IDEAS home Printed from https://ideas.repec.org/a/spr/joecin/v21y2023i3d10.1007_s10888-023-09565-x.html
   My bibliography  Save this article

Gender wage inequality: new evidence from penalized expectile regression

Author

Listed:
  • Marina Bonaccolto-Töpfer

    (University of Genova)

  • Giovanni Bonaccolto

    (Kore University of Enna, Cittadella Universitaria)

Abstract

The Machado-Mata decomposition building on quantile regression has been extensively analyzed in the literature focusing on gender wage inequality. In this study, we generalize the Machado-Mata decomposition to the expectile regression framework, which, to the best of our knowledge, has never been applied in this strand of the literature. In contrast, in recent years, expectiles have gained increasing attention in other contexts as an alternative to traditional quantiles, providing useful statistical and computational properties. We flexibly deal with high-dimensional problems by employing the Least Absolute Shrinkage and Selection Operator. The empirical analysis focuses on the gender pay gap in Germany and Italy. We find that depending on the estimation approach (i.e. expectile or quantile regression) the results substantially differ along some regions of the wage distribution, whereas they are similar for others. From a policy perspective, this finding is important as it affects conclusions about glass ceiling and sticky floors.

Suggested Citation

  • Marina Bonaccolto-Töpfer & Giovanni Bonaccolto, 2023. "Gender wage inequality: new evidence from penalized expectile regression," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(3), pages 511-535, September.
    • Handle: RePEc:spr:joecin:v:21:y:2023:i:3:d:10.1007_s10888-023-09565-x
    DOI: 10.1007/s10888-023-09565-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10888-023-09565-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10888-023-09565-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Expectile regression; Gender pay gap; Quantile regression; Penalized estimation;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J45 - Labor and Demographic Economics - - Particular Labor Markets - - - Public Sector Labor Markets
    • J51 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - Trade Unions: Objectives, Structure, and Effects

    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:spr:joecin:v:21:y:2023:i:3:d:10.1007_s10888-023-09565-x. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.