The Impact of Displacement on the Earnings of Workers in Ireland

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Nóirín McCarthy University College Cork and Peter W. Wright University of Sheffield

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This paper looks at the experiences of displaced workers in Ireland over the years 2005-2011, from a period of sustained economic growth followed by a sharp downturn in 2008. The previous years of high economic growth were followed by an increase in unemployment from approximately 4 per cent in 2005 to around 14 per cent in 2011 as noted by the World Bank (2018). In Ireland, Holton and O’Neill (2017) noted that the effects of the recession were exacerbated by the collapse in the construction sector.  Workers who lost their jobs due to a closure or mass layoff, referred to as “displaced workers” in this paper, often faced major adjustment costs including periods of unemployment.  Those who found a new job often experienced a fall in their earnings compared with those of their previous job.  The effect of job displacement, whether through a layoff or closure, on the subsequent earnings of workers has been studied in many countries.  This paper is the first to be look at displaced Irish workers.


This paper looks at the job earnings losses associated with displacement from both firm closure and mass layoffs.  This was done using a unique linked employer-employee dataset (P35) for the period 2005-2011. The dataset, based on information collected by the Revenue Commissioners and available through the Central Statistics Office (CSO) Ireland, contains approximately two million employee records in each file annually.  


This paper makes four key contributions.  Firstly, it provides estimates of earnings losses resulting from the displacement of Irish workers using a unique dataset of almost all paid employees in Ireland.  The countrywide data sets it apart from previous studies which used either survey data or a sample of administrative data. This dataset spans a period of remarkable shifts in the Irish economy and labour market. Its size allows for a detailed examination of displacement with matching techniques. Secondly, in an Irish context, the impact of the length of an unemployment spell on subsequent earnings losses is examined. Thirdly, the paper looks at the role of demographic characteristics (gender and age) in explaining the earnings losses of workers in Ireland.  Finally, the period of analysis is sub-divided and the experience of those displaced between 2005 and 2007 is compared to those displaced between 2008 and 2010.


Evidence is found of large earnings losses for displaced workers in Ireland.  Displaced workers are in one of two mutually exclusive groups.  Those who experienced a mass-layoff in enterprises with more than 50 employees, experienced an earnings loss of 77% immediately following displacement.   Displaced workers who experienced job closure  had an earnings loss of 46% relative to their earnings prior to displacement.  Although a recovery in earnings during the period studied was observed it should be noted that they did not return to their pre-displacement level for either the closure or mass-layoff groups.  However, those who found re-employment immediately after displacement experienced smaller losses in both the closure and mass-layoff samples.  Workers who experienced a period of unemployment (with no employment earnings) experienced greater longer-term financial losses as expected. Estimates from both samples suggest workers who switched employment to another industry experienced greater earnings losses relative to displaced workers who secured re-employment in the same industry they were displaced from. When analysing earnings losses associated with displacement before and after 2008, those displaced after 2008 experienced greater losses, particularly in the mass-layoff group. 


Holton, N. and D. O’ Neill, 2017. “The Changing Nature of Irish Wage Inequality from Boom to Bust”, The Economic and Social Review, Vol. 48, pp. 1-26.

The World Bank, 2018. The World Bank Development Indicators [Online]. Available: = 2&country = IRL&series = &period = #