Squeezing more out of existing data sources:
Small Area Estimation of Welfare Indicators
Berk Özler
The World Bank
Development Research Group, Poverty Cluster
November, 2001.
Outline
• Motivation:
– The demand for “poverty maps”
• Methods to obtain reliable welfare measures
for small areas?
• Our experience so far…
• Applications
• How can poverty maps help census?
• How can census help poverty maps?
• Conclusion
Motivation
• Access to reliable estimates of local welfare
are desirable to local administrations,
national policy-makers, and practitioners of
development in general.
– They can help in assessing need across
communities.
– They can teach us about factors that affect
decision-making at the local levels.
– They can also teach us about community
characteristics that affect individual and local
outcomes in social sectors (health, crime, etc.).
Examples
• In a number of countries (e.g. South Africa,
Nicaragua, Guatemala), central government
allocations to districts and municipalities
demand disaggragated welfare information.
• In Madagascar, provincial governments are
not satisfied with information from the
household survey that produces just one
poverty figure for the entire province.
Examples
• “Equitable Share” allocations for individual
municipalities in South Africa for FY
1998/99 states that (emphasis added):
• “The basic services (S) grant supports the ability of
municipalities to supply services to the poor. The approach
is to simply estimate the number of poor households with
incomes of less than R800 per month in 1998 Rand prices
and the current annual cost of providing basic services per
person. The two magnitudes are multiplied together and
...”
Examples
• Targeting poor areas or pro-poor areas?
• Poverty, inequality, and local elite capture.
• Causal relationships between:
– community level inequality and individual health
outcomes,
• or
– poverty, inequality and crime
How to Obtain a “Poverty Map”
1. Options:
1. Collect household data at highly
disaggregated levels
2. Produce “Basic Needs Index” from census
(and other data sources)
3. Apply methodology of combining survey
and census data.
We focus on option # 3...
How to Obtain a “Poverty Map”
1 Collecting disaggregated data
– Costly
– Time-consuming
– Quantity-Quality tradeoff
– Difficult to maintain comparability
How to Obtain a “Poverty Map”
2 Producing a “Basic Needs” index
Summary of Problems:
–
–
–
ad-hoc
often widely disputed (multiple maps)
how to interpret? (poverty=low income?)
–
Note that, in cases where income data are
collected in the population census, you may
still have a problem (South Africa).
Poverty Maps
• NEED:
• (1) Large data sets,
representative at
small geographical
units
• (2) Data on
consumption
expenditure
• PROBLEM:
• Household surveys
satisfy (2), not (1)
• Census data
satisfies (1), but not
(2)
Methodology
Basic Procedure:
– Estimate a model of (log) per capita
consumption, yh, using household survey data.
– Restrict explanatory variables to those that can
be linked to households in survey and census.
• Model thus provides an empirical weighting scheme
– Estimate expected level of poverty or inequality
for small populations of interest, using their
census-based characteristics, and the parameter
estimates from the model described above.
Methodology
• Combining census and survey data, we
impute a measure of welfare from
household survey into census, using
statistical prediction methods:
– Produces readily interpretable estimates
– Statistical precision can be gauged
– Encouraging results to date (although validation
is necessary)
– But, non-negligible data requirements
Experience
• Our experience in various countries (e.g.
Ecuador, South Africa, Madagascar...) is
positive:
– standard errors usually small enough to enable
disaggregation to 3rd administrative level.
– more than half of the pairwise comparisons at
this level yield statistically significant
differences in headcount index.
Experience
• An enormous impact on the policy
environment:
– South Africa (DPLG, cholera, crime)
– Panama (political credibility)
Applications
• Targeting localities
• Overlay poverty distributions against other
indicators (deforestation, crime, cholera)
• Survey to survey imputations
• “Cross-country” analysis
• Evaluation
How can poverty maps help census?
Poverty maps can...
• help increase demand for census data.
• be used as an advocacy tool for raising
funds for upcoming census.
• help raise the profile of statistical institutes.
• help statistical institutions by reconciling
information from different data sources.
• encourage more researchers to utilize
census data.
How can census help poverty maps?
Statistical institutes can help poverty
mapping by…
• adopting common design features for
census and household surveys.
• processing the census data faster.
• having clear guidelines regarding access to
aggregate and unit-record census data, and
hence making census data more available to
policy-makers and researchers alike.
Conclusion
• We can do more with existing data sets.
• Our approach is fairly cheap as it uses existing
data sources.
• Our method can also be used for monitoring
purposes over time by combining a small survey
with a larger survey