Socio-economic Data for Drylands Monitoring The Living Standards Measurement Study Integrated Surveys on Agriculture

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1 Socio-economic Data for Drylands Monitoring The Living Standards Measurement Study Integrated Surveys on Agriculture Alberto Zezza (Development Research Group, World Bank) Monitoring and Assessment of Drylands: Forests, Rangelands, Trees, and Agrosilvopastoral Systems Rome, FAO Headquarters January 20, 2015

2 Why does it matter for you? Share information on existing socio-economic data, with innovative features Address data gaps on people, socio-economics without duplicating efforts ( not as easily observables as forests are ) Possible uses Baseline on socio-economics, monitor change Derive/calibrate parameters for models Study household/community heterogeneity Understand micro incentives behaviors, outcomes Monitoring not enough: Evaluate interventions, policies

3 Key features of LSMS surveys Living Standard Measurement Study (LSMS) surveys key tool for national poverty and socioeconomic data collection since 1980s Integrated Surveys on Agriculture (-ISA) add-on with specific ag focus (2008- ) Country-owned, nationally representative Monitor, but more importantly understand, analyze Multi-topic, household-level and community data Typically every 3-5 years

4 Additional features of LSMS-ISA Focus on methodological development Forest module with FAO, CIFOR, IFRI, etc. Soil testing (ICRAF) Use of technology GPS for households and plots (area) Concurrent field-based data entry Computer Assisted Personal Interviews (CAPI) Open data Gender-disaggregated data Panel (longitudinal)

5 LSMS-ISA: Overview of Survey Instruments Household Expenditures Food & Nonfood Education Health Labour Nonfarm Enterprises Durable Assets Anthropometry Food Security Shocks, Coping Agriculture Plot Details Trees on farm Inputs Use Crops Cultivation & Production Livestock Fisheries Farm Implements & Machinery Forestry? NRM practices Community Demographics Services Facilities Infrastructure Governance Organizations & Groups Use of communal NR Prices

6 Survey Schedule Country Baseline Follow Up Tanzania 2008/ / /13 (Oct 2014) 2014/ /17 Uganda 2009/ / / /14 (Dec 2014) 2015 Malawi (Oct 2014) Nigeria 2010/ / / /18 Ethiopia 2011/ /14 (Dec 2014) 2015/ /18 Niger Mali 2014/ /17 Burkina Faso 2014/ / /18

7 Examples from recent livelihoods profile in 6 African countries Start exploiting geo-referencing in descriptive manner, but more can be done How many people, by area, and what they do (details on income sources, and more) High poverty incidence and numbers in drylands Compounded by malnutrition, food insecurity, lack of access to services Diversified livelihoods, role of education Interesting descriptives, highlighting heterogeneity within drylands

8 The data: Survey locations Ethiopia 2011: Rural Socioeconomic Survey (ERSS), n=4,000, rural and small towns Malawi, : 3 rd Integrated Household Survey (IHS3), n=12,271 Niger 2011: Enquête Nationale sur les Conditions de Vie des Ménages et l Agriculture (ECVMA); n=4,000 Nigeria : General Household Survey-Panel (GHS); n=5,000 Tanzania : National panel Survey (TZNPS) n=3,265 Uganda 2010: National Panel Survey (UNPS) n=3,200 Burkina, Mali in the pipeline

9 Poverty in the drylands Location of the poor Poverty headcount (%) within different drylands categories and non-drylands 80% 15% 70% 44% 22% 60% 19% 50% 40% Location of the poor 30% 20% 80% Poverty headcount (%) within different drylands categories and non-drylands 10% 70% 60% 50% 40% 30% 20% 10% Malawi Niger Nigeria North Eth.South Eth. Tanzania Uganda Poverty headcount by zone and by country

10 Education and Child Nutrition 6 Average years of education within HHs in the different drylands 80% Percentage of stunted in different drylands 70% 4 60% 50% 40% 2 30% 20% 10% 0 Niger Nigeria North Eth. South Eth. Tanzania Nigeria North Eth. South Eth. Tanzania Educational attainment: Years of schooling Stunting among children 0-5 yrs

11 Income shares by poverty status Crop income shares in drylands and among the poor Non-agricultural income shares in drylands and among the poor 80% 60% 50% 60% 40% 40% 30% 20% 20% 10% 0% Malawi Niger Nigeria North Eth.South Eth.Tanzania Uganda Drylands Non-drylands Crop income Malawi Niger Nigeria North Eth.South Eth.Tanzania Uganda Drylands Non-drylands Non-ag income Can break down by wealth or poverty status Need to look beyond ag, or at least at agriculture within the broader rural economy

12 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

13 Rainfall time series 2010 Rainfall as % of Normal Rainfall (mm)

14 Vegetation time series 2010 Max EVI Deviation from Mean NDVI 500 sparse moderate dense > 0.02

15 Temperature, rainfall, soil organic content as explanatory variables Variation in temperatures Variation in rainfall CoV max avg temp CoV seasonal avg temp CoV growing season rainfall over Ethiopia Malawi Niger Nigeria 2.5 Tanzania Organic content Ethiopia Organic Malawi CarbonNiger Nigeria Tanzania (TOC, %weight) FAO s EPIC project is using these data to study hh level agricultural productivity outcomes, incorporating spatial data Ethiopia Malawi Niger Nigeria Tanzania

16 Conclusions and way forward Data is there to be Used (available on the web) Improved - already working on forestry, soil testing (with FAO, ICRAF, ICRISAT, ) Understand determinants, and hh/community heterogeneity Calibrate models Build data collection in national systems, and plan ahead Limitations: Sample size; nomadic populations; forestry content but can work on this!

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