Small Area Estimation of Non-Monetary Poverty with Geospatial Data:
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 indic...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Washington, D.C
The World Bank
2020
|
| Schriftenreihe: | World Bank E-Library Archive
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| Online-Zugang: | kostenfrei |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (49 Seiten) |
| DOI: | 10.1596/1813-9450-9383 |
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| 520 | |a 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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| indexdate | 2024-10-12T04:02:46Z |
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| physical | 1 Online-Ressource (49 Seiten) |
| psigel | ZDB-1-WBA |
| publishDate | 2020 |
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| spellingShingle | Masaki, Takaaki Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title | Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_auth | Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_exact_search | Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_exact_search_txtP | Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_full | Small Area Estimation of Non-Monetary Poverty with Geospatial Data Takaaki Masaki |
| title_fullStr | Small Area Estimation of Non-Monetary Poverty with Geospatial Data Takaaki Masaki |
| title_full_unstemmed | Small Area Estimation of Non-Monetary Poverty with Geospatial Data Takaaki Masaki |
| title_short | Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_sort | small area estimation of non monetary poverty with geospatial data |
| url | https://doi.org/10.1596/1813-9450-9383 |
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