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

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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, including language modelling. According to our model, white collar occupations are most exposed to AI, and espe- cially 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 Swe- den, 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 groups of workers.

Suggested Citation

  • Engberg, Erik & Görg, Holger & Lodefalk, Magnus & Javed, Farrukh & Längkvist, Martin & Monteiro, Natália & Kyvik Nordås, Hildegunn & Pulito, Giuseppe & Schroeder, Sarah & Tang, Aili, 2023. "AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries," Ratio Working Papers 370, The Ratio Institute.
    • Handle: RePEc:hhs:ratioi:0370
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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. 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.
    3. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
    4. Edward W. Felten & Manav Raj & Robert Seamans, 2018. "A Method to Link Advances in Artificial Intelligence to Occupational Abilities," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 54-57, May.
    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.
    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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