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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. Elbers, Chris & Lanjouw, Jean O. & Lanjouw, Peter, 2002. "Micro-level estimation of welfare," Policy Research Working Paper Series 2911, The World Bank.
    2. Elbers, Chris & Lanjouw, Peter & Leite, Phillippe George, 2008. "Brazil within Brazil : testing the poverty map methodology in Minas Gerais," Policy Research Working Paper Series 4513, The World Bank.
    3. Alessandro Tarozzi & Angus Deaton, 2009. "Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 773-792, November.
    4. Demombynes, Gabriel & Elbers, Chris & Lanjouw, Jean O. & Lanjouw, Peter, 2007. "How good a map ? Putting small area estimation to the test," Policy Research Working Paper Series 4155, The World Bank.
    5. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    6. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    7. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
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    Cited by:

    1. 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.
    2. Permanyer, IƱaki, 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. 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.

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