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

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  • Jochen Kluve

    ()

  • Boris Augurzky

    ()

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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File URL: http://repec.rwi-essen.de/files/DP_04_021.pdf
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Bibliographic Info

Paper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung in its series RWI Discussion Papers with number 0021.

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Length: 42 pages
Date of creation: Sep 2005
Date of revision:
Handle: RePEc:rwi:dpaper:0021

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

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References

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  1. 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).
  2. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
  3. Dehejia, R.H. & Wahba, S., 1998. "Propensity Score Matching Methods for Non-Experimental Causal Studies," Discussion Papers 1998_02, Columbia University, Department of Economics.
  4. Joshua Angrist & Alan Krueger, 1998. "Empirical Strategies in Labor Economics," Working papers 98-7, Massachusetts Institute of Technology (MIT), Department of Economics.
  5. 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.
  6. 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.
  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. 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.
  9. Joshua D. Angrist & Jinyong Hahn, 1999. "When to Control for Covariates? Panel-Asymptotic Results for Estimates of Treatment Effects," NBER Technical Working Papers 0241, National Bureau of Economic Research, Inc.
  10. Blackburn, McKinley L & Neumark, David, 1993. "Omitted-Ability Bias and the Increase in the Return to Schooling," Journal of Labor Economics, University of Chicago Press, vol. 11(3), pages 521-44, July.
  11. Richard J. Murnane & John B. Willett & Frank Levy, 1995. "The Growing Importance of Cognitive Skills in Wage Determination," NBER Working Papers 5076, National Bureau of Economic Research, Inc.
  12. 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.
  13. Schmidt, Christoph M. & Augurzky, Boris, 2001. "The Propensity Score: A Means to An End," IZA Discussion Papers 271, Institute for the Study of Labor (IZA).
  14. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
  15. Kluve, Jochen & Lehmann, Hartmut & Schmidt, Christoph M, 1999. "Active Labour Market Policies in Poland: Human Capital Enhancement, Stigmatization or Benefit Churning?," CEPR Discussion Papers 2059, C.E.P.R. Discussion Papers.
  16. 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.
  17. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. Smith, Jeffrey & Todd, Petra, 2005. "Rejoinder," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 365-375.
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Citations

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Cited by:
  1. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating Continuous Training Programs Using the Generalized Propensity Score," IZA Discussion Papers 3255, Institute for the Study of Labor (IZA).
  2. 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, edition 2, volume 10, number pbk10, March.
  3. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating continuous training programs using the generalized propensity score1," Technical Reports 2007,39, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  4. 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, vol. 38(2), pages 103-130, June.
  5. 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.
  6. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
  7. 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.
  8. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
  9. 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.
  10. 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 2010-30, Department of Economics, University of St. Gallen.

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