2. A profile of NEETs in Slovenia

Prior to the COVID-19 crisis, strong economic growth over several years had translated into improved labour market outcomes for the Slovenian population. Younger generations also benefited, even though their employment and unemployment rates had yet to return to pre-financial crisis levels. For younger and older Slovenians alike, the spread of the COVID-19 virus and the necessary containment measures led to an increase in unemployment. Some of the newly unemployed are likely to land on their feet and re-enter the labour market quickly. However, those who were already persistently inactive or unemployed prior to the pandemic are likely to struggle to find a job, and the same may be true for those who worked in hard-hit sectors. Given their relative lack of labour market experience and seniority, young people are over-represented among those who are affected by the economic crisis. Understanding the profile of young people who are neither in employment nor in education or training (NEETs) is a pre-requisite for devising better support. This report builds on a wide range of survey and administrative data to provide more details about the characteristics of Slovenian NEETs and their needs (see Box 2.1).

The structure of the chapter is as follows: the first two sections describe the economic context and the employment and education outcomes of young Slovenians. These are followed by a section that presents the personal and well-being characteristics of NEETs, including a typology of different NEET groups and their respective size; and a section on the estimated fiscal costs of NEETs to the Slovenian State.

The COVID-19 crisis interrupted the (albeit already slowing) growth trajectory of the Slovenian economy. To contain the spread of the SARS-CoV-2 virus, the government imposed a lockdown from mid-March to mid-May 2020, during which citizens’ movements were limited and schools and non-food retail establishments were closed. The containment efforts were relatively successful, with comparatively few infections and deaths during spring and summer 2020. However, just like in almost all other European countries, infections started to accelerate again in fall 2020. As a result, the Slovenian Government re-imposed a series of restrictions, including limiting groups in the public sphere to six people maximum and prohibiting crossing municipal borders. As a result, the two infection waves scenario is now a reality rather than a conjecture, probably leading to even more drastic decreases in gross domestic product (GDP) than initially hoped. The projected 2020 decline in GDP, attributed in part to the restrictions to the service economy and to the decreased international demand for the outputs of Slovenia’s manufacturing sector, is similar to the projected OECD decline: Slovenia’s GDP is projected to contract by 9.1%, and the OECD’s by 9.3%, under the double-hit scenarios with two infection waves and lockdowns (Figure 2.1).

The 2020 decline in GDP may rival or even exceed the drop that occurred because of the international financial crisis in 2009, which amounted to -8.4% in per-capita GDP. Recovery in Slovenia following that crisis had been slow, as the country also experienced a domestic banking crisis in 2012-13 that led to a further 4% per-capita decline. These recessions pushed Slovenia’s per-capita GDP temporarily off its convergence path towards OECD and EU averages. Over the 2014-19 period, the economy displayed strong growth, with annual increases of GDP between 1.6% and 4.8%. The growth pattern outperformed that in the EU and OECD areas, where countries had an average per-capita GDP growth rate of 1.8% and 1.6% respectively over the same period. As a result, Slovenia’s per-capita GDP was slowly approaching the OECD GDP per-capita average, reaching 84% in 2019 compared with 77% in 2013. However, in 2017 and in particular 2018, per-capita growth had already started to slow down.

Government policies likely managed to blunt the COVID-19 caused increase in unemployment. Between March and April 2020, the OECD-wide unemployment rate rose by 3.0 percentage points. In Slovenia, the increase was only 0.4 percentage points (and a further 0.1 percentage points in May and June), though different classifications of workers on short-term work or temporary lay-off makes the cross-country comparison somewhat tenuous (OECD, 2020[2]). The newly created short-term work scheme certainly contributed to keeping the unemployment rate low. With around 277 000 participants at one point in time (OECD, 2020[3]), around one-third of dependent employees (as of December 2019)1 were covered by the short-term work scheme. This share is among the highest of OECD countries for which information is available, though still far below the more than two-thirds coverage observed in New Zealand (OECD, 2020[2]).

The pandemic ended the positive labour market trends of the previous years. The pickup in economic growth prior to the COVID-19 crisis had translated into higher employment and lower unemployment (Figure 2.2). Strong job creation reduced the unemployment rate for the population aged 15-64 to 4.5% in 2019 – drawing equal to the low rate before the onset of the international financial crisis in 2008. Unemployment rates were below the OECD and EU averages and employment rates were higher. Employment rates in 2019 even surpassed the rates that were attained in 2008. As most of the easy-to-employ jobseekers had found a job, the labour market was tightening up and labour shortages started to appear (OECD, 2020[4]). The shrinking size of the youth population – which shrank from 436 000 in 2000 to 381 000 in 2010 and 309 000 in 2018 (OECD, 2020[5]) – likely contributed to these shortages. Firms were increasingly hiring immigrants and cross-border commuters, especially from former Yugoslav countries – accounting for nearly three-quarters of new hires (Bank of Slovenia, 2019[6]).

Young people tend to be more affected by economic downturns than older generations. So far, the economic impact of the COVID-19 pandemic on Slovenian youth has had an important impact compared with other OECD countries, with unemployment rates among 15-24 year-olds 10 percentage points higher in the second quarter of 2020 than in the same period a year earlier (OECD average youth unemployment rates surged by 6.1 percentage points). Among Slovenian aged 25 years and over, unemployment rose by only 0.8 percentage points over the same period. Not only do young people have less seniority in their job and are more likely to have temporary contracts, which makes it easier to lay them off, the COVID-19 also hit sectors that employ a high share of young workers – in particular restaurants, bars and tourism – hard.

When the COVID-19 pandemic reached Slovenia, youth employment outcomes had not yet fully recovered from the previous crisis. At the depth of the economic downturn following the global financial crisis, the employment rate of 15-29 year-olds was only 80% of the 2007 rate (Figure 2.3, Panel A); by 2019, the rate was still below its pre-crisis level. In contrast, the share of employed 30-54 year-olds stayed relatively constant and the share of employed 55-64 year-olds even rose between 2007 and 2019. A similar pattern can be observed in youth unemployment rates (Figure 2.3, Panel B). Youth unemployment in Slovenia experienced a stronger increase in the years following the global financial crisis than in the average OECD and EU country, and the recovery took much longer. Nevertheless, by 2019, only 7.5% of the Slovenian youth labour force was unemployed, compared with OECD and EU averages of 9.1% and 11.9% respectively.

With 51.1% of 15-29 year-olds in employment in 2019, Slovenia has a lower employment rate than OECD countries on average (54.3%) – though slightly higher than the EU average of 50.9% (Figure 2.4, Panel A). One of the reason behind the relatively low employment rate is the long time young Slovenians spend in education. In 2018, the share of youth who were enrolled in education was the third-highest among OECD countries (Figure 2.4, Panel B). At the same time, the share of young people who combine education and work is comparable for Slovenia and the OECD on average, reaching 27.8% and 27.2% respectively. Among university students, the share of working students rises to one-half (European Commission, 2015[8]), as many engage in ‘student work’, a contract only available to students. A series of reforms increased the student work agency fees and established full social security insurance requirements, reducing the financial advantages of this type of employment. Nevertheless, several advantages for employers remain, such as fewer protections against dismissal and exemptions from meal and travel allowances.

Younger workers are often temporary employees, but their share has recently fallen. Among 15-24 year-old employees, 38% held a permanent contract in 2019, compared to 74% across the OECD and 57% across the EU 28 (Figure 2.5). Once they reach the 25-34 year age group, however, the share of employees with a permanent contract rises to about three-quarters (European Commission, 2018[9]). According to a recent study, the 2013 Employment Relations Act – aimed to decrease cost differences between temporary and permanent contracts – made it more likely for temporary employees and unemployed people of all age groups to transition towards employment with a permanent contract (Vodopivec, 2019[10]). This outcome is reflected in a rising share of 15-24 year-olds with permanent contracts since 2015.

The educational attainment of the Slovenian population has increased drastically since the beginning of the century. Non-completion of compulsory education (up to age 15) is quite rare, even though some population groups, including Roma, are over-represented among dropouts. In 2019, fewer than 5% of 25-34 year-olds had not completed upper secondary school, compared with 14.6% in 2000 (Figure 2.6). This outcome also compares favourably to the OECD average of 15.4%. A higher share of men (5.5%) than woman (4.2%) do not complete upper secondary school.

About two-thirds of under-25-year-olds (excluding internationally mobile students) enrol at college or university. As a result of the attractive funding conditions (see Box 2.2), Slovenia has, with 66%, the fourth highest rate of youth who enter tertiary education at any point before their 25th birthday among OECD countries for which the information is available, and well above the OECD(29) and EU(19) averages of 49%. However, the share who actually graduate with a bachelor’s degree during the theoretical duration plus three years 53%, the lowest value among the 21 OECD countries and regions for which data on the cohort are available (OECD, 2019[7]). The share of tertiary graduates among 25-34 year-olds nevertheless increased rapidly over the past two decades, reaching 44.1% in 2019 (close to the OECD average of 44.9%). Women in particular are likely to have graduated from university: In 2019, 55.1% of women in the age group 25-34 had a university degree, compared to 34.3% of Slovenian men (OECD, 2020[11]).

Many Slovenian students choose vocational tracks and degrees in Science, Technology, Engineering and Mathematics (STEM). With 71% of upper secondary students enrolled in vocational or professional programmes, the share is considerably above the OECD cross-country average of 42% (OECD, 2019[7]). The gap with the OECD average closes at university level, where 26% of bachelor graduates in 2017 studied a STEM subject, compared to the 23% OECD average (Figure 2.7). Slovenian graduates also more frequently obtain a degree in a discipline related to education or service, while a comparatively lower share than elsewhere obtains a business, administration and law; health and welfare; or arts and humanities degree. The 2017 OECD Skills Strategy Diagnostic report noted that the relatively low percentages of health and welfare and information and communications technology graduates may contribute to skill shortages, although health sector shortages are currently local and speciality-specific (OECD, 2017[12]).

A group of young people are neither in employment, nor in education or training, the so-called NEETs. This concept is widely used as an indicator to inform youth-oriented policies to lower youth unemployment and engage as many young people as possible in the world of work. Yet, the NEET rate captures a heterogeneous group of young people, who can have very different reasons for being NEET. Some – often women – stay home to care for children or relatives. Others are intensively searching for a job, but face barriers to finding one. Yet others simply take a break from or after education to travel or to pursue other personal interests. Each of these and other groups face different opportunities and obstacles to move into employment or re-enter education. Gaining an understanding of the characteristics of the different NEET groups is an important first step to design efficient and cost-effective policies that address the obstacles faced by those NEETs with the most difficulties for re-integration.

In Slovenia, nearly one in ten young people are NEET. In Slovenia, nearly one in ten young people are NEET. With around 29 500 NEETs and at a rate of 9.5% among 15-29 year-olds in 2018, Slovenia ranks in the lower third of OECD countries, where the average stood at 12.8% (Figure 2.8). About 53% of youth who were NEETs at any point over the prior four years experienced long or repeated periods of inactivity; and an equal share (53%) were not registered with the Employment Service of Slovenia (15 600 young people). Despite its relatively good position in the OECD ranking, Slovenia’s NEET rate has not yet fallen below the pre-crisis rate, which was 8.9% in 2007. At the height of the economic crisis, around 2014, the NEET rate had reached 13.9% in Slovenia, a considerable number, but still relatively low compared with some of the other countries in the region, like Italy and Greece, where the NEET rates were twice as high as in Slovenia. Important to note is that the share of unemployed NEETs in the total NEET rate is rather high for Slovenia: 43.8% of all NEETs reported that they were available for work and actively looking for a job in 2018, compared with 37.7% across the OECD.

Youth in Eastern Slovenia are more frequently NEETs than in Western Slovenia. According to 2018 registry data, almost one in five youth in the north-eastern Pomurska statistical region were NEETs. In contrast, in north-western Gorenjska, only one in ten were NEETs; and the NEET rate is only slightly higher in the capital region of Osrednjeslovenska (Figure 2.9). The evolution of NEET rates over the 2011 to 2018 period are relatively comparable across regions.

Women and older youth are both over-represented among NEETs. About 56% of Slovenian NEETs are women, close to the OECD cross-country average of 58% and the EU-28 average of 57% (Figure 2.10, Panel A). The NEET rate also tends to rise with age: both in Slovenia and in OECD countries on average, the NEET rates for 15-19 year-olds are less than half of those in the age group 20-24 and about one-third of those in the age group 25-29 (Figure 2.10, Panel B). For all age groups, the Slovenian NEET rates are significantly below the OECD and EU-28 averages, which is not surprising given the high share of young people who participate in education.

The NEET rate among foreign-born is nearly three time as high as among native-born in Slovenia: 24.2% compared to 8.3% (Figure 2.11). This difference is larger than for the OECD cross-country average (19.4% compared to 12.0%) and even more so than for the EU 28 cross-country average (17.6% compared to 12.2%). The difference was even more important in 2013 and 2014, in line with a common pattern that economic crises have stronger effects on the employment prospects of the foreign- compared to the native-born, and especially so among young workers (Chaloff, Dumont and Liebig, 2012[15]). However, while the native-born NEET rate continued to trend down between 2017 and 2018, the foreign-born rate once again increased. As a result, the share of NEETS who are foreign-born has increased steadily since 2015, reaching 19.8% in 2018.

Administrative data likewise suggests that NEETs more commonly were born abroad or had one foreign-born parent. Between 2011 and 2018, the gap in NEET rates between first-generation immigrants and young Slovenians without an immigrant background rose from 8.3 to 13.7 percentage points; and the gap between second-generation immigrants and Slovenians without an immigrant background from 5.6 to 6.3 percentage points (Figure 2.12). Falling NEET rates among native-born Slovenians contribute more to the rising difference in rates than rising NEET rates among first-generation immigrants do. Among first-generation immigrants themselves, NEET rates are highest among those stemming from one of the 15 countries that were members of the European Union prior to 2004, followed by ‘other’ countries, other EU and Balkan countries. But since a much larger share of 15-29 year-old immigrants were born in a Balkan country, three-quarters of immigrant NEETs stem from a Balkan country.

The NEET rate among Roma youth is generally thought to be much higher than among non-Roma youth, but precise figures are difficult to come by. Based on a specialised 2016 survey across nine EU countries (Bulgaria, Croatia, the Czech Republic, Greece, Hungary, Portugal, Romania, the Slovak Republic and Spain), it was found that 63% of the surveyed 16-24 year-old Roma were NEETs; and less than a quarter achieved an upper secondary qualification. Depending on the country, the rate is 3.6 to 7.3 times as high as the overall NEET rate among 15-24 year-olds. Moreover, with the exception of the Czech Republic, the NEET rate among young Roma women is significantly higher than among young Roma men. On average, the share is 55% among men and 72% among women (European Union Agency for Fundamental Rights, 2018[16]). It is likely that the same patterns hold in Slovenia. Relatively outdated data from the 2002 Census, for example, showed that 61% of self-declared Roma (which represent only 3 200 of the estimated 7 000 to 12 000 Roma living in Slovenia at the time) aged 20 to 24 had not attended or completed primary education, compared to less than 1% among the same age group of non-Roma origin. Another 21% completed only primary and 18% any type of secondary education. Only 3.6% had completed upper secondary education, compared to 61% in the general population (MIrovni Institut, 2012[17]).

NEETs tend to be less healthy than young people who work or study. One possible explanation is that health problems may prevent people from holding down a job or pursuing education. Another explanation is that not having a job may in itself lead to health problems, including because some unemployed people may have less access to high-quality health care. Indeed, the association between being unemployed and poor health is less pronounced in countries with higher unemployment benefit replacement rates (Vahid Shahidi, Siddiqi and Muntaner, 2016[18]). This meditating relationship of the welfare state may also help explain why for example in Germany (Schmitz, 2011[19]) and Finland (Böckerman and Ilmakunnas, 2009[20]), becoming unemployed was not associated with worsening health status (though the unemployment tended to be in worse health). Another factor that can influence the association is the extent of and speed at which individuals with health barriers are offered activation measures. The Employment Service of Slovenia’s assessments of health barriers, which otherwise follow good practices, often happen relatively late (OECD, 2021[21]). The NEET rates among those reporting poor health is 3.4 times higher than for those who do not in both Slovenia and across the OECD (Figure 2.13, Panel A). In contrast, the relative difference between those who report and do not report functional limitations is smaller in Slovenia than across the OECD. One in three male inactive Slovenian NEETs state they are inactive because they are ill or disabled (slightly more than the cross-country average of 30%). It is this the most frequently named motive for men, ahead of informal education and training (26% in Slovenia compared to 14% across the OECD) and caring or family responsibilities (22% compared to 12% across the OECD). In contrast, only 12% of inactive Slovenian women say they are so because they are ill or disabled (16% across the OECD). The large majority of inactive women (60%) have caring or family responsibilities, compared to the 53% cross-country average (Figure 2.13, Panel B).

As mentioned, poor mental health may be a risk for and a result of the NEET status. An association between poor mental health and being NEET was indeed found in a number of countries (OECD, 2016[22]; OECD, 2019[23]; OECD, 2018[24]). Different studies suggest that the causality can run in both directions: Rodwell et al. (2018[25]) show that adolescents in Australia with common mental health disorders were more likely to be NEET in their early twenties, whereas O’Dea et al. (2016[26]) did not find any associations between changes in depression and changes in NEET status in Australia among users of youth mental health services. Scottish individuals who had been NEETs 10 or 20 years earlier were 50% more likely to take antidepressant or antianxiety medication compared to individuals without a NEET background (Feng et al., 2015[27]). In the UK, rising self-reported mental health status have offset the ‘protective’ effects of rising education levels, leading to 2015 NEET rates that are very similar to those observed in the early 2000s (Holmes, Murphy and Mayhew, 2019[28]).

In Slovenia, the prevalence of mental distress does not vary as strongly between NEETs and non-NEETs as elsewhere. Based on the assumption that in each country, one fifth of the working-age population has some form of mental distress, it identifies the 20% with the most negative answers to eight mental-health related questions concerning for example whether they feel sad, fatigued, have lost appetite or interest in things they used to enjoy, etc. as being mentally distressed. The advantage of setting different cut-off points for mental distress in different countries is that cultural norms may affect how willing people are to admit that they struggle with mental health challenges. Around one-quarter of youth in Slovenia were classified as being in mental distress, meaning that it is more common for them than the working-age population as a whole (Figure 2.14). This higher prevalence among youth is by no means restricted to Slovenia. With the exception of Estonia and Poland, NEETs are more likely to belong to the 20% with the most mental distress than non-NEETs. Yet in Slovenia, the ratio of the prevalence in the two groups is among the lowest among the included European OECD countries.

Despite affordability not being an issue, relatively few young Slovenians consult psychologists or psychotherapists; 3.1% of young respondents in Slovenia reported that they had consulted a mental health professional in the past 12 months, compared to 6.5% across the included countries. The share of NEETs who did so is 1.4 times higher than among non-NEETs, a difference that is actually larger than the relative difference in mental distress. Only 1.7% of Slovenian youth indicated that they could not afford mental health care, with the difference between NEETs and non-NEETs being marginal. In contrast, across the included countries non-affordability was twice as frequently a problem among NEETs than non-NEETs. Together, these results suggest that mental health service access for NEETs is not a major issue in Slovenia; but that raising awareness of mental health issues and the potential benefits of seeing a mental health professional among all youth could be beneficial.

Many young people go through short NEET spells without major repercussions on their well-being and future economic opportunities. For example, recent graduates may take a few months to search for a job without future employers thinking any less of them. In contrast, being a NEET for a longer period can (but need not to have) adverse effects on a young person’s well-being and ability to complete further studies or find a job. It is therefore important to look in more detail at the length of NEET spells.

In Slovenia, about half of all NEETs remain in the status for a year or more during the 2014-17 period (dark bar in Figure 2.15, Panel A). This share nearly equals the cross-country average for the 18 European OECD countries for which the same information is available. However, the similarities hide differences across age groups: The share of long-term NEETs among 16-19 and 20-24 year-old Slovenian NEETs is below the cross-country average, whereas it is above average among 25-29 year-olds. Around three in five long-term NEETs in Slovenia are between 25 to 29-year-old, compared to only one in two on average in the 18 European OECD countries. Compared to the 2010-13 period, the share of long-term NEETs in Slovenia dropped in the 16-19 age group but rose in the other two (Figure 2.15, Panel B). This shift was most drastic among 25 to 29-year-olds. In this age group, the share of long-term NEETs among NEETs increased by 20 percentage points.

Further analysis suggests that long-term NEETs in the age group 25-29 are less likely to have completed tertiary education than those with short NEET spells and they are less likely to have a child (Figure 2.16). The difference in educational attainment is particularly large in the case of Slovenia, where 85% of short-term NEETs have a tertiary degree compared with only 48% among long-term NEETs. For the 18 OECD countries for which similar information is available, the gap is only 23 percentage points. Long-term NEETs in Slovenia are also less likely to be female than short-term NEETs, even though the share of women in both groups is quite high (64% and 72% respectively).

The different socio-economic characteristics of NEET tend to be interdependent. For example, 28-year-olds are far more likely to be parents than 17-year-olds are. In order to understand the relationship between a characteristic and NEET durations, these characteristics need to be analysed through a regression analysis. This analysis leads to several interesting results (Table 2.1):

  • In Slovenia, compared to single childless men, married men and fathers accrue on average fewer NEET months. This result is similar across the European OECD countries, although the coefficient is not statistically significant for single fathers for this group and the difference between childless single and married men is larger in Slovenia than across the included European OECD countries. As a comparison country, the relationships are less strong in Austria.

  • Over the entire 15-29 year-old youth group, the average NEET duration for childless women is not statistically different from the average NEET duration for childless single men with similar characteristics, whether or not these women are married. When the sample is restricted to 25-29 year-olds, however, Slovenian childless women living with a partner have average NEET durations that are five months longer than single childless men with similar other characteristics.

  • Across the included OECD countries and in Slovenia, single mothers spend 9-11 months longer as NEETs over a 48-months period than otherwise similar single childless men. In contrast, while married mothers across the OECD also spend about 9-11 months longer as NEETs as unmarried mothers, in Slovenia, the NEET duration of married Slovenian mothers is not statistically distinguishable compared to otherwise similar single childless men.

  • Self-reported poor health is associated with six and nine additional NEET months in first, Slovenia and Austria, and across the OECD, respectively. The link is more pronounced among 25-29 year-olds. In this age group, the association between poor health and NEET duration is stronger in Slovenia than across the OECD or in Austria (13.4 versus 10.5 and 9.6 months); and associated with a larger increase in the average NEET duration than any other characteristic analysed in the regression. However, given that the measure is self-reported and imprecise, the cross-country differences should be interpreted with caution.

  • Completing upper secondary and tertiary education shaves off 9-13 months of the average NEET duration in Slovenia, Austria and across the included OECD countries. The difference in the NEET durations between high school and university graduates is larger across the OECD than in Slovenia and even more so than in Austria. In Austria, by the late twenties there is no more relationship between educational attainment and NEET duration.

  • Parental employment status has a larger influence on the average NEET duration than parental education status, but both characteristics have less of an influence than the youth’s personal characteristics.

  • In sum, children for single women and low education are the strongest determinants of the NEET duration in Slovenia.

For several reasons, these results should be interpreted with caution. First, the included individual and family characteristics only account a small part of the variation in the duration of the NEET status over the 48 months: In the Slovenia-specific regressions, they account for around 16% and in the cross-country regression for 27%. Second, the NEET durations are censored, since it is not possible to observe whether someone who is NEET during the last month they are observed in the panel remains so afterwards or returns to education or finds a job. This means that the linear regression model used above does not yield consistent estimates. However, robustness checks using a Tobit model generally provide results that are qualitatively similar to the ones shown in Table 1.1, apart from the result that the marginal effect of being a mother no longer strongly differs between single and married women. Third, part of the analysed period includes a more difficult labour market environment. While it is possible that the relationship between certain characteristics and the probability of remaining NEET for a long time have changed since then, it is equally possible that the marginal effects of characteristics that make it harder to establish oneself on the labour market remain constant (despite the lower mean duration for all groups).

Working-age individuals in Slovenia, including young people, are eligible for a number of benefits:

  • Unemployment benefits: Unemployment insurance benefits are available to workers whose employers terminate their open-ended contracts or whose fixed-term contracts run out. The minimum insurance period is typically nine months over the previous two years, but workers under the age of 30 only need to have paid into the system for six months. The potential benefit duration for young and middle-aged workers is relatively short: two months for under-30-year-olds who were insured for six to ten months; three months for people insured for more than ten months to five years; and six months for people insured between five and 15 years. During the first three months, benefits can amount to 80% of the prior monthly earnings, but within minimum and maximum amounts from EUR 530 to EUR 893 (Employment Service of Slovenia, n.d.[15]).

  • Financial social assistance: This means-tested benefit is available for individuals whose families’ income and property are below a minimum level. For single individuals, the maximum amount is EUR 402 (Ministry of Labour, Family, Social Affairs and Equal Opportunities, 2020[16]). However, if a person works or volunteers, he or she can receive a supplemental activity allowance. For single individuals, the maximum amount is EUR 607 minus their labour income (provided that the person is active for more than 128 hours per month) or EUR 507 minus their labour income (provided that the person is active between 60 and 128 hours per month and volunteers). Additional emergency assistance is also available. The Centres of Social Work (CSW) administer the financial social assistance, but working-age individuals who are able to work also need to register with the Employment Service of Slovenia (ESS) in the registers of unemployed persons or job seekers. Students who have job seeker status only have to report to the ESS once during the six month following registration; while unemployed persons have to be active job seekers and are not allowed to refuse suitable employment.

  • Child benefits: Child benefits are granted to one of the parents or legal guardian with a registered residence in the Republic of Slovenia, up to the age of 18, if he or she also fulfils other conditions under the law governing family benefits. The level of the child benefit varies with the number of children, the monthly household income, single parent status, the age of the child and whether or not pre-school children are in childcare.

Calculations based on the European Union Statistics on Income and Living Conditions show that nearly four in five NEETs or their families receive some kind of social benefit (Figure 2.17). Even though only one in five NEETs receive unemployment benefit, close to one in two lives in a household that receives financial social assistance (45%) and/or child benefits (48%). The reliance on financial social assistance is particularly high compared with other OECD countries, where only 11% of NEETs lives in a household that benefits from social assistance.

NEETs may receive benefits more frequently because their need is larger due to their inactivity or personal situation, such as having children or being disabled, or because growing up in a socio-economically disadvantaged household increases their probability of being NEETs. Disaggregating the recipient rates by whether youth are living with their parents or not reveals that the higher receipt rate of financial social assistance among NEETS is particularly pronounced among NEETs who live with their parents (56%) compared to those that do not (29%). In contrast, the share receiving family assistance is much higher among those who no longer live with their parents (81% of NEETS and 45% of non-NEETs) compared to those who do (25% among NEETs and 32% among non-NEETs). Youth living with their parents tend to be younger and less likely to have children. The high share of NEETs receiving social assistance thus indicates that many among them come from households that are socio-economically disadvantaged. The high share of NEETS who are no longer living with their parents who receive family assistance indicates that many among them are young parents who are NEETs because of childcare obligations.

Despite the high benefit coverage, one in four Slovenian NEETs are poor. In contrast, only 8.5% of non-NEETs in 2017 lived in households with an equalised income below 60% of median income, a common poverty measure (Figure 2.18). While this outcome is nearly half the OECD average of 16.5%, the rate among NEETs is much closer to, though still below, the OECD average (27.9% compared to 34.3%). The poverty rate is higher for youth not living with their parents compared to those that do for both NEETs and non-NEETs in both Slovenia and across the OECD. But while among that non-NEETs who have moved out from home, the poverty rate is higher among those that do not have children (20%) compared to those that do (14%) in Slovenia; the opposite is true for NEETs (27% for those without children and 42% for those that do).

The income gap to ‘non-poverty’ is particularly high for young single people without children. In 2018, a 25-year-old who worked for one year and has been out of work for six months (and hence does not receive unemployment benefits) had an income (financial social assistance EUR 402) amounting to 61% of the at-risk-of-poverty threshold for a one-person household of EUR 662. If the same 25-year-old volunteered, his or her income (EUR 507) amounted to 77% of the poverty line.

Even NEETs with similar length of their NEET status may have quite different school-to-work transition patterns. Some may take a yearlong break once they graduate but find and stay with a job immediately after. Others may oscillate back and forth between short employment and unemployment spells, or find themselves unable to return to paid employment after a parental leave. A better understanding of these different pathways and of the socio-economic characteristics that are associated with them could potentially make it possible to create more targeted services that are appropriate for each group.

To get a picture of the different pathways from school to work, unemployment or inactivity, young people who were NEETs at least once during a four-year period are categorised into five to seven different groups, depending on their age group. The groupings are created through a cluster analysis (see the note of Table 2.2). Since the trajectories of 16-year-olds are likely to look very different from those of 28-years old, youth are divided into three age groups: 16-19 year-olds, 20-24 year-olds and 25-29 year-olds. The groups represent the following typologies of school-to-work transitions:

1. Students: Individuals in this group spend the majority of the four-year period enrolled in an educational institution.

2. Workers: Youth in this group of transition typology are on average working two to three out of the four years.

3. Stable transitioners: Young people in this group on average spend two years in education. Their integration into the labour market is often relatively smooth, with the time spent in employment amounting more than three times the time spent in unemployment. This transition pattern only applies to 20-24 year-olds.

4. Unstable transitioners: This transition pattern is predominately present among under-25-year-olds. These youth still spend a substantial time in education, but they are often unable to find a job when entering the labour force.

5. Unstable workers: Youth that follow this pattern have often already left education and are therefore represented in the over-20-year-old category. They only spend slightly more time working than being unemployed. Many of the individuals in the 20-24 year-old group are often initially unemployed but then transition to a more stable employment, while 25-29 year-olds often go back and forth between employment and unemployment.

6. Persistent unemployed: Individuals in this group on average are unemployed for three or more years.

7. Persistent inactives: This group mirrors the sixth group, but rather than being unemployed, youth are inactive for three or more years.

Figure 2.19 shows examples of activity trajectories of different groups of 16-29 year-old NEETs, whereas Table 2.2 provides information about the number of months spent in each activity.

The comparison of pattern frequencies between Slovenia and the other included European OECD countries suggests a number of conclusions:

  • Reflecting Slovenia’s high educational attainment compared to other European OECD countries, the ‘student’ pattern is over-represented in all age categories and the single largest group among 16-19 and 20-24 year-olds, at 59% and 39% compared to the OECD averages of 37% and 14%, respectively. The unstable transitioners group, in contrast, is less common among Slovenian teenagers than on average across the included OECD countries, but more common among Slovenians in their early twenties compared to the OECD average. Nevertheless, the share of unstable transitioners is higher among teenagers than among youth in their early twenties both on average and in Slovenia, suggesting that the immediate graduation-to-work transition is more difficult for younger than older youth.

  • The particularly problematic patterns – persistent unemployed and inactives – are less common in Slovenia than on average across European OECD countries, even though the share rises with age from 11% among 16-19 year-olds (OECD: 17%) to 28% (OECD: 39%) among 25-29 year-olds. The same patterns hold when the trajectories of unstable workers and unstable transitioners are included. This observation confirms that older NEETs in Slovenia tend to struggle more than younger NEETs.

It would be ideal to be able to predict whether a young person who recently became inactive or unemployed will remain so for a long time, but this is difficult to do on the basis of the current survey data. First, many of the factors that likely influence someone’s chances of easily transitioning from education to work – such as their school performance or whether they live in a region with a diversified and growing economy – are not captured in the panel data. Second, the panel is relatively short and therefore omits important background information. For example, for older youth we cannot always observe the period following graduation. Whether or not a person lands a job during this period may have long-term effects on their labour market success. Third, the number of young Slovenian respondents that belong in particular to the smaller groups is low, making it difficult to estimate the relationship between even a few characteristics and the probability of belonging to a given group.

Despite these caveats, a preliminary analysis nonetheless reinforces the conclusion that mothers are much more likely to be in one of transition patterns with longer average periods of unemployment and inactivity. This analysis is based on multi-nominal logit regressions that relate the likelihood of falling into any of the groups as compared to belonging to the ‘student’ group to individuals’ basic characteristics (age, sex, educational attainment, and being in poor health and being a parent during the first six month of panel inclusion). Many estimated coefficients in these regressions are not statistically significant. Among 16-19 year-olds, only an individual’s age provides any information about which pattern someone is likely to follow. Among 20-24 year-olds, the relative risk that a mother belongs to persistent inactives as opposed to the student group rises by a factor of 58 compared to men. For mothers, the relative risk that she falls into persistent unemployed, the worker and the unstable workers groups are also higher, but much less so than the persistent inactive group. Having completed secondary or tertiary education actually lowers the relative risk of being in any of the other groups compared to falling into the student group; though none of the coefficients are statistically significant for the stable transitioners group. In the 25-29 year age group, the coefficients on the educational attainment variables are no longer statistically significant. The relative risk for young mothers to belong to the groups of unstable workers or persistent inactives compared to being students rise by a factor of 7 and 61, respectively; while their relative risk of being workers or persistent unemployed change by a factor of 0.5 and 0.4, respectively.

Existing NEET cost estimates predominantly focus on economic rather than fiscal costs. A first estimate by the OECD (OECD, 2016[29]) concentrated solely on the opportunity costs of youths not working by estimating what their wages would likely have been based on their observed characteristics. The estimated cost of 1.4% of Slovenian GDP was equal to the OECD average. Eurofound (2012[30]) also estimated forgone earnings, but in addition included the ‘excess’ welfare benefits NEETs received in comparisons to non-NEETs. They arrived at a 1.5% of GDP cost for 2011, compared to the EU-26 average of 1.2%. The two existing estimates rely on a number of simplifying assumptions. First, they disregard some cost categories, such as the cost of employment services. Second, they implicitly assume that NEETs and their families and communities do not derive any value from the activities that NEETs pursue instead of working or studying. The example of a parent staying home to look after their children is one where it is particularly clear that this activity represents a benefit to the family and a saving for the community. A focus solely on the public budget costs associated with NEETs has the advantage of not requiring any assumptions about the value generated by NEETs’ non-market activities.

A number of researchers have estimated the long-term costs of NEETs for countries other than Slovenia. Since it is difficult to predict the life course of all NEETs and non-NEETs, they have often relied on fictitious life courses for different NEET sub-groups. In some cases, they can rely on longitudinal studies on the scarring effects of being a NEET. For example, a Swedish twin study found that long-term NEETs on average had 60% lower incomes ten years later than their twins who were not NEETs (Andersson, Gullberg Brännstrom and Mörtvik, 2018[31]).

Focussing on short-term public sector costs of NEETs, several components can be identified, including forgone tax revenues, social security contributions, higher benefit spending for NEETs compared to non-NEETs and the cost of the provision of services by the ESS and CSW. Costs that may arise from higher usage rates of health services (for example, because of negative mental health effects of being NEETs) and potentially higher incarceration are also part of NEET costs, but are even harder to estimate. The costs are counter-balanced by short-term savings, including public education costs of a NEET who would otherwise be in education and the public child-care costs of the children of NEETs who would need child care if their parent(s) were working or in education. Of course, in particular the educational cost saving is only a benefit in the short term. In the medium and long term, lower expected tax payments and higher public benefit payments can be expected to erase any short-term savings.

The income tax and social security contributions that NEETs would have made if they were not inactive tend to be quite limited. The estimation applies average tax and social security contribution rates (for single workers without children at average earnings) to the imputed annual labour income (using a Heckman correction that adjusts for the selection into employment based on observable characteristics). It likely overestimates the forgone tax and social security revenues because many NEETs would likely have a lower income and thus pay lower marginal tax rates. Despite this potential over-estimation, the fiscal cost of missing tax revenues from NEETs in Slovenia is minor: It only amounted to around 0.14% of GDP in 2017, compared to the average of around 0.16% for 16 European OECD countries (Table 2.3).

Since NEETs receive higher public benefit payments on average, they represent additional expenditures for the Slovenian state. Across European OECD countries in 2017, the difference in average public benefits received by NEETs compared to non-NEETs that year (including unemployment and disability as well as household size-equivalised social assistance, housing and family benefits) ranged from EUR 331 in Spain to EUR 8 749 in Denmark. In Slovenia, the difference was EUR 1 231. This amount translates to a public finance cost of 0.09% of GDP, compared to the average of 0.13% across the included European OECD countries. A validation based on administrative data shows that the survey-based estimate is of the right order of magnitude: In 2017, youth whom the demographic database listed as unemployed, inactive or social transfer recipients on average received EUR 1 097 more in financial assistance throughout the year than youth who were working or in education. In 2018, the difference amounted to EUR 1 239.

Registered NEETS also generate costs for the public employment service, though these short-term costs likely pay off quickly in terms of a faster labour market re-integration. For the PES services cost estimates, the ESS provided information on active labour market programme and labour costs, which were multiplied by the share of unemployed in 2018 who were aged 15-29 (to arrive at the cost that could be attributed to that age group)2 and divided by the total number of NEETs (to arrive at the per-NEET rather than per-registered unemployed youth estimate). The costs amount to EUR 653 per NEET, or 0.05% of GDP. A similar OECD average estimate could not be readily derived.

In the short run, countries can save expenditures on public education due to youth choosing to be NEETs rather than pursuing studies. For selected European OECD countries, these estimated savings range is from 0.06 to 0.27% of GDP. They are a function of the number of NEETS; the share among them that are more likely to be upper secondary or tertiary students rather than working; average educational expenditures per full-time student at the different levels of education and the share of these expenditures that are born by any level of government. At an estimated 0.21% of GDP, the Slovenian state realises comparatively larger short-term savings from youth being NEETs rather than studying compared to the average.

Similarly, the treasury may save some funds in the short run when NEETs look after their young children rather than sending them to public day care. Since the EU-SILC does not contain information about whether young children attend outside childcare, it is assumed that a child younger than four stays at home if their NEET parent defines their activity as fulfilling domestic tasks and care responsibilities. The number of NEETs for whom this is the case is then multiplied by the estimated public expenditure on public child care and early childhood education per attending child aged 0-5. The estimation assumes that the children of these NEETs do not currently attend public childcare and that they would do so if their parent became employed or returned to education. Based on this estimation, the ‘savings’ for the Slovenian state are close to zero. The main reason is that few NEET parents of young children in Slovenia define care responsibilities are their primary activity. As with the education of the NEETs themselves, the reduced short-term expenditures may entail higher expenditures in the longer term as the benefits of early childhood education are well established.

Summing up the different costs and savings, the short-term net fiscal costs of NEETs in Slovenia and across the OECD are relatively limited. They amount to 0.07 and 0.06% of GDP in Slovenia and on average across the included European OECD countries. However, these estimates need to be interpreted with extreme caution. First of all, the estimates are largely based on 2017 data and thus refer to a reference year that likely represents one of the years with the lowest share of NEETs in the recent past as well as the immediate future. When more young people become unemployed and inactive, the costs will necessarily rise. Second, the estimates rely on extreme simplifications even for the components that are included, and exclude other relevant costs such as potentially higher health expenditures. They also do not take into account any long-term costs that can arise from the scarring effects of being NEET, nor do they take into account the costs that being outside of education and the labour market imposes on the young people, their families and surroundings in terms of for example their reduced well-being and income. Finally, without the attributed ‘savings’ of education and child care expenditures that are likely a false economy because they entail longer-term costs, the estimated net costs would be significantly higher.

In 2019, nearly one in ten Slovenian youth were neither in employment, nor in education or training. This share is lower than in many OECD countries, but it is still higher than before the financial and economic crisis hit the country at the end of the 2000s. The COVID-19 crisis increased the number of unemployed youth and likely altered the composition of NEETs. Nevertheless, those who were already NEET prior to the crisis are also among the ones who will remain most vulnerable in the years to come, making it important to understand who they are. Young people can be NEET for very diverse reasons. It is thus important to better understand the characteristics of the different NEET groups in order to design better support.

Analysis of different national and international surveys reveals the following outcomes for Slovenia. Women and older youth are both over-represented among NEETs and an increasing share of NEETs are born abroad. NEET rates are also 3.4 times higher among those reporting poor health than among those who do not, though the NEET status itself may also cause health problems. Short bouts of inactivity or unemployment do not necessarily have negative repercussions on future employment opportunities and income. But about half of all Slovenian NEETs remain in this status for a year or more, which might affect their future chances of employment. The share of long-term NEETs is particularly high among 25-29 year-olds in comparison with other OECD countries. Further analysis suggests that low education and being a mother are the strongest determinants of the NEET duration in Slovenia. Nearly four in five NEETs or their families receive some kind of social benefit, yet, one in four Slovenian NEETs are poor.


[31] Andersson, F., S. Gullberg Brännstrom and R. Mörtvik (2018), “Long-term scarring effect of neither working nor studying”, International Journal of Manpower, Vol. 39/2, pp. 190-204, http://dx.doi.org/10.1108/IJM-12-2015-0226.

[6] Bank of Slovenia (2019), “Economic and Financial Developments”, No. ISSN 2385-9784, https://bankaslovenije.blob.core.windows.net/publication-files/gdhajPtofctjc_economic-and-financial-developments-july-2019.pdf.

[20] Böckerman, P. and P. Ilmakunnas (2009), “Unemployment and self-assessed health: evidence from panel data”, Health Economics, Vol. 18/2, http://dx.doi.org/10.1002/hec.1361.

[15] Chaloff, J., J. Dumont and T. Liebig (2012), “The Impact of the Economic Crisis on Migration and Labour Market Outcomes of Immigrants in OECD Countries”, CESifo DICE Report, Vol. 10/1, pp. 39-47.

[40] Employment Service of Slovenia (n.d.), Unemployment Benefits, http://english.ess.gov.si/jobseekers/unemployment_benefits (accessed on 20 August 2019).

[30] Eurofound (2012), Young people not in employment, education or training: Characteristics, costs and policy responses in Europe, Publications Office of the European Union, Luxembourg.

[9] European Commission (2018), Youth Guarantee Country Fiche Slovenia, European Commission , Brussels, https://ec.europa.eu/social/main.jsp?catId=1161&intPageId=3352&langId=en.

[8] European Commission (2015), 2015 European Semester: Country Reports - Slovenia, European Commission , Brussels, https://ec.europa.eu/info/publications/2015-european-semester-country-reports-slovenia_en.

[35] European Commission (2015), The 2015 Ageing Report - Economic and budgetary projections for the 28 EU Member States (2013-2060), Luxembourg: Publications Office of the European Union, Brussels, http://ec.europa.eu/economy_finance/publications/european_economy/2015/pdf/ee3_en.pdf.

[16] European Union Agency for Fundamental Rights (2018), Transition from education to employment of young Roma in nine EU Member States, European Union Agency for Fundamental Rights, Vienna, https://fra.europa.eu/sites/default/files/fra_uploads/fra-2018-eu-midis-ii-roma-transition-education-employment_en.pdf.

[27] Feng, Z. et al. (2015), Consequences, risk factors, and geography of young people not in education, employment or training (NEET), Scottish Government, Edinburgh, https://www.gov.scot/publications/consequences-risk-factors-geography-young-people-education-employment-training-neet/pages/7/.

[28] Holmes, C., E. Murphy and K. Mayhew (2019), “What accounts for changes in the chances of being NEET in the UK?”, SKOPE Working Papers, No. 128, Centre on Skills, Knowledge and Organisational Performance, University of Oxford.

[41] Ministry of Labour, Family, S. (2019), Financial Social Assistance, http://www.mddsz.gov.si/en/areas_of_work/social_affairs/financial_social_assistance/ (accessed on 20 August 2019).

[17] MIrovni Institut (2012), Slovenia: FRANET National Focal Focal Point Social Thematic Study - The situation of Roma 2012, MIrovni Institut, Ljubljana, https://fra.europa.eu/sites/default/files/situation-of-roma-2012-si.pdf.

[34] Naterer, A. et al. (2019), Youth Study Slovenia 2018/2019, http://library.fes.de/pdf-files/id-moe/15278.pdf.

[26] O’Dea, B. et al. (2016), “A prospective cohort study of depression course, functional disability, and NEET status in help-seeking young adults”, Social Psychiatry and Psychiatric Epidemiology, Vol. 51/10, pp. 1395-1404, http://dx.doi.org/10.1007/s00127-016-1272-x.

[21] OECD (2021), Sickness and Disability Insurance in Slovenia: new evidence, key challenges and OECD conclusions, OECD Publishing, Paris.

[3] OECD (2020), Active labour market policies to mitigate the rise in (long-term) unemployment in Slovenia [forthcoming], OECD Publishing, Paris.

[11] OECD (2020), Education at a Glance 2020: OECD Indicators, OECD Publishing, Paris, https://dx.doi.org/10.1787/69096873-en.

[5] OECD (2020), Historical Population, https://stat.oecd.org/Index.aspx?DataSetCode=HISTPOP (accessed on 16 April 2021).

[1] OECD (2020), OECD Economic Outlook, Volume 2020 Issue 1, OECD Publishing, Paris, https://dx.doi.org/10.1787/0d1d1e2e-en.

[4] OECD (2020), OECD Economic Surveys: Slovenia 2020, OECD Publishing, Paris.

[2] OECD (2020), OECD Employment Outlook 2020: Worker Security and the COVID-19 Crisis, OECD Publishing, Paris, https://dx.doi.org/10.1787/1686c758-en.

[43] OECD (2019), Education at a Glance 2019: Country Note Slovenia, OECD Publishing, Paris, https://www.oecd.org/education/education-at-a-glance/EAG2019_CN_SVN.pdf.

[7] OECD (2019), Education at a Glance 2019: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/f8d7880d-en.

[32] OECD (2019), Education at a Glance 2019: OECD Indicators, OECD Publishing, Paris, https://dx.doi.org/10.1787/f8d7880d-en.

[23] OECD (2019), Investing in Youth: Finland, Investing in Youth, OECD Publishing, Paris, https://dx.doi.org/10.1787/1251a123-en.

[44] OECD (2019), OECD Economic Outlook, Volume 2019 Issue 2, OECD Publishing, Paris, https://dx.doi.org/10.1787/9b89401b-en.

[38] OECD (2018), Education at a Glance 2018 Slovenia Country Note, OECD Publishing, Paris, http://gpseducation.oecd.org/Content/EAGCountryNotes/SVN.pdf.

[39] OECD (2018), Education at a Glance 2018: OECD Indicators, OECD Publishing, Paris, https://dx.doi.org/10.1787/eag-2018-en.

[24] OECD (2018), Investing in Youth: Norway, Investing in Youth, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264283671-en.

[36] OECD (2018), Migration-Demography Database - Note on Slovenia, http://www.oecd.org/els/mig/SVN.pdf (accessed on 13 June 2019).

[33] OECD (2017), OECD Economic Surveys: Slovenia 2017, OECD Publishing, Paris, https://dx.doi.org/10.1787/eco_surveys-svn-2017-en.

[14] OECD (2017), OECD Family Database - Key characteristics of parental leave systems, OECD Publishing, Paris, http://www.oecd.org/els/soc/PF2_1_Parental_leave_systems.pdf.

[12] OECD (2017), OECD Skills Strategy Diagnostic Report: Slovenia 2017, OECD Skills Studies, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264287709-en.

[13] OECD (2016), Education Policy Outlook Slovenia, OECD, Paris, http://www.oecd.org/slovenia/Education-Policy-Outlook-Country-Profile-Slovenia.pdf.

[22] OECD (2016), Investing in Youth: Sweden, Investing in Youth, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264267701-en.

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

[42] OECD (n.d.), Tax-benefit web calculator, http://www.oecd.org/els/soc/benefits-and-wages/tax-benefit-web-calculator/#d.en.500997 (accessed on 20 August 2019).

[45] OECD/European Union (2018), Settling In 2018: Indicators of Immigrant Integration, OECD Publishing, Paris/European Union, Brussels, https://dx.doi.org/10.1787/9789264307216-en.

[37] Republic of Slovenia Statistical Office (n.d.), SiStat, https://pxweb.stat.si/SiStatDb/pxweb/en/10_Dem_soc/10_Dem_soc__05_prebivalstvo__40_selitve__20_05N32_soc_ek_znac_odsel/?tablelist=true (accessed on 13 June 2019).

[25] Rodwell, L. et al. (2018), “Adolescent mental health and behavioural predictors of being NEET: a prospective study of young adults not in employment, education, or training”, Psychological Medicine, Vol. 48/5, pp. 861-871, http://dx.doi.org/10.1017/S0033291717002434.

[19] Schmitz, H. (2011), “Why are the unemployed in worse health? The causal effect of unemployment on health”, Labour Economics, Vol. 18/1, http://dx.doi.org/10.1016/j.labeco.2010.08.005.

[18] Vahid Shahidi, F., A. Siddiqi and C. Muntaner (2016), “Does social policy moderate the impact of unemployment on health? A multilevel analysis of 23 welfare states”, The European Journal of Public Health, Vol. 26/6, http://dx.doi.org/10.1093/eurpub/ckw050.

[10] Vodopivec, M. (2019), “Levelling the Playing Field: The Effects of Slovenia’s 2013 Labour Market Reform”, Economic and Business Review, Vol. 21/1, pp. 109-132.


← 1. Statistical Office (2020), “At the end of 2019 the average age of persons in employment was 42.8 years”, https://www.stat.si/StatWeb/en/News/Index/8658, accessed on 17 September 2020.

← 2. The costs of the youth counsellor project were fully attributed to the youth.

Metadata, Legal and Rights

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.

© OECD 2021

The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions.