IDEAS home Printed from https://ideas.repec.org/a/eee/deveco/v161y2023ics0304387822001584.html
   My bibliography  Save this article

Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan

Author

Listed:
  • Aiken, Emily L.
  • Bedoya, Guadalupe
  • Blumenstock, Joshua E.
  • Coville, Aidan

Abstract

Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor households nearly as accurately as survey-based measures of consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:deveco:v:161:y:2023:i:c:s0304387822001584
    DOI: 10.1016/j.jdeveco.2022.103016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304387822001584
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jdeveco.2022.103016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Angus Deaton, 2016. "Measuring and Understanding Behavior, Welfare, and Poverty," American Economic Review, American Economic Association, vol. 106(6), pages 1221-1243, June.
    2. 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.
    3. Rema Hanna & Benjamin A. Olken, 2018. "Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries," Journal of Economic Perspectives, American Economic Association, vol. 32(4), pages 201-226, Fall.
    4. Brown, Caitlin & Ravallion, Martin & van de Walle, Dominique, 2018. "A poor means test? Econometric targeting in Africa," Journal of Development Economics, Elsevier, vol. 134(C), pages 109-124.
    5. Dean Karlan & Bram Thuysbaert, 2019. "Targeting Ultra-Poor Households in Honduras and Peru," The World Bank Economic Review, World Bank, vol. 33(1), pages 63-94.
    6. Joshua E. Blumenstock, 2018. "Estimating Economic Characteristics with Phone Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 72-76, May.
    7. Grosh, M.E. & Baker, J.L., 1995. "Proxy Means Tests for Targetting Social Programs. Simulations and Speculation," Papers 118, World Bank - Living Standards Measurement.
    8. Vivi Alatas & Abhijit Banerjee & Rema Hanna & Benjamin A. Olken & Julia Tobias, 2012. "Targeting the Poor: Evidence from a Field Experiment in Indonesia," American Economic Review, American Economic Association, vol. 102(4), pages 1206-1240, June.
    9. Banerjee, Abhijit & Hanna, Rema & Olken, Benjamin A. & Sumarto, Sudarno, 2018. "The (Lack of) Distortionary Effects of Proxy-Means Tests: Results from a Nationwide Experiment in Indonesia," Working Paper Series rwp18-041, Harvard University, John F. Kennedy School of Government.
    10. 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.
    11. David Coady, 2004. "Targeting Outcomes Redux," The World Bank Research Observer, World Bank, vol. 19(1), pages 61-85.
    12. Sabina Alkire, James E. Foster, Suman Seth, Maria Emma Santos, Jose M. Roche and Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 9 - Distribution and Dynamics," OPHI Working Papers ophiwp090_ch9.pdf, Queen Elizabeth House, University of Oxford.
    13. 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.
    14. Sabina Alkire, James E. Foster, Suman Seth, Maria Emma Santos, José M. Roche and Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 7 - Data and Analysis," OPHI Working Papers ophiwp088_ch7.pdf, Queen Elizabeth House, University of Oxford.
    15. Emily Aiken & Suzanne Bellue & Dean Karlan & Chris Udry & Joshua E. Blumenstock, 2022. "Machine learning and phone data can improve targeting of humanitarian aid," Nature, Nature, vol. 603(7903), pages 864-870, March.
    16. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
    17. Sabina Alkire & James E. Foster & Suman Seth & Maria Emma Santos & Jose M. Roche & Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 9 - Distribution and Dynamics," OPHI Working Papers 90, Queen Elizabeth House, University of Oxford.
    18. Sabina Alkire, James E. Foster, Suman Seth, Maria Emma Santos, José M. Roche and Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 2 - The Framework," OPHI Working Papers ophiwp083_ch2.pdf, Queen Elizabeth House, University of Oxford.
    19. Bedoya Arguelles,Guadalupe & Coville,Aidan & Haushofer,Johannes & Isaqzadeh,Mohammad Razaq & Shapiro,Jeremy, 2019. "No Household Left Behind : Afghanistan Targeting the Ultra Poor Impact Evaluation," Policy Research Working Paper Series 8877, The World Bank.
    20. Sabina Alkire & James E. Foster & Suman Seth & Maria Emma Santos & Jose M. Roche & Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 7 - Data and Analysis," OPHI Working Papers 88, Queen Elizabeth House, University of Oxford.
    21. Sabina Alkire & James E. Foster & Suman Seth & Maria Emma Santos & Jose M. Roche & Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 2 - The Framework," OPHI Working Papers 83, Queen Elizabeth House, University of Oxford.
    22. Hernandez,Marco & Hong,Lingzi & Frias-Martinez,Vanessa & Frias-Martinez,Enrique, 2017. "Estimating poverty using cell phone data : evidence from Guatemala," Policy Research Working Paper Series 7969, The World Bank.
    23. Kathy Lindert & Tina George Karippacheril & Inés Rodriguez Caillava & Kenichi Nishikawa Chavez, 2020. "Sourcebook on the Foundations of Social Protection Delivery Systems," World Bank Publications - Books, The World Bank Group, number 34044, December.
    24. Alkire, Sabina & Foster, James & Seth, Suman & Santos, Maria Emma & Roche, Jose Manuel & Ballon, Paola, 2015. "Multidimensional Poverty Measurement and Analysis," OUP Catalogue, Oxford University Press, number 9780199689491.
    25. Adriana Camacho & Emily Conover, 2011. "Manipulation of Social Program Eligibility," American Economic Journal: Economic Policy, American Economic Association, vol. 3(2), pages 41-65, May.
    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. Dang, Hai-Anh & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," IZA Discussion Papers 16792, Institute of Labor Economics (IZA).
    2. Chowdhury, Shyamal & Hasan, Syed & Sharma, Uttam, 2024. "The Role of Trainee Selection in the Effectiveness of Vocational Training: Evidence from a Randomized Controlled Trial in Nepal," IZA Discussion Papers 16705, Institute of Labor Economics (IZA).
    3. Marco Letta & Pierluigi Montalbano & Adriana Paolantonio, 2024. "Climate Immobility Traps: A Household-Level Test," Papers 2403.09470, arXiv.org.

    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. 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.
    3. Emily Aiken & Guadalupe Bedoya & Joshua Blumenstock & Aidan Coville, 2022. "Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan," Papers 2206.11400, arXiv.org.
    4. 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.
    5. Dutta, Indranil & Nogales, Ricardo & Yalonetzky, Gaston, 2021. "Endogenous weights and multidimensional poverty: A cautionary tale," Journal of Development Economics, Elsevier, vol. 151(C).
    6. Guberney Muñetón-Santa & Daniel Escobar-Grisales & Felipe Orlando López-Pabón & Paula Andrea Pérez-Toro & Juan Rafael Orozco-Arroyave, 2022. "Classification of Poverty Condition Using Natural Language Processing," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(3), pages 1413-1435, August.
    7. Jacob Katuva & Rob Hope & Tim Foster & Johanna Koehler & Patrick Thomson, 2020. "Modelling Welfare Transitions to Prioritise Sustainable Development Interventions in Coastal Kenya," Sustainability, MDPI, vol. 12(17), pages 1-22, August.
    8. Vollmer, Frank & Alkire, Sabina, 2022. "Consolidating and improving the assets indicator in the global Multidimensional Poverty Index," World Development, Elsevier, vol. 158(C).
    9. Abre-Rehmat Qurat-ul-Ann & Faisal Mehmood Mirza, 2021. "Multidimensional Energy Poverty in Pakistan: Empirical Evidence from Household Level Micro Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(1), pages 211-258, May.
    10. Londari Yamarak & Kevin A. Parton, 2023. "Impacts of mining projects in Papua New Guinea on livelihoods and poverty in indigenous mining communities," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(1), pages 13-27, January.
    11. Jing Yang & Pundarik Mukhopadhaya, 2019. "Is the ADB’s Conjecture on Upward Trend in Poverty for China Right? An Analysis of Income and Multidimensional Poverty in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(2), pages 451-477, June.
    12. Pinaki Das & Bibek Paria & Shama Firdaush, 2021. "Juxtaposing Consumption Poverty and Multidimensional Poverty: A Study in Indian Context," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 469-501, January.
    13. Tavares, Fernando Flores & Betti, Gianni, 2021. "The pandemic of poverty, vulnerability, and COVID-19: Evidence from a fuzzy multidimensional analysis of deprivations in Brazil," World Development, Elsevier, vol. 139(C).
    14. Zaira Najam & John Gibson, 2022. "Does intra‐country poverty convergence depend on spatial spillovers and the type of poverty measure? Evidence from Pakistan," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 9(3), pages 516-535, September.
    15. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
    16. Gaurav Datt, 2019. "Multidimensional poverty in the Philippines, 2004–2013: How much do choices for weighting, identification and aggregation matter?," Empirical Economics, Springer, vol. 57(4), pages 1103-1128, October.
    17. Nisreen Salti & Jad Chaaban & Alexandra Irani & Rima Al Mokdad, 2021. "A Multi-Dimensional Measure of Well-being among Youth: The Case of Palestinian Refugee Youth in Lebanon," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(1), pages 1-34, February.
    18. M. Azhar Hussain & Nikolaj Siersbæk & Lars Peter Østerdal, 2020. "Multidimensional welfare comparisons of EU member states before, during, and after the financial crisis: a dominance approach," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 55(4), pages 645-686, December.
    19. Juan-Francisco Sánchez-García & María-del-Carmen Sánchez-Antón & Rosa Badillo-Amador & María-del-Carmen Marco-Gil & Juan-Vicente LLinares-Ciscar & Susana Álvarez-Díez, 2019. "A New Extension of Bourguignon and Chakravarty Index to Measure Educational Poverty and Its Application to the OECD Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(2), pages 479-501, September.
    20. Julien Hanoteau, 2023. "Do foreign MNEs alleviate multidimensional poverty in developing countries?," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 13(4), pages 719-749, December.

    More about this item

    Keywords

    Targeting; Machine learning; Mobile phone data; Afghanistan;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

    Statistics

    Access and download statistics

    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:eee:deveco:v:161:y:2023:i:c:s0304387822001584. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/devec .

    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.