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The (lack of) Distortionary Effects of Proxy-Means Tests: Results from a Nationwide Experiment in Indonesia

Author

Listed:
  • Abhijit Banerjee
  • Rema Hanna
  • Benjamin A. Olken
  • Sudarno Sumarto

Abstract

Many developing country governments determine eligibility for anti-poverty programs using censuses of household assets. Does this distort subsequent reporting of, or actual purchases of, those assets? We ran a nationwide experiment in Indonesia where, in randomly selected provinces, the government added questions on flat-screen televisions and cell-phone SIM cards to the targeting census administered to 25 million households. In a separate survey six months later, households in treated provinces report fewer televisions, though the effect dissipates thereafter. We find no change in actual television sales, or actual SIM card ownership, suggesting that consumption distortions are likely to be small.

Suggested Citation

  • Abhijit Banerjee & Rema Hanna & Benjamin A. Olken & Sudarno Sumarto, 2018. "The (lack of) Distortionary Effects of Proxy-Means Tests: Results from a Nationwide Experiment in Indonesia," NBER Working Papers 25362, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:25362
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    Cited by:

    1. Mounu Prem & Juan F. Vargas & Daniel Mejía, 2023. "The Rise and Persistence of Illegal Crops: Evidence from a Naive Policy Announcement," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 344-358, March.
    2. Altındağ, Onur & O'Connell, Stephen D. & Şaşmaz, Aytuğ & Balcıoğlu, Zeynep & Cadoni, Paola & Jerneck, Matilda & Foong, Aimee Kunze, 2021. "Targeting humanitarian aid using administrative data: Model design and validation," Journal of Development Economics, Elsevier, vol. 148(C).
    3. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
    4. Abhijit Banerjee & Paul Niehaus & Tavneet Suri, 2019. "Universal Basic Income in the Developing World," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 959-983, August.
    5. 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.
    6. Benjamin A. Olken, 2020. "Banerjee, Duflo, Kremer, and the Rise of Modern Development Economics," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(3), pages 853-878, July.
    7. 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).
    8. 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.

    More about this item

    JEL classification:

    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household
    • 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

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