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The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam

Author

Listed:
  • Francesco Carbonero

    (University of Turin)

  • Jeremy Davies

    (East Village Software Consultants)

  • Ekkehard Ernst

    (ILO Research Department)

  • Frank M. Fossen

    (University of Nevada
    IZA)

  • Daniel Samaan

    (ILO Research Department)

  • Alina Sorgner

    (IZA
    John Cabot University
    IfW Kiel)

Abstract

AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers’ skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (Am Econ Assoc Pap Proc 108:43-47, 2018) for the US and the World Bank’s STEP survey for Lao PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in urban Viet Nam, in comparison to Lao PDR, are more concentrated in occupations affected by AI, which requires them to adapt or puts them at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes.

Suggested Citation

  • Francesco Carbonero & Jeremy Davies & Ekkehard Ernst & Frank M. Fossen & Daniel Samaan & Alina Sorgner, 2023. "The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam," Journal of Evolutionary Economics, Springer, vol. 33(3), pages 707-736, July.
  • Handle: RePEc:spr:joevec:v:33:y:2023:i:3:d:10.1007_s00191-023-00809-7
    DOI: 10.1007/s00191-023-00809-7
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    References listed on IDEAS

    as
    1. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2020. "AI and Jobs: Evidence from Online Vacancies," Working Papers 279, Princeton University, Department of Economics, Center for Economic Policy Studies..
    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. Ernst Ekkehardt & Merola Rossana & Samaan Daniel, 2019. "Economics of Artificial Intelligence: Implications for the Future of Work," IZA Journal of Labor Policy, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 9(1), pages 1-35, June.
    4. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2020. "AI and Jobs: Evidence from Online Vacancies," Working Papers 279, Princeton University, Department of Economics, Center for Economic Policy Studies..
    5. Daniel Alonso Soto, 2020. "Technology and the future of work in emerging economies: What is different," OECD Social, Employment and Migration Working Papers 236, OECD Publishing.
    6. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    7. Arntz, Melanie & Gregory, Terry & Zierahn, Ulrich, 2017. "Revisiting the risk of automation," Economics Letters, Elsevier, vol. 159(C), pages 157-160.
    8. Mariscal, Judith & Mayne, Gloria & Aneja, Urvashi & Sorgner, Alina, 2019. "Bridging the gender digital gap," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-12.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Artificial intelligence; Machine learning; Digitalization; Labor; Skills; Developing countries;
    All these keywords.

    JEL classification:

    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • 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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