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Estimating impact with surveys versus digital traces: Evidence from randomized cash transfers in Togo

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  • Aiken, Emily
  • Bellue, Suzanne
  • Blumenstock, Joshua E.
  • Karlan, Dean
  • Udry, Christopher

Abstract

We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo, endline survey data indicate positive treatment effects on food security, mental health, and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict baseline poverty) are generally not statistically significant. When post-treatment outcome data are used in conjunction with predictions to estimate treatment effects, predicted impacts are similar to those estimated using surveys.

Suggested Citation

  • Aiken, Emily & Bellue, Suzanne & Blumenstock, Joshua E. & Karlan, Dean & Udry, Christopher, 2025. "Estimating impact with surveys versus digital traces: Evidence from randomized cash transfers in Togo," Journal of Development Economics, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:deveco:v:175:y:2025:i:c:s0304387825000288
    DOI: 10.1016/j.jdeveco.2025.103477
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    1. Anderson, Michael L., 2008. "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1481-1495.
    2. Tommaso Crosta & Dean Karlan & Finley Ong & Julius Rüschenpöhler & Christopher R. Udry, 2024. "Unconditional Cash Transfers: A Bayesian Meta-Analysis of Randomized Evaluations in Low and Middle Income Countries," NBER Working Papers 32779, National Bureau of Economic Research, Inc.
    3. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
    4. 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.
    5. Barriga-Cabanillas, Oscar & Blumenstock, Joshua E. & Lybbert, Travis J. & Putman, Daniel S., 2025. "Probing the limits of mobile phone metadata for poverty prediction and impact evaluation," Journal of Development Economics, Elsevier, vol. 174(C).
    6. Gharad Bryan & James J Choi & Dean Karlan, 2025. "Randomizing Religion: the Impact of Protestant Evangelism on Economic Outcomes," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(1), pages 293-380.
    7. Huang, Luna Yue & Hsiang, Solomon & Gonzalez-Navarro, Marco, 2021. "Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt1sp2w73b, Department of Agricultural & Resource Economics, UC Berkeley.
    8. Egger, Dennis & Miguel, Edward & Warren, Shana S. & Shenoy, Ashish & Collins, Elliott & Karlan, Dean & Parkerson, Doug & Mobarak, A. Mushfiq & Fink, Günther & Udry, Christopher & Walker, Michael & Hau, 2021. "Falling living standards during the COVID-19 crisis: Quantitative evidence from nine developing countries," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(6), pages 1-1.
    9. repec:oup:qjecon:v:136:y:2021:i:1:p:293-380. is not listed on IDEAS
    10. Thomas J. Kane & Douglas O. Staiger, 2008. "Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation," NBER Working Papers 14607, National Bureau of Economic Research, Inc.
    11. Tadesse, Getaw & Abate, Gashaw T. & Zewdie, Tadiwos, 2020. "Biases in self-reported food insecurity measurement: A list experiment approach," Food Policy, Elsevier, vol. 92(C).
    12. 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.
    13. Joshua D. Angrist & Peter D. Hull & Parag A. Pathak & Christopher R. Walters, 2017. "Erratum to “Leveraging Lotteries for School Value-Added: Testing and Estimation”," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 2061-2062.
    14. Isabella S. Smythe & Joshua E. Blumenstock, 2022. "Geographic microtargeting of social assistance with high-resolution poverty maps," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(32), pages 2120025119-, August.
    15. 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).
    16. Joshua E. Blumenstock, 2018. "Estimating Economic Characteristics with Phone Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 72-76, May.
    17. Michael Gilraine & Jiaying Gu & Robert McMillan, 2020. "A New Method for Estimating Teacher Value-Added," NBER Working Papers 27094, National Bureau of Economic Research, Inc.
    18. Juliana Londono-Velez & Pablo Querubin, 2022. "The Impact of Emergency Cash Assistance in a Pandemic: Experimental Evidence from Colombia," The Review of Economics and Statistics, MIT Press, vol. 104(1), pages 157-165, March.
    19. Karlan, Dean & Lowe, Matt & Osei, Robert & Osei-Akoto, Isaac & Roth, Benjamin N. & Udry, Christopher, 2026. "Social protection and social distancing during the pandemic: Mobile money transfers in Ghana," Journal of Development Economics, Elsevier, vol. 179(C).
    20. Johannes Haushofer & Jeremy Shapiro, 2016. "The Short-term Impact of Unconditional Cash Transfers to the Poor: ExperimentalEvidence from Kenya," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1973-2042.
    21. 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.
    22. Blumenstock, Joshua E. & Eagle, Nathan & Fafchamps, Marcel, 2016. "Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters," Journal of Development Economics, Elsevier, vol. 120(C), pages 157-181.
    23. Nathan Ratledge & Gabe Cadamuro & Brandon Cuesta & Matthieu Stigler & Marshall Burke, 2022. "Using machine learning to assess the livelihood impact of electricity access," Nature, Nature, vol. 611(7936), pages 491-495, November.
    24. Will Dobbie & Jae Song, 2020. "Targeted Debt Relief and the Origins of Financial Distress: Experimental Evidence from Distressed Credit Card Borrowers," American Economic Review, American Economic Association, vol. 110(4), pages 984-1018, April.
    25. Abhijit V. Banerjee & Rema Hanna & Gabriel E. Kreindler & Benjamin A. Olken, 2017. "Debunking the Stereotype of the Lazy Welfare Recipient: Evidence from Cash Transfer Programs," The World Bank Research Observer, World Bank, vol. 32(2), pages 155-184.
    26. Bottan, Nicolas & Hoffmann, Bridget & Vera-Cossio, Diego A., 2021. "Stepping up during a crisis: The unintended effects of a noncontributory pension program during the Covid-19 pandemic," Journal of Development Economics, Elsevier, vol. 150(C).
    27. 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.
    28. Isabella S. Smythe & Joshua E. Blumenstock, 2022. "Geographic microtargeting of social assistance with high-resolution poverty maps," Decision Analysis, INFORMS, vol. 119(32), pages 2120025119-, August.
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    Cited by:

    1. Emily Aiken & Anik Ashraf & Joshua E. Blumenstock & Raymond P. Guiteras & Ahmed Mushfiq Mobarak, 2025. "Scalable Targeting of Social Protection: When Do Algorithms Out-Perform Surveys and Community Knowledge?," Cowles Foundation Discussion Papers 2443, Cowles Foundation for Research in Economics, Yale University.
    2. Kilic, Talip & Letta, Marco & Montalbano, Pierluigi & Petruccelli, Federica, 2026. "CLARE : A Causal machine Learning Approach to Resilience Estimation," Policy Research Working Paper Series 11292, The World Bank.
    3. Klaus W. Deininger & Daniel Ayalew Ali, 2024. "Using Satellite Imagery and a Farmer Registry to Assess Agricultural Support in Conflict Settings : The Case of the Producer Support Grant Program in Ukraine," Policy Research Working Paper Series 10912, The World Bank.
    4. Letta, Marco & Montalbano, Pierluigi & Morales Opazo, Cristian & Petruccelli, Federica, 2025. "Measuring and testing vulnerability to food insecurity for prediction and targeting," Economics & Human Biology, Elsevier, vol. 59(C).

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    Keywords

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    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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

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