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An Introduction to Alternative Methods in Program Impact Evaluation

  • Nguyen Viet, Cuong

This paper presents an overview of several widely-used methods in program impact evaluation. In addition to a randomization-based method, these methods are categorized into: (i) methods assuming “selection on observable” and (ii) methods assuming “selection on unobservable”. The paper discusses each method under identification assumptions and estimation strategy. Identification assumptions are presented in a unified framework of counterfactual and two equation model. Finally, the paper uses simulated data to illustrate how these methods work under different identification assumptions.

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File URL: http://mpra.ub.uni-muenchen.de/24900/1/MPRA_paper_24900.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 24900.

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Date of creation: 01 Dec 2006
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Publication status: Published in Mansholt Working Papers No. 33.Year 2(2007): pp. 1-50
Handle: RePEc:pra:mprapa:24900
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  1. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
  2. Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
  3. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 487-535, October.
  4. Buddelmeyer, Hielke & Skoufias, Emmanuel, 2003. "An Evaluation of the Performance of Regression Discontinuity Design on PROGRESA," IZA Discussion Papers 827, Institute for the Study of Labor (IZA).
  5. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
  6. James J. Heckman, 1977. "Dummy Endogenous Variables in a Simultaneous Equation System," NBER Working Papers 0177, National Bureau of Economic Research, Inc.
  7. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  8. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
  9. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
  10. James J. Heckman & Lance Lochner & Christopher Taber, 1998. "General Equilibrium Treatment Effects: A Study of Tuition Policy," NBER Working Papers 6426, National Bureau of Economic Research, Inc.
  11. Wilbert van der Klaauw, 2002. "Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression-Discontinuity Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(4), pages 1249-1287, November.
  12. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
  13. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-09, January.
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