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.
Asia, Cambodia, poverty, principal component, small-area estimation, targeting
Asian Studies | Economics | Public Economics
How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia. (2008). World Development. 36, (10), 1830-1842. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1940
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.