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Assessing the Performance of Matching Algorithms When Selection into Treatment Is Strong

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  • Augurzky, Boris

    ()
    (RWI)

  • Kluve, Jochen

    ()
    (Humboldt University Berlin, RWI)

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.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 1301.

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Length: 39 pages
Date of creation: Sep 2004
Date of revision:
Publication status: published in: Journal of Applied Econometrics, 2006, 22 (3), 533-557
Handle: RePEc:iza:izadps:dp1301

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Keywords: optimal full matching; selection into treatment; matching algorithms;

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References

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  1. 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, American Statistical Association, vol. 17(1), pages 74-90, January.
  2. Augurzky, Boris & Schmidt, Christoph M., 2001. "The Propensity Score: A Means to An End," IZA Discussion Papers 271, Institute for the Study of Labor (IZA).
  3. 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 217-30, May.
  4. 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.
  5. Joshua D. Angrist, 1995. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," NBER Working Papers 5192, National Bureau of Economic Research, Inc.
  6. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 305-353.
  7. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, Elsevier, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
  8. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, Elsevier, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366 Elsevier.
  9. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 355-364.
  10. Kluve, Jochen & Lehmann, Hartmut & Schmidt, Christoph M, 1999. "Active Labour Market Policies in Poland: Human Capital Enhancement, Stigmatization or Benefit Churning?," CEPR Discussion Papers, C.E.P.R. Discussion Papers 2059, C.E.P.R. Discussion Papers.
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  12. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, Econometric Society, vol. 66(2), pages 315-332, March.
  13. Kluve, Jochen, 2001. "On the Role of Counterfactuals in Inferring Causal Effects of Treatments," IZA Discussion Papers 354, Institute for the Study of Labor (IZA).
  14. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
  15. Murnane, Richard J & Willett, John B & Levy, Frank, 1995. "The Growing Importance of Cognitive Skills in Wage Determination," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 251-66, May.
  16. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  17. 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.
  18. Joshua D. Angrist & Jinyong Hahn, 1999. "When to Control for Covariates? Panel-Asymptotic Results for Estimates of Treatment Effects," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0241, National Bureau of Economic Research, Inc.
  19. Orley Ashenfelter & Cecilia Rouse, 1997. "Income, Schooling, and Ability: Evidence from a New Sample of Identical Twins," NBER Working Papers 6106, National Bureau of Economic Research, Inc.
  20. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
  21. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, American Economic Association, vol. 76(4), pages 604-20, September.
  22. Smith, Jeffrey & Todd, Petra, 2005. "Rejoinder," Journal of Econometrics, Elsevier, Elsevier, vol. 125(1-2), pages 365-375.
  23. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 65(2), pages 261-94, April.
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Citations

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Cited by:
  1. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, Elsevier, vol. 175(1), pages 1-21.
  2. Gregory Price & William Spriggs & Omari Swinton, 2011. "The Relative Returns to Graduating from a Historically Black College/University: Propensity Score Matching Estimates from the National Survey of Black Americans," The Review of Black Political Economy, Springer, Springer, vol. 38(2), pages 103-130, June.
  3. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2007. "Evaluating Continuous Training Programs Using the Generalized Propensity Score," Ruhr Economic Papers, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen 0035, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  4. G. Atzeni & O. Carboni, 2006. "Regional Disparity in ICT Adoption: an Empirical Evaluation of The Effects of Subsidies in Italy," Working Paper CRENoS 200608, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  5. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(5), pages 1942-1960, May.
  6. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating continuous training programs using the generalized propensity score1," Technical Reports, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen 2007,39, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  7. 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 Ausl," DIW Berlin: Politikberatung kompakt, DIW Berlin, German Institute for Economic Research, DIW Berlin, German Institute for Economic Research, edition 2, volume 10, number pbk10, July.
  8. 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, Presses de Sciences-Po, vol. 97(5), pages 279-302.
  9. Martin Huber & Michael Lechner & Conny Wunsch, 2010. "How to control for many covariates? Reliable estimators based on the propensity score," University of St. Gallen Department of Economics working paper series 2010, Department of Economics, University of St. Gallen 2010-30, Department of Economics, University of St. Gallen.
  10. 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, Springer, vol. 1(2), pages 99-124, June.
  11. Kirchweger, Stefan & Kantelhardt, Jochen, 2014. "Structural Change and Farm Investment Support in Austria," 88th Annual Conference, April 9-11, 2014, AgroParisTech, Paris, France, Agricultural Economics Society 170545, Agricultural Economics Society.

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