not in education employment or training

Youth not in employment, education or training (NEET)

This indicator presents the share of young people who are not in employment, education or training (NEET), as a percentage of the total number of young people in the corresponding age group, by gender.

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This indicator presents the share of young people who are not in employment, education or training (NEET), as a percentage of the total number of young people in the corresponding age group, by gender. Young people in education include those attending part-time or full-time education, but exclude those in non-formal education and in educational activities of very short duration. Employment is defined according to the OECD/ILO Guidelines and covers all those who have been in paid work for at least one hour in the reference week of the survey or were temporarily absent from such work. Therefore NEET youth can be either unemployed or inactive and not involved in education or training. Young people who are neither in employment nor in education or training are at risk of becoming socially excluded – individuals with income below the poverty-line and lacking the skills to improve their economic situation.

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• Education and skills

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NEET Support - Not in Education, Employment or Training

The Education People provide the strategic leadership for reducing the number of young people classified as Not in Education, Employment or Training (NEETs) across the county, on behalf of Kent County Council.

The Skills & Employability service chairs the NEET Interdependencies Group that is made up of local authority and third-party services who support young people. This includes:

• Youth Justice
• Virtual School Kent (Looked After Children)
• Elective Home Education
• Children Missing Education
• Management Information Services

The group meets termly to review the annual NEET and Not Known Tracking Action Plan.

NEET Support Service

Who are the neet support service >>.

Our NEET Support Service helps young people aged 16 to 18 years old who are not participating in any form of education, employment or training. Last year they supported 1444 young people to progress into a positive destinations.

Here are some examples of the one to one support they offer:

• exploring options and pathways
• raising aspirations and challenging stereotypes
• looking for apprenticeships
• job searching and applications
• researching local and online training options
• support for other issues that might prevent young. people accessing education, employment or training and signposting to other support services as required.

The team take referrals from our Engagement and Tracking Officers, schools, colleges and other local authority or voluntary services that support young people.

Get in touch with the NEET Support Service >>

"Our aim is to help young people to  explore their options and develop a plan for their next steps towards independence, as well as have confidence in their long term goals. Our work is led by the young person's needs and we  seek to support them to overcome barriers and start their journey towards living as independent adults."

Meet the NEET Support Service

Thomas Campbell - NEET Service Manager

Tom has worked with young people for over 25 years. This has included working as a carer for young people with behavioural and learning difficulties... >>

Tony Hollingdale - Deputy NEET Service Manager

Tony has 13 years experience of working and managing staff to support young people and adults into positive outcomes. >>

Jane Batchelor - Senior NEET Support Worker

Jane has multiple years of experience working with young people in Kent in both education and community settings, >>

Rachael Howell - Senior NEET Support Worker

Rachael has been working with young people since 2007, supporting them into education, training and employment. >>

Baljit Dhillon-Sealey - NEET Support Worker

Bal is an experienced member of staff with over 25 years’ experience of working with young people in Kent and Medway. >>

Carley Heyes - NEET Support Worker

Carley has over 15 years’ experience working with children, young people, and their families. >>

Pauline Payne - NEET Support Worker

Pauline has worked with young people and their parents/carers for over 20 years to support them to achieve their goals and aspirations. >>

Vali Salari - NEET Support Worker

Vali has 14 years’ experience of working with young people and young people services. He has worked with 'looked after' children... >>

Claire Smith - NEET Support Worker

Claire works with young people who are 16 to 18 years old to support them into employment or further education... >>

Viv Snaith - NEET Support Worker

For the last 30 years Viv has been working with young people, starting as an Adviser and Placement Officer... >>

Karen Warburton - NEET Support Worker

Karen has worked in the careers and guidance sector careers for over thirty years in school, community and specialist settings. >>

Debbie Greensmith - NEET Support Worker

Debbie has worked in secondary education for the last thirteen years... >>

Bethanie Piper-Morris - NEET Support Worker

Bethanie has a background of working with and supporting children and young people of a range of ages in several different counties and contexts. >>

an LSA and then teacher in a special school, as a foster carer, careers adviser, Community Support PA, NEET Champion, team leader and a manager.

After leaving school with few qualifications, Tom worked in and ran a construction business but as the years progressed, he began to look for something more. After a chance stroll into a careers office, he spoke to a careers adviser who inspired him to go to university – something he previously felt was out of reach. From this point he realised the importance of/and huge positive impact that professional guidance can have.

Having gained a degree in Archaeology, he has since gained further higher education (HE) qualifications related to supporting young people, education and management. These include a post graduate certificate in education (SEN), a foundation degree, a level 5 qualification in management and leadership, as well as various HE certificates and diplomas related to supporting young people and staff.

He has also, in the past, volunteered as an education support worker for adults with special needs and as a Parish Councillor for 8 years.

Tom’s current role is to oversee operational management of staff, external stakeholders and contract commissioners of The Education People’s NEET Support Service (Delivered on behalf of Kent County Council). This role is wide ranging and very rewarding and involves partnership, project and strategic management. Tom likes to maintain his work with young people and often holds a small caseload which he enjoys and helps him to understand the changing of needs and culture of young people.

Tony leads on all of the South and East Kent District NEET Network meetings as well as coordinating the link work between the NEET Support Service and the Post 16 SEND team at Kent County Council, working to ensure NEET young people with an EHCP get appropriate advice and guidance. Qualified at Level 6 in Careers Guidance, Tony is passionate about supporting staff and enabling young people and vulnerable groups to overcome barriers to progress to education, employment and training.

Previous roles in the sector have included managing the Specialist Mentoring teams in both the 'Talent Match' and 'Launch Pad' lottery funded support programmes. Before moving to the NEET Support Service in 2020 , Tony was the Project Manager for the innovative 'Working Heads' programme, supporting jobseekers with creating video CVs for a unique portal which employers used for recruitment.

Jane has backed up her experiences with professional qualifications including careers advice and guidance.

Jane has worked with a number of young people from a variety of backgrounds and facing different barriers. However, this can not always be done solo therefore has built good relationships with other professionals and is happy to include parents /carers in her work.

Within her current role as a NEET Support Worker, Jane works predominantly in the Folkestone and Hythe district with those aged 16 to 18 helping them to find education, employment or training. She is keen to show that every young person has the potential to do well but some may just need a little more help along the way.

She started as an Intensive Support Worker within Kent secondary schools, while completing a foundation degree in 'Working with Young People and Young People’s Services'.

Knowing that information and guidance is key to success, Rachael strives to always work towards the goals her young clients want to achieve, enabling them to gain skills and experience needed to reach their aspirations.

Rachael loves her role within the NEET Support Service, being a sounding board for young people and parents and helping them to figure out their goals, barriers and pathways to their success.

She has a proven track record of working with all age groups to support, guide and assist people who have daily barriers back into education or employment.

She previously worked in London. Working with hard to reach/disadvantaged young people who were ‘at risk’ or ‘vulnerable’ such as ex-offenders, missing in education, teenage pregnancy, mental health, homeless and addictions. She facilitated CV & job workshops, working from various youth clubs breaking down barriers, tackling housing, anti-social behaviour, drugs, crime, and sexual health, reducing crime figures for young people.

Bal now works with 16 to 18 year olds in North Kent, carrying out initial assessments, action plans and reviews their journey along the way. She has a non-judgmental, client-centred approach, offering advice, guidance and support. She has strong networks across Kent and works closely with various agencies such as Kent Police, Social Services, Early Help to name but a few.

She is also the Exploitation Champion and designated safeguarding lead for the team. Delivering Safeguarding & Exploitation Training both internally and to external agencies. The training covers many subjects which include spotting the signs of exploitation, gang culture within Kent, FGM, Domestic Violence etc.

Bal loves to see young people succeed and is a strong believer that everyone can learn something new daily. Her proven work history shows that she is passionate about helping young people make a smooth transition to from NEET to EET (in education, employment or training).

She has worked on projects supporting young people to gain qualifications and develop new skills and led parenting support and domestic violence groups. She has worked in schools, pupil referral units and the community providing group sessions and 1 to 1 support.

Carley enjoys working with young people to help them explore their plans for the future and overcome any barriers that they may face along the way.

This role has continued to evolve and she is passionate about providing a positive experience for young people and empower them to make informed decisions regarding their future.

Pauline has years of experience supporting those that are affected by barriers to learning and strives to achieve positive outcomes for clients that are not only achievable, but also add value to their own goals and aspirations for their future career.

for three years as well as young people with Special Educational Needs.

Vali has a degree in 'Working With Young People and Young People Services' and has a Level 4 Careers Guidance qualification. Vali has been working with NEET young people for many years and has been involved with delivering engagement programmes at Canterbury College since 2010. With an established network of support, Vali has built relationships with the IAG team and many other departments at East Kent College; especially Canterbury College.

Vali has great knowledge of the needs of young people in the Canterbury district as well as opportunities for young people to gain support to progress. Vali is passionate about supporting NEET young people and enabling them to overcome barriers to progress to education, employment and training.

priding herself on being able to listen to and understand the needs of the young people she works with.

Claire is a qualified counsellor and volunteers at a counselling service in her own time. Therefore she has a vast knowledge and understanding of mental health issues, which she brings to her role as a NEET Support Worker. Claire has particular skills in supporting young people who experience anxiety and low mood. Claire can draw upon her skills in mentoring, coaching, motivational interviewing and solution focussed approaches to help young people realise their potential and take the (often scary) first steps towards their goals.

For more than 20 years, Claire has directly supported children and young people. She started as a teaching assistant in a mainstream secondary school after completing her degree in Early Childhood Studies. Later, she worked as a teaching assistant in a hospital school, providing one-on-one and small group support to students. Claire then joined the Connexions service, where she served as an advisor, focusing on supporting young people with low attendance or those at risk of becoming NEET in school settings. Her interest in mental health grew during this time, leading her to develop and deliver small group sessions to boost young people's confidence and self-esteem. Claire earned her Foundation Degree in working with young people and young people's services during her tenure as a Connexions adviser, incorporating many effective engagement approaches into her daily work.

Claire loves working with young people directly and enjoys helping them on their journey to achieve their goals.

with a local training provider. 22 years ago, she piloted the Intensive PA Role with Connexions Kent and Medway. In her role, she has worked in schools, the Youth Offending Service, and led targeted groups like 'Young Mums,' 'Parent Project,' and with young people on the verge of offending behaviour.

Viv took on the challenge of an intensive role within the Youth Offending Team, focusing exclusively on young offenders, their parents and local resources to help them find suitable training or employment. She also co-ran a solution-focused therapy group with a trained counsellor for parents of young offenders during this period.

Later in her career with the Youth Offending Service, Viv was given the added responsibility of overseeing all young offenders in the North and West areas of Kent serving custodial sentences. This involved regular home and prison visits to facilitate a smooth transition back into the community, and coordination with police and social workers to ensure this process.

In more recent times, Viv took on the role of NEET Support Worker covering North Kent. In this varied position, she provides advice and guidance to vulnerable young people seeking training and employment. She maintains strong connections with Youth Justice North, Social Services, Early Help, training providers, and the local college.

Qualifications include Diploma, Foundation Degree, National Supervision Certificate and Restorative Justice qualification.

Karen has a degree and a careers guidance qualification alongside additional specialist qualifications in supporting young people with mental health issues and clients on the autistic spectrum.

Karen worked for Connexions in Bromley as a schools careers adviser and then a NEET worker. Karen also worked as an Information specialist in schools and careers libraries and was responsible for writing publications for South London boroughs for dissemination to all pupils. Karen also spent time as an employment specialist working alongside local businesses to generate job and training roles for young people.

Karen has been in her current role as a NEET support worker for nine years and has worked across the North, East and West areas of Kent. Currently covering Maidstone, Tonbridge and Tunbridge Wells districts, Karen supports young people to progress into education, employment and training opportunities. This involves working with parents, carers, other professionals and a variety or external support agencies to ensure positive outcomes.

In her current role as a NEET Support Worker, Debbie works with young people in North Kent to help them find education, training and employment as well as other organisations that may be of help to them.

Debbie enjoys working with young people as no one day is ever the same and there are always challenges that make her think outside of the box.

One of her most notable achievements, is her First Class Degree in Education Studies which provided her with opportunities to develop her knowledge and experience in both school and prison education environments. She has also worked in a healthcare environment supporting young people experiencing mental health challenges, through therapeutic and educational activities. This role spanned over the recent pandemic which caused numerous challenges both to the staff and young people in their care, but also enabled the opportunity of developing greater resilience and creativity in her work.

Bethanie believes that with the right support any individual can learn and achieve great things regardless of background, status or perceived ability. Through her own experience of not being diagnosed with dyslexia until studying at university, she sees the importance in being able to listen to and identify a learner’s needs on a holistic level and then putting the measures in place to help them thrive.

Bethanie’s role within The Education People enables her to support young people who aren’t quite sure what they would like to do next and to help them explore their options. She works alongside individuals and their families to learn about their educational experiences, their interests and goals, in order to identify positive ways of moving forward. Bethanie enjoys her role as she gets to see young people taking steps towards embracing the possibilities of their future.

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Youth and the labour market

Youth not in employment, education or training (neet).

Participation in employment, education or training is important for youth to become established in the labour market and achieve self-sufficiency. Record high unemployment rates in a number of countries have hit youth especially hard. This has resulted in many youth unable to find work and in other youth withdrawing from the labour market entirely, becoming “inactive”.

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This indicator presents the share of young people who are not in employment, education or training (NEET), as a percentage of the total number of young people in the corresponding age group, by gender. Young people in education include those attending part-time or full-time education, but exclude those in non-formal education and in educational activities of very short duration. Employment is defined according to the OECD/ILO Guidelines and covers all those who have been in paid work for at least one hour in the reference week of the survey or were temporarily absent from such work. Therefore NEET youth can be either unemployed or inactive and not involved in education or training. Young people who are neither in employment nor in education or training are at risk of becoming socially excluded – individuals with income below the poverty-line and lacking the skills to improve their economic situation.

• NEET Youth in the Aftermath of the Crisis: Challenges and Policies
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Author(s) OECD

Share of young people not in education, employment or training

What you should know about this indicator, how is this data described by its producer.

Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups.

World Bank variable id: SL.UEM.NEET.ZS

Original source: International Labour Organization, ILOSTAT database. Data retrieved in March 1, 2020.

Aggregation method: Weighted Average

Statistical concept and methodology: The standard definition of unemployed persons is those individuals without work in a recent past period, and currently available for and seeking for employment. But there may be persons who do not actively "seek" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. NEET rates capture more broadly untapped potential youth, including such individuals who want to work but are not seeking work (often called the "hidden unemployed" or "discouraged workers").

Youth are defined as persons ages 15 to 24; young adults are those ages 25 to 29; and adults are those ages 25 and above. However, countries vary somewhat in their operational definitions. In particular, the lower age limit for young people is usually determined by the minimum age for leaving school, where this exists.

Limitations and exceptions: Data should be used cautiously because of differences in age coverage.

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The World Bank EdStats database offers a comprehensive array of over 8,000 internationally comparable indicators related to education access, progression, completion, literacy, teachers, demographics, and expenditures. It covers the education cycle from pre-primary to vocational and tertiary education, including data on learning outcomes from assessments like PISA, TIMSS, PIRLS, equity data from household surveys, and educational projections up to 2050.

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The mental health of young people who are not in education, employment, or training: a systematic review and meta-analysis

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• Volume 57 , pages 1107–1121, ( 2022 )

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• Geneviève Gariépy   ORCID: orcid.org/0000-0001-9209-4240 1 , 2 ,
• Sofia M. Danna   ORCID: orcid.org/0000-0003-4861-8400 3 ,
• Lisa Hawke   ORCID: orcid.org/0000-0003-1108-9453 4 ,
• Joanna Henderson   ORCID: orcid.org/0000-0002-9387-5193 4 &
• Srividya N. Iyer   ORCID: orcid.org/0000-0001-5367-9086 5 , 6  

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There are increasing concerns about the intersection between NEET (not in education, employment, or training) status and youth mental ill-health and substance use. However, findings are inconsistent and differ across types of problems. This is the first systematic review and meta-analysis (PROSPERO-CRD42018087446) on the association between NEET status and youth mental health and substance use problems.

We searched Medline, EMBASE, Web of Science, ERIC, PsycINFO, and ProQuest Dissertations and Theses (1999–2020). Two reviewers extracted data and appraised study quality using a modified Newcastle–Ottawa Scale. We ran robust variance estimation random-effects models for associations between NEET and aggregate groups of mental ill-health and substance use measures; conventional random-effects models for associations with individual mental/substance use problems; and subgroup analyses to explore heterogeneity.

We identified 24 studies from 6,120 references. NEET status was associated with aggregate groups of mental ill-health (OR 1.28, CI 1.06–1.54), substance use problems (OR 1.43, CI 1.08–1.89), and combined mental ill-health and substance use measures (OR 1.38, CI 1.15–1.64). Each disaggregated measure was associated with NEET status [mood (OR 1.43, CI 1.21–1.70), anxiety (OR 1.55, CI 1.07–2.24), behaviour problems (OR 1.49, CI 1.21–1.85), alcohol use (OR 1.28, CI 1.24–1.46), cannabis use (OR 1.62, CI 1.07–2.46), drug use (OR 1.99, CI 1.19–3.31), suicidality (OR 2.84, CI 2.04–3.95); and psychological distress (OR 1.10, CI 1.01–1.21)]. Longitudinal data indicated that aggregate measures of mental health problems and of mental health and substance use problems (combined) predicted being NEET later, while evidence for the inverse relationship was equivocal and sparse.

Our review provides evidence for meaningful, significant associations between youth mental health and substance use problems and being NEET. We, therefore, advocate for mental ill-health prevention and early intervention and integrating vocational supports in youth mental healthcare.

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Introduction

Transitioning from education into work is a milestone of emerging adulthood that about one in seven young people in economically developed countries struggle to attain [ 1 ], falling into the category of NEET—not in education, employment, or training. Concerns over these youth are growing worldwide [ 2 , 3 ]. The term NEET was coined in a 1999 report called “Bridging the Gap” from the United Kingdom [ 4 ]. By 2019, between 5.6% (Luxembourg) and 28.8% (Turkey) of 15 to 29-year-olds in Organisation for Economic Co-operation and Development countries were NEET [ 1 ]. Economic fallouts of the COVID-19 pandemic are expected to swell these numbers [ 3 ], as even early on in the pandemic, data showed that NEET rates were higher in the second quarter of 2020 than the previous year in 45 out of 50 countries [ 5 ]. Youth who are NEET are considered vulnerable as they face social exclusion and disempowerment, and disproportionately come from disadvantaged backgrounds [ 6 , 7 ]. Being outside school and the workforce limits their ability to gain skills and experience that could improve their prospects [ 8 , 9 , 10 ].

Being NEET is intertwined with mental health and substance use problems in young people. Studies have linked being NEET with the emergence of symptoms of depression, anxiety, substance use, and suicidality [ 11 , 12 , 13 , 14 , 15 ]. Conversely, mental health and substance use problems can deplete the drive and energy needed to enter the workforce or continue education/training and increase the risk of becoming NEET. However, the link between being NEET and poor mental health is unclear. Cross-sectional relationships are not always supported by longitudinal data [ 16 , 17 ] and there are indications that the relationship differs by type of mental health or substance use problem [ 14 , 18 ]. Furthermore, the association between being NEET and mental health problems may also differ in strength and significance across gender, depending on mental health problem [ 19 , 20 ]. For instance, Henderson [ 19 ] found that the association between internalizing disorders and being NEET was significant in only men. For externalizing disorders, however, the association with NEET status was significant for both men and women.

Previous reviews have reported on the association between mental health problems and youth unemployment [ 21 , 22 ] and school disengagement [ 23 , 24 ], but none investigated youth disengaged from both work and school. One narrative review examined the correlates of being NEET [ 6 ], but with little in-depth information on mental health. A synthesis of the literature is needed to inform the discussion on the growing youth population who are NEET and its intersection with youth mental health and substance use problems. This information is also needed to develop intervention studies and effective strategies to promote youth engagement in employment, education, and training.

Our primary objective was therefore to systematically review and synthesize via meta-analysis the literature on the associations between being NEET and mental health and substance use problems among youth. We expected NEET status to relate to mental ill-health measures, substance use measures, and all measures of mental ill-health and substance use combined; and the associations to vary across mental health and substance use problems. Our review thus extends the literature by focussing on youth disengagement from both education and employment and by examining the strength and consistency of associations across types of mental health and substance-use problems. Our secondary objectives were to investigate the directionality of the associations from longitudinal data and to examine subgroup differences by gender, age, and population-based versus clinical samples. We expected the association between NEET status and mental health and substance use problems to be bidirectional and to differ in strength by gender. We were agnostic as to differences by age and sample type.

Search strategy

We followed MOOSE reporting guidelines [ 25 ]. We searched Medline, EMBASE, ISI Web of Science, ERIC, PsycINFO, and ProQuest Dissertations and Theses Online from January 1, 1999 to May 2020, imposing no language restriction (see MEDLINE search strategy in Supplementary Appendix 1). We limited searches to the last 20 years because our objective was to synthesize contemporary knowledge of policy and practice relevance. The study is registered through PROSPERO (CRD42018087446) [ 26 ].

Selection criteria

We included observational studies with individual-level data that estimated the association between being NEET and mental and/or substance use symptoms or disorders among persons aged 15–34 years. We chose this age range to accommodate internationally diverse definitions of youth [ 27 ]. Studies had to identify NEET status by explicitly querying work and education or training status. Measures included those for any specific or general mental or substance use disorder; psychological or behavioural problems; psychological distress or well-being; or suicidality, measured on a dichotomous or continuous scale of symptoms, severity, or score. We excluded neurodevelopmental disorders and disabilities typically diagnosed in childhood (e.g., autism, intellectual disability) since we expected developmental and learning problems to have a unique association with becoming NEET. We excluded abstracts but considered unpublished studies if information was available for data extraction and quality assessment. We searched references of primary studies and review articles for additional studies. All references were uploaded to Covidence software [ 28 ].

The screening of titles and abstracts followed by screening of full texts for inclusion and exclusion criteria; data extraction from eligible studies (see data extraction form in Supplementary Appendix 2); and quality assessment of studies using a modified Newcastle–Ottawa Scale [ 29 ] (see description in Supplementary Appendix 3) were done independently by two reviewers, including first author/epidemiologist GG and either a psychiatry graduate student or a research assistant with a Master’s in mental health epidemiology (SD). Disagreements were resolved by consensus or by author SI. We emailed study authors for further information where necessary.

Data analysis

We conducted meta-analysis to quantitatively synthesize the literature. We ran robust variance estimation (RVE) random-effects models to obtain associations between NEET status and three aggregate groups; namely, mental ill-health (comprised of psychological distress, mood disorders, anxiety disorders, and behavioural disorders); substance use problems (comprised of alcohol, cannabis disorder, and drug use disorders); and all measures of mental ill-health and substance use problems taken together (comprised of the measures included in the mental ill-health and substance use groups, any other disorder, and suicidality). RVE allowed us to pool statistically dependent estimates (i.e., multiple estimates that are correlated because they arise from the same participant samples) into estimates incorporating all relevant measures for these aggregated groups without having to know or specify their covariance structures. Additionally, our analyses benefit from small-sample correction, which has been argued as necessary to implement when using RVE hypothesis testing [ 30 ]. It is important to note that, among small samples, hypothesis testing using RVE requires degrees of freedom be greater or equal to four to be accurate. Below four degrees of freedom, the t distribution approximation on which testing is based no longer holds, and the type I error will be greater than indicated by the p value being used [ 31 ]. We also conducted conventional random-effects models to obtain associations between NEET status and individual mental health and substance use problems. Forest plots were generated to display all main meta-analyses described above.

Our pooled results should be interpreted cautiously, given the highly heterogeneous study methodologies. We used the odds ratio (OR) as a summary measure since most studies with available quantitative data reported ORs. When multiple studies used the same dataset, we only included the study with the largest sample size to avoid double counting. For studies that only provided gender-stratified results, we combined the results using a fixed-effects model to include in the main meta-analysis. For studies that only reported a p value < 0.001, we calculated a confidence interval (CI) assuming a conservative p value of 0.001. For studies reporting only a p value > 0.05, we assumed a conservative p value of 0.10. All intervals reported are 95% CIs.

To explore sources of heterogeneity, we conducted subgroup analyses by gender, age group (< 18 vs ≥ 18 years old), and sample type (population-based vs clinical). Moreover, we investigated the potential directionality of associations from longitudinal studies that examined NEET status as a predictor of later mental health and substance use problems, and studies that examined the inverse relationship. We used fixed-effects models for subgroup analyses because there were too few studies by subgroup to estimate between-study variance with precision [ 32 ]. Study heterogeneity was evaluated using the I 2 index. We did not assess publication bias using quantitative methods because these are not recommended under conditions of high heterogeneity [ 33 ]. Meta-analyses were conducted in R (v3.6.1) using the meta, metafor, and robumeta packages [ 30 , 34 , 35 ].

From 6120 identified references, we included 24 studies (see PRISMA flow diagram in Fig.  1 ), which represented 548,862 unique individuals from the UK ( k  = 6 studies); Australia ( k  = 4, two using the same sample); Mexico ( k  = 3, all using the same sample); Sweden ( k  = 3); Italy ( k  = 2) [ 13 , 36 ]; Canada ( k  = 2); Brazil ( k  = 1); Norway ( k  = 1); Ireland ( k  = 1); Switzerland ( k  = 1); and Greece ( k  = 1). Study characteristics, detailed study findings, and quality assessment ratings appear in Table 1 and Supplementary Appendices 4 and 5, respectively. For 11 studies, the age range of the sample at baseline was under 18 years; three studies only included samples above the age of 19; and 10 studies included samples that were both below and above 18 years of age (e.g., 15–25). Cohort studies were most common ( k  = 13), followed by cross-sectional ( k  = 10) and case–control ( k  = 1) studies. Being NEET was associated with at least one measure of mental health or substance use problems in 75% of studies (18/24). Study quality was low in five studies; moderate in nine studies; and high in 10 studies. Measures of mental health and substance use problems included problems or symptoms of mood ( k  = 12), anxiety ( k  = 11), behaviour ( k  = 8), alcohol use ( k  = 9), cannabis use ( k  = 6), and drug use ( k  = 8) disorders, general psychological distress ( k  = 9), suicidal behaviours ( k  = 7), and any psychiatric disorder ( k  = 5).

PRISMA flow diagram

4 out of the 24 studies were excluded from meta-analyses of odds ratios because two reported chi-squared statistics [ 13 , 36 ] and 2 reported beta coefficients from linear regressions [ 11 , 37 ]. Further, two pairs of studies used the same data to calculate the same estimates [ 15 , 16 , 38 , 39 ]. We only included one study from each pair in the RVE meta-analyses [ 38 , 39 ].

The meta-analyses found significant associations between NEET and all three aggregated groups, i.e., mental health problems ( k  = 15 studies; n  = 25 effect sizes, OR 1.28, CI 1.06–1.54; see Supplementary Appendix 6 for forest plot); substance use problems ( k  = 11, n  = 16, OR 1.43; CI 1.08–1.89; see Supplementary Appendix 7 for forest plot); and all measures of mental health problems, substance use problems, and suicidality combined ( k  = 18, n  = 48, OR 1.38; CI 1.15–1.64).

Table 2 presents summaries of findings by type of mental health or substance use problem and Fig.  2 presents forest plot by each type of problem. The evidence most consistently pointed to an association between NEET and symptoms of mood disorders [ 12 , 14 , 17 , 38 , 40 , 41 ] ( k  = 6 non-overlapping studies; OR 1.43, CI 1.21–1.70); behavioural disorders [ 12 , 14 , 19 , 42 , 43 , 44 ] ( k  = 6; OR 1.49, CI 1.21–1.85); cannabis use problems [ 14 , 17 , 38 , 40 , 44 , 45 ] ( k  = 6; OR 1.62, CI 1.07–2.46); drug use problems [ 12 , 14 , 19 , 40 , 46 ] ( k  = 5; OR 1.99, CI 1.19–3.31); any psychiatric disorder [ 12 , 18 , 44 ] ( k  = 3; OR 1.72, CI 1.37–2.16); and suicidal behaviours [ 12 , 14 , 18 , 40 ] (k = 4; OR 2.84, CI 2.04–3.95).

Forest plot from the meta-analysis disaggregated by mental-ill health, substance use problems and suicidality measures

The evidence was more mixed—with fewer than 50% of non-overlapping studies reporting a significant finding—for NEET status being associated with anxiety disorders [ 12 , 14 , 18 , 40 , 46 ] ( k  = 5 non-overlapping studies; OR 1.55, CI 1.07–2.24); alcohol use problems ( k  = 5; OR 1.28, CI 1.12–1.46); and psychological distress ( k  = 7; OR 1.10, CI 1.01–1.21). Results were similar when we excluded low quality studies.

Sub-group analyses

For each of the three aggregate groups, subgroup analyses were conducted for directionality, age, and gender. There was evidence of mental health problems (first aggregated group; k  = 8 studies; n  = 12 effect sizes, OR 1.33, CI 1.01–1.74) and all measures combined (third aggregated group; k  = 9; n  = 19, OR 1.39, CI 1.03–1.86) being associated with subsequent NEET status. Other subgroup analyses conducted within the three aggregated groups had too few degrees of freedom ( df  < 4) to be reliable, so results should be considered exploratory (Supplementary Appendix 8).

Directionality of association

Longitudinal data provided some evidence for bidirectional associations (see Supplementary Appendix 9 for summary of significant findings of studies by directionality of association and type of mental or substance use disorder or symptoms). Ten studies measured symptoms of mental ill-health and/or substance use problems before the emergence of NEET status. Symptoms of mood disorders [ 14 , 16 , 17 , 41 ] ( k  = 4 non-overlapping studies; OR 1.12, CI 1.07–1.18); behavioural problems [ 42 , 43 , 44 ] ( k  = 3; OR 1.25, CI 1.19–1.32); cannabis use problems [ 17 , 44 ] ( k  = 2; OR 1.10, CI 1.04–1.15); drug use problems [ 14 ] ( k  = 1; OR 1.89, CI 1.29–2.77); any mental disorder [ 18 , 44 ] ( k  = 2; OR 1.83, CI 1.27–2.62); and suicidal behaviours [ 14 ] ( k  = 1; OR 3.30, CI 2.07–5.27) were associated with later NEET status. Alcohol use disorder [ 17 , 44 ] was not so associated ( k  = 2; OR 0.80, CI 0.48–1.34), and evidence was equivocal for anxiety symptoms/disorders [ 14 , 16 ] ( k  = 2; OR 1.38, CI 0.81–2.36) and psychological distress [ 11 , 17 , 42 , 47 ] ( k  = 4; OR 1.04, CI 1.00–1.08).

Five studies examined NEET status prior to mental health and substance use outcomes. NEET status predicted later suicidal behaviours in a single study [ 15 ] ( k  = 1; OR 2.40, CI 1.32–4.31); symptoms of mood disorder in one study [ 15 ] ( k  = 1; OR 1.67, CI 1.12–1.90), but not another [ 16 ] ( k  = 1; OR 1.94, CI 0.17–21.60); and alcohol use disorder in two studies [ 15 , 48 ] ( k  = 2; OR 1.22, CI 1.12–1.32), but not another [ 17 ] ( k  = 1; p  > 0.05, values unavailable). Being NEET did not predict later symptoms of anxiety [ 15 , 16 ] ( k  = 1; OR 0.40, CI 0.10–1.65); behavioural problems [ 15 ] ( k  = 1; OR 0.83, CI 0.45–1.50); cannabis use [ 17 ] ( k  = 1; p  > 0.05, values unavailable); drug use problems [ 15 ] ( k  = 1; OR 1.03, CI 0.71–1.50); or psychological distress [ 9 , 17 ] ( k  = 2; OR 1.78, CI 0.93–3.42).

Associations by gender

Six studies conducted gender-stratified analysis [ 19 , 20 , 42 , 46 , 49 , 50 ]. In Bania et al. [ 42 ], conduct problems at age 15–16 predicted becoming NEET 9 years later among men (OR 1.17, CI 1.07–1.28) and women (OR 1.25, CI 1.17–1.33), while emotional problems were associated with lower odds of becoming NEET in men (OR 0.88, CI 0.81–0.97), but not women (OR 1.04, CI 0.97–1.11).

Hale and Viner [ 49 ] found that psychological distress at age 13 predicted being NEET at age 19 among men (OR 1.72, CI 1.24–2.41) and women (OR 1.49, CI 1.11–1.99). Bynner and Parsons [ 20 ] found that being NEET at age 16–18 did not predict psychological distress at age 21 in men (OR 2.20, p  > 0.05, CI unavailable) and women (OR 1.69, p  > 0.05).

In their cross-sectional study, Stea et al. [ 50 ] found an association between NEET and psychological distress among women (OR 2.40, CI 1.00–5.20), but not men (estimate unavailable), whereas Basta et al. [ 46 ] found no association with distress among women (OR 0.98, CI 0.95–1.02) and men (OR 0.99, CI 0.96–1.03).

Basta et al. [ 46 ] found an association between anxiety problems and NEET among women (OR 1.05, CI 1.01–1.10), but not men (OR 1.01, CI 0.96–1.03). In a cross-sectional study, Henderson et al. [ 19 ] found that being NEET was associated with substance misuse among men (OR 1.83, CI 1.43–2.34) and women (OR 2.05, CI 1.58–2.66); externalizing disorders among both men (OR 0.93, CI 0.73–1.19) and women (OR 0.87, CI 0.67–1.12); and internalizing symptoms among men (OR 1.39, CI 1.08–1.78), but not women (OR 1.08, CI 0.80–1.45).

Associations by age

We compared findings for participants who were < 18 years ( k  = 4 studies) [ 12 , 19 , 20 , 42 ] and ≥ 18 years old ( k  = 7) [ 14 , 17 , 18 , 19 , 39 , 44 , 49 ]. The association was consistent among younger (< 18 years) youth between NEET status and mood problems [ 11 , 12 ] (beta coefficient 0.0710, p  < 0.05 in one study; OR 2.70, CI 1.77–4.12 in the other study); behavioural problems [ 12 , 19 , 42 ] ( k  = 3; OR 1.24, CI 1.18–1.30); and drug use problems ( k  = 2; OR 1.69, CI 1.38–2.07). Results were weaker for psychological distress [ 19 , 20 , 42 ] ( k  = 3; OR 0.97, CI 0.92–1.03) and anxiety problems [ 11 , 12 ] ( k  = 2; OR 1.30, CI 0.92–1.84 in one study; beta coefficient = 0.07, p  > 0.05 in other study).

In youth ≥ 18 years old, there was an association with anxiety disorders [ 14 , 18 , 39 ] ( k  = 3; OR 1.59, CI 1.12–2.26), behavioural disorders [ 14 , 19 , 39 , 44 ] ( k  = 4; OR 1.32, CI 1.12–1.55); cannabis use problems ( k  = 3; OR 1.11, CI 1.05–1.16); any disorder [ 18 , 44 ] ( k  = 2; OR 1.76, CI 1.21–2.54); and general psychological distress ( k  = 3; OR 1.15, CI 1.08–1.22).

In youth ≥ 18 years old, evidence was mixed for symptoms of mood disorder [ 14 , 17 , 18 , 39 ] ( k  = 4; OR ( n  = 3) 1.14, CI 1.07–1.21; and missing OR with p  > 0.05, CI unavailable in other study); drug use disorders [ 14 , 19 , 39 , 44 ] ( k  = 4; OR ( n  = 3) 1.99, CI 1.61–2.45; and missing OR with p  > 0.05, CI unavailable in other study); and alcohol use problems [ 14 , 17 , 18 , 39 , 44 ] ( k  = 5; OR ( n  = 3) 1.32, CI 1.14–1.54; missing OR with p  > 0.05, CI unavailable in other studies).

Associations by sample type

Similar patterns of association emerged between studies using clinical [ 16 , 19 , 37 , 38 ] ( k  = 4 studies) and population-based samples [ 11 , 12 , 15 , 17 , 18 , 20 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 48 , 49 ] ( k  = 18).

In clinical studies, service-seeking youth were more likely to be NEET if they presented with mood disorders [ 16 , 38 ]; current [ 19 ] or past [ 37 ] drug use disorders; or co-occurring mental health problems [ 19 ]; but not if they had problems with alcohol or cannabis use [ 16 , 38 ], anxiety disorders [ 16 , 38 ], psychological distress [ 37 ], externalizing problems [ 19 ], or a history of any mental health diagnosis [ 37 ].

This is the first comprehensive systematic review and meta-analysis on the association between NEET status and mental health and substance use problems in youth. Being NEET was associated with mental health problems, substance use problems, and all measures combined in aggregate analyses. When disaggregated, NEET was most consistently associated with suicidal behaviours, drug use problems, any psychiatric disorders, cannabis use problems, behavioural problems, and mood problems. Findings for the association between NEET and anxiety problems, alcohol use, and psychological distress were mixed. Results were generally consistent across clinical and population-based samples but mixed across gender. These associations were particularly consistent among younger youth (< 18 years old). Longitudinal data indicated that mental health problems in early youth predicted a later NEET status, while evidence for the inverse relationship was equivocal and sparse. Together, these results point to early youth as a sensitive period for mental health and substance use problems becoming related with being NEET and increasing the vulnerability to later becoming NEET.

The aggregate analyses showed meaningful and significant associations between mental health and substance use problems in youth and being NEET. Although the overall evidence is based on a relatively limited and heterogeneous body of literature, the studies were generally of moderate to high quality. These results align with previous reviews on youth unemployment [ 21 , 22 ] and school dropout [ 23 , 24 ] that report a close connection between vocational disengagement and poor mental health. Our review extends this literature by focussing on youth disengagement from both education and employment and by revealing that the strength and consistency of associations vary across types of mental health and substance-use problems.

In our study, meta-analytical evidence from longitudinal data suggested that mental health problems and all measures of mental health and substance use (combined) predicted becoming NEET later. This evidence for their increased risk of becoming NEET aligns with the well-documented [ 24 ] drain of mental and substance use disorders on youths’ ability to perform at school and work. Disengagement from school and work may further disadvantage those with mental health problems, widening the gap between them and peers who follow more engaged developmental trajectories. Disengagement may also further heighten feelings of shame, hopelessness, and social exclusion [ 20 , 51 ]. Analyses for NEET status predicting the later occurrence of mental health problems aggregated, substance use problems aggregated, and all measures combined were not conclusive. Nonetheless, there was evidence for NEET status predicting individual mental health/substance use problems, suggesting that being out of school and work, especially in early youth, could lead to mental health and substance use problems. Regardless of the directionality, school and work can provide crucial structures and experiences that enhance feelings of belonging, productivity, and hope for the future [ 52 ].

Contrary to our hypothesis, we discerned no clear gender-based pattern in the link between mental health problems and being NEET, although the evidence base was limited and most gender-stratified studies focussed on psychological distress. Nonetheless, there is evidence that the experience of being NEET could vary by gender. For instance, young women who are NEET are more likely to be stay-at-home parents or caretakers [ 53 , 54 ]. Further, the consequences of being NEET may differ by gender. A British longitudinal study [ 20 ] found that, for young men, being NEET mainly impacted their job prospects, while for young women, it further affected their psychological well-being. To develop tailored strategies to prevent youth from becoming NEET or developing mental health problems when NEET, further research is needed into the intersections between gender and other subgroupings of vulnerability, NEET status, and mental illness.

Our review was constrained by the heterogeneity of mental health measures used in the reviewed studies, ranging from specific disorder subtypes (e.g., generalized anxiety disorder) to broad categories (e.g., any anxiety disorder) and general symptom scales. Many mental disorders like psychosis, eating disorders, and personality disorders, were not represented and few studies reported on comorbidity [ 19 ]. Divergent definitions of NEET status also limited comparability. Non-paid work like parenting counted as employment in some studies [ 14 , 44 ], but not others. Most studies measured current NEET status, but some used timeframes from 1 month [ 16 ] to 9 years [ 42 ]. By assessing NEET status but not its duration, almost all studies captured the association of both short- and long-term vocational disengagement with mental health and substance use problems. To formulate more effective interventions and policies, research into the duration of NEET status and its association with mental and substance disorders is therefore needed. These definitional, methodological, and cultural challenges of measuring NEET status and the heterogeneity of its circumstances have also been previously noted [ 27 , 54 , 55 ].

We could not examine contextual/cultural influences on being NEET and mental health problems because the reviewed studies were from a limited number of specific geographical and political backgrounds. All the studies from this review were from Europe, North America, or Australia, with the exception of one study that included data from South America, limiting the generalizability of the evidence to other contexts like low- and middle-income countries. Furthermore, global and country-specific economic shifts may exacerbate associations between NEET status and mental-ill health and deserve exploration in the future. Evidence is from observational data thereby limiting direct causal inference. While we examined longitudinal studies to assess the potential directionality of association, none of the studies used specific panel regressions models and may therefore be biased by unobserved heterogeneity. Like other reviews, our findings may be affected by publication bias. While we could review papers in English, French, and Spanish, only English studies met our search criteria. We excluded studies that focussed on neurodevelopmental disorders or disabilities that are typically diagnosed in childhood. We recognize that these disorders could co-occur with mental and substance use disorders and contribute to being NEET and may even differ in their relationship with NEET compared to other mental disorders, and therefore should be examined in future work.

Notwithstanding these limitations, the studies provided data from diverse contexts and on a range of mental health and substance use outcomes, with generally consistent results despite methodological differences. Our review carefully assessed the association between being NEET and mental health and substance use problems, an emerging topic with important clinical and public health implications. We used rigorous methodology to search, systematically assess, and analyse current literature to explain our findings. Using RVE, we appropriately pooled multiple mental health measure estimates that were correlated because they came from the same participant samples. This allowed us to capture associations between NEET and overarching groups of mental health and substance use problems that reflect a generalized relationship between youth engagement and mental-ill health. In addition, we used subgroup analysis to investigate heterogeneity, directionality of association, and vulnerable subgroups.

We identified significant knowledge gaps in NEET and mental health research. First, the association between being NEET and mental health and substance use problems is likely context-sensitive and broadening the geographic ambit of studies is strongly recommended. Second, the association is likely marked by gender differences that bear teasing out. Third, rigorous research on the temporal relationship of mental disorders and being NEET is needed because the question of directionality remains unresolved. Moreover, the associations between duration and recurrence of NEET status and mental ill-health have yet to be systematically explored. Fourth, information about some mental disorders (e.g., psychosis) and comorbid disorders and their associations with being NEET is lacking. Finally, future research should include intervention studies to identify whether and for whom vocational and mental health supports are useful in averting and ending NEET status.

Realization of the loss to productivity and the wealth of nations from unaddressed youth mental health problems is increasing [ 56 ]. Although more longitudinal research is needed, our review found clear evidence for NEET status being a consequence of mental health problems and substance misuse. Efforts to prevent young people from becoming or remaining vocationally and socially disengaged should therefore include provisions for the prevention of and early intervention for mental health problems. Furthermore, because there is also evidence for a bidirectional relationship between NEET status and mental ill-health and because problems with vocational functioning are well-documented among youth with mental health problems [ 17 , 57 ], youth mental health services should integrate educational and employment supports and services to address vocational needs and promote recovery.

The connectedness of vocational disengagement and mental health problems among young people underlines the need for consistent, widespread policy support for broader-spectrum integrated youth-focussed services [ 58 , 59 ]. Our review also highlights the importance of schools, universities, and employers developing the will and capacity to address the needs of youth experiencing mental health problems. The socioeconomic disruptions and mental health implications of the ongoing pandemic make these needs ever more urgent. Our comprehensive synthesis can serve as a useful pre-pandemic reference point for future research on the associations between youth employment/education and mental health and substance use over the course of or after the COVID-19 pandemic.

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The authors would like to thank Nina Fainman-Adelman for help with the screening and data extraction of the studies.

This work was supported by a grant and a salary award from the Canadian Institutes of Health Research (SI) and the Fonds de Recherche du Québec—Santé (GG).

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Gariépy, G., Danna, S.M., Hawke, L. et al. The mental health of young people who are not in education, employment, or training: a systematic review and meta-analysis. Soc Psychiatry Psychiatr Epidemiol 57 , 1107–1121 (2022). https://doi.org/10.1007/s00127-021-02212-8

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Home > Articles > From ‘NEET’ to ‘Unknown’: Who is responsible for young people not in education, employment or training?

Article: From ‘NEET’ to ‘Unknown’: Who is responsible for young people not in education, employment or training?

From ‘neet’ to ‘unknown’: who is responsible for young people not in education, employment or training.

First Published: 3rd September 2017 | Author: Liam Wrigley | Tags: NEET , Neoliberalism , young people

Situating his discussion in its recent historical context, Liam Wrigley examines how young people labelled as ‘NEET’ have now become ‘unknown’ or ‘lost’, arguing that this is due to a lack of clear strategy concerning actors that have been responsibilised in responding to the employment, training and welfare needs of young people.

Introduction: What is ‘NEET’?

The number of young people (between 16-24 years of age) who experience being Not in Education, Employment or Training (NEET) has been of grave concern, with the rates of young people labelled as not in education, employment or training remaining high (Simmons et al, 2014). In the UK alone, the number of young people who are NEET has fluctuated between 15% in 2002 to 11.5% in 2016 (DfE, 2017). The label NEET has been successively adopted throughout Europe and internationally (Simmons et al, 2014), although there has been great variation in how this policy label has been defined globally (i.e. some countries count unemployed young people who are graduates or in precarious work situations or ‘zero hour’ contracts). The label reflects a growing trend in recognizing young people that have fallen outside the labour market or education. Throughout Europe, the rate of NEET young people remains high, with countries such as Spain, Ireland and Italy recording more than 17% of young people as out of education, employment or training (Eurofund, 2016).

In the UK, It should be noted that the production of young people into fixed policy categories like NEET is nothing new. Changes to the labour market from 1979-1997 marked a period where young people found themselves being placed on various employment initiatives (such as the ‘Youth Opportunities Program’, ‘Youth Training Scheme’ and ‘Restart Program’) that were designed to address the problem of youth unemployment and education/ training (Furlong and Cartmel, 2007). All of these policy responses have been critiqued due to their highly neoliberal nature, where disadvantaged young people increasingly fell foul of precarious and unstable labour market conditions. Neoliberalism for the purpose of this article can be best described by Harvey (2005: 2-3) as:

A theory of political economic practices that proposes that human well-being can best be advanced by liberating individual entrepreneurial freedoms and skills within an institutional framework characterized by strong private property rights, free markets, and free trade. The role of the state is to create and preserve an institutional framework appropriate to such practices […] deregulation, privatization, and withdrawal of the state from many areas of social provision have been all too common.

In light of this, these policy responses can be summarized by the following negative attributes: low wage, lacking a tangible education outcome, lack of investment by the private companies that provided the training contracts, and lack of long term stability in employment and education for young people (Hollands, 1990; Allatt and Yeandle, 1992). The policy shift in cuts to unemployment benefit in 1988 for 16-18 year olds resulted in a noticeable change to how young people were responded to from government and policy makers.

Istance et al (1994) and colleagues had first embraced the label ‘statusZero’ in a study of young unemployed 16-19 year olds in South Glamorgan. In this study, the first reference to NEET was made by a research observation that highlighted the lack of mainstream support for young people that had fallen outside the labour market or further education and training. This was due to various factors such as: problems with schooling, issues with the local labour market, and lack of support from family and services such as the Job Centre (ibid).

The responses to NEET

The label NEET was utilised by the New Labour government in 1997 to describe the growing numbers of young people age 16-24 years of age that were at risk or had failed to transition into further education or the labour market (Furlong and Cartmel, 2007). The New Labour government set up the Social Exclusion Unit in order to tackle the problem of NEET (ibid). Tony Blair declared that:

The best defence against social exclusion is to have a job […] the best way to get a job is to have a good education, with the right training […] The young people involved are disproportionately from poor backgrounds in deprived areas […] social exclusion in later life is disproportionately the result, They [NEETs] are much more likely to be unemployed, dependent on benefits, to live in unstable family structures and to be depressed about their lives – (Social Exclusion Unit, 1999:6-8)

The particular set of circumstances leading to an individual meeting the criteria of NEET are varied, and research in this area has brought to attention that young people who experience being NEET are far from being a homogenous group, despite the New Labour Government maligning responsibility onto young people to have ‘good education’ (ibid), irrespective of the great difficulty that would be faced in achieving such.  Much of the research surrounding NEET policy has focused on failed or disbanded interventions strategies such as ‘Connexions’ that was introduced under the Social Exclusion Unit (Yates and Payne, 2006). The Connexions strategy attempted to deal with the complexities of NEET, but ultimately became a ‘firefighting approach’ (Nudzor, 2010: 18) which evidence suggests attempted to tackle multiple sources of social exclusion, such as addiction, exclusion from education, issues with family, and homelessness. (Yates and Payne, 2006). 2011 onwards was marked by radical change towards young people’s services under the Coalition government, with the policy label ‘NEET’ remaining still favourable under ‘new’ strategies including the Big Society (Hancock et al, 2012), the Youth Contract (Roberts, 2013) and the National Citizen Service (NCS) (de St Croix, 2017).

In particular, a speech given in 2015 by former PM David Cameron drew attention to utilizing the NCS as part of a tripartite system, along with the voluntary sector and the market in addressing young people’s social problems:

I want National Citizen Service to become a rite of passage for all 16 and 17 year olds, changing attitudes by bringing together young people from every community and giving them the skills they need to get on in life and work […] And it’s also why I want us to continue pioneering world-leading social interventions – like our social impact bonds, so that private and voluntary sector organisations which succeed in helping the hardest to reach get into work can be rewarded with some of the savings they deliver to the taxpayer – (Cameron, 2015: Online)

This speech echoes the favoured anti-social democratic solution to being NEET, which ultimately prefers the free market being in control and young people having to be drivers of their own individual success stories, accompanied by the support of voluntary or private sector actors. The free market rule can be attributed towards the success of neoliberalism that the UK has experienced since 1979.

Much like the previous responses to the problem of youth unemployment, strategies such as the NCS have faced widespread criticism for being top-down, a neoliberal target culture that focuses on ‘outcomes’ and success. This profit motivation and success of the free market, evidently diverts the overall attention away from vulnerable young people in their transitions out of NEET (Roberts, 2013; de St Croix, 2011). The neoliberal policy approach has also been widely criticised due to the responsibilisation of communities and voluntary sector actors to deal with the issue of ‘at risk’ young people’s employment, education and training trajectories, which operate on minimal funding and absolve government of accountability and responsibility (Hancock et al, 2012).

Having established the definition of NEET in the UK and past policy developments relating to the reduction of numbers of NEET, the second part of this paper will turn attention towards what is currently happening to NEET young people since the collapse of New Labour initiated intervention strategies.

Why are ‘NEETs’ now ‘Unknown’ or ‘Hidden’?

Since the collapse of the Social Exclusion Unit in 2010, there has been a marked increase in debate and policy attention on ‘who’ is now responsible for the education, employment and training transitions of NEET young people. In spite of NEET becoming a bolt-on to strategies such as NCS and the Youth Contract, evidence from grey literature has centred on young people’s destinations out of NEET becoming obscured, with recent arguments suggesting that NEET to EET transitions are increasingly ‘unknown’ or ‘hidden’ (GM Talent Match, 2017; Brooks, 2014). This is principally due to the Education and Training Participation Age being increased to 18 in the UK (Furlong, 2016), which has effectively masked the NEET rate of 16-18 year olds in official statistics. As of 2017, there has been a lack of clear government initiative that effectively records the NEET rate across the country who are utilising such private or voluntary sector actors in arriving at ‘EET’ destinations (Hutchinson et al, 2016). Hence, in a Fabian Society Review ‘ Out of sight: How we lost track of thousands of NEETs ’, Ed Balls (2014, cited in Brooks: ix) suggested:

Since the Connexions service was cut we have lost track of over 50,000 young people who are NEET. No single organisation or individual controls all the levers necessary to bring down the numbers of NEET in a sustained way […] businesses and third sector organisations all have their part to play

It would be unrealistic to attempt to give a holistic analysis of NEET destinations for this article, since a devolved strategy has been employed by government in tackling young people’s problems with education, training and the labour market. The ‘devolved’ strategy differs drastically across each region in England and Wales, with each local authority having control of the service provision for NEET young people. (Dixon et al, 2011). However, the fact that the number of young people who qualify under ‘NEET’ status has become ‘lost’ clearly marks a discrepancy in the Coalition/ Conservative strategy targeting NEET to EET outcomes. As official data shows (figure 1), in areas such as the North West of England, a larger proportion (7.8%) of young people ending up in ‘unknown’ destinations than those designated as NEET.

Figure 1: Number of 16-18 year old NEETs in the North West in comparison to those who activities/ destinations are currently unknown, 2015.  ( Adapted from: DfE, 2016 )

This highlights an underlying issue as to who is now responsible for the transition of NEET young people into EET destinations. In the North West of England alone, limited evidence has emerged that various voluntary sector organisations are assisting NEET young people into education, employment and training propped up by various European Social Fund initiatives that strongly encourage an ‘outcome’ culture (Dixon et al, 2011; Furlong, 2016). Such outcomes do not necessarily have to be tangible in getting young people back into education, employment or training long term. Often, ‘ Payment by Results’ models (see Dixon et al, 2011) are being operated in creating such quick-fix outcomes, which, again, are top-down neo-liberal approaches (de St Croix, 2011), that do not engage young people into further education and training. As figure 2 demonstrates (below), in a wide selection of high NEET population Borough Councils in the North West of England, increasing numbers of young people’s destinations have become ‘Unknown’ in comparison to officially recorded NEET status .

Figure 2: Breakdown by Borough Council of 16-18 year old NEETs in the North West, in comparison to those who activities / destinations are currently unknown, 2015. ( Adapted from: DfE, 2016 )

Hutchinson et al (2016) argues that the lack of resources and clear policy direction under the Coalition and later Conservative government, has only exacerbated the problem of ‘NEET’ with more disadvantaged and ‘at risk’ young people becoming ‘hidden’ in cycles of poverty. For instance, in large metropolitan boroughs such as Liverpool and Manchester, the rate of ‘unknown’ destinations has skyrocketed in contrast to being ‘NEET’. In a review by Greater Manchester Talent Match (2017), some of the recurring reasons given to remaining ‘hidden’ or ‘unknown’ in comparison to ‘NEET’ included a lack of capacity within Job Centre Plus at dealing with the complexities of being NEET (such as sanctioning processes), and disengagement with Job Centre Plus and partner organisations due to the lack of contextual knowledge locally about services that are designed to work with NEET young people. The points illustrated above are a clear indication of the failings of the current neoliberal arrangements / policies.

The top down approach of utilising Job Centre Plus, partner organisations and the rolling back of state intervention for NEET young people onto communities and voluntary organisations, have created a state where thousands of would-be ‘NEET’ young people have gone missing in the system. Limited attempts have been made in trying to trace ‘new’ actors that facilitate transition towards ‘EET’ destinations. Dixon et al (2011) offers a critical explanation of how the voluntary sector’s capacity needs to be increased. Part of the ‘capacity building process’ is to enable successful bidding on behalf of the Voluntary Sector for services that are ‘profitable’, but also address NEET to EET outcomes. While these schemes have been enacted successfully at a local level, this piecemeal approach has yet to be acknowledged and trailed / implemented on a national level.

This article has not intended to critique the work of the voluntary sector that has historically offered aid and assistance to thousands as an alternative to the state. However, this raises the question as to whether the role of charity organisations should be delivering services that have a young person-centric approach, rather than focusing on profit and payment by result motivations.

Destination Unknown: A time to rethink or abandon NEET policy?

This article has argued that rising numbers of would be NEET young people are ending up in unknown or highly precarious education, employment or training situations. Holistically, this only problematises the actors such as the voluntary sector, Job Centre Plus and local communities that have been expected to facilitate transitions towards EET in recent times. Although it has not been the focus of this article to critique the agencies that have accommodated difficult youth transitions, the neoliberal policy responses that have arisen from recent NEET policy have only created more issues for young people and practitioners. In the interim these Coalition / Conservative policies have produced a myriad of inefficient schemes in comparison to the streamlined New Labour strategy for NEET young people. As the Coalition / Conservative responses have subsequently collapsed and been replaced it can be concluded that a shift in NEET policy thinking is in order. This could include abandonment of the NEET term, or replacement of current policies by a social democratic approach that puts young people central rather than peripheral to such policy thinking with youth practitioners in shaping EET outcomes.

Youth & Policy is run voluntarily on a non-profit basis. If you would like to support our work, you can donate below.

Last Updated: 1 October 2019

Acknowledgements:

Thank-you to the In Defence of Youth Work seminar that convened in Manchester (June 2017). The supportive environment and safe space to share ideas and knowledge was greatly appreciated. A personal thank-you to Dr Tania de St Croix, Dr Lisa Russell and  Dr Danielle White.

In memory of Cassandra Jayne Dulson (1992-2016), NEET TV, Merseyside.

References:

Allatt, P. & Yeandle, S. (1992).  Youth unemployment and the family: Voices of disordered times . London: Routledge.

Brooks, R. (2014). Out of Sight: How We Lost Track of Thousands of NEETs, and how We Can Transform Their Prospects . London: Fabian Society.

Cameron, D. (2015). PM Speech on Opportunity . 22 June 2015. [Online] 10/06/2017. URL. https:// www. gov. uk/ government/ speeches/ pm-speech-on-opportunity

de St Croix, T. (2011). Struggles and silences: Policy, youth work and the national citizen service. Youth and Policy , 106 (4).

de St Croix, T. (2017). Time to say goodbye to the National Citizen Service ? Youth and Policy. [ online ]  URL: https://www.youthandpolicy.org/articles/time-to-say-goodbye-ncs/

DfE (Department for Education) (2016) ‘NEET data by local authority’ . DfE: London. [Online] 11/06/2017.URL: https://www.gov.uk/government/publications/neet-data-by-local-authority-2012-16-to-18-year-olds-not-in-education-employment-or-training#history

DfE (Department for Education) (2017) ‘ Young people not in education, employment or training (NEET), UK Statistical bulletins’. Statistical First Release. ONS: London. [Online] 11/06/2017. URL: https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/bulletins/youngpeoplenotineducationemploymentortrainingneet/previousReleases

Dixon, L., Jones, E. & Southwood, S. (2011). Boosting capacity of third sector organisations to work with young people who are not in education, employment or training (NEET).  London: Department of Business Innovation and Skills.

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Hutchinson, J., Beck, V., & Hooley, T. (2016). Delivering NEET policy packages? A decade of NEET policy in England. Journal of Education and Work , 29 (6), 707-727.

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Nudzor, H. (2010). Depicting young people by what they are not: conceptualisation and usage of NEET as a deficit label. Educational futures , 2 (2), 12-25.

Roberts, K. (2013). Career guidance in England today: reform, accidental injury or attempted murder?. British journal of guidance & counselling , 41 (3), 240-253.

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Yates, S., & Payne, M. (2006). Not so NEET? A critique of the use of ‘NEET’in setting targets for interventions with young people. Journal of Youth Studies , 9 (3), 329-344.

Indicator 8.6.1

Proportion of youth (aged 15-24) not in education, employment or training (13th ICLS), %

This indicator conveys the proportion of youth (aged 15-24 years) not in education, employment or training - 13th ICLS (also known as "the youth NEET rate").

Proportion of youth (aged 15-24) not in education, employment or training (13th ICLS). This indicator conveys the proportion of youth (aged 15-24 years) not in education, employment or training - 13th ICLS (also known as "the youth NEET rate").

By 2020, substantially reduce the proportion of youth not in employment, education or training.

Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

United Nations Global SDG Database

SDG Metadata Repository

See United Nations Global SDG Database

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Gen z are increasingly becoming neets by choice—not in employment, education, or training.

Just like Peter Pan, there’s a growing cohort of Gen Zers who are refusing to grow up and embrace life’s major milestones to adulthood, like getting some form of qualification or joining the world of work.

Instead, they’re opting to become NEETs—not in employment, education, or training—and creating record levels of youth unemployment around the world.

According to the International Labour Organization , about a fifth of people between 15 and 24 worldwide in 2023 are currently NEETs.

In Spain alone, over half a million 15-to-24-year-olds are neither studying nor working. Meanwhile in the U.K., almost three million Gen Zers are now classed as economically inactive—with 384,000 youngsters joining the “workless” class since the Covid pandemic.

The studies don’t delve into what’s inspiring young people to ditch the rat race and opt for a life under their parent’s roof or on public subsidies, but separate research highlights that even if they did start climbing the corporate ladder, buying a home of their own still feels like an impossible task.

Adulthood milestones are seemingly out of reach anyway

Reams of research shows that those in their early 20s are earning less, have more debt and see higher delinquency rates than millennials did at their age.

Credit reporting agency TransUnion found that 20-somethings today are taking home around $45,500, while millennials at their age were earning $51,852 when adjusted for inflation.

Despite earning less, young people today are being forced to dig deep for basic necessities like food, groceries, and gas, thanks to inflation. Meanwhile, house prices have increased more than twice as fast as income has since the turn of the millennium.

This divergence goes a long way in explaining why young people may feel like saving—or even working—towards the future is futile.

As one Gen Zer noted in Fortune: “ I’m just focusing on the present because the future is depressing .”

Hustling is so last season

Hustling, girl bossing or “work hard, play harder” just doesn’t quite have the same grip on Gen Z as it did on millennials starting out.

Many young people today would rather protect their well-being than compete their way up the corporate ladder only to not be able to afford the McMansion their parents bought for a fraction of the price.

Even those who do want to work, don’t want a career. Instead, many Gen Zers are eyeing up easygoing jobs that don’t require regular overtime, antisocial working hours, or substantial responsibilities like managing a large team.

Others are avoiding office jobs: The hottest roles right now among Gen Z grads are in teaching , where low pay is balanced with weeks of vacation. Meanwhile, non-grad Gen Zers are picking up the tools and taking up trade jobs in record numbers.

Mental health struggles

At the same time as unemployment among the youth is rising, their mental health is in decline.

Gen Z are nearly twice as stressed out as millennials were at their age ; More than a third of 18-24-year-olds are suffering from a “common mental disorder” (CMD) like stress, anxiety, or depression; And Gen Zers who are working are taking significantly more sick leave than Gen Xers 20 years their senior.

“Youth worklessness due to ill health is a real and growing trend; it is worrying that young people in their early 20s, just embarking on their adult life, are more likely to be out of work due to ill health than those in their early 40s,” researchers at the think tank Resolution Foundation (RF) previously told Fortune .

Really, is it any surprise that those mentally struggling would avoid joining the world of work when more than half of CEOs even admit that their company's culture is toxic?

Have you chosen a life of unemployment over climbing the corporate ladder? We’d like to hear your story . Email [email protected]

This story was originally featured on Fortune.com

• Research article
• Open access
• Published: 25 October 2018

Emerging adults not in education, employment or training (NEET): socio-demographic characteristics, mental health and reasons for being NEET

• Raúl A. Gutiérrez-García 2 ,
• Corina Benjet 1 ,
• Guilherme Borges 1 ,
• Enrique Méndez Ríos 1 &
• María Elena Medina-Mora 1  

BMC Public Health volume  18 , Article number:  1201 ( 2018 ) Cite this article

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A growing group of emerging adults in many countries around the globe are not incorporated into the education system or the labor market; these have received the label “NEET: not in education, employment nor training”. We describe the mental health and socio-demographic characteristics of emerging adults who are NEET from Mexico City (differentiating between NEET who are homemakers and NEET who are not) compared to their peers who are studying, working or both, in a city in which education and employment opportunities for youth are limited. A secondary objective, because of the often inconsistent inclusion criteria or definitions of NEET, was to evaluate the heterogeneity amongst NEET emerging adults in terms of their perceived reasons for being NEET and to evaluate whether different reasons for being NEET are associated with different mental health characteristics.

The participants were 1071 emerging adults aged 19 to 26; they were interviewed in person by an interviewer in their homes as part of a follow-up study of the Mexican Adolescent Mental Health Survey. The Composite International Diagnostic Interview (WMH-CIDI) assessed psychiatric disorders, substance use and abuse, suicidal behavior and socio-demographic characteristics.

Of the total sample, 15.3% were NEET homemakers, 8.6% NEET non-homemakers, 41.6% worked only, 20.9% studied only and 13.5% worked and studied. Of those who were NEET, 12.6% were NEET by choice. NEET non-homemakers had overall greater odds of substance use, substance use disorders and some suicidal behaviors in comparison with all their peers, whereas NEET homemakers had reduced odds. Those who were NEET because they didn’t know what to do with their life had greater odds of mood, behavioral, and substance disorders, use of all substances and of suicide behaviors compared to those who were NEET by choice.

Conclusions

Non-homemaker NEET who lack life goals require targeted mental health intervention. The demographic reality of emerging adults not in education or employment and the varying reasons they give for being NEET are not consistent with how NEET is often conceptualized in terms of a societal problem.

Peer Review reports

Emerging adulthood is a recent concept proposed by Arnett in 2000 [ 1 , 2 , 3 ], and adopted by many American [ 4 , 5 ] and European [ 6 , 7 , 8 , 9 ] researchers, to encompass a stage of life occurring roughly between the ages of 18 and 26. The conceptual development of this new life stage is a response to changes in industrialized countries, such as later ages of adult roles like marriage, parenthood and work, an increase in the years dedicated to education and professional qualification, and thus a prolonged period of exploration of possible life directions [ 10 ]. These aspects of emerging adulthood contribute to this being one of the most demographically heterogeneous stages of life (in terms of employment, studying, marital status, having children, living or not with one’s family of origin), with no distinct normative reference [ 11 ]. This concept, however, is conceptualized primarily in terms of psychological development, whereas Bynner [ 12 ] argues that this stage of life is more greatly influenced by structural and social factors such as employment and educational opportunities.

How well this concept of emerging adulthood represents the experience of youth in developing countries and differing cultural contexts is starting to be investigated. Initial findings suggest that emerging adulthood, as described for Western developed countries, is not the norm in Latin America, particularly in lower socioeconomic levels [ 13 , 14 , 15 ]. The five psychological characteristics proposed by Arnett to describe emerging adults in developed countries include instability, possibilities, self-focus, in-betweenness, and identity exploration. While individuals in this age group in Latin America report some of these characteristics, younger ages of first marriage or union and of first parenthood, cultural attitudes towards living with one’s family of origin until first marriage or even after, coupled with more limited educational and employment opportunities, certainly makes emerging adulthood in this context distinct. In Mexico, the context of economic crisis hinders the access of emerging adults to key social institutions for their development, such as education and work; this limited access contributes to a process of social exclusion, instability and vulnerability in this population [ 16 ], which can cause adulthood postponement and low autonomy [ 17 ].

Research suggests college entrance and entry into the labor market typically takes place during emerging adulthood, and that the successful transition from school to work is a societal expectation for this stage [ 18 ]. Reality, however, deviates from social expectations as there is a significant proportion of the population of emerging adults who do not follow this path, partly due to limitations in access to higher education and high unemployment; this leads potentially to youth growing up faster even though they lack traditional employment opportunities. A growing group of emerging adults is not incorporated into the education system or the labor market; these have received the label “NEET: not in education, employment nor training” [ 19 , 20 , 21 ]. The National Institute of Statistics and Geography (INEGI) in Mexico defines NEET as all those above 14 years of age that are unemployed (whether or not they are actively looking for work and whether or not they are available to work with the exception of the severely disabled) and do not attend school [ 22 ].

However, this one-size-fits-all label masks the varied situations of these emerging adults not in education or employment. A person may be in this situation because of 1) inability to find employment, 2) inability to gain entrance into college or other levels of schooling 3) lack of economic resources to continue studying, 4) informal employment options, 5) lack of social recognition of unpaid work, 6) suffering from an illness, 7) taking time off to explore possibilities or because one is undecided about life plans, or 8) alternative life paths [ 23 ]. Work done outside formal employment structures (such as care work) is as important as work done within formal employment structures (market work) and should be recognized as such; however young adults, primarily females who are homemakers, are sometimes classified as NEET, for example the OECD [ 24 ] includes as NEET those who participate in the functions of caring for people and being housewives.

Mexico has had an economic crisis in recent years which has promoted the growth of informal work; 64% of youth do not have access to formal employment (in other words, employment that is taxed, monitored and subject to labor laws) leading to informal work (that which is off the books, tends to be precarious and can be exploitative) [ 25 ]. The National Institute of Statistics and Geography (INEGI) in Mexico reported that most informal work is carried out mainly as domestic work in other people’s homes and in agriculture [ 26 ]. In population studies, certain characteristics are over-represented among NEET youth. The key findings to date tend to be demographic and social factors, specifically, low socioeconomic status [ 27 , 28 ], parental factors (eg, low educational attainment, divorce, parental unemployment), living arrangements (eg, not living with either parent, homelessness), and negative school experiences (eg, low educational attainment, bullying, persistent truancy, expulsion and suspension, behavioral problems, learning difficulties) [ 29 ]. In most cases, very little attention is paid to mental health factors.

Both emerging adulthood and adolescence involve many life transitions and significant mental health risks. Fifty percent of people who develop a mental disorder do so before the age of 21 [ 30 ]. In Mexico, prior evidence from the Mexican Adolescent Mental Health Survey suggests that NEET adolescents aged 12 to 17 had greater psychopathology, substance use and suicidal behavior when compared to teens who studied exclusively [ 31 ], and subsequently had poorer mental health compared to their peers as they transitioned to early adulthood [ 32 ].

The conditions that lead to NEET status, as well as the experience of NEET status, may impact upon mental health through social disengagement and marginalization [ 33 , 34 ]. However, the causes and consequences of being NEET are likely to be different in emerging adulthood (a stage of greater socio-demographic heterogeneity) than in adolescence when NEET is a more deviant situation because by law adolescents should be in school [ 35 ]. In Mexico, compulsory education was the conclusion of middle school at age 15 until the year 2012, at which time compulsory education was extended to include high school.

Prior studies have shown that mental disorders are associated with lower educational attainment and higher risk of unemployment [ 36 ]. One such study found that 19% of youth seeking primary care attention in Australia were not engaged in study or work [ 37 ]; these youth were mostly male, had a criminal record, risky cannabis use, greater depressive symptomatology, more advanced mental illness, and poor social skills. In another study NEET youth in Britain had higher rates of mental health problems and substance abuse than non-NEET peers [ 38 ].

Therefore, the objective of this study was to describe the mental health and socio-demographic characteristics of emerging adults not in education or employment, termed NEET (differentiating between NEET who are homemakers and NEET who are not) compared to their peers who are studying, working or both, in a city in which education and employment opportunities for youth are limited. A secondary objective, because of the often-inconsistent inclusion criteria or definitions of NEET, was to evaluate the heterogeneity amongst NEET emerging adults in terms of their perceived reasons for being NEET and to evaluate whether different reasons for being NEET is associated with different mental health characteristics.

Participants

The participants were 1071 emerging adults aged 19 to 26; they were interviewed in person by an interviewer in their homes in 2013 as part of a follow-up study of the Mexican Adolescent Mental Health Survey, a general population representative survey of adolescents in the Mexico City Metropolitan Area conducted in 2005 [ 39 ]. Five groups were defined: 1. NEET who are homemakers, 2. NEET who are not homemakers, 3. those who study and work, 4. those who work only, and 5. those who study only; then, they were compared independently. We separated the NEET into homemakers and non-homemakers, as homemakers are included in some definitions of NEET (such as in the Mexican governmental definition), but not others, and we wanted to explore how they may be similar or different from each other and from their non-NEET peers. The NEET homemaker category included those who self-identified as homemakers. The NEET non-homemaker category included those who receive no financial compensation for work, those looking for employment, and those who are not enrolled in any educational institution.

The World Mental Health version of the WHO Composite International Diagnostic Interview 3.0 (WMH-CIDI) [ 40 ], a fully structured diagnostic interview, assessed psychiatric disorders using DSM-IV criteria [ 41 ], suicidal behavior, substance use, employment, education and other socio-demographic factors. The WMH-CIDI included the assessment of the following disorders in the 12 months prior to the interview: mood disorders (major depressive disorder, bipolar I and II, and dysthymia), anxiety disorders (specific phobia, social phobia, separation anxiety disorder and generalized anxiety disorder), substance use disorders (alcohol, tobacco and drug abuse and dependence), and behavioral disorders (attention-deficit hyperactivity disorder, oppositional-defiant disorder, conduct disorder and intermittent explosive disorder). A section on substance use asked about tobacco use, alcohol and drugs (marijuana, cocaine, tranquilizers or stimulants used without a medical prescription, heroin, inhalants, and other drugs) in the previous 12 months. A section on suicidality asked about suicidal ideation, plans and attempt in the previous 12 months. Participants were asked why they were in the situation of not working or studying and their answers were categorized as: to perform household duties, not finding work or gaining school admission, by choice, not knowing what to do with their life and other reasons that did not fit the aforementioned categories.

Emerging adults were recruited from the contact information that they gave as part of their prior participation in the Mexican Adolescent Mental Health Survey (MAMHS). Of the original sample, 91.9% gave contact information to be re-contacted. Of those, 89.4% were located eight years later. A response rate of 62.0% of located and eligible participants was obtained (participants were eligible if they continued to live in Mexico City and were not in prison or a hospital), though this was only 35.6% of the MAMHS sample. To make sure that the participants that were re-interviewed did not vary from those that were not re-interviewed in ways that might affect our results, we tested for possible attrition bias by performing χ2 tests, to evaluate possible differences in baseline socio-demographic and mental health characteristics of those participants that participated in this current survey versus those that did not. We found no differences in lifetime DSM-IV disorders between MAMHS respondents that participated in the current survey and those that did not. The variables that showed bias (i.e., sex, being a student, and living with both parents) were used to calculate weights to ensure that the current participants represented the initial MAMHS sample [ 42 ].

The Internal Review Board of the National Institute of Psychiatry approved the study. Fieldwork was carried out by survey research firm and supervised by the research team at the National Institute of Psychiatry. Therefore, we carried out extensive training and in situ supervision of field interviewers. These field interviewers located selected participants in their homes and after explaining the study, asked for their informed consent.

We weighted the data to adjust for differential probabilities of non-response and post-stratified by age and sex to represent the age and sex distribution of this age group in the population and to be representative of the wave I sample. We tabulated the weighted proportions and standard errors using the SUDAAN 11.0.1 statistics software for the five education/employment groups and then by reason for being NEET. To estimate the association of psychiatric disorder, substance use and suicidal behavior with education/employment status group and reason for being NEET we computed multivariate logistic regressions, and from the average marginal predictions from these fitted models we calculated adjusted odds ratios (aOR). Tables 1 , 2 and 3 , each present the results of a single multivariate multinomial logistic regression model; in each model all mental health variables (psychiatric disorders, substance use, suicidal behavior) are entered as independent variables, socio-demographic variables (sex, married, has children, some college education, living with family of origin) as covariates and education/employment category as the dependent variable with three levels such that the mental health characteristics of NEET homemakers and NEET non-homemakers are compared to the reference group (those who work only on Table 1 , those who study only on Table 2 and those who work and study on Table 3 ) controlling for their sociodemographic characteristics. Table 4 presents the results of a multivariate logistic regression model in which all mental health variables are entered as independent variables, socio-demographic variables as covariates and type of NEET as the dependent variable with NEET non-homemakers as the reference group. Finally, Table 5 presents the results of single multivariate multinomial logistic regression model among the NEET youth only, in which all mental health variables are entered as independent variables, socio-demographic variables as covariates and reason for being NEET as the dependent variable with 4 levels, the reference group being those who are NEET by choice.

Of the total sample, 15.3% were NEET homemakers, 8.6% NEET non-homemakers, 41.6% worked only, 20.9% studied only and 13.5% worked and studied. Table 1 shows the socio-demographic characteristics, psychiatric disorders, substance use and suicidal behavior of NEET versus working emerging adults. NEET homemakers were mostly female (98.5%), married (82.2%), had children (98.5%), roughly a third lived with their family of origin (38.5%), and few had attained any college education (8.5%). Only 6.3% of NEET homemakers were NEET by choice. Of NEETs who were not homemakers, roughly half were male (50.8%), few were married (21.7%), most lived with their family of origin (94.3%), and less than a quarter had attained any college education (22.3%). Almost 19 % of them were NEET by choice. When compared to those who worked exclusively, NEET homemakers were less likely to be male, to have a substance use disorder, and use illicit drugs (aORs ranging from 0.35 to 0.88) whereas they were more likely to married and to have children (aOR = 2.34; 95% CI = 1.04–3.56 and aOR = 2.55; 95% CI = 1.10–3.36, respectively). On the other hand, NEET who were not homemakers, compared to those who worked exclusively, were more than twice as likely to have suicide ideation (aOR = 2.55 95%CI = 1.08–5.63) and more than four times more likely to plan suicide (aOR = 4.40 95%CI = 1.06–10.70); they were also less likely to be male (aOR = 0.73; 95%CI = 0.56–0.90).

Table 2 presents socio-demographic characteristics, psychiatric disorders, substance use and suicidal behavior of NEETs versus students. NEET homemakers, compared to those who studied exclusively, were more likely to married and to have children (aOR = 4.66; 95% CI = 2.21–10.14 and aOR = 2.80; 95% CI = 1.81–4.53, respectively), but less likely to be male, to have any college education, to live with their family of origin and to plan suicide (aORs ranging from 0.10–0.78). NEET who were not homeworkers, compared to those who studied exclusively, were less likely to have any college education (aOR = 0.44; 95% CI = 0.21–0.75), but more likely to be married, to have a substance use disorder, to use alcohol, and to have made a suicide attempt (aORs ranging from 1.38 to 2.75).

Table 3 shows socio-demographic characteristics, psychiatric disorders, substance use and suicidal behavior of NEET versus studying and working emerging adults. NEET homemakers compared to those who studied and worked, are more likely to be married and to have children (aOR = 1.07; 95% CI = 1.01–1.16 and aOR = 1.60; 95% CI = 1.02–3.46, respectively), but were less likely to be male, to have any college education, and to have made a suicide plan (aORs ranging from 0.25 to 0.67). NEET who were not homeworkers, compared to those who studied and worked, were more likely to live with their family of origin, to have a substance use disorder, illicit drug use, suicide ideation and a suicide plan (ORs ranging from 1.15 to 7.50).

Socio-demographic characteristics, psychiatric disorders, substance use and suicidal behavior of NEET that are homemakers versus non-homemakers are shown on Table 4 . NEET homemakers, compared to those who are non-homemakers, were less likely to be male, to have any college education, to live with their family of origin, to have a substance use disorder, illicit drug use and suicide ideation (aORs ranging from 0.21 to 0.79).

Table 5 presents the socio-demographic characteristics, psychiatric disorders, substance use and suicidal behavior of emerging adults by reasons for being NEET. The most reported reason for being NEET was to perform household duties (64.5%). The next most frequently reported reasons for being NEET were not finding work or not being admitted to any school (13.8%), being NEET by choice (12.6%), and not knowing to do with their life (9.1%). Two participants reported other reasons. We, therefore, considered them as missing data for the analyses. Those who were NEET because of not knowing what to do with their life, in comparison to those who were NEET by choice, were more likely to be male, to have a mood disorder, a behavioral disorder, a substance disorder, alcohol use, tobacco use, illicit drug use, a suicide plan and a suicide attempt (aORs ranging from 1.30 to 5.44).

NEET because of not finding work or gaining school admission, compared to those NEET by choice, were more likely to be male and have some college education (ORs = 2.18 and 4.09, respectively), but were less likely to have a substance use disorder and illicit drug use (ORs = 0.14 and 0.11). NEET to perform household duties, compared to those who are NEET by choice, were more likely to have children and to be married (ORs = 2.64 and 1.41), and less likely to be male, to live with their family of origin, to have a substance use disorder, and illicit drug use (ORs ranging from 0.06–0.48).

Almost a quarter of the interviewed emerging adults from Mexico City were NEET, an estimation consistent with the 27% reported for the general population of youth aged 14–29 years living in Mexico [ 43 ]. However, we found that not all NEET were equally vulnerable to mental health conditions. A large proportion of these NEET were homemakers and NEET homemakers overall had less substance use, substance use disorders and some suicidal behaviors in comparison with all of their age-group peers, whereas NEET non-homemakers had greater substance use, substance use disorders and suicidal behavior compared to all their age-group peers. In fact, the most at risk emerging adults were non-homemaker NEET who didn’t know what to do with their life, a group that would be included in most definitions of NEET. Our data suggest that NEET in emerging adulthood may be experienced differently depending on the reason for being NEET and is not the same phenomena as NEET in adolescence. This is likely due to the heterogeneity of NEET emerging adults and that divergent life paths at this stage are more normative and less deviant than at earlier stages of life. For example, being a NEET homemaker is socially acceptable at this stage of life in the Mexico City context and thus does not present a mental health challenge.

The primary reason for being NEET, given by 62% was domestic duties. Those who gave this reason were almost exclusively female (99%) and married (81.7%). For this group, the transition to adult roles has mainly been made and it is unlikely they experience emerging adulthood as posited by Arnett [ 1 ] for developed countries. While Arias and Hernández [ 17 ] found that Mexicans aged 16 to 34 (particularly those in their twenties) largely endorsed agreement with descriptions of their life stage similar to those posited in the theoretical framework developed by Arnett for emerging adults, their study of Mexicans included primarily college students, and much fewer working persons and those with children than would be expected in a representative population study, and thus, likely represents the perspective of Mexicans from a higher socioeconomic and educational level, and not this group of NEET dedicated to domestic activities.

The second most important reason given for being NEET, (endorsed by 13.7% of all NEET and almost a fifth of non-homemaker NEET), reported they were NEET because they were unable to find employment or gain school entrance. This group most closely represents what is considered the primary reason for NEET among many academics and policy makers, lack of educational and employment opportunities for youth in a difficult economic climate. Unexpectedly, this group had less risk of a substance use disorder and illicit drug use than those NEET by choice. The lack of vulnerability found in this group, may perhaps be due to this being a temporary situation not long enough to impact upon their functioning.

In a similar proportion, NEET by choice was endorsed by 12.6% of all NEET (almost a fifth of non-homemaker NEET). While still primarily females (69%), those saying they were NEET by choice were less likely to be married and have children and almost exclusively lived with their family of origin. This group may be experiencing emerging adulthood more closely to the way it is characterized for developed countries, particularly in terms of identity exploration and postponement of adult roles.

A smaller proportion, (9%) reported being NEET because they did not know what to do with their lives. Forty-six percent male, this group is also likely in the process of identity exploration. This is the group with the greatest psychopathology. They had an increased risk of a mood disorder, a behavioral disorder, a substance use disorder, each type of substance use, suicidal plan and a suicide attempt. Suicidal behavior may reflect a negative view of the future in those that feel lost or lack life purpose. For example, Kleinman and Beaver found that meaning in life and search for meaning in life were associated with decreased suicidal ideation over time and reduced lifetime odds of a suicide attempt [ 44 ]. They also found that meaning in life partially mediated the association of perceived burdensomeness and thwarted belongingness (two factors that may be present in some NEET) with suicidal behavior. Furthermore, social exclusion has been found to be related to decreased meaning in life [ 45 ]. All this may explain the increased risk of suicidal behavior and emotional upheaval in those NEET who are in this situation due to lack of life direction. Additionally, substance use and mood disorders may contribute to disengagement, either because of the impairment caused through symptoms, such as apathy, reduced motivation and goal-directedness, and difficulties to make decisions. On the other hand, these problems may follow as a consequence of reduced social interactions and a lot of unstructured time leaving them lonely and without a sense of purpose. In a study of Mexican and Spanish NEET many reported loneliness [ 46 ]. Fergusson, McLeod and Horwood found that longer durations of unemployment were associated with increases in depression, alcohol and illicit substance abuse/dependency and other adverse psychosocial outcomes accounting for between four and 14 % of the risk. Less support in that longitudinal study was found for reverse causal explanations of prior psychosocial burden predicting unemployment. Directionality and causality, however, cannot be determined in this current study [ 47 ].

The varying reasons emerging adults in Mexico gave for being NEET and the most frequent reason (household responsibilities), however, do not conform to the general concept of NEET generally espoused by policy makers or the media when referring to NEET. For example, they generally don’t consider homemakers as part of the concept of NEET [ 48 ], nor those actively looking for employment. In Europe policy makers have been concerned that NEET youth may opt out of civic participation having lost trust in institutions and thus may be at risk of radicalization [ 49 ], whereas the Mexican media consider NEET youth to be vulnerable prey for organized crime [ 50 , 51 ]. Thus, the demographic reality of emerging adults not in education or employment, and the varying reasons they give for being NEET, are not consistent with how NEET is often conceptualized in terms of a societal problem.

Strengths and limitations of this study

A limitation of this study is that we did not consider the amount of time or duration the individuals have been NEET, which might be associated to mental health or reasons for being NEET. Studies of unemployment have shown an association between ill mental health and duration of unemployment [ 52 , 53 ]. There may be other reasons for being NEET which we did not address. Additionally, causal direction cannot be determined given the cross-sectional data.

Despite these limitations, a strength of this study is shedding light upon emerging adulthood in a country culturally, socially and economically different from where the majority of studies on emerging adults have been conducted and on a group of emerging adults considered to be vulnerable especially in contexts of limited educational and employment opportunities. An important contribution of this study is providing a more layered interpretation of the figures that agencies typically report for the number of NEET by understanding the various reasons for being NEET. We included all emerging adults that are not engaged in education, employment or training in our categorizations of NEET, and did not exclude any due to a preconceived notion of NEET (such as those engaged in domestic activities or searching for employment). Therefore, we were able to provide a greater understanding of this phenomenon in the context of heterogeneous life trajectories in emerging adulthood.

In conclusion, these results have important public policy implications. Strategies to facilitate the transition from school to work and those particularly focused on disengaged young adults need to consider the varied reasons for their disengagement, with focused attention on the mental health needs of those who are NEET because they don’t know what to do with their lives. Conversely, NEET homemakers have comparable or even better mental health than their same age peers and, rather than being considered a disadvantage, their unremunerated work should be recognized.

As we have shown, a considerable number of NEET emerging adults in our sample had clinically relevant levels of symptoms corresponding to established diagnostic criteria. In light of the age of the cohort studied, an age with low health service utilization due to generally good physical health, and low health coverage especially among the unemployed, targeted treatment and population-based interventions in non-health sector spheres is needed; these might include internet-based or mobile application interventions or interventions provided through community recreation centers. The absence of school and work in almost a quarter of these emerging adults is relevant for public health initiatives targeting individuals in this developmental stage, because psychiatric illness indirectly and directly poses significant risk to emerging adult health [ 54 ].

Abbreviations

Confidence interval

Adjusted Odds Ratios

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

National Institute of Statistics and Geography

Mexican Adolescent Mental Health Survey

Not in education, employment nor training

The Organisation for Economic Co-operation and Development

The World Health Organization World Mental Health Composite International Diagnostic Interview

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Acknowledgements

The survey was carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative. We would also like to thank the emerging adults who took part in the research.

Wave I of the Mexican Adolescent Mental Health Survey was supported by the National Council on Science and Technology and Ministry of Education (grant CONACYT-SEP-SSEDF-2003-CO1–22). Wave II was supported by the National Council on Science and Technology (grant CB-2010-01-155221) with supplementary support from Fundación Azteca. These specific analyses were supported by a postdoctoral fellowship from the National Council on Science and Technology (grant RG 2015–03-290804).

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Corina Benjet, Guilherme Borges, Enrique Méndez Ríos & María Elena Medina-Mora

Psychological Research, De La Salle Bajio University, Salamanca, Guanajuato, Mexico

Raúl A. Gutiérrez-García

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RG contributed to the conception and design of the study, analysis and interpretation of data, and drafted the manuscript. CB obtained funding for the survey, contributed to the conception and design of the study, collection and interpretation of data, and helped draft the manuscript. GB and MM contributed to the conception and design of the study, interpretation of data, and critically revised the manuscript. EM contributed to data cleaning, analysis and interpretation of data and critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.

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Correspondence to Raúl A. Gutiérrez-García .

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Gutiérrez-García, R.A., Benjet, C., Borges, G. et al. Emerging adults not in education, employment or training (NEET): socio-demographic characteristics, mental health and reasons for being NEET. BMC Public Health 18 , 1201 (2018). https://doi.org/10.1186/s12889-018-6103-4

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Not in education or employment: What's the problem?

Addressing the challenge of NEETS in the EU and beyond

Tackling the ever increasing challenge of young people not engaged in education, employment or training (NEETs) in the EU as well as in the neighbouring regions, the latest episode of the Skills Factory podcast involved Ummuhan Bardak, Expert on Labour Market Policies at the European Training Foundation, who shares updated research and expertise on the issue, its causes and future possible solutions.

“NEETs is a relatively new concept, not much used during the last two decades but now used as an indicator for understanding the wellbeing of youth” said Bardak.

According to statistics, the percentage of NEETs differs according to geographical location and socioeconomic context. For example, in 2019, the share of NEETs within the EU was 10%, with the lowest share in northern countries like Sweden and Denmark and highest share in Italy, Greece and Spain, at times reaching even 30%.

“Having high number of young people in this specific situation means there is a waste of young resources which could be used more efficiently either by training them better or by putting them in jobs that could contribute to the economy” added Bardak.

The episode also identifies the factors that are contributing to a worsening of the problem, not only in EU countries but also in neighbouring areas.

“The problem is exacerbated by other factors related to facilitating the transition from school to work such as a lack of support systems for career guidance and counselling, on-the-job training, work-based learning practises and the placement of young jobseekers in decent jobs. It is the whole system of labour markets and education system together which need to be combined” explained Bardak.

While the problem of NEETs is certainly related to education and employment regulations, gender is also a significant factor, with the latest figures confirming that women are more likely to be affected. 

Based on years of experience in this field, Bardak underscores the need, more than ever, to support  policymakers in the design and implementation of better policy interventions that serve the more vulnerable sectors of society and meet their needs for economic empowerment and social welfare.

This episode is the 18th episode broadcast by the European Training Foundation as part of its monthly Skills Factory Podcast series, which aims to raise awareness of issues related to human capital development, education, training and skills development in the EU neighbourhood.

Listen to the episode: https://anchor.fm/etf/episodes/18---Not-in-education-nor-employment-Whats-the-problem-e1kkrac/a-a86i58l

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• Release calendar
• Methodology

Young people not in education, employment or training (NEET), UK: May 2024

Estimates of young people (aged 16 to 24 years) who are not in education, employment or training, by age and sex. These are official statistics in development.

This is the latest release. View previous releases

Contact: Email Labour Supply team

Release date: 23 May 2024

Next release: 22 August 2024

Table of contents

• Main points
• Total young people who were not in education, employment or training
• Young people who were not in education, employment or training data
• Measuring the data
• Strengths and limitations
• Related links
• Cite this statistical bulletin

Print this Statistical bulletin

Download as PDF

1. Main points

Labour Force Survey (LFS) estimates have been reweighted for periods from July to September 2022 onwards; a discontinuity has been introduced at this point, so comparisons before this point are not possible.

Increased volatility of LFS estimates, resulting from smaller achieved sample sizes, means that estimates of quarterly change should be treated with additional caution.

There was an increase in the number of young people aged 16 to 24 years not in education, employment or training (NEET) in January to March 2024, with the total currently estimated to be 900,000, up from 812,000 in January to March 2023.

The percentage of all young people who were NEET in January to March 2024 was estimated at 12.6%, up 1.1 percentage points on January to March 2023.

The increase in the number of young people who were NEET was driven by young men, with an increase of 99,000 on the year to 506,000 (January to March 2023).

The number of young people who were NEET and unemployed in January to March 2024 was estimated to be 320,000, an increase of 11,000 on the year (January to March 2023).

The number of young men aged 16 to 24 years who were NEET and unemployed increased by 17,000 on the year to 203,000.

There were an estimated 580,000 young people in the UK who were NEET and economically inactive, an increase on the year of 77,000.

The ongoing challenges with response rates and levels mean that LFS-based labour market statistics will be badged as official statistics in development  until further review. Read more in  Section 5: Measuring the data .

2. Total young people who were not in education, employment or training

An estimated 12.6% of all people aged 16 to 24 years in the UK were not in education, employment or training (NEET) in January to March 2024. This is up 1.1 percentage points compared with January to March 2023, and up 0.6 percentage points on the previous quarter.

An estimated 13.9% of young men (up 2.6 percentage points on the year) and 11.2% of young women (down 0.5 percentage points on the year) were NEET. There were 900,000 young people who were NEET in total, an increase of 87,000 on the year. This increase was driven by young men. Of the total number of young people who were NEET, 506,000 were young men and 394,000 were young women.

The total number of people aged 18 to 24 years who were NEET was 829,000, up 74,000 on the previous year.

The percentage of those aged 18 to 24 years who were NEET was 15.0%, which was up 1.2 percentage points on the year, and up 0.8 percentage points on the quarter.

Figure 1: The percentage of young people who are not in education, employment or training (NEET) increased in January to March 2024 compared with the previous quarter (October to December 2023)

People aged 16 to 24 years who are neet as a percentage of all young people, seasonally adjusted, uk, july to september 2022 to january to march 2024..

Source: Labour Force Survey from the Office for National Statistics

Download this chart figure 1: the percentage of young people who are not in education, employment or training (neet) increased in january to march 2024 compared with the previous quarter (october to december 2023), unemployed young people who were not in education, employment or training.

There were an estimated 320,000 NEET young people aged 16 to 24 years who were unemployed in January to March 2024, up 11,000 from January to March 2023 and up 27,000 from October to December 2023. An estimated 203,000 of these unemployed NEETS were young men, and 116,000 were young women. The number of NEET men aged 16 to 24 years who were unemployed increased by 17,000 on the year from January to March 2023, while the number of NEET women aged 16 to 24 years who were unemployed decreased by 6,000 on the year from January to March 2023.

Economically inactive young people who were not in education, employment or training

In January to March 2024, there were an estimated 580,000 economically inactive young people aged 16 to 24 years who were NEET. This was up 77,000 on the year from January to March 2023, and up 21,000 on the quarter from October to December 2023. The number of young men who were NEET and economically inactive was 302,000 and the corresponding number of young women was 278,000. This was driven by young men who saw an increase of 82,000 on the year from January to March 2023, while young women aged 16 to 24 years who were NEET and economically inactive decreased by 5,000 on the year from January to March 2023.

Subnational not in education, employment or training estimates

Subnational estimates for people who are NEET are not published by us at the Office for National Statistics (ONS), but can be accessed by following the links in Section 7: Related links

3. Young people who were not in education, employment or training data

Young people not in education, employment or training (NEET) Dataset | Released 23 May 2024 Quarterly estimates for young people (aged 16 to 24 years) who are not in education, employment or training (NEET) in the UK.

Sampling variability for estimates of young people not in education, employment or training Dataset | Released 23 May 2024 Labour Force Survey sampling quarterly variability estimates for young people (aged 16 to 24 years) who are not in education, employment or training (NEET) in the UK.

4. Glossary

Young people.

For this release, young people are defined as those aged 16 to 24 years. Estimates are also produced for the age groups 16 to 17 years and 18 to 24 years by sex, and separately for the age groups 18 to 20 years, 21 to 22 years and 23 to 24 years.

Education and training

People are considered to be in education or training if they:

are enrolled on an education course and are still attending or waiting for term to start or restart

are doing an apprenticeship

are on a government-supported employment or training programme

are working or studying towards a qualification

have had job-related training or education in the last four weeks

Young people not in education, employment or training

Anybody who is not in any of the forms of education or training listed in the Education and training section of this glossary, and who is not in employment is considered to be NEET. As a result, a person identified as NEET will always be either unemployed or economically inactive.

Economic inactivity

People not in the labour force (also known as  economically inactive ) are not in employment, but do not meet the internationally accepted definition of unemployment because they have not been seeking work within the last four weeks and/or they are unable to start work in the next two weeks.

Employment  measures the number of people in paid work, or those who had a job that they were temporarily away from (for example, because they were on holiday or off sick). This differs from the number of jobs because some people have more than one job.

Unemployment

Unemployment  measures people without a job who have been actively seeking work within the last four weeks and are available to start work within the next two weeks.

A  more detailed glossary  is available in our Guide to labour market statistics.

5. Measuring the data

This statistical bulletin contains estimates for young people who were not in education, employment or training (NEET) in the UK. The bulletin is published quarterly in February or March, May, August and November. All estimates discussed in this statistical bulletin are for the UK and are seasonally adjusted.

Statistics in this bulletin are used to help monitor progress towards the Sustainable Development Goals (SDGs). Explore the UK Government's data on the SDGs reporting platform .

Our  NEET methodology  provides background information about how missing information for identifying someone as NEET is appropriated based on individual characteristics.

Official statistics in development

These statistics are labelled as "official statistics in development". Until September 2023, these were called "experimental statistics". Read more about the change in our  Guide to official statistics in development .

These statistics are based on information from the Labour Force Survey (LFS). The reweighting exercise has improved the representativeness of our LFS estimates for periods from July to September 2022, reducing potential bias in our estimates. Nonetheless, the ongoing challenges with response rates and levels mean that LFS-based labour market statistics are now badged as  official statistics in development  until further review. This is also in line with the  letter from the Office for Statistics Regulation (OSR) , stating that LFS statistics should not be published as accredited official statistics until OSR has reviewed them. We would advise caution when interpreting short-term changes in headline LFS rates and recommend using them as part of our suite of labour market indicators alongside Workforce Jobs, claimant count data and Pay As You Earn Real Time Information (PAYE RTI) estimates. 

We are transforming how we collect and produce the LFS data to improve the quality of these statistics. We have published a  Labour market transformation article  providing an update on the transformation of labour market statistics. It is currently planned that the Transformed Labour Force Survey will become the primary source of information on the labour market from September 2024. 

More quality and methodology information (QMI) on strengths, limitations, appropriate uses, and how the data were created is available in our  LFS QMI report .

Our  LFS performance and quality monitoring reports  provide data on response rates and other quality-related issues for the LFS.

We are responsible for NEET statistics for the UK, published within this release. Estimates of the number of young people who are NEET within the countries of the UK and for subnational areas are the responsibility of the Department for Education for England, and the devolved administrations for each of the other countries. There is further information on the availability of subnational estimates of young people who are NEET in  Section 7: Related links .

Coronavirus

View more information on how labour market data sources are affected in our Coronavirus (COVID-19) and the effects on UK labour market statistics article .

View our  Comparison of our labour market data sources methodology .

Relationship to other labour market statistics for young people

Our monthly  Labour market overview bulletin  inclldes the dataset  A06: Educational status and labour market status for people aged from 16 to 24 . The NEET statistics and the dataset A06 statistics are both derived from the LFS and use the same labour market statuses; however, the educational statuses are derived differently.

For dataset A06, the educational status is based on participation in full-time education only. For NEET statistics, the educational status is based on any form of education or training. Therefore, the dataset A06 category "not in full-time education" includes some people who are in part-time education and/or some form of training and who, consequently, should not be regarded as NEET.

Making our published spreadsheets accessible

Following the Government Statistical Service (GSS) guidance on  releasing statistics in spreadsheets , we will be amending our published tables over the coming months to improve the usability, accessibility and machine readability of our published statistics. To help users change to the new formats, we will be publishing sample versions of a selection of our tables and, where practical, initially publish the tables in both the new and current formats. If you have any questions or comments, please email  labour.market@ons.gov.uk .

6. Strengths and limitations

The figures in this bulletin come from the Labour Force Survey (LFS). Results from sample surveys are always estimates and not precise figures. As the number of people available in the sample gets smaller, the variability of the estimates that we can make from that sample size gets larger. In general, changes in the numbers and rates reported in this bulletin between three-month periods are small and are not usually greater than can be explained by sampling variability.

Our  Sampling variability dataset  shows sampling variability for estimates of young people who are not in education, employment or training (NEET) derived from the LFS.

7. Related links

NEET estimates for England Collection | Last updated 28 March 2024 Young people's participation in education, employment and training and those not in education, employment or training (NEET) from the Department for Education.

NEET estimates for Scotland Publication | Last updated 29 August 2023 Annual statistical publication reporting on learning, training and work activity of 16- to 19-year-olds in Scotland, from Skills Development Scotland  .

NEET estimates for Wales Report | Last updated 11 April 2024 Data for young people by age, gender, region and disability from the Welsh Government.

NEET estimates for Northern Ireland Tables | Last updated 14 May 2024 Tables from the Labour Market Report, including those not in education, employment or training (NEET), from the Northern Ireland Statistics and Research Agency (NISRA).

8. Cite this statistical bulletin

Office for National Statistics (ONS), published 23 May 2024, ONS website, statistical bulletin,  Young people not in education, employment or training (NEET), UK: May 2024

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The mental health of young people who are not in education, employment, or training: a systematic review and meta-analysis

Geneviève gariépy.

1 Montreal Mental Health University Institute, Montreal, QC Canada

2 School of Public Health, Department of Social and Preventive Medicine, University of Montreal, Montreal, QC Canada

Sofia M. Danna

3 Douglas Research Centre, Montreal, QC Canada

4 Centre for Addiction and Mental Health, Toronto, ON Canada

Joanna Henderson

Srividya n. iyer.

5 ACCESS Open Minds (Pan-Canadian Youth Mental Health Services Research Network), Montreal, QC Canada

6 Department of Psychiatry, McGill University, Montreal, QC Canada

Associated Data

There are increasing concerns about the intersection between NEET (not in education, employment, or training) status and youth mental ill-health and substance use. However, findings are inconsistent and differ across types of problems. This is the first systematic review and meta-analysis (PROSPERO-CRD42018087446) on the association between NEET status and youth mental health and substance use problems.

We searched Medline, EMBASE, Web of Science, ERIC, PsycINFO, and ProQuest Dissertations and Theses (1999–2020). Two reviewers extracted data and appraised study quality using a modified Newcastle–Ottawa Scale. We ran robust variance estimation random-effects models for associations between NEET and aggregate groups of mental ill-health and substance use measures; conventional random-effects models for associations with individual mental/substance use problems; and subgroup analyses to explore heterogeneity.

We identified 24 studies from 6,120 references. NEET status was associated with aggregate groups of mental ill-health (OR 1.28, CI 1.06–1.54), substance use problems (OR 1.43, CI 1.08–1.89), and combined mental ill-health and substance use measures (OR 1.38, CI 1.15–1.64). Each disaggregated measure was associated with NEET status [mood (OR 1.43, CI 1.21–1.70), anxiety (OR 1.55, CI 1.07–2.24), behaviour problems (OR 1.49, CI 1.21–1.85), alcohol use (OR 1.28, CI 1.24–1.46), cannabis use (OR 1.62, CI 1.07–2.46), drug use (OR 1.99, CI 1.19–3.31), suicidality (OR 2.84, CI 2.04–3.95); and psychological distress (OR 1.10, CI 1.01–1.21)]. Longitudinal data indicated that aggregate measures of mental health problems and of mental health and substance use problems (combined) predicted being NEET later, while evidence for the inverse relationship was equivocal and sparse.

Our review provides evidence for meaningful, significant associations between youth mental health and substance use problems and being NEET. We, therefore, advocate for mental ill-health prevention and early intervention and integrating vocational supports in youth mental healthcare.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00127-021-02212-8.

Introduction

Transitioning from education into work is a milestone of emerging adulthood that about one in seven young people in economically developed countries struggle to attain [ 1 ], falling into the category of NEET—not in education, employment, or training. Concerns over these youth are growing worldwide [ 2 , 3 ]. The term NEET was coined in a 1999 report called “Bridging the Gap” from the United Kingdom [ 4 ]. By 2019, between 5.6% (Luxembourg) and 28.8% (Turkey) of 15 to 29-year-olds in Organisation for Economic Co-operation and Development countries were NEET [ 1 ]. Economic fallouts of the COVID-19 pandemic are expected to swell these numbers [ 3 ], as even early on in the pandemic, data showed that NEET rates were higher in the second quarter of 2020 than the previous year in 45 out of 50 countries [ 5 ]. Youth who are NEET are considered vulnerable as they face social exclusion and disempowerment, and disproportionately come from disadvantaged backgrounds [ 6 , 7 ]. Being outside school and the workforce limits their ability to gain skills and experience that could improve their prospects [ 8 – 10 ].

Being NEET is intertwined with mental health and substance use problems in young people. Studies have linked being NEET with the emergence of symptoms of depression, anxiety, substance use, and suicidality [ 11 – 15 ]. Conversely, mental health and substance use problems can deplete the drive and energy needed to enter the workforce or continue education/training and increase the risk of becoming NEET. However, the link between being NEET and poor mental health is unclear. Cross-sectional relationships are not always supported by longitudinal data [ 16 , 17 ] and there are indications that the relationship differs by type of mental health or substance use problem [ 14 , 18 ]. Furthermore, the association between being NEET and mental health problems may also differ in strength and significance across gender, depending on mental health problem [ 19 , 20 ]. For instance, Henderson [ 19 ] found that the association between internalizing disorders and being NEET was significant in only men. For externalizing disorders, however, the association with NEET status was significant for both men and women.

Previous reviews have reported on the association between mental health problems and youth unemployment [ 21 , 22 ] and school disengagement [ 23 , 24 ], but none investigated youth disengaged from both work and school. One narrative review examined the correlates of being NEET [ 6 ], but with little in-depth information on mental health. A synthesis of the literature is needed to inform the discussion on the growing youth population who are NEET and its intersection with youth mental health and substance use problems. This information is also needed to develop intervention studies and effective strategies to promote youth engagement in employment, education, and training.

Our primary objective was therefore to systematically review and synthesize via meta-analysis the literature on the associations between being NEET and mental health and substance use problems among youth. We expected NEET status to relate to mental ill-health measures, substance use measures, and all measures of mental ill-health and substance use combined; and the associations to vary across mental health and substance use problems. Our review thus extends the literature by focussing on youth disengagement from both education and employment and by examining the strength and consistency of associations across types of mental health and substance-use problems. Our secondary objectives were to investigate the directionality of the associations from longitudinal data and to examine subgroup differences by gender, age, and population-based versus clinical samples. We expected the association between NEET status and mental health and substance use problems to be bidirectional and to differ in strength by gender. We were agnostic as to differences by age and sample type.

Search strategy

We followed MOOSE reporting guidelines [ 25 ]. We searched Medline, EMBASE, ISI Web of Science, ERIC, PsycINFO, and ProQuest Dissertations and Theses Online from January 1, 1999 to May 2020, imposing no language restriction (see MEDLINE search strategy in Supplementary Appendix 1). We limited searches to the last 20 years because our objective was to synthesize contemporary knowledge of policy and practice relevance. The study is registered through PROSPERO (CRD42018087446) [ 26 ].

Selection criteria

We included observational studies with individual-level data that estimated the association between being NEET and mental and/or substance use symptoms or disorders among persons aged 15–34 years. We chose this age range to accommodate internationally diverse definitions of youth [ 27 ]. Studies had to identify NEET status by explicitly querying work and education or training status. Measures included those for any specific or general mental or substance use disorder; psychological or behavioural problems; psychological distress or well-being; or suicidality, measured on a dichotomous or continuous scale of symptoms, severity, or score. We excluded neurodevelopmental disorders and disabilities typically diagnosed in childhood (e.g., autism, intellectual disability) since we expected developmental and learning problems to have a unique association with becoming NEET. We excluded abstracts but considered unpublished studies if information was available for data extraction and quality assessment. We searched references of primary studies and review articles for additional studies. All references were uploaded to Covidence software [ 28 ].

The screening of titles and abstracts followed by screening of full texts for inclusion and exclusion criteria; data extraction from eligible studies (see data extraction form in Supplementary Appendix 2); and quality assessment of studies using a modified Newcastle–Ottawa Scale [ 29 ] (see description in Supplementary Appendix 3) were done independently by two reviewers, including first author/epidemiologist GG and either a psychiatry graduate student or a research assistant with a Master’s in mental health epidemiology (SD). Disagreements were resolved by consensus or by author SI. We emailed study authors for further information where necessary.

Data analysis

We conducted meta-analysis to quantitatively synthesize the literature. We ran robust variance estimation (RVE) random-effects models to obtain associations between NEET status and three aggregate groups; namely, mental ill-health (comprised of psychological distress, mood disorders, anxiety disorders, and behavioural disorders); substance use problems (comprised of alcohol, cannabis disorder, and drug use disorders); and all measures of mental ill-health and substance use problems taken together (comprised of the measures included in the mental ill-health and substance use groups, any other disorder, and suicidality). RVE allowed us to pool statistically dependent estimates (i.e., multiple estimates that are correlated because they arise from the same participant samples) into estimates incorporating all relevant measures for these aggregated groups without having to know or specify their covariance structures. Additionally, our analyses benefit from small-sample correction, which has been argued as necessary to implement when using RVE hypothesis testing [ 30 ]. It is important to note that, among small samples, hypothesis testing using RVE requires degrees of freedom be greater or equal to four to be accurate. Below four degrees of freedom, the t distribution approximation on which testing is based no longer holds, and the type I error will be greater than indicated by the p value being used [ 31 ]. We also conducted conventional random-effects models to obtain associations between NEET status and individual mental health and substance use problems. Forest plots were generated to display all main meta-analyses described above.

Our pooled results should be interpreted cautiously, given the highly heterogeneous study methodologies. We used the odds ratio (OR) as a summary measure since most studies with available quantitative data reported ORs. When multiple studies used the same dataset, we only included the study with the largest sample size to avoid double counting. For studies that only provided gender-stratified results, we combined the results using a fixed-effects model to include in the main meta-analysis. For studies that only reported a p value < 0.001, we calculated a confidence interval (CI) assuming a conservative p value of 0.001. For studies reporting only a p value > 0.05, we assumed a conservative p value of 0.10. All intervals reported are 95% CIs.

To explore sources of heterogeneity, we conducted subgroup analyses by gender, age group (< 18 vs ≥ 18 years old), and sample type (population-based vs clinical). Moreover, we investigated the potential directionality of associations from longitudinal studies that examined NEET status as a predictor of later mental health and substance use problems, and studies that examined the inverse relationship. We used fixed-effects models for subgroup analyses because there were too few studies by subgroup to estimate between-study variance with precision [ 32 ]. Study heterogeneity was evaluated using the I 2 index. We did not assess publication bias using quantitative methods because these are not recommended under conditions of high heterogeneity [ 33 ]. Meta-analyses were conducted in R (v3.6.1) using the meta, metafor, and robumeta packages [ 30 , 34 , 35 ].

From 6120 identified references, we included 24 studies (see PRISMA flow diagram in Fig.  1 ), which represented 548,862 unique individuals from the UK ( k  = 6 studies); Australia ( k  = 4, two using the same sample); Mexico ( k  = 3, all using the same sample); Sweden ( k  = 3); Italy ( k  = 2) [ 13 , 36 ]; Canada ( k  = 2); Brazil ( k  = 1); Norway ( k  = 1); Ireland ( k  = 1); Switzerland ( k  = 1); and Greece ( k  = 1). Study characteristics, detailed study findings, and quality assessment ratings appear in Table ​ Table1 1 and Supplementary Appendices 4 and 5, respectively. For 11 studies, the age range of the sample at baseline was under 18 years; three studies only included samples above the age of 19; and 10 studies included samples that were both below and above 18 years of age (e.g., 15–25). Cohort studies were most common ( k  = 13), followed by cross-sectional ( k  = 10) and case–control ( k  = 1) studies. Being NEET was associated with at least one measure of mental health or substance use problems in 75% of studies (18/24). Study quality was low in five studies; moderate in nine studies; and high in 10 studies. Measures of mental health and substance use problems included problems or symptoms of mood ( k  = 12), anxiety ( k  = 11), behaviour ( k  = 8), alcohol use ( k  = 9), cannabis use ( k  = 6), and drug use ( k  = 8) disorders, general psychological distress ( k  = 9), suicidal behaviours ( k  = 7), and any psychiatric disorder ( k  = 5).

PRISMA flow diagram

Characteristics of the selected studies

a NEET: not in education, employment, or training; MH: mental health

b SES: socioeconomic status

4 out of the 24 studies were excluded from meta-analyses of odds ratios because two reported chi-squared statistics [ 13 , 36 ] and 2 reported beta coefficients from linear regressions [ 11 , 37 ]. Further, two pairs of studies used the same data to calculate the same estimates [ 15 , 16 , 38 , 39 ]. We only included one study from each pair in the RVE meta-analyses [ 38 , 39 ].

The meta-analyses found significant associations between NEET and all three aggregated groups, i.e., mental health problems ( k  = 15 studies; n  = 25 effect sizes, OR 1.28, CI 1.06–1.54; see Supplementary Appendix 6 for forest plot); substance use problems ( k  = 11, n  = 16, OR 1.43; CI 1.08–1.89; see Supplementary Appendix 7 for forest plot); and all measures of mental health problems, substance use problems, and suicidality combined ( k  = 18, n  = 48, OR 1.38; CI 1.15–1.64).

Table ​ Table2 2 presents summaries of findings by type of mental health or substance use problem and Fig.  2 presents forest plot by each type of problem. The evidence most consistently pointed to an association between NEET and symptoms of mood disorders [ 12 , 14 , 17 , 38 , 40 , 41 ] ( k  = 6 non-overlapping studies; OR 1.43, CI 1.21–1.70); behavioural disorders [ 12 , 14 , 19 , 42 – 44 ] ( k  = 6; OR 1.49, CI 1.21–1.85); cannabis use problems [ 14 , 17 , 38 , 40 , 44 , 45 ] ( k  = 6; OR 1.62, CI 1.07–2.46); drug use problems [ 12 , 14 , 19 , 40 , 46 ] ( k  = 5; OR 1.99, CI 1.19–3.31); any psychiatric disorder [ 12 , 18 , 44 ] ( k  = 3; OR 1.72, CI 1.37–2.16); and suicidal behaviours [ 12 , 14 , 18 , 40 ] (k = 4; OR 2.84, CI 2.04–3.95).

Summary of findings by type of mental health and substance use disorder

a Studies with overlapping data or with specific OR not reported

† 95% CI or specific p value not reported

‡ Specific p value not reported

Forest plot from the meta-analysis disaggregated by mental-ill health, substance use problems and suicidality measures

The evidence was more mixed—with fewer than 50% of non-overlapping studies reporting a significant finding—for NEET status being associated with anxiety disorders [ 12 , 14 , 18 , 40 , 46 ] ( k  = 5 non-overlapping studies; OR 1.55, CI 1.07–2.24); alcohol use problems ( k  = 5; OR 1.28, CI 1.12–1.46); and psychological distress ( k  = 7; OR 1.10, CI 1.01–1.21). Results were similar when we excluded low quality studies.

Sub-group analyses

For each of the three aggregate groups, subgroup analyses were conducted for directionality, age, and gender. There was evidence of mental health problems (first aggregated group; k  = 8 studies; n  = 12 effect sizes, OR 1.33, CI 1.01–1.74) and all measures combined (third aggregated group; k  = 9; n  = 19, OR 1.39, CI 1.03–1.86) being associated with subsequent NEET status. Other subgroup analyses conducted within the three aggregated groups had too few degrees of freedom ( df  < 4) to be reliable, so results should be considered exploratory (Supplementary Appendix 8).

Directionality of association

Longitudinal data provided some evidence for bidirectional associations (see Supplementary Appendix 9 for summary of significant findings of studies by directionality of association and type of mental or substance use disorder or symptoms). Ten studies measured symptoms of mental ill-health and/or substance use problems before the emergence of NEET status. Symptoms of mood disorders [ 14 , 16 , 17 , 41 ] ( k  = 4 non-overlapping studies; OR 1.12, CI 1.07–1.18); behavioural problems [ 42 – 44 ] ( k  = 3; OR 1.25, CI 1.19–1.32); cannabis use problems [ 17 , 44 ] ( k  = 2; OR 1.10, CI 1.04–1.15); drug use problems [ 14 ] ( k  = 1; OR 1.89, CI 1.29–2.77); any mental disorder [ 18 , 44 ] ( k  = 2; OR 1.83, CI 1.27–2.62); and suicidal behaviours [ 14 ] ( k  = 1; OR 3.30, CI 2.07–5.27) were associated with later NEET status. Alcohol use disorder [ 17 , 44 ] was not so associated ( k  = 2; OR 0.80, CI 0.48–1.34), and evidence was equivocal for anxiety symptoms/disorders [ 14 , 16 ] ( k  = 2; OR 1.38, CI 0.81–2.36) and psychological distress [ 11 , 17 , 42 , 47 ] ( k  = 4; OR 1.04, CI 1.00–1.08).

Five studies examined NEET status prior to mental health and substance use outcomes. NEET status predicted later suicidal behaviours in a single study [ 15 ] ( k  = 1; OR 2.40, CI 1.32–4.31); symptoms of mood disorder in one study [ 15 ] ( k  = 1; OR 1.67, CI 1.12–1.90), but not another [ 16 ] ( k  = 1; OR 1.94, CI 0.17–21.60); and alcohol use disorder in two studies [ 15 , 48 ] ( k  = 2; OR 1.22, CI 1.12–1.32), but not another [ 17 ] ( k  = 1; p  > 0.05, values unavailable). Being NEET did not predict later symptoms of anxiety [ 15 , 16 ] ( k  = 1; OR 0.40, CI 0.10–1.65); behavioural problems [ 15 ] ( k  = 1; OR 0.83, CI 0.45–1.50); cannabis use [ 17 ] ( k  = 1; p  > 0.05, values unavailable); drug use problems [ 15 ] ( k  = 1; OR 1.03, CI 0.71–1.50); or psychological distress [ 9 , 17 ] ( k  = 2; OR 1.78, CI 0.93–3.42).

Associations by gender

Six studies conducted gender-stratified analysis [ 19 , 20 , 42 , 46 , 49 , 50 ]. In Bania et al. [ 42 ], conduct problems at age 15–16 predicted becoming NEET 9 years later among men (OR 1.17, CI 1.07–1.28) and women (OR 1.25, CI 1.17–1.33), while emotional problems were associated with lower odds of becoming NEET in men (OR 0.88, CI 0.81–0.97), but not women (OR 1.04, CI 0.97–1.11).

Hale and Viner [ 49 ] found that psychological distress at age 13 predicted being NEET at age 19 among men (OR 1.72, CI 1.24–2.41) and women (OR 1.49, CI 1.11–1.99). Bynner and Parsons [ 20 ] found that being NEET at age 16–18 did not predict psychological distress at age 21 in men (OR 2.20, p  > 0.05, CI unavailable) and women (OR 1.69, p  > 0.05).

In their cross-sectional study, Stea et al. [ 50 ] found an association between NEET and psychological distress among women (OR 2.40, CI 1.00–5.20), but not men (estimate unavailable), whereas Basta et al. [ 46 ] found no association with distress among women (OR 0.98, CI 0.95–1.02) and men (OR 0.99, CI 0.96–1.03).

Basta et al. [ 46 ] found an association between anxiety problems and NEET among women (OR 1.05, CI 1.01–1.10), but not men (OR 1.01, CI 0.96–1.03). In a cross-sectional study, Henderson et al. [ 19 ] found that being NEET was associated with substance misuse among men (OR 1.83, CI 1.43–2.34) and women (OR 2.05, CI 1.58–2.66); externalizing disorders among both men (OR 0.93, CI 0.73–1.19) and women (OR 0.87, CI 0.67–1.12); and internalizing symptoms among men (OR 1.39, CI 1.08–1.78), but not women (OR 1.08, CI 0.80–1.45).

Associations by age

We compared findings for participants who were < 18 years ( k  = 4 studies) [ 12 , 19 , 20 , 42 ] and ≥ 18 years old ( k  = 7) [ 14 , 17 – 19 , 39 , 44 , 49 ]. The association was consistent among younger (< 18 years) youth between NEET status and mood problems [ 11 , 12 ] (beta coefficient 0.0710, p  < 0.05 in one study; OR 2.70, CI 1.77–4.12 in the other study); behavioural problems [ 12 , 19 , 42 ] ( k  = 3; OR 1.24, CI 1.18–1.30); and drug use problems ( k  = 2; OR 1.69, CI 1.38–2.07). Results were weaker for psychological distress [ 19 , 20 , 42 ] ( k  = 3; OR 0.97, CI 0.92–1.03) and anxiety problems [ 11 , 12 ] ( k  = 2; OR 1.30, CI 0.92–1.84 in one study; beta coefficient = 0.07, p  > 0.05 in other study).

In youth ≥ 18 years old, there was an association with anxiety disorders [ 14 , 18 , 39 ] ( k  = 3; OR 1.59, CI 1.12–2.26), behavioural disorders [ 14 , 19 , 39 , 44 ] ( k  = 4; OR 1.32, CI 1.12–1.55); cannabis use problems ( k  = 3; OR 1.11, CI 1.05–1.16); any disorder [ 18 , 44 ] ( k  = 2; OR 1.76, CI 1.21–2.54); and general psychological distress ( k  = 3; OR 1.15, CI 1.08–1.22).

In youth ≥ 18 years old, evidence was mixed for symptoms of mood disorder [ 14 , 17 , 18 , 39 ] ( k  = 4; OR ( n  = 3) 1.14, CI 1.07–1.21; and missing OR with p  > 0.05, CI unavailable in other study); drug use disorders [ 14 , 19 , 39 , 44 ] ( k  = 4; OR ( n  = 3) 1.99, CI 1.61–2.45; and missing OR with p  > 0.05, CI unavailable in other study); and alcohol use problems [ 14 , 17 , 18 , 39 , 44 ] ( k  = 5; OR ( n  = 3) 1.32, CI 1.14–1.54; missing OR with p  > 0.05, CI unavailable in other studies).

Associations by sample type

Similar patterns of association emerged between studies using clinical [ 16 , 19 , 37 , 38 ] ( k  = 4 studies) and population-based samples [ 11 , 12 , 15 , 17 , 18 , 20 , 39 – 44 , 46 , 48 , 49 ] ( k  = 18).

In clinical studies, service-seeking youth were more likely to be NEET if they presented with mood disorders [ 16 , 38 ]; current [ 19 ] or past [ 37 ] drug use disorders; or co-occurring mental health problems [ 19 ]; but not if they had problems with alcohol or cannabis use [ 16 , 38 ], anxiety disorders [ 16 , 38 ], psychological distress [ 37 ], externalizing problems [ 19 ], or a history of any mental health diagnosis [ 37 ].

This is the first comprehensive systematic review and meta-analysis on the association between NEET status and mental health and substance use problems in youth. Being NEET was associated with mental health problems, substance use problems, and all measures combined in aggregate analyses. When disaggregated, NEET was most consistently associated with suicidal behaviours, drug use problems, any psychiatric disorders, cannabis use problems, behavioural problems, and mood problems. Findings for the association between NEET and anxiety problems, alcohol use, and psychological distress were mixed. Results were generally consistent across clinical and population-based samples but mixed across gender. These associations were particularly consistent among younger youth (< 18 years old). Longitudinal data indicated that mental health problems in early youth predicted a later NEET status, while evidence for the inverse relationship was equivocal and sparse. Together, these results point to early youth as a sensitive period for mental health and substance use problems becoming related with being NEET and increasing the vulnerability to later becoming NEET.

The aggregate analyses showed meaningful and significant associations between mental health and substance use problems in youth and being NEET. Although the overall evidence is based on a relatively limited and heterogeneous body of literature, the studies were generally of moderate to high quality. These results align with previous reviews on youth unemployment [ 21 , 22 ] and school dropout [ 23 , 24 ] that report a close connection between vocational disengagement and poor mental health. Our review extends this literature by focussing on youth disengagement from both education and employment and by revealing that the strength and consistency of associations vary across types of mental health and substance-use problems.

In our study, meta-analytical evidence from longitudinal data suggested that mental health problems and all measures of mental health and substance use (combined) predicted becoming NEET later. This evidence for their increased risk of becoming NEET aligns with the well-documented [ 24 ] drain of mental and substance use disorders on youths’ ability to perform at school and work. Disengagement from school and work may further disadvantage those with mental health problems, widening the gap between them and peers who follow more engaged developmental trajectories. Disengagement may also further heighten feelings of shame, hopelessness, and social exclusion [ 20 , 51 ]. Analyses for NEET status predicting the later occurrence of mental health problems aggregated, substance use problems aggregated, and all measures combined were not conclusive. Nonetheless, there was evidence for NEET status predicting individual mental health/substance use problems, suggesting that being out of school and work, especially in early youth, could lead to mental health and substance use problems. Regardless of the directionality, school and work can provide crucial structures and experiences that enhance feelings of belonging, productivity, and hope for the future [ 52 ].

Contrary to our hypothesis, we discerned no clear gender-based pattern in the link between mental health problems and being NEET, although the evidence base was limited and most gender-stratified studies focussed on psychological distress. Nonetheless, there is evidence that the experience of being NEET could vary by gender. For instance, young women who are NEET are more likely to be stay-at-home parents or caretakers [ 53 , 54 ]. Further, the consequences of being NEET may differ by gender. A British longitudinal study [ 20 ] found that, for young men, being NEET mainly impacted their job prospects, while for young women, it further affected their psychological well-being. To develop tailored strategies to prevent youth from becoming NEET or developing mental health problems when NEET, further research is needed into the intersections between gender and other subgroupings of vulnerability, NEET status, and mental illness.

Our review was constrained by the heterogeneity of mental health measures used in the reviewed studies, ranging from specific disorder subtypes (e.g., generalized anxiety disorder) to broad categories (e.g., any anxiety disorder) and general symptom scales. Many mental disorders like psychosis, eating disorders, and personality disorders, were not represented and few studies reported on comorbidity [ 19 ]. Divergent definitions of NEET status also limited comparability. Non-paid work like parenting counted as employment in some studies [ 14 , 44 ], but not others. Most studies measured current NEET status, but some used timeframes from 1 month [ 16 ] to 9 years [ 42 ]. By assessing NEET status but not its duration, almost all studies captured the association of both short- and long-term vocational disengagement with mental health and substance use problems. To formulate more effective interventions and policies, research into the duration of NEET status and its association with mental and substance disorders is therefore needed. These definitional, methodological, and cultural challenges of measuring NEET status and the heterogeneity of its circumstances have also been previously noted [ 27 , 54 , 55 ].

We could not examine contextual/cultural influences on being NEET and mental health problems because the reviewed studies were from a limited number of specific geographical and political backgrounds. All the studies from this review were from Europe, North America, or Australia, with the exception of one study that included data from South America, limiting the generalizability of the evidence to other contexts like low- and middle-income countries. Furthermore, global and country-specific economic shifts may exacerbate associations between NEET status and mental-ill health and deserve exploration in the future. Evidence is from observational data thereby limiting direct causal inference. While we examined longitudinal studies to assess the potential directionality of association, none of the studies used specific panel regressions models and may therefore be biased by unobserved heterogeneity. Like other reviews, our findings may be affected by publication bias. While we could review papers in English, French, and Spanish, only English studies met our search criteria. We excluded studies that focussed on neurodevelopmental disorders or disabilities that are typically diagnosed in childhood. We recognize that these disorders could co-occur with mental and substance use disorders and contribute to being NEET and may even differ in their relationship with NEET compared to other mental disorders, and therefore should be examined in future work.

Notwithstanding these limitations, the studies provided data from diverse contexts and on a range of mental health and substance use outcomes, with generally consistent results despite methodological differences. Our review carefully assessed the association between being NEET and mental health and substance use problems, an emerging topic with important clinical and public health implications. We used rigorous methodology to search, systematically assess, and analyse current literature to explain our findings. Using RVE, we appropriately pooled multiple mental health measure estimates that were correlated because they came from the same participant samples. This allowed us to capture associations between NEET and overarching groups of mental health and substance use problems that reflect a generalized relationship between youth engagement and mental-ill health. In addition, we used subgroup analysis to investigate heterogeneity, directionality of association, and vulnerable subgroups.

We identified significant knowledge gaps in NEET and mental health research. First, the association between being NEET and mental health and substance use problems is likely context-sensitive and broadening the geographic ambit of studies is strongly recommended. Second, the association is likely marked by gender differences that bear teasing out. Third, rigorous research on the temporal relationship of mental disorders and being NEET is needed because the question of directionality remains unresolved. Moreover, the associations between duration and recurrence of NEET status and mental ill-health have yet to be systematically explored. Fourth, information about some mental disorders (e.g., psychosis) and comorbid disorders and their associations with being NEET is lacking. Finally, future research should include intervention studies to identify whether and for whom vocational and mental health supports are useful in averting and ending NEET status.

Realization of the loss to productivity and the wealth of nations from unaddressed youth mental health problems is increasing [ 56 ]. Although more longitudinal research is needed, our review found clear evidence for NEET status being a consequence of mental health problems and substance misuse. Efforts to prevent young people from becoming or remaining vocationally and socially disengaged should therefore include provisions for the prevention of and early intervention for mental health problems. Furthermore, because there is also evidence for a bidirectional relationship between NEET status and mental ill-health and because problems with vocational functioning are well-documented among youth with mental health problems [ 17 , 57 ], youth mental health services should integrate educational and employment supports and services to address vocational needs and promote recovery.

The connectedness of vocational disengagement and mental health problems among young people underlines the need for consistent, widespread policy support for broader-spectrum integrated youth-focussed services [ 58 , 59 ]. Our review also highlights the importance of schools, universities, and employers developing the will and capacity to address the needs of youth experiencing mental health problems. The socioeconomic disruptions and mental health implications of the ongoing pandemic make these needs ever more urgent. Our comprehensive synthesis can serve as a useful pre-pandemic reference point for future research on the associations between youth employment/education and mental health and substance use over the course of or after the COVID-19 pandemic.

Below is the link to the electronic supplementary material.

Acknowledgements

The authors would like to thank Nina Fainman-Adelman for help with the screening and data extraction of the studies.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by GG and SMD. The first draft of the manuscript was written by GG, SMD, and SNI and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

This work was supported by a grant and a salary award from the Canadian Institutes of Health Research (SI) and the Fonds de Recherche du Québec—Santé (GG).

Declarations

The authors declare that they have no conflict of interest.

Not applicable.

The manuscript does not contain clinical studies or patient data.

Sofia M. Danna was affiliated with the Douglas Research Centre at the time of working on this article.

NEET – Not in Education, Employment, or Training

NEET is an acronym for ‘ not in employment, education or training ’, used to refer to the situation of many young persons, aged between 15 and 29. The aim of the NEET concept is to broaden understanding of the vulnerable status of young people and to better monitor their problematic access to the labor market. European Foundation for the Improvement of Living and Working Conditions

NEET Implications

NEETs are more likely to become disenfranchised and suffer from poverty and social exclusion, including a considerable loss in productive capacity at a macroeconomic level.

This problem has been accelerated during the financial crisis of 2008.

Using NEET as a labeling factor can be problematic, due to how broad this world can actually be, considering how broad the terminology can be, a NEET can be a person that’s caring for his grandparents, or parents that are suffering from a disability, that have the potential to be a quality employee, down to children surviving off the well being of their parents, choosing to chase pleasure & non-productive activities all day, such as spending unearned money or simply playing video games without an economic incentive ( such as gaming streamers, or content creators, that produce entertainment through their gaming )

NEET Brackets, by EU Reporting

The EU has categorized NEETs in the following brackets :

• Re-entrants ( 7.8% ) – have already been hired or enrolled in education or training, and will soon leave the NEETs group.
• Short-term unemployed ( 29.8% ) – unemployed and seeking work, and have been unemployed for less than a year; moderately vulnerable.
• Long-term unemployed ( 22% ) –  unemployed, seeking work and have been unemployed for more than a year; at high risk of disengagement and social exclusion.
• Illness, disability ( 6.8% ) – Not seeking work due to illness or disability; includes those who need more social support because they cannot do paid work.
• Family responsibilities ( 15.4% ) – Cannot work because they are caring for children or incapacitated adults or have other family responsibilities.
• Discouraged ( 5.8% ) – Believe that there are no job opportunities and have stopped looking for work; at high risk of social exclusion and lifelong disengagement for employment.
• Other ( 12.5 ) – The most privileged and those who are following alternative paths, such as artistic careers; most vulnerable

NEET Growth Rate

Source : Data WorldBank

NEET Europe Growth Rate

Source : EuroStat – Young people neither in employment nor in education and training – annual data

Hiring NEETs

Depending on the bracket that potential employee is placed, a NEET might provide a good output for your business, of course, there are exceptions, such as the privileged ones, which most likely would lack the motivation to work, fortunately, the Conscientiousness from the Big Five Personality Traits can be a good indicator if the employee is fit for your position, in the scenario where the prospective employee was caring for a family member while out of the workforce, she’ll most likely score highly on the Agreeableness trait, it all depends on the position you’re hiring for.

The most important factor is the Neuroticism  trait, if the person is simply lacking a sense of purpose, and the job will provide that, most likely the employee’s life will become better, and get over his or her dark thoughts, and move forward to a better life.

Macromanagement might not be a suitable work style if the person requires hands-on experience & guidance, but it depends on the previous experience of the potential hire and current motivations.

To sum it up, NEETs are potentially great employees or very bad ones, it seems like the middle line is blurred, and properly segmenting during your hiring flow will help you pick the right one.

I’m focused on ensuring Enlivy will make it practical to hire people and go through 100’s of candidates with ease, and I cannot wait to leverage the opportunity to get more people in the workforce.

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Consequences, risk factors, and geography of young people not in education, employment or training (NEET) - Research Findings

Scottish Longitudinal NEET Study

Consequences, risk factors, and geography of young people not in education, employment or training (NEET)

Main findings

Consequences of NEET status

• Young people, who were NEET , remained disadvantaged in their level of educational attainment 10 and 20 years later. More than one in five of NEET young people in 2001 had no qualifications in 2011, compared with only one in twenty five of non- NEETS .
• There is a 'scarring effect' on economic activity. In comparison with their non- NEET peers, NEET young people in 2001 were 2.8 times as likely to be unemployed or economically inactive 10 years later.
• The scarring effect is also evident in the occupational positions that NEET young people take up, if they entered employment. For example, NEET young people in 2001 were 2.5 times as likely as their non- NEET peers to work in a low status occupation in 2011, if they found work.
• NEET experiences are associated with a higher risk of poor physical health after 10 and 20 years. The risk for the NEET group was 1.6-2.5 times that for the non- NEET group, varying with different health outcomes.
• NEET experiences are associated with a higher risk of poor mental health after 10 and 20 years. The risk of depression and anxiety prescription for the NEET group is over 50% higher than that for the non- NEET group.
• Young people who were NEET in 1991 and remained economically inactive in 2001 consistently demonstrated significantly poorer outcomes in 2011 than those who were non- NEET in 1991 and economically active in 2001 and those who were engaged in employment or education in either 1991 or 2001. This suggests that there is a cumulative effect of being out of employment or education on later life chances and this group is the most disadvantaged that need continuing support.
• Young people who changed from NEET status in 1991 to employment or education in 2001 have lower risks of poor life outcomes compared with those who were consistently in disadvantaged positions. However, the negative effect of NEET status in 1991 was not fully discounted by the later engagement in employment or education, indicating the long-lasting detrimental effect of NEET experiences.
• Young people who changed from being non- NEET in 1991 to being economically inactive or unemployed in 2001 have higher risks of poor life outcomes compared with those who were consistently in employment or education. This suggests that economic activity in 2001 (when this group are in their late twenties) is also predictive of later labour market and health outcomes regardless of NEET status in 1991.

Risk factors of becoming NEET

• Educational qualification is the most important risk factor. No qualifications increased the risk of being NEET by 6 times for males and 8 times for females (for those born in the 1980s). No qualifications at SCQF level 5 or higher obtained by school stage S4 increase the risk of being NEET by 10 times for males and 7 times for females (for those born in the 1990s).
• Risk factors are consistent across the two cohorts studied and between males and females.
• Other school factors are important, including time absent from school and number of exclusions.
• Two factors are important for females: being an unpaid carer for more than 20 hours per week and teenage pregnancy.
• Household factors are also important. Living in a social renting household, living in a family that is not headed by a married couple, living in a household with no employed adults, and having a large number of siblings all increased the risk of becoming NEET .
• Local NEET rate is an important factor for both cohorts and genders, with the risk of NEET increasing with local NEET rate.
• A risk score derived from the statistical modelling has potential to identify young people who are at risk of becoming NEET and perhaps guide interventions.

Geographies of NEET

• Census data reveals that there is a marked increase in the NEET rate with area deprivation and a tendency for higher NEET rates in more urban areas. The NEET rate varies greatly between local authorities, although some local authorities consistently have the highest rates in 1991, 2001 and 2011.

The proportion of 16-19 year olds who are not in education, employment or training ( NEET ) is a key measure which feeds into the Scottish Government's 'Opportunities for All' policy, which is the Scottish Government's commitment to an offer of a place in learning or training for every 16-19 year old (up to their 20th birthday), with a specific focus on young people not in education, employment or training. It brings together a range of existing national and local policies and strategies, including More Choices More Chances and 16+ Learning Choices, as a single focus to improve young people's participation in post-16 learning or training.

In Scotland, as in the rest of the UK , the Annual Population Survey ( APS , formerly Labour Force Survey) has been used to monitor the size of the NEET group at the national level. Based on the APS , the number of NEET young people was consistently around 30,000 in Scotland between 1996 and 2013, accounting for 11%-15% of young people aged 16-19 (Scottish Executive 2006; Scottish Government 2015). The latest statistics, however, show that the number of NEET s in 2014 has dropped to around 21,000, accounting for only 8% of young people aged 16-19 yrs (Scottish Government, 2015).

It is important to conduct research into the phenomena of NEET s, and to understand the causes and consequences of being NEET . A NEET individual is defined as one who, at the time of the Census, is aged 16 to 19, either unemployed, or economically inactive due to looking after home/family, permanently sick/disabled or other reasons. The findings from this research provides evidence of the long-term scarring effect of being NEET and will aid the identification of young people most at risk of becoming NEET . This research will help inform policies aimed at allowing Scottish Government to achieve its objectives around supporting young people into post-16 education, training and employment.

Outcomes - the 'scarring effect'

A number of socioeconomic and health outcomes have been examined for young people who were NEET . They include economic activity, occupations, limiting long-term illness, hospital admission following an A&E visit, hospital admission following an A&E visit due to self-harm, depression and anxiety, and drug misuse. Those who were NEET in 2001 and those who were NEET in 1991 consistently demonstrated highly significantly poorer labour market and health outcomes 10 and 20 years later.

The NEET group remained disadvantaged in their educational attainment 10 and 20 years later. More than one in five of NEET young people in 2001 had no qualifications by 2011 compared with only one in twenty five of non- NEET s.

For those aged 16-19 years in 2001 (Cohort 1), there is a scarring effect in economic activity. In comparison with their non- NEET peers, NEET young people were more than 2 times as likely to be unemployed or economically inactive 10 years later. The scarring effect is also evident in the occupational positions for NEET young people who found work. For example, NEET young people were 2 times as likely as their non- NEET peers to work in a low status occupation in 2011.

NEET experiences are associated with a higher risk of poor health in the long-term. The risk for the NEET group is 1.6 - 2.5 times that for the non- NEET group varying with different physical health outcomes. NEET experiences are associated with a higher risk of poor mental health; the risk of depression and anxiety for the NEET group is over 50% higher than that for the non- NEET group.

For those aged 16-19 years in 1991 (Cohort 2), outcomes can also be observed at 10 and 20 years. The reference group are those who were non- NEET in 1991 and were employed or in education in 2001 (the most advantaged group). The potentially most disadvantaged group are those who were NEET in 1991 and were also out of employment and education in 2001. Two further groups consist of those who changed their status from being NEET in 1991 to being in employment or in education in 2001 and those who changed from non- NEET in 1991 to unemployed and not in education in 2001.

Young people who were NEET in 1991 and remained economically inactive in 2001 consistently demonstrated significantly poorer outcomes by 2011 than those in the reference group and those who were engaged with employment or education in either 1991 or 2001. This suggests that there is a cumulative effect of being out of employment and education on later life chances and this group is the most disadvantaged and in need of continuing support. Those who changed from NEET in 1991 to economically active in 2001 showed poorer outcomes compared with those in the most advantaged group. This suggests that the negative effect of NEET status in 1991 was not fully discounted by the later engagement in employment or education, showing the long-lasting effect of NEET experiences.

Compared to the most advantaged group, the most disadvantaged group are about 9 times more likely to be unemployed/economically inactive in 2011, about 8 times more likely to have a hospital admission following a visit to A&E due to self-harm and about 9 times more likely to have a record in the Scottish Drugs Misuse Database.

Risk Factors (Predictors)

In general, the important risk factors are the same for both genders and for the two cohorts analysed - those observed as NEET or non- NEET at ages 16-19 in 2001 (Cohort 3) and 2011 (Cohort 4).

Educational qualification is the most important factor. Having no qualifications increases the risk of being NEET by 6 times for males and 8 times for females in Cohort 3 compared with those with Higher, HNC or degree level qualifications. No qualifications at SCQF level 5 or higher obtained by school stage S4 increases the risk of being NEET by 10 times for males and 7 times for females in Cohort 4 compared with those with at least 6 such qualifications.

Other school factors are important including the proportion of time absent from school, number of exclusions and being registered for free school meals.

Two factors are important for females: being an unpaid carer for more than 20 hours per week and teenage pregnancy. Females who were pregnant as a teenager were over 10 times as likely to be NEET as their non-pregnant counterparts. However, this is experienced by only a small proportion of females.

Local NEET rate is an important factor for both cohorts and genders, with the risk of being NEET increasing with local rate. This effect may work through more than one mechanism: fewer available opportunities, demotivating young people, a local culture where being NEET is the social norm and some areas may have been more affected by the loss of local employment.

Household factors are also important. Living in a renting household, living in a family that is not headed by a married couple, living in a household with no employed adults, having a large number of siblings all increase the risk of becoming NEET .

Census data analysis shows that deprived areas are consistently related to a higher proportion of NEET young people over the past two decades. Urban areas are also related to higher rates of NEET . Local authorities like Glasgow, North Lanarkshire, West Dunbartonshire, Inverclyde and North Ayrshire displayed higher NEET rates that were persistently above the national average between 1991 and 2011.

Policy implications

Our research has a number of policy implications.

• Disengagement from employment and education when young can lead to long-term consequences in employment, occupation and health. The social and economic costs can be considerable not only for individuals but also for society.
• Being consistently excluded from employment, education or training would exacerbate the long-term negative effect for NEET young people. Continuing support is needed for people who are excluded from employment or education persistently.
• The NEET problem should be tackled as part of wider strategies for social inclusion because many individual, household and local factors interplay and contribute to the risk of becoming NEET .
• Young people who have become disaffected with education are at greatest risk of becoming NEET . Measures to increase attendance and to boost attainment may help young people to avoid becoming NEET later on.
• In addition, area-based interventions and local coordination may be useful as NEET young people appear to be concentrated in more deprived areas and in some local authorities.

Conclusions

This study provides further evidence on the consequences, risk factors and geographies of being NEET in Scotland in the last two decades. There is strong evidence that there is a long-term scarring effect: the experience of being NEET appears to be harmful for all socioeconomic and health outcomes that we investigated. Education, teenage pregnancy, local NEET rate and household factors are consistently important risk factors of being NEET . NEET young people appear to be disproportionately concentrated in more deprived areas and some local authorities. Reducing the number of young people with NEET status should continue to be an important policy concern.

Acknowledgements

The authors gratefully acknowledge the support of the Scottish Longitudinal Study team at the Longitudinal Studies Centre Scotland ( LSCS ), the electronic Data Research and Innovation Service (e DRIS ) and the National Records of Scotland. The LSCS is supported by the ESRC / JISC , the Scottish Funding Council, the Chief Scientist Office and the Scottish Government.

The authors alone are responsible for the interpretation of the data. Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

How to access background or source data

☒ cannot be made available by Scottish Government for further analysis as Scottish Government is not the data controller.

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International Labour Organization: 62 million young people in Sub-Saharan Africa were not in employment, education or training in 2023

Reports indicate that around 62 million young people in 2023 were neither involved in employment, education or training (NEET) equating to 25.9% of the youth population, up from 22.2% in 2013.

These findings are from the World Employment and Social Outlook report by the International Labour Organization (ILO) which provides a comprehensive assessment of the latest labour market trends, including unemployment, job creation, labour force participation and hours worked.

The report on a number of youths who are neither in employment, education or training may not be unconnected to other findings which indicates that they are particularly at risk of disillusionment and labour market detachment as a result of being unable to secure decent and productive work on entry into the labour market.

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Furthermore, it indicates that although job creation is keeping pace with the rising labour force, it is not necessarily making improvements in the quality of jobs.

Sub-Saharan Africa’s labour force

Sub-Saharan Africa’s labour force continues to be driven by population growth.

The size of the labour force increased by 3.3 per cent in 2023. This translates into an additional 53 million people of working age in the labour force in 2023 compared with 2019.

Here are other findings which the report indicates:

• Labour Force Participation Rate: The labour force participation rate in sub-Saharan Africa has remained stable at around 67%, slightly lower than the average of 68% between 2010 and 2019.
• Expected Increase in Labour Force: The labour force is projected to increase by 14 million people in 2024.
• Unemployment: Unemployment has remained elevated post-pandemic, with youth particularly at risk. The overall unemployment rate in 2023 was 5.8%, compared to 5.9% in 2019, equating to 27 million people. The youth unemployment rate is higher at 8.9%, accounting for 9.4 million people.
• Total Hours Worked: The total hours worked increased by 3.4% in 2023, up from 2.8% growth in 2019. However, mean hours worked per person employed remained at 38.2 hours per week, indicating potential underemployment.
• Job Quality: Many employed individuals are not in decent and productive jobs. Informal employment accounted for 86.5% of total employment in 2023, nearly the same as in 2013 (87.2%).
• Around 75.5% of the employed population were own-account workers or contributing family workers in 2022, statuses associated with less job security and irregular income.

Recommendations for Governments of Sub-Saharan African Countries:

• Promote Job Creation: Implement policies to stimulate economic growth and create jobs, particularly for the youth. Additionally, supports entrepreneurship and small and medium enterprises (SMEs) through access to finance, training, and market opportunities.
• Enhance Job Quality: Focus on creating decent and productive jobs to address underemployment and improve working conditions and labour rights to ensure job security and fair wages.
• Invest in Education and Skills Development: Enhance vocational and technical training programs to equip the workforce with relevant skills and promote STEM (Science, Technology, Engineering, and Mathematics) education to meet the demands of modern economies.
• Strengthen Social Protection Systems: Expand social security coverage to include informal workers and those in precarious employment and implement policies to provide unemployment benefits and support during economic downturns.
• Foster Inclusive Economic Policies: Address gender disparities in the labour market by promoting equal opportunities and reducing barriers for women and ensure economic policies are inclusive and benefit all segments of the population.
• Formalize Informal Employment: Create incentives for informal businesses to transition to the formal economy simplify regulatory processes and reduce bureaucratic hurdles for small businesses.
• Enhance Labor Market Data Collection: Improve labour market information systems to gather accurate data on employment trends and utilize this data to inform policy decisions and monitor the impact of interventions.

By implementing these recommendations, sub-Saharan African governments can address the challenges of unemployment, underemployment, and informal employment, to foster a more resilient and inclusive labour market.

Ngozi Ekugo

A high-performing labour market analyst/ talent acquisition specialist providing research on labor availability, labour migration, workplace trends and career development opportunities. Having worked across various sectors such as the recruitment, consulting, investment banking (Goldman Sachs) and the media, both in Nigeria and the United Kingdom, I possess a unique blend of competencies and experience to thrive in any industry.

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Ask the Expert: Shortening Patient Wait Time, Providing Education and Training

July 25, 2024 - MSUToday

Originally published July 25, 2024 on MSUToday

In May, the Family Medicine Nurse Practitioner Clinic at the MSU Clinical Center marked its second year of service to Lansing-area residents. The clinic, part of MSU Health Care, provides educational opportunities for students in the Michigan State University College of Nursing , helps shorten the amount of time patients wait before seeing a health care provider, and sees more than 80 patients each day.

Ann Sheehan is the assistant dean for faculty practice and associate professor at the MSU College of Nursing. She also oversees health care providers at the Family Medicine Nurse Practitioner Clinic and manages partnerships with external organizations. She has been a pediatric nurse practitioner for more than 20 years in a variety of settings, including private practice, public health and in a nurse-managed health center.

Here, Sheehan discusses the value the clinic brings and how nurse practitioners support better and more accessible community health care.

Initially, the clinic shared a facility with the MSU College of Osteopathic Medicine and MSU College of Human Medicine . We were starting to run out of space, and we wanted to grow the family medicine practice. When space became available, the College of Nursing moved its nurse practitioners into the office on the first floor of the MSU Clinical Center off Service Road. The new space helped us provide better care for our community. Initially, new patients had to wait an average of 45 days to see a provider, but by adding more nurse practitioners and other nursing professionals, we have lowered that number to nine days — an 80% reduction in wait time.

What services does the clinic provide?

We are proud that our clinic provides wrap-around care. This means, ideally, many of our patients’ concerns can be resolved within the walls of the clinic. For example, we have a diabetes educator. This registered nurse, or RN, will sit down with patients and their families to educate them about lifestyle changes that can help keep their blood sugar under control and prevent the development of related chronic diseases. Ou psychiatric nurse practitioner can address ongoing mental health or behavioral health needs to holistically care for community members. This nurse also collaborates with the primary care nurse practitioners to optimize medication management when needed. As far as the business side of the clinic, our clinical nurse specialist focuses on project management and quality improvement and helps implement evidence-based practices into the clinic. They analyze workflows and help us optimize workflow to improve the patient experience. This is just the start of how we are looking to treat a person holistically. We are currently planning to add more providers and offer more virtual care options so a patient who needs to discuss their health concerns immediately can have access to a nurse practitioner even sooner.

How should patients use the clinic?

We have highly trained providers that can care for the whole family, including children. Right now, we see patients for preventive services (physical exams), chronic disease management and acute complaints — basically, services across the lifespan. We would like more patients to visit us for preventative health screenings so we can help them avoid chronic conditions and the complications that can accompany those conditions.

What does a nurse practitioner do?

A nurse practitioner is a registered nurse, or RN, who has earned a master’s degree or a doctorate. Their education focused on advanced physical assessment, differential diagnosis and treatment, or care management. A primary care nurse practitioner does many of the same things that a primary care physician does. They perform physical exams and sick visits, write prescriptions and order lab work and imaging. They review these results with the patient and make care plans based on the patient’s goals. One key difference in the NP clinic is that care is delivered through a nursing lens. We look at a patient holistically while partnering with them to create a plan that includes goal setting and healthy lifestyle changes.

How does the clinic impact the shortage of family practice providers?

Michigan is projected to have a shortage of 1,000 primary care physicians by 2025. If more providers implemented a clinic like ours, patients could have better access to care. In Lansing, we have health care “deserts,” where people do not have convenient access to quality care. We hope to expand this clinic concept in those areas so we can provide even more quality care to underserved communities. In addition, we can see patients in a shorter amount of time. The industry standard for a new patient to meet their provider is 11 days and, in Michigan, the average time is seven days. Our wait time to see a provider for acute visits is even faster — currently one to two days. We also can provide comprehensive care in one place that treats everything from acute issues, chronic conditions and mental health services to regular visits. We have prepared the clinic to serve our area and to provide patients with the best quality of care with shorter wait times.

What’s next?

We are expanding our wrap-around services by adding a social worker to our clinic team. To make a real change for patients, we want to start diving into social determinants of health, which encompass how socioeconomic, racial and other factors affect one’s health and their ability to access health care. Eventually, we will move into an even bigger facility. For now, we are working to have more virtual visits to keep up with the needs of our patients.

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• Initial teacher training performance profiles

Introduction

National and provider-level information about the outcomes for teacher trainees in England in the academic year 2022/23. Outcome measures presented are the proportion of trainees with course outcomes that gained Qualified Teacher Status (QTS), and the employment rates of these qualified teachers. The publication also includes further information on the trainees such as their characteristics, ITT subjects, and ITT routes. The publication also includes information on the number of assessment only candidates and the outcomes for early years ITT trainees. Please note that: 

• The publication covers trainees with course outcomes, that is, those who were awarded QTS or ended their training but were not awarded QTS.  
• Employment figures for trainees with outcomes in 2022/23 are provisionally estimated as it is too early in the reporting cycle to capture everyone who is employed in a state-funded school within 16 months of the end of the 2022/23 academic year. Revised figures will be calculated following collection of the November 2024 school workforce data and will be published as part of the 2023/24 Performance Profiles publication next year; see the methodology section for further details. 
• Revised employment figures for 2021/22 are calculated from more complete data using the November 2023 school workforce census; see the methodology section for further details. 

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Headline facts and figures - 2022/23

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ITT Performance Profiles 2022/23 Provider Tables (xlsx, 1 Mb)

These provider level tables contain the qualified teacher status and employment outcomes of postgraduate and undergraduate ITT trainees. Table 8 contains data on trainee characteristics by ITT provider and table 9 contains data on trainee qualified teacher status (QTS) and employment outcomes by ITT provider, subject phase and training route. The data in these tables cover 2017/18 to 2022/23 for qualified teacher status and 2017/18 to 2021/22 for employment outcomes.

About these statistics

The ITT performance profiles are designed to:  

•   provide transparent information on outcomes of trainee teachers to the public 

•   help potential trainee teachers make informed choices about where to train 

These statistics are based on trainees with course outcomes, i.e. those trainees who have been awarded QTS or ended their training but were not awarded QTS. Trainees who were not awarded QTS includes those who left the course before the end (excluding those who left the course within 90 days of the start) and trainees who did not meet the standards. For comprehensive statistics about new entrants to ITT and their characteristics, please refer to the ITT census publications, available on the ITT statistics webpage.  

These statistics cover those training to teach via both postgraduate and undergraduate routes, as well as separate sections on those undertaking Early Years Initial Teacher Training (EYITT) and assessment only (AO) courses. 

The following tables are included. All contain data on QTS award rates and employment rates. Employment rates for the latest year are provisional estimates and all previous years are revised: 

• national tables for the academic years 2017/18 to 2022/23 by route, subject phase (primary or secondary), subject, region, and trainee characteristics (main postgraduate and undergraduate routes). 
• provider-level tables for the academic years 2017/18 to 202/23 by route and phase. Provisional employment rates for the latest academic year are not published at provider level. 
• AO route: a national table from the academic years 2017/18 to 2023/23 by subject. 
• EYITT route: a national table for the academic year 2017/18 to 2022/23 by route and trainee characteristics. 

 In this year’s publication, the main tables contain six years of data, from 2017/18 to 2022/23. Please see the methodology for more details on how provisional and revised employment rates are calculated. 

Background on mainstream initial teacher training

To become a qualified teacher in England, trainees typically complete a programme of ITT. This provides them with training, mentoring and teaching practice in schools, and leads to the award of QTS for successful trainees. 

There are several pathways into teaching which include an undergraduate route, over a three or four-year course, and postgraduate routes which normally run for one year full-time. Postgraduate fee-funded courses can be undertaken through a Higher Education Institution (HEI), or via a group of schools delivering a School-Centred Initial Teacher Training (SCITT) programme or a School Direct fee-funded programme. Postgraduate salaried routes include the School Direct salaried programme, the High Potential ITT programme and the Postgraduate Teaching Apprenticeship (PGTA). High Potential ITT trainees were formerly reported as Teach First. The postgraduate teaching apprenticeship (PGTA) was a new route introduced in 2018/19. It is a growing route, although relatively small compared to other routes. Two non-mainstream routes, EYITT and AO, are covered in separate sections below. 

 At HEIs, the university or college delivers the pedagogy of teaching supplemented by placements in schools. Successful trainees are awarded QTS and a postgraduate certificate in education (PGCE). On school-led routes, trainees are placed in a school from the first day of training. Most school-led routes also include a PGCE as many school-led providers will pair with an HEI. School-led routes include all postgraduate routes except the Higher Education Institution Route. 

Overall QTS and employment rates of trainees

Postgraduate Summary  

• In 2022/23, there were 23,385 postgraduate trainee teachers with course outcomes, a reduction from 31,747 in 2021/22. Prior to this, the number of postgraduate trainees with course outcomes had increased every year since 2017/18. This is in line with a similar sharp decrease in postgraduate entrants to ITT in 2022/23, following an upwards trend peaking in 2020/21. 
• Of the 23,385 postgraduate trainees with course outcomes in 2022/23, 21,575 (92%) were awarded QTS. This is a reduction in both the number and percentage compared to 2021/22, when 29,511 (93%) of trainees were awarded QTS.  
• Of the 21,575 postgraduate trainees awarded QTS in 2022/23, we provisionally estimate that 76% will be teaching in a state-funded school within 16 months of the end of the 2022/23 academic year, an increase from 74% in 2021/22. However, in terms of absolute numbers, this is a reduction compared to last year, with 16,307 trainees in 2022/23 estimated to enter the workforce, compared to 21,830 in 2021/22.   
• Overall, from 2017/18 to 2020/21, the numbers of postgraduate trainee teachers awarded QTS increased steadily, and have decreased since. This is in line both with the trends in overall numbers of ITT trainees with course outcomes, and in ITT entrant numbers across the same time period. In parallel, the QTS award rate has decreased in the latest two years from 95% in 2020/21 to 92% in 2022/23, having been stable at 95% or 96% between 2017/18 to 2020/21.  
• The number of trainees going on to teach in a state-funded school is provisionally estimated to have fallen after having been relatively stable between 2017/18 and 2021/22. However, as a proportion of those awarded QTS, the employment rate has risen for the second year in a row, from 73% in 2020/21 to 76% in 2022/23, after steadily falling from 2017/18 to 2020/21. 

  Undergraduate Summary  

• In 2022/23, there were 5,787 undergraduate trainee teachers with course outcomes, an increase from 5,210 in 2021/22, and the highest since comparable statistics began in 2017/18. This is in line with an increase in the number of entrants to undergraduate ITT in 2020/21. 
• Of the 5,787 undergraduate trainees with course outcomes in 2022/23, 4,605 (80%) were awarded QTS. While the proportion awarded QTS remained constant, this is an increase in terms of numbers on 2021/22, when 4,162 undergraduate trainees were awarded QTS. 
• Of the 4,605 undergraduate trainees awarded QTS in 2022/23, we provisionally estimate that 62% will be teaching in a state-funded school within 16 months of the end of the 2022/23 academic year, a decrease from 65% in 2021/22. In terms of absolute numbers, this is a slight increase on 2021/22, with 2,868 trainees estimated to enter the workforce, compared to 2,721 in 2021/22. 
• The numbers of undergraduate trainee teachers awarded QTS fell from 4,733 in 2017/18 to 3,934 in 2020/21, and subsequently steadily increased to 4,605 in 2022/23. There was a similar pattern in the numbers of undergraduate trainee teachers teaching in a state-funded school, falling from 3,706 in 2017/18 to 2,677 in 2020/21, before increasing steadily to 2,868 it 2022/23.  
• However, QTS award rates and employment rates have been generally decreasing for undergraduate trainee teachers. QTS award rates fell from 92% in 2017/18 to 88% in 2020/21, then falling to 80% in 2021/22 and 2022/23. Employment rates have steadily fallen year-on-year from 78% in 2017/18 to 62% in 2022/23. Undergraduate award rates and employment rates have been lower than postgraduate award rates each year since 2017/18.  
• Note that the undergraduate cohort includes trainees who left the course before the end, regardless of which year of their training they were in.  

Outcomes of postgraduate trainees by subject

Primary summary  

•  In 2022/23, there were 11,106 primary postgraduate trainees with course outcomes, a decrease from 15,098 in 2021/22. 
• Of these, 10,268 were awarded QTS, a decrease from 14,140 in 2021/22. This equates to a QTS award rate of 92% for primary postgraduate trainees, a 2 percentage point decrease from 2021/22. QTS award rates for primary postgraduate trainees have been falling year-on-year since 2020/21, prior to which they were stable at 95% or 96%.  
• Of those primary postgraduate trainees awarded QTS, we provisionally estimate that 7,408 (72%) will be teaching in a state-funded school within 16 months of the end of the 2022/23 academic year. While the employment rate is unchanged from 2021/22, the numbers have decreased from 10,234. 
• The employment rate for primary postgraduate trainees has been stable at 72% since 2019/20, following a fall from 83% in 2017/18 and 78% in 2018/19. 

Secondary summary  

• In 2022/23, there were 12,279 secondary postgraduate trainees with course outcomes, a decrease from 16,649 in 2021/22. 
• Of these, 11,307 were awarded QTS, a decrease from 15,371 in 2021/22. This equates to a QTS award rate of 92%, unchanged from 2021/22. Prior to this, QTS award rates for secondary postgraduate trainees had been stable at 95% or 96% since 2017/18. 
• Of those secondary postgraduate trainees awarded QTS, we provisionally estimate that 8,899 (79%) will be teaching in a state-funded school within 16 months of the end of the 2022/23 academic year. While the employment rate has increased 4 percentage points from 75% in 2021/22, the numbers have decreased from 11,596.  
• Secondary postgraduate trainees have had higher employment rates than primary postgraduate trainees since 2019/20, while the QTS award rates have been consistently within 1 percentage point (except for 2021/22 where there was a 2 percentage point difference).  

Secondary subjects  

• QTS award rates varied by secondary subject in 2022/23, from 87% for Physics to 96% for Physical Education. Physics has had the lowest or joint lowest QTS award rate of the secondary subjects since 2017/18, with Physical Education consistently the highest or joint highest.  
• Of the secondary subjects, Biology, Business Studies, English, Mathematics, Chemistry, RE, Physics and Computing all had lower QTS award rates than the secondary average of 92%. However, Computing, Biology, and Business Studies all saw increases in their QTS award rate compared to 2021/22, while all other subjects’ QTS award rates decreased or remained the same. 
• Employment rates also vary by secondary subject. In 2022/23, the provisional employment rate ranged from 58% for Classics (very small numbers) and 72% for Business Studies (lowest excluding Classics) to 86% for Design & Technology. Design & Technology has had the highest employment rate since 2019/20 and the second highest in the two previous years, while Classics has had the lowest since 2017/18 with the second lowest varying each year. 

Outcomes of trainees by route

•  QTS award rates vary by postgraduate route. In 2022/23, 94% of postgraduate trainees with course outcomes on a school-led route were awarded QTS, compared to 90% of those on the HEI route, with the highest award rates seen for the School Direct Salaried (97%) and PGTA (97%) routes. In 2021/22, the overall QTS award rate was 94% for school-led routes and 92% for the HEI route. This is the second year in a row that the QTS award rate has been higher for school-led routes than for the HEI route. Between 2017/18 and 2020/21, the HEI route and school-led routes had identical QTS award rates.  
• In 2022/23, the provisional employment rate was 81% for postgraduate trainees on a school-led route, compared to 68% for those on the HEI route. The highest provisional employment rates were seen for HPITT (88%), PGTA (85%) and School Direct Salaried (84%). School-led routes have had higher employment rates than the HEI route consistently since 2017/18. For the HEI routes, employment rates have continued year-on-year increases since their lowest point in 2020/21, while the employment rates for school-led routes have remained stable at 81% or 80% since 2019/20. (Note that in 2019/20 and 2020/21, cohorts were seeking employment during the Covid-19 pandemic.) 

Outcomes of postgraduate trainees by trainee characteristics

Trainee sex  

• For postgraduate trainees with course outcomes in 2022/23, 94% of female trainees were awarded QTS, compared to 89% of male trainees. The award rate for female trainees decreased by 1 percentage point compared to 2021/22, while the award rate for male trainees stayed the same. Female trainees have had higher QTS award rates than male trainees every year since 2017/18. 
• Provisional employment rates in 2022/23 were 76% for female trainees and 75% for male trainees, an increase from 2021/22 in both cases (from 75% and 71% respectively). Similarly to award rates, employment rates for female trainees have been consistently higher than those for male trainees since 2017/18. 
• In 2022/23, 50 postgraduate trainees were recorded as other sex. These trainees had an 86% QTS award rate and a 76% provisional employment rate. 
• In 2022/23, of all postgraduate trainees with course outcomes, 1% (257) have unknown sex, compared to 1% in 2021/22. These trainees had a QTS award rate of 86% and a provisional employment rate of 77%. 

Trainee age  

• In 2022/23, of postgraduates with a course outcome, 94% of trainees aged under 25 were awarded QTS compared to 91% of trainees aged 25 and over. Figures for 2021/22 were 94% for under 25s and 92% for those aged 25 and over. Trainees aged under 25 have had higher QTS award rates than those aged 25 and over every year since 2017/18. 
• The provisional employment rate in 2022/23 was 75% for trainees aged under 25 and 76% for trainees aged 25 and over. The employment rates in 2021/22 were 74% for both age groups, and have been equal for both age groups since 2019/20. Prior to this they were slightly higher for those aged under 25 (by 1 percentage point in 2017/18 and 2018/19).  

Trainee disability status  

• In 2022/23, of postgraduates with a course outcome, 88% of trainees who declared a disability were awarded QTS compared to 93% of trainees who did not declare a disability. In 2021/22, QTS award rates were 89% for those who declared a disability and 93% for those who did not. Every year since 2017/18, QTS award rates have been lower for those who declared a disability than for those who did not.   
• In 2022/23, the provisional employment rate was 72% for trainees who declared a disability, and 76% for trainees who did not declare a disability, compared to 71% and 74% respectively in 2021/22. Similarly to QTS award rates, every year since 2017/18, trainees who declared a disability have had lower employment rates than those who did not. 
• In 2022/23, of all postgraduate trainees with course outcomes, 3% (802) have unknown disability status, compared to 10% in 2021/22. These trainees had a QTS award rate of 89% and a provisional employment rate of 77%. 

Trainee ethnicity  

•  In 2022/23, of postgraduate trainees with a course outcome that declared their ethnicity, QTS award rates were highest for White trainees (93%) and lowest for trainees of Other ethnicity (89%).  
• This year saw a slightly higher variation in QTS award rates between trainees of different ethnic groups, with a range of 4 percentage points. Previously, the highest range had been 3 percentage points in 2020/21 and 2017/18. This was driven by a 4 percentage point decrease in award rate for Other ethnicity trainees, from 93% in 2021/22. 
• Provisional employment rates in 2022/23 ranged from 68% for trainees of Other ethnicity to 77% for White trainees. The provisional employment rates increased compared to 2021/22 for all ethnic groups except Other, which saw a 1 percentage point decrease. This is consistent with historical trends for trainees of Asian/Asian British and Other ethnicity to have the lowest employment rates, and trainees of Mixed/Multiple ethnic groups and White trainees to have the highest employment rates. 
• In 2022/23, of all postgraduate trainees with course outcomes, 4% (929) have unknown ethnicity, compared to 10% in 2021/22. These trainees had a QTS award rate of 87% and a provisional employment rate of 74%.

Outcomes of postgraduate trainees by degree class on entry

• This section looks at the first degrees obtained by postgraduate trainees before entering ITT. Other degree class includes third class honours degrees, and ‘ordinary’ or ‘general’ degrees awarded after a non-honours course, and degrees awarded after a non-honours course that was not available to be classified. It also includes other categories from non-UK degrees. 
• In 2022/23, for postgraduate trainees with course outcomes and known degree class, QTS award rates were 95% for trainees with a first class degree on entry, 94% for those with an upper second, 91% for those with a lower second, and 94% for those with other degree class.  
• This was the first year that trainees with other degree class have had a higher QTS award rate than those with a lower second. Trainees with other degree class were the only group that saw an increase in award rate from 2021/22. However, this broadly continues the historical trend of trainees with higher degree classes having higher QTS award rates. 
• Similarly, provisional employment rates in 2022/23 were higher for trainees with a first class or upper second degree on entry (77% and 76% respectively) compared to those with a lower second or other degree class (73% and 74% respectively). This has been the case since 2017/18. 

Unknown degree class  

• In 2022/23, 6% (1,330) of postgraduate trainees with course outcomes had unknown degree class on entry. This compares to 7% in 2021/22, and 3% in 2020/21. 
• Postgraduate trainees with unknown degree class on entry had a QTS award rate of 73% and a provisional employment rate of 72%. 

Outcomes of postgraduate trainees by region

• In 2022/23, there was relatively little variation in postgraduate QTS award rates between different regions. The East of England had the highest QTS award rate at 94%, while the North East had the lowest at 91%. All other regions had a QTS award rate of 92%, equal to the national average. 
• The North East has consistently had the lowest or joint lowest award rates since 2019/20, however it saw a 4 percentage point increase in 2022/23 from 87% in 2021/22, its lowest award rate ever. This improvement led to decreased regional variation in QTS award rate in 2022/23 compared to 2021/22. This smaller range in QTS award rate was seen consistently in the years prior to 2021/22. 
• Postgraduate employment rates show significant regional variation. In 2022/23, the East of England had the highest provisional employment rate at 83%, and the North West had the lowest at 63%. The East of England has had the highest employment rate and the North West the lowest every year since 2017/18.  
• Please note that the region is determined by the location of the provider, which may not necessarily be where the trainee is located. Also note that since 2021/22, all HPITT trainees are reported under the Teach First provider which is located in London. Previously, HPITT trainees had been reported under providers across all regions.  

Outcomes of candidates undertaking assessment only (AO)

Background  

Gaining QTS through AO is a way for existing unqualified teachers, support staff or teaching assistants to demonstrate that they already meet all the QTS standards, without the need for any further training. AO is open to those with relevant teaching experience who hold a degree, or for those with a teaching qualification from another country. Candidates undertaking AO do not complete a course to achieve QTS but are instead assessed against the Teacher’s Standards. The entry criteria for AO are the same as those for all ITT courses and must be met in full prior to registration. Only DfE-approved accredited providers of ITT can assess and recommend AO candidates for QTS.  

Typical candidates for AO might include: 

• unqualified teachers with experience in settings where QTS is not a requirement, for example independent schools, who wish to move into state-funded schools 
• unqualified teachers with significant teaching experience 
• teachers from overseas who wish to be awarded QTS in England [1] 
• higher level teaching assistants with the necessary qualifications and teaching experience 

  Summary  

•  In 2022/23, there were 1,606 AO candidates, a 12% increase compared to 1,431 in 2021/22. This is also the highest recorded number of AO candidates in any academic year since 2017/18, when official statistics on AO candidates begin.  
• The proportion of AO candidates achieving QTS increased by 1 percentage point from 99% in 2021/22 to 100% in 2022/23. This is consistent with 100% QTS award rates seen in 2020/21 and from 2017/18 to 2019/20. 
• This higher rate of QTS award for candidates undertaking AO compared to mainstream ITT candidates is likely to be because candidates should already be experienced teachers, or hold a teaching qualification from another country, who can demonstrate that they meet all of the Teachers’ Standards without any further training. 

  Subject breakdown of AO candidates  

• In 2022/23, 46% of AO candidates took primary assessments. This is similar to the proportion of primary trainees within postgraduate entrants to mainstream ITT in 2022/23 (47%). 

The secondary subjects with the highest numbers of AO candidates were English (11% of AO candidates), Physical Education (8%) and Mathematics (6%). These three subjects have had the highest numbers of AO candidates since 2017/18. 

[1] Teachers who trained and qualified in specific overseas countries and regions can be awarded qualified teacher status (QTS) with no further ITT or AO in England. Figures for these teachers are published in the yearly Teacher Regulation Agency Annual Reports, which can be found online.

Outcomes of trainees undertaking early years initial teacher training (EYITT)

• EYITT provides specialist training covering the education and care of children from birth to the age of five and is distinct from primary education. Training is delivered by accredited ITT providers. Providers graded by the Office for Standards in Education, Children’s Services and Skills (Ofsted) as ‘requires improvement’, or a lower quality, cannot provide EYITT. 
•  Successful EYITT trainees are awarded early years teacher status (EYTS). They are not eligible for the QTS award and are therefore not qualified to lead classes in a maintained nursery or school (nurseries or schools where funding and oversight is provided through the local authority), unless they also hold QTS. Trainees with EYTS can work as level 3 support workers in a maintained nursery or school. They can work as unqualified teachers in maintained schools or academies but this status is dependent on the school. Early years teachers can lead teaching in all other early years settings in the private, voluntary and independent (PVI) sector. 
•  There are several routes leading to the award of EYTS. Trainees can undertake an undergraduate course, which allows them to earn a degree in an early childhood related subject and EYTS, normally over a three-year period full-time. Postgraduate EYITT courses can be undertaken through the graduate entry route (full time study, which includes the early years School Direct route) or the graduate employment based route (a one-year part-time route for graduates working in an early years setting). Postgraduate EYITT normally runs for one year full-time.  
•  Trainees can also undertake an assessment only route to earn EYTS. This is designed for graduates with experience of working with children from birth to five, who are able to demonstrate the Teacher’s Standards (early years) without further training; for example, overseas trained early years teachers.  The EYITT assessment only route is not included in this publication. 
•  There were 487 postgraduate EYITT trainees with course outcomes in 2022/23, of which 92% (450) were awarded EYTS. This is a 3 percentage point increase compared to 2021/22, when 89% of the 484 postgraduate EYITT trainees were awarded EYTS. Prior to 2021/22, EYTS award rates had been stable at 93% or 94% since 2018/19. When considering trends, it should be noted that numbers of EYITT trainees are relatively low compared to mainstream ITT. 
•  EYTS award rates were higher for the EYITT graduate entry route compared to the graduate employment based or undergraduate routes, although there were small numbers on the graduate entry and undergraduate routes (62 and 9 trainees respectively) so this trend should be treated with caution. This is the second year in a row that EYTS award rates have been highest for the graduate entry route. Between 2017/18 and 2020/21, the graduate employment route had the highest EYTS award rate every year. 
•  There were also differences in the EYTS award rates across several trainee characteristics. The trends for age and degree class broadly mirror the equivalent differences seen in mainstream ITT, while the trends in sex, ethnicity and disability status show some differences.  However, once again, these comparisons should be treated with caution due to very small numbers of EYITT trainees in some of the groups (see chart). 

2022/23 year specific methodology

Data collection  

The initial teacher training performance profiles are collected each year for trainees with ITT course outcomes in a given academic year. For 2022/23, trainees are included if they: 

• were awarded Qualified Teacher Status (QTS), 
• completed their course but were unsuccessful and not awarded QTS, 
• or left the course after at least 90 days of starting and before the course end 

between 1st August 2022 and 31st July 2023 (inclusive). 

For the academic year 2022/23, we extracted data for 229 providers. This consisted of 158 SCITTs, and 71 HEIs. All data were reviewed, confirmed and signed-off by a designated person at each provider, however 4 providers closed this year and were unable to officially sign-off their data (although they informally confirmed to DfE it was correct to the best of their knowledge). 

This statistical release presents trainee outcomes and provisional employment data for 2022/23 as well as revised employment data for 2021/22. 

  Quality assurance   

Data for the ITT performance profiles were completed, reviewed and signed-off by providers. The data collection and publication team within DfE carried out additional quality checks and data validations throughout the data entry process. After data were extracted on 11th June 2023 (excluding employment data which was extracted at a later date), a quality assurance process was undertaken by the publication production team. This process included detailed quality checks across the dataset.  

This quality assurance process identified a small number of issues. These, along with the solutions that have been implemented, are outlined below. 

• There were 420 trainees that were excluded as they have QTS award dates in August 2023 (outside of scope mentioned above). We are currently liaising with providers with regards to the best approach to trainees with August award dates. 
• This year we have seen a continued low response rate for the return of previous degree class (6% of postgraduates had unknown degree class compared to 3% in 2020/21). We do not feel this compromises the quality of the degree class information published in tables 1, 3, 7 or 8, but it impacts our ability to identify trainees who were eligible for a bursary in 2022/23. Therefore, the decision has been taken not to publish bursary eligibility data for this release. This will be reviewed for subsequent publications and we will investigate alternative methods for identifying trainees eligible for bursaries going forward. 

  Measuring Employment  

The Department uses internal administrative data sources to estimate how many final year trainees awarded QTS go on to employment in a state-funded school in England. For full details on the methodology for measuring employment, see the publication methodology .  

For this publication, we calculate two employment rates: 

• A provisional employment rate for final year trainees in the 2022/23 academic year 
• A revised employment rate for final year trainees in the 2021/22 academic year   

  Provisional employment rate: Departmental analysis has found that matching ITT trainee data to school workforce census data from the year following qualification does not fully capture how many trainees go onto employment because some teachers do not start in time to be recorded in that SWC, while others start up to sixteen months after the end of the academic year. We account for these teachers by applying an uplift to the 2022/23 employment figures to estimate a provisional employment rate for 2022/23. The uplift is derived by comparing with data from previous years to determine what proportion of new teachers employed during the year were not included in their first school workforce census but were captured in the following year’s census (the uplift applied for 2022/23 was around 27% of those not captured in the first school workforce census).  

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Methodology.

Find out how and why we collect, process and publish these statistics.

Official statistics

These are Official Statistics and have been produced in line with the Code of Practice for Official Statistics .

This can be broadly interpreted to mean that these statistics are:

• managed impartially and objectively in the public interest
• meet identified user needs
• produced according to sound methods
• well explained and readily accessible

Find out more about the standards we follow to produce these statistics through our Standards for official statistics published by DfE guidance .

Our statistical practice is regulated by the Office for Statistics Regulation (OSR).

OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

You are welcome to contact us directly with any comments about how we meet these standards. Alternatively, you can contact OSR by emailing [email protected] or via the OSR website .

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