Squeezing more out of existing data sources: Small Area Estimation of Welfare Indicators

1. 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.

2. 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

3. 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.).

4. 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.

5. 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 ...”

6. 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

7. How to Obtain a “Poverty Map” • 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...

8. How to Obtain a “Poverty Map” • Collecting disaggregated data • Costly • Time-consuming • Quantity-Quality tradeoff • Difficult to maintain comparability

9. How to Obtain a “Poverty Map” • 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).

10. 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) Poverty Maps

11. 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.

12. 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 • Statisticalprecision can be gauged • Encouraging results to date (although validation is necessary) • But, non-negligible data requirements

13. 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.

15. Experience • An enormous impact on the policy environment: • South Africa (DPLG, cholera, crime) • Panama (political credibility)

16. Applications • Targeting localities • Overlay poverty distributions against other indicators (deforestation, crime, cholera) • Survey to survey imputations • “Cross-country” analysis • Evaluation

17. 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.

18. 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.

19. 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