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How good a map? : putting small area estimation to the test

Author: Gabriel Demombynes
Publisher: [Washington, D.C.] : World Bank, Development Research Group, Poverty Team, [2007]
Series: Policy research working papers (Online), 4155.
Edition/Format:   eBook : Document : International government publication : EnglishView all editions and formats
Summary:
The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities.  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
How good a map?.
[Washington, D.C.] : World Bank, Development Research Group, Poverty Team, [2007]
(OCoLC)123435139
Material Type: Document, Government publication, International government publication, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Gabriel Demombynes
OCLC Number: 123435140
Notes: Title from PDF file as viewed on 4/19/2007.
Reproduction Notes: Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2010. MiAaHDL
Description: 1 online resource (41 pages) : illustrations.
Details: Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Series Title: Policy research working papers (Online), 4155.
Responsibility: Gabriel Demombynes [and others].

Abstract:

The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.

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