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 g...
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World Bank, Washington, DC
2020
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| Online Access: | http://documents.worldbank.org/curated/en/831041599576611927/Small-Area-Estimation-of-Non-Monetary-Poverty-with-Geospatial-Data http://hdl.handle.net/10986/34469 |
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okr-10986-344692022-09-20T00:10:32Z Small Area Estimation of Non-Monetary Poverty with Geospatial Data Masaki, Takaaki Newhouse, David Silwal, Ani Rudra Bedada, Adane Engstrom, Ryan POVERTY MEASUREMENT SMALL AREA ESTIMATE GEOSPATIAL ANALYSIS POVERTY RATE REMOTE SENSING 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. 2020-09-17T17:20:23Z 2020-09-17T17:20:23Z 2020-09 Working Paper http://documents.worldbank.org/curated/en/831041599576611927/Small-Area-Estimation-of-Non-Monetary-Poverty-with-Geospatial-Data http://hdl.handle.net/10986/34469 English Policy Research Working Paper;No. 9383 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Africa South Asia Sri Lanka Tanzania |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
POVERTY MEASUREMENT SMALL AREA ESTIMATE GEOSPATIAL ANALYSIS POVERTY RATE REMOTE SENSING |
| spellingShingle |
POVERTY MEASUREMENT SMALL AREA ESTIMATE GEOSPATIAL ANALYSIS POVERTY RATE REMOTE SENSING Masaki, Takaaki Newhouse, David Silwal, Ani Rudra Bedada, Adane Engstrom, Ryan Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| geographic_facet |
Africa South Asia Sri Lanka Tanzania |
| relation |
Policy Research Working Paper;No. 9383 |
| description |
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. |
| format |
Working Paper |
| author |
Masaki, Takaaki Newhouse, David Silwal, Ani Rudra Bedada, Adane Engstrom, Ryan |
| author_facet |
Masaki, Takaaki Newhouse, David Silwal, Ani Rudra Bedada, Adane Engstrom, Ryan |
| author_sort |
Masaki, Takaaki |
| title |
Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_short |
Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_full |
Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_fullStr |
Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_full_unstemmed |
Small Area Estimation of Non-Monetary Poverty with Geospatial Data |
| title_sort |
small area estimation of non-monetary poverty with geospatial data |
| publisher |
World Bank, Washington, DC |
| publishDate |
2020 |
| url |
http://documents.worldbank.org/curated/en/831041599576611927/Small-Area-Estimation-of-Non-Monetary-Poverty-with-Geospatial-Data http://hdl.handle.net/10986/34469 |
| _version_ |
1764480965696028672 |