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Microestimates of wealth for all low- and middle-income countries

Author

Listed:
  • Guanghua Chi

    (a School of Information, University of California, Berkeley, CA 94720;)

  • Han Fang

    (b Meta, Inc., Menlo Park, CA 94025)

  • Sourav Chatterjee

    (b Meta, Inc., Menlo Park, CA 94025)

  • Joshua E. Blumenstock

    (a School of Information, University of California, Berkeley, CA 94720;)

Abstract

Many critical policy decisions rely on data about the geographic distribution of wealth and poverty, yet only half of all countries have access to adequate data on poverty. This paper creates a complete and publicly available set of microestimates of the distribution of relative poverty and wealth across all 135 low- and middle-income countries. We provide extensive evidence of the accuracy and validity of the estimates and also provide confidence intervals for each microestimate to facilitate responsible downstream use. These methods and maps provide a set of tools to study economic development and growth, guide interventions, monitor and evaluate policies, and track the elimination of poverty worldwide.

Suggested Citation

  • 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.
  • Handle: RePEc:nas:journl:v:119:y:2022:p:e2113658119
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    References listed on IDEAS

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    1. Angus Deaton, 2005. "ERRATUM: Measuring Poverty in a Growing World (or Measuring Growth in a Poor World)," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 395-395, May.
    2. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    3. Tomas Hellebrandt & Paolo Mauro, 2015. "The Future of Worldwide Income Distribution," LIS Working papers 635, LIS Cross-National Data Center in Luxembourg.
    4. Joshua E. Blumenstock, 2018. "Estimating Economic Characteristics with Phone Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 72-76, May.
    5. 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.
    6. David Coady, 2006. "The Welfare Returns to Finer Targeting: The Case of The Progresa Program in Mexico," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 13(2), pages 217-239, May.
    7. Deon Filmer & Lant Pritchett, 2001. "Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India," Demography, Springer;Population Association of America (PAA), vol. 38(1), pages 115-132, February.
    8. Serajuddin,Umar & Uematsu,Hiroki & Wieser,Christina & Yoshida,Nobuo & Dabalen,Andrew L., 2015. "Data deprivation : another deprivation to end," Policy Research Working Paper Series 7252, The World Bank.
    9. Mohamed Almenfi & Ugo Gentilini & Ian Orton & Pamela Dale, 2020. "Social Protection and Jobs Responses to COVID-19," World Bank Publications - Reports 33635, The World Bank Group.
    10. Ravallion, Martin, 2016. "The Economics of Poverty: History, Measurement, and Policy," OUP Catalogue, Oxford University Press, number 9780190212773.
    11. Justin Sandefur & Amanda Glassman, 2015. "The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics," Journal of Development Studies, Taylor & Francis Journals, vol. 51(2), pages 116-132, February.
    12. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    13. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    14. Kathy Lindert & Tina George Karippacheril & Inés Rodriguez Caillava & Kenichi Nishikawa Chavez, 2020. "Sourcebook on the Foundations of Social Protection Delivery Systems [Les Systèmes de Mise en Œuvre de la Protection Sociale : Un Manuel de Référence]," World Bank Publications - Books, The World Bank Group, number 34044, April.
    15. Tobias G. Tiecke & Xianming Liu & Amy Zhang & Andreas Gros & Nan Li & Gregory Yetman & Talip Kilic & Siobhan Murray & Brian Blankespoor & Espen B. Prydz & Hai-Anh H. Dang, 2017. "Mapping the World Population One Building at a Time," World Bank Publications - Reports 33700, The World Bank Group.
    16. Blumenstock, Joshua & Aiken, Emily & Bellue, Suzanne & Udry, Christopher & Karlan, Dean, 2021. "Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance," CEPR Discussion Papers 16385, Centre for Economic Policy Research.
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