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Small Area Estimation of Non-Monetary Poverty with Geospatial Data / Takaaki Masaki.
World Bank Open Knowledge Repository (formerly "World Bank E-Library Publications") Available online
View online- Format:
- Book
- Government document
- Author/Creator:
- Masaki, Takaaki.
- Series:
- Policy research working papers.
- World Bank e-Library.
- Language:
- English
- Subjects (All):
- Geospatial Analysis.
- Inequality.
- Living Standards.
- Poverty Assessment.
- Poverty Lines.
- Poverty Measurement.
- Poverty Monitoring and Analysis.
- Poverty Rate.
- Poverty Reduction.
- Remote Sensing.
- Small Area Estimate.
- Local Subjects:
- Geospatial Analysis.
- Inequality.
- Living Standards.
- Poverty Assessment.
- Poverty Lines.
- Poverty Measurement.
- Poverty Monitoring and Analysis.
- Poverty Rate.
- Poverty Reduction.
- Remote Sensing.
- Small Area Estimate.
- Physical Description:
- 1 online resource (49 pages)
- Place of Publication:
- Washington, D.C. : The World Bank, 2020.
- System Details:
- data file
- Summary:
- This paper uses data from Sri Lanka and Tanzania to evaluate the benefits of combining household surveys with geographically comprehensive geospatial indicators to generate small area estimates of non-monetary poverty. The preferred estimates are generated by utilizing subarea-level geospatial indicators in a household-level empirical best predictor mixed model with a normalized welfare measure. Mean squared errors are estimated using a parametric bootstrap procedure. The resulting estimates are highly correlated with non-monetary poverty calculated from the full census in both countries, and the gain in precision is comparable to increasing the size of the sample by a factor of three in Sri Lanka and five in Tanzania. The empirical best predictor model moderately underestimates uncertainty, but coverage rates are similar to standard survey-based estimates that assume independent outcomes across clusters. A variety of checks, including adding noise to the welfare measure and model-based and design-based simulations, confirm that the main results are robust. The results demonstrate that combining household survey data with subarea-level geospatial indicators can greatly increase the precision of survey estimates of non-monetary poverty at comparatively low cost.
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