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Finite-Sample Bias and Inconsistency in the Estimation of Poverty Maps

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  • Jesse Naidoo

Abstract

I argue that the estimation technique - widely used in the poverty mapping literature - introduced by Elbers, Lanjouw and Lanjouw, is highly sensitive to specification, severely biased in finite samples, and almost certain to fail to estimate the poverty headcount consistently. First, I show that the specification of the first-stage model of household expenditure strongly influences the estimated headcount; the range of obtainable estimates is on the order of 20% for many districts, and is as high as 48% for some areas. Further, some specifications imply province-level headcounts which diverge from the direct estimates by many as six standard deviations. Secondly, I construct bootstrap confidence intervals for the difference between the estimates under alternative specifications, which shows that (at a 2% level of significance) finite sample-bias is present in more than 42% of districts in even the best-performing regions. I calculate approximate lower bounds for the bias; I find it to be on the order of 3% for most areas, but the lower bounds range as high as 19.6% in some provinces. Finally, I argue that consistent estimation of the first stage model is necessary for consistent second-stage imputations and I decompose the difference between the true and estimated headcount into a sampling component and a specification component, the latter of which is asymptotically persistent. Given these results, it appears that the poverty maps estimated by this technique reflect primarily the arbitrary and unexamined methodological choices of their authors rather than robust features of the data.

Suggested Citation

  • 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.
  • Handle: RePEc:ldr:wpaper:36
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    References listed on IDEAS

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    1. Harold Alderman & Miriam Babita & Gabriel Demombynes & Nthabiseng Makhatha & Berk Özler, 2002. "How Low Can You Go? Combining Census and Survey Data for Mapping Poverty in South Africa," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 11(2), pages 169-200, June.
    2. Jesko Hentschel & Peter Lanjouw, 1998. "Using Disaggregated Poverty Maps to Plan Sectoral Investments," World Bank Publications - Reports 11544, The World Bank Group.
    3. Minot, Nicholas & Baulch, Bob, 2005. "Spatial patterns of poverty in Vietnam and their implications for policy," Food Policy, Elsevier, vol. 30(5-6), pages 461-475.
    4. Simler, Kenneth R. & Nhate, Virgulino, 2005. "Poverty, inequality, and geographic targeting: Evidence from Small-Area Estimates in Mozambique," FCND discussion papers 192, International Food Policy Research Institute (IFPRI).
    5. 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.
    6. Gabriel DEMOMBYNES & Chris ELBERS & Jean O. LANJOUW & Peter LANJOUW, 2008. "How Good is a Map? Putting Small Area Estimation to the Test," Rivista Internazionale di Scienze Sociali, Vita e Pensiero, Pubblicazioni dell'Universita' Cattolica del Sacro Cuore, vol. 116(4), pages 465-493.
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    12. Hentschel, Jesko, et al, 2000. "Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador," The World Bank Economic Review, World Bank Group, vol. 14(1), pages 147-165, January.
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    14. Banerjee, Abhijit V & Duflo, Esther, 2003. "Inequality and Growth: What Can the Data Say?," Journal of Economic Growth, Springer, vol. 8(3), pages 267-299, September.
    15. Chris Elbers & Peter Lanjouw & Johan A. Mistiaen & Berk Özler & Kenneth Simler, 2003. "Are Neighbours Equal? Estimating Local Inequality in Three Developing Countries," WIDER Working Paper Series DP2003-52, World Institute for Development Economic Research (UNU-WIDER).
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    More about this item

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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