IDEAS home Printed from https://ideas.repec.org/p/has/discpr/1923.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.mtakti.hu/wp-content/uploads/2019/12/MTDP1923.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben Jann, 2008. "The Blinder–Oaxaca decomposition for linear regression models," Stata Journal, StataCorp LP, 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.
    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. 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.

    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. Olga Takács & János Vincze, 2020. "The gender-dependent structure of wages in Hungary: results using machine learning techniques," CERS-IE WORKING PAPERS 2044, Institute of Economics, Centre for Economic and Regional Studies.
    2. 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.
    3. Marco Caliendo & Frank M. Fossen & Alexander Kritikos & Miriam Wetter, 2015. "The Gender Gap in Entrepreneurship: Not just a Matter of Personality," CESifo Economic Studies, CESifo Group, vol. 61(1), pages 202-238.
    4. Huong Thu Le & Ha Trong Nguyen, 2018. "The evolution of the gender test score gap through seventh grade: new insights from Australia using unconditional quantile regression and decomposition," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-42, December.
    5. Kilic, Talip & Palacios-López, Amparo & Goldstein, Markus, 2015. "Caught in a Productivity Trap: A Distributional Perspective on Gender Differences in Malawian Agriculture," World Development, Elsevier, vol. 70(C), pages 416-463.
    6. Brahim Boudarbat & Marie Connolly, 2013. "The gender wage gap among recent post‐secondary graduates in Canada: a distributional approach," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 46(3), pages 1037-1065, August.
    7. Adam Pilny, 2017. "Explaining Differentials in Subsidy Levels Among Hospital Ownership Types in Germany," Health Economics, John Wiley & Sons, Ltd., vol. 26(5), pages 566-581, May.
    8. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    9. Sloczynski, Tymon, 2013. "Population Average Gender Effects," IZA Discussion Papers 7315, Institute of Labor Economics (IZA).
    10. Vassilis Monastiriotis & Angelo Martelli, 2021. "Crisis, Adjustment and Resilience in the Greek Labor Market: An Unemployment Decomposition Approach," International Regional Science Review, , vol. 44(1), pages 85-112, January.
    11. Powers, Daniel A. & Yun, Myeong-Su, 2009. "Multivariate Decomposition for Hazard Rate Models," IZA Discussion Papers 3971, Institute of Labor Economics (IZA).
    12. Oscar Molina Tejerina & Luis Castro Peñarrieta, 2020. "Unexplained Wage Gaps in the Tradable and Nontradable Sectors: Cross-Sectional Evidence by Gender in Bolivia," Investigación & Desarrollo 0120, Universidad Privada Boliviana, revised Nov 2020.
    13. Daniela Piazzalunga & Maria Laura Di Tommaso, 2019. "The increase of the gender wage gap in Italy during the 2008-2012 economic crisis," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(2), pages 171-193, June.
    14. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    15. Sonja C. Kassenboehmer & Mathias G. Sinning, 2014. "Distributional Changes in the Gender Wage Gap," ILR Review, Cornell University, ILR School, vol. 67(2), pages 335-361, April.
    16. Zhang, Li & Sharpe, Rhonda Vonshay & Li, Shi & Darity, William A., 2016. "Wage differentials between urban and rural-urban migrant workers in China," China Economic Review, Elsevier, vol. 41(C), pages 222-233.
    17. Grodecka, Anna & Hull, Isaiah, 2019. "The Impact of Local Taxes and Public Services on Property Values," Working Paper Series 374, Sveriges Riksbank (Central Bank of Sweden).
    18. Anna Adamecz-Völgyi & Morag Henderson & Nikki Shure, 2023. "The labor market returns to “first-in-family” university graduates," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(3), pages 1395-1429, July.
    19. Juan F. Castro, 2015. "Linear decompositions of cognitive achievement gaps a cautionary note and an illustration using peruvian data," Working Papers 15-08, Centro de Investigación, Universidad del Pacífico.
    20. Owen O’Donnell & Eddy van Doorslaer & Adam Wagstaff, 2012. "Decomposition of Inequalities in Health and Health Care," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 17, Edward Elgar Publishing.

    More about this item

    Keywords

    Oaxaca-Blinder decomposition; Random Forest Regression. CART;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:has:discpr:1923. 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: Nora Horvath (email available below). General contact details of provider: https://edirc.repec.org/data/iehashu.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.