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Targeting Ultra-poor Households in Honduras and Peru

Author

Listed:
  • Dean Karlan

    (Economic Growth Center, Yale University)

  • Bram Thuysbaert

    (KU Leuven)

Abstract

For policy purposes, it is important to understand the relative efficacy of various methods to target the poor. Recently, participatory methods have received particular attention. We examine the effectiveness of a hybrid two-step process that combines a participatory wealth ranking and a verification household survey, relative to two proxy means tests (the Progress out of Poverty Index and a housing index), in Honduras and Peru. The methods we examine perform similarly to one another by various metrics. They all target most accurately in the cases of the poorest and the wealthiest households but perform with mixed results among households in the middle of the distribution. Ultimately, given similar performance, the analysis suggests that costs should be the driving consideration in choosing across methods.

Suggested Citation

  • Dean Karlan & Bram Thuysbaert, 2013. "Targeting Ultra-poor Households in Honduras and Peru," Working Papers 1033, Economic Growth Center, Yale University.
    • Handle: RePEc:egc:wpaper:1033
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    File URL: http://www.econ.yale.edu/growth_pdf/cdp1033.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. Ravallion, M., 1998. "Poverty Lines in Theory and Practice," Papers 133, World Bank - Living Standards Measurement.
    3. Chambers, Robert, 1994. "Participatory rural appraisal (PRA): Analysis of experience," World Development, Elsevier, vol. 22(9), pages 1253-1268, September.
    4. Chambers, Robert, 1994. "Participatory rural appraisal (PRA): Challenges, potentials and paradigm," World Development, Elsevier, vol. 22(10), pages 1437-1454, October.
    5. Skoufias, Emmanuel & Davis, Benjamin & de la Vega, Sergio, 2001. "Targeting the Poor in Mexico: An Evaluation of the Selection of Households into PROGRESA," World Development, Elsevier, vol. 29(10), pages 1769-1784, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Mark Schreiner, 2015. "A Comparison of Two Simple, Low-Cost Ways for Local, Pro-Poor Organizations to Measure the Poverty of Their Participants," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 537-569, November.
    2. 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.
    3. repec:awi:wpaper:0623 is not listed on IDEAS
    4. Hannes Öhler & Mario Negre & Lodewijk Smets & Renzo Massari & Željko Bogetić, 2019. "Putting your money where your mouth is: Geographic targeting of World Bank projects to the bottom 40 percent," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-19, June.
    5. Stoeffler, Quentin & Mills, Bradford & del Ninno, Carlo, 2016. "Reaching the Poor: Cash Transfer Program Targeting in Cameroon," World Development, Elsevier, vol. 83(C), pages 244-263.
    6. Fanny Salignac & Julien Hanoteau & Ioana Ramia, 2022. "Financial Resilience: A Way Forward Towards Economic Development in Developing Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 160(1), pages 1-33, February.
    7. Henderson, Heath & Follett, Lendie, 2022. "Targeting social safety net programs on human capabilities," World Development, Elsevier, vol. 151(C).
    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).
    9. 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.

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    More about this item

    Keywords

    poverty targeting; participatory wealth rankings; proxy means tests;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • O20 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - General

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