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The Characteristics of the Artificial Intelligence Workforce across OECD Countries

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  • Andrew Green

    (OECD)

  • Lucas Lamby

    (Harvard Kennedy School)

Abstract

This study provides representative, cross-country estimates of the artificial intelligence (AI) workforce across the OECD countries. The AI workforce is defined as the subset of workers with skills in statistics, computer science and machine learning who could actively develop and maintain AI systems. For countries that wish to be at the forefront of AI development, understanding the AI workforce is crucial to building and nurturing a talent pipeline, and ensuring that those who create AI reflect the diversity of society. This study uses data from online job vacancies to measure the within-occupation intensity of AI skill demand. The within-occupation AI intensity is then weighted to employment by occupation in labour force surveys to provide estimates of the size and growth of the AI workforce over time. The study finds that the AI workforce in the OECD countries is still relatively small—less than 0.3% of employment—but growing rapidly. Workers with AI skills are not representative of the overall employed population in OECD societies: They tend to be disproportionately male with a tertiary education.

Suggested Citation

  • Andrew Green & Lucas Lamby, 2025. "The Characteristics of the Artificial Intelligence Workforce across OECD Countries," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 68(2), pages 541-568, June.
  • Handle: RePEc:spr:ijlaec:v:68:y:2025:i:2:d:10.1007_s41027-024-00549-7
    DOI: 10.1007/s41027-024-00549-7
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    References listed on IDEAS

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    1. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2022. "Artificial Intelligence and Jobs: Evidence from Online Vacancies," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 293-340.
    2. Brad Hershbein & Lisa B. Kahn, 2018. "Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings," American Economic Review, American Economic Association, vol. 108(7), pages 1737-1772, July.
    3. Nikolas Dawson & Mary-Anne Williams & Marian-Andrei Rizoiu, 2020. "Skill-driven Recommendations for Job Transition Pathways," Papers 2011.11801, arXiv.org, revised Aug 2021.
    4. Raghabendra Chattopadhyay & Esther Duflo, 2004. "Women as Policy Makers: Evidence from a Randomized Policy Experiment in India," Econometrica, Econometric Society, vol. 72(5), pages 1409-1443, September.
    5. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    6. Nikolas Dawson & Mary-Anne Williams & Marian-Andrei Rizoiu, 2021. "Skill-driven recommendations for job transition pathways," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-20, August.
    7. Shamena Anwar & Patrick Bayer & Randi Hjalmarsson, 2012. "The Impact of Jury Race in Criminal Trials," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(2), pages 1017-1055.
    8. David Deming & Lisa B. Kahn, 2018. "Skill Requirements across Firms and Labor Markets: Evidence from Job Postings for Professionals," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 337-369.
    9. Alicia Sasser Modestino & Daniel Shoag & Joshua Ballance, 2020. "Upskilling: Do Employers Demand Greater Skill When Workers Are Plentiful?," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 793-805, October.
    10. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    11. Enrico Moretti, 2021. "The Effect of High-Tech Clusters on the Productivity of Top Inventors," American Economic Review, American Economic Association, vol. 111(10), pages 3328-3375, October.
    12. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, October.
    13. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, December.
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    Keywords

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    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J44 - Labor and Demographic Economics - - Particular Labor Markets - - - Professional Labor Markets and Occupations

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