IDEAS home Printed from https://ideas.repec.org/a/gmf/journl/y2026i61p5592.html

Economic Growth, AI Adoption and Human Capital Across the OECD: Evidence from a Panel ARDL Model

Author

Listed:
  • Joshua Duarte

    (University of Coimbra, Faculty of Economics and CeBER)

  • Carina Freitas

    (University of Coimbra, Faculty of Economics)

  • Marta Simões

    (University of Coimbra, Faculty of Economics and CeBER)

Abstract

Technological progress is a key driver of long-term growth and increases in standards of living across generations, but its benefits often materialise with delay due to adjustment costs and the need for complementary investments. This study examines these dynamics for artificial intelligence (AI), whose rapid diffusion has raised expectations about its economic impact. As with previous waves of technological progress and innovation, there are also important concerns on how work will be shaped by the proliferation of AI as it may potentially benefit certain types of human capital. Using a panel ARDL model for 35 OECD countries from 1995 to 2017, we estimate the short- and long-run effects of AI adoption on growth and living standards. The ARDL framework captures the gradual adjustment process and allows us to incorporate human capital by interacting it with AI, assessing whether the benefits of AI differ across skill levels. Our preferred results, based on patents data and first differenced GMM, suggest that AI adoption already contributes to short-run growth and leads to long run improvements in standards of living, although these results are not supported in all contexts. Regarding the role of human capital, our aggregate data provides no evidence that AI benefits any particular group of workers, neither highly educated nor less-educated ones. Our research is limited by the current state of AI technology, which is advancing rapidly, and proxies available; therefore, the validity of our present, optimistic findings must be continually re-evaluated.

Suggested Citation

  • Joshua Duarte & Carina Freitas & Marta Simões, 2026. "Economic Growth, AI Adoption and Human Capital Across the OECD: Evidence from a Panel ARDL Model," Notas Económicas, Faculty of Economics, University of Coimbra, issue 61, pages 55-92, June.
  • Handle: RePEc:gmf:journl:y:2026:i:61:p:55:92
    DOI: 10.14195/2183-203X_61_3
    as

    Download full text from publisher

    File URL: https://impactum-journals.uc.pt/notaseconomicas/article/view/18969/12367
    Download Restriction: no

    File URL: https://libkey.io/10.14195/2183-203X_61_3?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

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    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:gmf:journl:y:2026:i:61:p:55:92. 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: Sofia Antunes (email available below). General contact details of provider: https://edirc.repec.org/data/fecucpt.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.