Spatial and Regional Economics Research Centre - Working Papers

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    Automation and Irish towns: who's most at risk?
    (Spatial and Regional Economics Research Centre, University College Cork, 2019) Crowley, Frank; Doran, Justin
    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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    Cross sectoral differences in the drivers of innovation: Evidence from the Irish community innovation survey
    (School of Economics, University College Cork, 2012-07) Doran, Justin; Jordan, Declan
    This paper analyses differences across sectors in firms’ propensity to innovate and the relative importance of inputs to innovation classifying firms into four broad sectors. The propensity and drivers of four types of innovation (new to firm, new to market, process and organisational) within these sectors are then analysed. The results indicate that, for new to firm and new to market innovation, there is a strong degree of heterogeneity in the drivers of innovation across sectors. The propensity to introduce process or organisational innovations varies slightly across sectors but that there is no evidence of differences across sectors in the drivers of innovation. These results have important implications for policy instruments to meet the needs of targeted firms.
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    The effects of geography on innovation in small to medium sized enterprises in the South-East and South-West of Ireland
    (School of Economics, University College Cork, 2009-04) Doran, Justin; Jordan, Declan; O'Leary, Eoin
    This paper analyses the effects of geography on innovation by small and medium sized enterprises in the South-West and South-East regions of Ireland. Using an augmented innovation production function it estimates, both directly and indirectly, the effects of interaction with geographically proximate external agents and agglomeration economies on product and process innovation in these enterprises. The findings question the premise that geography matters for innovation in the Irish case. There is little evidence that local/regional interaction is more important for innovation and the close availability of a skilled labour pool and a range of urbanization indicators have no effect.