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Estimating Poverty in Kinshasa by Dealing with Sampling and Comparability Issues

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  • Batana,Yele Maweki
  • Masaki,Takaaki
  • Nakamura,Shohei
  • Viboudoulou Vilpoux,Mervy Ever

Abstract

This paper proposes monetary poverty and inequality estimates for Kinshasa using a new Kinshasahousehold survey implemented in 2018. Given the obsolescence of the sampling frame, the survey was sampled usingsatellite imagery. However, the collection of data in the field was affected by sampling errors that are likely tocompromise the representativeness of the sample. After addressing these sampling issues and dealing with somecomparability issues with the 2012 survey, the paper shows that poverty and inequality increased significantly during2012–18 in Kinshasa. Poverty has increased in the city by 12 percentage points, from 53 to 65 percent, partly due to theloss of purchasing power following the sharp depreciation in 2017. Other explanatory factors include demographic factors,human capital, and spatial factors. The deterioration in well-being also appears to have been exacerbated by theonset of the COVID-19 pandemic through decline in labor and nonlabor income and disruptions in goods and servicesmarkets and public services.

Suggested Citation

  • Batana,Yele Maweki & Masaki,Takaaki & Nakamura,Shohei & Viboudoulou Vilpoux,Mervy Ever, 2021. "Estimating Poverty in Kinshasa by Dealing with Sampling and Comparability Issues," Policy Research Working Paper Series 9858, The World Bank.
    • Handle: RePEc:wbk:wbrwps:9858
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    References listed on IDEAS

    as
    1. Tarozzi, Alessandro, 2007. "Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 314-336, July.
    2. Ryan Engstrom & Jonathan Hersh & David Newhouse, 2022. "Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being," The World Bank Economic Review, World Bank, vol. 36(2), pages 382-412.
    3. Utz Pape & Philip Wollburg, 2019. "Estimation of Poverty in Somalia Using Innovative Methodologies," HiCN Working Papers 306, Households in Conflict Network.
    4. Torabi, Mahmoud & Rao, J.N.K., 2014. "On small area estimation under a sub-area level model," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 36-55.
    5. Engstrom,Ryan & Newhouse,David Locke & Soundararajan,Vidhya, 2019. "Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka," Policy Research Working Paper Series 8776, The World Bank.
    6. Claude Sumata & Jeffrey H. Cohen, 2018. "The Congolese diaspora and the politics of remittances," Remittances Review, Remittances Review, vol. 3(2), pages 95-108, October.
    7. Sumonkanti Das & Stephen Haslett, 2019. "A Comparison of Methods for Poverty Estimation in Developing Countries," International Statistical Review, International Statistical Institute, vol. 87(2), pages 368-392, August.
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