Automation and the changing nature of work

Cecily Josten and Grace Lordan

PLOS ONE, 2022, vol. 17, issue 5, 1-15

Abstract: This study identifies the job attributes, and in particular skills and abilities, which predict the likelihood a job is recently automatable drawing on the Josten and Lordan (2020) classification of automatability, EU labour force survey data and a machine learning regression approach. We find that skills and abilities which relate to non-linear abstract thinking are those that are the safest from automation. We also find that jobs that require ‘people’ engagement interacted with ‘brains’ are also less likely to be automated. The skills that are required for these jobs include soft skills. Finally, we find that jobs that require physically making objects or physicality more generally are most likely to be automated unless they involve interaction with ‘brains’ and/or ‘people’.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0266326

DOI: 10.1371/journal.pone.0266326

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