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Assessing the performance of matching algorithms when selection into treatment is strong

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  • Augurzky, Boris
  • Kluve, Jochen

Abstract

This paper investigates the method of matching regarding two crucial implementation choices, the distance measure and the type of algorithm.We implement optimal full matching – a fully efficient algorithm – and present a framework for statistical inference. The implementation uses data from the NLSY79 to study the effect of college education on earnings. We find that decisions regarding the matching algorithm depend on the structure of the data: In the case of strong selection into treatment and treatment effect heterogeneity a full matching seems preferable. If heterogeneity is weak, pair matching suffices.

Suggested Citation

  • Augurzky, Boris & Kluve, Jochen, 2004. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 21, RWI - Leibniz-Institut für Wirtschaftsforschung.
    • Handle: RePEc:zbw:rwidps:21
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    References listed on IDEAS

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    2. Augurzky, Boris & Schmidt, Christoph M., 2001. "The Propensity Score: A Means to An End," IZA Discussion Papers 271, IZA Network @ LISER.
    3. Lechner, Michael, 1999. "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 74-90, January.
    4. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
    5. Blackburn, McKinley L & Neumark, David, 1995. "Are OLS Estimates of the Return to Schooling Biased Downward? Another Look," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 217-230, May.
    6. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
    7. Kewei Ming & Paul R. Rosenbaum, 2000. "Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls," Biometrics, The International Biometric Society, vol. 56(1), pages 118-124, March.
    8. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    9. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
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    Cited by:

    1. Kurt Hornschild & Stephan Raab & Jörg-Peter Weiß, 2005. "Die Medizintechnik am Standort Deutschland: Chancen und Risiken durch technologische Innovationen, Auswirkungen auf und durch das nationale Gesundheitssystem sowie potentielle Wachstumsmärkte im Ausland ; Forschungsprojekt im Auftrag des Bundesminist," DIW Berlin: Politikberatung kompakt, DIW Berlin, German Institute for Economic Research, edition 2, volume 10, number pbk10.
    2. Dettmann, Eva & Becker, Claudia & Schmeißer, Christian, 2010. "Is there a Superior Distance Function for Matching in Small Samples?," IWH Discussion Papers 3/2010, Halle Institute for Economic Research (IWH).
    3. Ruben Atoyan & Patrick Conway, 2006. "Evaluating the impact of IMF programs: A comparison of matching and instrumental-variable estimators," The Review of International Organizations, Springer, vol. 1(2), pages 99-124, June.
    4. Gianfranco E. Atzeni & Oliviero A. Carboni, 2006. "The Effects of Subsidies on Investment: an Empirical Evaluation on ICT in Italy," Revue de l'OFCE, Presses de Sciences-Po, vol. 97(5), pages 279-302.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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