IDEAS home Printed from https://ideas.repec.org/p/oec/comaaa/25-en.html
   My bibliography  Save this paper

Measuring the demand for AI skills in the United Kingdom

Author

Listed:
  • Julia Schmidt
  • Graham Pilgrim
  • Annabelle Mourougane

Abstract

This paper estimates the artificial intelligence-hiring intensity of occupations/industries (i.e. the share of job postings related to AI skills) in the United Kingdom during 2012-22. The analysis deploys a natural language processing algorithm (NLP) on online job postings, collected by Lightcast, which provides timely and detailed insights into labour demand for different professions. The key contribution of the study lies in the design of the classification rule identifying jobs as AI-related which, contrary to the existing literature, goes beyond the simple use of keywords. Moreover, the methodology allows for comparisons between data-hiring intensive jobs, defined as the share of jobs related to data production tasks, and AI-hiring intensive jobs. Estimates point to a rise in the economy-wide AI-hiring intensity in the United Kingdom over the past decade but to fairly small levels (reaching 0.6% on average over the 2017-22 period). Over time, the demand for AI-related jobs has spread outside the traditional Information, Communication and Telecommunications industries, with the Finance and Insurance industry increasingly demanding AI skills. At a regional level, the higher demand for AI-related jobs is found in London and research hubs. At the occupation level, marked changes in the demand for AI skills are also visible. Professions such as data scientist, computer scientist, hardware engineer and robotics engineer are estimated to be the most AI-hiring intense occupations in the United Kingdom. The data and methodology used allow for the exploration of cross-country estimates in the future.

Suggested Citation

  • Julia Schmidt & Graham Pilgrim & Annabelle Mourougane, 2024. "Measuring the demand for AI skills in the United Kingdom," OECD Artificial Intelligence Papers 25, OECD Publishing.
  • Handle: RePEc:oec:comaaa:25-en
    DOI: 10.1787/1d6474ef-en
    as

    Download full text from publisher

    File URL: https://doi.org/10.1787/1d6474ef-en
    Download Restriction: no

    File URL: https://libkey.io/10.1787/1d6474ef-en?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
    ---><---

    More about this item

    Keywords

    AI-hiring intensity; artificial intelligence; job advertisements; natural language processing; united kingdom;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

    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:oec:comaaa:25-en. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/paoecfr.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.