Abstract
What are the socio-economic effects of the widespread introduction of robots, algorithms and digital technologies like artificial intelligence and machine learning? Following Frey and Osborne (London futures agiletown : the relentless march of technology and London’s response. Deloitte, 2014, Technol Forecast Social Change 114(C), 254–280, 2017, https://doi.org/10.1016/j.techfore.2016.08.019) we apply the computerization probabilities to occupations in Austria. We conclude that about 40% of the Austrian workforce is active in occupations that are very likely to undergo substantial changes regarding task structure, skill requirement and working environment in the future, causing challenges and opportunities. We also provide evidence that compared to men, women in Austria seem more likely to be affected by technological changes, with sectoral orientation playing a role. Following EBRD (Skills, employment and automation. Chapter 2 in: EBRD (2018): Transition Report 2018–19, European Bank for Reconstruction and Development, London, 2018),we see a broader move towards job polarization. We see this as distributive consequences of technological change and argue that the consequences of technology refashioning socio-economic development are influencing market processes, actors and inequalities. As in previous technological advances, coping with these changes will require efforts on the individual as well as on the political level.
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Notes
The work Frey and Osborne (2017) was already published in 2013 as a working paper. Inspired by this working paper, a series of similar works were created for other countries after 2013.
o*net: website that provides information on jobs in the US including task and skill profiles, for details see: https://www.onetonline.org/
This describes a tendency or a trend, but there are also significant deviations from the trend. We can find significant differences in income per capita between occupations with very similar computerization probabilities. When interpreting this relationship one should keep in mind that the estimated probabilities show purely technical (and not economic) potentials or possibilities. Therefore, more expensive workers do not automatically have higher probabilities. One can rather expect that occupations with lower technical replacement probabilities have by tendency higher income.
A comparison of our study with Nagl et al. (2017) with regard to the average probabilities instead of the proportion of employees with the highest probabilities shows that the results of both studies are quite similar.
The work Frey and Osborne (2017) was already published in 2013 as a working paper. Inspired by this working paper, a series of similar works were created for other countries after 2013.
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Acknowledgement
The authors gratefully acknowledge the valuable comments received on earlier versions of this paper from the participants of the international conference “Digitalization and Smart Green (R)evolution” in November 2018 in Bratislava (Slovak Republic), the Annual Meeting of the Austrian Economic Association in April 2019 in Graz (Austria), the 30th Anniversary SASE Meeting in June 2019 in New York City (USA), Andreas Eder, Wolfgang Koller, and one anonymous reviewer. Bernhard Mahlberg wishes to thank the Austrian Research Promotion Agency (FFG) for its financial support (project number: 854187). The opinions expressed are the authors’ personal views and not necessarily those of the institutions the authors are affiliated with. The usual disclaimers apply.
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Haiss, P., Mahlberg, B. & Michlits, D. Industry 4.0–the future of Austrian jobs. Empirica 48, 5–36 (2021). https://doi.org/10.1007/s10663-020-09497-z
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DOI: https://doi.org/10.1007/s10663-020-09497-z