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An Introduction to Alternative Methods in Program Impact Evaluation

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  • Nguyen Viet, Cuong

Abstract

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.

Suggested Citation

  • Nguyen Viet, Cuong, 2006. "An Introduction to Alternative Methods in Program Impact Evaluation," MPRA Paper 24900, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:24900
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    File URL: https://mpra.ub.uni-muenchen.de/24900/1/MPRA_paper_24900.pdf
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    References listed on IDEAS

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    2. Heckman, James J & Lochner, Lance & Taber, Christopher, 1998. "General-Equilibrium Treatment Effects: A Study of Tuition Policy," American Economic Review, American Economic Association, vol. 88(2), pages 381-386, May.
    3. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 487-535.
    4. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    5. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Robert Moffitt, 1991. "Program Evaluation With Nonexperimental Data," Evaluation Review, , vol. 15(3), pages 291-314, June.
    11. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
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    14. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
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    More about this item

    Keywords

    Program impact evaluation; treatment effect; counterfactual; potential outcomes; selection on observable; selection on unobservable.;

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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