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

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  • Jochen Kluve

    (RWI Essen, IZA Bonn, Germany)

  • Boris Augurzky

    (RWI Essen, IZA Bonn, Germany)

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. Copyright © 2007 John Wiley & Sons, Ltd.

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  • 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.
    • Handle: RePEc:jae:japmet:v:22:y:2007:i:3:p:533-557
    DOI: 10.1002/jae.919
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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 Ausla," DIW Berlin: Politikberatung kompakt, DIW Berlin, German Institute for Economic Research, edition 2, volume 10, number pbk10, Enero-Abr.
    2. 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.
    3. 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).
    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. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2007. "Evaluating Continuous Training Programs Using the Generalized Propensity Score," Ruhr Economic Papers 0035, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    6. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating Continuous Training Programs Using the Generalized Propensity Score," Ruhr Economic Papers 35, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    7. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2012. "Evaluating continuous training programmes by using the generalized propensity score," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 175(2), pages 587-617.
    8. Fichera, Eleonora & Emsley, Richard & Sutton, Matt, 2016. "Is treatment “intensity” associated with healthier lifestyle choices? An application of the dose response function," Economics & Human Biology, Elsevier, vol. 23(C), pages 149-163.
    9. 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.
    10. 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;National Economic Association, vol. 38(2), pages 103-130, June.
    11. 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.
    12. Thomas Stubbs & Bernhard Reinsberg & Alexander Kentikelenis & Lawrence King, 2020. "How to evaluate the effects of IMF conditionality," The Review of International Organizations, Springer, vol. 15(1), pages 29-73, January.
    13. Gianfranco E. Atzeni & OA 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.
    14. Egana-delSol, Pablo & Contreras, Dante & Valenzuela, Juan Pablo, 2019. "The impact of art-education on human Capital: An empirical assessment of a youth orchestra," International Journal of Educational Development, Elsevier, vol. 71(C).
    15. Kirchweger, Stefan & Kantelhardt, Jochen, 2014. "Structural Change and Farm Investment Support in Austria," 88th Annual Conference, April 9-11, 2014, AgroParisTech, Paris, France 170545, Agricultural Economics Society.
    16. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    17. repec:zbw:rwirep:0035 is not listed on IDEAS
    18. Jin, Hyun Joung & Cho, Sung Min, 2021. "Effects of cigarette price increase on fresh food expenditures of low-income South Korean households that spend relatively more on cigarettes," Health Policy, Elsevier, vol. 125(1), pages 75-82.
    19. 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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