IDEAS home Printed from https://ideas.repec.org/p/zbw/rwidps/21.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/18572/1/DP_04_021.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kluve, Jochen & Lehmann, Hartmut & Schmidt, Christoph M., 1999. "Active Labor Market Policies in Poland: Human Capital Enhancement, Stigmatization, or Benefit Churning?," Journal of Comparative Economics, Elsevier, vol. 27(1), pages 61-89, March.
    2. Augurzky, Boris & Schmidt, Christoph M., 2001. "The Propensity Score: A Means to An End," IZA Discussion Papers 271, Institute of Labor Economics (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 217-230, May.
    4. 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.
    5. 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.
    6. Orley Ashenfelter & Cecilia Rouse, 1998. "Income, Schooling, and Ability: Evidence from a New Sample of Identical Twins," The Quarterly Journal of Economics, Oxford University Press, vol. 113(1), pages 253-284.
    7. 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.
    8. 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.
    9. 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.
    10. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 605-654.
    11. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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 Ausla," DIW Berlin: Politikberatung kompakt, DIW Berlin, German Institute for Economic Research, edition 2, volume 10, number pbk10, Enero-Abr.
    2. 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.
    3. 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.
    4. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    2. Jochen Kluve & Boris Augurzky, 2005. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 0021, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
    3. repec:zbw:rwidps:0021 is not listed on IDEAS
    4. 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.
    5. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    6. 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).
    7. 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.
    8. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    9. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    10. Joseph Hotz, V. & Imbens, Guido W. & Mortimer, Julie H., 2005. "Predicting the efficacy of future training programs using past experiences at other locations," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 241-270.
    11. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
    12. 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.
    13. Martin Eichler & Michael Lechner, 2000. "Some Econometric Evidence on the Effectiveness of Active Labour Market Programmes in East Germany," William Davidson Institute Working Papers Series 318, William Davidson Institute at the University of Michigan.
    14. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 136(III), pages 247-268, September.
    15. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
    16. Aki Kangasharju, 2007. "Do Wage Subsidies Increase Employment in Subsidized Firms?," Economica, London School of Economics and Political Science, vol. 74(293), pages 51-67, February.
    17. Frölich, Markus & Michaelowa, Katharina, 2004. "Peer effects and textbooks in primary education: Evidence from francophone sub-Saharan Africa," HWWA Discussion Papers 311, Hamburg Institute of International Economics (HWWA).
    18. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
    19. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    20. Marco Caliendo & Reinhard Hujer & Stephan L. Thomsen, 2008. "The employment effects of job-creation schemes in Germany: A microeconometric evaluation," Advances in Econometrics, in: Modelling and Evaluating Treatment Effects in Econometrics, pages 381-428, Emerald Group Publishing Limited.
    21. Markus Frölich, 2004. "Programme Evaluation with Multiple Treatments," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 181-224, April.

    More about this item

    Keywords

    Matching algorithms; optimal full matching; selection into treatment;
    All these keywords.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:rwidps:21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/rwiesde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.