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Back to the future: Changing job profiles in the digital age

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  • Lorenz, Hanno
  • Stephany, Fabian

Abstract

In light of increasingly "smarter" technologies, the future of (human) labour is questioned on a daily basis. A study by Frey and Osborne (2013), one of the most recognised contributions in this domain, estimated that half of the US labour force is highly susceptible to computerisation in the near future. Their findings have been applied for several follow-up investigations in other countries. However, the transferability of the results is limited by the set-up of the study. In contrast to previous investigations, our approach tries to overcome past shortcomings by collecting assessments on the susceptibility to digital technologies for the Austrian labour market by Austrian experts. We show that the diversity of previous findings regarding the degree of job automatisation is to a large extent driven by model selection and not by controlling for personal characteristics or tasks. Our results indicate that while clerical computer-based routine jobs are likely to change in the next decade, professional activities, e.g., the processing of complex information, are prone to digital change.

Suggested Citation

  • Lorenz, Hanno & Stephany, Fabian, 2018. "Back to the future: Changing job profiles in the digital age," Working Papers 13, Agenda Austria.
    • Handle: RePEc:zbw:agawps:13
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    Citations

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    Cited by:

    1. Stephany, Fabian, 2020. "There is Not One But Many AI: A Network Perspective on Regional Demand in AI Skills," OSF Preprints 32qws, Center for Open Science.
    2. Lorenz, Hanno & Stephany, Fabian, 2018. "Back to the future: Changing job profiles in the digital age," Working Papers 13, Agenda Austria.
    3. Stephany, Fabian, 2021. "When Does it Pay Off to Learn a New Skill? Revealing the Complementary Benefit of Cross-Skilling," SocArXiv sv9de, Center for Open Science.
    4. 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.
    5. Fabian Stephany, 2020. "When Does it Pay Off to Learn a New Skill? Revealing the Complementary Benefit of Cross-Skilling," Papers 2010.11841, arXiv.org, revised Feb 2021.
    6. Gerten Elisa & Beckmann Michael & Bellmann Lutz, 2019. "Controlling Working Crowds: The Impact of Digitalization on Worker Autonomy and Monitoring Across Hierarchical Levels," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(3), pages 441-481, June.

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    More about this item

    Keywords

    Bayesian; Classification; Employment; GLM; Technological Change;
    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
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion
    • 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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