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Adaptive Efficiency in Coffee Clusters: Resilience Through Agglomeration, Global Value Chains, Social Networks, and Institutions A Dissertation Presented to The Academic Faculty by Thomas Hume Douthat In Partial Fulfillment of the Requirements for the Degree PhD in City and Regional Planning in the School of City and Regional Planning Georgia Institute of Technology [May, 2017] COPYRIGHT © 2017 BY THOMAS HUME DOUTHAT ADAPTIVE EFFICIENCY IN COFFEE CLUSTERS: RESILIENCE THROUGH AGGLOMERATION, GLOBAL VALUE CHAINS, SOCIAL NETWORKS, AND INSTITUTIONS Approved by: Dr. Michael Elliott, Advisor Dr. Nancey G. Leigh School of City and Regional Planning School of City and Regional Planning Georgia Institute of Technology Georgia Institute of Technology Dr. Subhrajit Guhathakurta Dr. Jennifer Clark School of City and Regional Planning School of Public Policy Georgia Institute of Technology Ivan Allan College of Liberal Arts Georgia Institute of Technology Dr. Raffaele Vignola Date Approved: [March 02, 2017] Climate Change and Watersheds Program Tropical Agricultural Research and Higher Education Center (CATIE) To coffee farmers in Costa Rica and Mexico. To my mother. To Rebeca. ACKNOWLEDGEMENTS I owe an enormous debt of gratitude to my wife, Rebeca, who has been a patient observer, collaborator, and supporter to this process on every level. My parents have also been extremely supportive, especially my mother. Also, thank you to my sister Kate for your last-minute editing help! At Georgia Tech, my advisor, Dr. Michael Elliott has been a patient and supportive presence in this process. Thank you for allowing me to seek out a project that in many ways is unusual for the department. Also, thank you to Dr. Bruce Stiftel for the department’s support of my time in the program, and to Dr. Subhro Guhathakurta and Dr. Nancey Green Leigh for being good mentors during my time helping with Journal of Planning Education and Research. Thank you to Dr. Jennifer Clark for your insights on my drafts, and to Dr. Raffaele Vignola (CATIE) for willing to be patient with all the difficulties of remote video and presentations. Also, to Dean Steven French, thank you for allowing me the opportunity to work on the CHAMP project. I would like to thank Dr. Juan Crespo for providing comments on one version of my questionnaire, and Dr. Elisa Baldassari, and Dr. Andrea Morrison for sharing versions of social network questionnaires used. In Costa Rica, I enjoyed important and extended conversations with Dr. Rafael Diaz- Porras, Dr. Wilson Picado, and Dr. Nicole Siblet. Dr. Siblet was generous with her time iv and accompanied me in the field for one week. Representatives of ICAFE, MAG, and INEC, were also very generous with their time, and helping me obtain data. In Mexico, would like to thank Dr. Bruce Fergusson and El Colegio de la Frontera Sur (ECOSUR) for hosting me as a visiting researcher in 2014. Also, thank you to COMEXUS and Fulbright, for allowing me to participate in the Fulbright Garcia-Robles program. May the bonds that tie our two nations only become stronger. Representatives of INEGI, SAGARPA, and AMECAFE, as well as other government entities, were also very generous with their time, and with providing invaluable data. I had several very informative conversations with Dr. Estaban Escamilla, Dr. Maria Elena Martinez, Dr. Ana Amelia Gonzalez, and Prof. Guillermo Montero, which undoubtedly informed my work. So many people in the coffee industry took time to talk with me, and I am truly grateful for their generosity, and patience. I hope this research starts to repay their time and openness. v TABLE OF CONTENTS ACKNOWLEDGEMENTS iv LIST OF TABLES x LIST OF FIGURES xii LIST OF SYMBOLS AND ABBREVIATIONS xv SUMMARY xvii CHAPTER 1. Introduction 1 1.1 Overview 1 1.2 Problem Statement 2 1.2.1 The problem in Central American and Mexican Coffee 5 1.2.2 Threats to Resilience in the Global Value Chain 6 1.3 Dissertation Outline 10 CHAPTER 2. Literature review 11 2.1 Resilience and Coffee 11 2.2 Sustainability and Resilience 16 2.3 Clusters in Agricultural Value Chains 19 2.4 Resilience in the Global Value Chain 23 2.5 Adaptive Efficiency and Clusters 28 2.6 Adaptive Efficiency Model Elements-Spatial/Agglomeration Factor 31 2.7 Adaptive Efficiency Model Elements-Relational: Social Capital 34 2.7.1 Social Networks and Clusters 34 2.7.2 Social Network Measures 36 2.8 Adaptive Efficiency Model Elements-Institutional: Land Tenure, Culture and Firm Structure 40 2.8.1 Land Tenure 43 2.8.2 Culture 44 2.8.3 Firm Structure 46 2.8.4 Ownership Structure 47 CHAPTER 3. Conceptual Framework 52 3.1 Model in Light of Literature Review 53 3.2 Resilience in Global Value Chains 55 vi 3.3 Model Element 1: Institutional Factors 57 3.3.1 Ownership and Operational Structure of Mills 57 3.3.2 Land Tenure 58 3.3.3 Culture and Language 58 3.4 Model Element 2: Relational Factors 59 3.4.1 Relational Factors Predictions 60 3.5 Model Element 3: Spatial Agglomeration Factors 61 3.5.1 Spatial Agglomeration Predictions 61 CHAPTER 4. Mixed Methods Research Design 62 4.1 Comparative Case Study Approach 65 4.2 Social Network Analysis 68 4.3 Qualitative Case Studies 70 4.4 Regression & Land Use Change 72 4.5 Convergent Analysis 73 4.6 Costa Rica and Mexico Comparisons: Enabling Environments 77 4.6.1 Place of Coffee in National Economy 77 4.6.2 Country Comparisons 78 4.6.3 Land Tenure in Costa Rica and Mexico 81 4.6.4 Coffee Governance in Costa Rica and Mexico 84 CHAPTER 5. Introduction to Cases 102 5.1 Description of Cases 102 5.1.1 Costa Rica Cases 102 5.1.2 Mexico Cases 111 5.1.3 Case Selection Summary 119 CHAPTER 6. Comparing Performance and Trajectories of Mexico and Costa Rica and Comparative Cases 125 6.1 Costa Rica v. Mexico 125 6.2 Costa Rica Performance 132 6.2.1 Land Use 132 6.2.2 Production 136 6.2.3 Milling Infrastructure 140 6.3 Mexico Performance 142 6.3.1 Veracruz 143 6.3.2 Chiapas 146 6.4 Summary of Outcomes by Case 150 CHAPTER 7. Social Network Analysis and Cluster Governance Assessment 154 7.1 Overview 154 7.2 Survey and Analysis Methods 157 7.2.1 Overview of Survey Sample and Data Collection 157 7.2.2 Data Collection Method 1: Knowledge Network Questions 160 7.2.3 Data Collection Method 2: Cluster Governance Questions 166 7.3 Results 167 7.3.1 Costa Rica Knowledge Network Questions: Cohesion 167 vii 7.3.1 Mexico Knowledge Network Questions: Cohesion 168 7.3.2 Costa Rica Knowledge Network Questions: Centrality 170 7.3.3 Mexico Knowledge Network Questions: Centrality 174 7.3.1 Costa Rica Knowledge Network Questions: Strength and Openness 178 7.3.1 Mexico Knowledge Network Questions: Strength and Openness 182 7.3.1 Mexico v. Costa Rica Knowledge Network Questions: Strength and Openness 186 7.4 Cluster Governance Questions: Costa Rica 189 7.5 Cluster Governance Questions: Mexico 190 7.5.1 Chiapas Mestizo & Chiapas Indigenous 190 7.5.2 Veracruz High Coop & Veracruz Low Coop 191 7.6 Cluster Governance Questions: Mexico versus Costa Rica 191 7.7 Discussion of Results 194 7.7.1 Hypothesis 5: Cluster resilience will correspond to stronger social networks and social capital within the cluster (akin to bonding capital). 194 7.7.2 Hypothesis 6: More resilient clusters will have more open knowledge and collaboration networks (akin to bridging capital). 198 7.7.3 Hypothesis 4 Clusters where producers are primarily from historically marginalized indigenous cultures will have weaker networks with external actors within public institutions and the coffee value chain. 200 7.7.4 Hypothesis 7: Social capital within an industry is driven by both local variation, and national institutional context 202 7.8 Summary 204 CHAPTER 8. Analysis of Comparative Case StuDies 205 8.1 Framework for Qualitative Case Studies 205 8.2 Data Collection & Analysis 208 8.3 CR Background Interviews 209 8.3.1 CR Prime Interviews 210 8.3.2 CR Non-Prime Interviews 211 8.4 Mexico Background Interviews 211 8.4.1 Chiapas Interviews 212 8.4.2 Veracruz Interviews 214 8.5 Analysis of Qualitative Data 216 8.6 Case Discussions 217 8.6.1 Costa Rica Prime Cases 217 8.6.2 Costa Rica Non-Prime Cases 245 8.6.3 Costa Rica Non-Prime High Coop 255 8.6.4 Comparison of CR Cases 265 8.6.5 Mexico-Veracruz 267 8.6.6 Mexico Chiapas 282 CHAPTER 9. Regression Based Analysis: Agglomeration and Cooperative Institutional Orientation and Land Use Change in Costa Rica Coffee 307 9.1 Introduction 307 9.2 Methods 313 9.2.1 Response Variable -- Percent Change in Land Use for Coffee Production 315 viii 9.2.2 Controls 318 9.2.3 Explanatory Variables for Agglomeration 320 9.2.4 Explanatory Variable: Institutional 322 9.2.5 Model Explanations 324 9.3 Results 325 9.4 Discussion 328 CHAPTER 10. Convergent Analysis 334 10.1 Institutional Hypotheses 334 10.1.1 Hypothesis 1: Clusters with strong cooperatives will be relatively more resilient 334 10.1.2 Hypothesis 2: Cluster resilience will correspond to areas whose institutions encourage value chain upgrading 338 10.1.3 Hypothesis 3: Communal land tenure will lead to lower levels of on-farm investment and upgrading in the absence of strong public mechanisms to support farmers. These differences will primarily manifest themselves at the national level. 341 10.1.4 Hypothesis 4: Clusters where producers are primarily from historically marginalized indigenous cultures will have weaker networks with external actors within public institutions and the coffee value chain.