IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/25362.html
   My bibliography  Save this paper

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
    Note: DEV PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w25362.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    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. 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.
    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. 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.
    5. 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.
    6. 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).
    7. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
    8. 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).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nbr:nberwo:25362. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.