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Matching as a regression estimator

Author

Listed:
  • Dan A. Black

    (University of Chicago and NORC at the University of Chicago, USA, and IZA, Germany)

Abstract

“Matching” is a statistical technique used to evaluate the effect of a treatment by comparing the treated and non-treated units in an observational study. Matching provides an alternative to older estimation methods, such as ordinary least squares (OLS), which involves strong assumptions that are usually without much justification from economic theory. While the use of simple OLS models may have been appropriate in the early days of computing during the 1970s and 1980s, the remarkable increase in computing power since then has made other methods, in particular matching, very easy to implement.

Suggested Citation

  • Dan A. Black, 2015. "Matching as a regression estimator," IZA World of Labor, Institute of Labor Economics (IZA), pages 186-186, September.
    • Handle: RePEc:iza:izawol:journl:y:2015:n:186
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    References listed on IDEAS

    as
    1. Dan A. Black & Amelia M. Haviland & Seth G. Sanders & Lowell J. Taylor, 2008. "Gender Wage Disparities among the Highly Educated," Journal of Human Resources, University of Wisconsin Press, vol. 43(3), pages 630-659.
    2. 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, July.
    3. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
    4. 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.
    5. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    6. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    7. 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.
    8. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
    9. O. Ashenfelter & D. Card (ed.), 1999. "Handbook of Labor Economics," Handbook of Labor Economics, Elsevier, edition 1, volume 3, number 3.
    10. Dan Black & Amelia Haviland & Seth Sanders & Lowell Taylor, 2006. "Why Do Minority Men Earn Less? A Study of Wage Differentials among the Highly Educated," The Review of Economics and Statistics, MIT Press, vol. 88(2), pages 300-313, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Dip, Juan Antonio & Gamboa, Luis Fernando, 2019. "The heterogeneity of effects of preschool education on cognitive outcomes in Latin America," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    2. Kritkorn Nawakitphaitoon & Can Tang, 2021. "Nonstandard Employment and Job Satisfaction across Time in China: Evidence from the Chinese General Social Survey (2006–2012)," Work, Employment & Society, British Sociological Association, vol. 35(3), pages 411-431, June.
    3. Seonho Shin, 2022. "Evaluating the Effect of the Matching Grant Program for Refugees: An Observational Study Using Matching, Weighting, and the Mantel-Haenszel Test," Journal of Labor Research, Springer, vol. 43(1), pages 103-133, March.

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    More about this item

    Keywords

    matching; ordinary least squares (OLS); functional form; regression;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • J00 - Labor and Demographic Economics - - General - - - General
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies

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