IDEAS home Printed from https://ideas.repec.org/p/has/discpr/1923.html

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," KRTK-KTI 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. 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.
    2. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    3. Ben Jann, 2008. "The Blinder–Oaxaca decomposition for linear regression models," Stata Journal, StataCorp LLC, vol. 8(4), pages 453-479, December.
    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," KRTK-KTI 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 Takacs & Janos Vincze, 2019. "The gender pay gap in Hungary: new results with a new methodology," KRTK-KTI WORKING PAPERS 1924, Institute of Economics, Centre for Economic and Regional Studies.
    2. Olga Takács & János Vincze, 2020. "The gender-dependent structure of wages in Hungary: results using machine learning techniques," KRTK-KTI WORKING PAPERS 2044, Institute of Economics, Centre for Economic and Regional Studies.
    3. 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.
    4. 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.
    5. Sloczynski, Tymon, 2013. "Population Average Gender Effects," IZA Discussion Papers 7315, IZA Network @ LISER.
    6. Powers, Daniel A. & Yun, Myeong-Su, 2009. "Multivariate Decomposition for Hazard Rate Models," IZA Discussion Papers 3971, IZA Network @ LISER.
    7. 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.
    8. 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).
    9. Mitsuhiro Hayashi & Mitsuhiko Kataoka & Takahiro Akita, 2014. "Expenditure Inequality in Indonesia, 2008–2010: A Spatial Decomposition Analysis and the Role of Education," Asian Economic Journal, East Asian Economic Association, vol. 28(4), pages 389-411, December.
    10. Karen Mumford & Cristina Sechel, 2020. "Pay and Job Rank among Academic Economists in the UK: Is Gender Relevant?," British Journal of Industrial Relations, London School of Economics, vol. 58(1), pages 82-113, March.
    11. Matthias Giesecke & Sarah Okoampah, 2014. "Inequality of Opportunity in Retirement Age – The Role of Physical Job Demands," Ruhr Economic Papers 0492, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    12. Jonathan Haughton & Wendi Sun & Le Thi Thanh Loan, 2018. "Discrimination against Migrants in Urban Vietnam," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 24(3), pages 211-232, August.
    13. Fernandez-Dominguez Amilcar Orlian, 2020. "Effect of Actual and Perceived Violence on Internal Migration: Evidence from Mexico’s Drug War," IZA Journal of Development and Migration, Paradigm & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 11(1), pages 1-27, January.
    14. Daniel Erdsiek, 2016. "Overqualification of graduates: assessing the role of family background [Überqualifikation von Hochschulabsolventen: Welche Rolle spielt der familiäre Hintergrund?]," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 49(3), pages 253-268, November.
    15. Nii-Aponsah, Hubert, 2022. "Automation exposure and implications in advanced and developing countries across gender, age, and skills," MERIT Working Papers 2022-021, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    16. Isaiah Hull & Anna Grodecka-Messi, 2022. "Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach," Papers 2203.14751, arXiv.org.
    17. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    18. 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.
    19. Ronald Bachmann & Mathias Sinning, 2016. "Decomposing the Ins and Outs of Cyclical Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 853-876, December.
    20. Jürgen Maurer, 2011. "Education and Male-Female Differences in Later-Life Cognition: International Evidence From Latin America and the Caribbean," Demography, Springer;Population Association of America (PAA), vol. 48(3), pages 915-930, August.

    More about this item

    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
    • 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 The email address of this maintainer does not seem to be valid anymore. Please ask Nora Horvath to update the entry or send us the correct address (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.