• Youth not in employment, education or training (NEET)

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

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  • Research article
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  • Published: 25 October 2018

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

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

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

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

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

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

Conclusions

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

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

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

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

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

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

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

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

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

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

Participants

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Strengths and limitations of this study

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

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

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

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

Abbreviations

Confidence interval

Adjusted Odds Ratios

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

National Institute of Statistics and Geography

Mexican Adolescent Mental Health Survey

Not in education, employment nor training

The Organisation for Economic Co-operation and Development

The World Health Organization World Mental Health Composite International Diagnostic Interview

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Acknowledgements

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

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

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

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

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not in education employment or training

Economics Help

NEET – ‘Not in Employment, Education or Training’

NEET stands for ‘Not in Employment, Education or Training’

It is used to measure social exclusion, economic inactivity and levels of disengagement from labour markets.

It was created to measure levels of labour market participation amongst young people in the UK.

In the UK, NEET measures the percentage of young people aged 16-24 who are not in work, education or training. The measure has been adopted by other countries, such as Japan and the EU. It is an important indicator of levels of inactivity amongst young people. Higher levels of NEET are seen as a cause for concern because of higher welfare payments, and the potential breakdown of normal social and economic activity.

Causes of NEET

  • unskilled and no relevant qualifications
  • Geographical factors, such as high rates of local unemployment and geographical unemployment
  • Poor expectations fostered by lack of role models and high unemployment
  • Recession. Levels of Neet has has increased in the 2008-13 EU recession.
  • See also: causes of youth unemployment
  • Lack of available education and training programmes.
  • Education and training programmes that are not suitable.
  • Unwillingness or poor information about available training and education programmes

Levels of NEET in UK

See ONS NEETs for latest data

Levels of NEETs in Europe

neets

In 2011, in the EU, there were some 7.5 million young people (15-24 years) in a NEET status (12.9 %) (1)

Economic Costs of NEETs

  • Higher welfare bill. Europeans aged 15 to 29 who are not in employment, education or training have reached record levels and are costing the EU €3bn a week in state welfare and lost production. (2)
  • Higher Crime. NEETs are 20 times more likely to commit a crime. They are 22 times more likely to be a single mum. (3)
  • Higher levels of social disengagement. Lack of prospects can create feelings of social exclusion and contribute to riots that have intermittently been seen across Europe.

Eurofound (2012a) estimated the economic costs of the disengagement of young people from the labour market (i.e. NEET costs at around €153 billion or 1.2 % of the aggregated GDP of EU-26 countries in 2011. This is an increase of €34 billion in comparison to 2008 (1)

  • How to deal with youth unemployment
  • Reasons for youth unemployment

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Young people are our hope for a better future, but targeted policies are needed to address the disadvantages they face in the labour market.

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

Geneviève gariépy.

1 Montreal Mental Health University Institute, Montreal, QC Canada

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

Sofia M. Danna

3 Douglas Research Centre, Montreal, QC Canada

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

Joanna Henderson

Srividya n. iyer.

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

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

Associated Data

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

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

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

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

Supplementary Information

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

Introduction

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

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

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

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

Search strategy

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

Selection criteria

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

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

Data analysis

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

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

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

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

An external file that holds a picture, illustration, etc.
Object name is 127_2021_2212_Fig1_HTML.jpg

PRISMA flow diagram

Characteristics of the selected studies

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

b SES: socioeconomic status

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

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

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

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

a Studies with overlapping data or with specific OR not reported

† 95% CI or specific p value not reported

‡ Specific p value not reported

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Object name is 127_2021_2212_Fig2_HTML.jpg

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

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

Sub-group analyses

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

Directionality of association

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

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

Associations by gender

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

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

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

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

Associations by age

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

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

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

Associations by sample type

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

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

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

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

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

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

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

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

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

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

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

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

Below is the link to the electronic supplementary material.

Acknowledgements

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

Author contributions

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

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

Declarations

The authors declare that they have no conflict of interest.

Not applicable.

The manuscript does not contain clinical studies or patient data.

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

Not in Education, Employment or Training: Incidence, Determinants and Costs in India

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not in education employment or training

  • Somtirtha Sinha 4 &
  • Zakir Husain 4  

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An individual is termed as NEET, who is neither employed, nor is (s)he attending any educational institution, nor is (s)he attending any sort of skill developing training. Thus, in a sense, NEET not only includes the currently unemployed population, but also includes the individuals who are more likely than not to be unemployed in the future, since they are not engaged in any productive activity whatsoever. NEET is essentially a measure of complete disengagement from the labor market. This study draws on the NSSO Employment and Unemployment Survey for the years 2004–05 and 2011–12 to firstly find the proportion and the absolute number of NEET in India. Secondly, we consider various socio-economic factors to assess whether they are significant determinants of being NEET using logistic regression models. Finally, we provide an estimate of the total loss in earnings of the individual. For this purpose, we calculate the proportions of NEET from the NSS data, and the total population figures were used using Census 2001 and 2011 data. We found that being a female and being married increase the odds of being a NEET significantly. Apart from this, family affluence, age, education level and socio-religious community of the individual were all found to be significant determinants of NEET. Finally, we found that the total income loss to the NEET was about 2% of GDP in 2004–05 and about 7% in 2011–12.

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The variable “src” was created by combining information on the religion and caste of the  respondents.

It should be noted that we are including two groups of people within NEET. They are persons engaged in illegal activities such as theft, or smuggling, or drug peddling all of which can prove to be quite financially rewarding. Unfortunately, there are no data on such activities as they are illegal, so that no one will own up to them. The second group comprises political party workers who get paid by the political party they belong to and are “allowed” to augment their legal incomes by bribes, hush money or cut-money. Joining politics is legal, so that part of the income earned by such grassroots full timers is legal. However, it is not considered to be an occupation, and NSSO does not give any figures for them. The inclusion of these two categories will reduce the estimate of NEET, so that our estimate of costs will be less than that stated in Table 14.9 .

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Sinha, S., Husain, Z. (2022). Not in Education, Employment or Training: Incidence, Determinants and Costs in India. In: Bagli, S., Chakrabarti, G., Guha, P. (eds) Persistent and Emerging Challenges to Development. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-16-4181-7_14

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Not in education, employment and training: pathways from toddler difficult temperament

Affiliations.

  • 1 Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
  • 2 Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • 3 Centre for Longitudinal Studies, UCL Social Research Institute, University College London, UK.
  • 4 ESRC Centre for Society and Mental Health, King's College London, London, UK.
  • PMID: 36001767
  • PMCID: PMC9403166
  • DOI: 10.1111/jcpp.13557

Background: Youths disengaged from the education system and labour force (i.e. 'Not in Education, Employment, or Training' or 'NEET') are often at reduced capacity to flourish and thrive as adults. Developmental precursors to NEET status may extend back to temperamental features, though this - and possible mediators of such associations such as attention deficit hyperactivity (ADHD) symptoms and antisocial behaviours (ASB) - have yet to be directly tested. This study investigates if i) difficult temperament in toddlerhood associates with NEET status in adulthood and ii) different subdomains of ADHD (i.e. hyperactivity-impulsivity vs. inattention) in late childhood and ASB in adolescence partially explain this pathway.

Methods: Participants were 6,240 mother-child dyads (60.7% female) from the Avon Longitudinal Study of Parents and Children. Mothers reported on their child's (a) difficult temperament (i.e. mood, intensity and adaptability) at age 2 and (b) ADHD symptoms at ages 8 and 10. Participants reported their own ASB at age 14 and NEET status in adulthood (ages 18, 20, 22 and 23).

Results: First, higher levels of difficult temperament in toddlerhood directly associated with an increased probability of being NEET in adulthood. Second, this effect was carried through hyperactivity-impulsivity, but not inattention, in late childhood, and ASB in adolescence; this demonstrates differential contribution to the pathway between the ADHD dimensions, with symptoms of hyperactivity-impulsivity playing a prominent role.

Conclusions: Early difficult temperament is a vulnerability factor for NEET status in adulthood. Our findings suggest that one developmental pathway for this vulnerability manifests through increased hyperactivity-impulsivity in childhood and ASB in adolescence. Of note, difficult temperament, as measured here, reflects difficulties in emotional and behavioural self-control (e.g. low adaptability and high intensity negative emotional expressions). Our results, therefore, suggest a prominent developmental role for lack of self-control from toddlerhood onwards in increasing risk for NEET.

Keywords: ADHD; ALSPAC; Difficult temperament; NEET; antisocial behaviours.

© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural
  • Attention Deficit Disorder with Hyperactivity* / diagnosis
  • Child, Preschool
  • Educational Status
  • Longitudinal Studies
  • Temperament*

Grants and funding

  • 217065/Z/19/Z/WT_/Wellcome Trust/United Kingdom
  • MC_PC_19009/MRC_/Medical Research Council/United Kingdom
  • MC_PC_15018/MRC_/Medical Research Council/United Kingdom
  • R01 HD068437/HD/NICHD NIH HHS/United States
  • G9815508/MRC_/Medical Research Council/United Kingdom
  • WT_/Wellcome Trust/United Kingdom

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India should worry about a generation of youth idling away

Skill up!

Lata’s expression that day was a mix of delight, surprise, disbelief, and pride. She had made it through the Marriott interview. She could not wait for Monday and her first day of work.

Among  423 million  young adults in India, Lata used to be one of the  13.2%  of youth her age unsuccessfully looking for a job. The critical part of that measure is “unsuccessful.” Choice is everybody’s imperative, which sometimes, even as egalitarian development sector folks, we seem to forget, especially when it comes to youth employability programmes.

Lata was willing and able to work, and hence she found a job. What about the millions of youth who want to be skilled enough for the workplace, but first want to study further and then enter the workforce? Are skilling programmes wasted on them? There is enough  evidence  that points to the fact that the positive effects of youth employment programmes are not instantaneous, and hence it is important that we view such programmes as long-term strategic investments.

Not in education, employment, or training

One of the key indicators proposed and accepted as a measure of the Sustainable Development Goal 8—promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all—is the not in education, employment, or training number (NEET).

The NEET was a little-known measure in the early 2000s to highlight the vulnerabilities faced by adolescents who had dropped out of education. It assumes even more significance now as we look at youth productivity as a whole—especially of young adults from disadvantaged and high-risk backgrounds.

The number is simply the total number of young adults minus the number of youth (between 15 and 29 years of age) in education, in training, or in jobs. These youth, who are potentially doing nothing, are the ones who we, as a nation, need to be most worried about.

A high NEET rate as compared with the youth unemployment rate could mean that a large number of youth are discouraged workers, or do not have access to education or training. As much as 27.2% of India’s 423 million youth population are NEET. These are the young adults who are currently isolated from the mainstream and have either given up or do not have access to any opportunities.

A matter of choice

It is intuitive to infer that the majority of young adults, especially in the 18 to 21 year age group in the higher socio-economic strata, are in education and not looking for employment. Hence, the same rationale could be extended to say that one of the early markers of privilege—along with a private-school education—could be to keep a young adult in education until such time that they wish or that their family can afford.

Why does that choice only remain the prerogative of the economically advantaged? Surely, we do not ask an undergraduate degree college only about their employment rates—just as the number of students taking up higher education is a matter of pride for most institutions, can a skill development programme not enthuse young adults, particularly school dropouts, to complete high school as a first step to achieving their career plan? In fact, that may be a more sustainable route for them to find and retain employment that stays relevant to the vagaries of the labour market.

It is an oft-repeated adage that we cannot really predict the jobs of the future. Yet, it seems counter-intuitive to most of us that building core work skills and self-reliance is perhaps the only sustained answer. For that to become a reality, we have to trust our young to make that choice to stay in education, get technically trained, or start working. And all three are equally significant indicators of success.

A nuanced understanding

Of all young boys in the 15 to 24 age group, only 6.4% are NEET, compared to a staggering 44.9% of girls in that age group. Indian culture looks down on families that “live off” a woman’s income and it is surprising to see the number of urban households also falling prey to societal pressure.

The honour of a girl is almost always a burden that the family of the girl carries and hence it is safer to keep her home rather than let her go out and work, lest she gains financial independence. Economic freedom is seen as being directly linked to the dreaded autonomous decision—making calls on life and marriage. This explains the large proportion of girls who are not in education, employment, or training.

Dropping out of education means social isolation, which also severely limits access to other basics such as healthcare, peer support, and so on. When a girl who had dropped out of education in class 7 or 8 goes back to school, battling all these societal shackles, it is most definitely a measure of success.

The government spends upwards of Rs3,000 crore on skill development every year. These are only the budgets of the ministry of skill development and entrepreneurship, not the other livelihood and technical education schemes. Yet these programmes are plagued by inefficiencies arising out of low enrollment.

According to the National Skill Development Corporation, 3.4 million youths have been trained in 2015-16. In this context, it would make sense to leverage the investments already made and ensure that young adults judiciously choose and enroll into technical training, as well after an intensive career counselling and core work skills module. This carefully matched profile of youth to skills training based on their aptitudes and interests is not only a smart move, it needs to be seen as a success metric even after a basic skilling programme.

It is time that we revisited the constricting and unilateral metric of job placement as the only indicator of success: we need to broaden our understanding of what fuels the productivity of young India. Perhaps learning from motivated young adults is the answer.

This post first appeared on India Development Review . We welcome your comments at [email protected] .

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NEET – Not in Education, Employment, or Training

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

NEET Implications

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

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

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

NEET Brackets, by EU Reporting

The EU has categorized NEETs in the following brackets :

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

NEET Growth Rate

Source : Data WorldBank

NEET Europe Growth Rate

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

Hiring NEETs

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

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

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

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

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

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More School Workers Qualify for Overtime Under New Rule. Teachers Remain Exempt

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School districts will be required to offer overtime pay to more employees under a federal rule finalized by the U.S. Department of Labor Tuesday, but teachers will remain exempt from the regulation.

The agency did not act on a request from the National Education Association, the nation’s largest teachers’ union, to end exemptions for teachers, who are currently included in the categories of employees that do not qualify for mandatory overtime pay under the Fair Labor Standards Act. The Labor Department said that the request was outside of the scope of the current review.

Such a change “ would have been groundbreaking in terms of what it would mean to district budgets because we all know teachers work more than 40 hours,” said Noelle Ellerson Ng, the associate executive director of advocacy and governance at AASA, The School Superintendents Association.

The new rule raises the minimum salary threshold for non-teaching worker exemptions. Since 2019, eligible employees who earn less than $35,308 a year have qualified for overtime pay if they work more than 40 hours a week. Effective July 1, the new rule will increase that salary maximum level to $43,888, and it will increase again to $58,656 on Jan. 1, 2025. Salary thresholds will update every three years starting in July 2027, relying on new federal data on average wages, the Labor Department said.

“This rule will restore the promise to workers that if you work more than 40 hours in a week, you should be paid more for that time,” said Acting Labor Secretary Julie Su in a statement.

While teachers and school administrators are exempt from the federal overtime rule, the change will lead to increased overtime costs for some district employees, like school nurses, athletic trainers, and librarians, school administrator groups previously warned. And it could increase the burden of recordkeeping and tracking hours for more employees, those organizations said.

AASA has been preparing district leaders for the shift for months, Ng said Tuesday. In some cases, districts will have to make the choice about whether to offer newly qualifying employees overtime or to hire additional employees to help lower their workloads, she said.

The Texas School Boards Association suggested in a September member advisory that it may be easier for school districts to avoid overtime by raising some employees’ pay to a level above the salary threshold if their current compensation falls slightly below the proposed cutoff.

“These are significant changes that will have a massive impact on the economy and millions of current and future workers,” said a September letter from 107 organizations representing a variety of industries, including AASA, the Association of School Business Officials International, the Association of Educational Service Agencies, and the National Association for Pupil Transportation.

The Labor Department estimates about 4 million employees will newly qualify for overtime after the rule fully takes effect.

Teachers not affected by new overtime rule

The Labor Department noted a flood of comments calling for the agency to remove the teacher exemption from the overtime rules. But the agency said it would require a separate rulemaking process to consider such a change.

The NEA argued for the change in a November letter to federal regulators.

“It no longer makes sense to treat teachers, 44 percent of whom are paid below the proposed salary threshold, the same as high-earning doctors and lawyers,” wrote Alice O’Brien, the general counsel for the NEA. “Instead, teachers, a heavily female profession that suffers from a large and growing wage gap compared with other similarly educated professionals, should be provided the same protections as other white-collar professionals whose exempt status depends not just on job duties, but also on salary.”

Doctors and lawyers are also among the employees exempt from mandatory overtime under the law, but tend to be much more highly paid than educators. Last year, the median salary for doctors was $229,300, and the median salary for lawyers was $135,740, the NEA’s letter noted. The median pay for teachers was $66,397.

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My job is classified as salaried, nonexempt: What does that mean? Ask HR

Despite being paid a salary, as a salaried, nonexempt employee, your employer is still obligated to track and record your work hours.

Johnny C. Taylor Jr. tackles your human resources questions as part of a series for USA TODAY. Taylor is president and CEO of the Society for Human Resource Management, the world's largest HR professional society and author of "Reset: A Leader’s Guide to Work in an Age of Upheaval.”

Have a question? Submit it here .

Question: My job is classified as ‘salaried, nonexempt.’ Though I’ve seen the term many times before, I’ve never understood what exactly it meant. What does that mean? How does it affect my pay? – Marlene

Most people are either salaried or nonexempt, but some assume you can’t be both. Well, that’s wrong. “Salaried” means you are paid a weekly rate and “nonexempt” means you are still entitled to overtime pay for any hours worked over 40 in a week. So, let’s say you make $52,000 per year (or $1000 per week) and you work 50 hours one week. That week, you would earn a $1000 salary plus $375 overtime pay (10 hours at $37.50 per hour) as both a salaried and nonexempt employee.

These salary, hourly, exempt, and nonexempt classifications are regulated at the federal level. However, some states may have different overtime pay requirements, such as daily overtime calculations.

While the term “nonexempt” is often associated with hourly employees, your employer is not necessarily required to pay you on an hourly basis. Instead, nonexempt employees can receive compensation through various methods, including salary, piece rate, commission, etc., provided their total weekly pay meets the minimum wage requirements and overtime is appropriately compensated for any hours worked beyond 40 in a workweek.

Despite being paid a salary, as a salaried, nonexempt employee, your employer is still obligated to track and record your work hours. If you work overtime, your employer must calculate your regular hourly rate based on your salary and pay you accordingly for all overtime hours worked. This ensures compliance with federal and state labor laws regarding compensation for nonexempt employees.

Again, thanks for asking, and I hope this makes your job designation clearer.

I’m considering putting in for a transfer to another department. What is the best way to inquire about a transfer without burning bridges with my current team and manager? – Dean

Navigating an internal transfer while maintaining positive relationships with your current team and manager requires careful consideration and communication. Here’s how you can approach the process without burning bridges:

  • Review company policies: Start by familiarizing yourself with your company's internal transfer policy and process. Ensure that you meet the eligibility requirements for a transfer and carefully assess the qualifications and skills required for the position you’re interested in.
  • Initiate a candid discussion: Transparency is vital in this situation. Schedule a meeting with your manager to have an open and honest conversation about your intention to apply for a transfer. Clearly communicate your reasons for seeking the transfer, such as a desire for career growth or a better alignment with your career goals. Emphasize that your decision does not reflect any issues with your current team or manager, but rather a personal career choice.
  • Involve human resources: Once you’ve discussed with your manager your intent to transfer, contact your HR department to kickstart the internal transfer process. They can provide guidance on the necessary paperwork and steps to formalize your request.
  • Exercise discretion with co-workers: While being transparent with your manager is essential, consider keeping your intention to transfer confidential from your co-workers until your move is confirmed. This can help minimize any disruptions within your team, especially if you’re not ultimately selected for the transfer. However, if you are chosen for the new position, offer your assistance in training your replacement and supporting your team during the transition period.

Following these steps and maintaining open communication allows you to conduct the internal transfer process smoothly while preserving the valued relationships with your current team and manager. Good luck as you pursue this new opportunity!

COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK

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Clinical Research Coordinator

  • Ophthalmology
  • Columbia University Medical Center
  • Opening on: Apr 23 2024
  • Job Type: Officer of Administration
  • Regular/Temporary: Regular
  • Hours Per Week: 35
  • Salary Range: $62,400 - $65,000

Position Summary

Under the direction of the Director of the Clinical Trials Unit (CTU) and Principal Investigators, the Clinical Research Coordinator will conduct clinical research studies (industry-sponsored and investigator-initiated) within the Columbia University Irving Medical Center (CUIMC) Department of Ophthalmology in adherence with assigned study protocols and manuals of operation and in accordance with clinical research principles.

Responsibilities

  • Serve as the contact person for those interested in study participation and assist with recruitment activities including pre-screening electronic medical records for eligibility, contacting potential subjects, explaining all study procedures, and consenting eligible subjects or assenting parents or guardians for children enrolled in research studies.
  • Coordinate day-to-day aspects of study related procedures, including, but not limited to scheduling visits and procedures, data entry, preparing for research visits, research visit documentation, maintenance of regulatory binders and study files, creation and/or maintenance of source documentation, preparation for monitoring visits, site initiation/closeout visits and audits as needed.
  • Be able to coordinate and perform research testing and imaging for clinical research studies including but not limited to visual acuity, refraction, dark adaptation, visual field, microperimetry, fluorescein angiography, fundus photography, optical coherence tomography (OCT), ICG angiography, slit lamp photography, MP1, corneal mapping, specular biomicroscopy including confocal imaging, HRT Analyzer (glaucoma), and ERGs.
  • Be able to administer surveys, such as the National Eye Institute Vision Function Questionnaire (NEI-VFQ-25), EuroQOL-5 Dimension, Reading speed, Health Utilities Index.
  • Work with the research team and ocular photography department to ensure that all required eye exams and ocular testing are scheduled and completed according to protocol.
  • Obtain and maintain study certifications for ETDRS, OCT, and photography for clinical trials.
  • Obtain access to sponsors’ electronic data capture (EDC) systems, complete EDC trainings, and enter data into the EDC within 5 days of seeing the study patient.
  • Maintain and organize study-related documentation and records using the EDC platforms, including capturing adverse events and serious adverse events and preparing for monitoring visits.
  • Respond to all sponsor-related queries in a timely manner.
  • Ensure that all aspects of Good Clinical Practice are followed at all times by developing and ensuring adherence with Standard Operating Procedure (SOP) for clinical studies being conducted in the Ophthalmology Clinical Trials Unit.
  • Work with the Regulatory Manager to gain CUIMC Institutional Review Board (IRB) approval in a timely manner by creating informed consent forms using sponsors’ templates, responding to IRB correspondents, submitting amendments, renewals, modifications, and other regulatory documents required by the sponsor and FDA, including progress reports.
  • Ensure that all appropriate Institutional, State, and Federal regulations are followed throughout the course of the study according to study-related protocols and manuals.
  • Work directly with sponsors’ designated Clinical Research Organizations (CRO) to complete all required study start-up documents including FDA 1572 forms, investigator signatures, CVs, medical licenses, Conflict of Interest, HIPAA, and Human Subjects Trainings in a timely manner.
  • Complete feasibility forms requested by sponsors in a timely manner to assess ophthalmic equipment and examination rooms to conduct the studies.

Minimum Qualifications

  • Bachelor’s degree or equivalent in education and experience, plus minimum of 1 to 2 years of related experience.
  • Conform to all applicable HIPAA, billing compliance and safety requirements.
  • Must be able to work effectively with minimal supervision.
  • Prior research experience to include recruiting study participants, conducting standardized protocol visits and data entry.
  • Excellent verbal and written communication skills and attention to detail required.
  • Computer skills (Word, Excel) required.
  • Excellent interpersonal skills.
  • Willingness to travel to different sites.

Preferred Qualifications

  • Working knowledge of Spanish
  • Phlebotomy license
  • Prior experience in ophthalmology

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.

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People walk next to a Google logo during a trade fair in Hanover

Google parent Alphabet hits $2tn valuation as it announces first dividend

Tech company’s shares rise as it plans to reward investors after strong quarterly results

Google’s parent company has hit a stock market value of $2tn (£1.6tn) as investors reacted to a declaration of its first ever dividend alongside strong results on Thursday.

Shares in Alphabet rose 10% in early Wall Street trading on Friday to give the tech group a stock market capitalisation – a measure of a corporation’s value – of more than $2tn. Alphabet last hit that level in intraday trading in 2021, but has yet to close above that benchmark after a day’s trading.

Alphabet’s shares rose after it posted results on Thursday that exceeded analyst’s expectations. Microsoft also reported strong figures on Thursday , amid heavy investment in artificial intelligence, and investors pushed the company past the $3tn mark, a level it has already crossed this year.

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Alphabet’s quarterly figures included better than expected results from its core Google search business as well as its YouTube platform, and strong figures from its cloud business, which has been boosted by the training and operation of artificial intelligence models. The company also announced its first ever dividend.

Russ Mould, the investment director at AJ Bell, an investment platform, said Alphabet joining the ranks of dividend-paying tech company was a “sign of the times”.

“Big tech firms have enjoyed stellar growth over the past decade and while most remain highly innovative, their cashflows have become so strong that there’s oodles of money left over post-reinvestment in the business to reward shareholders,” he added.

Alphabet joins a trio of US-listed companies with valuations of more than $2tn: Microsoft at more than $3tn; Apple at $2.6tn; and Nvidia, the leading chip supplier for AI products, at just more than $2tn. Apple also passed the $3tn mark last year.

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IMAGES

  1. NEET

    not in education employment or training

  2. Sri Lanka’s NEETs: An Analysis of Youth not in Education, Employment or Training

    not in education employment or training

  3. ONDH: 29% of Young People Are Not in Education, Employment, Training

    not in education employment or training

  4. Young people not in education, employment or training (NEET): January to March 2021

    not in education employment or training

  5. Share of youth not in education, employment, or training (NEET)

    not in education employment or training

  6. Youth not in employment, education or training (NEET), by age, % in...

    not in education employment or training

VIDEO

  1. Woodthorpe Youth Club Social Action Project

  2. saying NEET for 15 min

  3. Parkerville's Education, Employment & Training Program

  4. Why Kids NEED To Fail Early In Life & Why Teachers Quit: School Discipline, Unions, Burnout & More!

  5. Education is not for employment , education is for enlightenment!

  6. Prospectus

COMMENTS

  1. PDF Young People Not in Employment, Education or Training

    NOT IN EMPLOYMENT, EDUCATION OR TRAINING ILO/SIDA PARTNERSHIP ON EMPLOYMENT This brief has been prepared by Niall O'Higgins, based on a number of country background papers on the Sustainable Development Goals and structural transformation, as part of the ILO/Sida Partnership Programme. The research assistance of Luis Pinedo Caro is gratefully

  2. Youth not in employment, education or training (NEET)

    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.

  3. Young people not in education, employment or training (NEET): Recent

    The term NEET (not in education, employment or training) is now widely used to define and capture levels of disadvantage and disengagement among an increasingly diverse population of young people. In England, as in many other countries, the term embraces a wide catchment, in terms of age, characteristics and segmentation, with regard to young ...

  4. 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. Young people in education include those attending part-time or full-time education, but exclude those in non-formal education and in educational ...

  5. Emerging adults not in education, employment or training (NEET): socio

    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 ...

  6. Unravelling the NEET phenomenon: a systematic literature review and

    Introduction. There has been increasing concern over the rising number of young people not in employment, education, or training (NEET). NEET status has been found to have negative consequences for individuals, communities, and societies, such as lower well-being, increased social exclusion, and reduced economic growth (Rahmani & Groot, Citation 2023).

  7. NEET

    NEET stands for 'Not in Employment, Education or Training'. It is a measure of social exclusion, economic inactivity and levels of disengagement from labour markets. It is used to measure young people's participation in education and work in the UK and other countries. The web page explains the causes, levels, costs and related issues of NEET.

  8. There cannot be decent work for all without decent work for youth

    In 2022, at the global level, almost a quarter of the world's youth were not in education, employment, or training (NEET). This is over half a percentage point above the pre-COVID-19 level, and equivalent to about 289 million young people. Young women are even more likely to be NEET than young men. Though gender gaps in NEET rates have fallen ...

  9. NEET: Young People Not in Education, Employment or Training

    728,000 people aged 16-24 were Not in Education, Employment or Training (NEET) in January-March 2021, 10.6% of all people in this age group. This was a fall of 69,000 from the previous quarter and a fall of 54,000 from the year before. The proportion of 16-24 year olds who were NEET increased following the 2008 recession and peaked in July ...

  10. Young people not in education, employment or training (NEET): Recent

    526 Research in Comparative & International Education 10(4) education, employment or training (EET). Moreover, policy interventions to address the NEET 'problem' include prevention, reintegration and compensation measures targeted at specific sub-groups within the overall population. Also, programme interventions are increasingly delivered in

  11. The mental health of young people who are not in education, employment

    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 [], 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 ...

  12. Do young people not in education, employment or training experience

    Not in education, employment or training (NEET) is a contested concept in the literature. However, it is consistently used by policy-makers and shown in research to be associated with negative outcomes. In this paper we examine whether NEET status is associated with subsequent occupational scarring using the Scottish Longitudinal Study which ...

  13. Determinants of youth not in education, employment or training

    The presence of a large proportion of youth neither in education, employment, or training (NEET) signals problems in a country's education and labor market systems, and has wide-ranging negative consequences, extending beyond the individual to the economy and society. Using Sri Lankan Labour Force Survey data for the year 2016 and binomial ...

  14. The mental health of young people who are not in education, employment

    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 ...

  15. Not in education, employment and training: pathways from toddler

    'Not in Education, Employment, or Training' or 'NEET') are often at reduced capacity to flourish and thrive as adults. Developmental precursors to NEET status may extend back to temperamental features, though this - and possible mediators of such associations such as attention deficit hyperactivity (ADHD) symptoms and antisocial ...

  16. NEET Support

    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 ...

  17. Not in Education, Employment or Training: Incidence ...

    The International Labour Organization defines NEET as the section of the youth population, which is not engaged in education, employment or training. In this context, education includes any form of it as long as it is intentional, planned and most importantly institutionalized.

  18. Share of youth not in education, employment or training, total (% of

    Share of youth not in education, employment or training, male (% of male youth population) Unemployment, female (% of female labor force) (modeled ILO estimate) Unemployment with advanced education (% of total labor force with advanced education)

  19. Not in education, employment and training: pathways from toddler

    Background: Youths disengaged from the education system and labour force (i.e. 'Not in Education, Employment, or Training' or 'NEET') are often at reduced capacity to flourish and thrive as adults. Developmental precursors to NEET status may extend back to temperamental features, though this - and possible mediators of such associations such as attention deficit hyperactivity (ADHD) symptoms ...

  20. NEET Forum

    NEET Forum - Not in Education, Employment, or Training. New posts Meta. News & Announcements. Stay up to date with the latest news and updates to the site, including rule changes and feature additions. Threads 234 Messages 2.9K. Sub-forums: Feedback. Threads 234 Messages 2.9K. See who has you ignored. Yesterday at 10:43 PM;

  21. India should start worrying about the NEET: not in education

    Not in education, employment, or training. One of the key indicators proposed and accepted as a measure of the Sustainable Development Goal 8—promote sustained, inclusive, and sustainable ...

  22. NEET

    by Robert Rusu December 20, 2021. NEET is an acronym for ' not in employment, education or training ', used to refer to the situation of many young persons, aged between 15 and 29. The aim of the NEET concept is to broaden understanding of the vulnerable status of young people and to better monitor their problematic access to the labor market.

  23. Young people not in education, employment or training (NEET), UK

    An estimated 12.0% of all people aged 16 to 24 years in the UK were not in education, employment or training (NEET) in October to December 2023. This is up 0.2 percentage points compared with October to December 2022, but down 0.3 percentage points on the quarter.

  24. First early childhood apprentice completes training with Northeast

    "Apprenticeship is an 'Earn and Learn' program where students obtain paid employment while attending classes and training for a new career," said Henry Goeden, apprenticeship coordinator ...

  25. More School Workers Qualify for Overtime Under New Rule. Teachers

    Since 2019, eligible employees who earn less than $35,308 a year have qualified for overtime pay if they work more than 40 hours a week. Effective July 1, the new rule will increase that salary ...

  26. What does salaried, nonexempt really mean when it comes to employment?

    Most people are either salaried or nonexempt, but some assume you can't be both. Well, that's wrong. "Salaried" means you are paid a weekly rate and "nonexempt" means you are still ...

  27. Clinical Research Coordinator

    Job Type: Officer of Administration Regular/Temporary: Regular Hours Per Week: 35 Salary Range: $62,400 - $65,000 The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith ...

  28. Google parent Alphabet hits $2tn valuation as it announces first

    Google's parent company has hit a stock market value of $2tn (£1.6tn) as investors reacted to a declaration of its first ever dividend alongside strong results on Thursday. Shares in Alphabet ...