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Blinder-Oaxaca decomposition with recursive tree-based methods: a technical note

Author

Listed:
  • Olga Takacs

    (Corvinus University of Budapest and Center for Economic and Regional Studies, Institute of Economics)

  • Janos Vincze

    (Corvinus University of Budapest and Center for Economic and Regional Studies, Institute of Economics)

Abstract

The Blinder-Oaxaca decomposition was developed in order to detect and characterize discriminatory treatment, and one of its most frequent use has been the study of wage discrimination. It recognizes that the mere difference between the average wages of two groups may not mean discrimination (in a very wide sense of the word), but the difference can be due to different characteristics the groups possess. It decomposes average differences in the variable of interest into two parts: one explained by observable features of the two group, and an unexplained part, which may signal discrimination. The methodology was originally developed for OLS estimates, but it has been generalized in several nonlinear directions. In this paper we describe afurther extension of the basic idea: we apply Random Forest (RF) regression to estimate the explained and unexplained parts, and then we employ the CART (Classification and Regression Tree) methodology to identify the groups for which discrimination is most or least severe.

Suggested Citation

  • Olga Takacs & Janos Vincze, 2019. "Blinder-Oaxaca decomposition with recursive tree-based methods: a technical note," CERS-IE WORKING PAPERS 1923, Institute of Economics, Centre for Economic and Regional Studies.
    • Handle: RePEc:has:discpr:1923
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    References listed on IDEAS

    as
    1. Ben Jann, 2008. "The Blinder–Oaxaca decomposition for linear regression models," Stata Journal, StataCorp LLC, vol. 8(4), pages 453-479, December.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    4. David Neumark, 1988. "Employers' Discriminatory Behavior and the Estimation of Wage Discrimination," Journal of Human Resources, University of Wisconsin Press, vol. 23(3), pages 279-295.
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    Cited by:

    1. Olga Takacs & Janos Vincze, 2019. "The gender pay gap in Hungary: new results with a new methodology," CERS-IE WORKING PAPERS 1924, Institute of Economics, Centre for Economic and Regional Studies.

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    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
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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