Investing in Youth: North Denmark Region

1. Risk factors associated with a NEET status in European countries

1.1. Literature review on risk factors

The reasons why young people are not in education, employment, or training (NEET) is multifaceted and influenced by a range of factors of varied nature. Over the past four decades, research has explored the risk factors associated with having a so-called NEET status and yet, there have been only few studies that provide a systematic overview of this body of research.

To fill in this gap, a team of Danish researchers who collaborated with the OECD for this report conducted a systematic literature review on the range of factors that are likely to increase the NEET risk in Europe (see Box 1.1 for details). The review aims to deepen the understanding on the risk factors associated to the NEET status, with the ultimate objective of supporting the development of early interventions, inclusive policies, and more targeted support aimed at reducing NEET rates. Risk factors were categorised into four separate categories, discussed in the remaining sections of this chapter: social factors (Section 1.2), geographical factors (Section 1.3), mental health factors (Section 1.4), and physical health factors (Section 1.5).

Box 1.1. A systematic literature review

The PRISMA-ScR Checklist (Tricco et al., 2018[1]) guided the structuring of the literature review. Systematic literature searches were conducted in Medline, Embase, PsycINFO and Scopus on 21 February 2023, with an update on 15 January 2024.

The inclusion criteria were studies concerning young individuals aged 15 to 29 years, where data had been collected in a European country after 1980. Only studies making use of quantitative data were considered, with mixed-methods studies requiring distinguishable quantitative data. Articles written in English, German, Danish, Norwegian, and Swedish were included. Experimental studies were excluded, as the focus was on naturally occurring risk factors.

The initial search strategy yielded 51 126 studies. After removing duplicates, 33 314 studies underwent independent screening based on title and abstract, of which 32 655 were deemed irrelevant. Subsequently, 659 studies underwent full-text screening, with 434 studies being excluded and three studies unable to be retrieved. A total of 222 studies were found relevant for the literature review and categorised by the researchers across four main domains: physical health, mental health, social, and geographic factors.

Sources: Lindblad et al. (2024[2]), Mapping Social Landscapes: Youth NEET Risk and Social Issues in Europe – A systematic literature review; Lindblad et al. (2024[3]), Place Matters: Understanding Geographic Influences on Youth Not in Education, Employment, or Training (NEET) – A scoping review; Lindblad et al. (2024[4]), Beyond the mind: Understanding the influence of mental health on youth NEET status in Europe – A systematic literature review; and Lindblad et al. (2024[5]), From illness to inactivity: Exploring the influence of physical diseases on youth Not in Education, Employment, or Training status in Europe: A systematic literature review.

1.2. Social risk factors

The social factors identified in the literature review by Lindblad et al. (2024[2]) affecting the NEET risk can be classified into two overarching themes: household factors (parental situation and socio‑economic environment) and individual social factors (individual characteristics and adverse experiences).

First, family socio-economic resources have a crucial impact on the NEET likelihood (Rahmani and Groot, 2023[6]). Low parental educational (Yi̇ği̇t, Çakmak and Çakmak, 2023[7]; Odoardi, 2020[8]), household financial problems (Arifi, Fetai and Qirici, 2019[9]), and dependence on social assistance (Gustafsson, Katz and Österberg, 2017[10]) are all associated with an increased NEET likelihood. Growing up separated from one parent (Karhina et al., 2024[11]) or both parents (Essadek et al., 2023[12]; Fleming et al., 2020[13]; Pasanen et al., 2023[14]) and exposure to adverse behaviour from parents like maltreatment (Cahill, Hager and Chandola, 2022[15]) or parental substance abuse (Burdzovic Andreas, Torvik and Lund, 2022[16]; Pisinger et al., 2023[17]; Karsberg et al., 2023[18]) are also identified as risk factors.

Second, individual social factors, including both individual characteristics and adverse experiences, affect the likelihood to end up in a NEET situation. Poor academic performance (Barth et al., 2021[19]; Gladwell, Popli and Tsuchiya, 2022[20]; Rahmani and Groot, 2023[6]) lower IQ (Hegelund et al., 2019[21]), and cognitive impairments, particularly grade repetition (Legleye et al., 2010[22]) or struggles with mathematics and reading (Hakkarainen, Holopainen and Savolainen, 2013[23]) are associated with an increased risk of NEET status. Personality traits such as a difficult temperament (Wu et al., 2022[24]), high levels of cynicism (Bask and Salmela-Aro, 2013[25]), low levels of self-efficacy (Andersén et al., 2018[26]), and low psychological resources (Jensen, Andersen and Winding, 2020[27]) are also linked to dropout or unemployment. Similarly, factors such as violence (Kahlmeter, 2023[28]), substance abuse (Fabi et al., 2023[29]; Haddad et al., 2023[30]), stress (Winding, Nielsen and Grytnes, 2023[31]), and being uncertain of one’s aspirations for education or employment (Tayfur et al., 2022[32]) are associated with increased risk of NEET.

Potential mitigating factors to reduce the NEET risk include parental support for education (Alfieri et al., 2015[33]), a supportive teacher-student relationships in schools (Holen, Waaktaar and Sagatun, 2018[34]), peer acceptance (Plenty and la Roi, 2024[35]), as well as interventions addressing unstable living situations and poverty (Mussida and Sciulli, 2023[36]) and early intervention strategies aimed at reducing behavioural challenges (Campbell et al., 2020[37]). These factors can increase school engagement, thereby reducing the risk of NEET status (Duckworth and Schoon, 2012[38]; Schoon and Lyons-Amos, 2017[39]).

Across the three overarching spheres, Lindblad et al. (2024[2]) found that gender disparities influence the impact of the risk factor. Risks associated with having parents without employment are more pronounced for young men (Salminen and Lehti, 2023[40]), especially if their father belongs to a low social class or if their parents (or themselves) are immigrants. Other studies suggest that behavioural problems and lack of social belonging to peers have a more significant effect than emotional problems for young men and boys (Andrade and Järvinen, 2017[41]). In contrast, social/family obligations are a strong NEET determinant for women and their impact increase with age (Odoardi, D’Ingiullo and Quaglione, 2023[42]; Yi̇ği̇t, Çakmak and Çakmak, 2023[7]; Holmes, Murphy and Mayhew, 2021[43]).

The findings of Lindblad et al. (2024[2]) have policy and practice implications. With regards to risk factors associated with household factors, efforts could include additional efforts for households with a low socio-economic status or low parental labour market affiliation (Mussida and Sciulli, 2023[36]). The literature review also highlights the importance of helping young people gain self-efficacy and mental robustness. For gender disparities, increased support for young (single) parents (Dicks et al., 2022[44]), immigrant children and young people, and children exhibiting behavioural problems are identified as relevant efforts.

1.3. Geographical risk factors

According to the literature, geographical disparities do not only reflect but also amplify social and economic exclusions. The review by Lindbald et al. (2024[3]) identifies four dimensions of geographical disparities:

• Urban-Rural Divide: Young people in rural areas face a heightened NEET risk due to limited access to educational institutions and employment (Grinevica and Rivza, 2015[45]; Helgesson et al., 2018[46]; Myhr et al., 2017[47]), highlighting how access to opportunities affects young people’s life chances and demonstrating the disadvantage of young people living in rural areas in transitioning to work or higher education (OECD, 2018[48]).

• Neighbourhood Effects: Living in disadvantaged neighbourhoods emerges as a critical factor affecting the NEET likelihood (Brännström and Rojas, 2012[49]; Karyda and Jenkins, 2018[50]; Karyda, 2020[51]). High crime rates, low levels of educational, and poor infrastructure create an environment where young people are more likely to drop out of school and struggle to find work. The findings of these studies align with broader research on neighbourhood effects, which shows how socio-spatial environments can reinforce existing patterns poverty and exclusion (Sampson, Morenoff and Gannon-Rowley, 2002[52]).

• Local Labour Market Conditions: Local labour market conditions, including the availability of jobs and the nature of employment opportunities, were found to be crucial in determining the NEET likelihood (von Simson, 2015[53]). In regions with high unemployment rates and limited job prospects, young people encounter substantial barriers to entering the workforce, a challenge that is exacerbated for those without higher education qualifications (Eurofound, 2016[54]).

• Socio-Geographical Dynamics: Socio‑economic status, immigration background, and educational attainment all tend to intersect with geographical location to affect the NEET risk (Brattbakk and Wessel, 2013[55]; Gustafsson, Katz and Österberg, 2017[10]; Myhr et al., 2017[47]; Helgesson et al., 2018[46]; Karyda and Jenkins, 2018[50]; Lallukka et al., 2019[56]; Karyda, 2020[51]). This finding supports the idea of spatial inequality, suggesting that socio‑economic differences are strongly tied to geography, influencing young people’s opportunities depending on where they live (Dorling, 2019[57]).

The literature review by Lindbald et al. (2024[3]) emphasises the importance of regional economic development in shaping youth engagement in the labour market. There is a need for targeted educational initiatives, particularly in rural and disadvantaged urban areas, through investments in digital learning platforms and infrastructure improvements. Localised employment schemes, such as apprenticeships and entrepreneurship support, also have strong potential to create job opportunities and engage young people in meaningful work. Finally, the impact of neighbourhood effects on the NEET risk suggests that integrating youth services, recreational facilities, and crime reduction programmes into urban planning can significantly improve outcomes for young people in disadvantaged areas.

1.4. Mental health risk factors

The literature finds that both mental disorders and overall poor mental health status are strongly correlated with an increasing NEET risk, as well as with adverse education and employment outcomes in early adulthood (Lindblad et al., 2024[4]).

Several studies identify a positive association between Attention Deficit Hyperactive Disorder (ADHD) and the risk of being NEET (Fleming et al., 2017[58]; Hakulinen et al., 2019[59]; Halmøy et al., 2009[60]; Helgesson et al., 2023[61]; Riglin et al., 2023[62]). Young people who receive medication for ADHD are significantly worse off compared to their peers without ADHD, both in terms of NEET status and their overall health condition (Fleming et al., 2017[58]), and they have a higher risk of getting a disability pension (Helgesson et al., 2023[61]). However, Halmøy et al. (2009[60]) reveal that early recognition and early treatment of ADHD have a positive impact on future employment among young individuals with ADHD.

Young people with severe mental disorders, like psychotic disorders or personality disorders, are found to be at an increased risk of dropping out of upper secondary school or being unemployed (Hakulinen et al., 2019[59]; Liu et al., 2023[63]; Ringbom et al., 2023[64]; Spangenberg, Ramklint and Ramirez, 2023[65]), including those with schizophrenia (Holttinen et al., 2023[66]). Other specific mental conditions such as autism (Ringbom et al., 2023[64]; Toft et al., 2021[67]), social phobia (Amin, Svedberg and Narusyte, 2019[68]), and borderline personality disorder (Halling Hastrup et al., 2022[69]) are all associated with an increased risk of dropout or unemployment.

In addition to mental disorders, overall poor mental health status is positively correlated with an increased risk of adverse education and employment outcomes in early adulthood. Across the reviewed studies, it is consistently observed that young people with common mental conditions such as emotional challenges, depression, or anxiety are more likely to fail to complete upper secondary education and lack employment opportunities (Andersén et al., 2018[26]; Arvidsson, Widén and Tideman, 2015[70]; Bania, Eckhoff and Kvernmo, 2019[71]; Chen et al., 2024[72]; de Groot et al., 2021[73]; Hale and Viner, 2018[74]; Helgesson et al., 2018[46]), (Jensen, van der Wel and Bråthen, 2023[75]; Mikkonen et al., 2018[76]; Huegaerts, Wagener and Vanroelen, 2020[77]).

On a positive note, Tayfur et al. (2022[32]) suggest in their study that school‐based interventions during secondary education focusing on psychosocial factors, particularly physical activity could be helpful in supporting adolescents with mental health issues in their transition from education to work. A Spanish study also underscores the significance of teachers being knowledgeable about mental disorders (Granada-López et al., 2023[78]). Andersén et al. (2018[26]) and Chen et al. (2024[72]) recommend a strong focus on early assessment and self‐efficacy in the vocational rehabilitation of young adults with disabilities to support their transition and integration into the labour market. Finally, Mattila‐Holappa et al. (2018[79]) reveal an unmet need for psychotherapeutic interventions, in particular among young men, young people with lower socio-economic status, and young people with poor attachment to the labour market.

1.5. Physical health risk factors

According to the literature, physical health factors contributing to an increased NEET probability can be categorised into two primary domains: i) birth-related factors and congenital conditions, and ii) health conditions experienced during childhood and adolescence (Lindblad et al., 2024[5]).

With respect to the first group, premature birth (Cooke, 2004[80]; Lallukka et al., 2019[56]) and low birth weight (Kristensen, Bjerkedal and Irgens, 2004[81]; Kristensen et al., 2005[82]) have been consistently associated with higher unemployment rates in early adulthood. Rare physical health conditions also appear to be linked to a halving in the odds of having a full-time job (Ayorech, Plomin and von Stumm, 2019[83]), and young individuals who underwent early life (under age four) operations have been later found to have double the unemployment rate of the average population (Hövels-Gürich et al., 2023[84]). Finally, congenital heart disease (Mikkonen et al., 2018[76]) and cerebral palsy (Mikkonen et al., 2018[76]; Pedersen et al., 2023[85]) show strong associations with higher school dropout rates.

Health conditions during childhood and adolescence, such as digestive disorders and type 1 diabetes, are also found to heighten the risk of NEET status during adolescence (Calsbeek et al., 2006[86]; Kristensen et al., 2005[82]; Liu et al., 2023[63]; De Ridder et al., 2013[87]), and adolescents with disabilities appear to face more challenges both in education attainment and employment (Abebe, Helseth and Andenaes, 2019[88]). Cancer survivorship also poses obstacles to employment later in life (Dionisi-Vici et al., 2022[89]; Frobisher et al., 2017[90]), although one study could not find an association between previous cancer diagnosis and unemployment (Baughan et al., 2023[91]).

Common conditions like overweight (De Ridder et al., 2013[87]) and musculoskeletal complaints are positively linked to the NEET status (Homlong, Rosvold and Haavet, 2013[92]), while epilepsy (Fleming et al., 2019[93]; Frobisher et al., 2017[90]), liver and renal transplantation (Dommergues et al., 2010[94]; Haberal et al., 2000[95]), and exposure to anaesthesia early in life (Glatz et al., 2017[96]) are found to negatively influence educational outcomes. Multimorbidity, particular among refugees, is correlated with labour market marginalisation, with varying risks associated with different diagnosis (Chen et al., 2021[97]). A range of other physical health conditions also lead to an increased NEET risk, including musculoskeletal problems, rheumatic diseases, neurological diseases, etc. (Bania, Eckhoff and Kvernmo, 2019[71]; Boros et al., 2022[98]; Díaz-Mendoza, Modesto Caballero and Navarro-Cendejas, 2015[99]; Hövels-Gürich et al., 2023[84]; Kessel et al., 2022[100]; Mikkonen et al., 2018[76]).

The literature review by Lindblad et al. (2024[5]) underscores the profound impact illnesses during childhood and adolescence can have on educational and vocational outcomes and highlights the need for targeted interventions and support for affected individuals. Díaz-Mendoza, Modesto Caballero and Navarro-Cendejas (2015[99]) emphasise the importance of psychological support and vocational guidance in the management of patients, while Fleming et al. (2019[101]) propose educational interventions that mitigate the adverse effects of absenteeism. Abebe, Helseth and Andenaes (2019[88]) indicate that interventions addressing mental well-being and social competence can reduce the development of socio‑economic inequalities among young people with disability. Finally, Boros et al. (2022[98]) stress the importance of including the patient perspective in the assessment of long-term outcomes and relevant support strategy.

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