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

al

Poo

r

Tot

al

Poo

r

Tot

al

Poo

r

Tot

al

Poo

r

Tot

al

Poo

r

Tot

al

Poo

r

Tot

al

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

al

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