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Can census data alone signal heterogeneity in the estimation of poverty maps?

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  • Tarozzi, Alessandro

Abstract

Methodologies now commonly used for the construction of poverty maps assume a substantial degree of homogeneity within geographical areas in the relationship between income and its predictors. However, local labor and rental markets and other local environmental differences are likely to generate heterogeneity in such relationships, at least to some extent. The purpose of this paper is to argue that useful if only indirect and suggestive evidence on the extent of area heterogeneity is readily available in virtually any census. Such indirect evidence is provided by non-monetary indicators-such as literacy, asset ownership or access to sanitation-which are routinely included in censuses. These indicators can be used to perform validation exercises to gauge the extent of heterogeneity in their distribution conditional on predictors analogous to those commonly used in poverty mapping. We argue that the same factors which are likely to generate area heterogeneity in poverty mapping are also likely to generate heterogeneity in such kind of validation exercises. We construct a very simple model to illustrate this point formally. Finally, we evaluate empirically the argument using data from Mexico. In our empirical illustrations, the performance of imputation methodologies to construct maps of indicators typically feasible with census data alone is indeed informative about how effectively such methodologies can produce correct inference in poverty mapping.

Suggested Citation

  • Tarozzi, Alessandro, 2011. "Can census data alone signal heterogeneity in the estimation of poverty maps?," Journal of Development Economics, Elsevier, vol. 95(2), pages 170-185, July.
  • Handle: RePEc:eee:deveco:v:95:y:2011:i:2:p:170-185
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    References listed on IDEAS

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    1. Jesse Naidoo, 2009. "Finite-Sample Bias and Inconsistency in the Estimation of Poverty Maps," SALDRU Working Papers 36, Southern Africa Labour and Development Research Unit, University of Cape Town.
    2. Permanyer, Iñaki, 2013. "Using Census Data to Explore the Spatial Distribution of Human Development," World Development, Elsevier, vol. 46(C), pages 1-13.
    3. Luc Christiaensen & Peter Lanjouw & Jill Luoto & David Stifel, 2012. "Small area estimation-based prediction methods to track poverty: validation and applications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(2), pages 267-297, June.
    4. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    5. Van Der Weide,Roy & Blankespoor,Brian & Elbers,Chris T.M. & Lanjouw,Peter F., 2022. "How Accurate Is a Poverty Map Based on Remote Sensing Data ? An Application to Malawi," Policy Research Working Paper Series 10171, The World Bank.
    6. Skoufias, Emmanuel & Diamond, Alexis & Vinha, Katja & Gill, Michael & Dellepiane, Miguel Rebolledo, 2020. "Estimating poverty rates in subnational populations of interest: An assessment of the Simple Poverty Scorecard," World Development, Elsevier, vol. 129(C).
    7. Newhouse, D. & Shivakumaran, S. & Takamatsu, S. & Yoshida, N., 2014. "How survey-to-survey imputation can fail," Policy Research Working Paper Series 6961, The World Bank.

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