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How good a map ? Putting small area estimation to the test

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  • Demombynes, Gabriel
  • Elbers, Chris
  • Lanjouw, Jean O.
  • Lanjouw, Peter

Abstract

The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.

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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 4155.

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Date of creation: 01 Mar 2007
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Handle: RePEc:wbk:wbrwps:4155

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Keywords: Small Area Estimation Poverty Mapping; Rural Poverty Reduction; Science Education; Scientific Research&Science Parks; Population Policies;

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Cited by:
  1. Ferre, Celine & Ferreira, Francisco H.G. & Lanjouw, Peter, 2010. "Is there a metropolitan bias ? the inverse relationship between poverty and city size in selected developing countries," Policy Research Working Paper Series, The World Bank 5508, The World Bank.
  2. Escobal, J. & Ponce, C., 2011. "Spatial Patterns of Growth and Poverty Changes in Peru (1993 – 2005)," Working papers, Rimisp Latin American Center for Rural Development 078, Rimisp Latin American Center for Rural Development.
  3. Deepa Narayan & Lant Pritchett & Soumya Kapoor, 2009. "Moving Out of Poverty : Volume 2. Success from the Bottom Up," World Bank Publications, The World Bank, number 11838, August.
  4. Tarozzi, Alessandro, 2011. "Can census data alone signal heterogeneity in the estimation of poverty maps?," Journal of Development Economics, Elsevier, Elsevier, vol. 95(2), pages 170-185, July.
  5. Luc Christiaensen & Peter Lanjouw & Jill Luoto & David Stifel, 2012. "Small area estimation-based prediction methods to track poverty: validation and applications," Journal of Economic Inequality, Springer, Springer, vol. 10(2), pages 267-297, June.
  6. Jesse Naidoo, 2009. "Finite-Sample Bias and Inconsistency in the Estimation of Poverty Maps," SALDRU Working Papers, Southern Africa Labour and Development Research Unit, University of Cape Town 36, Southern Africa Labour and Development Research Unit, University of Cape Town.
  7. Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and Predicting Household Expenditures and Income Distributions," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung) 201147, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

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