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.