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The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis

Author

Listed:
  • Melanie Arntz

    (ZEW Mannheim)

  • Terry Gregory

    (ZEW Mannheim)

  • Ulrich Zierahn

    (ZEW Mannheim)

Abstract

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. Ces dernières années, les craintes que l’automatisation et la numérisation aboutissent finalement à un futur sans emploi se sont réveillées. Le débat a été alimenté par des études sur les États-Unis et l’Europe arguant qu’une grande partie des emplois étaient en « risque d’informatisation ». Ces études utilisent une méthode basée sur les professions proposée par Frey et Osborne (2013), c’est-à-dire qu’elles supposent que les professions dans leur ensemble et non les tâches isolées sont automatisées. Comme nous l’avançons, cette hypothèse peut mener à la surestimation de l’automatisation des emplois, puisque les professions dites à haut risque comprennent souvent une part substantielle de tâches difficiles à automatiser. Notre article a un double objectif. D’une part, nous estimons par une approche basée sur les tâches la possibilité d’automatiser les emplois pour 21 pays de l’OCDE. A la différence d’autres études, nous prenons en compte l’hétérogénéité des tâches au sein des professions. Globalement, nous estimons que 9 % des emplois sont automatisables en moyenne dans les 21 pays de l’OCDE. La menace générée par les avancées technologiques semble donc bien moindre que celle donnée par la méthode basée sur les professions. Nous trouvons également que les pays de l’OCDE sont hétérogènes en la matière. Par exemple, alors que la part des emplois automatisables représente 6 % en Corée, elle s’élève à 12 % en Autriche. Les différences entre pays peuvent être le reflet des diversités concernant l’organisation du lieu de travail en général, des différences dans les investissements faits auparavant dans les technologies d’automatisation ou encore des variations dans les niveaux d’éducation des travailleurs.

Suggested Citation

  • Melanie Arntz & Terry Gregory & Ulrich Zierahn, 2016. "The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis," OECD Social, Employment and Migration Working Papers 189, OECD Publishing.
  • Handle: RePEc:oec:elsaab:189-en
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    File URL: http://dx.doi.org/10.1787/5jlz9h56dvq7-en
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    More about this item

    JEL classification:

    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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