How Well Can We Target Aid with Rapidly Collected Data? Empirical Results for Poverty Mapping from Cambodia
Summary 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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- Elbers, Chris & Lanjouw, Peter & Mistiaen, Johan & Ozler, Berk & Simler, Kenneth, 2003. "Are Neighbours Equal? Estimating Local Inequality in Three Developing Countries," Working Paper Series UNU-WIDER Research Paper , World Institute for Development Economic Research (UNU-WIDER).
- Elbers, Chris & Tomoki Fujii & Lanjouw, Peter & Ozler, Berk & Yin, Wesley, 2004.
"Poverty alleviation through geographic targeting : how much does disaggregation help?,"
Policy Research Working Paper Series
3419, The World Bank.
- Elbers, Chris & Fujii, Tomoki & Lanjouw, Peter & Ozler, Berk & Yin, Wesley, 2007. "Poverty alleviation through geographic targeting: How much does disaggregation help?," Journal of Development Economics, Elsevier, vol. 83(1), pages 198-213, May.
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