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Brazil within Brazil : testing the poverty map methodology in Minas Gerais


  • Elbers, Chris
  • Lanjouw, Peter
  • Leite, Phillippe George


The small-area estimation technique developed for producing poverty maps has been applied in a large number of developing countries. Opportunities to formally test the validity of this approach remain rare due to lack of appropriately detailed data. This paper compares a set of predicted welfare estimates based on this methodology against their true values, in a setting where these true values are known. A recent study draws on Monte Carlo evidence to warn that the small-area estimation methodology could significantly over-state the precision of local-level estimates of poverty, if underlying assumptions of spatial homogeneity do not hold. Despite these concerns, the findings in this paper for the state of Minas Gerais, Brazil, indicate that the small-area estimation approach is able to produce estimates of welfare that line up quite closely to their true values. Although the setting considered here would seem, a priori, unlikely to meet the homogeneity conditions that have been argued to be essential for the method, confidence intervals for the poverty estimates also appear to be appropriate. However, this latter conclusion holds only after carefully controlling for community-level factors that are correlated with household level welfare.

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  • 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.
  • Handle: RePEc:wbk:wbrwps:4513

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    References listed on IDEAS

    1. In-Koo Cho & David M. Kreps, 1987. "Signaling Games and Stable Equilibria," The Quarterly Journal of Economics, Oxford University Press, vol. 102(2), pages 179-221.
    2. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November.
    3. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, Oxford University Press, vol. 87(3), pages 355-374.
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    Cited by:

    1. Lang, Corey & Barrett, Christopher B. & Naschold, Felix, 2013. "Targeting Maps: An Asset-Based Approach to Geographic Targeting," World Development, Elsevier, vol. 41(C), pages 232-244.
    2. Channing Arndt & Azhar M. Hussain & Vincenzo Salvucci & Finn Tarp & Lars Peter Østerdal, 2016. "Poverty Mapping Based on First‐Order Dominance with an Example from Mozambique," Journal of International Development, John Wiley & Sons, Ltd., vol. 28(1), pages 3-21, January.
    3. Douidich, Mohammed & Ezzrari, Abdeljouad & Lanjouw, Peter, 2008. "Simulating the impact of geographic targeting on poverty alleviation in Morocco : what are the gains from disaggregation ?," Policy Research Working Paper Series 4724, The World Bank.
    4. Sims, Katharine R.E., 2010. "Conservation and development: Evidence from Thai protected areas," Journal of Environmental Economics and Management, Elsevier, vol. 60(2), pages 94-114, September.
    5. 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.
    6. Francesca Ballini & Gianni Betti & Samuel Carrette & Laura Neri, 2009. "Poverty and inequality mapping in the Commonwealth of Dominica," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 0(Special i), pages 123-162.
    7. World Bank, 2013. "Nepal : Small Area Estimation of Poverty, 2011," World Bank Other Operational Studies 16569, The World Bank.

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    Statistical&Mathematical Sciences; Population Policies; Science Education; Scientific Research&Science Parks; Small Area Estimation Poverty Mapping;

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