The geography of job automation in Ireland: what urban areas are most at risk?

Frank Crowley () and Justin Doran
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Frank Crowley: University College Cork

The Annals of Regional Science, 2023, vol. 71, issue 3, No 10, 727-745

Abstract: Abstract Future automation and artificial intelligence technologies are expected to have a major impact on labour markets. There is a lack of analysis which considers the sub-national geographical implications of automation risk posed to employment. In this paper, we identify the proportion of jobs at risk of automation across all Irish towns, using the occupational methodology of Frey and Osborne (2017) and compare these results with those of the task-based methodology of Nedelkoska and Quintini (2018). The job risk of automation varies significantly across towns, and while there is a substantial difference in the magnitude of risk identified by the occupational and task-based approaches, the correlation between them is approximately 95% in our analysis. The proportion of jobs at high risk (> 70% probability of automation) across towns using the occupational based methodology varies from a high of 58% to a low of 25%. In comparison, the proportion of jobs at high risk using the task-based methodology varies from 26 to 11%. Factors such as education levels, age demographics, urban size, and industry structure are important in explaining job risk across towns. Our results have significant implications for local and regional urban policy development in the Irish case.

JEL-codes: J21 J38 R11 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00168-022-01180-4

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