1. Assessment and recommendations
Ireland has reformed its public employment service over the years to streamline delivery and better focus its resources on helping jobseekers. Efficient support to jobseekers is crucial to connect people with jobs, in an economy which exhibits larger than average cyclical fluctuations in its labour market. This support helps most unemployed individuals transition directly to employment. However, for those that do not, a core part of active labour market policy is delivered via two direct job creation schemes, Tús and Community Employment (CE).
This report shows that Tús and CE contribute to higher earnings in the long-term. CE also reduces dependence on unemployment benefits and future reliance on disability allowance. All groups of jobseekers benefit from participation, with the biggest effects on employment outcomes for young and prime-aged participants and women.
Enhancing and improving the collation and availability of data on ALMP participation and programme participants would foster future evaluation work in Ireland. Notably, such efforts would enable comprehensive assessments of the social impacts of the programmes, in addition to employment-related impacts.
Ireland’s labour market has been characterised by three main episodes over the last two decades. Employment grew strongly in the early 2000s, then plummeted as the global financial crisis unfolded, with the employment rate reaching a low of 60% in 2012, before recovering gradually and reaching 74% in 2022. Over this time period, the labour market in Ireland has tended to fluctuate more than in other OECD countries, and unemployment rates have been especially volatile, more so than other labour market indicators. This high level of volatility has led to large changes in the number of unemployed and long-term unemployed persons over the last two decades. For example, the long-term unemployment rate in 2022 stood at 1.5% for men and 1.1% for women, respectively, five times lower than in 2013. In 2013, 61% of the unemployed were long-term unemployed, against 32% in 2022.
Today, the Irish labour market fares well compared to other OECD countries. Employment rates in 2022 were well above the OECD average, with 74% of 15-64 year-olds employed compared to 69% in the OECD and the unemployment rate fell to its lowest level in decades in 2022, at 3.4%, lower than in most other countries. Nevertheless, the problem of unemployment, and in particular long-term unemployment, is not solved. Even with record-low unemployment levels in 2022, there were still almost 30 000 people in long-term unemployment in Ireland, many of whom face employment barriers and require tailored support to reconnect with the labour market. In a future economic downturn, this number could increase quickly.
People with low educational attainment and young people face bigger employment barriers than other groups. The employment gap between those with high and low levels of education is very wide in Ireland, at 48 percentage points, as many people with low levels of education are inactive and long-term unemployment is concentrated among the lower-educated. In addition, in common with other OECD countries, young people in Ireland are confronted with employment obstacles that people with more work experience do not face, leading to a large gap in unemployment between people aged 25-74 and young people aged 15-24. Therefore, active labour market policies (ALMPs) are particularly important for these groups.
One further challenge in the Irish labour market relates to the labour shortages amid a scarcity of skilled labour. Skills mismatches are high in Ireland, where some 31% of workers are underqualified for their job, more than in any other country for which data are available. In the years to come, Ireland’s ageing economy risks intensifying labour market shortages, even though Ireland faces slower ageing dynamics than many other OECD countries and will have more time to adapt to societal and economic consequences of population ageing. The role of ALMPs will be more important than ever in this context to support skills reorientation. In this context, it will be important to continue ensuring that public work schemes do not displace jobs and absorb labour that could have been otherwise used in the open labor market.
Ireland has gradually reformed its approach to helping jobseekers over the years, to place greater emphasis on jobseekers undertaking activities to support their job search. After the global financial crisis in 2008, a package of measures was agreed between Ireland, the EU and the IMF in response to the rapid rise in unemployment, which increased job-search requirements, improved work incentives and strengthened sanctions for non-compliance with job search requirements.
As a result of this new approach, unemployment benefit administration and active labour market services and measures were brought together into a combined service, which has been named Intreo since 2012. The introduction of Intreo was intended to simplify the pathway of support for jobseekers via the integration of previously disparate services relating to different aspects of employment support and to ensure there were no gaps between the start of an unemployment spell and the commencement of support for jobseekers. Bringing services together under one organisation decreased the number of contact points for customers and meant they could be delivered in a more joined up manner via a single, unified view of each unemployed person’s case. An evaluation of the introduction of Intreo found it introduced better early identification of invalid unemployment benefit claims and also improved exits to employment, although the effect was quantitatively small.
Intreo has modernised its service, both with the introduction of greater service differentiation for jobseekers requiring different levels of support and through enhanced digitalisation. One of the features of the new Intreo service was the introduction of a risk-scoring questionnaire in 2012 (the “Probability of Exiting” the Live Register, that is the unemployment register) for new jobseekers, which aims to categorise individuals based on their risk of long-term unemployment. This profiling allows Intreo to target resources on individuals that they deem at greater risk of extended unemployment. Furthermore, recent developments have centred around removing paper-based administration of benefit claims, to further enhance administrative efficiencies and to ensure seamless service delivery for jobseekers. A re-organisation of back-office functions to create centralised task-based teams means that individual Intreo offices do not have to conduct all aspects of claim administration for their jobseekers and can concentrate on delivering services for them. The specialisation offered by the back-office teams increases claim processing efficiency.
Ireland’s spending on ALMPs largely targets the long-term unemployed and is heavily focused on direct job creation (public works programmes). In total, 48% of Ireland’s expenditure on ALMPs was spent on direct job creation in 2021, much more than in most other OECD countries and a feature that has persisted for approximately the last two decades. Although Ireland’s total spending on ALMPs was only 0.21% of GDP, compared to an average in the OECD of 0.43% of GDP, its spending on direct job creation is the 7th highest of OECD countries (0.10% vs. 0.04%).
There are two flagship direct job creation schemes in Ireland, Community Employment (CE) and Tús, both of which are targeted to long-term unemployed people. Placements in both schemes are usually one year long, with individuals working for 19.5 hours per week in a range of jobs across voluntary sector organisations (for example, as caretakers or cleaners). Supervisors in the programmes offer guidance and support to participants. In CE, voluntary and third sector organisations apply directly to DSP to become approved CE schemes and to gain approval for specific placements. In Tús placements are managed via the use of implementing bodies who have approval from DSP and are then left to manage these placements with their community and third sector partners. There exists flexibility for consecutive placements in CE over multiple years whereas Tús participation is limited to a single year, and CE placements have a designated budget for training.
Since Tús’ introduction in 2011, there have been around 30 000 participants annually on either Tús (7 000) or CE (23 000), putting CE ahead of almost all other ALMPs in Ireland in terms of participant numbers. The strong focus of Ireland’s ALMPs on direct job creation has been a distinguishing feature for several decades.
Ireland’s national employment strategy, Pathways to Work 2021-25, commits to increasing the support that public employment service (PES) counsellors give to their clients and investing in digitalising PES to maximise its reach through blending in-person and digital service delivery. The PES will be required to invest in education and training and to increase further its engagement with employers to meet future labour market needs. This will play a part in ensuring labour force resilience and mobility in the Irish labour market. Data enhancements could support this ambition by improving the evidence base on PES interventions.
The development of an analytical framework that maximises the power of the available data and additional enhancements from external data could significantly enhance evidence generation on the effectiveness of ALMPs. Currently, the Department of Social Protection (DSP) does not have a longitudinal dataset for analytical purposes, that would enable the analysis of ALMPs and their outcomes within one database. Information on employment and most social welfare payments was only available as an annual aggregate for this report, limiting the identification of timing and sequences of short periods of work or participation in some support schemes during a year. Incorporating more frequent disaggregated information in future analytical datasets would facilitate the study of such short-term dynamics. The range of operational data available across employment services, benefits claim management, income support payments as well as ALMPs means there is a wealth of information that can be unleashed for analytical purposes. Exploiting this analytical potential requires restructuring operational data into a longitudinal framework suitable for research purposes. DSP has taken steps to achieve this objective and is currently developing the Work and Welfare Longitudinal Data Base (WWLD), with support from the Labour Market Advisory Council. Once the full longitudinal framework is complete, high-quality analysis could be conducted in a way that enables tracking outcomes across different ALMPs, analysing characteristics of shorter or longer unemployment benefit durations and for identifying the causal impact of the ALMPs and employment services to which DSP refers jobseekers. This report has directly contributed to the development of a synthesised analytical data framework, through the assimilation, cleaning and linking of disparate administrative data sources. It will be important to build further on this work to embed these compiled datasets into a strategic analytical framework that permits ongoing maintenance and updates so that future analytical work can utilise them.
Adding information on educational attainment would greatly enhance the capabilities of analytical datasets, and this is largely missing from the present suite of data for analysis. Education variables are critical when controlling for characteristics associated with labour market outcomes. At present, there are no administrative educational data linked to DSP data on unemployment claims. Linking to Department of Education and Department of Further and Higher Education, Research, Innovation and Science administrative data could allow the PES to better direct jobseekers towards the skills and qualifications that will enhance their employment prospects. Similarly, information on occupations classified according to international standards – e.g. ISCO – would further enrich the description of individual trajectories. Occupations can be merged to task information (e.g. from the European Jobs Monitor of Eurofound) to provide indirect information on qualifications gained outside the educational system.
Information on hours worked should be added to the employment data collected by Revenue as well. Whether an individual is employed on a part-time rather than full-time basis is an important factor in how earnings or weeks of employment are considered as outcomes. Incorporating these pieces of information into the administrative employment data in Ireland could yield a signification improvement to analysis of ALMPs, such as CE and Tús, so that labour market outcomes could encompass a measure of whether hours of work have increased.
A further data enhancement would be the incorporation of data on firms. Sector, size, capital investment, and financial health – to mention a few – are crucial determinants of earnings and of individual trajectories in and out of the labour market. Without them, any analysis is exposed to the risk of misattributing an effect to a policy intervention which is instead due to the sorting of individuals across firms. These data were not available for this study, but data on sector and firm size of the last employer since 2019 are available for future studies.
Incorporation of real-time information on earnings would also permit more timely and disaggregated analysis. The development of real-time taxation of earnings in 2019, where employers report their employees’ pay and deductions in real-time to the Revenue Commissioners each time they pay their employees, means administrative earnings data are now available at a periodicity more in line with administrative data on unemployment benefit claims. This will facilitate future examination of labour market status across multiple points in time, enabling the kind of analysis explored in Chapter 5 in a more structural way. It could also facilitate the evaluation of policies, such as labour market services, whose impacts are expected to be greater in the shorter-term.
Finally, the development of better metadata would facilitate a more standardised and consistent approach to different evaluations. Currently the administrative data offer a number of similar but potentially conflicting variables. Moreover, different databases provide information on the same supporting scheme, which are not always coherent across data sources. A one-off data modelling analysis could evaluate these variables and sources and establish a hierarchy of variables and data sources, taking account of the quality and timeliness of each data source, to enable reliable estimates of labour market status in cases of conflicting information. These data could then be combined into an analytical database suitable for evaluation across many different ALMPs. Sitting alongside enhanced metadata that accurately describe variable characteristics and provide information on data quality, advantages and limitations, this would foster faster and consistent analysis that is less prone to errors in interpretation.
This report demonstrates the potential of administrative data to shed light on individuals’ journeys through the labour market and the support provided by DSP. Combining datasets on CE and Tús eligibility and participations with demographic data, earnings, social security contributions and information on DSP payments, the report studies the individual trajectories of long-term unemployed people (and some individuals with eligibility via family-related benefits) across several different “states”. Beyond CE and Tús participation, a further six states are identified. These cover: employment with some kind of extra support via DSP, employment without support, and three other individual supports – Back to Education Allowance (BTEA), JobBridge and JobPath. A final state captures individuals who are not employed nor engaged in any of the supporting schemes covered in the analysis. Outcomes for jobseekers are observed for up to four years after becoming eligible for either CE or Tús.
The analysis of these trajectories reveals that four years after becoming eligible for either CE or Tús, almost half of jobseekers are employed without any form of support. Around 60% of individuals are observed in this state at some point over the four years considered. In contrast to this, 17% of individuals are never observed participating in any of the supporting schemes considered, nor in employment. Some subgroups of eligible individuals tend to move more frequently between different states, such as males, Irish nationals, and those aged under 50. Overall, annual transitions to CE or Tús are relatively infrequent. At most, 3.7% and 1.9% of all eligible individuals move into CE or Tús per year, respectively; only 7.5% of eligible individuals ever participate in CE over the time period considered, while for Tús the share is even lower at 6.6%. This underlines the importance to ensure that the measures are well targeted, so that those in need of support receive it.
Individuals aged less than 30 participate relatively less in CE, and more in BTEA or JobBridge. Those aged 50 or more, are more likely to go through CE or Tús at least once. Although entries into the programme are not that frequent, persistence in CE is high: 50% of the individuals who start a CE scheme are still observed there after three years and 20% after five years. Persistence in Tús beyond one year since entry into the programme is low due to institutional limits – maximum duration is one year. Month-by-month analysis of CE and Tús episodes reveal that exits to other states occur at specific points in time, namely after 12 months for Tús and at one-, two- and three-year durations for CE. At one-year duration after entry, the probability of moving to a job (either supported or not) is higher for Tús beneficiaries than for those with CE, although overall CE seems a better springboard to employment than Tús in the medium and long-term. In absolute terms, however, transitions to employment are low even at yearly spikes, which suggests that the lock-in into CE and Tús could be more a matter of a lack of alternatives than by choice.
CE was introduced in 1994 to provide part-time, temporary paid employment for the long-term unemployed, at a time of high long-term unemployment. It has a dual emphasis on providing opportunities for the long-term unemployed to find work in addition to acting as a resource for local communities. These placements are typically one-year in duration, but participants can take consecutive placements, particularly when they are older or are working towards qualifications.
As long-term unemployment fell from its highs of the mid-1990s, it precipitated a debate as to the appropriate number of participants in CE, as private sector employment opportunities increased and concerns over the lock-in effects of CE increased. As early as 1998, calls for a reduction in CE placements were made in a government report, at which point CE represented some 3% of total employment in the Irish economy. CE then fell from around 40 000 participants in 1998 to around 20 000 in 2003, a level at which it has hovered around for the last 20 years or so. After the adjustments made following the earlier advice, there was remarkable stability of participant levels over time, even in the face of rapidly changing long-term unemployment levels. A further evaluation in 2015 recommended a number of alterations to the scheme, including linking the number of CE places to changes to the stock of unemployed people. It also suggested formalising the split between “activation” objectives on the one hand, and “social inclusion” objectives on the other hand, though it made no precise definition of the latter and did not suggest metrics by which to measure it. This therefore gave rise to additional questions on how precisely to measure the impact of CE on communities and on this notion of “social inclusion”, as opposed to its role as a tool to help better connect individuals to jobs.
In the midst of these changes in implementation of CE over the years, Tús was introduced as a temporary measure following the Great Financial Crisis, designed to help provide extra support (in the form of more job placements) to the rising numbers of long-term unemployed people. The placements themselves are similar to CE in terms of their weekly working hours and the nature of the organisation and roles attached. However, it differs from CE in several noticeable respects. It has mostly randomly-assigned mandatory participation, a one-year participation limit, the jobs are managed by local development companies, which manage the number of jobs (as opposed to CE schemes, which apply to DSP for approval of individual placements) and there is no training budget. Somewhat similar to CE, and despite its inception as a temporary alleviation measure for the long-term unemployed, Tús participant numbers have remained remarkably steady, despite the large fall in the eligible population over recent years.
To properly assess the effectiveness of Ireland’s spending on ALMPs, it is critical to evaluate the role of these two large direct job creation schemes, their interplay and their function in providing services to long-term unemployed individuals. The last evaluation to offer a counterfactual assessment of the impact of CE was conducted in 2000 and there has yet to be one performed on Tús. Therefore, a large gap in the evidence base for Irish ALMPs exists, which is vital to fill to ensure public money is spent wisely and policy effectively helps to connect people with jobs, especially those individuals who have been unemployed for extended periods of time. This report aims to fill this gap.
The analysis in this report shows that CE helps to connect participants with the labour market in the longer term and that its effects are better for some groups of individuals than for others. The positive impact of CE on total annual earnings, the probability of any earnings in a given year and annual weeks of employment only occurs from around three years on average once participants have entered the programme. This long lag in effectiveness can be linked to repeated participation in the scheme, whereby the average total duration for individuals on CE is around 2.5 years. The report finds that employment earnings are around EUR 2000 per year higher for CE participants relative to similar non-participants, the incidence of any employment earnings (all unsubsidised) in a year is around 10 percentage points higher and individuals work an average of four weeks longer per year.
CE is more effective for some groups of long-term unemployed people than for others. Unemployed people aged between 30 and 50 and EU mobile workers from Member States that joined the EU during the 2004 and 2007 enlargements experienced higher effects of CE in terms of total earnings, incidence of positive earnings and weeks worked in the longer term. For women, there is also some evidence of a stronger effect on earnings and employment following CE participation in comparison to men. Those participating in the “activation” strand of CE tend to enjoy better outcomes than for those on the “social inclusion” strand. Similarly, those on the Health and Social Care scheme enjoy better outcomes than those on general or Childcare schemes.
The effects of CE on employment outcomes, including earnings, are of a similar magnitude to those found in the past studies of CE, and high when compared to the international literature on the effectiveness of direct job creation schemes. Nevertheless, these positive effects in the very long term do not necessarily imply that CE and other public works programmes, including Tús, are the most effective and cost-effective policies for long-term unemployed people. International research often concludes that some other types of ALMPs, such as training and employment services, tend to be more efficient in connecting job seekers with employment than public work schemes. This finding might also hold true in Ireland.
This report also evaluates those outcomes linked to social inclusion more generally that can be assessed using available linked administrative data on disability allowance and training uptake. More specifically, the receipt of the disability allowance is meant to capture, at least partially, the effects of CE on health, while the receipt of the Back to Education Allowance (BTEA) captures a pathway, through further education, towards a better connection to the labour market in the future.
The results show that CE participants are less likely to receive disability allowance than similar non-participants already shortly after entering CE. In the long run, five years after the start of the programme, the incidence of disability allowance receipt is some 6 percentage points lower for former CE participants. As expected, effects are largest for older age groups who are more likely to receive disability allowance, with an effect of 10 percentage points five years after CE participation for the over-50-year-olds, although some caution is needed on interpreting the absolute magnitude, particularly in the longer-term, as sensitivity analysis using alternative statistical models points to slightly smaller impacts.
Caution is also needed in the overall interpretation of the impact on receipt of disability allowance, as it is likely to be a relatively poor indicator of overall impacts on health. It will only capture those conditions that meet the eligibility criteria for the allowance and will also reflect individuals’ broader preferences and knowledge on whether to apply for it (which may be unrelated to health per se).
CE participation is associated with a higher probability of receiving the BTEA 3 and 4 years after CE participation, which dissipates in the longer term. Contrary to the impact on the disability allowance, this effect is largely focused on the younger age group, with most of the peak impact driven by receipt in the under 30 age group. These results should be interpreted with caution given the small sample sizes involved with BTEA receipt and their temporary nature. But it does suggest that CE can help to re-engage individuals with the labour market in ways that are not solely related to immediate job search or employment and may offer benefits to human capital accumulation that could help individuals enjoy better careers in the long-term. Such long-term outcomes are difficult for this report to evaluate, because an analysis would need to evaluate effects over more extended periods to determine long-term impacts.
Better data on education and training, such as incorporating all of the training organised via the training body SOLAS, would help to provide a more comprehensive picture of the extent to which CE impacts participants’ learning outcomes. This is an important element to quantify, particularly given that its dedicated training budget is one of CE’s oft-cited features.
Re-orientating the focus and emphasis on the training offered directly in CE may help participants to develop their skills further, regardless of any links to broader education. The dedicated training budget for CE is small and has fallen to less than half of its level in real terms since the scheme’s inception in the 1990s. In addition, much of the training undertaken while on CE is quite generic. Establishing better links with employers to focus training on the skills demanded in the local economy could help jobseekers equip themselves with skills that are in demand in the private sector. Increasing the training budget would allow more extensive skills development of individuals to aid re-skilling or to deepen skills and hence access to higher-paying jobs.
However, despite these suggestive positive findings on disability allowance and BTEA receipt, the report cannot fully assess the many outcomes CE might offer in terms of “social inclusion” because data on these outcomes are not available. For example, it cannot evaluate CE’s impact on the fabric of local communities, the services delivered to these communities, the mental and overall physical health of participants or their social isolation and integration.
Going forward, the precise goals of CE with respect to social inclusion should be properly defined and metrics to evaluate these objectives should be implemented as data availability permits, allowing more comprehensive evaluations of CE’s social inclusion objectives. Some of this may be achieved through better linkage to existing data (e.g. data on utilisation of health services), whilst others may necessitate data collection via the use of surveys. A rigorous assessment of the scheme’s objectives and the data needed to evaluate them is essential to properly determines the overall success of the programme.
The effect of Tús on earnings from employment is positive. The impact is modest at first but increases over time with former participants experiencing an annual boost to taxable earnings of EUR 1 500, on average, in the fifth year after participation in Tús starts. Alternative outcome measures, such as weeks in unsubsidised employment, are consistent with this finding. Compared to eligible non-participants, Tús participants spend three weeks per year more in unsubsidised employment three years after participation starts, rising to four weeks more in employment in the sixth year.
Tús appears to be more effective in terms of the increase in post-participation earnings for certain groups. The impact on earnings for women is considerably stronger than the impact on men, while participants aged under 30 experience an increase in earnings relative to their peers that is larger than any other age group. In the fifth year after participation, female participants experience an increase in earnings of about EUR 1900 while for their male counterparts the increase is around EUR 1 400. The positive results for women are consistent both with the literature and the findings from the CE analysis.
Former Tús participants do not move on to other DSP-provided benefits when they leave the unemployment register. In fact, participation on Tús appears to decrease the probability of receipt of other (i.e. non-jobseeker) social welfare payments. In the fourth year after beginning an episode of Tús, participants spend 3.25 fewer weeks a year in receipt of a welfare payment.
Re-balancing the ALMP basket and identifying good practices
While Tús and CE should be maintained, potential future increases in ALMP spending should be channelled towards training programmes that have shown to be effective and other types of ALMPs that perform well to ensure a balanced ALMP suite of provision in Ireland.
Regularly report on the performance of individual local Intreo offices to identify good practices and incentivise good performance.
Properly define the objectives for CE and collect the necessary data to monitor and evaluate CE against its objectives
Define precisely CE’s “activation” and “social inclusion” strands. Define specific objectives and outcome metrics for each of them to facilitate ongoing performance management and in-depth evaluations.
Define specific objectives of CE for different age-groups (notably young and prime-aged participants vs. participants close to retirement age), recognising that the effect of CE varies markedly depending on age and that the “activation” component is particularly important when the remaining time in the labour market is long. Define metrics to monitor and evaluate these objectives.
Collect all the data necessary to monitor and evaluate CE, including data on a range of social outcomes. Include, where applicable, information gathered via participant and provider surveys, to enable learning and sharing on best practices across schemes to support participants.
Conduct qualitative assessments of CE to complement the results of this report, especially regarding the effects of CE on social inclusion.
Optimise links between CE placements and ongoing job-search behaviour
Consider testing different scheme durations (below and above 1 year) to identify optimal scheme duration leading to the best labour market and social outcomes.
Consider testing lower lifetime limits of CE to reduce the lock-in period needed for positive impacts of CE to unfold, as some of the lock-in effect is likely to be linked to multiple participation.
Combine CE with other types of support, especially counselling towards the end of the placement, to facilitate quicker transitions into the primary labour market after CE.
Consider more flexibility in CE working hours, placement types and training
Possibility to work more than 19.5 hours for job seekers close to retirement age for whom the programme is mostly about social inclusion.
Possibility for younger individuals to deliver more working hours at the beginning of the placement, to gain intensive work experience, and less working time towards the end of the programme to be more available for counselling, other ALMPs or job search.
Possibility of fewer working hours at the beginning, combined with greater pastoral support (such as guidance and assistance), for individuals with multiple employment barriers, such as health barriers, family barriers or addictions. Increase working hours towards end of placement to aid transition to work.
Put a stronger focus on training for young participants, either in terms of training on the workplace or classroom training.
Ensure alignment of CE placements with labour market needs
Continue to offer CE, while continuing to ensure that CE is not in direct competition with open labour market jobs.
Consider introducing the possibility of random selection of long-term unemployed jobseekers into CE in light of its positive results, as is the case for Tús. Doing so would encourage participation among a broader group of long-term unemployed people, including those who have no knowledge about local CE programmes.
Give priority to placements that provide specific job skills that are relevant for the primary labour market.
Work closely with local employers, business organisation and unions to review training content of CE and ensure training provided in specific programmes builds skills for the open labour market.
Increase training budget and allow schemes more flexibility in application, particularly in distributing training budget across participants.
Consider adjusting the number of activation and social inclusion placements to the economic cycle, with more activation placements in economic downturns, depending on the needs of CE sponsors and DSP’s judgement.
Continue Tús selection model and consider adjusting in light of evaluation results
Random selection for Tús participation should continue as it offers possibilities for participation in the programme to a broad group of jobseekers, beyond those who have local knowledge of particular programmes or organisations.
It could be considered targeting the programme more to young and prime-aged long-term unemployed, for whom the impact of Tús on subsequent earnings is higher.
The process of referring individuals to Tús should be centralised, so that the same processes are followed by all the different Intreo centres. Currently there appears to be variation in how individuals are referred across different Intreo offices. A standardised referral process would ensure consistency in both data collection and monitoring and equitable access to Tús.
Data collection and storage can be improved to facilitate policy analysis
Ensure that all new job seekers are assigned a Probability of Exit (PEX) score.
Continue to develop a longitudinal dataset that maximises the power of the available administrative data to enable analysis of how interventions are associated with outcomes.
Further enhance the value of administrative data by linking DSP data with data from other registers to provide more detailed policy insights:
Linking to SOLAS data would permit exploration of how (particularly repeated) CE participation contributes to the attainment of formal qualifications and how this influences employability.
Capture information on hours worked in administrative employment data.
Link to Department of Education administrative data on education levels which are necessary data for any analysis of employment outcomes.
To make best use of administrative data, work is required to reconcile multiple, and sometimes conflicting, data sources. This may be due to non-standardised approaches to data collection or the use of operational data. Such work requires establishing a hierarchy of the data sources, taking account of the quality and timeliness of each source.
Complement administrative data with data collected through surveys, especially with regards dimensions that are difficult to collect data on from administrative records. This concerns for example health conditions where a survey, especially of older unemployed people, could help to get a detailed understanding of health impacts, e.g. distinguishing between effects on mental health and on physical health.
Finalise retrospective categorisation of activation and social inclusion job descriptions in CE to permit better historic analysis of the two CE strands.