The Origins of WUERC – Part 1

Sociology teaches us the importance of understanding that all things have histories, and of course WUERC is no exception. Today we will start at the beginning (or as near to the beginning as we can get).

It was June 2012, the world was still reeling from the fallout of the global financial crisis. The received wisdom instructed governments to pursue a programme of governance whereby spending would be cut, and taxes would be raised. This, we were told would balance the budget and enable a quick recovery, this strategy was known as austerity.

Along with the range of cost saving measures came different ways of ‘doing’ government. The old model of welfare being universal (or almost universal) and unconditional (or almost unconditional) was to draw to a close. Ireland was facing rates of unemployment in excess of 15%, meaning that the tax take of the government was reduced (due to fewer people working) at a time when spending was increasing (due to more people claiming unemployment benefits) and, so the argument went: something would have to be done.

Image Credit:

In 2011 a new coalition government was formed by the Fine Gael and Labour parties, and part of its strategy to reduce unemployment was the introduction of ALMP’s, or Active Labour Market Policies. The general argument was that, while in the immediate aftermath of 2007/2008 people had good reason to be unemployed, it was now almost five years later. It was time to put pressure on people to seek employment, which would aid in economic recovery. Like the austerity which inspired it, ALMP’s were as much a moral argument as they were an economic one. The unemployed were therefore characterised as ‘inactive’ (with its connotations of laziness), and required incentives and interventions in order to get a job, thereby becoming ‘active’.

Many of the schemes introduced by this coalition government have gone on to enjoy a level of infamy in Irish socio-political culture, but at the time while they were roundly criticised, it is also true that virtually everything every Irish government does is roundly criticised. There was a feeling that understanding was needed, consideration and analysis rather than immediately jumping to critique.

Thus in 2012, Ray Griffin (WIT), and Tom Boland (then at WIT, now at UCC), decided that they would run a summer project to study the experience of unemployment. This project was named the Waterford Unemployment Experiences Research Collaborative, and I can tell you that considerable effort was put in so that the acronym would spell ‘WUERC’ (pronounced ‘work’).

I don’t think anyone knew exactly what the project would produce, but our goal from the start was to understand unemployment. And a significant part of this was attempting to describe the experience of being unemployed. This sometimes caused confusion when we attempted to explain it to people, what did we mean the ‘experience’ of being unemployed? Unemployment, virtually by definition is a temporary state of being, when you are unemployed you are looking for work, and when you eventually find it then you are not unemployed anymore. But it was the very inability to speak about unemployment as a state of being that intrigued us, how could this experience be so common and yet go almost completely without qualitative or subjective description?

WUERC therefore recruited about a dozen undergraduate students who had finished their degrees and were about to graduate, I was in this group, along with Aisling Tuite who would go on to make a huge contribution towards the HECAT application and become an excellent researcher in her own right. To direct the students were half a dozen faculty members from WIT. Everyone who participated gave up their time for free, and we wished to inculcate our values of fairness and voluntarism into the project. WUERC had virtually no funding to speak of, though we were granted ethical approval by WIT to speak to the unemployed, which Waterford specifically, and the South-East generally had in abundance:

Image Credit: Michael Taft’s notesonthefront blog
Data from 2012

WIT also allowed us to use some of the rooms on the College St. Campus, which was very helpful to give us a place where we could meet and discuss our findings.

WIT says they're not closing College Street library | WLR
College St. Campus, WIT

In-fact, part of what would make the project work so well were the dozen undergraduate students who were (in the very near future) going to graduate. As we had finished all our coursework, we were hurtling towards the labour market at breakneck speed. This gave us a unique opportunity to study the unemployment system from the inside, and observe the ALMP’s as they were used in practice (on us in fact).

This was only to be the beginning, stay tuned for part 2.

Discussions of Welfare

The Cave

The Purgatorial Ethic and the Spirit of Welfare

August 27, 2017

Tom Boland talks about his recently published article “The Purgatorial Ethic and the Spirit of Welfare”.

WUERCing Blog – Latest Post

What is the PEX algorithm and why is it important to study it?

Published: 30th June 2017

Author: Aisling Tuite


In December 2016 we received funding from the Irish Research Council under the New Horizons Interdisciplinary Project Scheme to carry out a pilot project titled; Understanding Unemployment in the era of Big Data: Exploring how data-driven theory and algorithmic knowledge can support better policy and personal decision making. The purpose of the research is to explore the PEX (Probability of Exit) algorithm. Below are some initial notes on how we have begun to explore this algorithm and its effect on unemployed people.



There is no doubt that in the digital age the collation of large data sets and their organisation into useful ways for categorising and targeting is set to become increasingly prominent and an interesting topic to study. Algorithms are essentially a set of functions that use logic mathematical patterns to solve problems. They are part of the discrete mathematics family that feed into computing. We hear a lot about them, especially in the ‘online’ world where specific relevant adverts can be targeted towards us based on categories such as our age or gender. This is a very simplistic understanding of algorithms, many of which are far more complex. It is the everyday algorithms that we hear most about – those that control and influence our choices, whether as a consumer of goods or news. Algorithms are not new, but combined with large quantities of data stored online and increased computation power they will continue to influence our everyday lives. For this reason we need to study algorithms of all types, as Kavanagh, McGarry & Kelly (2015) note when exploring the possibility of carrying out an ethnography of an algorithm that their design is inaccessible to the observer, however, this is not always the case. The PEX algorithm is localised and has the benefit of being transparent, as opposed to those used by global corporate entities, it is therefore a good starting point for the study of algorithms.

Our interest in the PEX algorithm is two-fold. PEX came to our attention through our research into unemployment. At the time of our initial WUERC research project and the subsequent years of producing the Sociology of Unemployment PEX was in its infancy and was only being rolled out across the country with the introduction of the new DSP Intreo offices. While unemployment is our main interest we are also concerned with modern and future developments of policy on unemployment and the use of longitudinal datasets and algorithms to inform the DSP and Intreo agents is part of this ongoing process. As primarily social science researchers we have the tools and knowledge to develop an understanding of the social consequences of the use of algorithms but not the technical understanding. Therefore, we have teamed up with our STEM partner/mathematician Dr Aoife Hennessy to develop a more rounded understanding of how algorithms are developed and how they interact with society.


How and Why was PEX developed?

The Probability of Exit (PEX) algorithm was developed by the Economic and Social Research Institute (ESRI) at the request of the Department of Social Protection (DSP). It follows on from some previous attempts at understanding the requirements for reducing unemployment/live register numbers in the mid to late 1990s. It is important to note that attempts at understanding and profiling unemployed people have a longer history and that the driving force behind developing PEX was not the 2008 financial and employment crisis.

The algorithm was created by attaching an additional questionnaire to all new entrants into the social welfare system in a three month window from September 2006. The final number of relevant questionnaires was 30,762 people who received either Jobseekers Benefit and/or Jobseekers Allowance in this time. Respondents were then traced over a further period of 78 weeks which allowed for the development of six, twelve and fifteen month profiles. The types of questions asked were based on Age, Gender, Marital Status, Children, Perceived Health, Spousal Earnings, Employment/Unemployment History, Willingness to Relocate, Location, Transport and Education History. The result was a set of mathematical analyses that created two algorithms, one for male and one for female and with three classifications (low, medium, high) in each based on an individual’s probability of leaving the live register within 12 months. Subsequent intervention from case managers were to be informed by this classification with more intervention given to those with the lowest probability of exit.

The findings were reported in the document National Profiling of the Unemployed in Ireland in July 2009. This report largely presents a transparent process of collecting and analysing the data, it is available for anyone to read and download. The authors note that this is only a first attempt at profiling and welcome any comments on it. If we were to follow the Australians example (as detailed in the report) a series of readjustments would be necessary to accurately represent the current economic and social climate –  between 1994 and 2008 they made at least four significant changes to their profiling model. A second follow-up ERSI report was issued in June 2014 called Predicting the Probability of Long-Term Unemployment Using Administrative Data. This report focused on those who did not leave the live register when predictions suggest that they should have found employment, returned to education or otherwise ‘signed-off’. This study was in some ways an audit of PEX but, as I will discuss later could also be used for other, more sinister, reasons.


What are we interested in?

Over the past few months we have been considering the PEX algorithm and what it means to unemployed people and the future of welfare policy. To begin I will point out that in our discussions we are all open to the fact that the use of large datasets and algorithms are a part of our world and are something that will continue to be part of it. Where we see our research being of practical importance is in influencing policy at both national and European level. We want to ensure that the ‘human’ side is not forgotten. So, we have asked a number of questions that will inform our research.


Categories and classifications:

The categorisations that are chosen for the questionnaire are interesting. Age and Gender are pretty much the standard categories for any questionnaire. Yes, demographics are important but gender is not so clear cut. The report authors have divided this algorithm into male and female. It is based on anomalies between the two groups from their findings. But is it such a clear cut demographic? With marriage equality and a greater acceptance of personal gender identification is such a category fit for the future of profiling? Similarly age is a category that is not stable. The International Labour Organisation (ILO) defines youth unemployment as from the age of 15 to 24, whereas it is now appears that transitions to adulthood may be happening later. We question if these are perhaps somewhat lazy categories that we rarely question. I do not want to criticise the use of demographic categorisation, there cannot be an infinite number of categories, but this does highlight the inflexibility of using solely quantitative measures. The questionnaire does touch briefly on individual perceptions and responsibilities by asking questions about perceived health and willingness to relocate but for the most part there is little personal input into developing the PEX or, as I will discuss later in any future interventions. There are also mathematical elements that are questioned, which I discuss below.



As an overall discussion of the future use of algorithms and how they impact individuals who are unemployed we need to return to philosophical reasoning and question the ethics of using this method to profile and characterise individual people. As I have already mentioned there is very little personal input in the process of developing PEX and its subsequent use. Unemployment cannot be discussed without considering employment. Statistical profiling is rigid, the questions are asked and the answers determine where a person fits into an overall classification system. Intervention for unemployed people is (meant to be) given based on the likelihood of an individual finding work within 12 months. But what is this work and what type of interventions are given? Work needs to be sustainable and meaningful. We do not believe that individuals within the social welfare system are given much opportunity to discuss their aspirations for careers, their interests, or abilities to carry out particular types of work. If we do not question this we may fall into the trap of stereotyping people based on well-worn categorisations of class and gender; it may be assumed that a people from different social classes have differing perceptions on economic life, but such stereotyping is not cut and dry, what about those who fall outside these well-worn assumptions, we need to consider everyone. More involvement of individuals is an element which could be pivotal in helping individuals find employment that is meaningful and therefore sustainable in the long term. So, is it ethically moral to treat individuals as unthinking or unfeeling ‘numbers’ and force upon them employment that is uninteresting to them, where tasks may difficult to accomplish based on their skills, or in industries that expect high staff turnovers or are not sustainable for the long term?



We also question the robustness of the mathematical model both technically and in practice. This is something that the report authors invite. They note the iterations that other profiling models have gone through and recognise that improvements may be required and the development of the model is transparent. Some areas of interest are; how they picked the cut-off point for each of the classifications, the reliability of the data due to its age (as noted by the authors), and the usefulness of the model which is between 60 and 80% reliable on its predictions. So, we ask where the model is weakest and if there is any improvements that can be made. We are also considering what happens when questions are answered differently – that is could a different answer on a single question move a borderline case into a different category and what are the consequences of this? Currently we do not believe that many unemployed people are aware of this profiling, it is however information that is widely available. In the event that one classification of unemployed was seen to be treated better than another, what would the consequences of ‘gaming’ the questionnaire be?


The Consequences of PEX:

While we would like to delve further into the actual real life consequences of PEX, this is just a pilot project and of limited time. It is certainly something that needs further academic exploration. Currently our colleague Kenny Doyle is interviewing unemployed people for his PhD research. So, anecdotally we know some of the consequences. His work has allowed us some glimpse inside this system where subcontractors to the DSP are now engaged to carry out the job-finding intervention services. The early indications (although not rigidly proven, just anecdotal) are that these private companies may be giving more attention to those with a high PEX score (people who it would be easier to find a job for) rather than those with low PEX scores.

A second set of unintended consequences could be sanctions. The follow up report from 2014 may have been an attempt by the authors to audit their own work, but it presents a worrying opportunity for sanctions to be imposed on those who do not leave the live register within the timeframe that the PEX algorithm says they should.


What Next?

The above is a brief discussion of PEX and based on discussions of the project team over the initial months of the research.

We have two ongoing streams of research at the moment:

One is to gain access to the DSP’s longitudinal datasets, which is administered by the CSO. Then we can use the PEX model on a sample of claimants and see the effects that adjustments to the model have on PEX score.

The second is to conduct an ethnography of the algorithm. This is an emerging area of ethnography so the first stage is to conduct a literature review and then consider how this type of ethnography is possible. For this I am reading standard ethnography literature such as Van Maanen, literature on new forms of ethnographies, maths books, computing books, and design books. This is so I can understand the language, symbols and culture around an algorithm while pulling myself back from just concentrating on the social side. I will be putting some of my ideas and reports up here soon.


A symposium:

We are expecting to issue a call for papers soon for a Symposium to be held in December 2017. This will be designed to bring together anyone interested in unemployment for the future. We are also interested in expanding our research group to become more European focused and are looking for potential Horizon 2020 collaborators.

Understanding Unemployment in the Era of Big Data is funded through the IRC New Horizons Interdisciplinary Research Project Award. #loveirishresearch



Kavanagh, D., McGarry, S. & Kelly, S., 2015. Ethnography in and around an Algorithm. Athens, 30th EGOS Colloquium.

McGuiness, S., Kelly, E. & Walsh, J., 2014. Predicting the Probability of Long-Term Unemployment in Ireland using Administrative Data. [Online] Available at: [Accessed december 2016].

O’Connell, P. J., McGuiness, S., Kelly, E. & Walsh, J., 2009. National Profiling of the Unemployed in Ireland. [Online] Available at: [Accessed December 2016].

Rosen, K. H., 1999. Discrete Mathematics and Its Applications. 4th ed. s.l.:WCB/McGraw-Hill.







May 2017

The annual Economy and Society Summer School will take place from 8th – 12th May 2017 in Blackwater Castle Co. Cork.

Addressing the multiple and complex intersections of the economy and society, the week long doctoral symposium brings together scholars from many disciplines to discuss ‘economic’ themes such as; the market, the state, production, consumption, value, money, work, commodities, poverty and inequality.

The summer school is offered to post-graduate students in anthropology, economics, geography, history, organisation studies, management and marketing, politics and sociology. It is specifically tailored to the academic development of doctoral candidates

The Economy & Society summer school is hosted by the Waterford Institute of Technology (WIT) and University College Cork (UCC) research group Centre for the Study of the Moral Foundations of Economy & Society and has been developed under the auspices of the President of Ireland’s Ethics Initiative, an attempt to address recent and on-going crises, to understand and criticise the thinking which put these processes in train.


Economy and Society Summer School

Centre for the Study of the Moral Foundations of Economy and Society

New Labour Market

The New Labour Market

In recent times the focus of jobs and work have shifted from steady employment and jobs-for-life into boundaryless careers and precarity of employment. This stream explores entrepreneurship and innovation, new forms of work and the shifting landscape of industry. More information on this project can be found in the New Labour Market tab and throughout the blog postings.

Welfare Conditionality and Sanctions

Welfare Conditionality and Sanctions

This stream explores the increasing trend towards imposing conditions and sanctions on unemployed people, whether through demographics (e.g. age) or failure to find employment. Increasingly social welfare recipients note the additional challenge of responding to the demands of social welfare departments while trying to remain focused on finding paid, quality and sustainable employment. More information on this project can be found in the Welfare Conditionality tab and throughout the blog postings.

History of Labour and Welfare

Historical Labour Markets and Welfare

Unemployment and job creation is of central importance to political life in Ireland. The growth from the 1950s hailed an economic recovery with a singular aspiration of full employment. This stream explores the genealogy of political interference in the creation of jobs and the administration of social welfare. More information on this project can be found in the Historical Labour and Welfare tab and throughout the blog postings.

PEX-Understanding Unemployment in the Era of Big Data

PEX – Understanding Unemployment in the Era of Big Data.

This branch of research is supported by a funding grant from the Irish Research Council (IRC) under the New Horizons Interdisciplinary Research Project Award.

The primary aim of the research project “Understanding Unemployment in the Era of Big Data: Exploring how data-driven theory and algorithmic knowledge can support better policy and personal decision making” is to explore how emerging analytical methods and computational tools can improve the experience of unemployment. It is a future driven stream of the WUERC project, exploring concepts like the Industrial Revolution 4.0 and its potential consequences on future work and unemployment. Taking the recent deployment of the PEX algorithm (probability of exit from the live register) by the Irish Department of Social Protection (DSP) as the point of departure, the project aims to produce data-driven models of unemployment dynamics that incorporate conventional and emerging theory on the experience of unemployment. In this way, the project aspires contribute by: Improving, broadening and strengthening the deployment of algorthmic knowledge on unemployment in Ireland and beyond; contribute to the emerging sociological and organisaiton studies work on the social life of algorthimic knowledge; contribute to the nascent field of operational algorithms, by creating an up to date tool to capture the important individual characteristics that fall outside of the scope of the PEX algorithm while making the tool assessable to those at the centre of the model.

The research will expand the WUERC project by developing a broader national and international scope with an ultimate goal of participating in a European-wide Horizon 2020 research collaborative. More information on this project can be found in the PEX tab and throughout the blog postings.



The Waterford Un/Employment Research Collaborative (WUERC) came into being against the backdrop of a rapid increase in the number of unemployed people in Ireland (from 4.2% to 15% between February 2006 and February 2012). The research group, comprising of colleagues in from the schools of Business and Humanities in Waterford Institute of Technology, set out to develop large scale datasets around the experience of unemployment and produced the 2015 book “The Sociology of Unemployment” published by Manchester University Press. Since the initial phase of the research the group has continued to expand its interests and now has four main areas of interest.