Approach To and Findings From Farming Practices Survey

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Approach To and Findings From Farming Practices Survey

  1. 1. Approach To and Findings From Farming Practices Survey October 17, 2008
  2. 2. Overview of Presentation <ul><li>How an Impact Evaluation Works </li></ul><ul><li>Evaluation Design for Water-to-Market Training </li></ul><ul><li>About the Farming Practices Survey </li></ul><ul><li>Findings from the Farming Practices Survey </li></ul>
  3. 3. Impact Evaluation <ul><li>Was the program effective? </li></ul><ul><li>Estimating impacts involves comparing: </li></ul><ul><ul><li>Outcomes with the program with outcomes if there were no program </li></ul></ul><ul><li>Counterfactual: What participants would have experienced if there were no program </li></ul><ul><ul><li>True counterfactual is not directly observed </li></ul></ul><ul><li>Goal of impact study is to identify a comparison group to approximate the counterfactual </li></ul>
  4. 4. Importance of the Counterfactual: An Illustrative Example <ul><li>Rural poverty has decreased in recent years, prior to MCA-Armenia programs </li></ul><ul><li>In this case, we want to see how much more did poverty decline because of MCA-Armenia? </li></ul>
  5. 5. How Much More Did Poverty Decline Because of MCA-Armenia? Impact
  6. 6. Random Assignment: Often Considered the “Gold Standard” <ul><li>Randomly assign into two groups, similar to a lottery </li></ul><ul><li>Program and comparison groups are the same on average, except one group has access to program </li></ul><ul><ul><li>Subsequent differences in outcomes can be attributed to the program </li></ul></ul><ul><li>Random assignment most feasible when there are limited resources/excess demand </li></ul>
  7. 7. II. Evaluation for Water-to-Market Training
  8. 8. Water-to-Market Activities <ul><li>Focus on evaluation of training programs </li></ul><ul><ul><li>Water management techniques </li></ul></ul><ul><ul><li>Transition to higher value agriculture </li></ul></ul><ul><li>ACDI/VOCA providing training </li></ul><ul><ul><li>Primarily in Compact Years 2-5 </li></ul></ul>
  9. 9. Evaluation Design for Water-to-Market Training <ul><li>Ideally, would want to randomly assign farmers who apply for training </li></ul><ul><li>Not practically feasible or politically viable </li></ul><ul><ul><li>New program starting, so no excess demand </li></ul></ul><ul><ul><li>Intervention at the village level (potential for spillover/contamination) </li></ul></ul><ul><li> </li></ul><ul><li>We randomly assigned villages to program or “control” group </li></ul><ul><ul><li>Use a phased-in approach </li></ul></ul>
  10. 10. Shortcomings of the Approach <ul><li>Less statistical power to detect impacts than random assignment at the individual level </li></ul><ul><ul><li>Not all farmers in a village may want to participate in training </li></ul></ul><ul><ul><li>Clustering effects (village specific effects) </li></ul></ul><ul><li>Challenge can be overcome to some extent if: </li></ul><ul><ul><li>Take-up rates of training high </li></ul></ul><ul><ul><li>More villages included </li></ul></ul><ul><li> </li></ul>
  11. 11. Random Assignment Design <ul><li>Randomly assign when training will start in each village, with three groups: </li></ul><ul><ul><li>Compact Year 2 </li></ul></ul><ul><ul><li>Compact Years 3 and 4 </li></ul></ul><ul><ul><li>Compact Year 5 </li></ul></ul><ul><li>Compare Compact Year 2 villages to Compact Year 5 villages </li></ul>
  12. 12. Random Selection of Villages (Implemented in August 2007) <ul><li>Started with a list of villages with “good water” and which could benefit from training </li></ul><ul><ul><li>Excluded villages served in pilot phase </li></ul></ul><ul><li>Random selection conducted publicly to: </li></ul><ul><ul><li>Ensure transparency </li></ul></ul><ul><ul><li>Allow for greater accountability and implementation fidelity </li></ul></ul>
  13. 13. Selection of Villages <ul><li>Total of 277 village clusters were assigned </li></ul><ul><ul><li>Year 2: 120 clusters </li></ul></ul><ul><ul><li>Years 3 and 4: 77 clusters </li></ul></ul><ul><ul><li>Year 5: 80 clusters </li></ul></ul><ul><li>Selection of clusters stratified by WUAs </li></ul><ul><ul><li>In proportion to the number of villages in the WUA </li></ul></ul><ul><li>To ensure balance across treatment and control villages </li></ul><ul><ul><li>Also for political reasons </li></ul></ul>
  14. 14. Selection of Villages (cont’d) <ul><li>No. of Villages By Year of Training and Ag. Zone </li></ul>
  15. 15. Overall Approach to the Impact Evaluation <ul><li>Compare outcomes for farmers in treatment and control villages </li></ul><ul><li>Examine impacts on key outcomes </li></ul><ul><ul><li>Participation in training and adoption of new practices </li></ul></ul><ul><ul><li>Changing crop patterns, improved yields, and increases in income </li></ul></ul><ul><li>Data and Sample </li></ul><ul><ul><li>Survey of farmers in the treatment and control villages </li></ul></ul><ul><ul><li>Sample of 5,000 farmers to detect impact on poverty of 5 percentage points </li></ul></ul>
  16. 16. About the Farming Practices Survey
  17. 17. Farming Practices Survey <ul><li>Baseline survey conducted by AREG </li></ul><ul><li>Implemented during Nov 2007- Feb 2008 </li></ul><ul><ul><li>In 223 communities </li></ul></ul><ul><ul><li>Target sample of 5,000 farmers </li></ul></ul><ul><li>Topics covered in the survey </li></ul><ul><ul><li>Demographic characteristics </li></ul></ul><ul><ul><li>Agricultural practices and productivity </li></ul></ul><ul><ul><li>Income and consumption </li></ul></ul>
  18. 18. Identifying the Sample Frame <ul><li>Sample frame is critical as it defines what population the study represents </li></ul><ul><ul><li>Need comparable frame for treatment and control villages </li></ul></ul><ul><ul><li>Want persons who farm as their main occupation </li></ul></ul><ul><li>No viable sample frame exists for the surveys </li></ul><ul><ul><li>Originally attempted to use WUA member lists </li></ul></ul><ul><ul><li>Worked with WUA heads to get names of farmers </li></ul></ul><ul><ul><li>During pretest, AREG found some lists were bad </li></ul></ul><ul><ul><li>All lists had to be verified by AREG in the field and developed as necessary </li></ul></ul>
  19. 19. Implementing the Survey and Checking Data Quality <ul><li>Interactive interviewer training (Q by Q, etc) </li></ul><ul><li>Piloting of both survey instrument and sample list </li></ul><ul><ul><li>Revisions to questionnaire </li></ul></ul><ul><ul><li>Sample verification process </li></ul></ul><ul><li>Regular generation of field reports </li></ul><ul><li>MCA did field visits to review processes </li></ul><ul><li>Independent review of initial batches of data </li></ul>
  20. 20. Planned Improvements to the Upcoming Survey <ul><li>More use of photos in training interviewers on farming practices </li></ul><ul><li>Pretesting both survey instrument and sample list </li></ul><ul><li>Double key data entry </li></ul>
  21. 21. Baseline FPS Survey <ul><li>Completed interviews with 4,855 farmers </li></ul><ul><li>Sample focuses on households farming for five years or more in these communities </li></ul><ul><li>Sample not representative of all farmers in Armenia </li></ul><ul><li>However, sample internally valid for study </li></ul><ul><ul><li>Will show comparison of farmers in treatment and control villages later </li></ul></ul>
  22. 22. IV. Baseline Findings <ul><li>Farmer and Household Characteristics </li></ul><ul><li>Crop Cultivation and Sales </li></ul><ul><li>Income and Poverty </li></ul>
  23. 23. General Approach to Analysis and Data Cleaning <ul><li>Descriptive analysis </li></ul><ul><ul><li>Averages and distributions </li></ul></ul><ul><ul><li>Examined conditional and unconditional measures </li></ul></ul><ul><ul><li>Used weights to adjust for treatment-control balance </li></ul></ul><ul><li>Data checks </li></ul><ul><ul><li>Missing values and outliers </li></ul></ul><ul><ul><li>Logical skips </li></ul></ul><ul><ul><li>Internal consistency </li></ul></ul><ul><li>Measuring income and consumption is challenging </li></ul><ul><ul><li>Sensitivity tests </li></ul></ul><ul><ul><li>Looking at distributions critical </li></ul></ul>
  24. 24. Farmer and Household Characteristics
  25. 25. Farmer and Household Characteristics <ul><li>Characteristics (Percentages) </li></ul>
  26. 26. Farmer and Household Characteristics (cont’d) <ul><li>Land Cultivated by Respondents (Sq. Meters) </li></ul>
  27. 27. Few Farmers Use Water Management Practices <ul><li>Respondents’ Irrigation Practices </li></ul><ul><li>(Percentages) </li></ul>
  28. 28. Crop Cultivation and Sales
  29. 29. Crop Cultivation and Sales <ul><li>Respondents Growing and Selling </li></ul><ul><li>Crops (Overall Percentages) </li></ul>
  30. 30. Crop Cultivation and Sales Vary by Agricultural Zone <ul><li>Farmers in Ararat Valley grow more fruit, and much more likely to sell their fruits and vegetables </li></ul><ul><li>Farmers in the Mountainous Zone grow much more grain and potatoes </li></ul><ul><li>Mountainous Zone farmers sell very little </li></ul>
  31. 31. Income and Poverty
  32. 32. How to Measure Agricultural Income? <ul><li>Crop sales tell us monetary income </li></ul><ul><li>Much of production is consumed by household; not reflected in sales </li></ul><ul><li>Assign monetary value to crops consumed by household </li></ul><ul><li>Subtract agricultural costs to get profit </li></ul>
  33. 33. Crop Sales and Value of Production <ul><li>Respondents’ Average Crop Sales and Values (AMD) </li></ul>
  34. 34. Measuring Income <ul><li>Survey is relatively short (30 minutes) </li></ul><ul><li>Focus on agricultural production, but learn about non-agricultural income/consumption as well </li></ul><ul><li>Income measures include employment income, pensions, remittances, other benefits </li></ul><ul><li>Consumption measures include groceries, household products, utilities, transportation </li></ul>
  35. 35. Household Income, Only Considering Monetary Income <ul><li>Annual Household Monetary Income (AMD) </li></ul>
  36. 36. Household Income, Including Value of Crops Consumed <ul><li>Annual Household Economic Income (AMD) </li></ul>
  37. 37. Measuring Poverty <ul><li>Consumption-based measure </li></ul><ul><li>Adopt similar approach as NSS/World Bank </li></ul><ul><li>Food poverty line: Meets minimal caloric needs </li></ul><ul><li>Complete poverty line: Allowance for other basic necessities </li></ul>
  38. 38. Estimates of Poverty Rates <ul><li>Respondent Households in Poverty (Percentages) </li></ul>
  39. 39. Poverty Varies Appreciably by Zone <ul><li>Respondent Households in Poverty by Zone (Percentages) </li></ul>
  40. 40. Many Households are Near Poverty Line <ul><li>Consumption Relative to Complete Poverty Line (CPL) </li></ul>
  41. 41. Values of Variables by Treatment Status
  42. 42. Demographic Characteristics and Land Size of Sample Members <ul><li> Treatment Control </li></ul><ul><li>Respondent’s Age (years) 49.2 49.2 </li></ul><ul><li>Female Respondent 13.0 10.8 </li></ul><ul><li>Respondents Education </li></ul><ul><li>Less than Secondary 13.8 13.4 </li></ul><ul><li>Full Secondary 40.6 41.1 </li></ul><ul><li>Secondary Vocational 28.9 27.1 More than Secondary 16.7 18.4 </li></ul><ul><li>Area of land cultivated (ha) 1.99 1.98 </li></ul><ul><li>Kitchen plot size (ha) .171 .173 </li></ul><ul><li>Use varying furrow spacing 7.5 7.4 </li></ul>
  43. 43. Crop Cultivation and Sales and Income for Sample Members <ul><li> Treatment Control </li></ul><ul><li>Percent Cultivating </li></ul><ul><li>Grain 45.7 43.5 </li></ul><ul><li>Fruit 76.3 77.6 Vegetable 33.1 31.7 </li></ul><ul><li>Revenue from Crops Sold (AMD) </li></ul><ul><li>Grain 21,711 32,531 </li></ul><ul><li>Fruit 317,619 206,355 Vegetable 88,858 57,653 </li></ul><ul><li>Market Value of Harvest </li></ul><ul><li>Grain 112,784 108,144 </li></ul><ul><li>Fruit 332,365 282,692 Vegetable 110,924 142,928 </li></ul>
  44. 44. Household Income and Poverty <ul><li> Treatment Control </li></ul><ul><li>Household Income (AMD) </li></ul><ul><li>Nonagricultural Income 674,780 630,414 </li></ul><ul><li>Total Monetary Income 929,592 708,041** </li></ul><ul><li>Total Economic Income 1,115,105 1,036,766 </li></ul><ul><li>Poverty </li></ul><ul><li>Complete Poverty Rate 17.9% 18.7% </li></ul><ul><li>Consumption Relative to CPL 238% 237% </li></ul>
  45. 45. Lessons Learned and Plans for the Future <ul><li>Modifications to the questionnaire </li></ul><ul><ul><li>More detailed measure of employment income </li></ul></ul><ul><ul><li>Consistent units for crop production </li></ul></ul><ul><li>Enhancements to data collection procedures </li></ul><ul><ul><li>Use pictures of irrigation practices </li></ul></ul><ul><ul><li>Focus on high response rates </li></ul></ul><ul><li>More focus on longitudinal analysis </li></ul><ul><ul><li>Useful to factor out preexisting differences </li></ul></ul><ul><li>Final impact report (2011) </li></ul><ul><ul><li>Possibility of interim report </li></ul></ul>

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