Alberto Zezza Development Data Group World Bank 2018 ReSAKSS Annual Conference Addis Ababa, 24-26 October 2018

1. Scaling up the LSMS-ISA to Monitor Progress
on CAADP Indicators
Alberto Zezza
Development Data Group
World Bank
2018 ReSAKSS Annual Conference
Addis Ababa, 24-26 October 2018

2. Outline
• The LSMS & LSMS-ISA programs and related survey initiatives
• Data collection
• Methodological research
• Capacity development
• Role of surveys in measuring development outcomes (CAADP, but also
SDGs)
• The relevance and potential for [biofortification] analysis
• Yields
• Food consumption
• Varietal identification, technology adoption
• What next for a closer collaboration?

3. LSMS-ISA and scaling up of ag surveys
• LSMS, est. 1980
• LSMS-ISA: Africa and ag focus, 8
countries, 28+ surveys
• Understand not just monitor
• Sustainability of approach
• Value for money by integrating different
domains (e.g. ag, food consumption,
nutrition), technology
• Within national statistical system
• Scaling-up: The 50 by 2030 initiative
• FAO’s AGRISurvey program

4. LSMS-Integrated Surveys on Agriculture:
Working on 3 Fronts
• Collecting & disseminating multi-topic
household survey data with a focus on
agriculture in 8 African countries
• Improving methods in agricultural
statistics based on rigorous
experimentation and tool development
• -- land, soil, labor, yields, variety, food…
• -- sourcebooks
• Conducting and promoting policy
research in agriculture and rural
development

5. SDG Indicators by Goal and Tier
• 77 indicators in total identified
as coming from household
surveys
• Goal 3 with highest number
followed by goals 16, 8, 5, 7, 1
and 2
• About 80% are either Tier I or
Tier II, 13 of the indicators are
Tier III
Mitra and Walsh, 2017
By Goal: Tier I Tier II Tier
III
Mixed Total
Goal 1: Poverty 2 2 2 0 6
Goal 2. Hunger 4 0 1 0 5
Goal 3. Health 8 9 1 0 18
Goal 4. Education 1 3 1 2 7
Goal 5. Gender equality 2 7 0 0 9
Goal 6. Water and sanitation 2 0 0 0 2
Goal 7. Energy 2 0 0 0 2
Goal 8. Decent work 6 2 1 0 9
Goal 9. Infrastructure 1 0 0 0 1
Goal 10. Inequality 1 0 3 0 4
Goal 11. Cities 1 1 1 0 3
Goal 16. Justice 1 6 3 0 10
Goal 17. Partnership 1 0 0 0 1
Total 32 30 13 2 77

6. LSMS potential for monitoring and understanding
CAADP
• Poverty, inequality
• Productivity
• Post Harvest Losses
• Nutrition: Stunting
• Jobs for youth in
agriculture
• Women in
agribusiness
• Resilience
Household
• Expenditures – Food &
Nonfood
• Education
• Health
• Labour
• Nonfarm Enterprises
• Durable Assets
• Anthropometry
• Food Security
• Shocks
Agriculture
• Plot Details
• Inputs – Use & Access;
Labor & Non-Labor Alike
• Crops – Cultivation &
Production
• Implements & Machinery
• Extension services
• Livestock, Fisheries
• Forestry
Community
• Demographics
• Services
• Facilities
• Infrastructure
• Governance
• Organizations & Groups
• Prices

7. How can LSMS-ISA data contribute to the
CAADP agenda: Focus on biofortification
• Food consumption data:
• Design: Potential for biofortification
• Who consumes biofortified foods?
• Productivity measures
• Innovation in land and labor data collection
• Technology adoption
• Varietal identification

8. Uganda: Validating remotely-sensed maize
yields
Source: Lobell, D. B., Azzari, G., Burke, M., Gourlay, S., Jin, Z., Kilic, T., and Murray, S. (2018). “Eyes in the sky, boots on the
ground: assessing satellite- and ground-based approaches to crop yield measurement and analysis in Uganda.” World Bank
Policy Research Working Paper No. 8374.

9. Varietal identification
• Partnership with CGIAR Standing Panel on Impact
Assessment (SPIA) to improve data and survey
methods on varietal adoption
• Status quo: Farmer’s self-reporting, expert opinions
• Survey experiments assessing the accuracy of
prevailing subjective approaches to data collection
vis-à-vis DNA fingerprinting
• Maize in Uganda (completed)
• Sweet Potato in Ethiopia (completed)
• Cassava in Malawi (completed)
• Sorghum in Mali (on-going)
• Banana in Uganda (on-going)
• Sub-objective: Remote sensing
• Integrate at scale, guidebooks

10. Farmer-Reporting vs. DNA Fingerprinting Correspondence:
Uganda Maize Varietal Identification
Farmer-Reporting DNA Fingerprinting
Source: Ilukor, J., Kilic, T., Stevenson, J., Gourlay, S., Kosmowski, F., Kilian, A., Sserumaga, J., and Asea, G. (Forthcoming). “Blowing
in the Wind: The Quest for Accurate Crop Variety Identification in Field Research, with an Application to Maize in Uganda.”

11. Farmer-Reporting vs. DNA Fingerprinting Correspondence:
Ethiopia Sweet Potato Varietal Identification
Farmer-ReportingDNA Fingerprinting
Source: Kosmowski, F., Aragaw, A., Kilian, A., Ambel, A., Ilukor, J., Yigezu, B., and Stevenson, J. (2018). “Varietal identification in household surveys:
results from three household-based methods against the benchmark of DNA fingerprinting in Southern Ethiopia.” Experimental Agriculture, 1-15.

12. Final thoughts
• The agricultural data landscape is
changing, but much remains to be done
• LSMS-ISA: Engagement, take up by
researchers and country policy analysts -
weak interaction with regional programs
• Scaling-up opportunities going forward: 50
by 2030 initiative
Can we leverage ‘data smart
agriculture’ to achieve nutrition-
smart agriculture?

13. Scaling up the LSMS-ISA to Monitor Progress
on CAADP Indicators
Alberto Zezza
Development Data Group
World Bank
2018 ReSAKSS Annual Conference
Addis Ababa, 24-26 October 2018

14. LSMS-Led Research: Cross-Country
Gender & Agriculture
• Partners: IFAD, Africa Gender Innovation Lab, IFPRI, FAO
• World Bank Policy Research Working Papers
• World Bank-ONE Campaign Report – Leveling the Field
• Agricultural Economics Special Issue
Nutrition & Agriculture
• Partners: BMGF, IFPRI
• World Bank Policy Research Working Papers
• Journal of Development Studies Special Issue
Agriculture in Africa: Telling Facts from Myths
• Partners: AfDB, World Bank Africa CE, Yale, Cornell, Maastricht
• World Bank Policy Research Working Papers
• Food Policy Special Issue

15. Selected (LSMS & Non-LSMS) Peer-Reviewed Uganda NPS-Based
Research
Welfare
• World Bank Poverty Assessment
• Inequality in Uganda
• Poverty Dynamics
• Is Poverty Reduction Overstated?
• Combining Satellite Imagery and Machine Learning to Predict Poverty
• Measuring Poverty for Food Security Analysis
• Household Income Portfolios
• Rise of Middle Class and Food System Transformation
• Staple Food Consumption and Undernourishment
• Food Price Seasonality
• Targeting for Development Programs
Nutrition
• Household Income and Child Nutrition
• Livestock Ownership and Child Nutrition
• Agricultural Commercialization and Nutrition

16. Selected (LSMS & Non-LSMS) Peer-Reviewed UNPS-Based Research (3)
Agriculture
• Agricultural Input Use
• Agricultural Input Credit
• Agricultural Intensification
• Inorganic Fertilizer Profitability
• Agricultural Factor Markets and Market Failures
• Smallholder Access to Land
• Gender Differences in Agricultural Productivity
• Women’s Contribution to Agricultural Labor
• Post-Harvest Losses
• Adoption of Modern Varieties and Welfare
• Technological Change in Agriculture Sector
• Improved Spatially-Disaggregated Livestock Measures
• Socio-environmental Drivers of Forest Change
• Coffee Certification and Welfare

17. LSMS-Led Research: Sector/Topic-Specific
• Agricultural Productivity
• Land: Guesstimates to GPStimates, Missing(ness) in Action, Sourcebook
• Debunking IR: Ethiopia, Uganda
• Agricultural Productivity and Poverty: Nigeria, Malawi
• ADePT Crop Module (Automated Analysis, Data Files, including Uganda)
• Soil quality: Ethiopia, X-Country, including Uganda
• Livestock
• Livestock Data for Evidence-Based Policies (Uganda, Tanzania)
• Livestock Ownership, Animal Source Food Consumption and Child Nutrition (Uganda)
• Milk Off-Take in Extensive Livestock Systems (Niger)
• Livestock Module Guidebook (Uptake: ISA countries, IFPRI)
• ADePT Livestock Module (Automated Analysis, Data Files)