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Targeting Ultra-Poor Households in Honduras and Peru

Author

Listed:
  • 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 by various metrics. They all identify most accurately 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, 2019. "Targeting Ultra-Poor Households in Honduras and Peru," The World Bank Economic Review, World Bank, vol. 33(1), pages 63-94.
    • Handle: RePEc:oup:wbecrv:v:33:y:2019:i:1:p:63-94.
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    File URL: http://hdl.handle.net/10.1093/wber/lhw036
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    Cited by:

    1. Trachtman, Carly & Permana, Yudistira Hendra & Sahadewo, Gumilang Aryo, 2026. "How much do our neighbors really know? The limits of community-based targeting," Journal of Development Economics, Elsevier, vol. 178(C).
    2. 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.
    3. 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.
    4. Lyons, Angela C. & Montoya Castano, Alejandro & Kass-Hanna, Josephine & Zhang, Yifang & Soliman, Aiman, 2025. "A machine learning approach to assessing multidimensional poverty and targeting assistance among forcibly displaced populations," World Development, Elsevier, vol. 192(C).
    5. 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.
    6. Pascale Schnitzer, 2016. "How to Target Households in Adaptive Social Protection Systems?," World Bank Publications - Reports 25387, The World Bank Group.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Henderson, Heath & Follett, Lendie, 2022. "Targeting social safety net programs on human capabilities," World Development, Elsevier, vol. 151(C).
    12. 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).
    13. 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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    Keywords

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    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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