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Poverty mapping with aggregate census data: what is the loss in precision?

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  • Baulch, Bob
  • Minot, Nicholas

Abstract

Spatially disaggregated maps of the incidence of poverty can be constructed by combining household survey data and census data. In some cases, however, statistical authorities are reluctant, for reasons of confidentiality, to release household-level census data. This paper examines the loss in precision associated with using aggregated census data, such as village- or district-level means of the data. We show analytically that using aggregated census data will result in poverty rates that are biased downward (upward) if the rate is below (above) 50 percent and that the bias approaches zero as the poverty rate approaches zero, 50 percent, and 100 percent. Using data from Vietnam, we find that the average absolute error in estimating provincial poverty rates is about 2 percentage points if the data are aggregated to the enumeration-area level and around 3-4 percentage points if they are aggregated to the provincial level. Even census data aggregated to the provincial level perform reasonably well in ranking the 61 provinces by the incidence of poverty: the average absolute error in ranking is 0.92.

Suggested Citation

  • Baulch, Bob & Minot, Nicholas, 2002. "Poverty mapping with aggregate census data: what is the loss in precision?," MSSD discussion papers 49, International Food Policy Research Institute (IFPRI).
    • Handle: RePEc:fpr:mssddp:49
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    2. Gilligan, Daniel O. & Veiga, Alinne, 2003. "An Evaluation Of Geographic Targeting In Bolsa Alimentação In Brazil," 2003 Annual meeting, July 27-30, Montreal, Canada 21915, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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    5. Bellon, Mauricio R. & Hodson, David & Bergvinson, David & Beck, David & Martinez-Romero, Eduardo & Montoya, Yinha, 2005. "Targeting agricultural research to benefit poor farmers: Relating poverty mapping to maize environments in Mexico," Food Policy, Elsevier, vol. 30(5-6), pages 476-492.
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    7. Bruno Arpino & Arnstein Aassve, 2014. "The role of villages in households’ poverty exit: evidence from a multilevel model for rural Vietnam," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2175-2189, July.
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    10. Mduma, John K. & Wobst, Peter, 2005. "Village Level Labor Market Development in Tanzania: Evidence from Spatial Econometrics," Discussion Papers 276260, University of Bonn, Center for Development Research (ZEF).
    11. Szonyi, Judit A. & de Pauw, Eddy & La Rovere, Roberto & Aw-Hassan, Aden, 2006. "Poverty Mapping in Rural Syria for Enhanced Targeting," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25564, International Association of Agricultural Economists.
    12. Benami, Elinor & Bell, Anne & Messer, Kent D. & Zhang, Wei & Cecil, Michael, 2024. "Seeding Change to Manage Climate Change: Growing Insights from Four USDA Programs to Support Climate-Smart Agriculture," IAAE 2024 Conference, August 2-7, 2024, New Delhi, India 344333, International Association of Agricultural Economists (IAAE).
    13. Delgado, Christopher L. & Rosegrant, Mark W. & Wada, Nikolas & Meijer, Siet & Ahmed, Mahfuzuddin, 2002. "Fish as food," MTID discussion papers 52, International Food Policy Research Institute (IFPRI).
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    15. Francesco Balducci, 2021. "Mapping the invisibles: Using non-conventional point-level data to analyse residential patterns of deprived people in a mid-sized city," Urban Studies, Urban Studies Journal Limited, vol. 58(8), pages 1634-1654, June.

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