The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis
Open accessAbstract
In recent years, there has been a revival of concerns that automation and digitalisation might after all result in a jobless future. The debate has been fuelled by studies for the US and Europe arguing that a substantial share of jobs is at “risk of computerisation”. These studies follow an occupation-based approach proposed by Frey and Osborne (2013), i.e. they assume that whole occupations rather than single job-tasks are automated by technology. As we argue, this might lead to an overestimation of job automatibility, as occupations labelled as high-risk occupations often still contain a substantial share of tasks that are hard to automate. Our paper serves two purposes. Firstly, we estimate the job automatibility of jobs for 21 OECD countries based on a task-based approach. In contrast to other studies, we take into account the heterogeneity of workers’ tasks within occupations. Overall, we find that, on average across the 21 OECD countries, 9 % of jobs are automatable. The threat from technological advances thus seems much less pronounced compared to the occupation-based approach. We further find heterogeneities across OECD countries. For instance, while the share of automatable jobs is 6 % in Korea, the corresponding share is 12 % in Austria. Differences between countries may reflect general differences in workplace organisation, differences in previous investments into automation technologies as well as differences in the education of workers across countries.
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Cites
The Skill Content of Recent Technological Change: An Empirical Exploration
Autor, D. H., Levy, F., & Murnane, R. J. (2003) · The Quarterly Journal of Economics · doi:10.1162/003355303322552801 · Cited by 6,205
Why Are There Still So Many Jobs? The History and Future of Workplace Automation
Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917
Skills, Tasks and Technologies: Implications for Employment and Earnings
Acemoglu, D., & Autor, D. (2011) · Handbook of Labor Economics · doi:10.1016/s0169-7218(11)02410-5 · Cited by 3,799
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Skills, Tasks and Technologies: Implications for Employment and Earnings
Acemoglu, D., & Autor, D. (2010) · doi:10.3386/w16082 · Cited by 2,180
Explaining Job Polarization: Routine-Biased Technological Change and Offshoring
Goos, M., Manning, A., & Salomons, A. (2014) · American Economic Review · doi:10.1257/aer.104.8.2509 · Cited by 2,111 · cited 3×
“Similarly, Goos et al. (2014) develop a model of labour demand at the industry level for 16 European countries and find that routine-intensive industries that are at a higher risk of introducing labour-saving technologies gain”
BackgroundPolanyi's Paradox and the Shape of Employment Growth
Autor, D. (2014) · doi:10.3386/w20485 · Cited by 323
OECD Digital Economy Outlook 2015
OECD (2015) · OECD Digital Economy Outlook · doi:10.1787/9789264232440-en · Cited by 272
Cited by
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Brave new world: service robots in the frontline
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018) · Journal of Service Management · doi:10.1108/josm-04-2018-0119 · Cited by 2,315
Artificial Intelligence and the Public Sector—Applications and Challenges
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019) · International Journal of Public Administration · doi:10.1080/01900692.2018.1498103 · Cited by 1,341
Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.08.018 · Cited by 431
“In contrast, some scholars predict that AI technology at work will affect the nature of tasks not jobs (Arntz et al., 2016).”
Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F. J., Molina, D., Fernández, B., et al. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02264-y · Cited by 276
Metadata
- Venue
- OECD Social, Employment and Migration Working Papers
- Published
- 2016-05-14
- ISSN
- 1815-199X (ISSN-L)
- OpenAlex
- W2372356850
- Cited by
- 847
- References
- 8
- Type
- report
- Language
- en
- Open access
- bronze
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