Socio-economic Data for Drylands Monitoring The Living … · 2021. 2. 8. · 0.01 0.015 0.02 0.025...
Transcript of Socio-economic Data for Drylands Monitoring The Living … · 2021. 2. 8. · 0.01 0.015 0.02 0.025...
Socio-economic Data for Drylands Monitoring
The Living Standards Measurement Study – Integrated Surveys on Agriculture
Alberto Zezza
(Development Research Group, World Bank) www.worldbank.org/lsms
Monitoring and Assessment of Drylands: Forests, Rangelands, Trees, and Agrosilvopastoral Systems
Rome, FAO Headquarters January 20, 2015
• 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
Why does it matter for you?
• Living Standard Measurement Study (LSMS) surveys key tool for national poverty and socio-economic 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
Key features of LSMS surveys
• 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)
Additional features of LSMS-ISA
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
Survey Schedule Country Baseline Follow Up
Tanzania 2008/09 2010/11 2012/13 (Oct 2014) 2014/15 2016/17
Uganda 2009/10 2010/11 2011/12 2013/14 (Dec 2014) 2015 …
Malawi 2010 2013 (Oct 2014)
2016 2018 2020
Nigeria 2010/11 2012/13 2015/16 2017/18
Ethiopia 2011/12 2013/14 (Dec 2014) 2015/16 2017/18
Niger 2011 2014
Mali 2014/15 2016/17
Burkina Faso 2014/15 2015/16 2017/18
• 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
Examples from recent livelihoods profile in 6 African countries
The data: Survey locations • Ethiopia 2011: Rural Socioeconomic
Survey (ERSS), n=4,000, rural and small towns
• Malawi, 2010-11: 3rd 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 2010-11: General Household Survey-Panel (GHS); n=5,000
• Tanzania 2008-09: National panel Survey (TZNPS) n=3,265
• Uganda 2010: National Panel Survey (UNPS) n=3,200
• Burkina, Mali in the pipeline
Poverty in the drylands
15%
22%
19%
44%
Arid Semi-aridDry sub-humid Other
Location of the poor
10%
20%
30%
40%
50%
60%
70%
80%
Poverty headcount (%) within different drylands categories and non-drylands
Arid Semi-aridDry sub-humid Other
Location of the poor
10%
20%
30%
40%
50%
60%
70%
80%
Malawi Niger Nigeria North Eth.South Eth. Tanzania Uganda
Poverty headcount (%) within different drylands categories and non-drylands
Arid Semi-aridDry sub-humid Other
Poverty headcount by zone and by country
Education and Child Nutrition
10%
20%
30%
40%
50%
60%
70%
80%
Nigeria North Eth. South Eth. Tanzania
Percentage of stunted in different drylands
Arid Semi-aridDry sub-humid Other
Stunting among children 0-5 yrs
0
2
4
6
Niger Nigeria North Eth. South Eth. Tanzania
Average years of education within HHs in the different drylands
Arid Semi-aridDry sub-humid Other
Educational attainment: Years of schooling
Crop income 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
Income shares by poverty status
0%
10%
20%
30%
40%
50%
60%
Malawi Niger Nigeria North Eth.South Eth.Tanzania Uganda
Tot
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Poo
r
Tot
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Poo
r
Tot
al
Poo
r
Tot
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Poo
r
Tot
al
Poo
r
Tot
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Poo
r
Tot
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Poo
r
Non-agricultural income shares in drylands and among the poor
Drylands Non-drylands
20%
40%
60%
80%
Malawi Niger Nigeria North Eth.South Eth.Tanzania Uganda
Tot
al
Poor
Tot
al
Poor
Tot
al
Poor
Tot
al
Poor
Tot
al
Poor
Tot
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Poor
Tot
al
Poor
Crop income shares in drylands and among the poor
Drylands Non-drylands
Theme Variable
Distance Plot distance to household
Household distance to paved road Household 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 class
Typology Agro-ecological zone
Elevation
Slope class
Topographic wetness index
Landscape-level soil characteristics
Time series, Short-term average crop season rainfall total
crop season 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
Rainfall (mm)
1 10 25 50 75 100 150
Rainfall time series
2010 Rainfall as % of Normal
0 500
1000 1500 2000 2500
Vegetation time series 2010 Max EVI Deviation from Mean
0
500
1000
1500
-0.02 -0.01 0 0.01 0.02 > 0.02 sparse dense moderate
NDVI
sparse dense moderate
Temperature, rainfall, soil organic content as explanatory variables
0
0.005
0.01
0.015
0.02
0.025
0.03
Arid
Sem
i-arid
Dry
sub-
hum
id
Oth
er
Sem
i-arid
Dry
sub-
hum
id
Oth
er
Arid
Sem
i-arid Arid
Sem
i-arid
Dry
sub-
hum
id
Oth
er
Arid
Sem
i-arid
Dry
sub-
hum
id
Oth
er
Ethiopia Malawi Niger Nigeria Tanzania
Variation in temperaturesCoV max avg temp 1989-2010
CoV seasonal avg temp 1989-2010
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Arid
Semi
-arid
Dry s
ub-h
umid
Othe
r
Semi
-arid
Dry s
ub-h
umid
Othe
r
Arid
Semi
-arid
Arid
Semi
-arid
Dry s
ub-h
umid
Othe
r
Arid
Semi
-arid
Dry s
ub-h
umid
Othe
r
Ethiopia Malawi Niger Nigeria Tanzania
Variation in rainfall
CoV growing seasonrainfall over 1983-2012
0
0.5
1
1.5
2
2.5
Arid
Semi
-arid
Dry s
ub-h
umid
Othe
r
Semi
-arid
Dry s
ub-h
umid
Othe
r
Arid
Semi
-arid
Arid
Semi
-arid
Dry s
ub-h
umid
Othe
r
Arid
Semi
-arid
Dry s
ub-h
umid
Othe
rEthiopia Malawi Niger Nigeria Tanzania
Organic content
Total Organic Carbon(TOC, %weight)
FAO’s EPIC project is using these data to study hh level agricultural productivity outcomes, incorporating spatial data
• 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!
Conclusions and way forward
www.worldbank.org/lsms