Publication: Developing Gender-Disaggregated Poverty Small Area Estimates: Technical Report

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Small area estimates of poverty and inequality statistics, through survey-to-census imputation that lets consumption be estimated for each and every household in a census, are useful for at least three reasons. First, they can help improve the effectiveness of public spending, by targeting to prevent the leakage of benefits to the non-poor (and prevent the under-coverage of the poor). If poor people are concentrated in certain areas, spatial targeting by directing extra development projects and public services to those areas, may be more feasible than trying to individually target the poor. Geographic targeting is highly relevant in countries like Timor Leste, where mountainous topography contributes to high levels of heterogeneity. In similar environments, such as Papua New Guinea, the enclave nature of some modern economic development has created high levels of spatial inequality. The basic details are that household survey data are used to estimate a model of consumption, with explanatory variables restricted to those that have overlapping distributions from a census. The coefficients from this model are then combined with the variables from the census, and consumption is predicted for each household in the census. With these predictions available for all households, inequality and poverty statistics can be estimated for small geographic areas (Elbers et al, 2003).2 In the results below, the poverty statistics that are calculated by using the predicted consumption data for each census household are reported at the suco level (n=442). For the headcount poverty rate, the standard errors at the suco level (relative to the poverty index) average one-quarter and so this is a comparable degree of precision to what the survey offered at the municipality level (n=13) for a variable like the poverty severity index.
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World Bank. 2019. Developing Gender-Disaggregated Poverty Small Area Estimates: Technical Report. © World Bank, Washington, DC. License: CC BY 3.0 IGO.
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