Investing in Youth: North Denmark Region

2. Labour market outcomes of young people in North Denmark Region

2.1. Youth labour market outcomes in Denmark and the OECD

Denmark has a higher labour force participation rate among young people aged 15‑29 than the OECD average and especially the EU average, at respectively 71.3%, 60.4% and 56.5% in 2023 (Figure 2.1, Panel A). Differences in the labour force participation rate between Denmark and OECD/EU‑27 averages are driven by the younger age cohorts (15‑19 and 20‑24) while the differences become negligible for the 25‑29 year‑olds. The Danish youth employment rate, at 64.1% in 2023, displays a similar positive gap with OECD and EU averages (54.3% and 50.2%) (Figure 2.1, Panel B), steered by a system where young people often pursue vocational education and training (VET) and start working at a young age (OECD, 2010[1]). Denmark’s youth unemployment rate, at 10.1% in 2023, is below both the OECD average (10.3%) and the EU‑27 average (11.2%) (Figure 2.1, Panel C). The unemployment rate is highest among the youngest age cohort and lowest for the 25‑29 age cohort, a finding that holds for Denmark as well as for the OECD and EU27 on average.

After the huge shock of the Global Financial Crisis, all youth labour market outcomes in Denmark started recovering from 2013 onwards, though with a short setback during the COVID‑19 pandemic (Figure 2.1). However, even a decade after the Global Financial Crisis, youth labour market outcomes in Denmark have not fully recovered. Unlike the OECD and EU averages, Denmark’s youth labour force participation and employment rates remain below their 2008 rates and the unemployment rate above. While initially part of the decreased labour force participation was offset by an increased participation in education (Figure 2.1, Panel D), education participation experienced a huge drop in 2016 that was not entirely offset by an increase in labour force participation, suggesting a shift towards inactivity among young people in Denmark. A recent report by an expert group contracted by the Ministry of Employment (2024[2]) unveils a similar increase in inactivity among the total population.

Figure 2.1. Denmark has better youth labour market outcomes than OECD and EU27 on average

Labour market outcomes for 15‑29 year‑olds in Denmark, OECD average and EU27 average, 2022

Note: OECD and EU27 refer to the unweighted OECD and EU27 averages.

Source: OECD employment and education database and EU-LFS.

2.2. NEET rates in Denmark and the North Denmark region

The share of young people who are neither in employment, nor in education or training (NEET rate) reached 10% of all 15‑29 year‑olds in Denmark in 2022, below the OECD average of 12.8% (Figure 2.2). With one in ten young people being NEET, Denmark ranks in the lowest third of OECD countries, though above most other Nordic countries. The OECD NEET rate is higher than the rates usually reported by Danish institutions due to differences in the NEET definition (see Box 2.1 for details), yet for comparative purposes this report uses the OECD definition.

Splitting the group of NEETs into those who are actively looking for a job (unemployed) and those who are not actively looking for a job (inactive) reveals that a large part of NEETs in Denmark are inactive. In 2022, 7.1% of all young people are classified as inactive NEETs and 2.9% as unemployed NEETs. Comparing the relative importance of both groups in Denmark and the OECD reveals a larger weight of inactive NEETs over unemployed NEETs in Denmark than in OECD countries on average.

The trend in the unemployed and inactive NEET rates over the past decade and a half follow the labour market indicators described in Figure 2.1. The unemployed NEET rate mirrors the general improvement in the labour market, while the inactive NEET rate mirrors the increase and then sudden drop in education participation (Figure 2.3). The overall NEET trend therefore reflects both the improved labour market situation and the worsening education situation in Denmark. The strong variation in the Denmark’s inactive NEET rate over time is in contrast with the rather stable trend in the average OECD inactive NEET rate. The latter is usually less dependent on economic fluctuation and more dependent on a country’s policy setting (OECD, 2016[3]).

Figure 2.2. Denmark’s NEET rate is among the lowest third in the OECD

NEET rate for 15‑29 year‑olds split by NEETs who are unemployed and inactive, in 2022

Note: Data missing for Australia, Japan, Korea and Colombia.

Source: OECD calculations using EU and country labour force surveys. OECD (2023[4]), Education at a Glance 2023: OECD Indicators, for Israel and New Zealand.

Figure 2.3. NEET rates have increased since the financial crisis but have recently fallen

NEET rates for 15‑29 year‑olds split by NEETs who are unemployed and inactive, for Denmark, OECD34 and EU27, in 2022

Note: OECD34 refers to the unweighted average of NEET rates of 34 OECD countries, excluding South Korea, Japan, Australia, and Colombia.

Source: OECD calculations based on EU-LFS 2007‑22.

Box 2.1. NEET definitions differ between the OECD and Danish institutions

The NEET definitions used by various institutions in Denmark differ sometimes significantly from the OECD definition, leading to generally lower NEET rates reported by Danish institutions than those reported by the OECD.

The OECD considers young people aged 15‑29 as NEET if they are neither in employment nor in formal education. The underlying data sources to compute the OECD NEET rates are country labour force surveys, hence relying on individuals’ self-reported employment, education and job search status.

Statistics Denmark uses registry data to compute the NEET rate by classifying young people as NEET if they are neither in employment nor in education in the last week of November of a given year (Statistics Denmark, n.d.[5]). The crucial difference between the OECD and Statistics Denmark definition lies in the age groups considered: while the OECD definition considers youth 15‑29 year‑olds, Statistics Denmark focuses only on 16‑24 year‑olds when computing NEET rates. Since NEET rates are generally highest for 25‑29 year‑olds (see Figure 2.4 below), omitting the age group 25‑29 leads to lower reported NEET rates by Statistics Denmark compared to OECD rates.

Definitions further differ within Denmark. The NEET definition used by the Danish Ministry of Employment diverges in important ways from the definition used by both the OECD and Statistics Denmark. The Danish Ministry of Employment classifies a young person as NEET if all of the following conditions apply (Beskæftigelsesministeriet, 2024[6]):

• Aged 15‑24 years

• NOT in the process of an education

• NOT graduated from VET education or education at a higher level

• Has been enrolled in Danish primary school in at least one of the following grades: 8th, 9th or 10th grade

• MAXIMUM of 18 hours/week in employment (average of the months of October)

• NOT receiving early retirement benefits

• NOT receiving any financial support for students (SU benefits)

Accordingly, the NEET rate reported by the Danish Ministry of Employment is generally lower than both the OECD and the Statistics Denmark rate, as it does not consider anyone who has VET education or higher as NEET, independent of their current education and employment status. In contrast, the definition would classify someone who participates in the labour market but works less than 18 hours per week as NEET nonetheless, which contributes to a slightly higher rate again.

Overall, the NEET rates are highest according to the OECD definition across all municipalities in the North Denmark Region, followed by the NEET rates reported by Statistics Denmark and by the Ministry of Employment (Figure 2.4). While the definitions of the OECD and Statistics Denmark generate the same ranking of municipalities in North Denmark Region, the ranking obtained using the definition of the Ministry of Employment differs.

Figure 2.4. NEET rates are highest according to the OECD definition

NEET rates for the North Denmark Region according to three definitions: OECD, Statistics Denmark, and the Ministry of Employment, 2022

Source: OECD NEET rates computed using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership. Statistics Denmark rates downloaded from the Statistics Denmark website (Statistics Denmark, n.d.[5]). NEET rates from the Danish Ministry of Employment are taken from Beskæftigelsesministeriet (2024[6]), Table B.6.

Using administrative data provided by Statistics Denmark to calculate the NEET rate according to the OECD definition, the average NEET rate (11.3% in 2022) for the North Denmark Region is very similar to the average NEET rate for the rest of Denmark (11.0%).1 Nevertheless, many individual municipalities within the North Denmark Region exhibit a higher NEET rate than the North Denmark average and the average of the rest of Denmark (Figure 2.5), indicating that the largest municipalities have a relatively low NEET rate. Figure 2.6 indicates the NEET rate for municipalities in North Denmark Region and the rest of Denmark on a map of Denmark, with municipalities with higher NEET rates shaded in darker blue.

Contrary to the findings in the academic literature for European countries described in Section 1.3, there is no strong correlation between the size of a municipality or the degree of urbanisation and the local NEET rate for 15‑29 year‑olds among municipalities in the North Denmark Region. This is also evident in Figure 2.6, where the lowest NEET rates are found in Aalborg and Rebild, with Aalborg by far the largest municipality in North Denmark Region with many universities and jobs, and Rebild the second smallest. The other two more urban municipalities (Hjørring and Frederikshavn) both show higher NEET rates instead. More important than size is the development a municipality has undergone in recent years. For example, in municipalities that experienced population growth between 2019 and 2024 (such as Aalborg and Rebild), the NEET rate is generally lower. In line with this observation, municipalities with more young people (higher share of 15‑29 year‑olds among total population) tend to have a lower NEET rate.

Also important is the structure of the local labour market. In municipalities with a larger share of jobs in either public or private services, trade or transport related, the NEET rates are lower. Contrary, municipalities with a higher share of jobs in industry and agriculture have higher NEET rates. Surprisingly however, the correlation between municipal unemployment rates for the total population and the NEET rates is negative, and the correlation with the total employment rate is small.

In line with the literature review in Section 1.2, the social circumstances are important as well. The higher the share of disadvantaged youth in a municipality (measured by the share of 0‑22 year‑olds who receive preventive measures or are placed outside of their home as provided by Statistics Denmark (2023[7])), the higher the NEET rate and the higher average disposable family income in a municipality, the lower the NEET rate.

Figure 2.5. NEET rates are higher than the Danish average for most municipalities in the North Denmark Region

NEET rates for 15‑29 year‑olds by municipality in the North Denmark Region and the rest of Denmark, 2022

Note: North Denmark Region and rest of Denmark refer to weighted averages for all municipalities in the North Denmark Region and in the rest of the country excluding North Denmark Region respectively.

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

Figure 2.6. NEET rates are lowest in Aalborg and Rebild

Municipalities in North Denmark Region and rest of Denmark, with blue shades indicating the magnitude of the NEET rate for 15‑29 year‑olds, 2022

Note: The light blue shade for the rest of Denmark indicates the weighted average NEET rate for all Danish municipalities that are not in North Denmark Region.

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

2.3. Characteristics of NEETs in the North Denmark Region

2.3.1. NEET rates are higher among older young people

Splitting NEET rates by age group reveals that the higher NEET rates across many North Denmark Region municipalities are mainly driven by high rates among the two older age groups, i.e. 20‑24 and 25‑29 year‑olds (Figure 2.7). NEET rates are considerably lower for the youngest age group (15‑19 year‑olds) than for 25‑29 year‑olds across Denmark. At respectively 4.2% and 16.7% on average for the North Denmark Region, the difference is substantial. The middle group, 20‑24 year‑olds, often exhibit either slightly lower or similar NEET rates to 25‑29 year‑olds.

Figure 2.7. NEET rates are considerably higher for the older age groups 20‑24 and 25‑29 than for the age group 15‑19

NEET rates for different age groups for municipalities in North Denmark Region and the rest of Denmark, 2022

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

2.3.2. Older NEETs are more likely to be female

Women are slightly more likely to be NEET, with a rate of 11.7% compared to 10.3% among men (Figure 2.8). These findings hold for both the North Denmark Region and the rest of the country. The gender gap is particularly large in Morsø (6 percentage points), Mariagerfjord (5 percentage points), and Rebild (4 percentage points). Looking at the gender difference by age cohort reveals that the gender gap grows considerably with age and can become rather large in some of the municipalities. In Morsø, the gender gap reaches 14 percentage points for the age group 25‑29, 10 percentage points in Mariagerfjord, and 9 percentage points in Vesthimmerland. Rebild, in contrast, has a particularly large gender gap for the age cohort 20‑24, reaching 11 percentage points. The particularly high female NEET rates for this age group in these municipalities may potentially be linked to childcare obligations as Morsø and Mariagerfjord exhibit some of the lowest age at first birth for mothers and Rebild one of the highest total fertility rates among all municipalities in the North Denmark Region (Statistics Denmark, 2022[8]).

Figure 2.8. Women are more likely to be NEET, especially among the older youth cohort

NEET rate by gender and gender gap by municipality, for young people aged 15‑29 in North Denmark Region and the rest of Denmark, 2022

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

2.3.3. NEET rates are highest among low educated young people

Consistent with other OECD countries, NEET rates are highest among young people with low education levels (Figure 2.9). However, contrary to many other OECD countries, NEET rates in municipalities of the North Denmark Region are higher among those with high education levels than those with medium levels of education (VET education or upper secondary education). The average NEET rate across the OECD for low educated young people (15‑29) is 14.8%, for young people with medium education the OECD average is 12.8%, and for those with high levels of education it is 9.6%. The average rates for the North Denmark Region are respectively 15.7%, 6.9%, and 9.9%. The Danish pattern of higher NEET rates among highly educated compared to medium educated could on the one hand be partly driven by age, as those with high education levels are likely older than those with a VET education and Figure 2.7 confirms higher NEET rates among older cohorts. Unfortunately, the data at hand do not allow to test to which extent age is driving this pattern.

On the other hand, the pattern is likely also connected to the Danish labour market which suffers from a shortage of VET-labour. This is reflected in overall unemployment rates in Denmark which are indeed lowest for the middle education group at 2.8% in 2022, compared to 5.6% for the low educated and 3.7% for the highly educated (OECD Data Explorer, n.d.[9]).

Low-educated young people are by far the largest NEET group in all municipalities of the North Denmark Region, accounting for 58% of all NEETs (Figure 2.10). There are however considerable differences across municipalities. While almost three out of four NEETs in Jammerbugt and Morsø have low education levels, the share is only about two out of four in Aalborg. In contrast, Aalborg has the highest share of highly educated young people among its NEET population in line with Aalborg having many universities and therefore a higher share of highly educated youth. The education patterns also reflect the overall ranking of municipalities well, with those municipalities with the highest overall share of low educated among its young population also having the highest share of low educated among its NEET population. In line with Figure 2.9, the share of low educated among NEETs is however still significantly larger compared to the share low educated among all young in all municipalities.

The impact of low education on NEET rates is supported by the correlation between municipal NEET rates and the share of young people with low education across municipalities. Municipalities with a larger share of young people with only low education levels are associated with considerably higher NEET rates. Similarly, the higher the share of highly educated young people, the lower the NEET rate.

Figure 2.9. NEET rates are highest among young people with low education levels

NEET rates for 15‑29 year‑olds by highest completed education and municipality, 2022

Note: VET education refers to vocational education and training.

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

Figure 2.10. The majority of NEETs in Denmark have low education

Composition of NEET rate among 15‑29 year‑olds, by highest level of education achieved and municipality, 2022

Note: VET education refers to vocational education and training.

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

2.3.4. NEET rates are considerably higher among foreign-born young people

Comparing NEET rates of young people born in Denmark with those born abroad reveals that NEET rates are often around twice as large among young people born abroad compared to Danish-born young people (Figure 2.11). However, NEET rates for foreign-born are often largest where the share foreign born among all 15‑29 is lowest. For example, Aalborg has the highest share of foreign-born young people among all 15‑29 year‑olds according to Statistics Denmark but the NEET rate for foreign-born is among the lowest. In contrast, Hjørring has the highest NEET rate among foreign born, but ranks among the lowest three in terms of share of foreign-born young people among all 15‑29 year‑olds.

High NEET rates among foreign-born youth are not attributable to low education levels. In 2022, calculations based on EU-LFS data show that foreign-born youth (aged 15‑29) accounted for 8.8% of the total youth population, but only 8.7% of all low-educated youth, indicating that foreign-born are slightly less likely to be low-educated compared to native‑born. In line with higher NEET rates among foreign-born (Figure 2.11), foreign-born individuals are over-represented among all NEETs, comprising 11.1% of the NEET population, despite making up only 8.8% of the total youth population. However, this overrepresentation is not driven by low education levels as foreign-born individuals make up just 10.7% of NEETs with low education levels, compared to 11.1% of NEETs across all education levels.

Figure 2.11. NEET rates among foreign-born young people are twice as high as NEET rates among Danish-born young people

NEET rates for 15‑29 year‑olds by municipality and migrant status, 2022

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

2.4. NEET duration and entry paths

Dividing the NEET group by duration of their NEET status reveals that half of them (52%) have been NEET for at least three years (Figure 2.12). This finding holds for all municipalities of the North Denmark Region as well as for the rest of Denmark on average. The same pattern is observed for both genders across municipalities, although the share of women who have been NEET for more than three years tends to be slightly larger than the share of men. This pattern is to some extent responsible for the higher NEET rates of women as observed in Figure 2.8.

Among 25‑29 year‑olds, more than two‑thirds of NEETs have been in that situation for at least three years in the North Denmark Region on average. While older cohorts have had more time to remain in their NEET situation than younger cohorts, they also had more time to find an exit to their situation. The high share of long-term NEETs among the older youth cohort indicates that special attention for this group is necessary.

To better understand how young people become NEET, Figure 2.13 divides NEET rates by activity status of NEETs one year earlier (i.e. 2021). In line with the majority of NEETs having been NEET for more than three years, the figure reveals that a large majority of current NEETs was already in a NEET status one year earlier. The remaining group more often moves directly from education into NEET status in the case of the younger cohorts (aged 15‑19 and 20‑24), while older cohorts move into NEET status from education or employment at about an equal rate.

Figure 2.12. The majority of NEETs have been in this situation for at least three years

Share of NEETs among 18‑29 year‑olds by years spent in NEET status and municipality, 2022

Note: NEET rates per municipality are higher in this figure than in Figure 2.5 since this figure only covers young people aged 18‑29 and only those who have been residing in Denmark the past three years in order to be able to compute NEET status three years ago.

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

Figure 2.13. Younger NEETs often enter NEET status from education while older NEETs tend to enter NEET status from both employment and education

NEET rate split by activity status one year earlier, by age group and region, 2022

Note: The figure splits the NEET rate in 2022 into activity status of NEETs one year prior.

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

2.5. Mental health and NEET status

Mental health has been shown to be increasingly important to understand NEET rates in OECD countries (Section 1.4). In Denmark overall, the report by the Ministry of Employment finds that 43% of NEETs in Denmark have been diagnosed with a mental health problem (Beskæftigelsesministeriet, 2024[6]).

For North Denmark Region, mental health issues can be assessed using survey data from the health profile survey “How are you?” (Region Nordjylland, 2024[10]), which is a survey on well-being, health and illnesses among citizens above the age of 16 in the North Denmark Region. The survey is run by the North Denmark Region and is carried out as part of a nationwide survey on health and well-being. The survey has been run roughly every four years since 2010 with the latest wave available conducted in 2021.

The survey assesses various measures of mental well-being. It includes the 12 questions from the Short-Form Health Survey (SF‑12), allowing to compute the SF‑12 mental health score and physical health score, as well as the WHO‑5 well-being index – for more information on measurement of population mental health and the SF‑12 and WHO‑5 measures refer to OECD (2023[11]). To measure stress and loneliness, the survey includes questions from the Cohen Perceived Stress Scale (PSS) and the UCLA three‑item loneliness scale (TILS) – more information on how to measure social connectedness and the UCLA three‑item loneliness scale can be found in Mahoney et al. (2024[12]). The health profile survey also collects information on self-reported anxiety disorder, depression, and overall mental disorders.

Data from the latest wave of the health profile survey (2021) reveals that nearly half of the NEET population in the North Denmark Region has mental health issues. 46% of 16‑29 year‑old NEETs exhibit a low score on the mental component of the SF‑12 score2 (Figure 2.14), compared to only 21% of non-NEETs of the same age group. While the physical component of the SF‑12 score shows a much smaller prevalence of physical ill-health overall, NEETs are still significantly more likely to suffer from physical ill-health (10%) compared to non-NEETs (2%).

This pattern is repeated across different measures of mental ill-health. The WHO‑5 score reveals that 57% of NEETs are at risk of depression and 11% exhibit severe depression, while numbers are much lower among non-NEETs with 32% and 2% respectively. Studying self-reported measures of mental ill-health also confirms high levels of mental ill-health among NEETs compared to non-NEETs. The share of NEETs who self-report to suffer from anxiety (41%) is twice as high as the share among non-NEETs (20%) and the difference between the two groups is even larger for self-reported depression and general mental disorders.

A similar gap between NEETs and non-NEETs is also observed for measures of stress and loneliness. Stress levels among NEETs are high with 60% scoring above 18 on the PSS stress score, indicating high levels of stress. Further, more than one in three NEETs also classify as lonely according to the UCLA TILS score compared to less than one in five for non-NEETs.

Figure 2.14. Almost half of NEETs in North Denmark Region exhibit mental health issues

Share of NEETs and non-NEETs aged 16‑29 in North Denmark Region who exhibit mental ill health measured via various indicators, 2021

Note: The figure shows various measures of mental ill-health computed with data from the 2021 wave of the health profile survey (Region Nordjylland, 2024[10]). The first two measures indicate whether someone exhibits a score in the bottom 10% of the Danish distribution on the mental and physical component of the Short-Form Health Survey (SF‑12). The third and fourth measure use the five questions of the WHO‑5 well-being index to classify individuals into at risk of depression (WHO‑5 score lower or equal to 50) and severe depression if the WHO‑5 score is equal to or below 12. The cut-off of 12 is selected by North Denmark Region. Anxiety, depression, and mental disorders are self-reported, by asking respondents whether they suffer from anxiety, depression or a mental disorder or not. Loneliness is assessed using the UCLA three‑item loneliness scale (TILS) where an individual is classified as lonely if the TILS score is equal to or above 7. Stress is measured via the Cohen Perceived Stress Scale (PSS) where the North Denmark Region follows the Danish national cut-off of 18 to define high stress.

Source: OECD calculations based on the 2021 wave of the health profile survey (Region Nordjylland, 2024[10]).

The gap in mental health between NEET and non-NEET populations has further widened over time. Figure 2.15 shows that the share of NEETs who exhibit low mental health on the mental component of the SF‑12 score has been increasing from 23% in 2010 to 46% in 2021, while the increase was less pronounced among non-NEETs (from 10% to 21% over the same period). The faster rise in mental ill-health among NEETs is observed only since 2017, while the rates developed similarly for NEETs and non-NEETs between 2013‑17. The same pattern is observed for self-reported mental disorders and for the share of young people who exhibit high stress levels.3

Figure 2.15. Mental health has been worsening faster for NEETs

Percentage of young people (aged 16‑29) who have a low score on the mental component of the SF‑12 indicator, split by NEET status, 2013‑21

Note: The figure plots the percentage of young people who exhibit a score in the bottom 10% of the Danish distribution of the mental component of the Short-Form Health Survey (SF‑12) measure for mental health. The underlying data are the 2013, 2017 and 2021 waves of the health profile survey (Region Nordjylland, 2024[10]). The first wave from 2010 is not included as the questions to distinguish young people by NEET status were not asked.

Source: OECD calculations based on the 2021 wave of the health profile survey (Region Nordjylland, 2024[10]).

Splitting further by gender reveals that young women are more likely to exhibit mental ill-health compared to young men, both among NEET and non-NEET populations. 52% of NEET women aged 16‑29 show a low score of the mental component of the SF‑12 measure compared to 40% of NEET men (Figure 2.16, Panel A). Mental ill-health is also more prevalent among young people with low education levels, for both NEET and non-NEET populations but the differences by education level are more pronounced for NEETs (Figure 2.16, Panel B). The share of NEETs who have a low mental SF‑12 score is highest for those with low education (48%), slightly lower for NEETs with medium education (40%), and lowest among those with high education (31%). A similar pattern is observed for non-NEETs, although the differences are smaller: 16% of non-NEETs with low education exhibit mental health issues, compared to 14% non-NEETs with medium education and 13% with high education.

Figure 2.16. Women and young people with low education exhibit the highest shares of mental health issues

Percentage of young people (aged 16‑29) who exhibit a low score on the SF‑12 mental component measure of mental health, split by NEET status and gender (Panel A) and NEET status and highest education level (Panel B).

Note: The figure plots the percentage of young people who exhibit a score below the bottom 10% of the Danish distribution on the mental component of the Short-Form Health Survey (SF‑12) measure for mental health. The underlying data for Panel A are the 2021 waves of the health profile survey (Region Nordjylland, 2024[10]), while Panel B pools data from the 2017 and 2021 wave to avoid low sample size issues. The overall share of NEETs with a low SF‑12 mental score is therefore 46% and 21% for non-NEETs in Figure 2.14, while it is lower for Panel B at 38% and 18% as this pools waves 2017 and 2021 and the shares of mental ill-health have been increasing over time (Figure 2.15).

Source: OECD calculations based on the 2021 wave of the health profile survey (Region Nordjylland, 2024[10]).

Although mental health issues are prevalent among young people across all municipalities in North Denmark Region, there is significant municipal variation in the share of young people who have a low score on the mental component of the SF‑12 scale, i.e. who exhibit mental health issues. In Aalborg, more than one in four (26%) young people aged 16‑29 exhibit a low mental SF‑12 score while the lowest share is observed in Mariagerfjord at 16% (Figure 2.17). Comparing other measures of mental ill-health confirms that Aalborg consistently ranks among the three municipalities with the highest share of young people with mental health issues, together with Vesthimmerland and Frederikshavn. On the lower end, there is more variation across the different indicators. It should be noted however, that sample sizes in the health profile survey are small on municipality level which may impact municipality level results.

In comparison with the total population, young people display much more frequently mental ill-health. The share of people aged 16 and over with a low score on the SF‑12 mental health scale lies around 9‑11% in most municipalities (Figure 2.17). As with young people, the highest share is observed in Aalborg with 13% of the population exhibiting mental health issues.

Figure 2.17. There is significant variation in mental ill-health among young people across municipalities in North Denmark Region, with the highest share observed in Aalborg

Percentage of young people (aged 16‑29) and total population (16 years and older) who exhibit a low score on the mental component of the Short-Form Health Survey (SF‑12) scale, 2021.

Note: A low score on the SF‑12 scale refers to a score in the bottom 10% of the Danish distribution. Due to the small sample size on municipality level, it is not possible to distinguish mental ill-health by NEET status.

Source: OECD calculations based on the 2021 wave of the health profile survey (Region Nordjylland, 2024[10])

Mental ill-health in a municipality is negatively correlated with visits to one’s general practitioner among young people in a municipality. Municipalities with a high share of young people suffering from mental ill-health tend to have fewer young people visiting their general practitioner in the last year on average. This finding is especially striking when studying the share of young people who visited their general practitioner in the past 12 months among those with a low mental SF‑12 score: Frederikshavn, Aalborg and Vesthimmerland – the three municipalities with the highest shares of young people with mental health issues (Figure 2.17) – exhibit the three lowest shares of young people with mental health issues who visited their general practitioner in the past year among all municipalities in the North Denmark Region. The highest share of young people visiting their general practitioner in the last year is observed in Mariagerfjord (84%), the municipality with the lowest share of young people suffering from mental health issues according to the mental SF‑12 score (Figure 2.17). The correlation between the share of young people suffering from mental ill-health and the share of those who visited their general practitioner in the last year is ‑0.8.

However, the health profile survey only inquires about visits to one’s general practitioner while mental ill-health would rather require visits to a psychologist. Register data on the number of visits to the psychologist in a municipality is available, although only for visits through the public health system (i.e. visits that are covered by private insurance or out of pocket are not covered and the data therefore understates the true number of visits to a psychologist). Figure 2.18 plots the average number of annual psychologist visits per individual for both NEETs and non-NEETs by municipality. In line with significantly higher rates of mental health issues among NEETs (Figure 2.14), the figure shows that NEETs also have considerably more annual psychologist visits compared to non-NEETs across all municipalities in the North Denmark Region. Further, the figure confirms that municipalities with higher shares of young people suffering from mental health issues exhibit fewer annual visits to a psychologist on average: Vesthimmerland and Aalborg – municipalities with consistently high shares of mental health issues among young people (Figure 2.14) – are among the three municipalities with the lowest number of annual visits to the psychologist per individual at 0.42 and 0.45 for NEETs. Mariagerfjord on the other hand – a municipality with consistently lower shares of mental health issues (Figure 2.14) – has the second highest number of average annual visits to the psychologist per NEET individual at 0.6.

In line with rising mental ill-health rates over time (Figure 2.15), the average number of visits to the psychologist have also been rising continuously since 2017, for both NEET and non-NEET groups. While in 2017 around 0.4 annual visits to the psychologist were registered for NEETs across Denmark, the number has increased to close to 0.6 in 2022. A similar trend is observed for non-NEETs albeit at a lower level, in line with a smaller increase in mental ill-health rates among non-NEETs (Figure 2.15).

Figure 2.18. NEETs have considerably more visits to the psychologist per year compared to non-NEETs

Average number of yearly visits through the public health insurance system to the psychologist for NEETs and non-NEETs for the age group 15‑29, by municipality, cumulative 2017‑22

Note: The average number of yearly visits to the psychologist is computed as the total number of visits to the psychologist per year by a given group (for example NEETs aged 15‑29 in a given municipality), divided by the total number of individuals in that group. Importantly, the data only covers visits through the public health insurance system and neglects visits by patients who seek help outside the limits of the public health insurance (for example through private insurance or covering expenses themselves). To avoid working with very small samples, numbers are pooled for years 2017‑22.

Source: OECD computation using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership.

2.6. Conclusions

Denmark has higher labour force participation and employment rates among 15‑29 year‑olds than the OECD and EU averages, and a lower youth unemployment rate. With about one in ten young people not in employment, education, or training (NEETs), Denmark ranks in the lowest third of OECD countries, though above most other Nordic countries. Inactive NEETs, i.e. those who are not looking for a job, account for seven out of ten NEETs in Denmark and their weight in the total NEET rate is larger than the OECD average. The larger share of inactive in total NEETs in Denmark seems to be related to a drop in the share of young people in education combined with an improvement in youth labour market outcomes.

The NEET rate in the North Denmark Region (11.3%) is comparable to the rate in the rest of the country (11.0%) and below the OECD average (12.8%). Even so, there is considerable variation within the Northern region, with many of the small municipalities reporting higher NEET rates than the two largest municipalities. Municipal NEET rates are lowest among 15‑19 year‑olds and increase rapidly with age. NEET rates are highest for 25‑29 year‑olds across Denmark, yet many municipalities in the North Denmark Region exhibit particularly high rates for this age group, especially among women. Municipalities with larger gender differences for the 25‑29 age cohort are often characterised by lower age at first birth for mothers or higher total fertility rates, suggesting that childcare responsibilities may play a role in explaining these gender differences.

Low education is one of the most important factors to understand NEET rates in Denmark. The country has a high share of low educated among the NEET population and high NEET rates for the low educated group. While low educated young people account for 55% in Denmark overall, the effect is particularly large (around 60‑75%) in most municipalities of North Denmark Region except for Aalborg (51%). Overall, there is no strong correlation between the size of a municipality or the degree of urbanisation and a municipality’s NEET rate, but the structure of the local labour market does matter. NEET rates tend to be lower in municipalities with a larger share of jobs in services, trade, or transport, and higher in municipalities with a higher share of jobs in industry and agriculture. Surprisingly however, the correlation between municipal unemployment rates for the total population and the NEET rates is negative, and the correlation with the total employment rate is small. Social circumstances do matter though. The higher the share of disadvantaged youth in a municipality, the higher the NEET rate and the higher average disposable family income in a municipality, the lower the NEET rate.

Mental ill-health is another important risk factor. NEETs in Denmark and North Denmark Region are often in a vulnerable situation with mental health issues, social problems, and other resulting challenges. Across North Denmark Region, almost one in two (46%) NEETs suffer from mental ill-health compared to 21% for non-NEETs, and the gap between both groups has been increasing over time. Regional variations in mental ill-health are considerable, with Aalborg displaying the highest shares of mental ill-health, both among young people and among the total population.

Finally, young people tend to remain in a NEET status for a long time. Across Denmark, more than half of NEETs have been without employment or education for more than three years, with this share increasing up to 60% for some municipalities in the North Denmark Region. This group is particularly challenging as they are increasingly further away from the labour market. Younger NEETs (aged 15‑19 and 20‑24) most often come from education, while the share NEETs aged 25‑29 moving into NEET status from education is about equal to the share coming from employment.

References

[6] Beskæftigelsesministeriet (2024), Status på arbejdsmarkedet, https://bm.dk/media/dlqlwita/status-paa-arbejdsmarkedet_digital.pdf (accessed on 19 August 2024).

[12] Mahoney, J. et al. (2024), “Measuring social connectedness in OECD countries: A scoping review”, OECD Papers on Well-being and Inequalities, No. 28, OECD Publishing, Paris, https://doi.org/10.1787/f758bd20-en.

[2] Ministry of Employment (2024), Anbefalinger til fremtidens beskæftigelsesindsats (Recommendations for future employment efforts), https://fremtidensbeskæftigelsesindsats.dk/media/zxbnsxyy/bm-full_report.pdf (accessed on 10 July 2024).

[4] OECD (2023), Education at a Glance 2023: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/e13bef63-en.

[11] OECD (2023), Measuring Population Mental Health, OECD Publishing, Paris, https://doi.org/10.1787/5171eef8-en.

[3] OECD (2016), Society at a Glance 2016: OECD Social Indicators, OECD Publishing, Paris, https://doi.org/10.1787/9789264261488-en.

[1] OECD (2010), Jobs for Youth/Des emplois pour les jeunes: Denmark 2010, Jobs for Youth/Des emplois pour les jeunes, OECD Publishing, Paris, https://doi.org/10.1787/9789264075160-en.

[9] OECD Data Explorer (n.d.), Unemployment rates by education levels, 2024, https://data-explorer.oecd.org/vis?lc=en&df[ds]=DisseminateArchiveDMZ&df[id]=DF_DP_LIVE&df[ag]=OECD&df[vs]=&av=true&pd=2022%2C2022&dq=DNK%2BOECD%2BOAVG.UNEMPEDU...A&to[TIME_PERIOD]=false&vw=tb (accessed on 17 September 2024).

[10] Region Nordjylland (2024), Sundhedsprofil: Hvordan har du det?, https://rn.dk/sundhedsprofil (accessed on 5 November 2024).

[7] Statistics Denmark (2023), Documentation “Share of disadvantaged youth”, https://www.dst.dk/Site/Dst/SingleFiles/GetArchiveFile.aspx?fi=380404374985&fo=0&ext=kvaldel (accessed on 16 September 2024).

[8] Statistics Denmark (2022), Total Fertility Rate Statistics, https://www.statbank.dk/20018 (accessed on 4 September 2024).

[5] Statistics Denmark (n.d.), Not in Employment, Education or Training (NEET), https://www.dst.dk/en/Statistik/emner/arbejde-og-indkomst/befolkningens-arbejdsmarkedsstatus/unge-uden-for-beskaeftigelse-og-uddannelse-neet (accessed on 19 August 2024).

Notes

← 1.  Note that this NEET rate is computed using administrative data provided by Statistics Denmark via the CRT SAM-K/LINE model partnership while the NEET rates in Figure 2.2 are computed using Labour Force Survey data. The NEET rate for Denmark is slightly higher using the former data since it refers to employment and education status on 1 November, while the Labour Force Survey data refers to employment and education status in the four weeks prior to the survey date. The former rate is higher as it measures the employment and education status on one single day while the latter measures the status over four weeks, making it more likely that someone has worked or studied at least on some of the days in the four weeks prior.

← 2.  A low score indicates that an individual’s SF‑12 score ranks in the bottom 10% among all individuals who have taken the health profile survey.

← 3.  The other measures of mental ill-health considered in Figure 2.14 are only included in the 2021 wave of the health profile survey.