Contents

This publication is also available online in a web-accessible version at https://pub.norden.org/temanord2023-501.

1. Executive summary

Labour market attachment in the Nordic countries is strong, but differences between the Nordic countries exist. This indicates that there is potential to improve the labour market attachment of the Nordic populations.

The Nordic Council of Ministers wishes to promote green growth in the Nordic Region.^{[1]The Nordic Council of Ministers (2022). Programme for the Nordic Council of Ministers’ Co-operation on Labour 2022–2024.} Getting more people from vulnerable groups into work is vital for a well-functioning labour market and for the individual's socio-economic conditions. Furthermore, the Nordic Council of Ministers sees it as the basis for sustainable welfare models and as crucial for creating a competitive and socially sustainable Nordic Region. The ageing population further contributes to the need of getting even more people into work in the Nordic countries.

To get more people from vulnerable groups into work, a stronger knowledge base concerning how to support the weaker groups in society, including people who face the risk of labour market exclusion, is necessary.

This report is the first of a series of research reports in a project examining how to increase labour market attachment among vulnerable groups in the Nordic countries. This report specifically focuses on the following traditional groups of people with a high risk of facing labour market exclusion:

• Young people
• Seniors
• Immigrants
• Persons with health issues or disabilities

Knowledge of how to increase labour market attachment, particularly the labour market attachment of the weaker groups in society, will help the Nordic Council of Ministers in ensuring that everyone gets the opportunity to achieve a good working life.

Potential to improve labour market attachment

The research in this report demonstrates that there is potential for improving labour market attachment in the Nordics. This potential can be looked at from different angles.

Our research shows that, across all the Nordic countries, certain population groups struggle with relatively bad labour market attachment, namely young people, seniors, and immigrants. For example, the average employment rate of immigrants in the Nordic countries is 66 pct., which is 9 percentage points lower than the average for the working-age population as a whole. It is therefore relevant to look at the potential of the Nordic countries to collaborate on supporting them.
 

Differences between the Nordic countries may indicate a potential for increasing labour market attachment by learning from the best in class. For example, if Sweden, Denmark, Finland, and Norway had the same employment rate as that of these countries with the highest employment rate for relevant subgroups in the labour market, then the combined employment of these countries would increase by 700,000 individuals. This is equivalent to an increase in the employment by approximately 6 pct. for 15–64-year-olds in the Nordic countries (see chapter 5 for more about the underlying benchmark calculations). The differences in employment between the Nordic countries might be caused by different policy contexts and differences in population characteristics in the Nordic countries. Some of these characteristics are of a permanent character, so it is not possible for all countries to perform as the ‘best in class’. Hence, 700,000 individuals should be seen as an upper estimate of the potential. Still, it illustrates that, even though the differences in labour market attachment between the Nordic countries are small, there is great potential in learning from each other.

700,000-individual

…increase in Nordic employment if all countries
had the same employment rate as the best country.

Finally, we find that around 14 pct. of the working-age population in the Nordic countries are employed with either a very low amount of hours or in temporary contracts, i.e., they have weak labour market attachment. This demonstrates a further potential for improving labour market attachment.

No country outshines all the others with regards to labour market attachment

Labour market attachment in the Nordics varies between countries and across different subgroups of the population. No country has the best labour market attachment for all the analysed groups. Rather, some countries have had success in ensuring a strong labour market attachment for some groups, while other countries have had success regarding other groups. When analysing labour market attachment in this report, we look at two main indicators:

• Labour force participation rate, i.e., the share of the working-age population in the labour force
• Employment rate, i.e., the share of the working-age population that is working

Hence, each country has its own challenges with regards to labour market attachment, and all the Nordic countries are likely to benefit from cross-country learning and collaboration. Taking one country at a time, we find that:

Sweden has a very high labour force participation rate for immigrants in 2020. Nonetheless, the employment rate is around the Nordic average. This indicates that there might be a challenge with unemployment among immigrants in Sweden.

Denmark is particularly challenged with regards to young people. In Denmark, a relatively high share of young people are neither in employment nor in education. Moreover, the labour force participation has decreased over the past 10 years, and a large share of employed young people have weak labour market attachment (i.e., work few hours or have temporary contracts).

In Finland, labour market attachment for young people and seniors seems to be at a lower level than in the rest of the Nordic countries, when looking at both labour force participation and employment. However, Finland is the Nordic country with the lowest share of unskilled workers in the working-age population.

In Norway, the labour force participation rates of seniors and immigrants are relatively low compared to the other Nordic countries. Moreover, these rates have not increased as much in the last 10 years as in the other Nordic countries (for immigrants, it has decreased). However, the Norwegian employment rates for these two groups exceed the Nordic average.

Iceland is characterised by very high labour force participation and employment rates in both a Nordic and a EU27 context (and probably also in a global context). Conversely, compared to the other Nordic countries, we see fewer young people enrolled in education in Iceland. This, combined with an increased share of young people neither in employment nor in education in the last four years, could leave more young people at risk of labour market exclusion in a longer-term perspective.

Small improvements in labour market attachment across time – but many young people at risk

Labour force participation in the Nordics has increased by 2 percentage points in the last 10 years. Still, the developments look different depending on population group and country.

Seniors, in particular, have experienced large increases in labour force participation, which is likely driven, at least in part, by changes in retirement schemes in the Nordic countries.

In young people’s labour force participation rate, however, there is only a one-percentage-point increase. Furthermore, the share of young people neither in employment nor in education has remained more or less unchanged during the last ten years. Hence, young people have also not increased their participation in education. Stagnating numbers of young people with no education entail that considerable swathes of Nordic youth are at risk of exclusion in fast-changing labour markets.

More knowledge on vulnerable groups and barriers is needed

To realise the potential of increased employment for vulnerable groups, more knowledge is needed.

Better Nordic collaboration on supporting vulnerable groups, with the aim of devising measures and programmes to address these challenges, requires identification and knowledge of the specific challenges a particular group faces. Also, there is a need of more knowledge of the barriers that result in some people being employed in jobs with very few hours or on temporary contracts, be that due to lack of qualifications or labour conditions in particular parts of the labour market.

Finally, in-depth knowledge of the interventions, administrative proceduces, etc., bringing about good performance in one country is required so that other countries can learn from it.

In the coming phases of this research project, we will analyse and assess relevant barriers that lead to labour market exclusion (or risk thereof) in the Nordics. Also, we will develop a method to group people according to the barriers they face, the aim being to identify and formulate concrete and actionable policy recommendations for how the Nordic countries can further improve the labour market attachment among vulnerable groups.

2. Methodology

In this chapter, we briefly describe the important terms and definitions as well as the data sources used to analyse the labour market attachment in the Nordic countries.

2.1 Data source

The analyses in this report are based on data from the Labour Force Survey (LFS) from Eurostat from the years 2010–2020.^{[1]LFS microdata from 2021 is planned to be released at the end of October/beginning of November 2022.} The LFS is the most common source used to monitor employment, unemployment, and labour force participation in the EU. The statistics and facts in this report will therefore be easily comparable with the general labour market statistics.

LFS microdata for scientific purposes contains data for all member states of the European Union, as well as for Iceland, Norway, Switzerland, and the United Kingdom. In this report, we use data for the five Nordic countries (Sweden, Denmark, Finland, Norway, and Iceland) and the remaining EU27 countries. We compare the numbers for the Nordic countries with the EU27 average. We focus on the working-age population, here defined as individuals who are 15–64 years old.

The purpose of LFS is to give a description of the labour market status of the population. The population is classified into two main categories: people in the labour force and people outside the labour force. Moreover, people in the labour force are classified into two categories: employed people and unemployed people. The definitions in LFS follow the EU definitions, along with recommendations from the International Labour Organization (ILO), which means that a respondent in the survey is interviewed about one specific week and that the classification into main categories relate to this specific week. The definitions of the main categories in LFS are further described in Box 2.1.

Box 2.1 Definition of labour market status in LFS

The labour force: all persons who are employed or unemployed.

Employment: all persons who worked for payment, as self-employed, or for family gain, in the reference week. Persons who were temporarily absent from work in the reference week are also considered to be employed.

Unemployment: all persons without employment who were actively seeking work in the four weeks prior to the reference week and who are able to start a job within two weeks following the reference week.

Source: Eurostat (2021): Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/SEPDF/cache/20687.pdf

The classification of labour market status based on LFS differs from the classifications based on administrative register data often used in the Nordic countries. For example, a student who is actively seeking work and is able to begin a job within two weeks is classified as unemployed in LFS, while this would not be the case in a classification based on register data. The two types of classifications have different advantages and disadvantages. However, LFS is very suitable for comparative studies as the data collection and definitions are comparable between the European countries.

2.2 Definitions used in the project

Labour force attachment and exclusion

Barriers in the labour market may affect not only vulnerable persons’ possibilities of obtaining employment but also their chances of obtaining permanent employment instead of temporary employment and full-time employment instead of employment with restricted hours. Thus, limiting attention to only the unemployed or non-employed disregards the true extent of labour market difficulties for vulnerable groups in the labour market. Therefore, in this report, we use a broad definition of labour market exclusion.

Labour market exclusion is defined as either:

• being outside the labour force
• being unemployed
• having a weak labour market attachment, which includes:
  • unstable jobs (working only sporadically)
  • working persistently with restricted working hours (less than 20 hours a week)
    
     

This definition is inspired by the OECD paper “Faces of Joblessness” (Fernandez, R.; Immervoll, H.; Pacifico, D.; Thévenot, C., 2016. Faces of Joblessness. OECD).^{[1]The OECD paper also adds persons with near-zero or negative income to the group of persons with weak labour market attachment. They do this because self-reported activity status may be subject to measurement/classifications errors. We do not have income information for all the countries in LFS, but we also expect that the classification of employment status is more precise in LFS than in EU-SILC, which is the data source in the OECD paper.}

This definition includes both voluntary and involuntary joblessness as well as both voluntary and involuntary restricted working hours. The reason is that it is difficult – both empirically and theoretically – to distinguish voluntary and involuntary joblessness. Whenever possible, we will try to focus our analyses and policy recommendations on the involuntary part.

Vulnerable groups in the labour market

Work-oriented policy measures in the Nordics traditionally define vulnerable groups as young people, seniors, immigrants, and persons with disabilities or health issues. In other words, these are the groups identified by the literature as having a relatively high risk of facing labour market exclusion. Because of this, most policy measures aimed at increasing labour market participation focus on these groups. In this report, we will therefore focus on the labour market participations of these groups.

More concretely, we define the four traditional target groups as follows:

• Young people: 15–29 years old
• Seniors (older workers): 55–64 years old
• Immigrants: persons who are foreign-born
• Persons with disabilities or health issues^{[2]LFS publications from 2010-2020 do not include information that makes it possible to define people with disabilities or health issues. However, the LFS 2021 includes questions about health status and limitations in activities, and we will provide an appendix to this report when we get access to LFS2021.}

2.3 Main indicators of labour market attachment in this report

Our analyses of the Nordic labour markets focus on two main indicators:

• Labour force participation rate, i.e., the share of the working-age population in the labour force
• Employment rate, i.e., the share of the working-age population that is working

Employment follows the business cycles, as shown in section 3.1. Therefore, we generally do not analyse development in employment, and when we compare employment across countries, we use a three-year average from 2017 to 2019 to even out business cycles. We generally do not include employment data for 2020 as this is heavily influenced by Covid-19. In the yearly reports “Nordic Economic Outlook” by the Nordic Council of Ministers, it is concluded that all Nordic countries in these years experienced growth but were in somewhat different stages of the business cycle (the Nordic Council of Ministers, 2017, 2018, 2019). We also considered a five-year average over the years 2015–2019, but this did not change the results significantly.

The labour force participation rate is generally more stable over time and less affected by business cycles than the employment rate. We look at labour force participation to analyse the development in the labour markets over time. Regarding young people, only looking at participation in employment disregards that a large proportion of young people are enrolled in education and in that way invest in their future. Therefore, we also present statistics on the percentage not in employment nor in education or training (NEET). Young people are defined as belonging to the NEET group if they meet the following two conditions: (1) they are not employed in the reference week, and (2) they have not received any formal or nonformal education or training in the four weeks preceding the time of the interview.

Weighted averages

When analysing the above indicators of labour market attachment, we look at the indicators for each of the Nordic countries and compare them with a Nordic average and the EU27 average. The averages are weighted by the population in the included countries.

2.4 Information on population characteristics used in this report

Besides the labour market indicators, we use several sociodemographic variables in the comparisons of the Nordic countries. These variables – and how we divide them into subcategories – are shown in Table 2.1.

Table 2.1 Population characteristics in 2020, pct. of 15–64-year-olds

The variables concerning urbanisation and education are described in more detail in the following two paragraphs.

Urbanisation

In LFS, the degree of urbanisation describes the character of the municipality where the respondent lives. The classification of the municipalities with respect to urbanisation is based on a grid with square cells of 1 km². These square cells are classified into the following types of units:

• Urban centres: contiguous grid cells of 1 km² with a density of at least 1,500 inhabitants per km² and a minimum population of 50,000
• Urban clusters: clusters of contiguous grid cells of 1 km² with a density of at least 300 inhabitants per km² and a minimum population of 5,000
• Rural grid cells: grid cells outside urban clusters

The variable describing urbanisation has the following three subcategories in LFS:

• Cities: municipalities where at least 50% of the population live in urban centres
• Towns and suburbs: municipalities where at least 50% of the population live in urban clusters that are not cities
• Rural area: municipalities where at least 50% of the population live in rural grids

Education

The variable describing education is based on the International Standard Classification of Education (ISCED11). We have classified education into the following subcategories:

• Unskilled: pre-primary, primary and lower-secondary education
• Upper secondary: upper secondary education
• Short tertiary: post-secondary non-tertiary education and short-cycle tertiary education
• Medium tertiary: bachelor’s or equivalent
• Long tertiary: master’s or equivalent, and doctorate or equivalent

A person is classified as enrolled in education if the person has been in regular education during the last four weeks (before the reference week). 

3. Labour market attachment in the Nordics

In this chapter, we describe the Nordic labour markets and highlight differences between the Nordic countries. People in the Nordic countries generally have strong labour market attachment, but some differences between the Nordic countries exist.

3.1 Characteristics of the Nordic labour markets and working-age populations

In this section, we will briefly look at some main differences between the populations in the Nordic countries as well as in the business cycles of the Nordic economies. These differences are important to keep in mind when analysing and interpreting the labour market attachment in the following sections.

Population characteristics

Table 3.1 presents key characteristics of the working-age population (15–64-year-olds) in the Nordic countries.

Table 3.1 Population characteristics in 2020, pct. of 15–64-year-olds

Table 3.1 shows that the Nordic countries are very similar in terms of population characteristics. We see almost the same gender distribution (women making up close to 49 percent of the population in all five countries) and a very similar age distribution. In all five countries, young people (15–29 years) make up approximately 29 percent, and seniors (55–64 years) make up approximately 19 percent. In terms of the married share of the population, these countries also resemble each other, although differences in this field are slightly larger. All countries are close to the Nordic average of 41 percent, yet there is a five-percentage-point difference between Denmark and Norway.

In terms of level of education, share of immigrants, and level of urbanisation, the five countries are significantly more different. Taking education first, Iceland and Denmark have a much higher share of unskilled people than Finland and Sweden. In Iceland and Denmark, 28 and 26 percent of the population are unskilled, while this is the case for merely 17 and 21 percent of the population in Finland and Sweden, with Finland having the lowest share.

Turning to immigrants, we see far higher shares in the populations of Norway and Sweden than in the other three countries. In Sweden, immigrants constitute 25 percent of the population; for Norway, the figure is 21 percent of the population. Hence, as a share of the population, there are three times as many immigrants in Sweden than in Finland, and twice as many in Sweden compared to Denmark. Immigrants can be a very heterogeneous group, though, and their labour market participation will likely depend on where they come from and how long they have been in the country. We analyse immigrants further in section 4.3.

Finally, the share of the population living in rural areas is much larger in Norway than in any other Nordic country. In Norway, 46 percent of the population live in rural areas, while only 15 percent do so in Iceland. Denmark is the country resembling Norway the most: 37 percent live in rural areas in Denmark. Sweden and Finland are placed between Denmark and Iceland, with 26 percent living in rural areas in both these countries.

In conclusion, we may argue that the Nordic countries resemble each other relatively closely in terms of gender distribution, age distribution, and the size of the married share of the population. There are larger differences between the countries if we compare levels of education, immigrants as a share of the population, and the share of the population living in rural areas. Comparing the Nordic countries to EU27, we see that young people constitute a larger share of the population in the Nordic countries, that fewer people are married, that the population is generally better educated, that immigrants constitute a larger share of the population, and that more people live in rural areas.

Labour market regulations and educational systems

Besides the above-mentioned differences in the Nordic populations, there are regulatory and organisational differences between the Nordic countries which are important in how their labour markets work. In this report, we do not go in depth with these differences as they will be further analysed in later stages of the research project, where we look at barriers of entry for people outside the labour market (including regulatory barriers) as well as relevant policy recommendations.

When looking at the different statistics, it is important to have in mind that some of the differences between the Nordic countries might be explained by regulatory and organisational differences. For example, there are big differences in the vocational education systems in the Nordics. Denmark has a considerable number of apprenticeship-based educations, while vocational educations in Sweden are mainly school-based. This may lead to higher employment for young people in Denmark as a larger share are apprentices. Another example could be the criteria for receiving unemployment benefits or social assistance, which is likely to influence the degree to which people are actively seeking employment, hence whether they will be categorized as unemployed or outside the labour force.

Business cycles in the Nordic countries

In this section, we will briefly account for and compare the economic situations in the Nordic countries in the past 15 years. We do so because it is important to recognise that countries may be in different economic cycles at different points in time, and this will typically affect economic indicators, such as the (un)employment rate. This is important to keep in mind when trying to identify potential structural differences in the Nordic labour markets.

Figure 3.1 shows the employment rate in the Nordic countries over a period of 15 years. The figure illustrates the similarities in the economic cycles of the Nordic countries. Some national variation, however, is worth mentioning. For example, the financial crisis in the years 2008 and 2009 affected Iceland more severely than the other Nordic countries. Norway’s employment rate was affected to a lesser degree immediately after the crisis, but the country experienced a decreasing employment rate between 2012 and 2017, which is likely connected with the decrease in the oil price around 2015. The other Nordic countries (excluding Iceland) have experienced increasing employment rates from approximately 2013 up until 2019.^{[1]The Nordic Council of Ministers, Nordic Economic Outlook (2016)}

In the yearly reports “Nordic Economic Outlook” by the Nordic Council of Ministers, it is concluded that all Nordic countries experienced growth but were in somewhat different stages in the business cycle in the period 2017–2019.^{[2]The Nordic Council of Ministers, Nordic Economic Outlook (2017, 2018, 2019)} Looking at the most recent period, all Nordic economies have been impacted negatively by Covid-19, but national variation in how severely the economies have been affected exists.

Figure 3.1 Employment rates, pct. of 15–64-year-olds

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.
Note: The y-axis starts at 55 pct.

As employment follows the business cycles, we generally do not analyse development in employment. Also, when comparing employment across countries, we use a three-year average from 2017 to 2019 to even out business cycles, as explained in section 2.3.

3.2 Labour force participation

In this section, we will examine and compare the labour force participation in the Nordic countries and discuss potential drivers behind Nordic differences. Figure 3.2 presents the labour force participation among 15–64-year-olds in the Nordic countries. Generally, the labour force participation is higher in the Nordic countries than in EU27. Further, the labour force participation has increased in Finland and Sweden but has remained at a constant level between 2010 and 2020 in the other Nordic countries.

Figure 3.2 Labour force participation, pct. of 15–64-year-olds 

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

In 2020, Iceland and Sweden have the highest labour force participation, with 85 pct. and 83 pct., respectively. In the other Nordic countries, the labour force participation is at a slightly lower level, around 78 pct.

One reason for differences in the labour force participation in the Nordic countries could be varying labour force participation across age levels. Figure 3.3 presents the labour force participation rate for different age groups. The figure shows that one explanation for the higher labour force participation in Sweden and Iceland is that both countries are more successful in keeping seniors on the labour market, which is one of the traditional target groups. For example, the labour force participation among the 55–64-year-olds is around 82 pct. in Iceland and Sweden, whereas it is around 73 pct. in the other Nordic countries. Differences in retirement schemes and cultures can partially explain the cross-Nordic differences in labour force participation. Further, the traditional target groups – young people and seniors – also seem to be the main drivers behind the differences in labour force participation between the Nordic countries and EU27.

The increase in the labour force participation in Sweden and Finland from 2010 to 2020 can be explained by an increase in the labour force participation among seniors in these countries (not shown graphically). Between 2010 and 2020, the labour force participation among seniors increased with 7 percentage points in Sweden and 13 percentage points in Finland.

Figure 3.3 Labour force participation in 2020 across age groups, pct. of population in age group

Note: The averages of the Nordic countries and of EU27 are weighted by the size of the population in each category.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Another reason for cross-Nordic differences in labour force participation could be varying participation across different educational levels. Figure 3.4 presents labour force participation rates across educational levels in 2020 in the Nordic countries. Figure 3.4 shows that individuals with tertiary educations have very similar labour force participation rates in the Nordic countries. However, there is a larger variation between the Nordic countries when it comes to the labour force participation among individuals who either have an upper secondary education as the highest attained education or who are unskilled. Therefore, the higher labour force participation of this group in Iceland and Sweden is the reason for the cross-Nordic differences in the labour force participation in 2020.

Figure 3.4 Labour force participation in 2020 for different levels of education, pct. of population in education group

Note: The averages of the Nordic countries and of EU27 are weighted by the size of the population in each category.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Increases in the labour force participation of certain education groups can also explain the general increase in the labour force participation in Finland (not shown graphically). Specifically, Finland has experienced an increase in the labour force participation among individuals with a long tertiary education of 4 percentage points between 2014 and 2020.^{[1]It is not possible to analyse labour force participation of the educational groups further back than 2014 because of changing definitions in the LFS data.}

After having looked at the labour force participation and what drives differences in the labour force participation in the Nordic countries, we will now look more closely at the individuals who are outside the labour force.

People outside the labour force

In the previous section, we saw that 20 percent of the 15–64-year-old population was outside the labour force in the Nordic countries in 2020. For Iceland and Sweden, this situation affects 15–17 percent, while the shares vary between 21 and 22 percent in the other Nordic countries.

A number of questions in the European Labour Force Survey (LFS) guide us toward a better understanding of some of the characteristics of the persons outside the labour force (see Box 3.1).

Box 3.1 People outside the labour force in LFS

The LFS divides individuals outside the labour force into the following subgroups:

1. Individuals seeking employment, but who are unable to start working within two weeks
2. Individuals not seeking employment, but who would still like to work
   1. and who can start to work immediately (within two weeks)
   2. and who cannot start to work immediately (within two weeks)
3. Individuals not seeking employment and not wanting work

Individuals outside the labour force distributed into subgroups, 2020

The table in Box 3.1 shows us that the vast majority of individuals outside the labour force belong to subgroup 3, i.e., they do not look for work, and they do not want work. The other three subgroups (1, 2a, 2b) make up far smaller shares of persons outside the labour force. Persons who are actually looking for work but who cannot start within two weeks (subgroup 1) make up only 6 percent. Persons in subgroups 2a and 2b add up to 21 percent. These two subgroups are interesting because persons in both subgroups are not looking for work, yet they still would like to work. Moreover, the persons in subgroup 2a even indicate that they would be able to start working within two weeks. Hence, they are not looking for work, but they want to work, and they are ready to start working at short notice.

The LFS questionnaire also contains a question that provides us with some explanations of why persons in the subgroups 2a, 2b and 3 refrain from seeking employment. Figure 3.5 illustrates the distribution of replies to this question, split by country.

Figure 3.5 Reasons for not seeking employment, 2020

Note: This figure shows answers from persons in category 2a, 2b, and 3. Iceland is not included due to too few observations.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Figure 3.5 shows that two types of reasons prevail among the 94 percent of the working-age population outside the labour market who are not seeking employment (i.e., categories 2a, 2b, 3). In most Nordic countries – Norway excepted – the main explanation provided is participation in education and training. In the Nordic countries, 36 percent on average of those who do not look for work indicate this reason. The second-most-common explanation is being ill or disabled, as indicated by 28 percent of this group of respondents at the Nordic level. Retirement and looking for children or incapacitated adults are reasons chosen by far smaller shares of the respondents – 4 and 8 percent, respectively – while 24 percent chose to indicate ‘other reasons’.

Moreover, the LFS also provides us with some answers to why some individuals cannot start working immediately (subgroup 1 and 2b). Hence, the LFS asks the respondents in these two subgroups for the reason for not being available to start work within two weeks. Figure 3.6 illustrates the distribution of replies to this question, split by country.

Figure 3.6 Reasons for not being able to start work immediately, 2020

Note: This figure shows answers from persons in category 2b and 3. Iceland is not included due to too few observations.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Figure 3.6 shows the two main reasons for not being able to start work immediately. These two reasons are: firstly, obligations to complete education and training, and, secondly, illness and incapacity. The Nordic averages for these two types of replies are 44 and 27 percent, respectively. Finland diverges from the other Nordic countries by having far more respondents choosing the first rather than the second category. Obligations to complete education and training, and illness or incapacity are less important reasons for not being able to start work immediately in EU27 compared to the Nordic countries, while personal or family responsibilities is more widely used as a reason in EU27.

Summing up, our analysis shows that among persons aged 15–64 years who are outside the labour force, the largest subgroup indicate that they do not seek work and do not want to work (73 percent, subgroup 3). A large and interesting subgroup indicate that they refrain from seeking work, yet they still would like to work (21 percent, subgroup 2a and 2b). Among those not seeking employment or not able to start working immediately (i.e., within two weeks), the main explanation chosen is that they have to complete training or education, and the second-most-common explanation is ill health or disability/incapacity.

3.3 Employment rates

In section 3.2, we looked at labour force participation. In this section, we look at the overall employment rates in the different Nordic countries and EU27. We also compare the shares in the different Nordic populations that have either a strong or a weak attachment to the labour market. People with strong attachment have a permanent job with at least 20 working hours a week, while people with weak attachment have either a permanent job with less than 20 working hours a week or a temporary job (see section 2.2).

Figure 3.7 Average employment rate (2017–2019), pct. of 15–64-year-olds

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Figure 3.7 shows that the overall employment rates differ, but not very much when we compare Sweden, Denmark, Finland, and Norway. The total employment rate (i.e., the sum of persons with a strong or weak attachment to the labour market) varies between 77 percent in Sweden and 71 percent in Finland. Iceland differs substantially more from the other Nordic countries since the total employment rate in Iceland reaches 84 percent as a 2017–2019 average. The average employment rate for 2017–2019 for all the Nordic countries is 75 percent, somewhat above the EU27 average at 67 percent.

In all five Nordic countries, the share of persons in a weak employment situation amounts to 13–15 percent. The Nordic average is 14 percent, while the EU27 average is 13 percent.

When we inspect gender differences, we see that more women than men hold ‘weak’ jobs in the Nordics and in EU27 (figures not shown). Across the Nordic countries, 11 percent of men are in a weak job situation, and 65 percent of men are in a strong job situation. For women, the same figures are 16 and 55 percent, respectively. An internal comparison of the Nordic countries shows no major differences between the countries in this respect. Slightly more women than men are in a weak labour market situation in all the Nordic countries. For EU27, we see a rather similar gender pattern. For men, 61 and 12 percent, respectively, are in a strong or weak employment situation; for women, 48 and 14 percent, respectively, are in a strong or weak employment situation.

Table 3.2 Average employment rates (2017–2019) for different age groups, pct. of population in age group

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

We have also analysed the average employment rate 2017–2019 for different age groups (see Table 3.2). Our results show that there is a lower degree of employment among young people, i.e., persons 15–24 years old, in Sweden and Finland than in Iceland, Denmark, and Norway. This could be explained partially by differences in the educational systems, as Denmark and Norway both have apprenticeship-based vocational educations. For the age group 25–34 and upwards, employment rates rise steeply in all countries. This is the case in especially Sweden, as well as, to some extent, Finland. Disregarding the youngest age group, the employment rate in Sweden exceeds the rates in the other Nordic countries – apart from Iceland, which exceeds all other Nordic countries in all age groups. For example, if we look at seniors in Sweden and Iceland, we see a higher degree of employment among the 55–64-year-olds than among the same age group in Finland, Denmark, and Norway.

Comparing to EU27, we see a notable difference in the Nordic countries for, in particular, the young and the seniors. For the young, i.e., 15–24 years old, the average employment rate in the Nordics is 47 percent, while the figure is 33 percent for EU27. For the seniors, i.e., 55–64 years old, the Nordic and the EU27 average employment rates are 72 and 58 percent, respectively. The Nordic countries appear to be clearly better at integrating young people into the labour market, as well as keeping the seniors there.

Figure 3.8 Average employment rate for different levels of education, pct. of population in education group (2017–2019)

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Figure 3.8 shows the relationship between average employment rate 2017–2019 and different levels of education for the different Nordic countries and EU27. Overall, the employment rate increases with a rising level of education in all the Nordic countries and EU27. Hence, in terms of gaining employment, education clearly appears to pay off. Figure 4.8. also shows a much higher employment rate among unskilled Icelanders and a much lower employment rate among unskilled Finns, when comparing to other Nordic countries. In Denmark, Norway, and Sweden, we see very similar employment rates for all levels of education. Comparing the Nordic countries as a whole to EU27, we see that the trend, i.e., the relationship between levels of education and employment, is very similar.

Weak attachment to the labour market

We have already touched upon the distribution of persons with a strong or weak attachment to the labour market in the different Nordic countries. In this subsection, we analyse which types of weak jobs prevail in the different Nordic countries. We distinguish between three types of weak jobs: (1) permanent jobs with 1–19 hours a week, (2) temporary jobs with 1–19 hours a week, and (3) temporary jobs with 20 hours or more a week.

Figure 3.9 Average weak employment in the Nordic countries (2017–2019)

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Figure 3.9 reveals large differences between the Nordic countries with respect to the percentage of persons with weak employment in a temporary job. Especially in Sweden and Finland, temporary employment dominates among workers with weak employment. Among workers with weak employment during the period 2017–2019, 85 percent in Sweden and 71 percent in Finland held a temporary job. The corresponding numbers for Denmark, Norway, and Iceland were 40–52 percent. There are also notable differences between the countries in terms of average working hours for people in temporary employment. 20+ working hours tend to dominate temporary weak jobs, especially in Sweden. In Denmark and Norway, permanent part-time jobs (1–19 hours a week) make up roughly half of the weak jobs. Permanent part-time jobs constitute a much larger share of the weak jobs in Denmark, Iceland, and Norway than in Finland and Sweden. If we regard temporary jobs as a more vulnerable type of weak job than permanent part-time jobs, more people are in a vulnerable position in Sweden and Finland than in the other three Nordic countries.

If we look at the overall Nordic average, temporary jobs with 20+ hours are the dominant type of weak job since they constitute 49 percent of these jobs in the Nordic countries, while 31 percent of weak jobs are permanent jobs with 1–19 hours. The EU27 pattern resembles the overall Nordic pattern, although temporary jobs with 20+ hours are even more dominant (as in Sweden), while permanent-contract jobs with 1–19 hours constitute almost a quarter of weak jobs (as in Finland). Again, if we regard temporary jobs as a more vulnerable type of weak job than permanent part-time jobs, more people in EU27 are in a vulnerable job situation than in the Nordics.

Figure 3.10 Percentage of employed persons with weak employment across different age groups (2017–2019)

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

When we look at the shares of weak jobs among different age groups, we see, unsurprisingly, the highest share of persons with a weak attachment among young persons, 15–24 years. This is unsurprising given that young people often undergo education or training, which entails limited possibilities for gaining permanent fulltime employment. Looking at country differences, we see the lowest share of persons with weak employment in Iceland, and that is in all age groups. For young people, we find the highest share in Denmark. Among all age groups, persons 45–54 years exhibit the lowest share of persons in weak jobs. Finally, we should notice the uniform age pattern characterising this distribution in all countries. The highest share of persons in a weak labour market situation is found among young people, while the lowest share is found among persons 45–54 years old. For seniors, the share rises slightly in all countries (apart from Iceland). The EU27 pattern closely resembles that of the Nordic countries.

Figure 3.11 Percentage of employed persons with weak employment across different educational levels (2017–2019)

Note: Iceland is not included due to too few observations.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Figure 3.11 shows the share of weak jobs for different educational groups. Notice that numbers for Iceland are not included in the figure due to too few observations for some of the educational groups. It is interesting to contrast the very uniform Nordic pattern for the distribution of weak jobs with respect to age with the pattern we see when we look at education. As we would expect, higher educational levels generally correlate with lower shares of persons in a weak labour market situation. More education provides a greater chance of finding a permanent full-time job in the Nordic countries. Nonetheless, the patterns characterising the different Nordic countries show a more uneven distribution compared to what we saw in relation to age. For EU27, we see a pattern relatively similar to the average Nordic pattern, although the share of persons with weak jobs only diminishes marginally for persons with upper secondary education or above.

Overall, figure 3.11 shows that a relatively large share of the employed with weak labour market attachment in the Nordic countries are unskilled (34–39 percent). Still, figure 3.11 shows differences between the Nordic countries with respect to weak labour market attachment among persons with short tertiary education. Low levels of weak labour market attachment characterise such persons in Finland (9 percent) and Denmark (11 percent), as compared to the higher levels found among such persons in Norway (19 percent) and Sweden (17 percent). Finally, we see from figure 3.11 that weak labour market attachment characterises both medium and long-term tertiary education to a higher extent in Finland than in the other Nordic countries.

Summing up, we see that higher age and levels of education generally correlate with lower shares of persons holding weak jobs. Still, this pattern is not homogeneous across the different Nordic countries since Norway and Sweden diverge from this overall pattern as regards persons with short tertiary education. For the Nordic countries as a whole, the trend of more education reducing the amount of weak jobs is stronger than what we see in EU27.

3.4 Overall labour market attachment

Figure 3.12 shows the overall labour market status in the Nordic countries and EU27, comparing 2010 and 2020. We see that the share of employed persons rises marginally from 2010 to 2020. In the Nordic countries, the share of employed persons rises from 72 to 74 percent, while there is a rise from 63 to 68 percent in EU27. Conversely, in three out of five Nordic countries, the share of persons outside the labour force shrinks when we compare 2010 and 2020. This trend is strongest in Sweden and Finland, where the share of persons outside the labour force shrinks from 21 to 17 and from 26 to 22 percent, respectively.

Figure 3.12 Labour market status in the Nordic countries, pct. of 15–64-year-olds 

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey

In the Nordic countries taken together, the share of persons outside the labour force diminishes by 2 percentage points from 2010 to 2020 (from 22 to 20 percent), while the share diminishes by 3 percentage points (from 30 to 27 percent) in EU27. In conclusion, the Nordic countries and EU27 appear to be following the same path. Still, the relative size of the group of persons outside the labour market is much smaller in the Nordic countries than in EU27.

Summing up on important characteristics concerning labour market attachment in the Nordic countries, we have seen that the Nordic populations are characterised by relatively high levels of education. Across the Nordics, 41 percent of the population aged 15–64 years old hold tertiary education. This is 8 percentage points higher than the EU27 average at 33 percent. We have also seen that all the Nordic countries experienced economic growth during the years 2017–2019. In 2020, the Nordic labour force participation rate reached 80 percent, which was 2 percentage points higher than the level in 2010 and 8 percentage points higher than the 2020 level in EU27.

Comparing the different Nordic countries in 2020, Iceland and Sweden have the highest labour force participation rates, while Denmark, Finland, and Norway are at a slightly lower level. We have also seen that the difference in labour force participation rate is largest for older age groups. Sweden and Finland, in particular, have experienced a large increase in their respective labour force participation rates between 2010 and 2020.

We have also looked more closely at persons outside the labour force, i.e., people either not seeking jobs or people not able to start working within two weeks. We find that the main explanation for their employment status as being outside the labour force is that they have to complete training or education. The second-most-common explanation is ill health or disability/incapacity.

When we look at employment, we find that the average employment rate for 2017–2019 for all the Nordic countries is 75 percent. This rate is 8 percentage points above the EU27 average at 67 percent. The Nordic-EU27 differences in labour force participation rate (2020) and employment rate (2017–2019) are exactly the same, i.e., 8 percentage points. Looking at the strength of labour market attachment, we find that 61 percent of the population in the Nordic countries have a strong attachment to the labour market, while 14 percent have a weak attachment. While employment rate is lower for EU27, this destitution in EU27 is very similar. We also find that the share of the population holding weak jobs, as a general rule, diminishes with rising age and higher levels of education in the Nordic countries taken as a whole.

4. Vulnerable groups in Nordic labour markets

In this chapter, we analyse groups with weak labour market attachment and identify differences between the Nordic countries. Underlying the Nordic differences in labour market attachment are differences in population characteristics and the labour market attachment of vulnerable groups in each country.

4.1 Young people

The first traditional target group we analyse is young people. Young people aged 15–29 years represent between 28 pct. (in Finland) and 30 pct. (in Denmark and Iceland) of the working-age population in the Nordic countries. Table 4.1 presents key characteristics of this group in the Nordic countries in 2020.

Table 4.1 Population characteristics of young people in the Nordic countries, pct. of young people in 2020

Table 4.1 shows some interesting differences and similarities among the young people in the Nordic countries. In the Nordic countries, approximately the same share of the young people are married. There are greater differences between the five countries when it comes to the young people’s highest attained education. For example, a larger fraction of young people is unskilled in Denmark; consequently, there are also relatively few with a tertiary education in Denmark. This could be the case because young people spend more time completing their education in Denmark than in the other Nordic countries. Further, it is important to notice that a relatively large share (51 pct. to 54 pct.) of young people in the Nordic countries are enrolled in education. These large shares of young people undergoing education are important to keep in mind when interpreting the labour market statistics in the forthcoming sections.

Neither in employment nor education

In this section, we present statistics on how many young people in the Nordic countries are neither in employment nor in education or training (NEET). As already highlighted, a large fraction of the young people is enrolled in education. Therefore, the concept of NEET (Not in Employment nor in Education or Training) is introduced in specific relation to young people since countries having a large fraction of young people in education will perform badly on employment statistics, even though having young people in education to develop their human capital is not considered bad. Notice that NEET shares are highly sensitive to the economic situation, so we present the data for a 10-year period to be as transparent as possible.

Figure 4.1 presents NEET shares for young people in the Nordic countries over time – and several interesting patterns appear.

Figure 4.1 Young people neither in employment nor in education or training (NEET), pct. of all young people

Note: A person is considered a student if they are enrolled in either formal or informal education.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

First, the NEET shares are generally lower in the Nordic countries compared to EU27. Second, both Denmark and Iceland have experienced increasing NEET shares between 2016 and 2019/2020, whereas the other Nordic countries have experienced either a decreasing or a constant NEET share in this period. Third, comparing the levels in 2019, Denmark and Finland both had a relatively high NEET share, with approximately 9–10 pct. of the young people in the NEET group in these countries, compared to around 6 pct. in the other Nordic countries in 2019^{[1]According to recent statistics from the Danish Ministry for Children and Education, the NEET share in Denmark has decreased between 2016 and 2020, specifically from 7.1 pct. to 6.6 pct. However, these statistics are based on Danish register data, whereas this analysis is based on Eurostat’s Labour Force Survey, which explains the difference (together with differences in the methodologies used).}.

Labour force participation

In this section, we examine the labour force participation among young people in the Nordic countries. Figure 4.2 shows the labour force participation among young people in the Nordic countries over time. In general, the labour force participation rate is lower for young people than the rest of the population, which is partially explained by the large share of students who are often outside the labour force.

Figure 4.2 Labour force participation for young people, pct. of all young people

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Between 2010 and 2020, every Nordic country except Denmark experienced an increasing labour force participation among young people, whereas Denmark experienced a decrease from 70 pct. in 2010 to 68 pct. in 2020. This could be explained by an increase in the number of students in Denmark. However, combining the decreasing labour force participation with the increasing NEET share, we can conclude that Denmark has experienced an increase in the number of vulnerable young people in this 10-year period.

Looking at the levels in 2020, Iceland has a high labour force participation rate, with 79 pct. of the young people being in the labour force, whereas it varies from 62 pct. in Finland to 68 pct. in Denmark for the other countries. However, many young people’s attachment to the labour market is characterised as weak, which we will touch upon in the next section.

Employment

In this section, we compare the employment rates among young people and distinguish between strong and weak labour market attachment. Figure 5.3 shows the average employment rate between 2017 and 2019 in the Nordic countries. Iceland has a high employment rate of almost 80 pct. among the young people, while the employment rate varies between 54 pct. and 60 pct. for the same share in the rest of the Nordic countries.

Figure 4.3 Average employment rate for young people (2017–2019), pct. of young people

Note: Weak labour market attachment is defined as having a temporary job and/or working less than 20 hours.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Finland has a relatively low employment rate among young people, which could contribute to the high NEET share among young people we saw in the previous section. Meanwhile, Denmark has a relatively high employment rate, which coupled with the high NEET share indicates that students form a large share of the employed young people in Denmark. Further, almost 50 pct. of the employment is characterised as weak, which is a large fraction of the employment, compared to that of seniors, for example, among whom almost all employment is strong. One reason for the high percentage of weak labour market attachment is, again, that students, who typically have a job where they work less than 20 hours, are included in this group.

4.2 Seniors

In this section, we take a closer look at the characteristics of seniors, i.e., persons 55–64 years old, and at their attachment to the labour market. Seniors represent between 18 pct. (in Iceland) and 21 pct. (in Finland) of the working-age population.

Table 4.2 Population characteristics of seniors in the Nordic countries, pct. of seniors in 2020

Table 4.2 shows that no major differences exist between the Nordic countries or the EU27 in terms of gender distribution among seniors. The table also shows that more than half of seniors in all Nordic countries are married. Iceland and Denmark are the two countries closest to the EU27 average of 69 percent, with 67 and 65 percent married seniors, respectively. Conversely, we see larger shares of single seniors in Sweden (44 percent), Norway, and Finland (both 42 percent) than in Denmark (35 percent) and Iceland (33 percent). Furthermore, we find larger shares of senior immigrants in the population in Sweden (19 percent), Norway (13 percent) than in Denmark (9 percent), Iceland (4 percent), and Finland (5 percent). Highly relevant to the labour market, we see rather large differences between the Nordic countries in terms of tertiary education. In Finland, 42 percent of the senior population have tertiary education, while only 30 percent of the seniors have such education in Denmark. Hence, in this respect, Denmark is closer to the EU27 average of 24 percent than the other Nordic countries. Finally, while slightly more than a third (37 percent) of seniors in all countries live in rural areas, this is the case for 48 and 43 percent in Norway and Denmark, respectively. 

Labour force participation

In this section, we analyse the labour force participation rate among seniors.

Figure 4.4 Labour force participation among seniors, pct. of seniors

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Figure 4.4 shows a substantially higher labour force participation rate in the Nordic countries as a whole compared to EU27. Although this rate has risen markedly in the Nordic countries and especially EU27 between 2010 and 2020, the Nordic rate at 77 percent in 2020 still exceeds the EU27 rate by 10 percentage points.

When we compare the Nordic countries, we also find considerable differences, e.g., labour force participation rates in Sweden and Iceland in 2020 exceeding the similar rates in Norway and Finland by almost 10 percentage points. Concerning developments in labour force participation, Denmark and Finland, in particular, but also Sweden and Norway have successfully increased labour force participation among seniors between 2010 and 2020. The Icelandic participation rate – already at a very high 82 percent level in 2010 – has declined marginally.

Employment

As previously in this report, we follow up on our analyses of labour force participation rates by analysing average 2017–2019 employment rates in the Nordic countries and EU27. Figure 4.5 presents the average employment rate of the years 2017–2019 for the Nordic countries.

Figure 4.5 Average employment rate for seniors (2017–2019), pct. of seniors

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Similar to the trend we found in relation to labour force participation, we find a considerably higher employment rate in the Nordic countries compared to EU27. 64 percent of the Nordic population of seniors have a strong attachment to the labour market compared to 49 percent in EU27. Relatively similar shares in the Nordic countries and EU27 have a weak labour market attachment, 7 and 8 percent, respectively.

Among the Nordic countries, we again find the highest employment rate in Sweden, totally reaching 78 percent for the period 2017–2019. This is 14 percentage points higher than the rate in Finland for this period, 64 percent. Finally, our results show that a weak attachment to the labour market affects seniors in the Nordic countries to a very limited extent. This reflects our previous results (see Figure 3.10), namely that weak attachment to the labour market decreases as the age of the population share increases.

4.3 Immigrants

In this section, we analyse the characteristics of immigrants in the Nordic countries and EU27 and their attachment to the labour market.

Table 4.3 Population characteristics of immigrants in the Nordic countries, pct. of immigrants in 2020

Table 4.3 shows some key statistics pertaining to immigrants in the Nordic countries. Firstly, we find no differences between the countries pertaining to the gender distribution among immigrants. We find slightly more variation when we look at the share of immigrants who are married. In Finland, 59 percent are married, while this is the case for 50–53 percent in the other Nordic countries.

Comparing the age distribution among immigrants and the working-age population in all the Nordic countries, we find that 25 percent of working-age immigrants are between 15–29 years, while this age group constitutes 29 percent of the general working-age population. Reflecting overall Nordic differences pertaining to people living in rural areas, we find that more immigrants live in rural areas in Norway and Denmark than in the other Nordic countries. In Iceland, only 10 percent of immigrants live in such areas.

When we analyse the country of origin of the immigrants in the different Nordic countries, we distinguish between immigrants originating in Europe and immigrants originating outside Europe. Analysing the immigrant population in this perspective, we find the highest shares of European immigrants in Iceland (70 percent) and Finland (55 percent). In Sweden, 62 percent of immigrants originate outside Europe, while around 50 percent of the immigrants in Denmark and Norway originate outside Europe. Sweden also has the largest share of immigrants who have lived five years or less in the country. This is the case for 29 percent of the immigrants in Sweden, compared to only 12 percent in Finland.

Finally, concerning skills and education, relatively large shares of immigrants are unskilled: between 26 percent in Norway and 36 percent in Sweden. For the Nordic countries as a whole, 32 percent of immigrants are unskilled, while 22 percent of the general working-age population are unskilled. Concerning education, we see that larger shares of immigrants are enrolled in education in Sweden (26 percent) and Finland (22 percent) than in the other Nordic countries, where the overall weighted average is 22 percent. Denmark has the lowest share enrolled in education at 16 percent.

Labour force participation

The labour force participation among immigrants varies between the Nordic countries (see Figure 4.6). For 2020, Iceland and Sweden have the highest rates. However, the differences between the Nordic countries are not large (if we take Iceland out of account). It is remarkable that the immigrant labour force participation rate in the Nordic countries taken together merely exceeds the labour force participation rate in EU27 by 3 percentage points in 2020 given that the rate of the Nordic countries exceeds the EU27 rate by 8 percentage points when comparing general working-age population labour force participation rates.

Figure 4.6 Labour force participation among immigrants, pct. of immigrants

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

As shown in table 4.3, the composition of immigrants differs between the Nordic countries. As European immigrants typically are likely to integrate more easily into the Nordic labour markets than non-European immigrants, one would expect that the variations in the ratio of European to non-European immigrants would contribute to explaining the differences in participation rates between the Nordic countries. However, this is not the case, as shown in Figure 4.7. Iceland has a relatively high share of immigrants from Europe. In Iceland, however (as opposed to the other Nordic countries), non-European immigrants have a higher participation rate than European immigrants. Furthermore, despite a relatively high share of non-European immigrants (table 4.3), Sweden has a higher labour force participation rate among immigrants than the other Nordic countries (apart from Iceland). Figure 4.7 shows that both European and non-European immigrants in Sweden have a higher participation rate than corresponding groups in the other Nordic countries (apart from Iceland).

Figure 4.7 Labour force participation for immigrants in 2020 by country of origin

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey

Employment

We have also analysed the average Nordic employment rate between 2017 and 2019 among immigrants.

Figure 4.8 Average employment in the Nordic countries (2017–2019)  

Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

Inspecting Figure 4.8 from the left towards the right, we see very similar average employment rates for immigrants in different Nordic countries (apart from Iceland, with the rate at 86 percent). In the rest of the Nordic countries, we see employment rates varying between 63 percent in Denmark and 69 percent in Norway. In the Nordic countries as well as in EU27, around two thirds of immigrants hold a job, while close to 15 percent have a weak attachment to the labour market, and approximately 50 percent have a strong attachment.

We have also analysed gender differences among immigrants in terms of male and female share of workers with either a strong or weak attachment to the labour market. We find that all Nordic countries are relatively close to the Nordic average in terms of shares of immigrant men and women in weak employment, with rates at 13 and 16 percent, respectively.

We have previously found a positive correlation between level of education and rate of employment. We have conducted the same analysis for immigrants for the different Nordic countries, as can be seen in Figure 4.9. We have added a Nordic natives average to serve as a relevant comparison group.

Figure 4.9 Employment among immigrants, by educational level and country

Note: The y-axis starts at 40 pct. Iceland is not included due to too few observations.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey.

With little variation, we find the same results as in the previous analysis, i.e., employment rates among immigrants similarly rise as their level of education increases. Still, Figure 4.9 also shows that immigrants’ employment rates rise in a less uniform and continuous manner as a function of education when comparing to Nordic natives. Moreover, Figure 4.9 shows that we find a higher increase in level of employment among immigrants with higher levels of education in Norway when comparing to the other Nordic countries. We find the reverse situation in Finland for higher levels of education among immigrants, i.e., stagnating or decreasing employment levels when comparing short tertiary education with medium or long tertiary education.

5. The potential for better labour market attachment

If the Nordic countries could learn from each other so that all countries could attain the same employment as the country with the highest employment rate, it would amount to 700,000 more people in employment in the Nordic countries. This shows that, even though the differences between the Nordic countries are small, there is great potential in learning from each other.

In this section, we illustrate the potential for increasing the employment in the Nordic labour markets by learning from each other in the Nordic countries. We do so to demonstrate that even just an increase of a few percentage points in the employment rate can translate into a large increase in the number of individuals that are employed. The calculations are based on a simple benchmarking approach and do not take into account the reasons for the existing differences in employment rates between the Nordic countries. As some of the differences might be caused by circumstances that cannot easily be changed, such as demographic characteristics, it is likely that not all the countries will be able to achieve as high an employment rate as the best performing country. Hence, the potential increase in employment should be seen as an upper estimate of the potential.

To illustrate which target groups show the greatest potential for increasing labour market participation, we have made the analysis for each of the traditional target groups analysed in the previous chapter separately. As some of the target groups overlap, i.e., you can be in the young people and immigrant target groups concurrently, we have divided the population into the following mutually exclusive groups:

• Native young people (15–29 years)
• Immigrant young people (15–29 years)
• Immigrants (30–54 years)
• Immigrant seniors (55–64 years)
• Native seniors (55–64 years)
• The rest of the population

Box 5.1 Benchmark calculations methodology

Methodologically, we use an approach similar to that which Dansk Arbejdsgiverforening (DA) uses to demonstrate the potential of the Danish municipalities all performing as the best municipality regarding various employment statistics (see, for example, Dansk Arbejdsgiverforening, 2019).^{[1] Dansk Arbejdsgiverforening, Danmarks Bedste Beskæftigelseskommune (2019)} This is a simple methodology and does not take into consideration the complexities and differences between each of the Nordic countries. As some of the differences between the countries might be caused by circumstances that cannot easily be changed, such as demographic characteristics, it is likely that not all the countries will be able to achieve as high an employment rate as the best performing country. Hence, the potential increase in employment should be seen as an upper estimate of the potential.

We decided to exclude Iceland from this analysis since the country contitutes a relatively small part of the Nordic labour market and is not representative of the Nordic countries as a whole. The analysis in the preceding sections have emphasised this fact by showing that Iceland does not resemble the other Nordic countries in a number of statistics.

Based on data from Sweden, Denmark, Finland, and Norway, we calculated the average employment rate from 2017–2019 for each subgroup. This allowed us to assess which of the four countries have been relatively successful in providing employment for the specific subgroup.

After having found the most successful country for each subgroup, we simply asked and answered the following question for each subgroup and country: How many more persons in subgroup i in country j would be employed if country j had the same employment rate as the best country?

The results are presented in Figure 5.1, which shows the potential for each subgroup. Our analysis shows that Norway has the highest employment rate for the two subgroups native young people and immigrants (30–54 years old), whereas Denmark has the highest average employment rate for immigrant young people among the Nordic countries. Sweden has the highest employment rate among both immigrant and native seniors as well as for the rest of the population, which should not come as a surprise our previous analysis in mind, which showed that Sweden has a high employment rate among seniors and immigrants.

Figure 5.1 Potential increase in employment if all the Nordic countries did as well as the best Nordic country

Note: Iceland is excluded from the analysis. The analysis is based on the average employment rate for 2017–2019. The flag represents the country which has the highest average employment rate in 2017–2019 for the specified subgroup.
Source: Own calculations based on Eurostat’s Labour Force Survey.

This demonstrates that not one Nordic country is superior to the others in terms of employment for all subgroups (which the preceding analysis has also shown). However, the largest potential lies in learning from Sweden, where more than 2/3 of the potential lies. In other words, if the other Nordic countries had the same employment rate among native seniors and the rest of the population as Sweden, the employment would increase by more than 500,000 people in the Nordic labour market. But this must also be seen in the light of the fact that the rest of the population and native seniors constitute large groups in the Nordic labour markets; consequently, small increases in the employment rate will increase the number of employed people significantly.

We have tried to take this into account by putting the potential of each subgroup in relation to the total number of employed people in the subgroup. This is shown in Figure 5.2, where the dark green column shows the number of employed people in the subgroup in the Nordic countries and the light green column shows the potential of the specific subgroup.

Figure 5.2 Potential relative to employment rate for subgroups in the Nordic labour market

Note: The number above each bar represents the increase in the employment rate (in percentage points) if the potential is realised in all countries.
Source: Own calculations based on microdata from Eurostat’s Labour Force Survey

The number above each bar in the figure shows how much the potential is compared to the total employment for the subgroup. This number can be interpreted in terms of how much the employment rate (in percentage points) will increase in the Nordic countries for this subgroup if all the Nordic countries did as well as the best Nordic country for this subgroup. When looking at the potentials relative to the employment, it is evident that the largest potentials no longer lie with the rest of the population, but instead among the young immigrants. If the other Nordic countries learned from Denmark as regards increasing the employment among young immigrants, the employment rate for this subgroup would increase by 12 pct. on the Nordic labour market. It is, though, still fruitful to learn from Sweden as regards increasing the employment rate among native seniors since it is possible to increase the employment rate by 11 pct. in the Nordic labour market for this group. Again, it is important to stress that these numbers are based on a simple benchmarking approach, and we recognise that the question of how to improve the labour market attachment among young immigrants or other subgroups is a complex one, requiring – among other things – more in-depth analysis of causes and consequences of different policies.

About this publication

Labour market attachment in the Nordic countries

Vibeke Jakobsen, Frederik Thuesen, Andreas Højbjerre, Sarah Kildahl Scerdocz Nielsen and Rasmus Lang Thomsen

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ISBN 978-92-893-7509-2 (PDF)
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http://dx.doi.org/10.6027/temanord2023-501

TemaNord 2023:501
ISSN 0908-6692

© Nordic Council of Ministers 2023

Cover photo: SHVETS production / Pexels

Published: 10/2/2023

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Nordic co-operation

Nordic co-operation is one of the world’s most extensive forms of regional collaboration, involving Denmark, Finland, Iceland, Norway, Sweden, and the Faroe Islands, Greenland and Åland.

Nordic co-operation has firm traditions in politics, economics and culture and plays an important role in European and international forums. The Nordic community strives for a strong Nordic Region in a strong Europe.

Nordic co-operation promotes regional interests and values in a global world. The values shared by the Nordic countries help make the region one of the most innovative and competitive in the world.

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