The Welfare Returns to Finer Targeting: The Case of The Progresa Program in Mexico
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DOI: 10.1007/s10797-006-4824-2
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Citations
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Cited by:
- John A. Maluccio, 2009.
"Household targeting in practice: The Nicaraguan Red de Protección Social,"
Journal of International Development, John Wiley & Sons, Ltd., vol. 21(1), pages 1-23.
- John Maluccio, 2008. "Household Targeting In Practice: The Nicaraguan Red De Protección Social," Middlebury College Working Paper Series 0802, Middlebury College, Department of Economics.
- Andrés Camilo ÁLVAREZ-ESPINOSA & Daniel Alejandro ORDOÑEZ & Alejandro NIETO & William WILLS & German ROMERO & Silvia Liliana CALDERÓN, 2015. "Compromiso de Reducción de Emisiones de Gases de Efecto Invernadero: Consecuencias económicas," Archivos de Economía 14157, Departamento Nacional de Planeación.
- 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.
- Chikako Yamauchi, 2010.
"Community-Based Targeting and Initial Local Conditions: Evidence from Indonesia's IDT Program,"
Economic Development and Cultural Change, University of Chicago Press, vol. 59(1), pages 95-147, October.
- Chikako Yamauchi, 2008. "Community-based Targeting and Initial Local Conditions: Evidence from Indonesia’s IDT Program," CEPR Discussion Papers 584, Centre for Economic Policy Research, Research School of Economics, Australian National University.
- Viviane Azevedo & Marcos Robles, 2013. "Multidimensional Targeting: Identifying Beneficiaries of Conditional Cash Transfer Programs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(2), pages 447-475, June.
- 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.
- Emily Aiken & Suzanne Bellue & Dean Karlan & Christopher R. Udry & Joshua Blumenstock, 2021. "Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance," NBER Working Papers 29070, National Bureau of Economic Research, Inc.
- Mr. David Coady & Susan Parker, 2009. "Targeting Social Transfers to the Poor in Mexico," IMF Working Papers 2009/060, International Monetary Fund.
- Lang, Corey & Barrett, Christopher B. & Naschold, Felix, 2013. "Targeting Maps: An Asset-Based Approach to Geographic Targeting," World Development, Elsevier, vol. 41(C), pages 232-244.
- Garcia-Diaz, Rocio & Sosa-Rub, Sandra G., 2011. "Analysis of the distributional impact of out-of-pocket health payments: Evidence from a public health insurance program for the poor in Mexico," Journal of Health Economics, Elsevier, vol. 30(4), pages 707-718, July.
- Yörük, Erdem & Öker, İbrahim & Şarlak, Lara, 2019. "Indigenous unrest and the contentious politics of social assistance in Mexico," World Development, Elsevier, vol. 123(C), pages 1-1.
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"(Machine) Learning What Policies Value,"
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17364, C.E.P.R. Discussion Papers.
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"Microestimates of wealth for all low- and middle-income countries,"
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(3), pages 2113658119-, January.
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Keywords
geographic targeting; proxy-means targeting; self-selection; finer targeting; combining targeting methods; welfare; Mexico;All these keywords.
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