Developing Gender-Disaggregated Poverty Small Area Estimates : Technical Report
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 effe...
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| Format: | Report |
| Language: | English |
| Published: |
World Bank, Washington, DC
2019
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| Online Access: | http://documents.worldbank.org/curated/en/486731560917670303/Timor-Leste-Poverty-Developing-Gender-Disaggregated-Poverty-Small-Area-Estimates-Technical-Report http://hdl.handle.net/10986/32018 |
| Summary: | 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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