IDEAS home Printed from https://ideas.repec.org/h/eme/rleczz/s0147-91212024000052b029.html
   My bibliography  Save this book chapter

Using Domain-Specific Word Embeddings to Examine the Demand for Skills

In: Big Data Applications in Labor Economics, Part B

Author

Listed:
  • Sugat Chaturvedi
  • Kanika Mahajan
  • Zahra Siddique

Abstract

We study the demand for skills by using text analysis methods on job descriptions in a large volume of ads posted on an online Indian job portal. We make use of domain-specific unlabeled data to obtain word vector representations (i.e., word embeddings) and discuss how these can be leveraged for labor market research. We start by carrying out a data-driven categorization of required skill words and construct gender associations of different skill categories using word embeddings. Next, we examine how different required skill categories correlate with log posted wages as well as explore how skills demand varies with firm size. We find that female skills are associated with lower posted wages, potentially contributing to observed gender wage gaps. We also find that large firms require a more extensive range of skills, implying that complementarity between female and male skills is greater among these firms.

Suggested Citation

  • Sugat Chaturvedi & Kanika Mahajan & Zahra Siddique, 2024. "Using Domain-Specific Word Embeddings to Examine the Demand for Skills," Research in Labor Economics, in: Big Data Applications in Labor Economics, Part B, volume 52, pages 171-223, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:rleczz:s0147-91212024000052b029
    DOI: 10.1108/S0147-91212024000052B029
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0147-91212024000052B029/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0147-91212024000052B029/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0147-91212024000052B029/full/epub?utm_source=repec&utm_medium=feed&utm_campaign=repec&title=10.1108/S0147-91212024000052B029
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/S0147-91212024000052B029?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.

    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:eme:rleczz:s0147-91212024000052b029. 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: Emerald Support (email available below). General contact details of provider: .

    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.