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Automation and Irish Towns: Who's Most at Risk?

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  • Crowley, Frank
  • Doran, Justin

Abstract

Future automation and artificial intelligence technologies are expected to have a major impact on the labour market. Despite the growing literature in the area of automation and the risk it poses to employment, there is very little analysis which considers the sub-national geographical implications of automation risk. This paper makes a number of significant contributions to the existing nascent field of regional differences in the spatial distribution of the job risk of automation. Firstly, we deploy the automation risk methodology developed by Frey and Osborne (2017) at a national level using occupational and sector data and apply a novel regionalisation disaggregation method to identify the proportion of jobs at risk of automation across the 200 towns of Ireland, which have a population of 1,500 or more using data from the 2016 census. This provides imputed values of automation risk across Irish towns. Secondly, we employ an economic geography framework to examine what types of local place characteristics are most likely to be associated with high risk towns while also considering whether the automation risk of towns has a spatial pattern across the Irish urban landscape. We find that the automation risk of towns is mainly explained by population differences, education levels, age demographics, the proportion of creative occupations in the town, town size and differences in the types of industries across towns. The impact of automation in Ireland is going to be felt far and wide, with two out of every five jobs at high risk of automation. The analysis found that many at high risk towns have at low risk nearby towns and many at low risk towns have at high risk neighbours. The analysis also found that there are also some concentrations of at lower risk towns and separately, concentrations of at higher risk towns. Our results suggest that the pattern of job risk from automation across Ireland demands policy that is not one size fits all, rather a localised, place-based, bottom up approach to policy intervention.

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  • Crowley, Frank & Doran, Justin, 2019. "Automation and Irish Towns: Who's Most at Risk?," SRERC Working Paper Series SRERCWP2019-1, University College Cork (UCC), Spatial and Regional Economic Research Centre (SRERC).
    • Handle: RePEc:zbw:srercw:srercwp20191
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    Cited by:

    1. Martin Labaj & Materj Vitalos, 2019. "Automation and labor demand in European countries: A task-based approach to wage bill decomposition," Department of Economic Policy Working Paper Series 021, Department of Economic Policy, Faculty of National Economy, University of Economics in Bratislava.
    2. Crowley, Frank & Doran, Justin & Ryan, Geraldine, 2020. "The impact of Covid-19 restrictions on workers: Who is most exposed?," SRERC Working Paper Series SRERCWP2020-3, University College Cork (UCC), Spatial and Regional Economic Research Centre (SRERC).
    3. Crowley, Frank & Daly, Hannah & Doran, Justin & Ryan, Geraldine & Caulfield, Brian, 2021. "The impact of labour market disruptions and transport choice on the environment during COVID-19," Transport Policy, Elsevier, vol. 106(C), pages 185-195.
    4. Crowley, Frank & Doran, Justin, 2020. "Covid-19, occupational social distancing and remote working potential in Ireland," SRERC Working Paper Series SRERCWP2020-1, University College Cork (UCC), Spatial and Regional Economic Research Centre (SRERC).

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