1. Assessment and recommendations
Finland invests heavily in active labour market policies (ALMPs) and is one of the top OECD countries when it comes to spending on training programmes for the unemployed. An evaluation of its two main training programmes for unemployed people (labour market training and self-motivated education or studies while maintaining unemployment benefits) suggests that they have positive effects on employment after an initial long lock-in period and support labour mobility across occupations. Further benefits could be derived by targeting these programmes to the groups who benefit the most from them, notably older jobseekers and women. At the same time, Finland should invest more in evidence-informed policy making by increasing the capacity of the Ministry of Economic Affairs and Employment to conduct and outsource research and by improving data coverage, quality and availability for analysis and research on ALMPs. Building the evidence base will be even more important in the context of the ongoing reforms of the ALMP system.
1.1.1. Finland has a well performing labour market but with room for improvement on adult skills development
Over the past decade, Finland’s labour market has been characterised by relatively high participation and employment alongside relatively high unemployment. In 2021, Finland’s employment rate of adults aged 15-64 was 73% and the labour market participation rate 79%, 5 and 6.6 percentage points higher than the OECD average respectively. Labour market performance has continued to improve in Finland in 2022, with the employment rate increasing to 73.8%, the participation rate increasing to 79.3% and unemployment falling to 6.9%. However, unemployment is still comparatively high relative to the OECD. In 2021 Finland’s unemployment rate of 7.8% was 1.4 percentage points higher than the OECD average (6.3%). In addition, in 2021, individuals who were unemployed for one year or longer made up 24.2% of total unemployment in Finland, below the OECD average of 28.4% but above the 20.4% of other Nordic countries. Finland also has a high share of individuals who are marginally attached to the labour force. The proportion of its labour force who are not actively looking for work, but who would be willing to take work if it was offered, was 6.8% in 2021, almost four times larger than for the OECD (1.8%). To address these labour market challenges, more can be done to reach out to people needing support and help them connect with jobs.
Further disparities exist in Finland in labour market outcomes for specific groups of individuals. Outcomes tend to be poorer for younger and older individuals, for men and the less well educated. Like many OECD countries, young people in Finland have higher unemployment rates than their older peers. In 2021, unemployment rates for men and women aged 15-24 in Finland were similar, at around 16%, around twice as high as the overall unemployment rate. For women aged 55-64, unemployment in Finland was 6.8%, more than 2 percentage points higher than the OECD average of 4.5%. Similarly, men aged 55-64 had an unemployment rate in Finland of 8%, relative to 4.8% in the OECD. Unlike women, men also experience higher unemployment relative to the OECD across all other ages. At the same time, whilst for the high educated, unemployment is similar in Finland and the OECD, the unemployment rate for the low educated (13.8%) is significantly higher in Finland than for the OECD as a whole (10.7%). Finland has also had a high share of job shortages in predominantly high-skilled occupations, far above the average for other OECD countries. In 2021, the Finland’s number of open job vacancies was at its highest level in 30 years. This highlights the need for active labour market policies (ALMPs) that can serve these different groups of individuals and equip them with the skills required in the labour market.
The unemployment rate in Finland increased less than the OECD average after the COVID-19 pandemic struck. However, it took 27 months to return to pre-pandemic levels, six months longer than the OECD average, and despite a relatively stronger recovery in employment and participation rates.
1.1.2. Spending on active labour market policies is generous, but outreach by the public employment service could be improved
Finland has a relatively generous set of ALMPs. In 2020, Finland spent 0.86% of GDP on ALMPs versus 0.48% for the OECD on average and only below ALMP spending in Denmark and Sweden. This higher-than-average spending is a feature that has persisted over time. Spending is heavily focused on training and direct job creation in Finland. In 2020, close to half of ALMP budget (41.9%) was spent on training and 15.1% on direct job creation (versus 22.9% and 8.2% in the OECD respectively). Finland also has slightly more generous spending on labour market policies for people with disabilities and job placement and related services by the Public Employment Service (PES) relative to its GDP, although their share in the overall ALMP budget is lower than in the OECD on average.
The provision of ALMPs in Finland is undertaken by a mixture of government bodies. The main stakeholders are the Ministry of Economic Affairs and Employment (TEM), Employment and Economic Development Offices (TE Offices), Centres for Economic Development, Transport and the Environment (ELY Centres), the Development and Administrative Centre for TE Offices and ELY Centres (KEHA Centre), and the Ministry of Education and Culture (OKM). TEM has policy responsibility for labour market policies including ALMPs. It manages and monitors the implementation of the public employment and business services (TE services, the Finnish equivalent of public employment services) and has oversight and control of the TE Offices. The TE Offices directly implement the delivery of TE services. There are 15 offices located around Finland that offer a range of services to support job search and employment. Individuals are primarily instructed to register directly for TE services online in the first instance. This is complemented by in-person services such as vocational education and career guidance, which do not require registration at the TE office. ELY centres are tasked with promoting regional competitiveness, well-being and sustainable development and belong to the administrative branch of TEM. They offer numerous services to support the TE Offices. This includes forecasts of long-term regional education and training needs, which support the TE Offices to plan training and employment services. The ELY centres are also responsible for the procurement and tendering of training contracts for the TE Offices. Other ministries guide the ELY centres wider functions, such as those relating to the environment, traffic or fisheries. The KEHA centre was formed in 2015 to provide administrative and development support to the network of ELY and TE Offices. Its services include human resources management, budgeting and payment services for some benefits and IT development. OKM is involved in both main training programmes that are available to jobseekers and are evaluated in this report. For labour market training (LMT), OKM directly funds training that leads towards an accredited education award. OKM also grants providers of education permits to provide vocational educational training. For self-motivated training (SMT), OKM directly funds the programmes and delivers them through its existing educational institutions.
This network of stakeholders works together to offer a generous system of support to help individuals acquire skills and connect with jobs. However, this co-exists with a large number of individuals who do not use the support that is available to them. Strengthening the outreach of the PES may also go some way to improving job matching for jobseekers but also strengthening labour market ties for those marginally attached. In 2020, European Union Labour Force Survey data show that only 44% of jobseekers in Finland contacted the PES to search for work. At the same time, only 23% of jobseekers contacted a private employment service. This means that a relatively large proportion of jobseekers undertake job search completely independently. Some of these persons could benefit from PES support.
1.1.3. Ambitious reforms will radically alter active labour market policy delivery
Two sets of reform are underway in Finland to transform the delivery of its labour market support to jobseekers. On 2 May 2022, a new customer service model came into force, designed to provide jobseekers with more support, alongside obligations on job search. Quarterly interviews with job counsellors were replaced by fortnightly meetings and stipulations on required numbers of job applications came into force. This reform is set to be accompanied by a transfer of responsibilities from central government to municipalities, so that the latter are directly responsible for providing ALMPs. To ensure effective ALMP provision, financial incentives for municipalities will be altered, by making them increasingly financially responsible for a part of social security of jobseekers as unemployment duration lengthens. In this sense, getting individuals into work faster has a direct benefit to municipalities’ financial position. Each of these reforms is estimated to move an additional 10 000 individuals into employment.
It will be important to ensure the proper evaluation of both reforms so that they can be appraised against their objectives. The transfer of responsibilities to municipalities is being accompanied by a large set of pilots to inform the final roll-out. This is welcome as it will provide evidence on how the reform can be refined prior to full-scale roll-out. However, learning from this stage should be tempered and contextualised taking into account the more tightly controlled and monitoring phase that can accompany such trials. This may mean that inferences from the pilots do not generalise well when the policy is implemented more widely. Finland has opted to transfer responsibilities on the 1 January 2025, which precludes the immediate ability for further testing of the municipalities not involved in the earlier pilots. Careful consideration should be given on how best to generate evidence and metrics that will still allow to test its original objectives. Similarly, in order to test the success of increasing job counsellor meeting frequency and job-search requirements, data that allow an investigation at the counsellor or office level may allow some inference to be gained, by looking at how variation in meeting frequency impacts job finding rates.
1.2.1. TEM needs a long-term strategy and more resources for research to drive policy making on active labour market policies
To design ALMPs and their delivery in an evidence-based manner, the analysis and research activities of TEM need to systematically collect and generate sufficient and relevant evidence. TEM uses a decentralised model for these activities, which can lead to people with more diverse skills and expertise conducting these tasks but might also cause fragmentation in knowledge generation. With the tasks for analysis and research scattered across the ministry, it is difficult to ensure systematic knowledge generation, avoid duplication and ensure that data and knowledge sharing are streamlined for maximum efficiency. A co-ordination group for policy analysis has been established to overcome fragmentation and an annual plan for research is agreed for research projects to be contracted out. Nevertheless, TEM does not have a longer-term strategy for research and analysis in order to be able to manage and develop analytical capacity, and above all, ensure continued improvement in evidence-based policy making. In addition to specific research projects in the annual plan, the longer-term strategy should outline the objectives and priorities of research activities, and the milestones to reach the objectives, considering both internal capacity and contracting out.
TEM’s underlying principle for labour market research has been to contract major policy evaluations out to external researchers. The annual research plan defines the research to be contracted out and the allocated budget across policy fields, with the topic of labour market policy being just one of the many topics covered. Furthermore, a large share of the research budget goes towards annual reviews with only a low budget for actual research, which leaves a limited possibility to conduct ALMP evaluations within TEM’s research budget.
TEM has only a few researchers and economists as most staff (including those partially conducting analysis) have a qualification related to legal affairs. Hence there is currently only a limited number of staff that can conduct policy impact evaluations or design research projects for contracting out and steer these projects in partnership with external researchers. Furthermore, these skills and knowledge are not currently actively developed or maintained, as evaluation activities are not conducted in-house. In addition, a limited number of staff qualified or available for analytical tasks more generally means that some of the needs for evidence are not met, as only the more urgent needs for policy analysis can be addressed. To overcome the capacity challenges, TEM should allocate more resources for analysis and research, and preferably hire some economists who could support these functions full time.
Since 2014, Finland implements a cross-ministry research instrument – the joint analysis, assessment and research activities (VN TEAS) – co-ordinated by the government (the Prime Minister’s Office) that TEM has used to fill the gaps in its internal capacity for research. The government working group for the co-ordination of research, foresight and assessment activities (TEA Working Group) consisting of representatives across the ministries in Finland, assesses the research requests of the ministries and makes a proposal for the VN TEAS annual plan. The project assessment process by the TEA Working Group ensures that only strategic research needs of cross-ministry relevance get funded via the VN TEAS. The VN TEAS can be instrumental in generating evidence in case of more major ALMP reforms that could potentially affect other policy areas, such as education, social and health policy. Yet it cannot be the only, or even the main instrument, to fund systematic ALMP evaluations due to its nature.
The Ministry of Finance has a greater capacity to conduct policy evaluations than many other ministries in Finland as it employs a substantial number of economists, some of whom have the skills to conduct counterfactual impact evaluations. The Ministry of Finance has been covering some of the other ministries’ research needs when it finds the topic politically and financially relevant. While research conducted by the Ministry of Finance can overcome some gaps in evidence generation in the framework of limited research budget in TEM, this mechanism does not ensure that evidence generation on ALMPs meets the strategic needs of TEM. As TEM is the policy designer in the field of ALMPs, it needs to assign sufficient resources to related knowledge generation to be able to drive labour policy and do so based on evidence.
1.2.2. Disseminating research results is systematic but the channel to policy makers needs to be strengthened
There are no systematic dissemination channels between analysts and policy makers in TEM, as the initiative to learn about the new research results comes occasionally from the minister and key policy makers, but these exchanges are not initiated by the analysts’ side. Nevertheless, the dissemination of research results is generally very comprehensive and well established, being potentially a good practice example for other countries. The results of analysis and research on labour policy issues conducted internally or contracted-out by TEM are systematically published in a dedicated publication series, accompanied by occasional press conferences and press releases in case the key results could be of wider public interest. The research conducted by the VN TEAS has its own dedicated publication series managed by the Prime Minister’s Office.
The evidence gained via the research projects of TEM and the VN TEAS is not systematically taken into consideration in policy design. The reasons for this can be needs to make changes faster than the evidence can be generated, election cycles, insufficient dissemination of evidence, dismissing evidence due to inconclusive research results, as well as gaps in evidence. Although piloting and experiments in the field of ALMPs are more common in Finland than in many other OECD countries, even these results do not necessarily feed into policy design. A pilot was carefully designed for the currently on-going major reform in the institutional set-up of ALMP provision, but the decision to go on with the reform was taken before the end and evaluation of the pilot.
To support evidence-based policy design and implementation, analysts in TEM need to take initiative to disseminate the analysis results more systematically to policy makers, policy implementers and the broader public. The content and channel of communication need to be defined based on the specific audience. For example, the communication to policy designers might need to focus less on the evaluation methods, and more on policy design elements necessitating change. Communication to policy implementers might need to take a form of guidelines for employment counsellors.
To continuously improve the quality of draft laws, and particularly the impact assessments in the government proposals, Finland established the Finnish Council of Regulatory Impact Analysis (FCRIA) in 2016. Above all, this council assesses whether the impact evaluations of the proposed legal changes are appropriate and makes proposals to improve them. To further enhance the link between evidence and policy design, Finland could consider extending the tasks of the FCRIA to monitor if all relevant evidence available has been indeed used as inputs in the draft legal proposals. As a key priority, the FCRIA could check whether above all the research results of the VN TEAS have been considered, as these are ought to be the key strategic research projects. Furthermore, the FCRIA and the VN TEAS could co-operate in exchanging information on on-going research projects and draft legislation being assessed to avoid a situation where a major legal change is taking place while the evidence generation is still in process.
1.2.3. TEM has a well-established and transparent system to contract out research
TEM designs research and analysis projects internally, regardless of whether the project is to be conducted internally or contracted out. External researchers are generally not involved in the design of the project descriptions as the procurement process needs to be transparent and fair for any potential bidder. Hence, exchanging research ideas with external researchers takes place only via more general research seminars. Yet, the design and methodology of the internal projects, particularly when designing major trials to be evaluated later, should be discussed more explicitly and openly with external researchers to generate credible evidence.
The procurement process to outsource research in TEM is well-established and transparent, following the national procurement legislation. TEM focuses on quality components in its assessment criteria in procurement, selecting the most economically advantageous tender. The assessments of tenders are considered to be very transparent by the applicants (research organisations). Thus, there have been almost no cases of contesting the assessment results over the years. A dedicated steering group is set up for each contracted out research project in TEM, involving relevant experts from policy design as well as analysis. The steering group guides and monitors the research projects, ensuring that the external researchers have all of the relevant information available and the generated evidence is sound for policy making.
The greatest challenge for researchers in the outsourced projects is the foreseen project timeline. As policy makers expect the evidence quickly, the timeline in the procurement documents sometimes underestimates the time it takes to generate credible evidence. The analysts in TEM need to create more awareness among the policy makers on the feasible timelines of research projects, such as allowing some time after policy implementation for the impacts to materialise before evaluating these. In addition, Statistics Finland and administrative registers need to continue their efforts to shorten time lags in data availability for research.
TEM aims to ensure research quality via its sound procurement process, project management and publication of research results. TEM could additionally consider a peer review process for the research projects it conducts internally. Moreover, if this approach would be used, TEM could consider conducing ALMP impact evaluations internally as these are currently fully contracted out due to objectivity concerns. Internal analysis with external peer reviewing could be, for example, used when the evidence needs to be generated quickly, as TEM then has better control over data access as well as the timeline more generally. Nonetheless, conducting ALMP evaluations internally may require building the capacity first for this kind of research, and above all, hiring more staff with appropriate skills and knowledge.
1.2.4. The available evidence on impact could be augmented by cost-benefit analyses
In total, a considerable volume of evidence on ALMPs has been generated via the different funding mechanisms over past years. Some evidence on the effectiveness of the key ALMPs (jobseeker counselling, wage subsidies, work-related rehabilitation, business start-up subsidies) is publicly available via the research reports of the VN TEAS. A few additional counterfactual impact evaluations of ALMPs (above all training) have been conducted by the Ministry of Finance. The research and analysis conducted and outsourced by TEM provides inputs for policy making via ex-ante evaluations and descriptive analysis of ALMP measures and services, as well as reforms and digital tools in the Finnish ALMP system. The impact evaluations of ALMPs use counterfactual evaluation methodology, combining quasi-experimental and experimental designs. Although Finland is already using experimental design for ALMP impact evaluation more than many OECD countries, applying pilots and trials could be used even more to generate credible evidence as the evidence generated using quasi-experimental design is often criticised or even dismissed by policy makers as being inconclusive.
Finland has scope for improvement in conducting systematically cost-benefit analyses to demonstrate the value added of different ALMPs more explicitly. Cost-benefit analyses should build on counterfactual impact evaluations, examining the impact of ALMPs in relation to the costs of implementing the ALMP and, if possible, the opportunity costs for participants (e.g. foregone earnings) as well as indirect costs on non-participants (e.g. negative externalities). Conducting cost-benefit analyses requires Finland to make cost data available for research purposes across the many registers containing cost data on services, measures and benefits for jobseekers.
1.3.1. Modernising the IT infrastructure of employment services would improve the availability and quality of research data
When the KEHA Centre became responsible for the IT infrastructure for ALMPs in 2017, it started quickly replacing the legacy systems. Nevertheless, the staff in TE Offices and ELY Centres still use the legacy IT system (URA system) to register jobseekers and provide ALMPs, which can affect the quality of the data used in ALMP impact evaluations, in addition to negative implications on ALMP provision. For example, some data fields that could be structured (classifications and code lists) are implemented as free text, not supporting well the work of employment counsellors or statistics and research based on these data. Some fields are mandatory to fill, although employment counsellors rarely have information on these issues, resulting in incorrect data in the database. The URA system is not sufficiently exchanging data with other administrative registers in Finland (such as on employment or education) to fully support services provision, as well as data accuracy. In addition, it has not been possible to adjust the URA system to align it with all recent changes in policy design.
An automatic data exchange is set up to provide data from the URA system to TEM monthly, supporting the production of timely statistics. The individual level dataset that TEM receives from the KEHA Centre, is further shared with Statistics Finland that uses the data for national statistics and can make these available for researchers, including for ALMP evaluation. Nonetheless, the outdated IT infrastructure for ALMP provision, including the data analytics tools, hinders the data availability for statistics, analysis and research on ALMPs. The dataset shared regularly with TEM and Statistics Finland is inflexible and has remained the same over the past years regardless of changes in policy design.
A sudden decision on changing the institutional set-up of ALMP provision in September 2021 has further delayed the IT modernisation process, as the exact responsibilities of each stakeholder need to be assigned before the IT solutions to support these roles can be defined. In addition to a central operational IT system and a data analytics solution to support data availability for monitoring and evaluation, some municipalities might want to set up their own IT platforms that have interfaces with the national IT infrastructure. Hence, before the developments of the national IT infrastructure can be adjusted to the new institutional set-up, the government and TEM need to be clear on which responsibilities will be transferred to municipalities, what will be their scope of freedom regarding their operating models and business processes in implementing ALMPs, and what kind of support needs to be provided by the central level.
As TEM is governing the KEHA Centre, it needs to drive and enable the process of modernisation of the IT infrastructure to ensure well-performing ALMP provision, as well as data availability for evidence generation. First, TEM needs to be in a systematic dialogue with the KEHA Centre in preparing for the reform and developing the IT infrastructure to meet the needs of the new set-up. The operational IT system needs to maximise its support to its users, i.e. employment counsellors. Hence, the planning, development and testing needs to involve employment counsellors (currently in the TE Offices, but to be transferred to the municipalities), as well as the municipalities more generally. The plans need to be discussed not only with those municipalities that are eager to go through the reform, have been part of the piloting of the new system and tend to have a higher capacity, but also with those for which the reform might be challenging and thus might have different needs of support. Second, in addition to the careful design of data exchange and integration of IT infrastructure between the core stakeholders of ALMP provision, data exchanges with other administrative registers need to be strengthened to support employment counsellors, jobseekers and employers, as well as to ensure data accuracy. Data already available in other administrative registers should not be collected again but received automatically, requiring Finland to potentially revise some of the regulation to enable additional data exchange for relevant operational purposes. Third, TEM (and the government) needs to find a sufficient and sustainable funding model for the KEHA Centre to enable it to carry out its responsibilities, particularly in terms of projects and developments that have longer than one year horizon.
1.3.2. Rich research data are available securely via Statistics Finland, although costs, timelines and metadata quality can be hurdles for research
The amendments of the Statistics Act in 2013 made it possible for Statistics Finland to share their data remotely for research purposes in a pseudonymised form. This has significantly widened the data availability for researchers as the uniquely pseudonymised format enables researchers access full datasets from Statistics Finland and combine different datasets according to the research needs. Data exchange between Statistics Finland and various registers in Finland has been established often many years ago, although continuous work takes place to keep the data exchange up to date when the IT infrastructure, collected data and policies change. Contrary to quite a few other OECD countries, Finland uses a unique identification number (the social security number) for all its residents across administrative registers, enabling to link data accurately across registers.
The scope of data available in Statistics Finland can support well ALMP impact evaluations. First, the datasets include the original pre-defined datasets on jobseekers, ALMPs and vacancies from URA system, as well as individual level indicators calculated for statistics based on URA data, that can on some occasions further facilitate conducting evaluations. Second, the datasets include rich data on socio-economic characteristics of the population to construct a counterfactual for the evaluation (e.g. family composition, household data), as well as observe the effects of ALMPs on different outcomes (using employment, education and firm data). As the data on ALMPs and jobseekers from the URA register are available securely via Statistics Finland, TEM refers the researchers there for data needs rather than sharing data directly with them. In case researchers’ data needs go beyond the dataset shared by KEHA Centre with TEM (which are available through Statistics Finland), they are referred to request additional data from the KEHA Centre, which can also be optionally made available via Statistics Finland, enabling to link these data with other register data already in Statistics Finland.
Nevertheless, the data in Statistics Finland are generally not fit to evaluate very recent changes in ALMP design. First, many datasets are shared with Statistics Finland only once a year. Second, it takes some time for the registers to share their data with Statistics Finland. Third, the data shared from the registers with Statistics Finland go through a thorough quality check and pseudonymisation that take some time. And fourth, Statistics Finland is not currently sharing the data for research purposes before they have published the official statistics based on these data. Further modernisation of the IT infrastructure across the public sector in Finland is necessary to ensure better data quality in the registers, prepare operational data better for data analytics and establish more timely and frequent automatic data exchanges. Better financing of Statistics Finland would enable it to shorten data preparation periods and produce more timely statistics, simultaneously shortening the time lags of research data as a side effect.
Statistics Finland collects metadata systematically together with data from the administrative registers and makes these available for researchers. While the metadata quality has improved over the years, it is still limited due to the metadata shared with Statistics Finland by the registers and because of the shortage of resources of Statistics Finland. Further descriptions of data and information on addressing certain issues in the data are also not collected and shared by any other organisation, regardless of different researchers continuously facing the same issues. This approach is inefficient and can mean that the ministry outsourcing the research pays several times for the same work.
In the outsourced research projects, TEM should request that the researchers also share with TEM the codes used to conduct the research and publish these together with the reports discussing the results. First, this would bring down the costs of research projects as some of the work does not need to be repeated again (enable TEM to get more evidence within the same budget). Second, publishing the codes would serve as an additional quality assurance, as the exact methodology would be more transparent for the research community and any questionable steps in the methodology could be identified more easily.
As Statistics Finland does not receive any funding from the Ministry of Finance for the processes to share data for research purposes, the researchers accessing data need to cover the associated costs themselves. However, this funding scheme has not covered all related costs in the past, resulting in a shortage of staff to carry out the research services in Statistics Finland and long waiting times for researchers to access the data. The processing time of data applications has tended to stretch over a few months in most cases during the past years, while the waiting times have been around one year in case additional data from administrative registers has been requested to be linked with the data already in Statistics Finland. To overcome the funding issues, Statistics Finland significantly increased its prices to access data for research in the beginning of 2022 from an already high level. This means that a significantly higher share of budgets for research projects needs to be allocated to data access. In addition, ALMP impact evaluations would be possible only essentially within publicly procured research projects, and not for example for purely academic reasons. Finland needs to consider funding the research services of Statistics Finland sustainably, for example, from the state budget to cut the data application processing times and cap research data prices to support evidence generation across policy fields and encourage research not only tied to a current political agenda.
1.4.1. Quasi-experimental techniques seek to resolve the problem of comparison of training participants and non-participants
Where programmes have a wide diversity of potential participants, these participants may differ from their counterparts who choose not to participate in the programme. This is true for both LMT and SMT. In these programmes, individuals must express an interest in participation and subject to agreement of the job counsellor that the training would help them in the labour market, they can participate. This makes it likely that there will be differences between participants and non-participants. Simply comparing the labour market outcomes of participants to non-participants may reflect some of these innate differences, rather than the effect the training has had on their success in the labour market. Because there was no trial period prior to full-scale implementation of these programmes, where eligibility was restricted, or other kind of randomisation in participation, quasi-experimental techniques need to be used to evaluate the impact of these programmes.
In order to account for and remove potential differences between participants and non-participants in LMT and SMT this report uses propensity score matching. This technique uses detailed information on individuals to determine which characteristics are important in explaining who participates in the training. Once this has been determined, it is then possible to compare participants to non-participants that, based on their background characteristics, look similar to the participants. In this way, impacts that are not due to the training are removed and only the effects of training on labour market outcomes are left.
1.4.2. Comprehensive linked administrative data underpin the analysis and allow to look at different labour market outcomes
The analysis in the report uses Statistics Finland “off-the-shelf” administrative datasets, which offer excellent research possibilities. These datasets are comprehensive in the number of different dimensions they cover, as well as in the full coverage of the Finnish population. The data include unemployment spells, participation in ALMPs, earnings histories, previous occupation and industry of employment and detailed socio-economic data, including educational attainment, age, gender, native language, marital status, presence and age of children in the family, dwelling and tenancy status.
These data enable the assessment of several different outcomes. It is possible to look at impacts on employment, annual earnings, monthly wages and unemployment duration. In addition to this, the availability of occupational information permits an investigation into how training programmes affect progression on the “occupational ladder”. An occupational index is constructed, which utilises information on average earnings in each occupation and allows occupations to be ranked depending on their average earnings. This index allows a tractable investigation into whether and how training programmes affect jobseekers’ mobility within this distribution. This provides insight into how occupational choices are affected in addition to earnings and thus greater evidence is generated on the mechanisms by which these programmes affect job matching.
1.5.1. Self-motivated training and labour market training are the two main training programmes for jobseekers in Finland
SMT and LMT are the two primary training offers for jobseekers in Finland. In 2022 there are over 50 000 jobseekers receiving unemployment benefits participating in this training.
LMT consists mostly of traditional vocational training and is open to all jobseekers. The acceptance into LMT depends on the TE Office that determines whether candidates have the characteristics required to participate and whether the training will address a gap in their skills. Participants are selected using a mixture of the information provided in their application, interviews and aptitude tests. Training can also contain an initial training period, to determine participants’ suitability for continuing in the training in LMT. A team of experts from the TE Offices and a representative of the training provider makes selection decisions. An employer representative may participate if they are part of planning and funding the training in question. LMT was the main training offer for jobseekers in Finland until the introduction of SMT in 2010, which allowed jobseekers to study in degree-level programmes whilst retaining unemployment benefits for up to two years.
SMT allows jobseekers to enrol in longer formal full-time courses that are part of the general education system provided by OKM, with the aim of obtaining a degree. The TE office needs to assess that the individual has need for the training and that it is the best way to improve their employment opportunities. If this is the case the individual can continue receiving the unemployment benefit for a period of up to 24 months while studying. Jobseekers must be 25 years or older to participate in this training, though this restriction is relaxed for those participating under the Integration Act (such as migrants accessing basic educational training). There is a requirement for monitoring of progress on the studies to continue receiving unemployment benefit, which is conducted by the state insurance fund, KELA, or the individual private unemployment insurance funds. In 2022 reforms to SMT introduced a requirement for participants to apply for three jobs per quarter, therefore introducing conditionality back into this educational pathway. This effectively removes the possibility of using SMT as a means to complete education whilst retaining unemployment benefits.
LMT and SMT offer somewhat alternative forms of education to jobseekers. The intensity and amount of educational content of the courses is one of the main differences. This can be clearly seen when looking at course duration. Between 2014 and 2018 the median duration for LMT was 43 days, whilst for SMT it was 341 days.
1.5.2. Self-motivated training and labour market training attract different types of jobseekers that are not representative of the rest of the registered unemployed
Both programmes attract younger jobseekers relative to the pool of all registered unemployed people. However, the average age of participants in SMT is even lower (by two years) than that of LMT participants. In addition, women are slightly less likely than men to participate in LMT while they are significantly more likely than men to enter SMT. On average SMT participants have more children while LMT participants are more likely to be foreign nationals and less likely to speak Finnish.
For both programmes the educational level of participants is slightly higher than for other unemployed persons as they are less likely to have an upper-secondary level education at most. The fields of the highest level of education attained by jobseekers prior to unemployment differ considerably between participants in the two programmes. Arts and humanities degrees are overrepresented, while engineering diplomas are underrepresented among SMT participants. The opposite is true for LMT beneficiaries who are also more likely to possess an information and communication technology degree. While no differences stand out in terms of the profession of the job held before unemployment between LMT participants and other unemployed persons, SMT participants are more likely than other jobseekers to have worked in the service and sales industry and less likely in craft and related trades.
Regarding past unemployment history, SMT participants have spent fewer days and periods in unemployment over the past (two) year(s) than other unemployed persons. For LMT participants no significant difference is observed.
The characteristics of participants in LMT and SMT do not clearly indicate that groups which are close or further from the labour market are more likely to participate in the programmes. For instance, LMT participants are less likely to have a low educational level but are also more likely to lack language skills. Therefore, this descriptive analysis does not raise strong concerns for creaming, which would exist if LMT or SMT participants would have better employment prospects than other unemployed people, even without their participation in the training programmes.
1.5.3. Self-motivated training helps jobseekers become employed in the long term, but with slight negative effects on earnings and upward occupational mobility
The analysis in this report focuses mainly on SMT and LMT that last at least three months. The results of the counterfactual impact evaluation (CIE) show that SMT has a small positive and statistically significant effect on employment three years after the start of the programme. At this point individuals that participate in this programme are 1.4 percentage points more likely to be employed. In contrast to the impact on employment probability, SMT has no significant positive effects on earned income. The year the programme starts, the effect of this programme on earnings reaches its lowest level representing a loss of EUR 855. This negative effect diminishes over time and does not become positive three years after the start of the programme.
These not so positive results on employment and earnings three years after the start of the programme reflect the so-called “lock-in” effect. In fact, since individuals that participate in SMT are not actively looking for a job and might not be willing to accept a job offer until obtaining their target degree, they are less likely to find a job than their unemployed counterparts that do not participate in training. The lock-in period observed for SMT in this evaluation is between two and three years. It is consistent with the fact that SMT can last up to 24 months supported with unemployment benefit (and may continue after this without unemployment benefit receipt), while classical labour market trainings are usually shorter. When the period of observation of the analysis is expanded to measure the impact of SMT on a sub-sample of unemployed four years after the start of the programme, the effects on the employment probability are twice as high as the previous year and the effect on earnings is positive but statistically non-significant.
SMT participation increases the probability of changing occupations, but participants seem to be moving down the occupational ladder. Individuals that participate in SMT end up in occupations ranked 0.6 percentage points lower (and paid EUR 25 less per month) than their initial occupation compared to their control counterparts.
1.5.4. Labour market training helps individuals become employed but the evaluation results detect no impact on upward occupational mobility and earnings
The analysis in this report shows that LMT programmes that last at least three months improve the likelihood of participants finding a job. Two years after the start of the programme jobseekers who participate in LMT have a higher chance of being employed. The effect on employment found in the long term (three years from the start of LMT) is of 4 percentage points and is consistent with the effects found in the international literature. The lock-in effect of LMT thus lasts between one and two years. This shorter lock-in effect compared to SMT is partially explained by the fact that LMT have shorter durations.
Three years from the start of the training, the effect of LMT is not statistically different from zero for the two measures of job quality studied: earnings and upward occupational mobility.
1.5.5. The programmes shift the distribution of occupational quality from bottom and top occupations to occupations in the middle of the distribution
SMT and LMT do not exhibit positive effects regarding upward occupational mobility. However, this average effect does not provide detailed information on how SMT and LMT affect the shape of the distribution of occupations. A null average effect could come from an absence of change along the distribution, but different tails of the distribution could also be disproportionately affected. The analysis in this report looks at the changes in the distribution of the occupational index, for individuals who participated in the programmes compared to similar individuals who did not. It shows that both SMT and LMT generate important changes in the distribution of occupations.
For both programmes, the null average effect on occupational mobility hides a decrease in the frequency of bottom and top occupations in favour of occupations in the middle of the distribution. Therefore, even if the programmes do not improve the quality of jobs on average, they contribute to reducing inequalities in job quality leading to a more concentrated distribution of occupational quality.
1.5.6. Women and older jobseekers exhibit larger gains in terms of employment and income from participating in self-motivated and labour market training
The effect of the two programmes varies across different subgroups of the population. For both SMT and LMT, the heterogeneity of the effects goes in the same direction for gender and age. Women seem to benefit more from the two programmes in terms of employment and income than men do. Three years after the start of the programme, women who entered SMT are 4 percentage points more likely to be employed and earn on average EUR 104 (over the year) more than similar women who did not participate (against -2 percentage points and EUR -248 for men). Regarding LMT, the estimates for women are respectively 6.2 percentage points and EUR 146 (against 2.5 percentage points and EUR -55 for men). These results echo the international evidence which shows that programmes targeted at women are more effective than the average programme or programmes targeting men. Regarding gender, SMT is indeed attracting jobseekers more likely to benefit from it since women are more likely to participate, but the opposite is true for LMT.
The results also vary with age, the highest programme effects on employment and income in both programmes are found for older individuals (50 and above). For both LMT and SMT the estimates on this sub-group of the population rise to around 19 percentage points and EUR 400 respectively. However, despite significant gains in terms of employment and earnings, older individuals participating in these training programmes move down the occupational ladder.
The effects of SMT on earnings and upward occupational mobility are negative for jobseekers with an education level above upper secondary. Highly educated SMT participants earn EUR 218 less and end up in occupations ranked 3.4 percentage points below their initial occupation compared to their control group counterparts.
All in all, these heterogeneity results highlight the importance of encouraging the groups of the population that are more likely to benefit from it to increase their participation in the programmes. Further analysis is needed for groups that are adversely affected by participating in LMT and SMT. For example, they might benefit from participating in certain modules of training or training in certain fields only, but the data available for the current analysis did not enable to explore these questions. Furthermore, some groups, such as highly-educated jobseekers might benefit more from other ALMPs than degree or occupational training altogether, such as better support for job search and placement.
1.5.7. Longer labour market training programmes exhibit better results in the long term
LMT training is quite heterogeneous in terms of its duration, therefore it is important to examine the extent to which trainings of different durations may impact differently labour market outcomes of participants. The evaluation results show that for longer LMT, the negative lock-in effect at first is bigger while the effects on employment and earnings later on also increase. Thus, longer LMT courses seem to be more effective than shorter ones in raising the likelihood of employment and earnings. More information is needed on the content of LMT in order to understand what makes longer programmes more effective. Furthermore, a cost-benefit analysis could complement this evaluation by shedding light on whether the positive effects of these programmes are sufficient to offset their cost.
1.6.1. It is not possible to establish whether or not self-motivated training changed training participation for jobseekers relative to the situation when only labour market training was available
An investigation into whether the introduction of SMT helped to improve outcomes for jobseekers, by widening the educational pathways available to them (relative to LMT only) was hindered by its implementation method. SMT was introduced across Finland simultaneously in 2010. This means it is not possible to look at variation in access across individuals to analyse how this introduction affected training participation of jobseekers. It was also a period of relative labour market flux, coming very soon after the global financial crisis. An attempt is made in this report to compare across two years immediately prior and post SMT introduction. However, robustness checks against unemployed non-participants groups in the same years, which should not have displayed impacts, highlight that potential effects from the economic cycle are confounding this analysis.
Due to the nature of SMT, which is driven by voluntary participation by individuals, it is not possible to compare variation of the intensity of SMT use across regions or TE Offices because any variation is likely to be driven by individuals themselves, rather than any sort of rationing or selection by the individual offices or regions. In order to determine how the introduction of SMT affects take-up and outcomes of individuals (either via encouraging individuals who may not otherwise have taken any training, or by enabling jobseekers to participate in longer training), a randomised trial would be necessary in order to isolate the impacts that are solely caused by the introduction of SMT. At present this may not be possible due to constitutional requirements.
1.6.2. There is some evidence that self-motivated training participants enjoy better long-term outcomes relative to those participating in training with the less generous study subsidy
Before 2010, a jobseeker could study for a degree and receive the study subsidy from KELA. A full-time student did not have a right to unemployment benefits and only those part-time students judged to have sufficient availability for full-time work would be eligible. Today, for a single individual aged over 18 and living alone this study subsidy is EUR 268 per month (against basic unemployment assistance of EUR 594, after provisional income taxes have been applied). SMT introduced the possibility of retaining unemployment benefits for up to two years whilst continuing in further education without having to satisfy any job seeking conditions (although this legislation was amended in 2022, to introduce some jobseeking requirements for individuals studying in this manner). There, SMT made it more financially advantageous for jobseekers to undertake such studies. This raises questions as to whether SMT encourages unemployed people to take up education, whether it improves labour market outcomes by providing greater financial assistance for individuals to finish education or whether the same outcomes could have been achieved with the less costly public funding option to support these educational pathways with the study subsidy.
To address this question, individuals undertaking SMT are compared to similar individuals using the study subsidy to continue their studies. The analysis in this report provides tentative evidence that those using SMT were more likely to enjoy better employment and earnings four years after enrolling in education. However, the small sample sizes involved mean it is challenging to be definitive about this.
The fact that study subsidy use is concentrated at lower ages, with an average participant age of 22, and SMT is undertaken more by prime-age people (on average 35 years old) who have already a family to support, indicates that SMT is appropriately targeted and offers these slightly older jobseekers a route in which they are more likely to gain education that affords them better long-term labour market outcomes.
Strengthen the role of the Ministry of Economic Affairs and Employment as the driver for evidence-based policy making in the field of ALMPs
Establish a long-term strategic view on research and evaluation activities in the Ministry of Economy and Employment (TEM) to manage and develop the analytical capacity and ensure continuous improvement in evidence-based policy making. In addition to specific research projects in the annual plan, the longer-term strategy should outline the objectives and priorities of research activities in the long term, and the milestones to reach the objectives, considering both internal capacity and contracting out.
Allocate more resources to build the internal analytical and research capacity in TEM, preferably by hiring additional economists to conduct these functions full time. Additional skilled staff would enable some impact evaluations of ALMPs to be conducted in-house. This would help to close evidence gaps and increase the capacity to design research projects for contracting out. It could improve communication with external researchers, control research quality and feed research results into policy design.
Establish systematic communication practices between analysts in TEM and policy makers, policy implementers and the broader public. The content and channels of communication need to be defined based on the specific audience, for example, by putting more focus on policy design elements in communication with policy makers.
Extend the tasks of the Finnish Council of Regulatory Impact Analysis (FCRIA) to monitor that all relevant evidence available has been used as inputs in the draft legal proposals. As a key priority, the FCRIA could check whether the research results of the inter-ministerial research instrument VN TEAS have been considered. Furthermore, the FCRIA and the VN TEAS should exchange information on on-going research projects and assessments of draft legislation to avoid a major legal change taking place while the evidence generation is still in progress.
Ensure sound evidence on ALMPs
Involve external researchers more in designing research projects to be conducted internally in TEM, and in designing major trials and pilots regardless of whether the evaluation would be later contracted out or not.
Allocate sufficient time in TEM’s contracted-out research projects for researchers to be able to access relevant data and raise awareness on the feasible timelines for credible evidence generation among policy makers.
Consider applying a peer review process by external researchers for the key research projects that TEM conducts internally to enhance overall quality. Internal analysis with external peer reviewing could be used when the evidence needs to be generated quickly, as TEM has then better control over data access, the timeline for evidence generation, and the research costs.
Extend the use of pilots and trials of ALMP designs, (digital) tools for employment services and business models for ALMP provision before nation-wide rollout to fine-tune the design and increase confidence in programme effectiveness, as well as have more credible inputs for future ex-ante evaluations.
Conduct cost-benefit analyses systematically in addition to impact evaluations to demonstrate the value added of different ALMPs more explicitly and understand whether the funding invested in ALMPs could generate benefits for the society exceeding the investments.
Use the reform to PES services to build evidence and improve performance and outreach
Ensure evidence building is embedded in the two major ALMP reforms. A comprehensive research strategy is essential to properly assess whether the reforms have met their objectives and deliver value-for-money to government and taxpayers. In addition, consider the use of small-scale trials to deviate from the new operating model and build further evidence.
Clearly define the responsibilities to be transferred to municipalities, their freedom in operating models to implement ALMPs and the support to be provided centrally, before developing the new national IT infrastructure to support the new set-up and ensure data collection for analysis and research purposes.
Strengthen PES outreach by building links between local employment services and other institutions providing services. Reviewing how municipalities engage and contact jobseekers may allow for extra learning and innovation between municipalities.
Improve data coverage, quality and availability for analysis and research on ALMPs
Set up systematic dialogue between TEM and the KEHA Centre to develop the IT infrastructure for the new institutional set-up and provide the KEHA Centre with sustainable funding to fulfil its tasks. Involve employment counsellors and municipalities in the planning, development and testing of the new IT platforms.
In addition to the careful design of data exchange and integration of IT infrastructure between the core stakeholders of ALMP provision, data exchanges with other administrative registers need to be strengthened to support employment counsellors, jobseekers and employers, as well as to ensure data accuracy. Regulations need to be revised and amended to enable data exchange relevant for operational purposes.
Continue investing in a data analytics system that provides monitoring reports and dashboards for municipalities, TE Offices and employment counsellors to manage their work and caseloads, ELY Centres to steer TE Offices and regional ALMP provision, TEM to govern the overall ALMP system, and the public to gain knowledge on ALMPs interactively.
Modernise the IT infrastructure further across the public sector in Finland to ensure better data quality in the registers, to better prepare operational data for data analytics and to establish more timely and frequent automatic data exchanges, including with Statistics Finland. Extend the availability of cost data for Statistics Finland on services, measures and benefits across the public sector to facilitate conducting systematic cost-benefit analyses.
Consider funding (part of) the research services of Statistics Finland sustainably from the state budget to cut the data application processing times and cap research data prices to support evidence generation across policy fields and encourage research not only tied to a current political agenda.
Request researchers conducting projects for TEM to share the programming codes used to conduct the research and publish these together with the reports discussing the results to avoid duplication in data preparation activities, conduct research projects more efficiently and establish an additional quality assurance, as the exact methodology would be more transparent for the research community.
Target SMT and LMT to groups more likely to benefit from them and redesign these programmes for those groups who do not benefit currently
Improve SMT and LMT effectiveness by targeting them to those groups which benefit from them the most, notably older jobseekers aged 50 and above and women.
Continue re-designing SMT and LMT to improve the labour market outcomes of groups that are not yet experiencing the beneficial effects of these measures. This is of particular importance for individuals below 30 years old who are overrepresented in those programmes. Explore new features to re-shape the programmes and adapt them to the needs of youth, for instance by learning from youth work best practices (i.e. mentoring, project-based learning, etc.).
Analyse which ALMPs would better support those groups for which LMT and SMT have adverse or weaker effects and adjust targeting accordingly. Above all, consider better job search and placement services for highly-educated people with low job search skills.
Extend skill mapping of jobseekers and vacancies within the job mediation platform “Job Market Finland” to better target training to cover skill gaps.
Continue evaluating SMT in the coming years to enable observing its effects on labour market outcomes beyond the three-year-threshold to better understand its long-term effects across sub-groups of jobseekers.
Exploit SMT and LMT as tools to promote labour mobility and reduce labour market shortages while taking into account job quality
Strengthen the targeting of these programmes towards sectors and occupations that are experiencing labour market shortages to support labour mobility and reduce labour market mismatch.
Ensure labour mobility through upskilling and reskilling policies does not come at the expense of job quality. Consider complementing the Occupational Barometer with information on job quality indicators to guide jobseekers to bottleneck occupations of higher quality and higher value-added for the Finnish economy.