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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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    1. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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 201-209, January.
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    10. Robert Moffitt, 1991. "Program Evaluation With Nonexperimental Data," Evaluation Review, , vol. 15(3), pages 291-314, June.
    11. Buddelmeyer, Hielke & Skoufias, Emmanuel, 2003. "An Evaluation of the Performance of Regression Discontinuity Design on PROGRESA," IZA Discussion Papers 827, Institute of Labor Economics (IZA).
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    14. 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.
    15. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
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    More about this item

    Keywords

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

    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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