this thesis led us to forward the following recommendations:

 This study found that PSNP had not brought any significant impact on the participants’ asset accumulation, and on SLM practices in the study area. Thus, program designers at higher levels, implementers at lower levels, and funding agents should re-evaluate the program design and implementation to bring the positive effect on the participants in terms of asset accumulation and SLM practices.

 Soil bund construction was positively influenced by perceived slope level of plots. Hence, raising the farmers’ understanding of the level of plot slope should be focused on by the woreda bureau of agriculture to bring positive effect.

 Productive safety net program had a negative effect on the intensity of soil bund while other food security program had a positive effect on it. Therefore, care should be taken while designing huge social programs such as PSNP so that the public work should not compete for agricultural activities performed at farm level.

 Experienced and educated farmers were found to use less chemical fertilizer while perceived profitability of using it influenced the intensity of use positively in the study area implying that educated and experienced farmers use less expensive alternatives like natural fertilizer. Hence raising the perception level of farmers towards understanding the profitability of using chemical fertilizer and availing it at lower cost by producing in the country should be focused on by policy makers.

 Households who own larger and whose plots situated at far distance were found to use less chemical fertilizer while livestock ownership and larger plot size had positive effect on the intensity of using it. Hence, increasing the number of livestock holding and avoiding land fragmentation should be focused on to bring a positive effect.

 Plot size and perceived profitability of using manure influenced positively both the likelihood of participation and the intensity of using manure while total land size and plot distance had negative effect on it. Hence, raising the farmers’ perception level, avoiding land fragmentation and arranging means of transporting such heavy fertilizer such as animal cart to distant plots are recommended to bring a positive effect on manure use in the study area.

 Finally, additional researches should be carried out using much larger sample size at different locations to acquire more empirical findings on the impact of PSNP on asset accumulation and on its impact on SLM practices.

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Zaman, H., 2001. Assessing the poverty and vulnerability impact of micro credit in Bangladish: A Case study of Bangladesh Rural Advancement Committee (BRAC). Office of the chief economist and senior vice president (DECVP), The World Bank

7. APPENDICES

Appendix 1. Multicollinearity test for continuous variables included in PSM model

Fertility improving practices SWC practices Asset accumulation Variables VIF Variables VIF Variables VIF HHEDUC 2.31 HHEDUC 2.36 HHEDUC 2.27 FMEMBEDUC 2.16 FMEMBEDUC 2.25 FMEMBEDUC 2.09 SZPLOT 1.84 SZPLOT 1.88 LABFORCE 1.67 LAND 1.82 LAND 1.84 FAMILYSZ 1.66 LABFORCE 1.72 FAMILYSZ 1.71 HHAGE 1.46 FAMILYSZ 1.70 LABFORCE 1.69 LIVESTOCK 1.32 HHAGE 1.56 HHAGE 1.53 LAND 1.27 LIVESTOCK 1.44 LIVESTOCK 1.45 DAVISTFREQ 1.16 DISPLOT 1.30 DISPLOT 1.33 FOODSECPROB 1.14 FOODSECPROB 1.21 DAVISTFREQ 1.19 Mean VIF 1.56 DAVISTFREQ 1.19 FERTPLOT 1.18 FERTPLOT 1.12 FOODSECPROB 1.15 Mean VIF 1.61 Mean VIF 1.63 Source: Own estimation result

Appendix 2. Contingency coefficient for discrete variables included in PSM model

Variable Fertility improving practices SWC practices Asset accumulation Value of C Value of C Value of C HHSEX 0.123 0.123 0.123 IRONROOFEHOME 0.007 0.007 0.007 CREDITACCESS 0.133 0.133 0.133 DISTRICTDUMMY 0.015 0.015 0.015 Source: Own estimation result

Appendix 3. Multicollinearity test for continuous variables included in Heckman two-steps model

Fertility improving practices SWC practices Variables VIF Variables VIF LIVESTOCK 1.79 LIVESTOCK 1.79 LAND 1.78 LAND 1.78 SZPLOT 1.71 SZPLOT 1.71 HHEDUC 1.43 HHEDUC 1.43 HHAGE 1.31 HHAGE 1.31 OFFARMINC 1.28 OFFARMINC 1.28 DISPLOT 1.14 DISPLOT 1.14 LABFORCE 1.14 LABFORCE 1.14 DAVISTFREQ 1.12 DAVISTFREQ 1.12 Mean VIF 1.41 Mean VIF 1.41 Source: Own estimation result

Appendix 4. Contingency coefficient for discrete variables included in the Heckman two step model

Fertility improvement practices SWC practices Variable Value of C Variable Value of C INFERTILEPLOTS 0.042 MEDIUMSLOPE 0.249 MEDIUMFERTPLTS 0.067 STEEPSLOPE 0.325 PERPROF 0.344 VERYSTEEPSLOPE 0.512 CREDIT 0.109 PERPROF 0.090 PARTPSNP 0.001 PARTPSNP 0.009 PARTOFSP 0.071 PARTOFSP 0.053 DISTRDUMMY 0.055 DISTRDUMMY 0.179 Source: Own estimation result

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Appendix 5. Conversion factors used to estimate tropical livestock units (TLU)

Livestock category Conversion Factor

Horse 1.1

Ox 1

Cow 1

Woyfen (weaned male calf) 0.34

Heifer 0.75

Calf 0.25

Donkey (adult) 0.7

Donkey (young) 0.35

Sheep (adult) 0.13

Sheep (young) 0.06

Goat (adult) 0.13

Goat (young) 0.06

Hen 0.013

Source: Storck, et al., 1991

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Appendix 6. Logit results of program participation based on SWC outcome indicators

Variables Coefficients Robust Std. Errorz- values P-value CONSTANT -2.292216 1.11695 -2.05** 0.040 FAMILYSZ 0.2178732 0.1050115 2.07** 0.038 HHSEX -1.123344 0.4343617 -2.59** 0.010 HHAGE 0.0058417 0.0145265 0.40 0.688 HHEDUC 0.1073762 0.080871 1.33 0.184 FMEMBEDUC -0.2172818 0.0860189 -2.53** 0.012 LABFORCE 0.1610619 0.2034086 0.79 0.428 LAND 0.1280716 0.3326882 0.38 0.700 AVERAGEPLOTSZ 1.000146 0.7513434 1.33 0.183 AVERAGEPLOTDIST 0.0221775 0.0211669 1.05 0.295 STEEP/VERYSTEEPPLOT (%) 0.0009475 0.0052963 0.18 0.858 LIVESTOK -0.0565767 0.0531936 -1.06 0.288 IRONROOFEHOME 0.1437737 0.5509596 0.26 0.794 DACONTACTFREQ 0.0577357 0.0223682 2.58** 0.010 CREDITACCESS 0.3048799 0.3554642 0.86 0.391 FOODSECPROB 0.1611351 0.084378 1.91* 0.056 DISTRICTDUMMY 0.5508928 0.6616137 0.83 0.405 Sample size (N) = 186 Prob> χ 2 = 0.0019 Pseudo-R2 = 0.1445 LRχ 2 (14) = 37.25 Log likelihood = -110.25765 Source: Own estimation result **and * means significant at 5% and 10% probability levels, respectively.

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Appendix 7. Logit results of program participation based on asset accumulation outcome indicators

Variables Coefficients Robust Std. Error z-values P-value CONSTANT -1.288375 0.876744 -1.47 0.142 FAMILYSZ 0.2123698 0.1031714 2.06** 0.040 HHSEX -1.026088 0.4273571 -2.40** 0.016 HHAGE 0.0042532 0.014267 0.30 0.766 HHEDUC 0.0785635 0.0778334 1.01 0.313 FMEMBEDUC -0.193937 0.0827616 -2.34** 0.019 LABFORCE 0.1316071 0.2004267 0.66 0.511 LAND 0.3426935 0.3031752 1.13 0.258 LIVESTOK -0.0457358 0.0498138 -0.92 0.359 IRONROOFEHOME 0.0010681 0.5193505 0.00 0.998 DACONTACTFREQ 0.0534581 0.0219539 2.44** 0.015 CREDITACCESS 0.2164023 0.3432045 0.63 0.528 FOODSECPROB 0.1618258 0.0835382 1.94* 0.053 DISTRICTDUMMY -0.1174685 0.4604439 -0.26 0.799 Sample size (N) =186 Prob> χ 2 = 0.0009 Pseudo-R2 = 0.1345 LRχ 2 (14) = 34.68 Log likelihood = -111.54224 Source: Own estimation result **and * means significant at 5% and 10% probability levels, respectively.

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Appendix 8. Matching performance of different estimators for SWC outcome indicators and asset accumulation outcome variables

Matching SWC outcome variables Asset accumulation outcome variables estimator Performance criteria Performance criteria Balancing Pseudo-R2 Matched Balancing Pseudo-R2 Matched test* sample size test* sample size Caliper 0.1 16 0.026 123 16 0.019 127 0.25 14 0.127 141 14 0.081 144 0.5 14 0.198 156 14 0.153 161 Kernel Matching Band width 0.1 12 0.201 164 12 0.180 168 Band width 0.25 12 0.201 164 12 0.180 168 Band width 0.5 12 0.201 164 12 0.180 168 NN matching 1 neighbour 12 0.235 164 12 0.180 168 2 neighbour 16 0.034 174 16 0.025 176 3 neighbour 16 0.028 174 16 0.022 176 4 neighbour 16 0.022 174 16 0.019 176 5 neighbour 16 0.024 174 16 0.024 176 Source: Own estimation result. * Number of explanatory variables with no statistically significant mean differences between the matched groups of program and non-program households

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Appendix 9. Propensity score and covariate balance test for SWC outcome variables

Variable Sample mean %bias %reduction T-test treated control |bias| T p>|t| pscore Unmatched 0.601 0.416 95.3 6.51*** 0.000 Matched 0.591 0.588 1.3 98.6 0.10 0.918 FAMILYSZ Unmatched 5.4 4.5934 38.4 2.62** 0.010 Matched 5.2717 5.538 -12.7 67.0 -0.90 0.371 HHSEX Unmatched 0.695 0.802 -24.8 -1.69* 0.093 Matched 0.685 0.764 -18.2 26.7 -1.19 0.234 HHAGE Unmatched 39.895 39.923 -0.2 -0.01 0.989 Matched 39.88 40.19 -2.2 -993.1 -0.17 0.863 HHEDUC Unmatched 2.0211 2.703 -20.2 -1.38 0.170 Matched 1.989 2.288 -8.8 56.2 -0.64 0.523 FMEMBEDUC Unmatched 4.4737 5.066 -19.2 -1.31 0.192 Matched 4.5 4.372 4.1 78.4 0.29 0.775 LABFORCE Unmatched 2.505 2.264 22.5 1.54 0.126 Matched 2.489 2.465 2.3 89.9 0.14 0.887 LAND Unmatched 1.045 0.929 15.1 1.03 0.305 Matched 1.1163 -14.7 2.8 -0.92 0.358 AVERAGEPLOTSZ Unmatched 0.438 0.396 10.3 0.70 0.486 Matched 0.437 0.495 -14.3 -38.9 -0.87 0.387 AVERAGEPLOTDIST Unmatched 11.767 11.498 2.5 0.17 0.866 Matched 11.922 11.21 6.6 -164.1 0.45 0.651 STEEPSLOPLOT (%) Unmatched 28.099 27.628 1.4 0.09 0.925 Matched 28.802 31.551 -8.0 -483.0 -0.54 0.589 LIVESTOK Unmatched 3.5871 4.0151 -9.9 -0.67 0.501 Matched 3.608 4.165 -12.8 -30.0 -0.95 0.343 IRONROOFEHOME Unmatched 0.137 0.132 1.5 0.10 0.921 Matched 0.109 0.052 16.6 -1047.3 1.43 0.156 DACONTACTFREQ Unmatched 12.305 8.121 52.4 3.56*** 0.000 Matched 11.967 11.06 11.4 78.3 0.75 0.452 CREDITACCESS Unmatched 0.484 0.352 27.0 1.84* 0.068 Matched 0.468 0.486 -3.9 85.7 -0.26 0.798 FOODSECPROB Unmatched 4.032 3.099 43.4 2.96*** 0.003 Matched 3.902 3.848 2.5 94.2 0.18 0.857 DISTRICTDUMMY Unmatched 0.579 0.593 -2.9 -0.20 0.842 Matched 0.598 0.589 1.6 43.6 0.11 0.911 Source: Own estimation result ***,**and* means significant at 1%, 5% and 10% probability levels, respectively.

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Appendix 10. Propensity score and covariate balance test for asset accumulation

Variable Sample mean %bias %reduction T-test treated control |bias| T p>|t| pscore Unmatched 0.597 0.422 91.8 6.27*** 0.000 Matched 0.584 0.581 1.5 98.4 0.11 0.910 FAMILYSZ Unmatched 5.4 4.593 38.4 2.62** 0.010 Matched 5.261 5.394 -6.3 83.5 -0.43 0.668 HHSEX Unmatched 0.695 0.802 -24.8 -1.69* 0.093 Matched 0.696 0.755 -13.8 44.4 -0.91 0.366 HHAGE Unmatched 39.895 39.923 -0.2 -0.01 0.989 Matched 39.848 41.272 -10.0 -4924.4 -0.79 0.430 HHEDUC Unmatched 2.021 2.703 -20.2 -1.38 0.170 Matched 2.011 1.986 0.7 96.4 0.05 0.957 FMEMBEDUC Unmatched 4.474 5.066 -19.2 -1.31 0.192 Matched 4.457 4.2663 6.2 67.9 0.42 0.674 LABFORCE Unmatched 2.5053 2.2637 22.5 1.54 0.126 Matched 2.467 2.641 -16.2 28.0 -0.98 0.331 LAND Unmatched 1.045 .929 15.1 1.03 0.305 Matched 0.992 1.035 -5.6 62.8 -0.36 0.719 LIVESTOK Unmatched 3.587 4.015 -9.9 -0.67 0.501 Matched 3.590 3.531 1.4 86.1 0.11 0.913 IRONROOFEHOME Unmatched 0.137 0.132 1.5 0.10 0.921 Matched 0.119 .07337 13.5 -828.7 1.06 0.291 DACONTACTFREQ Unmatched 12.305 8.1209 52.4 3.56*** 0.000 Matched 12.043 11.122 11.5 78.0 0.76 0.446 CREDITACCESS Unmatched .484 0.352 27.0 1.84 0.068 Matched 0.467 0.478 -2.2 91.8 -0.15 0.883 FOODSECPROB Unmatched 4.032 3.099 43.4 2.96*** 0.003 Matched 3.924 3.823 4.7 89.2 0.33 0.740 DISTRICTDUMMY Unmatched 0.579 0.593 -2.9 -0.20 0.842 Matched 0.598 0.628 -6.0 -106.7 -0.41 0.679 Source: Own estimation result ***,**and* means significant at 1%, 5% and 10% probability levels, respectively.

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Appendix 11. Survey questionnaire

A. GENERAL INFORMATION

1. Household marital status___ 1. Single 2. Married 3. Widowed 4. Divorced 2. Household type____ 1. Polygamous 2. Monogamous 3. Religion: 1. Orthodox Christian 2. Muslim 3. Protestant 4. Others (Specify)___

Household background before and after intervention (write figure before intervention in bracket)

ID code (4) Name R/n to head sex (6) Age Grade Major Occupations (5) (7) (8) (9) 1 2 . . 13

Codes: Relation to household: Codes: Highest grade attended: 1 = household head 0= illiterate (unable to read and write), 2 = wife 1= able to read and write only 3 = son or daughter Codes: Sex: 4 = son-in-law / daughter-in-law 1. Male 5 = grandson / granddaughter 2. Female 6 = father / mother of head or wife Codes: Occupation: 7 = brother / sister of head / wife 1=farmer 8 = other relative of head/ wife 2=trader 9 = adopted 3=Student 10 = non-relative / servant 4=disabled 5=Other(specify)_____

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B. Land holding , SLM Practices and Crop production

10. Details of the Parcels that household operating as of 2009 budget year

Parcel ID (List all including homestead) 1 2 3 4...10 Parcel Name Tenure type (1=own; 2= rented/shared in/out) Plot size(timad) Distance from homestead (min. of walk) Soil type (local name) Slope (1=flat; 2=medium; 3=steep; 4=very steep) Soil depth (1=shallow; 2=medium; 3=deep) Fertility status (1=low; 2=medium; 3=fertile) seed type (1=local,2=improved) Intercropping (major/minor) Yield of major crop in quintals Yield of minor crop1 in quintals Yield of minor crop2 in quintals Major crop last year Minor crop1 last year Minor crop2 last year Farming system (irrigated=1, Rain fed=2) Trees planted on each plot (numbers) Tree1 Tree2 Tree3...tree n

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11. Investments on sustainable land management technologies

SLM Practice/ Parcel ID 1 2 3 4 5...10 Soil Bunds(m) Stone bunds(m) Ditches(m) Fertilizer Urea (Kg) Fertilizer DAP (Kg) Manure (quintals) Compost (Quintals) Trees planting on farmlands (number)

12. Farmers' Perceived profitability of SLM practices (: 1=Yes, 2=No)

Perceived Soil Stone boyi Fertilizer Manure Compost Trees Profitability of Bunds bunds planting practices In the short run 1 2 1 2 1 2 1 2 In the medium term 1 2 1 2 In the long run 1 2

13. In the last farming season (2009), how much of each crop grown was harvested and what did you do with the harvest? code of crops Crop Total Harvested (qunts) How many quintals were eaten given away Sold 1 Maize 2 Sorghum 3 Barely 5 Teff . . . .

13 Others specify

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C. Farmers' Perception on Land Degradation Problem

14. Do you think soil erosion is a problem for your farm plot? 1. Yes 2. No 15. If Yes, Which indicators lead you to believe that soil erosion exists? 1. Decrease production 2. Visible rills/gully formation 3. Decrease soil depth 4. Soil colour changes 5.Others______16. If yes, what do you think are the main causes of soil erosion on your farm plot? 1. Overgrazing 2. Deforestation 3. Heavy rain 4. Improper plough 5. Steepness 6. Others (specify)______17. What do you think the consequences of soil erosion in your plots? 1. Loss of soil fertility 2. Loss of fertilizer 3. Loss of seeds 4. Loss of crop land 18. Do you think soil fertility decline is a problem for your farm plot? 1. Yes 2. No 19. If Yes, what are the symptoms for soil fertility decline? 1. Decrease production 2. Soil colour changes 3. others (specify)______20. What do you think the consequences of soil fertility decline in your plots? (Please explain) ______

D. Land Tenure Security 21. Do you think that you have the right to inherit your land to your children? 1. Yes 2. No 22. If no, Why? ______

E. Access to Extension Services and Credit Facilities 23. How often were you visited by development agents (D.As) last year? ______days. 24. Do you get extension advices and trainings on soil and water conservation practices last year?1. Yes 2. No 25. If Yes, how many times?______26. Did you take credit for the last five years? 1. Yes 2. No 27. If Yes, what was the purpose of the credit? 1. Fertilizer credit 2. Improved seed credit 3. Livestock credit 4. Money to buy farm tools 5. Money to hire labour for soil/stone bund construction 6. Other (specify)_____ 28. Have you got credit for bund construction in the past years? 1. Yes 2. No 29. If No, why? 1. No credit access 2. Not profitable 3. High interest rate 110

4. No need of money to construct bund 5. Other ___ 30. Have you got credit for fertilizer in past 5 years? 1. Yes 2. No 3. Yes except last year 31. If No, why? 1. Lack of credit access 2. We don't want 3. Not profitable 4. Other___

F. INFORMAL and FORMAL TRANSFERS

In the last 4years, has your household received any of the following types of assistance from any relative or friend living outside the household (Informal)? (Not from government or NGO.) Or from government or aid agencies as a formal (PSNP and OFSP TRANSFERS)?

INFORMAL TRANSFERS (32) Yes No FORMAL TRANSFERS(33) Yes No Remittances (from relatives) 1 2 Free food aid 1 2 Other cash gift 1 2 Free cash 1 2 Cash loan (no interest) 1 2 Food-for-work employment 1 2 Food or grain gift 1 2 Cash-for-work employment 1 2 Grain loan (no interest) 1 2 Free seeds or tools 1 2 Seed gift 1 2 Free fertilizer 1 2 Seed loan 1 2 Credit/ Loan 1 2 Free labour 1 2 Livestock 1 2 Free use of oxen or plough 1 2 Other (specify):______1 2 Free use of pack animals 1 2 1 2 Other (specify): 1 2 1 2

34. Have you given loan in the past 4 years to others? 1. Yes 2. No

G. SHOCKS ENCOUNTERED 35. For the last 4 years, have you encountered the following shocks? Types of shocks Yes No Copping mechanism weather-related shocks like flood, drought etc. 1 2 Pests and other crop diseases or crop theft Family illness and /or death Livestock death or theft others

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H. ASSET INVENTORY

As of today, how many of the following assets do your household own? (Write 0 if none) For livestock, include any animals that belong to you, but are being raised by households. Don't include any animals that you are rearing for someone but don't belong to you. Asset No. owned today (36) No. owned 4yrsIf there is difference, ago (37) why?(write codes (38) Livestock Cows . . . Poultry Productive Assets Plough . . . Household Assets . . Consumer durable goods . .

Codes: Differences in asset ownership (1-10) reason for decrease, (11-14) reasons for decrease 1. We were forced to sell the asset to buy food 8. The asset was stolen 2 .We were forced to sell the asset to pay for health expense 9. Livestock died 3. We were forced to sell asset to pay for education expense10. Livestock was slaughtered for food 4. We were forced to exchange the asset for food 11. Livestock reproduced 5. We had to sell the asset to meet social obligations (e.g. wedding) 12. We bought this asset 6. We used the asset for social obligations (e.g. wedding) 13. Someone gave us for free 7. We sold the asset for other reasons for decrease (specify) ______14. Other reason for increase _

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I. SOURCES OF INCOME OF HOUSEHOLDS Livelihood Activity For how many months Ave. monthly Total annual you/r family member income (Br.) income (Br.) earned income from (39) (40) (41) AGRICULTURE Rearing /selling cattle Rearing /selling sheep Rearing /selling goats Fattening Selling animal products Poultry rearing and sales Bee keeping (Honey and wax) From fishery Off-farm Activities Daily worker (labourer) Trading in food crops Trading in livestock or livestock product Trading in other commodities Arekie (tella) Selling firewood or charcoal Selling grass or fodder (for livestock) Selling construction materials Spinning, tailoring or weaving Making traditional utensils or farm tools Blacksmithing or metal-work, Pottery Traditional healer Carpenter Land rent Oxen rent Donkey/horse rent

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J. FOOD SECURITY

42. During the last 4years, did your household suffer from any food shortage? 1. Yes 2 No.

If Yes which months in the last 5years did your household have problems of satisfying food needs? (Put √ mark) Months (put √ mark wherever appropriate Year Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. 2006 2007 2008 2009

K. COPING STRATEGIES 43. During the hunger seasons, what did your household do to survive? Coping strategy Yes No Coping strategy Yes No Ate less food (smaller portions) 1 2 Rented out land to buy food 1 2 Reduced the number of meals/day 1 2 Sold land to buy food 1 2 Collected bush products to eat or sell 1 2 Sold livestock to buy food 1 2 Relied on help from relatives 1 2 Sold other assets to buy food 1 2 Household members migrate 1 2 Sold firewood or charcoal 1 2 Borrowed food or cash to 1 2 Withdrew children from school 1 2 purchase food Reduced spending on non-food items 1 2 Sent children to work 1 2 Sent children to stay with relatives 1 2 Other (specify): 1 2

L. TARGETING 44. Has your household received any food or cash from the new government Safety Net Programme since January 2005? 1. Yes 2. No, If Yes continue to Q 45. If No, Pass to L2

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L1. BENEFICIARY HOUSEHOLDS (Q45-76) only for PSNP beneficiaries)

45. Why do you think your household was selected to receive food or cash from the new government PSNP? 1. We are landless 2. We have poor quality land 3. We don't have or have a few livestock 4. We have no family support or remittance from relatives 5. We received food aid/ emergency cash transfer before selection 6. Other reasons (specify)______46. Who selected which household would receive the transfer from PSNP? 1. The D.A 2. Kebele Food security task force 3. Kebele administration 4. The community 5. Woreda Food security task force 6 Woreda administration 7. I don't know 8. Other (specify) ____ 47. Do you think the selection is fair? 1. Yes 2.No 48. Had you participated in food for work program (FFWP) before PSNP? 1. Yes 2.No 49. If Yes, Is there any difference between FFWP and PSNP? 1. Yes 2. No. 50. If Yes, Please mention:______51. Do you know about graduation from PSNP? 1. Yes 2. No 52. If yes, what are you planning to do after your graduation? Please explain:______53. Do you know the criteria for being graduated from PSNP? 1. Yes 2. No 54. If Yes, please mention:______55. Do you know the objective of PSNP? 1. Yes 2. No 56. Have you participated in one or more of the following Other Food Security Programs (OFSP) or Household Extension Packages (HEP)?

Types of OFSP Quantity unit Unit Receiving Repayment price Year(E.C) period (E.C) Dairy No. Sheep rearing No. Sheep or Goat fattening No. Oxen fattening No. Beehives No. Credit at 0 interest rate Birr Others (specify)______

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57. If you haven't participated, what are the reasons? 1. Due to lack of land 2. Due to lack of labour 3. Due to lack of sufficient knowledge 4. Due to the fear of repayment for the loan in case of failure (e.g. death of livestock) 5. The loan is larger than we need 6 I don’t know 7. .Others (specify)______58. How much food or cash did your household receive, in which months from the PSNP? Months February March April ... September Yr/pmt wheat cash wheat cash wheat cash ...... wheat cash 2006 2007 2008 2009

59. Did any members of the household work for this food or cash? 1. Yes 2. No 60. If NO, why not? 1. There are no public works projects 2. Disability or sickness. 3. No able bodied person 4.Other______61. If YES, record the number of days s/he worked each year

Yr/m Jan Feb mar Apr May June July Aug Sept Oct Nov Dec 2006 2007 2008 2009

62. If s/he had not been working on the PSNP project during those months, what would s/he has been doing instead? 1= Domestic work, 2= Childcare, 3= Attending school, 4=Farming work, 5= Livestock tending, 6= something else (specify): ______

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M. USE OF PSNP CASH OR FOOD 63. If you received free cash from the Safety Net Programme, or worked on a cash-for-work project in the last 12 months, what did you do with all the money you received? (Circle all that apply) Consumption items Yes No Investment items Yes No Bought staple food (e.g. grain) 1 2 Debt repayment 1 2 Bought other food (e.g. meat) 1 2 Bought seeds for farming 1 2 Bought groceries (salt, sugar, soap 1 2 Bought fertilizer for farming 1 2 Bought clothes or cloth 1 2 Paid for health costs 1 2 Gave some cash to help others 1 2 Paid for education costs 1 2 Paid taxes 1 2 Bought livestock (specify): 1 2 Social obligations (specify): 1 2 paid for soil/stone bund workers 1 2 Other (specify): Other (specify): 1 2

64. If you received free food aid from the Safety Net Programme, or worked on a food-for- work project in the last 4 years what did you do with all the food you received? Consumption items Yes No Investment items Yes No We ate all the food for cash 1 2 We ate all the food 1 2 We sold food to buy other food 1 2 We gave it to livestock for feed 1 2 Others (specify) Others (specify)

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N. ASSET PROTECTION AND BUILDING Trends in Assets Yes-b/c yes-b/c of No of PSNP others 65 Have you enrolled more of your children in school in 1 2 3 these 4 years than 4 years ago? 66 Have you kept your children in school for longer 1 2 3 these 4 years than 4 years ago? 67 Have you used healthcare facilities these 4years more 1 2 3 than 4 years ago? 68 Have you consumed more or better food these 4 years 1 2 3 than 4 years ago? 69 Have you avoided having to sell household assets to 1 2 3 buy food these 4years? 70 Have you avoided having to use your savings to buy 1 2 3 food these 4 years? 71 Have you retained your own food production to eat 1 2 3 yourselves these 4 years, rather than selling it? 72 Have you acquired any new HH assets in these 4 years? 1 2 3 73 Have you acquired new skills or knowledge which has 1 2 3 increased your income in these 4 years?

74. Within your household, who actually collected most or all of the food or cash from the Safety Net Program? ______75. Within your household, who decided how to use the cash or food from the Safety Net Program? 1 I decided alone 2. I consulted with my spouse 3 my spouse decided 4. The whole household 76. If you could choose, would you prefer to get assistance from the Safety Net Programme in food, cash, or a mix of half food and half cash? (Circle one only) 1. Cash only 2. Food only 3. Half cash and half food

End of interview for PSNP beneficiary households.

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L2. ENON-BENEFICIARY HOUSEHOLDS (Q77-82 Only for Excluded Hhlds) 77. Why was your household not selected to receive food or cash from the new government Safety Net programme? (Circle all that apply) 1. We have some land/ enough land/ or better quality land 2. We own livestock 3. We have other income 4. We are not registered on the kebele household list 5. We are not so poor as the selected 6. Our household did not receive food aid or emergency cash transfer in previous years 7. Other reason (specify): ______78. Who decided which households in the community would receive the food or cash? 1. The D.A 2. Kebele Food security task force 3. Kebele council/administration 4. The community 5. Woreda Food security task force 6. Woreda council/administration 8. Other (specify) ______79. Did you think the decision was fair? 1. Yes 2.No Explain why or why not:______80. If not fair, did you complain? 1. Yes 2. No 81. If Yes, Who did you complain to? Circle all thatcomplained to Circle all that Why not? apply apply 1 Kebele authorities 1 There is no-one to complain to 2 Woreda authorities 2 We don’t know who to complain to 3 Zonal authorities 3 It would not do any good to complain 4 Regional authorities 4 I am too frightened to complain 5 NGO, WFP or another 5 The decision-makers are the same organisation people who hear the appeals 6 Community meeting 6 Other reason (specify): 7 Rapid Response Team 8 Other (specify:)

82. If YES (complained), was your complaint successful? 1. Yes 2. No Explain what happened: ______

End of interview for non-beneficiary households.

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