IDEAS home Printed from https://ideas.repec.org/a/eee/streco/v79y2026icp386-399.html

Gender dividends in the digital economy: Exploring employment quality for female migrant workers

Author

Listed:
  • Wang, Chen-Guang
  • Yin, Xu-Kang
  • Lu, Wei
  • Wang, Wei-Win

Abstract

Owing to institutional segmentation and the gendered division of labor within households, female migrant workers in China face more severe employment discrimination. Using repeated cross-sectional data from the 2013–2021 Chinese General Social Survey (CGSS), this study finds that the digital economy significantly improves the employment quality of female migrant workers, generating a discernible gender dividend. Mechanism analysis suggests that this effect operates through two main channels: alleviating the motherhood penalty and strengthening gender equality awareness. Heterogeneity analysis shows that the effect is stronger in the eastern and central regions, in cities with higher levels of marketization, and among disadvantaged groups such as low-skilled workers and those from lower social strata. However, these gains are uneven across dimensions of employment quality, being concentrated in expanded job opportunities and higher earnings, while improvements in rights-based dimensions such as social security and occupational mobility remain limited.

Suggested Citation

  • Wang, Chen-Guang & Yin, Xu-Kang & Lu, Wei & Wang, Wei-Win, 2026. "Gender dividends in the digital economy: Exploring employment quality for female migrant workers," Structural Change and Economic Dynamics, Elsevier, vol. 79(C), pages 386-399.
  • Handle: RePEc:eee:streco:v:79:y:2026:i:c:p:386-399
    DOI: 10.1016/j.strueco.2026.05.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X26000950
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.strueco.2026.05.015?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eee:streco:v:79:y:2026:i:c:p:386-399. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/525148 .

    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.