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Targeting ultra-poor households in Honduras and Peru

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  • Dean Karlan
  • Bram Thuysbaert

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," NBER Working Papers 19646, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:19646
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    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.
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    3. Chambers, Robert, 1994. "Participatory rural appraisal (PRA): Challenges, potentials and paradigm," World Development, Elsevier, vol. 22(10), pages 1437-1454, October.
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    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.
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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. Schleicher, Michael & Souares, Aurélia & Pacere, Athanase Narangoro & Sauerborn, Rainer & Klonner, Stefan, 2016. "Decentralized versus Statistical Targeting of Anti-Poverty Programs: Evidence from Burkina Faso," Working Papers 0623, University of Heidelberg, Department of Economics.
    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. 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.
    6. 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.
    7. Henderson, Heath & Follett, Lendie, 2022. "Targeting social safety net programs on human capabilities," World Development, Elsevier, vol. 151(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.
    9. Ligon, Ethan & Trachtman, Carly, 2024. "Assessing Targeting Peformance: The Case of Ghana’s LEAP Program," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2zk0m608, Department of Agricultural & Resource Economics, UC Berkeley.
    10. 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).

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    More about this item

    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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