IDEAS home Printed from https://ideas.repec.org/p/nip/nipewp/14-2023.html
   My bibliography  Save this paper

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

Author

Listed:
  • Erik Engberg

    (Örebro University)

  • Holger Görg

    (University of Kiel; Kiel Institute for the World Economy)

  • Magnus Lodefalk

    (Department of Economics, Örebro University)

  • Farrukh Javed

    (Örebro University)

  • Martin Längkvist

    (Örebro University)

  • Natália Monteiro

    (NIPE/Center for Research in Economics and Management, University of Minho, Portugal)

  • Hildegunn Kyvik Nordås

    (Council on Economic Policies)

  • Giuseppe Pulito

    (Rockwool Foundation Berlin)

  • Sarah Schroeder

    (Aarhus University)

  • Aili Tang

    (Örebro University
    NIPE/Center for Research in Economics and Management, University of Minho, Portugal)

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 groups of workers.

Suggested Citation

  • Erik Engberg & Holger Görg & Magnus Lodefalk & Farrukh Javed & Martin Längkvist & Natália Monteiro & Hildegunn Kyvik Nordås & Giuseppe Pulito & Sarah Schroeder & Aili Tang, 2023. "AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries," NIPE Working Papers 14/2023, NIPE - Universidade do Minho.
    • Handle: RePEc:nip:nipewp:14/2023
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    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. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    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. 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.
    5. 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.
    6. 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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fossen, Frank M. & McLemore, Trevor & Sorgner, Alina, 2024. "Artificial Intelligence and Entrepreneurship," IZA Discussion Papers 17055, Institute of Labor Economics (IZA).
    2. Ekaterina Prytkova & Fabien Petit & Deyu Li & Sugat Chaturvedi & Tommaso Ciarli, 2024. "The Employment Impact of Emerging Digital Technologies," CEPEO Working Paper Series 24-01, UCL Centre for Education Policy and Equalising Opportunities, revised Feb 2024.
    3. Albanesi, Stefania & Dias da Silva, Antonio & Jimeno, Juan Francisco & Lamo, Ana & Wabitsch, Alena, 2023. "New Technologies and Jobs in Europe," CEPR Discussion Papers 18220, C.E.P.R. Discussion Papers.
    4. Rafael Novella & David Rosas-Shady & Alfredo Alvarado, 2023. "Are we nearly there yet? New technology adoption and labor demand in Peru," Science and Public Policy, Oxford University Press, vol. 50(4), pages 565-578.
    5. Albanesi, Stefania & Da Silva, António Dias & Jimeno, Juan Francisco & Lamo, Ana & Wabitsch, Alena, 2023. "Reports of AI ending human labour may be greatly exaggerated," Research Bulletin, European Central Bank, vol. 113.
    6. Pons Rotger, Gabriel & Rosholm, Michael, 2020. "The Role of Beliefs in Long Sickness Absence: Experimental Evidence from a Psychological Intervention," IZA Discussion Papers 13582, Institute of Labor Economics (IZA).
    7. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    8. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2020. "AI and Jobs: Evidence from Online Vacancies," NBER Working Papers 28257, National Bureau of Economic Research, Inc.
    9. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    10. Songul Tolan & Annarosa Pesole & Fernando Martinez-Plumed & Enrique Fernandez-Macias & José Hernandez-Orallo & Emilia Gomez, 2020. "Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks," JRC Working Papers on Labour, Education and Technology 2020-02, Joint Research Centre.
    11. Dario Guarascio & Jelena Reljic & Roman Stollinger, 2023. "Artificial Intelligence and Employment: A Look into the Crystal Ball," LEM Papers Series 2023/34, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    12. Jin Liu & Xingchen Xu & Xi Nan & Yongjun Li & Yong Tan, 2023. ""Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets," Papers 2308.05201, arXiv.org, revised Jun 2024.
    13. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    14. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2023. "A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    15. Pelin Ozgul & Marie-Christine Fregin & Michael Stops & Simon Janssen & Mark Levels, 2024. "High-skilled Human Workers in Non-Routine Jobs are Susceptible to AI Automation but Wage Benefits Differ between Occupations," Papers 2404.06472, arXiv.org.
    16. Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
    17. Pablo Casas & Concepción Román, 2024. "The impact of artificial intelligence in the early retirement decision," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(3), pages 583-618, August.
    18. Borsato, Andrea & Lorentz, André, 2023. "The Kaldor–Verdoorn law at the age of robots and AI," Research Policy, Elsevier, vol. 52(10).
    19. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
    20. Pablo Casas & José L. Torres, 2023. "Automation, automatic capital returns, and the functional income distribution," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 32(1), pages 113-135, January.

    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

    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:nip:nipewp:14/2023. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: NIPE (email available below). General contact details of provider: https://edirc.repec.org/data/nipampt.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.