IDEAS home Printed from https://ideas.repec.org/a/nas/journl/v119y2022pe2113658119.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.pnas.org/content/119/3/e2113658119.full
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Angus Deaton, 2005. "Measuring Poverty in a Growing World (or Measuring Growth in a Poor World)," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 1-19, February.
    2. Tomas Hellebrandt & Paolo Mauro, 2015. "The Future of Worldwide Income Distribution," LIS Working papers 635, LIS Cross-National Data Center in Luxembourg.
    3. Joshua E. Blumenstock, 2018. "Estimating Economic Characteristics with Phone Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 72-76, May.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Ugo Gentilini & Mohamed Almenfi & Ian Orton & Pamela Dale, 2020. "Social Protection and Jobs Responses to COVID-19," World Bank Publications - Reports 33635, The World Bank Group.
    12. Ravallion, Martin, 2016. "The Economics of Poverty: History, Measurement, and Policy," OUP Catalogue, Oxford University Press, number 9780190212773.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nathan Ratledge & Gabe Cadamuro & Brandon de la Cuesta & Matthieu Stigler & Marshall Burke, 2021. "Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access," Papers 2109.02890, arXiv.org.
    2. Aiken, Emily L. & Bedoya, Guadalupe & Blumenstock, Joshua E. & Coville, Aidan, 2023. "Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan," Journal of Development Economics, Elsevier, vol. 161(C).
    3. 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, C.E.P.R. Discussion Papers.
    4. Nicolò Golinucci & Nicolò Stevanato & Negar Namazifard & Mohammad Amin Tahavori & Lamya Adil Sulliman Hussain & Benedetta Camilli & Federica Inzoli & Matteo Vincenzo Rocco & Emanuela Colombo, 2022. "Comprehensive and Integrated Impact Assessment Framework for Development Policies Evaluation: Definition and Application to Kenyan Coffee Sector," Energies, MDPI, vol. 15(9), pages 1-19, April.
    5. Till Koebe & Alejandra Arias-Salazar & Timo Schmid, 2023. "Releasing survey microdata with exact cluster locations and additional privacy safeguards," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    6. Lee, Kamwoo & Braithwaite, Jeanine, 2022. "High-resolution poverty maps in Sub-Saharan Africa," World Development, Elsevier, vol. 159(C).
    7. Dumedah, Gift & Abass, Kabila & Gyasi, Razak M. & Forkuor, John Boulard & Novignon, Jacob, 2023. "Inefficient allocation of paratransit service terminals and routes in Ghana: The role of driver unions and paratransit operators," Journal of Transport Geography, Elsevier, vol. 111(C).

    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. Emily Aiken & Suzanne Bellue & Dean Karlan & Christopher R. Udry & Joshua Blumenstock, 2021. "Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance," NBER Working Papers 29070, National Bureau of Economic Research, Inc.
    2. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    3. Abbate Nicolás & Gasparini Leonardo & Gluzmann Pablo Alfredo & Montes Rojas Gabriel & Sznaider Iván & Yatche Tobías, 2023. "Ingreso Estructural Por Área Geográfica: una aplicación para Argentina," Asociación Argentina de Economía Política: Working Papers 4622, Asociación Argentina de Economía Política.
    4. Piotr Wójcik & Krystian Andruszek, 2022. "Predicting intra‐urban well‐being from space with nonlinear machine learning," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 891-913, August.
    5. Atamanov, Aziz & Tandon, Sharad & Lopez-Acevedo, Gladys & Vergara Bahena, Mexico Alberto, 2020. "Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region: Data Gaps and Different Options to Address Them," IZA Discussion Papers 13363, Institute of Labor Economics (IZA).
    6. Jung, Woojin, 2023. "Mapping community development aid: Spatial analysis in Myanmar," World Development, Elsevier, vol. 164(C).
    7. 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.
    8. Dang,Hai-Anh H., 2018. "To impute or not to impute ? a review of alternative poverty estimation methods in the context of unavailable consumption data," Policy Research Working Paper Series 8403, The World Bank.
    9. Hannes Mueller & André Groeger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2021. "Monitoring War Destruction from Space Using Machine Learning," Working Papers 1257, Barcelona School of Economics.
    10. Aiken, Emily L. & Bedoya, Guadalupe & Blumenstock, Joshua E. & Coville, Aidan, 2023. "Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan," Journal of Development Economics, Elsevier, vol. 161(C).
    11. Dang, Hai-Anh H. & Serajuddin, Umar, 2020. "Tracking the sustainable development goals: Emerging measurement challenges and further reflections," World Development, Elsevier, vol. 127(C).
    12. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    13. Ngoc Thien Anh Pham & Nicholas Sim, 2020. "Shipping cost and development of the landlocked developing countries: Panel evidence from the common correlated effects approach," The World Economy, Wiley Blackwell, vol. 43(4), pages 892-920, April.
    14. Gaurav Datt & Martin Ravallion & Rinku Murgai, 2020. "Poverty and Growth in India over Six Decades," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 4-27, January.
    15. 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).
    16. 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.
    17. Lee, Kamwoo & Braithwaite, Jeanine, 2022. "High-resolution poverty maps in Sub-Saharan Africa," World Development, Elsevier, vol. 159(C).
    18. Grimm, Michael & Harttgen, Kenneth & Klasen, Stephan & Misselhorn, Mark, 2008. "A Human Development Index by Income Groups," World Development, Elsevier, vol. 36(12), pages 2527-2546, December.
    19. 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.
    20. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), 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:nas:journl:v:119:y:2022:p:e2113658119. 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: Eric Cain (email available below). General contact details of provider: http://www.pnas.org/ .

    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.