Better Data for Better Agricultural Policies:The Living Standards Measurement Study

Integrated Surveys on Agriculture

Gero Carletto and Alberto ZezzaDevelopment Research GroupWorld BankRavelloJune 18, 2013

• 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

Outline

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

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

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

The LSMS – ISA Project• Collecting household survey data with focus on agriculture in 7+ SSA countries• 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

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

Schedule of surveysCountry Baseline Additional wavesTanzania 2008/09 2010/11 2012/13 2014/15

Uganda 2009/10 2010/11 2011/12 2013/14Malawi 2010/11 2013Nigeria 2010/11 2012/13Ethiopia 2011/12 2013/14Niger 2011 2014Burkina Faso 2014Mali 2014

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)

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

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?

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

Better Data for Better Agricultural Policies:The Living Standards Measurement Study

Integrated Surveys on Agriculture

Gero Carletto and Alberto ZezzaDevelopment Research GroupWorld Bankazezza@worldbank.org

Surveys: Going Beyond Rates

Understanding secondary school enrollments, 12-18 year olds, Albania 2002

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• 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

Understanding secondary school enrollments, 12-18 year olds, Albania 2002

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• 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?

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Understanding secondary school enrollments, 12-18 year olds, Albania 2002

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• …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

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Understanding secondary school enrollments, 12-18 year olds, Albania

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• …With a survey we can show enrollment rates broken down by consumption level--and thus understand an additional dimension

>>Consumption quintile

Female, urbanMale, urban

Male, rural

Female, urban

Average

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

Dependent Variable: Z-Score of Height-for-Age Definitions of Woman’s Share of Household Income V1Assumption100 to Head

V2Assumption50/50 Split V3 Assumption a la HH

V4 PreferredChild: Male -0.130 -0.129 -0.147 -0.186**Woman's Share of Household Income x Male Child -0.735 -0.070 -0.008 0.155***Observations 2,522 2,522 2,522 2,522R2 0.711 0.710 0.710 0.711note: *** p<0.01, ** p<0.05, * p<0.1

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Everyone rounds up…

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GPS Farmers' Estimate

Plot Size Measured with GPS and Farmers ' Estimate

Source: Carletto, Savastano, Zezza (2013). “Fact or Artifact: the Impact of Measurement Errors on the Farm size - Productivity Relationship”, Journal of Development Economics.

…large farmers under report…

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1 2 3 4 5 6 7 8 9 10Deciles of Land Cultivated

Land Self-Reported Land GPS

UGANDA : Inverse Farm Size Productivity Relationship

The IR is strengthened if we use GPS! >>

Missing Plot Measurements

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The case of missing plot measurements

High initial rates of missing gps data in months 1 & 2

Missing Plot Measurements

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The case of missing plot measurements

Intervention - High rate of missing data observed and new instructions to field

disseminated.

Concurrent Data Entry

Missing Plot Measurements

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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.

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?

Integrate space, agro-ecology into ag micro-economics

What we do• Record household and plot locations with GPS

– Protocol to avoid releasing this information as it would violate confidentiality

Theme VariableDistance Plot distance to household 

Household distance to paved roadHousehold distance to major market (if available)Climatology Annual mean temperature  Mean temperature of wettest quarter  Mean temperature of driest quarter  Annual precipitation  Precipitation of wettest quarter  Precipitation of driest quarter  Precipitation seasonality (coefficient of variation)Landscape Land cover classTypology Agro-ecological zoneElevation  Slope class  Topographic wetness index  Landscape-level soil characteristicsTime series, Short-term average crop season rainfall totalcrop season Specific crop season rainfall total  Short-term average NDVI crop season aggregatesSpecific crop season NDVI crop season aggregates

• 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

Integrate geo-spatial data

Coverage of African Drylands (descriptive)

Household Distance to Major Road (km)

10 20 30 40 50 60 70 80 90 100> 100050010001500200025003000

• 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; Stifel and Minten 2008)

Distance

Average Annual Rainfall (mm) Average Annual Temperature (°C)

23 24 25 26 27 28 29050010001500200025003000

700 900 1100 1300 1500 1700 1900 2100 2300 2500> 2,50002004006008001000

Climatology

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 requirements150 300 400 500 600 700 800 900 1000 1100> 1100

0500100015002000

Landscape typology

Rainfall (mm)1 10 25 50 75 100 150

Rainfall time series2010 Rainfall as % of Normal

<80% 80-90% 90-100% 100-110% 110-120% >120%05001000150020002500

Vegetation time series >> 2010 Max EVI Deviation from Mean

-0.02 -0.01 0 0.01 0.02 > 0.020500

10001500

sparse densemoderate

NDVIsparse densemoderate