How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia
We compare commune-level poverty rankings in Cambodia based on three different methods: small-area estimation, principal component analysis using aggregate data, and interviews with local leaders. While they provide reasonably consistent rankings, the choice of the ranking method matters. In order t...
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sg-smu-ink.soe_research-29392017-04-10T06:30:58Z How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia Tomoki FUJII, We compare commune-level poverty rankings in Cambodia based on three different methods: small-area estimation, principal component analysis using aggregate data, and interviews with local leaders. While they provide reasonably consistent rankings, the choice of the ranking method matters. In order to assess the potential losses from moving away from census-based poverty mapping, we used the concentration curve. Our calculation shows that about three-quarters of the potential gains from geographic targeting may be lost by using aggregate data. The usefulness of aggregate data in general would depend on the cost of data collection. 2008-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1940 info:doi/10.1016/j.worlddev.2007.10.013 https://ink.library.smu.edu.sg/context/soe_research/article/2939/viewcontent/TargetAidDataCambodia_2008.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Asia Cambodia poverty principal component small-area estimation targeting Asian Studies Economics Public Economics |
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Asia Cambodia poverty principal component small-area estimation targeting Asian Studies Economics Public Economics Tomoki FUJII, How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia |
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We compare commune-level poverty rankings in Cambodia based on three different methods: small-area estimation, principal component analysis using aggregate data, and interviews with local leaders. While they provide reasonably consistent rankings, the choice of the ranking method matters. In order to assess the potential losses from moving away from census-based poverty mapping, we used the concentration curve. Our calculation shows that about three-quarters of the potential gains from geographic targeting may be lost by using aggregate data. The usefulness of aggregate data in general would depend on the cost of data collection. |
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Tomoki FUJII, |
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Tomoki FUJII, |
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How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia |
title_short |
How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia |
title_full |
How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia |
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How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia |
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How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia |
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how well can we target aid with rapidly collected data? empirical results for poverty mapping from cambodia |
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Institutional Knowledge at Singapore Management University |
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2008 |
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https://ink.library.smu.edu.sg/soe_research/1940 https://ink.library.smu.edu.sg/context/soe_research/article/2939/viewcontent/TargetAidDataCambodia_2008.pdf |
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