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Shooting a Moving Target: Choosing Targeting Tools for Social Programs

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  • Beuermann, Diether
  • Hoffmann, Bridget
  • Stampini, Marco
  • Vargas, David
  • Vera-Cossio, Diego A.

Abstract

A key challenge for policymakers is how to design methods to select beneficiaries of social programs when income is volatile and the target population is dynamic. We evaluate a traditional static proxy-means test (PMT) and three policy-relevant alternatives. We use a unique panel dataset of a random sample of households in Colombia's social registry that contains information before, during, and after the 2020 economic crisis. Updating the PMT data does not improve social welfare relative to the static PMT. Relaxing the eligibility threshold reduces the exclusion error, increases the inclusion error, and increases social welfare. A dynamic method that uses data on shocks to estimate a variable component of income reduces exclusion errors and limits the expansion in coverage, increasing social welfare during the economic crisis. We consider these targeting metrics together with the curvature of governments social welfare function and budgetary and political constraints.

Suggested Citation

  • Beuermann, Diether & Hoffmann, Bridget & Stampini, Marco & Vargas, David & Vera-Cossio, Diego A., 2024. "Shooting a Moving Target: Choosing Targeting Tools for Social Programs," IDB Publications (Working Papers) 13359, Inter-American Development Bank.
  • Handle: RePEc:idb:brikps:13359
    DOI: http://dx.doi.org/10.18235/0005502
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    References listed on IDEAS

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    1. 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).
    2. Letícia Bartholo & Joana Mostafa & Rafael Guerreiro Osorio, 2018. "Integration of administrative records for social protection policies: contributions from the Brazilian experience," Working Papers 169, International Policy Centre for Inclusive Growth.
    3. Bosch, Mariano & Schady, Norbert, 2019. "The effect of welfare payments on work: Regression discontinuity evidence from Ecuador," Journal of Development Economics, Elsevier, vol. 139(C), pages 17-27.
    4. 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.
    5. Ashley Pople & Ruth Hill & Stefan Dercon & Ben Brunckhorst, 2021. "Anticipatory Cash Transfers in Climate Disaster Response," CSAE Working Paper Series 2021-07, Centre for the Study of African Economies, University of Oxford.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Social protection; Household income; Poverty;
    All these keywords.

    JEL classification:

    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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