IDEAS home Printed from https://ideas.repec.org/p/wbk/wbrwps/9858.html
   My bibliography  Save this paper

Estimating Poverty in Kinshasa by Dealing with Sampling and Comparability Issues

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://documents.worldbank.org/curated/en/629051637331820967/pdf/Estimating-Poverty-in-Kinshasa-by-Dealing-with-Sampling-and-Comparability-Issues.pdf
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Newhouse David, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 45-50, August.
    2. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    3. Hai‐Anh H. Dang, 2021. "To impute or not to impute, and how? A review of poverty‐estimation methods in the absence of consumption data," Development Policy Review, Overseas Development Institute, vol. 39(6), pages 1008-1030, November.
    4. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    5. Hussein, Mohamud & Law, Cherry & Fraser, Iain, 2021. "An analysis of food demand in a fragile and insecure country: Somalia as a case study," Food Policy, Elsevier, vol. 101(C).
    6. Dang, Hai-Anh & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    7. Jakubowski, Maciej & Pokropek, Artur, 2015. "Reading achievement progress across countries," International Journal of Educational Development, Elsevier, vol. 45(C), pages 77-88.
    8. Hai‐Anh Dang & Dean Jolliffe & Calogero Carletto, 2019. "Data Gaps, Data Incomparability, And Data Imputation: A Review Of Poverty Measurement Methods For Data‐Scarce Environments," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 757-797, July.
    9. Hai-Anh H. Dang & Peter F. Lanjouw, 2023. "Regression-based imputation for poverty measurement in data-scarce settings," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 13, pages 141-150, Edward Elgar Publishing.
    10. Lee, Kamwoo & Braithwaite, Jeanine, 2022. "High-resolution poverty maps in Sub-Saharan Africa," World Development, Elsevier, vol. 159(C).
    11. Talip Kilic & Thomas Pave Sohnesen, 2019. "Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(1), pages 144-165, March.
    12. Najam, Zaira, 2020. "The Sensitivity of Poverty Trends to Dimensionality and Distribution Sensitivity in Poverty Measures - District Level Analysis for Pakistan," MPRA Paper 102383, University Library of Munich, Germany.
    13. Guanghua Chi & Han Fang & Sourav Chatterjee & Joshua E. Blumenstock, 2022. "Microestimates of wealth for all low- and middle-income countries," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(3), pages 2113658119-, January.
    14. Theresa Beltramo & Hai-Anh H. Dang & Ibrahima Sarr & Paolo Verme, 2020. "Estimating Poverty among Refugee Populations: A Cross-Survey Imputation Exercise for Chad," Working Papers 536, ECINEQ, Society for the Study of Economic Inequality.
    15. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
    16. Eric V. Edmonds & Nina Pavcnik & Petia Topalova, 2010. "Trade Adjustment and Human Capital Investments: Evidence from Indian Tariff Reform," American Economic Journal: Applied Economics, American Economic Association, vol. 2(4), pages 42-75, October.
    17. Zaira Najam, 2021. "The sensitivity of poverty trends to dimensionality and distribution sensitivity in poverty measures—District level analysis for Pakistan," Poverty & Public Policy, John Wiley & Sons, vol. 13(4), pages 368-411, December.
    18. Song Cai & J.N.K. Rao, 2022. "Selection of Auxiliary Variables for Three-Fold Linking Models in Small Area Estimation: A Simple and Effective Method," Stats, MDPI, vol. 5(1), pages 1-11, February.
    19. Hai-Anh H. Dang & Peter F. Lanjouw, 2018. "Poverty Dynamics in India between 2004 and 2012: Insights from Longitudinal Analysis Using Synthetic Panel Data," Economic Development and Cultural Change, University of Chicago Press, vol. 67(1), pages 131-170.
    20. Lu Chen & Luca Sartore & Habtamu Benecha & Valbona Bejleri & Balgobin Nandram, 2022. "Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs," Stats, MDPI, vol. 5(3), pages 1-18, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wbk:wbrwps:9858. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Roula I. Yazigi (email available below). General contact details of provider: https://edirc.repec.org/data/dvewbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.