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    <link>https://hdl.handle.net/10419/195050</link>
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    <pubDate>Sat, 02 May 2026 02:05:24 GMT</pubDate>
    <dc:date>2026-05-02T02:05:24Z</dc:date>
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      <title>The impact of Covid-19 restrictions on workers: Who is most exposed?</title>
      <link>https://hdl.handle.net/10419/219047</link>
      <description>Title: The impact of Covid-19 restrictions on workers: Who is most exposed?
Authors: Crowley, Frank; Doran, Justin; Ryan, Geraldine
Abstract: The coronavirus is severely disrupting labour markets. Businesses that rely on face-to-face communication or close physical proximity between co-workers and with customers are particularly vulnerable. While interventions such as occupational social distancing and remote working have become widespread responses to the pandemic, we know very little about which workers will be affected the most by these interventions. Does our age, gender, marital status, educational attainment, occupation, or location affect our ability to practice occupational socially distancing, or our ability to work remotely? Social distancing and remote working potential indices are constructed, by occupation, using O*Net data, and this is matched to individual level data on over 150,000 individuals in employment from the Irish Census 2011. This allows us to identify, at the individual level, worker characteristics which can explain the degree to which a given individual working in a certain occupation may be able to effectively socially distance in their workplace or engage in remote work. Our results indicate that Covid-19 restrictions are unequal across workers. Notably younger, male, less educated, non-nationals, the self-employed and those located outside the capital will find it more difficult to work remotely and more difficult to practice socially distancing in the workplace.</description>
      <pubDate>Wed, 01 Jan 2020 00:00:00 GMT</pubDate>
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      <dc:date>2020-01-01T00:00:00Z</dc:date>
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      <title>Death, demography and the denominator: New Influenza-18 mortality estimates for Ireland</title>
      <link>https://hdl.handle.net/10419/218898</link>
      <description>Title: Death, demography and the denominator: New Influenza-18 mortality estimates for Ireland
Authors: Colvin, Christopher L.; McLaughlin, Eoin
Abstract: Using the Irish experience of the Spanish flu, we demonstrate that pandemic mortality statistics are sensitive to the demographic composition of a country. We build a new demographic database for Ireland's 32 counties with vital statistics on births, ageing, migration and deaths. We then show how age-at-death statistics in 1918 and 1919 should be reinterpreted in light of these data. Our new estimates suggest the very young were most impacted by the flu. New studies of the economic impact of Influenza-18 must better control for demographic factors if they are to yield useful policy-relevant results. Covid-19 mortality statistics must go through a similar procedure so policymakers can better target their public health interventions.</description>
      <pubDate>Wed, 01 Jan 2020 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/218898</guid>
      <dc:date>2020-01-01T00:00:00Z</dc:date>
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      <title>Covid-19, occupational social distancing and remote working potential in Ireland</title>
      <link>https://hdl.handle.net/10419/218897</link>
      <description>Title: Covid-19, occupational social distancing and remote working potential in Ireland
Authors: Crowley, Frank; Doran, Justin
Abstract: The Covid-19 pandemic has had a sudden and drastic impact on labour supply and output In Ireland. As the Irish government responds, a key question is how covid-19 will impact people and places differently. There is considerable uncertainty around the implications of social distancing measures and remote working for the Irish labour market. The objective of this paper is to get a better understanding of the social distancing and remote working potential at an occupational, sector and regional level in Ireland. We generate two indices which capture the potential impact of Covid-19 through identifying (i) the occupations which may have the most potential to engage in social distancing procedures and (ii) the occupations which may have the most scope for remote working. This is accomplished using occupational level data from O*NET which provides very detailed information of the tasks performed by individuals with their occupations. The paper identifies that social distancing and remote working potential differs considerably across occupations, sectors and places. Examples of large employment which have relatively high indices are teaching occupations at secondary and third level and programme and software developers. While occupations which have large employment but which possess relative low indices are nurses and midwives and care workers. The potential for social distancing and remote work favours occupations located in the Greater Dublin region and provincial city regions. At a town level - more affluent, more densely and highly populated, better educated and better broadband provisioned towns have more jobs with greater potential for social distancing and remote working.</description>
      <pubDate>Wed, 01 Jan 2020 00:00:00 GMT</pubDate>
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      <dc:date>2020-01-01T00:00:00Z</dc:date>
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      <title>Automation and Irish Towns: Who's Most at Risk?</title>
      <link>https://hdl.handle.net/10419/195055</link>
      <description>Title: Automation and Irish Towns: Who's Most at Risk?
Authors: 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.</description>
      <pubDate>Tue, 01 Jan 2019 00:00:00 GMT</pubDate>
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      <dc:date>2019-01-01T00:00:00Z</dc:date>
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