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Evaluation of development programs : randomized controlled trials or regressions ?

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  • Elbers, Chris
  • Gunning, Jan Willem

Abstract

Can project evaluation methods be used to evaluate programs: complex interventions involving multiple activities? A program evaluation cannot be based simply on separate evaluations of its components if interactions between the activities are important. In this paper a measure is proposed, the total program effect (TPE), which is an extension of the average treatment effect on the treated (ATET). It explicitly takes into account that in the real world (with heterogeneous treatment effects) individual treatment effects and program assignment are often correlated. The TPE can also deal with the common situation in which such a correlation is the result of decisions on (intended) program participation not being taken centrally. In this context RCTs are less suitable even for the simplest interventions. The TPE can be estimated by applying regression techniques to observational data from a representative sample from the targeted population. The approach is illustrated with an evaluation of a health insurance program in Vietnam.

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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 6587.

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Date of creation: 01 Sep 2013
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Handle: RePEc:wbk:wbrwps:6587

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Keywords: Poverty Monitoring&Analysis; Health Monitoring&Evaluation; Science Education; Scientific Research&Science Parks; Statistical&Mathematical Sciences;

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  1. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  2. Das, J. & Dercon, S. & Habyarimana, J. & Krishnan, P., 2004. "Teacher Shocks and Student Learning: Evidence from Zambia," Cambridge Working Papers in Economics 0514, Faculty of Economics, University of Cambridge.
  3. Alain de Janvry & Frederico Finan & Elisabeth Sadoulet, 2012. "Local Electoral Incentives and Decentralized Program Performance," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 672-685, August.
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  5. Rodrik, Dani, 2008. "The New Development Economics: We Shall Experiment, but How Shall We Learn?," Working Paper Series rwp08-055, Harvard University, John F. Kennedy School of Government.
  6. Chen, Shaohua & Mu, Ren & Ravallion, Martin, 2009. "Are there lasting impacts of aid to poor areas?," Journal of Public Economics, Elsevier, vol. 93(3-4), pages 512-528, April.
  7. Angus Deaton, 2010. "Instruments, randomization, and learning about development," Working Papers 1224, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
  8. Ravallion, Martin, 2008. "Evaluation in the practice of development," Policy Research Working Paper Series 4547, The World Bank.
  9. Abhijit V. Banerjee & Esther Duflo, 2008. "The Experimental Approach to Development Economics," NBER Working Papers 14467, National Bureau of Economic Research, Inc.
  10. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
  11. Elbers, Chris & Gunning, Jan Willem & de Hoop, Kobus, 2009. "Assessing Sector-wide Programs with Statistical Impact Evaluation: A Methodological Proposal," World Development, Elsevier, vol. 37(2), pages 513-520, February.
  12. Almeida, Rita K. & Galasso, Emanuela, 2010. "Jump-starting Self-employment? Evidence for Welfare Participants in Argentina," World Development, Elsevier, vol. 38(5), pages 742-755, May.
  13. White, Howard, 2006. "Impact evaluation: the experience of the Independent Evaluation Group of the World Bank," MPRA Paper 1111, University Library of Munich, Germany.
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