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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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Bibliographic Details
Main Authors: Masaki, Takaaki, Newhouse, David, Silwal, Ani Rudra, Bedada, Adane, Engstrom, Ryan
Language:English
Published: World Bank, Washington, DC 2020
Subjects:
POVERTY MEASUREMENT
SMALL AREA ESTIMATE
GEOSPATIAL ANALYSIS
POVERTY RATE
REMOTE SENSING
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

Internet

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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