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231006 ||| eng |
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1 |
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|a Van Der Weide, Roy
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| 245 |
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|a How Accurate is a Poverty Map based on Remote Sensing Data?
|h Elektronische Ressource
|b An Application to Malawi
|c Roy Van Der Weide
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| 260 |
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|a Washington, D.C
|b The World Bank
|c 2022
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| 300 |
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|a 61 pages
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| 653 |
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|a Poverty Mapping
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| 653 |
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|a Remote Sensing Data
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| 653 |
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|a Targeting Transfers
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| 653 |
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|a Poverty Monitoring and Analysis
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| 653 |
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|a Poverty Monitoring
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| 653 |
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|a Poverty Assessment
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| 653 |
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|a Poverty Reduction
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| 653 |
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|a Geography of Poverty
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| 653 |
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|a Development Patterns and Poverty
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| 653 |
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|a Small Area Poverty Estimation
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| 700 |
1 |
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|a Blankespoor, Brian
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| 700 |
1 |
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|a Elbers, Chris
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| 700 |
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|a Lanjouw, Peter
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b WOBA
|a World Bank E-Library Archive
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| 028 |
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|a 10.1596/1813-9450-10171
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| 856 |
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|u http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-10171
|x Verlag
|3 Volltext
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|a 330
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| 520 |
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|a This paper assesses the reliability of poverty maps derived from remote-sensing data. Employing data for Malawi, it first obtains small area estimates of poverty by combining the Malawi household expenditure survey from 2010/11 with unit record population census data from 2008. It then ignores the population census data and obtains a second poverty map for Malawi by combining the survey data with predictors of poverty derived from remote sensing data. This allows for a clean comparison between the two poverty maps. The findings are encouraging - although that assessment depends somewhat on the evaluation criteria employed. The two approaches reveal the same patterns in the geography of poverty. However, there are instances where the two approaches obtain markedly different estimates of poverty. Poverty maps obtained using remote sensing data may do well when the decision maker is interested in comparisons of poverty between assemblies of areas, yet may be less reliable when the focus is on estimates for specific small areas
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