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 Selected program highlights, innovations – Gender – Use of technology in surveys – Geospatial data Policy and methods research Some final considerations

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 surveys

Country

Tanzania

Baseline

2008/09 Uganda Malawi 2009/10 2010/11 Nigeria Ethiopia Niger Burkina Faso Mali 2010/11 2011/12 2011 2014 2014 2010/11 2010/11 2013 2012/13 2013/14 2014

Additional waves

2012/13 2014/15 2011/12 2013/14

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 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 …

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 Zezza Development Research Group World Bank [email protected]

Surveys: Going Beyond Rates Understanding secondary school enrollments, 12-18 year olds, Albania 2002

Percent 100% 90% 80% 70% 60% 50% 40% 30%

• In almost all countries we have a single statistic: mean enrollment at the national level. In this case it is 61%.

Average

•This is interesting for monitoring purposes, but it doesn’t say much about poverty or other factors.

20% 10% 0%

•... A regional disaggregation would be useful

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

100% 90% 80% 70% Percent 60% 50% 40% 30% 20% 10% 0%

Urban Rural

• In some countries we have regional breakdowns, with marked contrasts

Average

•The contrast between urban and rural rates emphasizes the disadvantages faced by rural communities.

•What other breakdown would be useful?

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

100% 90% 80% 70% Percent 60% 50% 40% 30% 20% 10% 0%

• …with luck, official statistics can add the gender dimension

Male Female Urban Male Female Rural Average

•…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

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

100% 90% Female, urban Male, urban

>>

80% Male, rural 70% Percent 60% 50% 40% 30% 20% 10% Female, urban

Average

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

0% Q1 Q5

>>

Q2 Q3 Q4 Consumption quintile

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

>>

Dependent Variable: Z-Score of Height-for-Age Child: Male Woman's Share of Household Income x Male Child Observations R2 note: *** p<0.01, ** p<0.05, * p<0.1

Definitions of Woman’s Share of Household Income V1 Assumption 100 to Head V2 Assumption 50/50 Split V3 Assumption a la HH V4 Preferred -0.130

-0.129

-0.147

-0.186** -0.735

2,522 0.711

-0.070

2,522 0.710

-0.008

2,522 0.710

0.155*** 2,522 0.711

Everyone rounds up…

Plot Size Measured with GPS and Farmers ' Estimate 0 1 GPS 2 Acres 3 Farmers' Estimate 4

…large farmers under report…

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

The IR is strengthened if we use GPS! >>

UGANDA : Inverse Farm Size Productivity Relationship 1 2 3 4 5 6 7 Deciles of Land Cultivated Land Self-Reported 8 9 Land GPS 10

Concurrent Data Entry The case of missing plot measurements

Missing Plot Measurements

10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 1

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

2 3 4 5 6 7

Month

8 9 10 11 12

Concurrent Date Entry ( cont’d) The case of missing plot measurements

Missing Plot Measurements

10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 1 2 3

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

4 5 6 7

Month

8 9 10 11 12

Concurrent Data Entry

The case of missing plot measurements >>

Missing Plot Measurements

10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 1 2 3

Substantial decrease in missing data. Because of revisit of households in month 4-6, part of the missing data was now captured.

4 5 6 7

Month

8 9 10 11 12

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?

What we do

• Record household and plot locations with GPS – Protocol to avoid releasing this information as it would violate confidentiality

Integrate geo-spatial data

Theme Distance Climatology Landscape Typology Time series, crop season Variable Plot distance to household Household distance to paved road Household distance to major market (if available) 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) Land cover class Agro-ecological zone Elevation Slope class Topographic wetness index Landscape-level soil characteristics Short-term average crop season rainfall total Specific crop season rainfall total Short-term average NDVI crop season aggregates Specific 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

Coverage of African Drylands (descriptive)

Distance

Household Distance to Major Road (km) • Remoteness negatively affects household-level agricultural productivity & incomes 3000 2500 2000 1500 1000 500 0 • 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)

Climatology

Average Annual Rainfall (mm) Average Annual Temperature (°C) 1000 800 600 400 200 0 3000 2000 1000 0 23 24 25 26 27 28 29

Landscape typology

Elevation (m) 2000 1500 1000 500 0 • 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

Rainfall time series

2010 Rainfall as % of Normal 2500 2000 1500 1000 500 0 Rainfall (mm) 1 10 25 50 75 100 150

Vegetation time series

>>

2010 Max EVI Deviation from Mean NDVI 1500 1000 500 0 -0.02 -0.01

0 0.01

0.02 > 0.02