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Gero Carletto and Alberto Zezza Development Research Group World Bank Ravello June 18, 2013

Better Data for Better Agricultural Policies: The Living Standards Measurement Study Integrated Surveys on Agriculture. Gero Carletto and Alberto Zezza Development Research Group World Bank Ravello June 18, 2013. Outline. Background and overview on LSMS and LSMS-ISA

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Gero Carletto and Alberto Zezza Development Research Group World Bank Ravello June 18, 2013

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  1. Better Data for Better Agricultural Policies:The Living Standards Measurement Study Integrated Surveys on Agriculture Gero Carletto and Alberto Zezza Development Research Group World Bank Ravello June 18, 2013

  2. Outline • Background and overview on LSMS and LSMS-ISA • Selected program highlights, innovations • Gender • Use of technology in surveys • Geospatial data • Policy and methods research • Some final considerations

  3. The LSMS over time • Est. 1980 • Evolution … • Poverty monitoring and measurement: the “McNamara anecdote” • Technical assistance, capacity building • Back to the “roots”: strong research agenda (methodological and policy) • Focus on agriculture, and on Africa: LSMS-ISA

  4. The LSMS ‘philosophy’ • Need to understand living standards, and the correlates and determinants not just monitor… >>

  5. The LSMS ‘philosophy’ • Need to understand living standards, and the correlates and determinants not just monitor… the sum is greater than the parts! • Demand driven, country owned, capacity • Priority given to meeting the policy needs of each country, but an eye to x-country comparability • Strict quality control • Dissemination, open data

  6. The LSMS – ISA Project • Collectinghousehold survey data with focus on agriculture in 7+ SSAcountries • Motivation:Dismal availability, quality and relevance of ag stats in Africa • Building capacity in national institutions • Improving methodologies in agricultural statistics, producing best practice guidelines & research • Documenting & disseminating micro data, policy research

  7. Main Features • 6+ year program (2009-2015) • 7 Sub-Saharan African countries • Panel • Sample: 3-5,000 households • Population-based frame • Representative at national- and few sub-national levels • Tracking: Movers, Subsample of split-offs • Open data access policy • Micro-data publicly available within 12 months of data collection

  8. Schedule of surveys

  9. Main Features (cont’d) • Gender-disaggregated data • Use of technology • GPS for households and plots (area) • Concurrent field-based data entry • Computer Assisted Personal Interviews (CAPI) • Integration via Geo-referencing (links to other data sources)

  10. Our research agenda: Policy and Methods Policy: • Gender Differentials in Productivity • Farm Household Production and Nutritional Outcomes” • Fact and Myths in African Agriculture Anno 2012 Methods: • Productivity measurement (inputs, outputs) • Technology adoption • Gender • …

  11. Take home messages: The PhD perspective? • Agenda still huge • Data availability • Methods/Tools/Technologies • Analytical work • Open data: A gold mine for theses, and post-docs… • An employment opportunity?

  12. http://www.worldbank.org/lsms-isa

  13. Better Data for Better Agricultural Policies:The Living Standards Measurement Study Integrated Surveys on Agriculture Gero Carletto and Alberto Zezza Development Research Group World Bank azezza@worldbank.org

  14. Surveys: Going Beyond RatesUnderstanding secondary school enrollments, 12-18 year olds, Albania 2002 • In almost all countries we have a single statistic: mean enrollment at the national level. In this case it is 61%. • This is interesting for monitoring purposes, but it doesn’t say much about poverty or other factors. • ... A regional disaggregation would be useful Average Percent

  15. Understanding secondary school enrollments, 12-18 year olds, Albania 2002 • In some countries we have regional breakdowns, with marked contrasts • The contrast between urban and rural rates emphasizes the disadvantages faced by rural communities. • What other breakdown would be useful? Urban Average Percent Rural

  16. Understanding secondary school enrollments, 12-18 year olds, Albania 2002 • …with luck, official statistics can add the gender dimension • …the figures show that, in urban areas, there is no gender differential but a large gap in rural areas. • But we still don’t know much about who sends their children to school Male Urban Female Average Percent Male Rural Female

  17. Understanding secondary school enrollments, 12-18 year olds, Albania >> Female, urban Male, urban Male, rural Female, urban Average Percent • …With a survey we can show enrollment rates broken down by consumption level--and thus understand an additional dimension • >> Consumption quintile

  18. Is women’s control of income important for child nutrition? >>

  19. Everyone rounds up…

  20. …large farmers under report… Source: Carletto, Savastano, Zezza (2013). “Factor Artifact: the Impact of Measurement Errors on the Farm size - Productivity Relationship”, Journal of Development Economics.

  21. The IR is strengthened if we use GPS! >>

  22. Concurrent Data Entry The case of missing plot measurements High initial rates of missing gps data in months 1 & 2

  23. Concurrent Date Entry (cont’d) The case of missing plot measurements Intervention - High rate of missing data observed and new instructions to field disseminated.

  24. Concurrent Data Entry The case of missing plot measurements >> Substantial decrease in missing data. Because of revisit of households in month 4-6, part of the missing data was now captured.

  25. Integrate space, agro-ecology into ag micro-economics Data to understand inter-relationships between agriculture & behavior • How does variability in climate affect productivity? What are the indirect effects on nutrition, health, human capital development? • How does distance to market affect value of farm product? And off-farm work opportunities? • How does length of crop season affect productivity and seasonality of wellbeing, hunger, children?

  26. What we do • Record household and plot locations with GPS • Protocol to avoid releasing this information as it would violate confidentiality

  27. Integrate geo-spatial data • Geo-spatial variables describing physical environment, mostly using public domain data sources (NASA, NOAA, AfSIS, ISRIC..) • Focus on factors affecting agricultural productivity: • Distance • Climatology • Landscape Typology • Time series

  28. Coverage of African Drylands (descriptive)

  29. Household Distance to Major Road (km) Distance • Remoteness negatively affects household-level agricultural productivity & incomes • Analysis of household data on the effects of road connectivity on input use, crop output, and household incomes in Madagascar and Ethiopia (Chamberlin and others 2007; Stifeland Minten2008)

  30. Climatology Average Annual Rainfall (mm) Average Annual Temperature (°C)

  31. Landscape typology Elevation (m) • Topography can have a significant influence on yields • Elevation and derivatives (slope, relief roughness, topographic wetness index) affect water availability, soil fertility, land degradation & management requirements

  32. Rainfall time series 2010 Rainfall as % of Normal Rainfall (mm) 1 10 25 50 75 100 150

  33. NDVI sparse sparse dense dense moderate moderate Vegetation time series >> 2010 Max EVI Deviation from Mean

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