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AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries

Author

Listed:
  • Erik Engberg

    (Orebro University)

  • Holger Gorg

    (Kiel Institute for the World Economy)

  • Magnus Lodefalk

    (Örebro University)

  • Farrukh Javed
  • Martin Langkvist
  • Natalia Monteiro
  • Hildegunn Nordas
  • Giuseppe Pulito

    (Rockwool Foundation Berlin)

  • Sarah Schroeder

    (Aarhus University)

  • Aili Tang

    (Örebro University)

Abstract

We unbox developments in artificial intelligence (AI) to estimate how exposure to these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark,and Portugal, and estimating firm labour demand regressions. We find a positive (negative) association between AI exposure and labour demand for high-skilled white (blue) collar work. Overall, there is an up-skilling effect, with the share of white-collar to blue collar workers increasing with AI exposure. Exposure to AI within the subdomains of image and language are positively (negatively) linked to demand for high-skilled white collar (blue collar) work, whereas other AI-areas are heterogeneously linked to groupsof workers.

Suggested Citation

  • Erik Engberg & Holger Gorg & Magnus Lodefalk & Farrukh Javed & Martin Langkvist & Natalia Monteiro & Hildegunn Nordas & Giuseppe Pulito & Sarah Schroeder & Aili Tang, 2024. "AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries," RF Berlin - CReAM Discussion Paper Series 2414, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    • Handle: RePEc:crm:wpaper:2414
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    References listed on IDEAS

    as
    1. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
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    4. 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.
    5. Gmyrek, Pawel, & Berg, Janine, & Bescond, David,, 2023. "Generative AI and jobs a global analysis of potential effects on job quantity and quality," ILO Working Papers 995324892702676, International Labour Organization.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial intelligence; Labour demand; Multi-country firm-level evidence;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • N34 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Europe: 1913-
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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