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Grantee Final Report Seeing is Believing? Evidence from an Extension Network Experiment in Mozambique Florence Kondylis Development Research Group (DIME), World Bank Valerie Mueller Development Strategy and Governance Division International Food Policy Research Institute Siyao Zhu Development Research Group (DIME), World Bank Grantee Final Report Accepted by 3ie: August 2014 Note to readers This impact evaluation has been submitted in partial fulfilment of the requirements of grant OW3.1118 issued under open window 3. 3ie is making it available to the public in this final report version. All content is the sole responsibility of the authors and does not represent the opinions of 3ie, its donors or its board of commissioners. Any errors and omissions are the sole responsibility of the authors. All affiliations of the authors listed in the title page are those that were in effect at the time the report was accepted. Any comments or queries should be directed to the corresponding author, Florence Kondylis and Valerie Mueller [email protected] and [email protected] Suggested citation: Kondylis, F, Mueller, V and Zhu, S, 2014. Seeing is Believing? Evidence from an Extension Network Experiment in Mozambique, 3ie Grantee Final Report. New Delhi: International Initiative for Impact Evaluation (3ie) Funding for this impact evaluation was provided by 3ie’s donors, which include UKaid, the Bill & Melinda Gates Foundation, Hewlett Foundation and 12 other 3ie members that provide institutional support. A complete listing is provided on the 3ie website. 3ie Final Report: Seeing is Believing? Evidence from an Extension Network Experiment Florence Kondylis∗ Development Research Group (DIME) World Bank Valerie Mueller Development Strategy and Governance Division International Food Policy Research Institute Siyao Zhu Development Research Group (DIME) World Bank June 27, 2014 ∗Corresponding authors' emails are: [email protected]; [email protected]. Research discussed in this publication has been funded by the International Initiative for Impact Evaluation, Inc. (3ie) through the Global Development Network (GDN), the Mozambique oce of the United States Agency for In- ternational Development, the Trust Fund for Environmentally and Socially Sustainable Development, the Belgian Poverty Reduction Partnership and the Gender Action Plan, and the CGIAR Research Program on Policies, Insti- tutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI) and nanced by the CGIAR Fund Donors. The authors beneted from comments provided by Jenny Aker, Madhur Gautam, and Markus Goldstein and during presentations at the CSAE (Oxford), the Mid-Western Economic Development Conference, the Development Impact Evaluation Seminar Series at the World Bank, and the IFPRI Seminar Series. The views expressed in this article do not reect those of the World Bank, 3ie or its members. The authors would like to thank Pedro Arlindo, Jose Caravela, Destino Chiar, Isabel Cossa, Beatriz Massuanganhe, and Patrick Verissimo for their collaboration and support throughout the project. John Bunge, Ricardo da Costa, and Cheney Wells provided excellent eld coordination; Siobhan Murray impressive research assistance. Usual disclaimers apply. 1 Contents 1 Introduction 6 2 Agricultural Extension Constraints in Mozambique and Intervention 9 2.1 Extension Network in Mozambique's Zambezi Valley . 10 2.2 External Validity . 11 3 Experimental Design, Data and Identication 11 3.1 SLM Trainings . 13 3.2 Data . 16 3.3 Balance . 17 3.4 Measuring Information Diusion and Behavioral Change . 17 3.5 Empirical Strategy . 19 3.6 Summary Statistics . 21 4 Results 21 4.1 CF Adoption and Learning-by-doing . 21 4.2 CF Substitution of Techniques . 23 4.3 Others' Knowledge and Adoption . 23 4.4 Farmers' Perceptions of Cost Savings . 24 4.5 CF and Others' Heterogeneity . 25 5 Discussion 26 Appendix A: Additional Figures and Tables 45 Appendix B: Sample Design 65 Appendix C: Household Survey Instrument 67 Appendix D: Power Calculations 69 Appendix E: Study Design and Implementation 73 2 List of Figures 1 Timeline of Trainings and Contact Farmer and Household Surveys . 32 2 Geographical Distribution of (Non-CF) Households . 33 3 Eect of SLM Training Intervention on Contact Farmers . 34 E.1 Randomized Multi-Arm Treatment Design . 76 List of Tables 1 Advantages of SLM Techniques . 35 2 Contact Farmers' Characteristics in Treated and Control Communities . 36 3 Other Farmers' Characteristics in Treated and Control Communities . 37 4 Characteristics Comparison between Contact Farmers and Other Farmers . 38 5 Eect of SLM Training Intervention on Contact Farmers . 39 6 Eect of SLM Training Intervention on Contact Farmers' SLM Adoption . 40 7 Eect of SLM Training Intervention on Contact Farmers' Adoption Controlling for Previous Adoption . 41 8 Eect of SLM Training Intervention on Other Farmers . 42 9 Selective Attrition, Balanced Sample & Lee's Bounds . 43 10 Heterogeneity of ITT on Other Farmers' Adoption of Micro-Catchments . 44 A.1 Advantages of Being a Contact Farmer in Treated and Control Communities . 46 A.2 Extension Agents' Characteristics in Treated and Control Communities at Midline . 47 A.3 Eect of SLM Training Intervention on Contact Farmers (Includes Intercropping) . 48 A.4 Eect of SLM Training Intervention on Other Farmers (Includes Intercropping) . 49 A.5 SLM Learning before 2010 in Treated and Control Communities (Recall) . 50 A.6 SLM Adoption before 2010 in Treated and Control Communities (Recall) . 51 A.7 Gender Barriers to Adoption (Mean Dierences within the Control Group) . 52 A.8 Other Farmers' Characteristics between Attrition Groups . 53 A.9 Attrition of CFs and Households . 54 A.10 Probability of CF and Household Attrition . 55 A.11 Descriptive Statistics of Contact Farmers' Characteristics . 56 A.12 Descriptive Statistics of Other Farmers' Characteristics . 57 A.13 Eect of SLM Training Intervention on Contact Farmers' SLM Knowledge . 58 A.14 Eect of SLM Training Intervention on Other Farmers' SLM Adoption . 59 A.15 From Whom Other Farmers Claim to Learn SLM Techniques . 60 A.17 Other Farmers' Perceptions of Technique's Labor Savings Compared to Traditional Method . 62 A.18 Contact Farmers' Perceptions of SLM Techniques . 63 A.19 Eect of SLM Training Intervention on Contact Farmers' Labor Time . 64 D.1 Power Calculations for Row Planting . 70 D.2 Power Calculations for Crop Rotation . 71 D.3 Reverse Power Calculations for Micro-Catchments . 72 3 Abstract Farmers' adoption of existing, improved techniques is a central challenge in Sub-Saharan Africa. Extension agents (EAs) are commonly used to disseminate agricultural techniques and technologies. There is no evidence on what activities and incentive mechanisms can make ex- tension services work for farmers. Within an existing extension network in Mozambique, we compare diusion of sustainable land management (SLM) practices in the classic training and visit (T&V) model and a revised T&V model. Both models rely on extension agents (EAs) to transmit information about new technologies to contact farmers (CFs), model farmers who serve as points-of-contacts between EAs and other farmers within their communities. The re- vised T&V model oers CFs a direct, centralized training on SLM of similar content and breadth as the EA training. The direct training program avoids two pitfalls in the classic model: i) low transmission of information due to infrequent EA visits, and ii) receipt of poor quality informa- tion, e.g., through EA lters on information. Two hundred communities in central Mozambique were randomly assigned to the classic or modied T&V arms. We track information transmission through two nodes: from EAs to CFs, and from CFs to others. Knowledge does not propagate eciently under the classic T&V model. Directly trained CFs are more likely to demonstrate and adopt techniques and learn-by-doing. Subsequent diusion within the community is limited: only the technique with perceived labor savings is practiced by other males, with an eect size of 75 percent. The results imply that the eectiveness of even a revised T&V model continues to depend on the prole of the contact farmer and fails to address the demand-side constraints of the average farmer. First, female farmers' responses were particularly unaected by the revised T&V model. Only when we stratied the treatment eects by whether the CF had similar crop portfolios as the farmer did we witness an eect on women's adoption rates. These ndings are consistent with previous work which shows farmers are more likely to learn from seed adopters with similar characteristics. Providing messengers with amenable farming conditions may improve the targeting of female farmers in the provision of extension services. Second, in spite of the CF rendering three SLM techniques valuable enough to adopt after receiving the training, the average farmer chose to adopt only one of the three techniques, micro-catchments. Our ndings suggest that the farmer might have been inuenced by the immediate labor savings of the technique. Farmers exposed to the intervention were more likely to perceive micro-catchments as labor saving. Furthermore, trained, CFs on average spent 4-hours less preparing land the week prior to the interview. Although the labor saving estimates for CFs are inclusive of all techniques they adopted, the descriptive ndings are consistent with farmer's beliefs updating in response to the intervention following their adoption of micro-catchments or after observing the CF's demonstration activities. The Market-led Smallholders Development in the Zambezi Valley Project (MSDVP) was implemented by the Government of Mozambique (GoM) in the context of a large national agricultural reform, with technical supervision from the World Bank (WB). Its main objective was to rebuild the agricultural extension network in 5 districts of the Zambezi Valley. The project provides extension services at 2 levels: (1) 2 extensionists at the level of the posto administrativo, each provides services to 8-10 communities; and (2) a contact farmer (CF) in each community. Yet, the structure of the extension network implemented by the project does not necessarily guarantee a ow of information from CFs to farmers.
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