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Back to the Future - Changing Job Profiles in the Digital Age

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  • Stephany, Fabian
  • Lorenz, Hanno

Abstract

The uniqueness of human labour is at question in times of smart technologies. The 250 years-old discussion on technological unemployment reawakens. Frey and Osborne (2013) estimate that half of US employment will be automated by algorithms within the next 20 years. Other follow-up studies conclude that only a small fraction of workers will be replaced by digital technologies. The main contribution of our work is to show that the diversity of previous findings regarding the degree of job automation is, to a large extent, driven by model selection and not by controlling for personal characteristics or tasks. For our case study, we consult Austrian experts in machine learning and industry professionals on the susceptibility to digital technologies in the Austrian labour market. Our results indicate that, while clerical computer-based routine jobs are likely to change in the next decade, professional activities, such as the processing of complex information, are less prone to digital change.

Suggested Citation

  • Stephany, Fabian & Lorenz, Hanno, 2019. "Back to the Future - Changing Job Profiles in the Digital Age," EconStor Preprints 202035, ZBW - Leibniz Information Centre for Economics.
  • Handle: RePEc:zbw:esprep:202035
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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

    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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