Young people not in employment, education or training – NEETs – were at the heart of The Ami Foundation’s work in 2024. We explored the issue extensively by publishing articles on the topic, organising a seminar and a workshop, and running an advocacy campaign on social media. We also piloted our new web pages About The Nordic Labour Market (in Finnish) specifically through the NEET theme. By making the situation of NEET youth visible, we invite researchers, research institutes, and voluntary sector organisations, among others, to apply to The Ami Foundation for research and project funding that supports NEET youth’s transition to working life and education, and prevents the NEET stage from becoming prolonged. Our aim is to have the lowest NEET rate in the Nordic countries and to create a future labour market where everyone’s potential can be realised.
Readers of The Pathfinder blog will have noticed that the NEET rate in Finland, especially in the Helsinki Metropolitan Area, is higher than in other Nordic countries. In Sweden, on the other hand, NEET figures have remained low for years. In this blog post, I explore what recent research tells us about NEET youth and their situation in Sweden. The sources for this blog include recent studies from the Swedish Agency for Youth and Civil Society MUCF and The Institute for Evaluation of Labour Market and Education Policy IFAU.
Swedish NEET youth
NEET youth in Sweden are a very diverse group – exactly like in Finland. Statistically, the largest group of them falls into the category “unknown”, which corresponds to the “other unknown” classification group of population outside the labour force in Finland. The second largest group of NEETs in Sweden consists of unemployed young people who participate in state labour market measures, and receive compensation through the Swedish Public Employment Service.
A significant proportion of NEET youth in Sweden are disabled or chronically ill, and receive sickness or activity benefits. As in Finland, a number of risk factors for prolonged NEET status have been identified in Sweden, including long-term illness and disability, immigrant background, dropping out of upper secondary education, and becoming a mother at a young age.
There are significant differences in NEET rates between different municipalities in Sweden. The lowest municipal percentage is 4.8%, while in the municipality with the highest percentage 16.7% of young people are not working or studying. This means that, proportionally, the municipality with the highest share of young people not working or studying has almost four times the proportion of NEETs than the municipality with the lowest share. According to reports, part of the difference between Swedish municipalities can be explained by structural factors. However, much of the variation in NEET rates between municipalities is not explained by structural conditions, suggesting that there are other factors that do explain why the proportion of young people not working or studying varies between municipalities.
Municipalities with a low NEET rate
A significant part of the differences in the NEET rate between municipalities cannot be explained by structural conditions alone, such as industry structure, age distribution, or unemployment rate. This suggests that other factors, such as local policies, target group understanding, and tailored measures may influence why the share of young people not in employment, education or training varies across municipalities.
Swedish municipalities that have managed to achieve low NEET rates share common features that explain their success. Studies highlight the strong commitment of policy-makers in these municipalities to reducing the NEET rate and to the measures that are needed to achieve this. Successful municipalities also show ambition and perseverance in achieving this goal. Studies have also shown that adequate education and service provision alone are not enough to reduce the NEET rate. Measures to reduce the NEET rate must be strategically interlinked, and start at the highest level of local policy-making.
Municipalities with low NEET rates have also developed a deep target group understanding of different NEET youth groups and their situations. This understanding allows them to tailor interventions to different subgroups of NEET youth, such as those with chronic illnesses or young people from a migrant background. Services for young people are designed, and their effectiveness is evaluated, on an evidence-basis by these municipalities.
Successful municipalities have developed comprehensive data on young people outside work and education, and this information serves as a basis for action plans, and for planning and launching projects. A data-driven approach is therefore key to achieving low NEET rates.
Cooperation between different authorities has proved crucial in reducing NEET rates and preventing NEET status. As authorities have their own statutory roles and boundaries, effectively coordinated models of cooperation are key to improving the situation of young people. Accordingly, Swedish studies emphasise the importance of coordination and the creation of a shared understanding of the situation. The studies also point out that there is no one right way to coordinate cooperation and organise services, but that any measures have to be tailored to the needs of young people in each municipality.
Research shows that the one-stop shop model where all the services a young person needs are available in one place has been found to be an effective service model. From a one-stop shop, the young person can be referred to other services, if necessary. In this respect, the Swedish model is similar to the Finnish One-Stop Guidance Center which offers multidisciplinary support to young people under one roof.
The challenges
Although Sweden has long had the lowest NEET rate in the Nordic countries, the exclusion of young people from education and employment has also been recognised as a major challenge there. Reports from the MUCF highlight a number of problems relating to the situation of young people that still need to be addressed.
According to MUCF reports, a key challenge is to reach the young people for whom specific measures are aimed at. Several municipalities have set themselves the target of reaching the NEETs who are not in contact with the authorities, in particular, and creating opportunities for them to access the support they need.
However, municipalities report that certain groups are particularly difficult to reach. For example, foreign-born girls are challenging to reach. In some municipalities, motivating young people with criminal convictions to participate in services is also a major issue. These challenges underline the need to develop new approaches and methods to support hard-to-reach groups.
Projects to improve the situation of NEET youth have been identified as important and often effective, but there are significant challenges around longevity. Although individual projects have proven effective in improving the situation of young people, they are generally of limited duration. This means that when projects end, their benefits and good practices are often not followed up in a sustainable way. Projects based on fixed-term funding therefore represent a challenge for Swedish municipalities in terms of improving the situation of NEET youth.
Indeed, a key problem is that good practices developed with project funding are not integrated into the permanent service structure. This underlines the need to design projects in such a way that their results can be integrated into long-term activities and service systems.
In Sweden, improving the situation of NEET youth is also complicated by cooperation challenges between different stakeholders. According to reports, there are issues with cooperation between municipalities, health regions (Regioner), and the Public Employment Service (Arbetsförmedlingen). This is a major challenge as there are groups of NEET youth who need the services of several organisations at the same time.
Effective cooperation between these organisations is essential to ensure that young people in need of multidisciplinary support are able to receive comprehensive and coordinated services. However, lack of cooperation can lead to young people not getting the support they need, or to services not meeting their needs. This underlines the need to develop more effective structures and processes to improve cooperation between different stakeholders.
Takeaways from Sweden?
The risk factors for prolonged NEET status in Sweden are largely similar to those in Finland, which is not surprising as international research literature identifies similar risks in all Western countries. The challenges are also similar in many respects between these countries. NEET youth are a diverse group, and the different challenges they face require extensive cooperation between employment services, youth work, voluntary organisations, educational institutions, and healthcare. Coordinating the work of different organisations is a challenge in both Finland and Sweden. In addition, both countries have identified problems in bringing young people within the service provision.
Sweden’s much better economic situation and higher number of job vacancies partly explain its low NEET rate. However, it would be very important to have more comparative research between Finland and Sweden. Has Sweden succeeded in developing action models or coordinated services at municipal level from which Finland could learn?
Sources
- Korpi Tomas, Minas Renate: Lokala åtgärder för unga som varken arbetar eller studerar – Utformning, organisation och funktion av åtgärder på kommunal nivå. Institutet för arbetsmarknads- och utbildningspolitisk utvärdering (IFAU). 2024.
- Abelin Charlotte, Ilhammar Cecilia: Stöd till de aktörer, i första hand kommuner, som arbetar i verksamheter som bidrar till etableringav unga som varken arbetar eller studerar. Myndigheten för ungdoms- och civilsamhällesfrågor (MUCF). 2024.
- MUCF: Olika villkor för etablering – Lokala förutsättningar och stöd till unga som varken arbetar eller studerar. 2023.
Blog post illustration created with the AI tool Adobe FireFly.