not in education employment or training

'NEETS' and 'new unemployables': Why some young adults aren't working

Although the  unemployment rate  has spent 30 months at or below  below 4%  — a near record — not everyone who wants a job has one. And not everyone even wants a job at all.

Some, referred to as “NEETs,” which stands for “not in employment, education, or training,” are opting out of the labor force largely because they are discouraged by their economic standing.

Others, alternatively, are well-qualified but often younger candidates who are struggling to find positions, comprising a contingent of “new unemployables,” according to a recent report by  Korn Ferry . 

Among 16- to 24-year-olds, the unemployment rate rose to 9% in May, which is “typical,” according to Alí Bustamante, a labor economist and director of the Worker Power and Economic Security program at the Roosevelt Institute, a liberal think tank based in New York City.

Although the youth unemployment rate fell below 7% in 2023, according to the U.S. Bureau of Labor Statistics, such lows were “emblematic of how hot the labor market was at that point,” Bustamante said.

“9% is basically what we should be expecting during relatively good economic times for younger workers,” he added.

‘NEETS’ feel ‘left out and left behind’

Still, some young adults in the U.S. are neither working nor learning new skills.

In 2023, about 11.2% of young adults ages 15 to 24 in the U.S. were considered as NEETs, according to the International Labour Organization.

More from CNBC

• How thousands of Americans got caught in fintech’s false promise and lost access to bank accounts
• Tesla shares rise on better-than-expected Q2 deliveries report
• The salary Americans say they’d need to earn to feel rich at every age—it’s less than $1 million

In other words, roughly one in 10 young people are “being left out and left behind in many ways,” Bustamante said.

Even though “that’s typically the norm,” he said, “we should be expecting these rates to be lower.”

Young men, especially, are increasingly disengaged, according to Julia Pollak, a labor economist at ZipRecruiter.

“The NEET trend is mostly a male phenomenon,” she said.

Pollak explained that’s in part due to declining opportunities in traditionally male occupations, such as construction and manufacturing, while “women’s enrollment in schooling, education outcomes, and employment outcomes have mostly trended upwards.”

‘Talent hoarding’ has led to ‘new unemployables’

According to Korn Ferry’s report, a “perfect storm” has also created a glut of “new unemployables,” or highly trained workers who struggle to find job opportunities.

“Employers are holding on to the talent they have and increasingly focusing on talent mobility,” said David Ellis, senior vice president for global talent acquisition transformation at Korn Ferry.

This “talent hoarding” has led to fewer available job openings even for well-qualified candidates, he said.

At the same time, firms are scaling back on new hires,  limiting the opportunities  at the entry level, as well.

While the teen employment rate is the highest it has been in over a decade, early 20-somethings are struggling to find jobs, Pollak said. “It’s the 20- to 24-year-olds that saw a massive drop off in the labor force participation during the pandemic, and who have lagged behind ever since.”

Overall, hiring projections for the class of 2024 fell 5.8% from last year, according to a  report from the National Association of Colleges and Employers , or NACE.

As more candidates compete for fewer positions, stretches of unemployment are also lengthening. Now, the number of people unemployed for longer than six months is up 21%, Korn Ferry found.

‘Unemployable’ to employable

Despite those trends in the job market, “all is not lost,” Ellis said.

“Don’t wait to reach out,” he advised. Get back in touch with former employers or colleagues through LinkedIn or email and set up informational interviews. After that initial approach, ask for any job leads or contacts.

In the meantime, make yourself more visible by writing about noteworthy topics in the industry and  updating your resume  to include keywords and so-called “title tags,” which highlight important elements at the top.

Finally, don’t limit yourself to roles that include a promotion or a raise, Ellis also advised. Rather, aim for a “career lattice,” which could entail taking lower position to gain skills that will pay dividends later.

Jessica Dickler is a personal finance writer for CNBC.

Ana Teresa Solá is a personal finance reporter for CNBC.

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.

Select a language

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.

• Measuring the labour market
• Directorate for Education and Skills
• Youth employment and social policies
• Labour markets surveillance
• Education and skills

Access the source dataset in Data Explorer

Related data.

• Compulsory education This dashboard displays the starting and ending ages, the duration of compulsory and free education, as well as recent expansions in compulsory education.
• Indicator Net childcare costs Net childcare costs are the household expenses of using full-time centre-based childcare, after any benefits designed to reduce the gross childcare fees.
• Indicator Unemployment rate Unemployment rate is the share of the labour force without work. Unemployed people are those of a working age who do not have a job, are available for work and have taken specific steps to find a job in the previous four weeks.
• Indicator Labour productivity and utilisation Labour productivity and utilisation are indicators of economic effects from labour efficiencies.
• Indicator Unemployment rates by education level This indicator shows the unemployment rates of people according to their education levels.

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—which stands for “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 ages 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 3 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 twenties are earning less, have more debt, and see higher delinquency rates than millennials did at their age.

Credit reporting agency TransUnion found that twentysomethings 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—toward 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, girlbossing, 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 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- to 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 twenties, just embarking on their adult life, are more likely to be out of work due to ill health than those in their early forties,” 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]

Latest in Success

Inside the routine of Red Lobster’s 35-year-old CEO from when he ran P.F. Chang’s

How ‘breadwinner wives’ who out-earn their husbands navigate success—and social judgement

Shark Tank’s Kevin O’Leary tells couples to separate their finances no matter how in love they are

An airline passenger’s unruly behavior led to a canceled flight. Now he’s on the hook for almost $6,000 in fuel costs 

Boeing at risk of losing investment-grade credit rating at Moody’s as worker strike looms

I’m a founder—and ‘founder mode’ is missing a key to growth

Most popular.

Protecting a billionaire CEO costs millions. Here’s how the world’s biggest companies measure up

Rupert Murdoch demanded his kids give him $100 million each as a sign of respect

Elon Musk faces moment of truth in Europe as buyers turn their backs on Tesla

Michael O’Leary calls Wetherspoons chief’s bluff by pledging a 2-drink limit on Ryanair flights ‘tomorrow’ if pubs follow

Montana miner laying off 700 as price for metal used in catalytic converters collapses

The top 4 states where the most young and rich Americans are moving to

Advancing social justice, promoting decent work ILO is a specialized agency of the United Nations

Migrated Content

We have recently updated the ILO website and are in the process of rebuilding a number of pages. You might encounter layout issues on pages as we work on them. Thank you for your understanding while we improve your experience.

ILO/Sida Partnership on Employment Technical brief No. 3

• My Favorites

You have successfully logged in but...

... your login credentials do not authorize you to access this content in the selected format. Access to this content in this format requires a current subscription or a prior purchase. Please select the WEB or READ option instead (if available). Or consider purchasing the publication.

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”.

English Also available in: French

• OECD Employment Outlook
• International student assessment (PISA)
• Education resources
• Education attainment
• OECD Education Statistics
• https://doi.org/10.1787/7b765a3b-en
• Subscribe to the RSS feed Subscribe to the RSS feed

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
• Click to access:
• Click to access indicator DATA

Cite this content as:

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.

Related research and writing

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

Sources and processing, this data is based on the following sources, world bank – world bank education statistics (edstats).

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.

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Reuse this work

• All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
• All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license . You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

How to cite this data

In-line citation If you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Full citation

Our World in Data is free and accessible for everyone.

Help us do this work by making a donation.

Advertisement

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

• Open access
• Published: 21 December 2021
• Volume 57 , pages 1107–1121, ( 2022 )

Cite this article

You have full access to this open access article

• 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  

7410 Accesses

37 Citations

16 Altmetric

Explore all metrics

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.

Similar content being viewed by others

Secular trends in mental health problems among young people in Norway: a review and meta-analysis

Sociodemographic, psychological, and clinical characteristics associated with health service (non-)use for mental disorders in adolescents and young adults from the general population

Associations over the COVID-19 pandemic period and the mental health and substance use of youth not in employment, education or training in Ontario, Canada: a longitudinal, cohort study

Avoid common mistakes on your manuscript.

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.

Youth not in employment, education or training (NEET) (indicator) (2021) https://data.oecd.org/chart/5MP5 Accessed 21 Apr 2021

International Labour Office (2020) Global employment trends for youth 2020: technology and the future of jobs. International Labour Office, Geneva

Google Scholar  

Kassid S (2020) What about us? Youth (un)employment in times of COVID-19. World Future Council. https://www.worldfuturecouncil.org/covid19-what-about-us/ . Accessed May 2020

Social Exclusion Unit (1999) Bridging the gap: new opportunities for 16–18 year olds not in education, employment or training. Cabinet Office, London

Karkee V, Sodergren M-C (2021) How women are being left behind in the quest for decent work for all. International Labour Office Department of Statistics. https://www.ilostat.ilo.org/how-women-are-being-left-behind-in-the-quest-for-decent-work-for-all/ . Accessed 29 Mar 2021

Sadler K, Akister J, Burch S (2015) Who are the young people who are not in education, employment or training? An application of the risk factors to a rural area in the UK. Int Soc Work 58(4):508–520. https://doi.org/10.1177/0020872813515010

Article   Google Scholar  

Alfieri S, Sironi E, Marta E, Rosina A, Marzana D (2015) Young Italian NEETs (not in employment, education, or training) and the influence of their family background. Eur J Psychol 11(2):311–322. https://doi.org/10.5964/ejop.v11i2.901

Article   PubMed   PubMed Central   Google Scholar  

Backman O, Nilsson A (2016) Long-term consequences of being not in employment, education or training as a young adult. Stability and change in three Swedish birth cohorts. Eur Soc 18(2):136–157. https://doi.org/10.1080/14616696.2016.1153699

Bynner J (2012) Policy reflections guided by longitudinal study, youth training, social exclusion, and more recently NEET. Br J Educ Stud 60(1):39–52. https://doi.org/10.1080/00071005.2011.650943

Ralston K, Feng Z, Everington D, Dibben C (2016) Do young people not in education, employment or training experience long-term occupational scarring? A longitudinal analysis over 20 years of follow-up. Contemp Soc Sci 11(2–3):203–221. https://doi.org/10.1080/21582041.2016.1194452

Symonds J, Dietrich J, Chow A, Salmela-Aro K (2016) Mental health improves after transition from comprehensive school to vocational education or employment in england: a national cohort study. Dev Psychol 52(4):652–665. https://doi.org/10.1037/a0040118

Article   PubMed   Google Scholar  

Benjet C, Hernandez-Montoya D, Borges G, Mendez E, Medina-Mora ME, Aguilar-Gaxiola S (2012) Youth who neither study nor work: mental health, education and employment. Salud Publica Mex 54(4):410–417. https://doi.org/10.1590/s0036-36342012000400011

Nardi B, Lucarelli C, Talamonti M, Arimatea E, Fiori V, Moltedo-Perfetti A (2015) NEETs versus EETs: an observational study in Italy on the framework of the HEALTH25 European project. Res Post Compuls Educ 20(4):377–399. https://doi.org/10.1080/13596748.2015.1081749

Goldman-Mellor S, Caspi A, Arseneault L, Ajala N, Ambler A, Danese A, Fisher H, Hucker A, Odgers C, Williams T, Wong C, Moffitt TE (2016) Committed to work but vulnerable: self-perceptions and mental health in NEET 18-year olds from a contemporary British cohort. J Child Psychol Psychiatry 57(2):196–203. https://doi.org/10.1111/jcpp.12459

Gutierrez-Garcia RA, Benjet C, Borges G, Rios EM, Medina-Mora ME (2017) NEET adolescents grown up: Eight-year longitudinal follow-up of education, employment and mental health from adolescence to early adulthood in Mexico City. Eur Child Adolesc Psychiatry 26(12):1459–1469. https://doi.org/10.1007/s00787-017-1004-0

O’Dea B, Lee RSC, McGorry PD, Hickie IB, Scott J, Hermens DF, Mykeltun A, Purcell R, Killackey E, Pantelis C, Amminger GP, Glozier N (2016) A prospective cohort study of depression course, functional disability, and NEET status in help-seeking young adults. Soc Psychiatry Psychiatr Epidemiol 51(10):1395–1404. https://doi.org/10.1007/s00127-016-1272-x

Baggio S, Iglesias K, Deline S, Studer J, Henchoz Y, Mohler-Kuo M, Gmel G (2015) Not in education, employment, or training status among young Swiss men. Longitudinal associations with mental health and substance use. J Adolesc Health 56(2):238–243. https://doi.org/10.1016/j.jadohealth.2014.09.006

Power E, Clarke M, Kelleher I, Coughlan H, Lynch F, Connor D, Fitzpatrick C, Harley M, Cannon M (2015) The association between economic inactivity and mental health among young people: a longitudinal study of young adults who are not in employment, education or training. Ir J Psychol Med 32(1):155–160. https://doi.org/10.1017/ipm.2014.85

Article   CAS   PubMed   Google Scholar  

Henderson JL, Hawke LD, Chaim G (2017) Not in employment, education or training: mental health, substance use, and disengagement in a multi-sectoral sample of service-seeking Canadian youth. Child Youth Serv Rev 75(Supplement C):138–145. https://doi.org/10.1016/j.childyouth.2017.02.024

Bynner J, Parsons S (2002) Social exclusion and the transition from school to work: the case of young people not in education, employment, or training (NEET). J Vocat Behav 60(2):289–309. https://doi.org/10.1006/jvbe.2001.1868

Reneflot A, Evensen M (2014) Unemployment and psychological distress among young adults in the Nordic countries: a review of the literature. Int J Soc Welf 23(1):3–15. https://doi.org/10.1111/ijsw.12000

Vancea M, Utzet M (2017) How unemployment and precarious employment affect the health of young people: a scoping study on social determinants. Scand J Public Health 45(1):73–84. https://doi.org/10.1177/1403494816679555

Bowman S, McKinstry C, McGorry P (2017) Youth mental ill health and secondary school completion in Australia: time to act. Early Interv Psychiatry 11(4):277–289. https://doi.org/10.1111/eip.12357

Esch P, Bocquet V, Pull C, Couffignal S, Lehnert T, Graas M, Fond-Harmant L, Ansseau M (2014) The downward spiral of mental disorders and educational attainment: a systematic review on early school leaving. BMC Psychiatry 14(1):237. https://doi.org/10.1186/s12888-014-0237-4

Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA, Thacker SB (2000) Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 283(15):2008–2012. https://doi.org/10.1001/jama.283.15.2008

Gariepy, G., Iyer, S.N. The mental health of youth not employed or in education: a systematic review of current knowledge. PROSPERO: International prospective register of systematic reviews. 2018. CRD42018087446. Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018087446

Batini F, Corallino V, Toti G, Bartolucci M (2017) NEET: a phenomenom yet to be explored. Interchange 48(1):19–37. https://doi.org/10.1007/s10780-016-9290-x

Veritas Health Innovation Covidence systematic review software, Melbourne, Australia. http://www.covidence.org . Accessed 01 May 2020

Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, Tugwell P The Newcastle–Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Health Research Institute. http://www.ohri.ca/programs/clinical_epidemiology/oxford.htm . Accessed 01 Jan 2021

Tanner-Smith EE, Tipton E, Polanin JR (2016) Handling complex meta-analytic data structures using robust variance estimates: a tutorial in R. J Dev Life Course Criminol 2(1):85–112. https://doi.org/10.1007/s40865-016-0026-5

Tipton E (2015) Small sample adjustments for robust variance estimation with meta-regression. Psychol Methods 20(3):375. https://doi.org/10.1037/met0000011

Lin E, Tong T, Chen Y, Wang Y (2020) Fixed-effects model: the most convincing model for meta-analysis with few studies. arXiv preprint arXiv: 2002. 04211

Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L, Moreno SG (2010) Assessing publication bias in meta-analyses in the presence of between-study heterogeneity. J R Stat Soc A Stat Soc 173(3):575–591. https://doi.org/10.1111/j.1467-985X.2009.00629.x

Balduzzi S, Rücker G, Schwarzer G (2019) How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health 22(4):153–160. https://doi.org/10.1136/ebmental-2019-300117

Viechtbauer W (2010) Conducting meta-analyses in R with the meta for package. J Stat Softw 36(3):1–48. https://doi.org/10.18637/jss.v036.i03

Nardi B, Arimatea E, Giunto P, Lucarelli C, Nocella S, Bellantuono C (2013) not employed in education or training (NEET) adolescents with unlawful behaviour: an observational study. J Psychopathol 19(1):42–48

Cairns AJ, Kavanagh DJ, Dark F, McPhail SM (2018) Comparing predictors of part-time and no vocational engagement in youth primary mental health services: a brief report. Early Interv Psychiatry 12(4):726–729. https://doi.org/10.1111/eip.12445

O’Dea B, Glozier N, Purcell R, McGorry PD, Scott J, Feilds KL, Hermens DF, Buchanan J, Scott EM, Yung AR, Killacky E, Guastella AJ, Hickie IB (2014) A cross-sectional exploration of the clinical characteristics of disengaged (NEET) young people in primary mental healthcare. BMJ Open 4(12):8. https://doi.org/10.1136/bmjopen-2014-006378

Gutierrez-Garcia RA, Benjet C, Borges G, Mendez Rios E, Medina-Mora ME (2018) Emerging adults not in education, employment or training (NEET): socio-demographic characteristics, mental health and reasons for being NEET. BMC Public Health 18(1):1201. https://doi.org/10.1186/s12889-018-6103-4

Gariépy G, Iyer S (2019) The mental health of young canadians who are not working or in school. Can J Psychiat 64(5):338–344. https://doi.org/10.1177/0706743718815899

López-López JA, Kwong AS, Washbrook E, Pearson RM, Tilling K, Fazel MS, Kidger J, Hammerton G (2019) Trajectories of depressive symptoms and adult educational and employment outcomes. BJPsych Open 6(1):E6. https://doi.org/10.1192/bjo.2019.90

Bania EV, Eckhoff C, Kvernmo S (2019) Not engaged in education, employment or training (NEET) in an Arctic sociocultural context: the NAAHS cohort study. BMJ Open 9(3):e023705. https://doi.org/10.1136/bmjopen-2018-023705

Hammerton G, Murray J, Maughan B, Barros FC, Gonçalves H, Menezes AMB, Wehrmeister FC, Hickman M, Heron J (2019) Childhood behavioural problems and adverse outcomes in early adulthood: a comparison of Brazilian and British birth cohorts. J Dev Life Course Criminol 5(4):517–535. https://doi.org/10.1007/s40865-019-00126-3

Rodwell L, Romaniuk H, Nilsen W, Carlin JB, Lee KJ, Patton GC (2018) Adolescent mental health and behavioural predictors of being NEET: a prospective study of young adults not in employment, education, or training. Psychol Med. https://doi.org/10.1017/s0033291717002434

Stea TH, de Ridder K, Haugland SH (2019) Comparison of risk-behaviors among young people who are not in education, employment or training (NEET) versus high school students. A cross-sectional study. Norsk Epidemiologi 28(1–2):39–47. https://doi.org/10.5324/nje.v28i1-2.3049

Basta M, Karakonstantis S, Koutra K, Dafermos V, Papargiris A, Drakaki M, Tzagkarakis S, Vgontzas A, Simos P, Papadakis N (2019) NEET status among young Greeks: association with mental health and substance use. J Affect Disord 253:210–217. https://doi.org/10.1016/j.jad.2019.04.095

Hale DR, Bevilacqua L, Viner RM (2015) Adolescent health and adult education and employment: a systematic review. Pediatrics 136(1):128–140. https://doi.org/10.1542/peds.2014-2105

Manhica H, Lundin A, Danielsson A-K (2019) Not in education, employment, or training (NEET) and risk of alcohol use disorder: a nationwide register-linkage study with 485 839 Swedish youths. BMJ Open 9(10):e032888. https://doi.org/10.1136/bmjopen-2019-032888

Hale DR, Viner RM (2018) How adolescent health influences education and employment: investigating longitudinal associations and mechanisms. J Epidemiol Community Health 72(6):465. https://doi.org/10.1136/jech-2017-209605

Stea TH, Abildsnes E, Strandheim A, Haugland SH (2019) Do young people who are not in education, employment or training (NEET) have more health problems than their peers? A cross-sectional study among Norwegian adolescents. Norsk Epidemiologi. https://doi.org/10.5324/nje.v28i1-2.3055

Hammarström A, Ahlgren C (2019) Living in the shadow of unemployment—an unhealthy life situation: a qualitative study of young people from leaving school until early adult life. BMC Public Health 19(1):1661. https://doi.org/10.1186/s12889-019-8005-5

Mitchell DP, Betts A, Epling M (2002) Youth employment, mental health and substance misuse: a challenge to mental health services. J Psychiatr Ment Health Nurs 9(2):191–198. https://doi.org/10.1046/j.1365-2850.2002.00466.x

Brunet S (2018) The transition from school to work: the NEET (not in employment, education or training) indicator for 25-to 29-year-old women and men in Canada. Education Indicators in Canada: Fact Sheet. Statistics Canada, Ottawa

Eurofound (2016) Exploring the diversity of NEETs. Publications Office of the European Union, Luxembourg

Holte BH (2018) Counting and meeting NEET young people: methodology, perspective and meaning in research on marginalized youth. Young 26(1):1–16. https://doi.org/10.1177/1103308816677618

Bloom DE, Cafiero E, Jané-Llopis E, Abrahams-Gessel S, Bloom LR, Fathima S, Feigl AB, Gaziano T, Hamandi A, Mowafi M, O’Farrell D, Ozaltin E, Pandya A, Prettner K, Rosenberg L, Seligman B, Stein AZ, Weinstein C, Weiss J (2012) The global economic burden of noncommunicable diseases, vol 8712. Program on the Global Demography of Aging, Harvard University, Cambridge

Rodwell L, Romaniuk H, Nilsen W, Carlin JB, Lee K, Patton GC (2018) Adolescent mental health and behavioural predictors of being NEET: a prospective study of young adults not in employment, education, or training. Psychol Med. 48(5):861–871. https://doi.org/10.1017/S0033291717002434 .

Fusar-Poli P (2019) Integrated mental health services for the developmental period (0 to 25 years): a critical review of the evidence. Front Psychiatry. https://doi.org/10.3389/fpsyt.2019.00355

Malla A, Iyer S, McGorry P, Cannon M, Coughlan H, Singh S, Jones P, Joober R (2016) From early intervention in psychosis to youth mental health reform: a review of the evolution and transformation of mental health services for young people. Soc Psychiatry Psychiatr Epidemiol 51(3):319–326

Download references

Acknowledgements

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).

Author information

Authors and affiliations.

Montreal Mental Health University Institute, Montreal, QC, Canada

Geneviève Gariépy

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

Douglas Research Centre, Montreal, QC, Canada

Sofia M. Danna

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

Lisa Hawke & Joanna Henderson

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

Srividya N. Iyer

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

You can also search for this author in PubMed   Google Scholar

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.

Corresponding author

Correspondence to Srividya N. Iyer .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Availability of data and material/code availability

Not applicable.

Ethics approval and consent to participate

The manuscript does not contain clinical studies or patient data.

Consent for publication

Additional information.

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 775 KB)

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

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

Download citation

Received : 28 April 2021

Accepted : 05 December 2021

Published : 21 December 2021

Issue Date : June 2022

DOI : https://doi.org/10.1007/s00127-021-02212-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Education or employment
• Youth mental health
• Substance use
• Systematic review
• Meta-analysis
• Find a journal
• Publish with us
• Track your research

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

You are using an outdated browser. Please upgrade your browser to improve your experience and security.

• Job Vacancies

Please tell us which products you are interested in.

Early career teachers, early years & childcare, enrichment activities, equality & inclusion, financial management, governor services & clerking, outdoor learning, primary school improvement, professional development, room booking & equipment hire, safeguarding, secondary school improvement.

• Skills & Employability

Supported Employment

Tell us which area you are interested in, all training, primary & early years in school, college & higher education, foster carer & supported homes, conferences.

• Our Services Our Services
• Contact Us Contact Us
• Our Expertise

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.

Holiday Activities and Food Summer Programme

The Education People Show 2024 Opening Speaker Update

Upcoming events.

Kent Briefing on the new Early Years Foundation Stage Reforms

Developing Conceptual Fluency Using Knowledge Organisers

Share of youth not in education, employment or training, total (% of youth population)

All Countries and Economies

Country Most Recent Year Most Recent Value

• Privacy Notice
• Access to Information

This site uses cookies to optimize functionality and give you the best possible experience. If you continue to navigate this website beyond this page, cookies will be placed on your browser. To learn more about cookies, click here.

• 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

6720 Accesses

22 Citations

Metrics details

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

Arnett J. Emerging adulthood: a theory of development from the late teens through the twenties. Am Psychol. 2000;55:469–80.

Article   CAS   Google Scholar  

Arnett J. Emerging adulthood: the winding road from the late teens through the twenties. New York, NY: Oxford University Press; 2004.

Google Scholar  

Arnett J. Identity development from adolescence to emerging adulthood: what we know and (especially) don’t know. In: McLean KC, Syed M, editors. the Oxford handbook of identity development (pp. 53–64). New York, NY: Oxford University Press; 2015.

Marshall E, Butler K. School-to-work transitions in emerging adults. In: Arnett JJ, editor. Oxford handbook of emerging adulthood. New York, NY: Oxford University Press; 2015.

Murphy KA, Blustein DL, Bohlig J, Platt M. The college -to-career transition: an exploration of emerging adulthood. J Couns Dev. 2010;88(2):174–81.

Article   Google Scholar  

Buhl HM, Lanz M. Emerging adulthood in Europe: common traits and variability across five European countries. J Adolesc Res. 2007;22(5):439–43.

Hendry LB, Kloep M. 2010. How universal is emerging adulthood? An empirical example. J Youth Stud. 2010;13(2):169–79.

Luyckx K, De Witte H, Goossens L. Perceived instability in emerging adulthood: the protective role of identity capital. J Appl Dev Psychol. 2011;32(3):137–45.

Westberg A. Forever young? Young people’s conception of adulthood: the Swedish case. J Youth Stud. 2004;7(1):35–53.

Marshall K. Youth neither enrolled nor employed. Perspectives on Labour and Income. 2012;24:1–15.

Goldscheider F, Goldscheider C. Leaving and returning home in 20th century America (population bulletin). Washington, DC: Population Reference Bureau; 1994.

Bynner J. Rethinking the youth phase of life-course: the case for emerging adulthood? J Youth Stud. 2005;8(4):367–84.

Dutra-Thomé L, Koller SH. Emerging adulthood in Brazilians of differing socioeconomic status: transition to adulthood. Paidéia (Ribeirão Preto). 2004;24(59):313–22.

Facio A, Resett S, Micocci F, Mistrorigo C. Emerging adulthood in Argentina: an age of diversity and possibilities. Child Dev Perspect. 2007;1:115–8.

Galambos NL, Martínez ML. Poised for emerging adulthood in Latin America: a pleasure for the privileged. Child Dev Perspect, 2007; 1(2), 109–114. https://doi.org/10.1111/j.1750-8606.2007.00024.x .

Echarri C, Pérez J. En Tránsito hacia la Adultez: Eventos en el Curso de Vida de los Jóvenes en México [trad] in transition to adulthood: events in the life course youth in Mexico. Revista Estudios Sociodemográficos y Urbanos. 2007;22:43–77.

Arias DF, Hernández AM. Emerging adulthood in Mexican and Spanish youth: theories and realities. J Adolesc Res. 2007;22:476–503.

Arnett J. Are college students adults? Their conceptions of the transition to adulthood. J Adult Dev. 1994;1:154–68.

Bynner J, Parsons S. Social exclusion and the transition from school to work: the case of young people not education, employment or training (NEET). J Vocat Behav. 2002;60:289–309.

Eurofound. NEETs—young people not in employment, education or training: characteristics, costs and policy responses in Europe . Luxembourg: publications Office of the European Union; 2002. Retrieved February. 2017:15. https://www.eurofound.europa.eu/sites/default/files/ef_files/pubdocs/2012/54/en/1/EF1254EN.pdf .

Wanberg C. The individual experience of unemployment. Annu Rev Psycho. 2012;63:369–96.

Negrete R, Leyva G. Los NiNis en México: una aproximación crítica a su medición [trad] The NEET in Mexico: a critical approach to measurement. Revista Internacional de Estadística y Geografía . 2013;4(1):90–121.

Gutiérrez RA, Martínez KI, Pacheco AY. Los Jóvenes que no estudian ni trabajan en México [trad] the young who neither study nor work (NEET) in Mexico. Enseñanza e Investigación en Psicología. 2014;19(2):58–67.

OECD, Organization for Economic Cooperation and Development. Un primer plano sobre los jóvenes. La situación de México [trad] A close up on young people. The situation in Mexico; 2016.

International Labour Organization -ILO. What does NEETs mean and why is the concept so easily misinterpreted? Geneva: ILO; 2015. Retrieved December 16, 2016. http://www.ilo.org/wcmsp5/groups/public/@dgreports/@dcomm/documents/publication/wcms_343153.pdf .

INEGI, [National Institute of Statistics and Geography]. Encuesta Nacional de Ocupación y Empleo [trad] National Survey of Occupation and Employment; 2010. Retrieved February 10, 2017. http://www.inegi.org.mx/est/lista_cubos/consulta.aspx?p=encue&c=4 .

Quintini G, Martin J, Martin S. The changing nature of the school-to-work transition process in OECD countries. Institute for the Study of labour (IZA): OCDE; 2007.

Stoneman P, Thiel D. NEET in Essex: a review of the evidence. UK: University of Essex; 2010.

Alfieri S, Sironi E, Marta E, Rosina A, Marzana D. Young Italian NEETs (not in employment, education, or training) and the influence of their family background. Eur J Psychol. 2015;11(2):311–22.

Kessler R, Berglund P, Demler O, Merikangas K, Walters E. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005; 62, 593–602. http://dx.doi.org/10.1001/archpsyc.62.6.593 .

Benjet C, Hernández D, Borges G, Medina-Mora M, Aguilar S. Youth who neither study nor work: mental health, education and employment. Salud Publica Mexico. 2012;54:410–7.

Gutiérrez-García RA, Benjet C, Borges G, Méndez Ríos E, Medina-Mora ME. NEET adolescents grown up: eight-year longitudinal follow-up of education, employment and mental health from adolescence to early adulthood in Mexico City. European Child & Adolescent Psychiatry. 2017;26(12):1459–469.

Gutiérrez R, Martínez K, Pacheco A, Benjet C. La construcción social de la identidad en jóvenes que no estudian ni trabajan. [trad] The social construction of identity in young people who neither study nor work Revista Iberoamericana de Ciencias. 2014;1:1–12.

Scott J, et al. Adolescents and young adults who are not in employment, education, or training. BMJ. 2013;9:347–55 https://doi.org/10.1136/bmj.fe5270 .

Lisha NE, et al. The relationship of emerging adulthood trajectories to drug use, and other correlates. Salud y drogas. 2015;15:91–102.

PubMed   PubMed Central   Google Scholar  

Lee R, et al. Neuropsychological and socio-occupational functioning in young psychiatric outpatients: a longitudinal investigation. PLoS One. 2013;8(3):176–84.

O’Dea B, Glozier N, Purcell R, et al. A cross-sectional exploration of the clinical characteristics of disengaged (NEET) young people in primary mental healthcare. BMJ Open. 2014;4:e006378.

Goldman-Mellor S, Caspi A, et al. Committed to work but vulnerable: self-perceptions and mental health in NEET 18-year olds from a contemporary British cohort. J Child Psychol Psychiatry. 2016;57:196–203.

Benjet C, Borges G, Méndez E, Albor Y, Casanova L, Orozco R, Curiel T, Fleiz C, Medina-Mora M. Eight-year incidence of psychiatric disorders and service use from adolescence to early adulthood: longitudinal follow-up of the Mexican adolescent mental health survey. Eur Child Adolesc Psychiatry. 2016;25:163–73. https://doi.org/10.1007/s00787-015-0721-5 .

Kessler R, ÜstÜn S. The world mental health (WMH) survey initiative version of the World Health Organization (WHO) composite international diagnostic interview (CIDI). Int J Methods Psychiatr Res. 2004;13:93–121.

American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-IV). Washington, DC: APA; 2000.

Benjet C, Borge G, Medina-Mora ME, Zambrano J; Aguilar-Gaxiola S. Youth mental health in a Populous City of the developing world: results from the Mexican adolescent mental health survey. J Child Psychol Psychiatry 2009;50:386–95. http://dx.doi.org/10.1111/j.1469-7610.2008.01962.x .

OECD, Organization for Economic Cooperation and Development. Youth not in education or employment (NEET) (indicator). Retrieved April 10, 2016. http://dx.doi.org/10.1787/72d1033a-en .

Kleiman EM, Beaver K. A meaningful life is worth living: meaning in life as a suicide resiliency factor. Psychiatry Res. 2013;(3):934–9.

Stillman T, Baumeister R, Lambert N, Crescioni A, DeWall C, Fincham F. Alone and without meaning: life loses meaning following social exclusion. J Exp Soc Psychol. 2009;45:686–94.

Gutiérrez R, Moral M, Martínez K, Pacheco A. Discursos de los jóvenes que no estudian ni trabajan en México y España. [trad] Narrativies of young people who neither study nor work in Mexico and Spain Revista Alternativas en Psicología. 2015;1:92–108.

Fergusson D, McLeod G, Horwood L. Unemployment and psychosocial outcomes to age 30: a fixed-effects regression analysis. Aust N Z J Psychiatry. 2014;48(8):735–42.

Furlong A. Not a very NEET solution: representing problematic labour market transitions among early school-leavers. Work Employ Soc. 2006;20(3):553–69.

Mascherini M. Young people and NEETs in Europe: first findings. Dublin: Eurofound; 2012.

Camarillo M. 22 millones de “Ninis” en AL, acechados por el narco. [trad] 22 million “NEET” in AL, stalked by drug trafficking. México; 2015. Crónica. Retrieved October 20, 2016. http://www.cronica.com.mx/notas/2013/768376.html .

Michel V. Sicarios de cárteles son 'ninis', sostiene el Banco Mundial [trad] Hitmen poster child NEET' says World Bank. México: El financiero 2016;. Retrieved October 15, 2016. http://www.elfinanciero.com.mx/nacional/sicarios-de-carteles-son-ninis-sostiene-el-banco-mundial .

Drydakis N. The effect of unemployment on self-reported health and mental health in Greece from 2008 to 2013: a longitudinal study before and during the financial crisis. Soc Sci Med. 2014;128:43–51.

Zenger M, Brähler E, Berth H, Stobel-Ricter Y. Unemployment during working life and mental health of retirees: results of a representative survey. Aging Ment Health. 2011;15(2):178–85.

Tanner J, Arnett J. Approaching young adult health and medicine from a developmental perspective. Adolescent medicine: State of the Art Reviews. 2013;24:485–506.

Download references

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).

Availability of data and materials

Data collected as part of the study can be obtained from the corresponding author for the purpose of verification.

Author information

Authors and affiliations.

Epidemiologic and Psychosocial Research, National Institute of Psychiatry Ramón de la Fuente Muñiz, Mexico City, Mexico

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

You can also search for this author in PubMed   Google Scholar

Contributions

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.

Corresponding author

Correspondence to Raúl A. Gutiérrez-García .

Ethics declarations

Ethics approval and consent to participate.

This study was approved by the Research Ethics Committee of the National Institute of Psychiatry (Mexico City, Mexico), and therefore has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All participants gave their written informed consent.

Consent for publication

Not applicable. The submission does not contain any personally identifiable information.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 06 June 2018

Accepted : 08 October 2018

Published : 25 October 2018

DOI : https://doi.org/10.1186/s12889-018-6103-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Mental health

BMC Public Health

ISSN: 1471-2458

• General enquiries: [email protected]

NEET Forum - Not in Education, Employment, or Training

News & announcements.

• Yesterday at 6:46 PM

NEET Lifestyle

• 13 minutes ago
• TellEmNoYellin

Anime & Manga

• 1 minute ago

Newest Threads

• Started by AsgardTheFatcel
• 12 minutes ago
• Started by xan
• 39 minutes ago

• Started by DirtyCurryCell
• 40 minutes ago

Forum statistics

• This site uses cookies to help personalise content, tailor your experience and to keep you logged in if you register. By continuing to use this site, you are consenting to our use of cookies. Accept Learn more…

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

Did you like this article? If you would like to be notified when new content like this is published, subscribe to receive our email alerts.

The leading source of labour statistics

• Country profiles
• Standards & methods

Data by region

For data producers, publications, selected topics.

Home > Topics

• © Marcel Crozet / ILO

Statistics on youth

Table of contents, related pages.

Labour statistics for the Sustainable Development Goals (SDGs)

Statistics on unemployment and labour underutilization

Labour Force Statistics (LFS, STLFS, RURBAN databases)

Youth Labour Market Statistics (YouthSTATS database)

Mastercard Foundation

Decent Jobs for Youth Knowledge Facility

UN Global SDG Indicators Database

Introduction

In many countries, the youth labour situation is worrisome. Informality and vulnerable employment remain an unfortunate reality for the majority of employed youth around the world. Moreover, when they are not in employment, youth face difficulties accessing the labour market. This is reflected in high youth unemployment rates, high NEET (not in employment, education or training) rates, and the often difficult transition from school to work.

In the 2030 Agenda for Sustainable Development, the international community committed to increase youth employment opportunities and to substantially reduce the proportion of youth not in education, employment or training ( SDG 8.6 ). In this context, detailed labour statistics on youth provide vital information to support governments and civil society in their efforts to design, implement and monitor policies to promote better youth employment outcomes.

Data catalogue

Below is a subset of the indicators available on youth, which is defined as persons ages 15 to 29 in the Youth Labour Market Statistics (YouthSTATS) database only. For all available indicators by age, which includes a category for persons ages 15 to 24, refer to the data page .

Latest posts

How population trends shape our workforce

African youth face pressing challenges in the transition from school to work

Youth skills: tackling challenges and seizing opportunities for a brighter future of work

The ILO project to produce comprehensive national data on youth in the labour market, including indicators on the transition from school to work, was possible thanks to the support of the Mastercard Foundation.

School-to-work transition indicators provide a detailed classification of young people’s transition path into the labour market, shedding light on employment prospects for youth and barriers to young people’s access to decent jobs. There are two main indicators: the school-to-work transition stage and the school-to-work transition form.

The school-to-work transition stage classifies youth into three groups according to their stage in the transition: transited, in transition, and transition not yet started. According to this classification, a person has not “transited” until they are settled in a job that meets very basic criteria of stability or satisfaction. The transited population is subdivided according to two types of transition: (1) youth transited in a stable job; and (2) youth transited in satisfactory self-employment or a satisfactory temporary job.

The school-to-work transition form indicator classifies those youth that are “in transition” into four forms: those that are (1) in school and currently in the labour force (employed or not employed but available and looking for a job); (2) not in school and unemployed (looking and available for a job); (3) not in school and currently employed in a temporary and unsatisfactory job; and (4) not in school but with the intention to be employed in the future. In addition, the youth population that has not yet started the transition is classified into those who (1) are still in school and outside the labour force (not employed and not available and/or looking for a job); and those who are (2) not in school, outside the labour force and with no intention of looking for a job.

Concise description of concepts and definitions, uses, sources and limitations for labour force statistics focused on youth.

Resolution concerning the methodology of the SDG indicator 8.b.1 on youth employment

Can we measure the school-to-work transition of young persons with labour force surveys a feasibility study.

The purpose of the paper is to examine the feasibility of obtaining data on school-to-work transitions of young persons from conventional labour force surveys.

Note: Many publications are available only in English. If available in other languages, a new page will open displaying these options. 

World Employment and Social Outlook: September 2024 Update

This brief focuses on the evolving relationship between labour and capital income, challenges facing youth in the current job market, and progress towards key SDGs as 2030 approaches.

Global Employment Trends for Youth 2022: Investing in transforming futures for young people

The 2022 edition discusses the impact of the COVID-19 pandemic on young people and their labour market prospects during the recovery and beyond.

Volunteer work and its links to the labour market experiences of young people

This paper looks at how volunteering can benefit young people at the start of their careers. It uses existing literature and undertakes further longitudinal analysis, including on the effects of volunteering on young people as they seek to access good jobs.

From school to work: An analysis of youth labour market transitions

This brief presents an analysis of youth labour market outcomes, with a particular focus on two new school-to-work transition indicators published on ILOSTAT. It first introduces the new indicators. It then analyses the distribution of youth by stages of transition across a set of 60 countries for which the ILO has derived indicators from national labour force survey microdatasets.

Labour market access – a persistent challenge for youth around the world

The fifth issue of our series Spotlight on work statistics uses the first ever global estimates of youth not in employment, education or training along with other youth labour market indicators to explore the situation of youth in labour markets around the world, and unveil the additional challenges they face.

Need help? Click here to email us.

Sign up for our quarterly newsletter

The ILO Department of Statistics is the focal point to the United Nations on labour statistics. We develop international standards for better measurement of labour issues and enhanced international comparability; provide relevant, timely and comparable labour statistics; and help Member States develop and improve their labour statistics.

Tell us whether you accept cookies

We would like to use cookies to collect information about how you use ons.gov.uk .

We use this information to make the website work as well as possible and improve our services.

You’ve accepted all cookies. You can change your cookie settings at any time. Hide

• Release calendar
• Methodology

Young people not in education, employment or training (NEET), UK: August 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: 22 August 2024

Next release: 21 November 2024

Table of contents

• Main points
• Total young people who were not in education, employment or training (NEET)
• Data on young people who were not in education, employment or training
• Data sources and quality
• Related links
• Cite this statistical bulletin

Print this Statistical bulletin

Download as PDF

1. Main points

• Labour Force Survey (LFS) estimates have been weighted to population estimates published in November 2023 for periods from July to September 2022; 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 April to June 2024, with the total currently estimated to be 872,000, up from 798,000 in April to June 2023.
• The percentage of all young people who were NEET in April to June 2024 was estimated at 12.2%, up 0.9 percentage points on the year.
• The increase in the number of young people who were NEET was driven by young men, who saw an increase of 69,000 on the year to 493,000.
• The number of young people who were NEET and unemployed in April to June 2024 was estimated to be 332,000, a decrease of 1,000 on the year.
• The number of young men aged 16 to 24 years who were NEET and unemployed decreased by 24,000 on the year to 213,000.
• There were an estimated 540,000 young people in the UK who were NEET and economically inactive, an increase on the year of 75,000.

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: Data sources and quality .

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

An estimated 12.2% of all people aged 16 to 24 years in the UK were not in education, employment or training (NEET) in April to June 2024. This is up 0.9 percentage points compared with April to June 2023, and down 0.4 percentage points on the previous quarter.

An estimated 13.5% of young men (up 1.7 percentage points on the year) and 10.8% of young women (largely unchanged on the year) were NEET. There were 872,000 young people who were NEET in total, an increase of 74,000 on the year. This increase was driven by young men. Of the total number of young people who were NEET, 493,000 were young men and 379,000 were young women.

The total number of people aged 18 to 24 years who were NEET was 807,000, up 65,000 on the previous year.

The percentage of those aged 18 to 24 years who were NEET was 14.5%, which was up 1.1 percentage points on the year, and down 0.4 percentage points on the quarter.

Figure 1: The percentage of young people who are not in education, employment or training (NEET) decreased over the quarter (January to March 2024)

People aged 16 to 24 years neet as a percentage of all people aged 16 to 24 years by age, seasonally adjusted, uk, july to september 2022 to april to june 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) decreased over the quarter (january to march 2024), unemployed young people who were not in education, employment or training.

There were an estimated 332,000 NEET young people aged 16 to 24 years who were unemployed in April to June 2024, down 1,000 from April to June 2023 and up 13,000 from January to March 2024. An estimated 213,000 of these unemployed NEETS were young men, and 120,000 were young women. The number of NEET men aged 16 to 24 years who were unemployed decreased by 24,000 on the year from April to June 2023, while the number of NEET women aged 16 to 24 years who were unemployed increased by 23,000 on the year.

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

In April to June 2024, there were an estimated 540,000 economically inactive young people aged 16 to 24 years who were NEET. This was up 75,000 on the year from April to June 2023, and down 40,000 on the quarter from January to March 2024. The number of young men who were NEET and economically inactive was 280,000 and the corresponding number of young women was 260,000. The total increase of 75,000 on the year was driven by young men, who saw an increase of 93,000 on the year from April to June 2023, while young women aged 16 to 24 years who were NEET and economically inactive decreased by 18,000 on the year.

Subnational not in education, employment or training (NEET) estimates

Subnational estimates for people not in education, employment or training are not published by the Office for National Statistics (ONS), but can be accessed through the links in Section 6: Related links .

3. Data on young people who were not in education, employment or training

Young people not in education, employment or training (NEET) Dataset | Released 22 August 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 22 August 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 (NEET)

Anybody who is not in any of the forms of education or training listed previously and 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. Data sources and quality

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 data on our SDGs reporting platform .

Our NEET methodological article providing background information explains 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. As stated in the article, we are planning a further reweighting exercise, based on the population projections published in January 2024. We plan to introduce the reweighted LFS series into our labour market publication by the end of 2024.

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

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

The Office for National Statistics (ONS) is 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 6: Related links .

Coronavirus

View more information on how labour market data sources are affected by the coronavirus (COVID-19) pandemic .

View a comparison of our labour market data sources and the main differences .

Relationship to other labour market statistics for young people

Our monthly Labour market statistical bulletin includes 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 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 [email protected] .

Strengths and limitations

The figures in this bulletin come from the 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 variabilities for estimates of young people who are NEET derived from the LFS.

6. 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.

NEET estimates for Wales Statistics and research | Last updated 27 June 2024 Data for young people by age, gender, region and local authority from the Welsh Government.

NEET estimates for Northern Ireland Tables | Last updated 14 May 2024 Tables from the Labour Market Report, including Not in education employment or training (NEET).

7. Cite this statistical bulletin

Office for National Statistics (ONS), published 22 August 2024, ONS website, statistical bulletin, Young people not in education, employment or training (NEET), UK: August 2024

Contact details for this Statistical bulletin

COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK

Reporting Analyst

• Columbia University Medical Center
• Opening on: Sep 12 2024
• Job Type: Officer of Administration
• Bargaining Unit:
• Regular/Temporary: Regular
• End Date if Temporary:
• Hours Per Week: 35
• Standard Work Schedule:
• Salary Range: 63,700-87,700

Position Summary

The Revenue Cycle/Financial Reporting Analyst provides practice operations and budget/finance support in the Finance Office for multiple Surgical Departments.

Responsibilities

• Responsible for quarterly and annual Clinical revenue projections for use by both Surgery and the FPO and other external offices.
• Prepare quarterly and annual revenue projections for new recruits for use and evaluation by department, FPO and NYPH.
• Accurately analyze metrics and collect data for various types of Epic reports to drive critical business decisions.
• Maintain the Monthly Metric Sheet report to conduct quantitative and qualitative analyses on a variety of issues.
• Review and coordinate ad-hoc reports for multiple departments to ensure billing compliance.
• Regularly examine data reports to locate and resolve mistakes with recommended corrective actions.
• Monitor data to identify and communicate changes in financial trends within the practices.
• Coordinate data implementation projects to present to the management team.
• Perform other duties as needed.

Minimum Qualifications

Bachelor's degree or equivalent in education and experience, plus three (3) years of related experience.

Preferred Qualifications

Preferred advanced degree in Finance, Accounting or Business Administration.

Other Requirements

• Must have strong analytical abilities.
• Proficiency with the Microsoft Office suite, especially Excel
• Must be able to manage and analyze large and complex data sets with a high degree of accuracy.
• Must be able to manage a high-volume workload.
• Must have strong organizational skills and capable of working independently as well as collaboratively.
• Must be detail oriented and be able to handle competing priorities.
• Effective interpersonal and communication skills required, excellent customer service.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.

Commitment to Diversity 

Columbia university is dedicated to increasing diversity in its workforce, its student body, and its educational programs. achieving continued academic excellence and creating a vibrant university community require nothing less. in fulfilling its mission to advance diversity at the university, columbia seeks to hire, retain, and promote exceptionally talented individuals from diverse backgrounds.  , share this job.

Thank you - we'll send an email shortly.

Other Recently Posted Jobs

Research Project Coordinator

Assistant director, sexual violence response, clinical coordinator, mhp.

Refer someone to this job

• ©2022 Columbia University
• Accessibility
• Administrator Log in

Wait! Before you go, are you interested in a career at Columbia University? Sign up here! 

Thank you, for sharing your information. A member of our team will reach out to you soon!

This website uses cookies as well as similar tools and technologies to understand visitors' experiences. By continuing to use this website, you consent to Columbia University's usage of cookies and similar technologies, in accordance with the Columbia University Website Cookie Notice .