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

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

15. 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

16. 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

17. 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

18. 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

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

20. Everyone rounds up…

21. …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.

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

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

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

25. 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.

26. 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?

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

28. 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

29. Coverage of African Drylands (descriptive)

30. 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)

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

32. 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

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

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