1. Executive summary

The Nordic Council of Ministers aims to make the Nordic region the most socially sustainable and competitive region in the world by, among other things, improving the employment prospects of vulnerable groups in the entire region. This is essential for a well-functioning labour market and the socio-economic well-being of individuals (The Nordic Council of Ministers, 2022). To achieve this goal, a stronger knowledge base is needed to understand the employment barriers faced by vulnerable groups and how to overcome them.

This report is the third in a large research project examining how to increase labour market participation among vulnerable groups in the Nordic countries. In the first report, we shed light on the labour force participation among vulnerable groups in the Nordic countries. In the second report, we developed a framework over employment barriers in the Nordic countries affecting the employment chances of vulnerable groups. The framework covers 24 specific employment barriers relating to either individual characteristics, economic incentives and motivation, the employer and labour market structures, or public services. The purpose of this third report is to deepen our understanding of the prevalence and combination of these employment barriers among individuals with no or weak labour market attachment in the Nordic countries. Furthermore, we seek to categorise these individuals based on the specific employment barriers they face.

First, we use our developed framework over employment barriers in the Nordic countries. By utilising microdata from Eurostat, we operationalise 10 employment barriers which relate to 9 out of the 24 employment barriers identified in the second report, making it possible to observe these barriers in data.

Second, we focus on individuals with no or weak labour market attachment and investigate the prevalence of the operationalised barriers, determine the typical number of barriers that individuals face, and identify the most common combinations of barriers. About 21 pct. of the working-age population in the Nordic countries (excluding students and individuals enrolled in compulsory military service) have no or weak labour market attachment.

Finally, we employ a statistical segmentation method (latent class analysis or LCA) to group individuals based on the barriers they encounter rather than solely relying on their observable demographic characteristics, such as age, country of origin, and health status. We call the subgroups, derived from the latent class analysis, data-driven target groups as counterpart to the traditional target groups for labour market interventions (young people, seniors, immigrants, and persons with disabilities).

During previous phases of this project, we observed indications that individuals outside the labour market often face several barriers that hinder their participation on the labour market. However, these were only preliminary indications. With this report, we are now able to confirm that two-thirds of individuals with no or weak labour market attachment in the Nordic countries face at least two barriers. Further, we show that 14 pct. of the individuals face at least four barriers, and a common set of barriers prevalent among this subgroup comprises health issues, lack of education, no recent experience, and lack of skills. The simultaneous presence of these barriers amplifies the complexity of the task at hand, making it clear that a multifaceted approach is required to effectively assist these individuals in entering the labour market. Moreover, these results are most likely bottom-edge estimates of the barrier complexity in the Nordic countries since we are only able to operationalise 10 employment barriers related to 9 out of 24 employment barriers, as identified in previous work in this project.

We use latent class analysis (LCA) to separate the highly heterogeneous population of individuals with no or weak labour market attachment in the Nordic countries into 8 distinct subgroups that are close to homogeneous with respect to the types of employment barriers they face. These 8 distinct data-driven target groups are shown in Table 1.1. The model identifies an overall distinction between individuals who have no recent labour market experience and individuals who have recent labour market experience. Basically, the model sorts the individuals into 5 groups who, to a large degree, have no recent experience and 3 groups of individuals who to a larger degree, have recent labour market experience. Further, the table shows the primary and secondary barriers that identify the group, the size of the group, as well as a short description of the group.

The results shown in Table 1.1 contribute to highlight important sets of barriers that individuals with no or weak labour market attachment face in the Nordic countries. The data-driven target groups, each of which are close to homogeneous in terms of employment barriers, could be used to facilitate discussions of the strengths and limitations of different policy interventions for concrete groups of policy clients. They can also be used to help inform decisions on whether to channel additional efforts towards specific priority groups.

The LCA reveals another significant finding: the traditional demographic target groups cannot be neatly categorised into the 8 groups. In other words, not all young people exclusively belong to Group 1, nor do all seniors exclusively fall into Group 2, and so on. This suggests that the traditional demographic target groups do not uniformly face the same barriers. Instead, these barriers are shared across different traditional demographic groups. Consequently, relying solely on traditional demographic characteristics for precise targeting has its limitations. Nevertheless, despite the variations within the groups, there are certain primary demographic characteristics that partially define the data-driven target groups. These primary demographic characteristics are highlighted in the description of the group. By considering these primary demographic features, a more nuanced understanding of the target audience can be attained, leading to more effective strategies and tailored approaches. For instance, Group 1 and Group 6 consist of relatively high shares of immigrants from non-EU countries. However, it is crucial to recognise that these immigrants face distinct employment challenges. Group 1's primary barrier is the lack of prior experience in the labour market, whereas Group 6's primary challenge stems from an absence of competence-giving education and a lack of skills. Acknowledging these differences within the traditional demographic target groups is essential for shaping future employment policies.

These empirical findings demonstrate that future policies may derive greater benefits by focusing on the specific employment barriers faced by individuals, as indicated by the 8 data-driven target groups, rather than relying on broad demographic categories as a shorthand for the unique challenges experienced by individuals with no or weak labour market attachment. For instance, young people and seniors are often used as proxy groupings in policy discussions, assuming that these categories effectively capture distinct sets of employment barriers that can inform policy formulation and implementation (Fernandez et al., 2016). However, it is important to note that being young or being a senior, in and of itself, does not constitute an employment barrier.

When formulating future employment policies in the Nordic countries, it is crucial to consider the identified data-driven target groups. This analysis demonstrates that paying particular attention to the central distinction between individuals with recent labour market experience and those without is vital. Furthermore, recognising the significant group of individuals temporarily out of the labour market, who typically face only one employment barrier (Group 8), is essential. Early identification of this group should be prioritised when designing new policy initiatives. Lastly, it is important to recognise the existence of individuals with more complex sets of barriers, such as those who have never worked (Group 1) or those who lack both work experience and education (Group 2). On average, individuals in these groups face three and four employment barriers, respectively.

Nevertheless, it is essential to acknowledge that the traditional demographic target groups cannot be entirely disregarded, especially when addressing certain barriers. For instance, policy actions aimed at addressing the lack of education among young people will differ from those aimed at addressing lack of education among seniors. Therefore, a nuanced approach that considers both the specific employment barriers identified through data analysis and the relevant demographic characteristics is crucial for effective policy design and implementation.

This is invaluable information in future phases of this project when we will assess how current activation and labour support policies in the Nordic countries adequately support the identified groups of individuals as well as the traditional data-driven target groups. This evaluation will be achieved through a combination of extensive literature reviews and qualitative research, including interviews with relevant practitioners, with the ultimate aim of presenting concrete evidence-based policy recommendations to enhance labour force participation and inclusion among vulnerable groups in the Nordic countries.