not employment education or training

• 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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Introduction

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

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

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

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

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

COVID-19 and the Sustainable Development Goals: Reversing progress towards decent work for all

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

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

6177 Accesses

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Metrics details

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

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

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

Conclusions

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

Peer Review reports

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

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

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

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

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

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

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

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

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

Participants

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Strengths and limitations of this study

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

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

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

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

Abbreviations

Confidence interval

Adjusted Odds Ratios

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

National Institute of Statistics and Geography

Mexican Adolescent Mental Health Survey

Not in education, employment nor training

The Organisation for Economic Co-operation and Development

The World Health Organization World Mental Health Composite International Diagnostic Interview

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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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Skills intelligence

Youth unemployment is a problem which tends to affect all countries with weak or negative employment growth. When growth in the economy slows, so may employment growth, as businesses hold back on expanding and creating new jobs - or worse, go into reverse, as they make cuts to their workforce to decrease costs. It is often young people who bear the heaviest cost of this, as they compete for fewer jobs against older workers with much more experience under their belt.

One way young people who cannot find work can improve their labour market prospects is to acquire, via education and training, those skills which employers are known to value. Where, however, young people are not in employment and neither are they participating in education nor training, they run an increased risk of becoming disconnected from the labour market and facing social exclusion. This has the potential to blight their entire working lives.

The young persons neither in education nor employment or training (NEET) rate is an indication of how many people aged 15-24 in an economy are neither in work, nor in formal education or training. This is expressed as a percentage of the total population aged 15-24 .

Note: All estimations are Skills intelligence Team own calculations based on Eurostat data.

IJPDS International Journal of Population Data Science

Not in Employment, Education or Training (NEET); more than a youth policy issue

Main article content.

Introduction Australians who are Not in Employment, Education or Training (NEET) and receive income support span a wide spectrum of working ages. Australian research has concentrated on NEETs aged 15--29 years, in line with international standards. This paper investigates extending the NEET concept to include all working age persons 15--64 years and the value added to welfare policy through analysis of a new linked dataset.

Methods An observational study design was implemented with individuals aged 15-64 years recorded as receiving Department of Social Services (DSS) income support payments from September 2011 being linked with Australian Bureau of Statistics (ABS) Census data from August 2011 to create a linked dataset for analysis. Descriptive analyses were undertaken of NEET status by Census socio-demographic characteristics, and we modelled the adjusted likelihood of NEET status by Census demographics.

Results Some 1.37 million or 45.2% of linked DSS payment recipients qualified as NEET. Of NEETs, more than twice as many were female, nearly half were aged 45--64 years, and under 1-in-5 were aged 15--29 years. Multivariate analyses showed that NEETs were more likely to be older, have low educational attainment, have a disability, and to be Indigenous.

Conclusions Young NEETs aged 15--29 years represented less than 20% of linked DSS payment recipients classified as NEET, suggesting that standard NEETs reporting neglects information on around 80% of the working age NEET population in Australia. Combined with other demographic insights, these results have implications for welfare policy, and indicate a wider range of demographics should be considered under the NEET classification. This may also have implications for Organisation for Economic Co-operation and Development (OECD) reporting.

Introduction

Persons of working age who are Not in Employment, Education or Training (NEET) and who also receive income support payments from government welfare services are a diverse population of people which is an ongoing challenge to social and fiscal policy across the developed world. Some people categorised as NEET may suffer from a physical or mental disability that prevents them from either working or enrolling in any form of training. Others may have voluntarily exited the workforce for a period to raise children while others may be experiencing long-term involuntary unemployment and disengagement with education and training opportunities [ 1 ]. There is also a view that for many NEET cases disengagement is closely associated with long-term or multi-generational socio-economic deprivation [ 2 , 3 ]. Whatever the reasons for any individual being classified NEET, one common factor in NEET analysis and policy development across OECD nations has been a focus on young populations [ 4 ]. Given the lasting influence of a young person’s formative years in education on later joblessness and social exclusion, this focus on youth is critical, and important to retain as a key strategy to reducing long-term welfare dependency [ 4 ]. However, there may be an argument to consider older working age NEETs as an additional group requiring targeted support as part of an expanded NEET policy framework. This paper explores that premise through analyses of a unique linked administrative data set.

The term “NEET” came to prominence in the late 1990s when the British Government’s Social Exclusion Unit published Bridging the Gap – New Opportunities for 16–18 year olds not in Education, Employment or Training [ 5 ]. The report observedthat “ ….where life goes wrong, or continues to go wrong, for young people in this age group, social exclusion in later life is disproportionately the result. They are much more likely to be unemployed, dependent on benefits, to live in unstable family structures, and to be depressed about their lives ” (p. 6). Hence, the motivation for focussing on young people who were NEET, as opposed to all working age persons, was to create targeted policies aimed at preventing the entrenchmentof multiple forms of disadvantage amongst Britain’s most educationally vulnerable youth. Britain had excluded young people aged 16–18 years from official unemployment figures following changes to their social security rules in 1988, leaving a knowledge gap in relation to young people disengaged from education and training services who were also unemployed. Bridging the Gap was designed to help address this information gap, and in doing so brought the concept of NEET status to public policy attention in Britain and latterly across the OECD. Thus, the original NEETs classification referred to persons aged 16–18 years, with this age range subsequently widened to 16–24 years for official statistics in the UK [ 6 ] and to 15–29 years in OECD publications examining the NEET phenomenon across developed nations [ 4 ]. As with Bridging the Gap , the overarching policy perspective has been focused on preventing the entrenchment of multiple forms of disadvantage amongst jobless and disengaged OECD youth.

In line with the established use of NEET status as a youth-centric concept, previous investigations of NEET populations in Australia have also concentrated on young people aged 15–29 years. For example, a comprehensive 2016 OECD report estimated that as of 2015 Australia had 580,000 young people classified NEET, representing 11.8% of all young Australians aged 15–29 years, and lower than the OECD average NEET rate of 14.6% [ 1 ].

Almost two-thirds of young Australian NEETs were not searching for work and were subsequently described as inactive NEETs. Young females were twice as likely to be NEET as young males, and much of this was driven by early parenthood and resultant childcare responsibilities dovetailing with unaffordable childcare and inflexible employment opportunities for young parents with children. Young NEETs were also more likely to be Indigenous, disabled, and to have low educational attainment, and these characteristics were especially true for those who had been NEET for longer than 12 months [ 1 ].

The OECD report stopped short of describing NEETs across the broader working age population aged 15–64 years. This working age population makes up the majority income-tax base and the entire pre- Age Pension welfare recipient population in Australia and most other OECD nations. A youth focussed approach to studying NEETs has left Australia and other OECD nations with a narrower and less informed view of their NEET populations; an information gap that hinders development of evidence-based policy for NEETs who fall outside of the 15–29 years age band. For long-term younger NEETs who remain NEET into their 30s, and people who only become NEET between the ages of 30 and 64 years, governments, policy makers and service agencies have been operating with limited published research and commentary. However, the majority of individuals classified as NEET will require income support from their government. While the lifetime welfare cost of young people who are NEET and remain NEET across their life course will be high, there is also evidence from administrative data in Australia that the lifetime welfare cost of older people who become NEET at a later stage can be high and that the requirement for a transition to income support may be preventable if the characteristics of the individuals undergoing these transitions is better understood [ 7 ]. Prior to the current study there has been little awareness of the scale of NEET status for those aged 30–64 years and who are presently outside the bounds of NEET policy. Young NEETs attract more policy attention and the lifetime consequences of long-term NEET status at a young age may be particularly costly at the individual level. In aggregate terms, however, older NEETs may be costlier to the welfare system simply because there are many more of them. It is possible that some older NEETs are more amenable to employment-based interventions as they are less likely to have young dependent children and may already have relevant work experience. However, other older NEETs may be in poorer health or less adapted to modern technology-driven workplaces compared with younger NEETs. Therefore, young NEETs, mid-life NEETs and older NEETs each face different challenges and may require different policy responses. Effective policy is not necessarily easier or cheaper to implement for any of these groups; yet each group is worthy of policy attention for different reasons. Our study sheds light on these policy issues for the first time.

In 2015 the Australian Commonwealth Government made a commitment to implement the Australian Priority Investment Approach (PIA) to welfare, designed to help reduce long-term dependency on welfare and improve the lifetime well-being of Australians [ 7 ]. The primary aim of the PIA was to estimate the lifetime costs of groups of individuals in receipt of welfare and to identify groups that would benefit from early intervention to prevent long-term dependence on income support payments, hence reducing the cost of the welfare burden. In addition to intervention for long-term income support recipients, which naturally includes young people less than 30 years of age classified as NEET, the framework for the PIA also included consideration of early intervention when individuals first received income support and intervention at critical stages that may otherwise lead to movement from one payment type to another. Several groups of older ages are identified in the report (p.113) as being of relatively high cost, in addition to young students, young carers and young parents, and should be investigated as potential respondents to early intervention. These include both males and females who enter into working age income support after age 55 years, parents transitioning to working age payments, working age to disability transitions and older people entering carer payments. These findings highlight the need to better understand the presence of NEET status in people of all ages, for the development of appropriate policy interventions at critical stages in an individual’s life course.

There are plausible reasons why predictors of NEET status might differ by age-group across the working age spectrum, as different factors are likely to be more prominent at different life stages and require different policy responses. For example, age at first childbirth has risen steadily over the past few decades for women in developed countries [ 8 , 9 ], creating the potential for periods of NEET status for many women that would be unobserved using a youth-oriented NEET measure that stops at 24 or 29 years. Some of these women may elect to stay out of education or the labour force until their children are school-aged, explaining some of the dip in labour force participation observed in recent statistics for women in their 30s [ 10 , 11 ]. Older men or women from low-skilled employment backgrounds may be affected by globalisation and industry closure into their 40s and 50s, finding they are less able to compete for the remaining jobs in the modern economy, and perhaps disengaged from education and training opportunities primarily aimed at youth [ 9 , 11 ]. Individuals may be more likely to develop health problems and disabilities with age and are more likely to transition onto carer payments if their partners, parents or other family members become ill [ 7 ].

Demographics such as gender, migrant status, Indigenous status and parenting status may all vary by age in relation to NEET status, as might levels of foundational skill, payment types and time on payment, number of NEET occurrences and total time spent NEET. Recognising differences in reasons behind NEET status for people of all working ages is important to deriving effective policies targeted at reducing periods of avoidable NEET status. Clearly policies aimed at addressing the reasons for NEET status of those aged less than 25 years for example, will necessarily be different to those aimed at addressing NEET status for older groups. While the younger group may respond best to programs designed to improve their baseline educational attainment, older NEETs may respond better to programs designed to match their existing skills with appropriate employment or job training/re-training opportunities. In line with the Australian Government’s Priority Investment Approach to welfare, reducing the lifetime cost of the welfare burden and improving the lifetime well-being of all Australians is something that policy is better able to achieve when NEET status is considered as a working age problem rather than a youth problem. While not all NEETs will be able to transition out of income support due to permanent or irredeemable barriers mentioned previously, those young NEETs, mid-life NEETs, and older NEETs with the potential to respond to tailored policy stand to deliver substantial cost savings to government over their remaining working life [ 7 ]. Our approach will investigate the relationship between different socio-demographic factors and NEET status by age and consider these as a proxy for the different reasons and critical life stages associated with a person’s NEET status.

What are the characteristics of all working age NEETs who receive income support payments, and what does this mean for human services policy? Should older NEETs be more visible on the policy radar? How do younger, mid-life and older NEETs differ by demographics? Do these differences have implications for policy responses designed to assist NEETs to become economically active? Our research seeks to address these gaps by describing NEET status across the working age population and bringing a life course perspective to the issue.

We analyse data from a novel linked dataset to investigate these issues for the first time in Australia. National census information held by the Australian Bureau of Statistics (ABS) was linked to income support recipient information held by the Australian Government Department of Social Services (DSS). This unique linked population dataset permits new insights into Australia’s NEET population by supporting investigation into the nature and pattern of NEET status among income support recipients of working age that was not previously possible.

Note that this investigation reflects the situation for NEETs in Australia as of 2011, per data availability for this project. Therefore, some observations made within may not reflect current circumstances. However, the issue of considering NEET status across all working ages remains just as relevant today.

The SSRI–census linked dataset

In December of 2014 DSS and ABS entered into a Memorandum of Understanding to conduct a three-year data-integration program with the overarching aim of showcasing the power of linked administrative data to inform public policy. The first outcome from this agreement was the linkage of ABS Census and DSS payment data, described below. In April 2015, researchers from the Australian Research Council Centre of Excellence for Children and Families over the Life Course (The Life Course Centre) were invited to assist DSS in delivering on its goal by leading a demonstration project on a topic of policy concern that new information from this dataset was able to address.

Access to these data were enabled by close partnerships between researchers in the Life Course Centre, DSS and ABS. The Life Course Centre has worked closely with a number of Commonwealth agencies to facilitate improved access to linked government administrative data for Australian researchers. As part of this work, the Life Course Centre trialled a number of different proof-of-concept models for accessing and analysing linked administrative data. A DSS employee was seconded to ABS to undertake the data analyses, using de-identified analytical data in a secure ABS environment. Life Course Centre researchers worked closely with the DSS analyst to provide instructions, advice and feedback on the results, but did not have direct access to the research dataset due to ABS data access protocols. Following the analyses, confidentialised, tabular data was released to DSS for use in this research. Confidentialisation is the term used by the ABS to describe their statistical disclosure control process, which supports the publication of safe and reliable statistical outputs while minimising the risks to identification for individual persons or organisations represented within the data. This approach met legal, security and privacy obligations about access to the data, but at the same time, enabled social science researchers to obtain unique insights into important social policy questions that can assist policymakers to devise appropriately targeted programs to tackle barriers faced by the NEET population.

The Social Security and Related Information (SSRI) dataset, held by DSS and representing all recipients of the 22 most relevant DSS funded welfare payments in Australia, was linked to the Australian Census of Population and Housing (the Census) held by ABS, with linkage performed in-house at the ABS. ABS is an Accredited Data Integration Authority under the Commonwealth Data Integration Guidelines [ 12 , 13 ].

While DSS has a wide range of payment classes that include such categories as natural disaster recovery and once-off emergency payments, we extracted for linkage only persons receiving payment types that relate to general and on-going income support and family support. A full list of the DSS payments extracted for linkage is available from the corresponding author. SSRI as linked for this project explicitly excluded Paid Parental Leave recipients, as this scheme is designed to provide up to 18 weeks of paid leave from an existing job for parents of newborn children. Parents are expected to return to this job, which they hold open whilst on parental leave.

Records for approximately 9 million individuals appearing in the September 2011 quarter of SSRI were matched to records on the August 2011 Census using a rules-based deterministic linkage methodology [ 14 ]. Linkage to the full Census occurred, resulting in a linkage rate of approximately 83% SSRI records matching a Census record. Only those record pairs meeting the linkage criteria were accepted as links, all other records being assigned ‘non-link’ status and not used for this analysis. Data cleaning methods at DSS and ABS were not disclosed to the authors, but as a national statistical agency ABS require data supplied for linkage to be of a high standard. Eligible records were complete for the purposes of this research, with no missingness reported on variables of interest. The September 2011 quarter was chosen for the SSRI data as it provided the closest time-alignment with the Census month of August 2011.

NEET status

NEET status was calculated from Census employment and education fields, and defined as: Labour Force status of ‘Not in the Labour Force’; ‘Unemployed, looking for part-time work’; and ‘Unemployed, looking for full-time work’; and Full-time/part-time student status of ‘Not attending an education institution’.

DSS payment recipients on SSRI and who successfully linked to Census and met the NEET status criteria are referred to throughout as ‘NEETs’, and those who did not meet the above criteria for NEET status are referred to throughout as ‘non-NEETs’.

Linked and non – linked records

Non-linked SSRI records are not included in the analysis presented here. We were unable to disaggregate ‘people who do not receive DSS welfare payments’ from those who were ‘receiving DSS welfare payments, but not linked to the Census’. Therefore the ‘Not Linked’ category is confounded for the purpose of direct comparison with the ‘Linked’ population.

All results presented here comprise Linked SSRI-Census records for those persons classified as NEET and non-NEET by the NEET status criteria described above.

Data analysis

Data on 3,031,000 persons aged 15-64 years and receiving DSS payments were available for analysis via the linked SSRI–Census dataset. A further 11,321,000 Census records were not linked to SSRI. Most of the Not-Linked population were either non-NEET and/or not DSS payment recipients; however, as described above, we could not disaggregate this group. Note that a total of 1.29 million or 11.4% of these Not-Linked records met the criteria for NEET status. Not-Linked records were not included in any further analysis and are not represented here. De-identified information on individuals aged 15-64 years recorded in SSRI from September 2011 was extracted from the SSRI–Census Linked dataset.

Approximately 3 million additional records were excluded for people aged 65 years and over. Though the NEETs analysis is designed to place Age Pension recipients out of scope where possible, Age Pension was being received by a small proportion of the population aged less than 65 years due to two reasons. At the reference period for linkage (August – September 2011) women aged 64 years and six months were eligible for the Age Pension, and men who turned 65 years in the six-week period between the August 2011 Census and the September 2011 end-of-quarter SSRI cut-off would have become eligible for the Age Pension. These people represent less than 1% of the linked population and do not affect the outcomes observed in the data. Subsequent changes to Age Pension eligibility will raise the minimum age to receive this payment from 65 years and six months in 2017, to 67 years by 2023 for both men and women [ 15 ].

The Census variables that were included as population descriptors in the analysis are shown in Table 1 . Further information about the construction and content of these Census variables is accessible via the ABS 2011 Census Dictionary [ 16 ].

Initial analysis derived a set of basic descriptive tables to describe the linked and not-linked populations and grouping of the linked population into NEET and non-NEET categories. Further analysis included development of a multi-variate, main-effect binary logistic regression model predicting the odds of NEET status among linked DSS payment recipients.

Statistical analyses were undertaken using SAS Enterprise Guide version 9.1. As noted above, an authorised DSS officer trained in population data analytics was seconded to ABS and accessed the linked data file via secure ABS servers under direct supervision of ABS officers. No detailed microdata was viewed outside secure ABS facilities.

Confidentiality of data and personal information

The data for this study were collected under the Social Security Act 1991 and the Census and Statistics Act 1905 [ 17 , 18 ]. Personal information supplied to the agencies operating under each of these Acts becomes property of the Commonwealth of Australia. Each agency is subject to tight disclosure rules under their respective Acts, forbidding public release of information in a way that might identify an individual. As such these data custodians can only release aggregated and de-identified outputs to researchers. This is the basis under which this study has been undertaken.

As an Accredited Data Integration Authority, when undertaking linkage projects, the ABS is bound by strict data handling conditions and procedures to protect the integrity of the data and the privacy of individuals. These legal obligations, conditions and procedures are described in full elsewhere [ 12 , 13 ].

To ensure that no individual person can be identified from the data and that privacy is maintained, statistical disclosure control techniques have been applied to the outputs. For example, all population numbers presented here are subject to variation due to rounding and perturbation. This means that sub-totals may not always add to the same grand total. This affects published outputs. Observed variations are very small relative to the size of the dataset and contribute no substantive impact with regard to interpretation of findings.

All results refer to the population of linked records for persons aged 15–64 years from the SSRI-Census linked dataset described previously in Methods. Note that population numbers presented here are subject to variation due to rounding and perturbation to protect privacy of individuals in the datasets. Therefore, numbers appearing in tables may not add exactly to grand totals, but this has no bearing on conclusions drawn from the statistical results.

NEET population characteristics

Table 2 describes characteristics of the Linked NEET population by gender. Of particular interest are the characteristics by which males and females differ, such as in age distribution, care of dependent children, marital status, and population size. In a population of just over 3 million individuals aged 15–64 years who were receiving DSS payments and linked to Census, some 1.37 million (45.2%) were classified NEET. The first column of Table 2 is based on all 3 million DSS payment recipients. For all payment recipients aged 15–29 years some 32.6% were classified as NEET. For DSS payment recipients aged 30–44 years this figure was 8.6%, and for those aged 45–64 years 60.9% were classified NEET.

Columns 2–5 in Table 2 refer to the 1.37 million payment recipients classified as working age NEETs compared with the 1.66 million payment recipients who were non-NEET. Among NEETs, more than twice as many were female (910,200 persons or 66.5%) compared with male NEETs (459,400 persons or 33.5%). This pattern was evident regardless of age group. A similar pattern by gender was also observed among non-NEETs receiving DSS payment, possibly reflecting the dominant role of women in child-rearing activity in Australia, discussed further below.

In terms of age group representation among the 1.37 million persons classified as working age NEETs, those aged 15–29 years represented 18.5%, those aged 30–44 years represented 32.5%, and NEETs aged 45–64 years represented 49.0%, or almost half the population of working age NEETs. These are aggregate proportions for males and females combined, whereas Table 2 displays these figures by gender.

Those aged 60–64 years represented 18.1% of working age NEETs, the highest proportion for any five-year age group. The lowest representation was for those aged 15–19 years at just 2.7%.

A higher proportion of NEET women were in the 30–44 years age category (36.6% vs. 24.5% for males), which may be a further indicator of women’s greater role in child rearing, as this age-range represents the peak years for female fertility and care for young children in Australia [ 8 ]. A greater proportion of NEET men was aged 45–64 years, at 58.0%, compared with 44.4% of female NEETs.

Some 56.0% of NEET women were caring for dependent children, compared with 23.5% of NEET males. Caring for children is the number one reason given by women of working age for being “Not in the Labour Force” (NILF) in Australia [ 11 ]. Only 19.5% of NEET women had never been married compared with 39.0% of NEET men.

All NEETs had strikingly lower levels of educational attainment than non-NEET recipients of DSS payments. This may have implications for their future employment prospects in comparison to non-NEETs. Male NEETs were disadvantaged by a factor of three when compared to the proportion of non-NEET males having Degree or Higher Degree education. In terms of numbers of persons, this translates to just 23,500 (out of 459,400) male NEETs being degree qualified, compared to 76,300 (out of 502,200) non-NEET males. When you consider that 201,100 male NEETs had Year-10 or below education, the observation that fewer than 25,000 had a degree qualification gives some sense of the magnitude of the skew towards lower levels of education among male NEETs.

The discrepancy in Degree or Higher Degree education among females was less pronounced, but still very apparent at almost two-fold in favour of non-NEET females. This smaller difference than that observed among males may indicate that more degree qualified women are leaving the workforce during peak child rearing years. This requires further research. The 8.9% of female NEETs with a degree qualification represents about 80,600 women.

Around half the proportion of female NEETs (11.4%) required assistance with core activities (a proxy for disability) compared with male NEETs (22.6%), and regardless of gender NEETs required assistance with core activities at around four times the rate of non-NEETs. As with higher rates of low education, higher rates of disability may have implications for onward employment opportunities in comparison to non-NEETs.

Lastly, Indigenous persons were overrepresented in the NEET category by almost double compared with non-NEET recipients of DSS payments.

NEET status and age group

Figure 1 shows the relationship between NEET status and age group for recipients of DSS payments, and clearly demonstrates that NEET status is more than just a youth issue. The increased proportion of NEET status for DSS payment recipients aged from 45–49 years onwards is quite dramatic, and suggests further investigation is warranted.

Figure 1: NEETs in Australia – proportion of linked DSS payment recipients aged 15–64 years classified as NEET, by age group.

Adjusted likelihood of NEET status

Table 3 shows that DSS payment recipients were more likely to be NEET if aged 30–64 years compared with those aged 15–29 years. Regardless of age group, NEETs were more likely to be female.

Overall, persons in need of assistance with core activities (i.e. disabled people) were almost six times more likely to be NEET, with older NEETs being twice as likely as younger NEETs to require such assistance. Compared to those holding degree level qualifications, persons holding any lower level of educational attainment were more likely to be NEET. Likelihood of NEET status showed no consistent pattern by educational attainment across the three age-groups.

Younger people were more likely to be NEET if they had young children in the family, compared with NEETs aged over 30 years. People of both age groups were equally more likely to be NEET if providing unpaid care to a family member, but younger people were more likely to be NEET if providing unpaid care for a child.

People were more likely to be NEET if renting their house from the government or a housing charity, and more so for younger people. People with more than four persons in their household were at greater odds of being NEET, with younger people at generally higher odds.

While Indigenous recipients of DSS payments were around twice the odds of being NEET, it was interesting to note that this was one of the few demographics where NEET status was more likely in the 15–29 years age group. We also observed this independently for the region “Very Remote Australia”, where a higher proportion of young people are Indigenous [ 19 ].

Lastly, linked SSRI – Census records were almost five times more likely to be NEET than unlinked records.

Our findings highlight several critical issues for policy consideration with respect to Australians aged 15-64 who are not in employment, education or training: the current focus on younger NEETs, while important, misses a potential fiscal “iceberg” in the form of NEETs aged over 30-years; two-thirds of NEETs are female, with those having dependent children being a major contributor; older NEETs are far more likely to have a disability, and a higher proportion of males are classified NEET once aged over 45-years.

As of September 2011 approximately 1.37 million working age direct recipients of Australian Government Department of Social Services income and family support payments, who also linked to the August 2011 Census, were classified as NEET. This represented 45% of all linked recipients of DSS payments aged 15–64 years. Those aged 15–29 years, which is the standard age range for OECD analysis of NEETs, numbered just over 250,000 persons. These younger NEETs represented less than 20% of the total linked DSS payment recipients classified as NEET, suggesting that standard NEETs reporting may neglect information on around 80% of the working age NEET population in Australia. Regardless of what age groups are used to compare NEET status, persons above the age of 30 but below Age Pension age who meet the criteria for NEET status are not assigned that term in official reporting. The NEET classification seems reserved for those aged 15–29 years only. We argue that all working-age welfare recipients meeting NEET criteria should be categorised and reported as NEET and placed into meaningful age-groups for analysis and targeted policy development.

Why is this important? Australia is entering a period of population aging, shrinking of the working-age tax base, an impending revolution in workplace automation, and planned elevation of the minimum age for Australian age-pension eligibility from 65 years to 67 years by 2023 [ 9 , 15 ]. DSS presided over AUD $72 billion in personal welfare benefit expenditure in 2015/16, a figure that excludes an additional $43 billion in Age Pension expenses [ 20 ]. Our findings show almost half of working age DSS payment recipients may be classified as NEET, and almost half of these are aged 45–64 years, which is well outside the existing policy focus for those who are NEET. But older NEETs may represent a greater financial burden to the welfare budget for several reasons associated with their demographic characteristics. Recent policy initiatives from the Australian Government Department of Education, Skills and Employment (DESE) have sought to address the issue of unemployed welfare recipients aged over 45 years who want to work but face barriers in finding work due to their age, health, caring responsibilities and/or outdated skills. The range of schemes on offer can be accessed via the Mature Age Hub on the DESE website, which is set up to help mature-age job seekers with free and subsidised training and other career transition assistance. The Hub also assists employers with wage and training subsidies, to help tackle the issue from both sides [ 21 ]. Even with these new supports, our research shows the scale of this issue is large, and there is room for policy frameworks to be more proactive in understanding older NEETs and assisting them to reduce their welfare dependence where possible.

The current Australian and OECD focus on NEET status as a youth problem remains relevant, as effective support and diversion strategies at this life stage can prevent entrenchment of costly disadvantage across an entire adult life course. Many younger NEETs are not long-term NEET, and analysis shows most are NEET for less than one-year for reasons including travel, “gap” years, volunteering, and caring, and most transition out of NEET status as their priorities change [ 22 ]. OECD calculations showed that about half of all Australians aged 15–29 years experienced a period of NEET status in the 48-month period from 2009-12. While this sounds high, most were NEET for short spells of less than 6-months, indicating NEET status is transient for most. When analysed further, only 16% of young Australians spent more than 12-months total classified as NEET across the 48-month period [ 1 ]. Australians aged 15–29 years who were long-term NEET (classified NEET for 7 consecutive months or longer) were more likely to have low education levels, have parents with low education levels, be female, and be Indigenous. Further, for females, they were far more likely to have at least one child under-5 years [ 1 ]. NEET welfare policy tends to focus on young people who are vulnerable to becoming NEET and staying NEET, and this group may represent a small number in comparison to those who transition to NEET status when aged over 30 years. The Australian Government identifies older age-groups as representing “areas for further investigation” as part of their Priority Investment Approach, including transition to working age income support for parents, disabled persons, adult carers, and over-55s. These policy areas are divided into issues for prevention, or intervention at critical stages, while early intervention approaches are flagged only for young carers and parents aged under 24 years [ 7 ]. Our study shows that older NEETs are far more numerous that younger NEETs and have a different disability profile. However, the data used in this study represent NEETs at a single point in time. We are thus unable to determine the point at which each individual first acquired a NEET status, how many periods of NEET status they have endured, or when their longest continuous period of NEET status occurred. Therefore, describing the longitudinal welfare journey or cumulative financial burden associated with each NEET group is not possible from this study. While further research is required to ascertain whether older NEETs are more likely to be NEET for longer, the simple fact that people aged 30–64 years represent four-in-five working age NEETs suggests they also represent a large cost burden to the welfare system that would benefit from the “further investigation” that the Australian Government identifies in their Priority Investment Approach [ 7 ].

Our extension of the age-range for NEET status from the OECD standard of 15–29 years to a ‘working age’ perspective covering 15–64 years reflects the need to address several factors important in social and fiscal policy that can be missed or diminished in importance when viewing NEETs from the constrained perspective of 15–29 years, while still allowing us to look at younger age groups. For example, while formal education and training is generally seen as something young people are involved in, with shifts in economic conditions further training can be increasingly important for older NEETs who may have been victims of industry closure and do not possess the skills to transition to another industry. This may be evident in the higher proportion of older males we found to be NEET, compared with females in the 45–64 years age group. Other NEETs may not have completed their education due to parenting responsibilities and find themselves with limited employment options into their thirties and beyond or have caring responsibilities for ageing or sick family members, and these types of factors have been identified by DSS as requiring further investigation in relation to transitions to welfare [ 7 ]. Restructuring of modern OECD economies away from primary and manufacturing industries, and towards technology and knowledge-based work brings challenges for welfare systems dealing with younger NEETs who may not have completed their education to the point where they have a marketable skill, and older ‘refugees’ from shrinking sectors of the economy who also do not possess the skills to gain employment in new growth sectors of the economy [ 9 ]. This does not mean that governments should prioritise helping older NEETs at the expense of supporting younger NEETs; on the contrary, our findings demonstrate that a focus on young NEETs remains as important as ever. However, our results on the age distribution of NEETs also suggest that constructive policy responses are required for NEETs of older working ages, and that these policies should be tailored to each age group on the basis of their relevant demographic profiles.

Limitations

The SSRI–Census linked dataset used for this project was cross-sectional in nature. We were unable to ascertain how long each recipient had been receiving welfare payments, nor how long they had been continuously NEET up to the reference period, nor how many times they may have moved between NEET and non-NEET status over their lifetimes. This has made it impossible to view NEET status over time and to accurately gauge the long-term cost of NEET status to the welfare system. Future linkages may overcome these issues, such as the longitudinal components of the Multi-Agency Data Integration Project (MADIP), a data partnership among six Australian Government agencies currently being curated by ABS [ 23 ]. Further, as stated in Methods, Not-Linked SSRI records are not included in the analysis. Lastly, these data are from 2011, so may not reflect current circumstances as well as newer data. However, the issue of extending NEET status classification to incorporate all working ages remains salient.

While NEET status is typically framed with a policy focus towards young people, we show here that it is relevant across a substantial portion of the life course. We suggest that any focus on persons disengaged from education, training and employment and supported by government welfare payments might usefully be expanded to include all persons of working age. This approach to the NEET concept continues to support an understanding of younger NEETs via age-group segmentation of data, whilst allowing a more complete overview of the wider NEET population, including those aged over 30 years who represent the vast majority of NEET cases. Further research seeks to inform policy around NEET payment demographics, NEETs disengaged from labour markets, and longitudinal pathways into and out of NEET status.

Results from our study clearly demonstrate that NEET status remains important beyond 15–29 years, and the sheer number of NEETs aged over-30 years may represent an even greater issue for policy makers than young NEETs. That around 80% of the Australian working age NEET population were aged 30–64 years should be reason enough to widen the scope of NEET classification to include older age groups. The reasons for being NEET, characteristics of persons who are NEET, and their welfare payment types are likely to differ by age and gender, and our investigation supports much of this. Extending the age-range to a practical definition of working age allows analysts to capture a complete picture of those who are NEET, describe the characteristics of NEET status for multiple age-groups, and to generate a quasi-life course perspective on NEET status that offers governments across the OECD an opportunity to view the NEET phenomenon in its entirety, and formulate appropriate responses for each age group. NEET classification appears valid across the entire working age range for people who are physically, mentally and circumstantially able to be engaged in education, training or work, but are instead disengaged from each whilst also being reliant on taxpayer funded welfare payments. Our research suggests it is both viable and desirable to generate policy relevant information for young NEETs (15–29 years), mid-life NEETs (30–44 years), and older NEETs (45–64 years) of working age and this can only occur if the concept of NEET status is expanded to encompass all working ages. This concept of a three-category NEET classification allows governments to retain and grow their important focus on young NEETs while also extending targeted support to older NEETs who may be facing different challenges and require different policy solutions.

Importantly, these analyses highlight the kinds of insights to be obtained from linked government administrative datasets. The results reported here are only possible due to the linkage of SSRI and Census data and partnerships across agencies that support collaborative research to analyse these data. Effective evidence-based policy design requires strong evidence to support decision making about programs and policies. In addition to the important new insights on NEETs shown here, we hope that this paper also showcases the value of linking administrative data in collaborative partnerships between data custodians, policy makers and social researchers to unlock the value of these data for social policy design and development.

Ethics Statement

No institutional ethics clearance was required for this research to occur. At the time of this study the Department of Social Services did not require independent ethics approval to use service data collected under the Social Security Act 1991 for research and planning purposes. The Australian Bureau of Statistics does not require independent ethics approval to use information collected under the Census and Statistics Act 1905 to assist another Commonwealth agency by making Census data accessible in a confidential de-identified manner.

Statement of Conflicts of Interest

None declared.

Acknowledgments

This research was supported by the Australian Research Council Centre of Excellence for Children and Families over the Life Course (project number CE140100027). The views expressed herein are those of the authors and are not necessarily those of the Australian Research Council. The ARC Life Course Centre acknowledges the roles and assistance of the Australian Government Department of Social Services and the Australian Bureau of Statistics in the implementation of the DSS-ABS Data Integration Initiative. The authors wish to acknowledge the valuable contribution of Waylon Nielsen from the Department of Social Services, who was in-posted to the Australian Bureau of Statistics to interrogate the linked data file under secure conditions. Information on how to access more information about the study protocol or data used for this research can be obtained by contacting the Australian Government Department of Social Services.

Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License .

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23 Australian Bureau of Statistics. Microdata: Multi-Agency Data Integration Project, Australia. Catalogue No. 1700.0 [Internet]. Canberra: ABS; 2018. https://www.abs.gov.au/ausstats/[email protected]/mf/1700.0 .

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Not in Employment, Education or Training (NEET): A Portending Challenge for Youth Labour Market in India

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Not in Employment, Education or Training (NEET) status of youth has become a worldwide threat to the inclusion of youth in production activities (education and employment). Henceforth, this chapter investigates the surmounting challenge of NEET youth in the Indian context. The chapter presents a conceptual framework addressing the interplay of demographic and socio-economic characteristics in defining the NEET/Non-NEET status of youth in the Indian context. The chapter finds gender disparity in the functioning of youth labour market in India and identifies that female youth represents 33% higher chances of being a NEET in the year 2018/19. The chapter suggests that it is crucial to create full decent employment for the youth to actively engage them in the mainstream of inclusive and sustained economic development. Indeed, capitalizing on youth—the potential human capital-through proactive measures to ensure the reintegration of NEET youth will contribute to India's economic growth.

Not in Employment, Education or Training (NEET): The Missing Youth. Do They Exist or Not—Where are they?

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Bisht, N., Pattanaik, F. (2023). Not in Employment, Education or Training (NEET): A Portending Challenge for Youth Labour Market in India. In: Youth in India. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-99-4330-2_4

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Labour Statistics at a Glance Young people not in employment, education or training: What did they do in the past 12 months?

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Introduction

Who was neet in september, overall, young people who were neet had a wide range of main activities in the past 12 months, most neet individuals had either been going to school or working as a main activity in the past 12 months, caring for children a common main activity for neet women aged 25 to 29, some young canadians were neet due to a physical or mental health concern or disability, other activities and looking for paid work, most of the neet population had not volunteered as a main activity in the past 12 months, data sources and definitions.

• In September 2018, 779,000 young people aged 15 to 29 were not in employment, education or training (NEET). This represented 11.3% of those in this age category.
• Slightly more than half of these young people had actually gone to school or worked as a main activity in the past 12 months—some of whom may have been in-between jobs in September, or in a transition stage between school and work. Almost 4 in 10 young NEET Canadians that had worked during the past 12 months had held a temporary, rather than permanent, job.
• Women who were NEET commonly reported caring for children. This was especially true of women aged 25 to 29, who also had the highest NEET rate in September 2018.
• A little more than 1 in 10 NEET individuals said that a physical or mental health concern or disability had kept them from doing something else as a main activity.
• Few NEET individuals volunteered for a group or organization as their main activity in the previous 12 months, although about 3 in 10 had volunteered to some extent during this period.

Young people (aged 15 to 29) who are not in employment, education or training (NEET) are often considered to be more vulnerable than their peers, as they may face a risk of becoming disengaged or socially excluded, and could miss out on gaining skills or experience in the labour market.

While Statistics Canada has previously examined the characteristics of the NEET population, Note  this is the first study to examine the main activities of NEET 15- to 29-year-olds over a 12-month period using Labour Force Survey (LFS) data. Note  Among the activities to be analyzed are going to school, working, caring for children, and volunteering both as a main and secondary activity.

Overall, there were 6.9 million young people aged 15 to 29 in Canada in September 2018 (Figure 1). Of those, 4.0 million were non-students (57.8%), while 2.9 million were students Note  (42.4%). Both categories (students and non-students) are then divided into the employed and the not employed. The NEET population consists of all non-students who are not employed: in September 2018, 779,000 people were in this category (11.3% of the total population aged 15 to 29).

This diagram divides young people aged 15 to 29 in September 2018 into different groups based on their student and labour market statuses.

In total there were 6,920,000 young Canadians aged 15 to 29 in September 2018. They are divided into two groups: non-students (3,998,000) and students (2,922,000). Each of these two groups is further divided.

Students are divided into three groups: those who were employed (1,233,000), those who were inactive, or not looking for work (1,529,000), and those who were unemployed, or looking for work (160,000). These three groups are not further divided.

Non-students are divided into two groups. The first group, including those who were not employed, in education or training (NEET) (779,000), is further divided. The second group, those who were employed (3,219,000), is not further divided.

Those who were NEET are divided into two groups: unemployed NEET, who were looking for work (269,000), and inactive NEET, who were not looking for work (510,000). These two groups are not further divided.

Those aged 25 to 29 comprised the largest proportion (46.8%) of young people who were NEET during the LFS reference week, followed by 20 to 24 (36.9%), and 15 to 19 (16.2%). While NEET individuals were slightly more likely to be female (52.1%) than male (47.9%) overall, those aged 15 to 19 were a few percentage points more likely to be male, and those aged 25 to 29 were similarly likely to be female.

Of young people who were NEET in September 2018, 34.5% were unemployed (looking for work and available for work), and 65.5% were inactive (not looking for work). While each of these groups may be at risk of falling behind their peers on work experience, this concern is generally greater for those who are inactive, as they may face challenges entering or re-entering the labour force.

Both male and female NEET individuals were more likely to be inactive than unemployed, though the share of women that were out of the labour force (72.2%) was greater than the share of men (58.2%).

Over the previous 12 months, the NEET population reported a variety of main activities. A little more than a quarter (26.5%) reported that they had been in school, while a similar percentage (26.1%) said they had been working Note  (Chart 1). The main activities of the remaining NEET included caring for children, Note  being ill or having a disability, looking for paid work, or other activities. Note  In comparison, almost all of the non- NEET population either worked or had been in school.

More than half of the NEET population in September 2018 indicated that their main activity in the last 12 months had actually been either going to school or working (52.6%). Though a bit counter-intuitive, this simply means that some NEET individuals in September 2018 could have been in-between jobs, or perhaps transitioning from school to the labour force. In addition, most people in the group that had been either working or going to school as a main activity reported that they were unemployed (available and looking for work), rather than inactive (not looking for work).

NEET population more likely to have held a temporary job

NEET individuals that had been in paid employment were more likely to have held a temporary position (37.9%) in the past 12 months than the non- NEET population (19.0%). A little less than 1 in 5 NEET individuals in this group said that they had worked in a temporary job because they could not find a permanent job. This further reinforces the suggestion that some NEET individuals were in-between jobs, and also highlights the precarious work or income situations that some of these young people may experience. Holding a temporary position may make it harder to gain long-term work experience, as well as financial stability.

Among the NEET population, 14.4% indicated that their main activity in the past 12 months was caring for children. This answer was much more common among women (26.4%) than men (1.3% Note  ). Women aged 25 to 29 were the most likely to have cared for children (38.8%), followed by women aged 20 to 24 (17.7%).

Interestingly, among the entire Canadian population aged 15 to 29, women aged 25 to 29 were the most likely to be NEET (16.4%) in September 2018. Additionally, this group was more likely to have been inactive than unemployed. Another recent study noted this higher inactive rate for 25- to 29-year-old women as well, and tied it to motherhood (Brunet, 2018).

A little more than 1 in 10 NEET individuals (11.5%) said that a physical or mental health concern or disability had kept them from doing something else as a main activity in the past 12 months. This category is important to consider in the context of the NEET population, as some studies, such as Brunet (2018) point out that these youth may face a greater risk of social exclusion and depression. A greater share of men than women indicated “own illness or disability” (13.8% compared with 9.3%), especially among those aged 25 to 29. People who were NEET and inactive had a larger proportion that had stated they had an illness or a disability (15.4%) compared with the unemployed (4.0%).

The 13.3% of NEET people in the “other activities” category can be broken down further. Specifically, 4.4% of the NEET population said they had done household work as their main activity, while 1.0% Note  stated volunteering or care-giving other than for children. The activities of the remaining NEET included things such as unpaid family work, travelling, learning a language, or “nothing”.

Looking for paid work as a main activity was indicated by 8.2% of those who were NEET , with a greater share of men reporting this answer than women (12.6% compared with 4.2%). The unemployed NEET population was more likely to have looked for paid work than their inactive counterparts (15.8% and 4.2% respectively).

Differences between the Canadian born and immigrants

In September 2018, the 15 to 29 NEET rate for landed immigrants (14.8%) was higher than the rate for those born in Canada (10.4%). This was due to the higher NEET rate for the 25- to 29-year-old immigrant group (20.0% compared with 12.8% for the Canadian born).

Time since landing had an impact on the NEET rates for immigrants. Very recent immigrants (5 years or less) had a NEET rate of 18.3% in September, compared with a rate of 13.2% for immigrants who landed more than 5 years ago.

Looking at the main activity in the previous 12 months, the proportion of NEET individuals that had been working or going to school was similar for both immigrants and people born in Canada. The immigrant NEET group was more likely to have cared for children compared with those born in Canada (19.3% and 13.3% respectively). Among very recent immigrants who were NEET , 30.6% indicated that caring for children was their main activity in the previous 12 months, while the share was 12.5% among immigrants who had landed in Canada more than 5 years ago.

NEET individuals who were born in Canada were more likely to have indicated an illness or a disability compared with their immigrant counterparts (13.7% and 6.8% respectively).

Another way to stay active and engaged is to help others. In September, young people aged 15 to 29 were asked two questions Note  about their volunteer work for a group or organization, and any other assistance provided to others of their own initiative without pay. As mentioned above, only 1.0% Note  of the NEET population reported that they had been volunteering or care-giving other than for children as their main activity.

A small share of the NEET population volunteered for a group or organization

Though not as a main activity, about 29.1% of NEET individuals had volunteered for a group or organization to some extent over the period, compared with 37.0% of the non- NEET population. Among those who had volunteered, having done so “a few times in the past 12 months” was the most common answer (Table 1).

Close to half of young NEET people provided help to others without pay

Looking at assistance provided to others of their own initiative and without pay, Note  48.0% of the NEET population provided this type of help in the past 12 months (Table 2), compared with 53.4% of the non- NEET population. “A few times in the past 12 months” was the most common frequency for providing this help, followed by “at least once a week”.

This study considered the 11.3% of young Canadians (aged 15 to 29) who were NEET in September 2018, and their activities over the previous 12 months. While most had previously been going to school or working, caring for children was also a common answer among women who were NEET . In September 2018, women aged 25 to 29 had the highest NEET rate, and most commonly reported caring for children.

Overall, very few NEET individuals had volunteered as their main activity, though a little less than a third had volunteered to some extent. Slightly over 1 in 10 NEET individuals said that a physical or mental health concern or disability had prevented them from doing something else as a main activity in the previous 12 months.

This analysis uses data from the September 2018 Labour Force Survey (LFS) in order to examine youth aged 15 to 29. These youth may be:

• In education—including both full-and part-time students at primary and secondary educational institutions, colleges and universities. For the purpose of this analysis, young people attending “other schools” are not considered in education or training.
• Employed—youth who, during the LFS reference week, were working, or had a job but were temporarily absent from work (for reasons such as illness, vacation, labour dispute or personal or family responsibilities).
• Unemployed (available and looking for work)—youth who had looked for work in the past four weeks ending with the reference period and were available for work.
• Inactive (not looking for work)—youth who were neither employed nor unemployed. These youth may be doing productive activities, such as travelling, volunteering, or caring for a family member, or activities that are a cause for concern, such as dropping out of school or abandoning a job search.
• This report uses data that were derived from questions added to the September 2018 LFS to determine, among other topics, the main activity of respondents in the past 12 months, and specifically for people aged 15 to 29, the frequency of volunteering activities over that period.
• During the past 12 months, was your main activity working at a paid job, self-employed or something else?
• During the past 12 months, was your main job permanent, or is there some way that it was not permanent? (for example, seasonal, temporary, term or casual)
• What was the main reason you were in a non-permanent job?
• In the past 12 months, what was your main activity?
• Did you work at a job or business at any time in the past 12 months?
• In the past 12 months, how often did you volunteer for a group or organization?
• Now think about helping people on your own, not on behalf of an organization. For example, caring for someone’s home, driving someone to an appointment, visiting the elderly, shovelling snow or unpaid babysitting. In the past 12 months, how often did you carry out any of these types of activities without pay?

For more information see the Guide to the Labour Force Survey ( 71-543-G ).

Brunet, Sylvie. 2018. “ The transition from school to work - the NEET (not in employment, education or training) indicator for 25- to 29-year-old women and men in Canada .” Education Indicators in Canada: Fact Sheet. Statistics Canada Catalogue no.  81-599-X.

Marshall, Katherine. 2012. “ Youth neither enrolled nor employed .” Perspectives on Labour and Income . Vol. 24, no.  2. May. Statistics Canada Catalogue no.  75-001-X.

Statistics Canada. 2018. “ The transition from school to work - the not in employment, education or training (NEET) indicator for 15 to 19 year olds in Canada .” Education Indicators in Canada: Fact Sheet . Statistics Canada Catalogue no.  81-599-X.

More information

Note of appreciation.

Canada owes the success of its statistical system to a long-standing partnership between Statistics Canada, the citizens of Canada, its businesses, governments and other institutions. Accurate and timely statistical information could not be produced without their continued co-operation and goodwill.

Standards of service to the public

Statistics Canada is committed to serving its clients in a prompt, reliable and courteous manner. To this end, the Agency has developed standards of service which its employees observe in serving its clients.

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

Migrated Content

120 apprentices have graduated from the first phase of the programme.

15 June 2022

Dar es Salaam. On 20 April 2022, 90 apprentices aged 15 to 25 started their three-month theoretical training at the JKU Vocational Training Centre. The programme will last 6 months in total and trainees are expected to graduate in one of the three occupations approved by the Government of Zanzibar, including air conditioning and refrigeration, carpentry and tailoring. In the second half of the programme, apprentices will participate in on-the-job training to gain relevant skills and work experience in their respective occupations to compete in the labour market or become self-employed. The second phase of the modular apprenticeship programme came after the successful conclusion of the previous intake on 29 May 2021. Compared to the previous phase, the programme has expanded by including an additional occupation, which is tailoring. The addition of more traditionally female-based occupations to the programme in Zanzibar, as well as more inclusive advocacy, including through the advertisement of the programme, has resulted in achieving gender parity in the intake. Much of the informal sector in Zanzibar is made up of a workforce with low levels of formal education and training. According to the Integrated Labour Force Survey 2020/21 , over 33 percent of Zanzibar’s youth population is unemployed. The Government of Zanzibar has therefore identified apprenticeship programmes as a way to curb youth unemployment and lack of formal training. In addition, the government has planned to expand the programme by committing financial resources but also by considering additional occupations in this programme, including welding, decoration, mechanical, aluminium and glasswork, aquaculture, and livestock keeping. The objective is to reach 500 youth not in education, employment, or training to be trained in the selected occupations by the end of 2022. The modular apprenticeship programme is supported by the ILO through the Global Programme on Skills and Lifelong Learning (GPSL3), which is financially supported by the Norwegian Agency for Development Cooperation (NORAD).  

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Many in Gen Z ditch colleges for trade schools. Meet the 'toolbelt generation'

Windsor Johnston

Sy Kirby dreaded the thought of going to college after graduating from high school. He says a four-year degree just wasn't in the cards for him or his bank account.

"I was facing a lot of pressure for a guy that knew for a fact that he wasn't going to college," Kirby says. "I knew I wasn't going to sit in a classroom, especially since I knew I wasn't going to pay for it."

Instead, at the age of 19, Kirby took a job at a local water department in southern Arkansas. He said the position helped him to develop the skills that helped him start his own construction company.

Sy Kirby, who runs his own construction company, says a four-year degree just wasn't in the cards for him or his bank account. Will Anderson hide caption

Sy Kirby, who runs his own construction company, says a four-year degree just wasn't in the cards for him or his bank account.

Now at age 32, Kirby finds himself mentoring many of his employees, who also opted to learn a skilled trade rather than shelling out tens of thousands of dollars to pursue a degree that they wouldn't use after graduating.

Kirby says blue-collar work is lucrative and allows him to "call the shots" in his life. But, he says the job also comes with a downside, mainly because of the stigma attached to the industry.

"I think there's a big problem with moms and dads coming home from quote-unquote 'dirty' jobs. Coming home with dirty clothes and sweating. You had a hard day's work and sometimes that's looked down upon," he says.

High-paying jobs that don't need a college degree? Thousands of them sit empty

Kirby is among the growing number of young people who have chosen to swap college for vocational schools that offer paid, on-the-job training.

Skilled trades make a comeback

Lisa Countryman-Quiroz is the CEO of JVS, or Jewish Vocational Service, a nonprofit in San Francisco that provides career training for unemployed workers to find jobs, including in skilled trades. She says that over the years there has been a shift — with skilled trade making a comeback, especially among members of Generation Z.

"Folks have really prioritized a college education as a path to the middle class and a path to a cushy office job." But, Countryman-Quiroz says, "over the last 10 to 15 years, we are seeing a trend among young people opting out of universities. Just the crushing debt of college is becoming a barrier in and of itself."

More than half of Gen Zers say it's possible to get a well-paying job with only a high school diploma, provided one acquires other skills. That's according to a survey by New America, a Washington Think Tank that focuses on a range of public policy issues, including technology, education and the economy.

The driver of the big rig one lane over might soon be one of these teenagers

The high cost of college prompts a change in career paths.

In addition, the Education Data Initiative says the average cost of college in the United States has more than doubled in the 21st century.

With that price tag increasing, many Gen Zers say they've been left with no choice but to leave the college path. Many say living with their parents until they can pay off their college debt isn't an option.

The Indicator from Planet Money

Do i need a four-year degree.

Nitzan Pelman is founder of Climb Hire, a company that helps low-income and overlooked people break into new careers. She says many young people say graduating from college with a six-figure debt is a non-starter.

"It's not a secret that the cost of college has gone up so dramatically in the last decade that it's really cost prohibitive at this point," she says.

Pelman says pursuing skilled trades can also help "level the playing field," especially for young people from less-privileged backgrounds and for people of color.

Construction boom helps fuel job gains in March

"We don't see a lot of Black men in construction, but more Latino men in construction and you don't see many women in construction. Social capital is a really big gatekeeper and a door-opener for accessing high-quality jobs and helping people break into certain industries," she says.

In 2021, President Biden signed a $1.2 trillion bipartisan infrastructure bill. Since then, he's been traveling the country promoting the law, which he says will open up thousands of new jobs in trades.

Comparing college costs to the amount a student expects to earn after graduation

"you can expect to get your hands dirty and that's ok".

The high cost of college isn't the only factor driving many young people toward skilled trades. With the use of artificial intelligence on the rise, many Gen Zers see manual labor as less vulnerable to the emerging technology than white-collar alternatives. They also say vocational schools are a straight path to well-paying jobs.

Pelman says increasing salaries and new technologies in fields such as welding, plumbing and machine tooling are giving trade professions a face-lift, making them more appealing to the younger crowd.

"There are a lot of vocational jobs out there that are pretty attractive — HVAC repair and installation, electricians, solar panel installer — there's so much demand for wind turbine installers who, in many cases, make more than $100,000 a year — so there's a lot of demand for manual labor," she stresses.

Diego Aguilar works at a trade center at East Bay Municipal Utility District in Oakland, Calif. Marla Aufmuth/JVS hide caption

Diego Aguilar works at a trade center at East Bay Municipal Utility District in Oakland, Calif.

That was the case for 25-year-old Diego Aguilar, who says a traditional desk job was out of the question for him. Aguilar now works full time at a trade center at East Bay Municipal Utility District in Oakland, Calif., after going through the JVS training program.

"When I went into a trade program I learned how much money I could make performing a very specific kind of work. You need mechanics, you need machinists, you need carpenters, operators you need painters. You can expect to get your hands dirty and that's OK," Aguilar says.

Jobs Friday: Why apprenticeships could make a comeback

Data from the National Student Clearinghouse Research Center shows the number of students enrolled in vocational-focused community colleges increased 16% from 2022 to 2023.

As for Kirby, he says his mission is to keep raising awareness about what he calls the "toolbelt generation."

"Where they can walk out of the school of hard knocks, pick an industry, work your 10 years, take your punches, take your licks and hopefully you're bringing jobs and careers back to the community," he says.

When asked if he regrets his decision to go into skilled trades, Kirby chuckles. "Not for a second," he says.

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Young people not in education, employment or training (NEET), NI: Jan-Mar 2024

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

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FTC Announces Rule Banning Noncompetes

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Today, the Federal Trade Commission issued a final rule to promote competition by banning noncompetes nationwide, protecting the fundamental freedom of workers to change jobs, increasing innovation, and fostering new business formation.

“Noncompete clauses keep wages low, suppress new ideas, and rob the American economy of dynamism, including from the more than 8,500 new startups that would be created a year once noncompetes are banned,” said FTC Chair Lina M. Khan. “The FTC’s final rule to ban noncompetes will ensure Americans have the freedom to pursue a new job, start a new business, or bring a new idea to market.”

The FTC estimates that the final rule banning noncompetes will lead to new business formation growing by 2.7% per year, resulting in more than 8,500 additional new businesses created each year. The final rule is expected to result in higher earnings for workers, with estimated earnings increasing for the average worker by an additional $524 per year, and it is expected to lower health care costs by up to $194 billion over the next decade. In addition, the final rule is expected to help drive innovation, leading to an estimated average increase of 17,000 to 29,000 more patents each year for the next 10 years under the final rule.

Noncompetes are a widespread and often exploitative practice imposing contractual conditions that prevent workers from taking a new job or starting a new business. Noncompetes often force workers to either stay in a job they want to leave or bear other significant harms and costs, such as being forced to switch to a lower-paying field, being forced to relocate, being forced to leave the workforce altogether, or being forced to defend against expensive litigation. An estimated 30 million workers—nearly one in five Americans—are subject to a noncompete.

Under the FTC’s new rule, existing noncompetes for the vast majority of workers will no longer be enforceable after the rule’s effective date. Existing noncompetes for senior executives - who represent less than 0.75% of workers - can remain in force under the FTC’s final rule, but employers are banned from entering into or attempting to enforce any new noncompetes, even if they involve senior executives. Employers will be required to provide notice to workers other than senior executives who are bound by an existing noncompete that they will not be enforcing any noncompetes against them.

In January 2023, the FTC issued a  proposed rule which was subject to a 90-day public comment period. The FTC received more than 26,000 comments on the proposed rule, with over 25,000 comments in support of the FTC’s proposed ban on noncompetes. The comments informed the FTC’s final rulemaking process, with the FTC carefully reviewing each comment and making changes to the proposed rule in response to the public’s feedback.

In the final rule, the Commission has determined that it is an unfair method of competition, and therefore a violation of Section 5 of the FTC Act, for employers to enter into noncompetes with workers and to enforce certain noncompetes.

The Commission found that noncompetes tend to negatively affect competitive conditions in labor markets by inhibiting efficient matching between workers and employers. The Commission also found that noncompetes tend to negatively affect competitive conditions in product and service markets, inhibiting new business formation and innovation. There is also evidence that noncompetes lead to increased market concentration and higher prices for consumers.

Alternatives to Noncompetes

The Commission found that employers have several alternatives to noncompetes that still enable firms to protect their investments without having to enforce a noncompete.

Trade secret laws and non-disclosure agreements (NDAs) both provide employers with well-established means to protect proprietary and other sensitive information. Researchers estimate that over 95% of workers with a noncompete already have an NDA.

The Commission also finds that instead of using noncompetes to lock in workers, employers that wish to retain employees can compete on the merits for the worker’s labor services by improving wages and working conditions.

Changes from the NPRM

Under the final rule, existing noncompetes for senior executives can remain in force. Employers, however, are prohibited from entering into or enforcing new noncompetes with senior executives. The final rule defines senior executives as workers earning more than $151,164 annually and who are in policy-making positions.

Additionally, the Commission has eliminated a provision in the proposed rule that would have required employers to legally modify existing noncompetes by formally rescinding them. That change will help to streamline compliance.

Instead, under the final rule, employers will simply have to provide notice to workers bound to an existing noncompete that the noncompete agreement will not be enforced against them in the future. To aid employers’ compliance with this requirement, the Commission has included model language in the final rule that employers can use to communicate to workers. 

The Commission vote to approve the issuance of the final rule was 3-2 with Commissioners Melissa Holyoak and Andrew N. Ferguson voting no. Commissioners Rebecca Kelly Slaughter , Alvaro Bedoya , Melissa Holyoak and Andrew N. Ferguson each issued separate statements. Chair Lina M. Khan will issue a separate statement.

The final rule will become effective 120 days after publication in the Federal Register.

Once the rule is effective, market participants can report information about a suspected violation of the rule to the Bureau of Competition by emailing  [email protected] . 

The Federal Trade Commission develops policy initiatives on issues that affect competition, consumers, and the U.S. economy. The FTC will never demand money, make threats, tell you to transfer money, or promise you a prize. Follow the  FTC on social media , read  consumer alerts  and the  business blog , and  sign up to get the latest FTC news and alerts .

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Flexible programme opens doors to training opportunities and jobs

Jobs Growth Wales+ at Whitehead-Ross Education and Consulting is for 16- to 19-year-olds not engaged in education, employment, or training

• 13:39, 29 APR 2024

A learning programme in Merthyr Tydfil provides young people with the necessary skills, qualifications, and experience to pursue employment opportunities or further training.

Jobs Growth Wales+ at Whitehead-Ross Education and Consulting (WREC) is designed for 16- to 19-year-olds who are currently not engaged in education, employment, or training.

It’s a very flexible programme designed around you - whether you want just a little help or more support.

The qualifications available are Essential skills and Employability qualifications, including interview skills, job application, personal finance, healthy living, H&S in the workplace, and many more.

WREC also offers NVQ certificates in Customer Service, Business Administration, Hospitality and Childcare, as well as the CSCS card.

The best thing is that you will receive a weekly training allowance while learning, although the amount will depend on how many hours you participate.

And for those with busy schedules, WREC offers weekly inductions, ensuring that the door to opportunity is always open.

You can find out more, look around the training centre and meet the team at an Open Day, from 3pm to 7pm on May 23, 2024. Click here to book your tickets.

Flexible programme

A WREC spokesperson said: "You might be searching for the right job or next steps in education and just need some help to get you there.

"That's where Jobs Growth Wales+ comes in. It's a great way to boost your confidence and get a taste of work you might be interested in.

"You’ll also have access to free training along with paid work experience opportunities within the local area.

"Our clients include local authorities, the Department for Work and Pensions, and the Education & Skills Funding Agency."

Employability training and support

Jobs Growth Wales+ offers employability training and all the support you need to fast forward to the next stage of your life - all while being paid.

• Try out jobs you might be interested in through work trials and placements
• Get paid with weekly training allowances while learning and a real wage when employed
• Receive ongoing advice, guidance, and one-to-one coaching to help you achieve your goals
• Build your confidence as you grow your skills and experience
• Train and gain recognised qualifications to boost your career options
• Get a foot in the door and access to jobs with local employers

Diverse offerings

Founded in 2012, Whitehead-Ross Education and Consulting (WREC) delivers high-quality skills-related programmes and social services provision.

With sites across Wales and England, it is committed to delivering top-notch programmes to equip youth with the skills, qualifications, and experience needed to thrive in today's competitive landscape.

Indeed, WREC's offerings are as diverse as the dreams it helps realise.

From CSCS Training to Essential Skills in Communication, Numeracy, and Digital Literacy, from Customer Service to Childcare, Hospitality, and Retail Training, the possibilities are endless.

Supporting young people in Merthyr

Indeed, WREC's dedication to providing tailored support and diverse opportunities truly sets it apart.

The spokesperson continued: "We're not just in the business of education and training; we're in the business of transformation.

"What makes us proud is the positive impact we are having on the lives of young people within Merthyr, supporting people into positive destinations such as employment or further learning.

"Seeing the positive impact we've had on the lives of over 11,000 individuals since our inception fills us with immense pride."

Find out more

Whitehead-Ross Education and Consulting is at Office 13, 14 & 15, Crownford House, Swan Street, Merthyr Tydfil, CF47 8EU. The Open Day runs from 3pm to 7pm on May 23, 2024. Click here to book your tickets.

For further information, call 07896 024336 or see www.wrecltd.co.uk

• Merthyr Tydfil
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Department of human resources, search form, recruitment details.

This page contains information on the definition of terms for the hiring process.

• At least 30 days of active duty in time of war or peace in a campaign or expedition for service in which a medal has been authorized by the government of the United States; or
• During the period from September 16, 1940, through January 31, 1955; or
• After January 31, 1955, at least 181 consecutive days of active duty.
• Applicants must not have been discharged under dishonorable conditions, or as the result of a court martial.
• Widows/widowers or surviving domestic partners of deceased eligible veterans may also qualify for Veteran's Entitlement credit.
• Veteran's Entitlement is limited to an application for entrance employment.
• The applicant must notify the Department of Human Resources of his/her veteran status when he/she submits the initial job application, complete the Application for Veteran's Preference form and verify eligibility.
• The applicant must attain a passing score on an entrance selection process to be entitled to the Veteran's Entitlement credit.
• Applicants must not have already used Veteran's Entitlement in an entrance examination which resulted in the applicant's permanent appointment.
• Once the probationary period is passed, Veteran's Entitlement points are to be removed from all other eligible lists on which there is standing.
• Veterans with a permanent service connected disability that is of record in the US Veteran's Administration may apply for a disability credit of ten percent (10%) of the qualifying score.
• Disabled veterans as defined above shall be afforded all rights under the Americans with Disabilities Act, including any reasonable accommodation if appropriate.
• Veteran's Preference Application

Last updated 4/25/2024

An official website of the United States government

Apprenticeship and Training Representative

Department of labor, office of apprenticeship.

All current Department of Labor employees with competitive status. This position is that of State Director located within a Region of the Office of Apprenticeship (OA), Employment and Training Administration (ETA), responsible for planning, directing, administering, and coordinating the agency's apprenticeship and other training programs in a State comprising several area (local) offices. This position is outside the bargaining unit. Click here to learn more about Office of Apprenticeship!

• Accepting applications

Open & closing dates

04/26/2024 to 05/06/2024

$104,356 - $160,315 per year

Pay scale & grade

1 vacancy in the following location:

• Indianapolis, IN

Telework eligible

Yes—as determined by the agency policy.

Travel Required

25% or less - You may be expected to travel for this position.

Relocation expenses reimbursed

Appointment type.

Permanent -

Work schedule

Full-time -

Competitive

Promotion potential

Job family (series).

0243 Apprenticeship And Training

Supervisory status

Security clearance.

Not Required

Position sensitivity and risk

Moderate Risk (MR)

Trust determination process

Suitability/Fitness

Announcement number

MS-24-DAL-ETA-12399623-CAR

Control number

This job is open to, career transition (ctap, ictap, rpl).

Federal employees who meet the definition of a "surplus" or "displaced" employee.

Internal to an agency

Current federal employees of this agency.

Clarification from the agency

Open to current permanent Employment and Training Administration (ETA) employees with competitive status; CTAP eligibles in the local commuting area.

• Strategically manages resources to ensure high performance, quality service, customer satisfaction, in order to enhance the Registered Apprenticeship System
• Plans and directs work of subordinates, setting, and adjusting priorities and establishing deadlines for completion of work. Articulates and communicates to subordinates the assignment, project, problem to be solved, actionable events, milestones, and deadlines/timeframes. Develops and/or assists in the development of performance standards and evaluating subordinate performance.
• Ensures the strategic plan, mission, vision, and values are communicated to subordinates and integrated into the team's strategies, goals, objectives, work plans, products, and services.
• Interpret and implement National policy within the State, making significant contributions to the development of policy at the National and Regional Levels, ensuring success of the programs at the State level, providing high performance, and quality customer service and satisfaction.
• Plans, coordinates, and implements an outreach program designed to encourage employers, organized labor and their representatives, and apprenticeship program sponsors to actively participate in the workforce investment system.
• Incorporates the input of customers, stakeholders, and partners into goal setting and operational outcomes.
• Decides on the approach to be used to interpret program data, develops proposed changes, and anticipates the effects and outcome.

Requirements

Conditions of employment.

• Must be a U.S. Citizen.
• Must be at least 16 years old.
• Candidate required to obtain the necessary security/investigation level.
• Requires a probationary period if the requirement has not been met.

Qualifications

The Apprenticeship and Training Series, GS-0243, has basic requirements for positions covered by this standard. Applicants must meet both the basic requirements (IOR) and specialized experience for the specific grade level to qualify. Individual Occupational Requirement (IOR): Specialized Experience (for positions above GS-5): Experience in the development or administration of apprenticeship programs or other occupational training programs involving the systematic development of worker skills on the job. This experience must have demonstrated:

• Knowledge of the functions, purposes, and practices of apprenticeship or other industrial training; and understanding of the knowledge, skills, and techniques involved in the practice of apprenticeable trades;
• Knowledge of the practices, operations, and content of occupations in one or more fields;
• Knowledge of training methods and related worker utilization practices for the improvement and better use of workers' skills; and the ability to apply this knowledge in identifying work force and training problems, analyzing such problems, and advising on their solution;
• Understanding of industrial relations problems and practices and of the traditions and attitudes of labor and management in relation to programs involving apprenticeship or other training on the job; and
• Ability to deal effectively with management, labor, and other appropriate officials on training matters.
• Working member of a functioning joint apprenticeship committee.
• Employer training official or officer of a union or trade association whose duties included the development or administration of an apprenticeship program or other industrial training program.
• Coordinator or director of a training program for apprenticeable occupations or other training activities.
• Training representative or administrator in the field of apprenticeship, working with labor or management.
• Vocational teacher or instructor in an apprenticeable occupation.

Specialized experience must include 3 out of the 5 following statements: 1. Develops relationships with and/or manages relationships with state or federal agencies/entities involved with Registered Apprenticeship such as; Workforce Development Agencies, Dept of Education, Veterans Affairs, Licensing Boards etc. 2. Experience interpreting and/or reporting data and making sound recommendations for appropriate changes to senior management. 3. Experience conducting on-the-job training in Apprenticeship Programs and compliance reviews according to Federal regulations and policies. 4. Responsible for coordinating and/or promoting National Apprenticeship System administered programs with other Federal, State, Tribal, local programs, or organizations. 5. Identifying issues, making recommendations for new approaches to possible solutions and evaluating the outcome in the operations of state and/or federal government programs. GS-14 Specialized Experience Requirements : In additional to meeting the IOR basic requirements described above, for the GS-14 grade level, applicants must have 52 weeks of specialized experience equivalent to at least the next lower grade level, GS-13, in the Federal service. Specialized experience is experience that equipped the applicant with the particular knowledge, skills and abilities to perform successfully the duties of the position and that is typically in or related to the position to be filled.

Specialized experience must include 4 out of the 5 following statements: 1. Develops relationships with and/or manages relationships with state or federal agencies/entities involved with Registered Apprenticeship such as; Workforce Development Agencies, Dept of Education, Veterans Affairs, Licensing Boards etc. 2. Experience interpreting and/or reporting data and making sound recommendations for appropriate changes to senior management. 3. Experience conducting on-the-job training in Apprenticeship Programs and compliance reviews according to Federal regulations and policies. 4. Responsible for coordinating and/or promoting National Apprenticeship System administered programs with other Federal, State, Tribal, local programs, or organizations. 5. Identifying issues, making recommendations for new approaches to possible solutions and evaluating the outcome in the operations of state and/or federal government programs.

IN DESCRIBING YOUR EXPERIENCE, PLEASE BE CLEAR AND SPECIFIC, WE WILL NOT MAKE ASSUMPTIONS REGARDING YOUR EXPERIENCE; otherwise, your application may be considered incomplete or not qualified. Assumptions will not be made based on job titles alone.

There is no educational substitution at these grade levels.

Additional information

The mission of the Department of Labor (DOL) is to protect the welfare of workers and job seekers, improve working conditions, expand high-quality employment opportunities, and assure work-related benefits and rights for all workers. As such, the Department is committed to fostering a workplace and workforce that promote equal employment opportunity, reflects the diversity of the people we seek to serve, and models a culture of respect, equity, inclusion, and accessibility where every employee feels heard, supported, and empowered.

Refer to these links for more information: GENERAL INFORMATION , REASONABLE ACCOMMODATION , ADDITIONAL DOCUMENTATION , FORMER FEDERAL EMPLOYEES

As a condition of employment, all personnel must undergo a background investigation for access to DOL facilities, systems, information and/or classified materials before they can enter on duty: BACKGROUND INVESTIGATION Click here for Career Ladder Promotion Information . Click here for Telework Position Information Based on agency needs, additional positions may be filled using this vacancy. The Department of Labor may use certain incentives and hiring flexibilities, currently offered by the Federal government to attract highly qualified candidates. Click here for Additional Information . The Fair Chance Act (FCA) prohibits Federal agencies from requesting an applicant's criminal history information before the agency makes a conditional offer of employment. If you believe a DOL employee has violated your rights under the FCA, you may file a complaint of the alleged violation following our agency's complaint process Guidelines for Reporting Violations of the Fair Chance Act . Note: The FCA does not apply to some positions specified under the Act, such as law enforcement or national security positions.

A career with the U.S. government provides employees with a comprehensive benefits package. As a federal employee, you and your family will have access to a range of benefits that are designed to make your federal career very rewarding. Opens in a new window Learn more about federal benefits .

Review our benefits

Eligibility for benefits depends on the type of position you hold and whether your position is full-time, part-time or intermittent. Contact the hiring agency for more information on the specific benefits offered.

How You Will Be Evaluated

You will be evaluated for this job based on how well you meet the qualifications above.

• Decision Making
• Interpersonal Skills
• Oral Communication
• Organizational Awareness
• Planning and Evaluating
• Written Communication

As a new or existing federal employee, you and your family may have access to a range of benefits. Your benefits depend on the type of position you have - whether you're a permanent, part-time, temporary or an intermittent employee. You may be eligible for the following benefits, however, check with your agency to make sure you're eligible under their policies.

The following documents must be submitted by 11:59 p.m. (ET) on the vacancy closing date. ELIGIBILITY REQUIREMENTS: Applicants must meet the eligibility requirements of time-in-grade (52 weeks at the next lower grade), time-after-competitive-appointment (90 days), and minimum qualifications (52 weeks equivalent to the next lower grade in federal service). These requirements must be met within 30 days of 05/06/2024 the announcement closing date.

The following documents are required from all applicants (PLEASE READ CAREFULLY):

• STATUS FEDERAL EMPLOYEES: If applying as a status candidate with current or former Federal Service, please provide a copy of your last or most recent SF-50, Notification of Personnel Action which shows your appointment eligibility for the position for which you are applying. Your SF-50 must identify the highest grade you held on a permanent basis, Pay Plan/Pay Schedule, Series, Grade/Pay Band, and career status. Multiple SF-50 may be submitted to demonstrate your highest previous rate and eligibility.

* WARNING: An award SF-50 (ex. cash or time off award) may not indicate grade, step, and competitive status. For current DOL employees, an award SF-50 DOES NOT indicate grade and step.

• Resumes are required- provide a resume either by creating one in USAJOBS or uploading one from your profile. To receive full consideration for relevant and specialized experience, please list the month, year, and number of work hours worked for experience listed on your resume. We also suggest that you preview the vacancy questions, and confirm that your resume supports your question responses.
• Most recent performance appraisal/evaluation signed and dated within 18 months is requested by the agency, but will not disqualify candidates if not submitted. If not submitted during the application process, one may/will be requested at the time of the interview if the opportunity is extended. *In most cases, CTAP applicants must submit this document - see the CTAP link below for more details.

The following documents are required (if applicable):

• Displaced Employee Placement Documents: Only required if requesting priority consideration under Career Transition Assistance Plan ( CTAP ) Eligibility. Click/Review this CTAP link to confirm what must be submitted as proof that the requirement has been met.

Do not upload password-protected documents. Applicants selected for employment who are not current DOL employees will be required to provide proof of citizenship , and the E-Verify system will be used to confirm the employment eligibility of all new hires.

If you are relying on your education to meet qualification requirements:

Education must be accredited by an accrediting institution recognized by the U.S. Department of Education in order for it to be credited towards qualifications. Therefore, provide only the attendance and/or degrees from schools accredited by accrediting institutions recognized by the U.S. Department of Education .

Failure to provide all of the required information as stated in this vacancy announcement may result in an ineligible rating or may affect the overall rating.

Persons who are deaf, hard of hearing, blind, or have speech disabilities, please dial 711 to access telecommunications relay services. To apply for this position, you must complete the initial online application, including submission of the required documentation specified in the Required Documents section.

Your application and ALL required supplemental documents MUST be received by 11:59 pm Eastern Time on the vacancy closing date to receive consideration. Paper applications and supplemental documents submitted in any other manner without prior approval from the vacancy contact will not be considered. Click here for information on Reasonable Accommodations.

Important - Save your information before the 30 minute system timeout! For help, go to USAJOBS Help Center .

STEP 1 - Create USAJOBS Account on www.usajobs.gov , including Resume and Saved Documents. Your resume must provide sufficient information to substantiate your responses to the self-assessment vacancy questions. If not, HR may amend your responses to more accurately reflect the competency indicated by resume content. For each employment period , include start/end month & year and note full-time or part-time (if part-time, include # of hours worked per week) otherwise, your application may be considered incomplete.

STEP 2 - Complete the 1st part of the application process (USAJOBS) Once you have identified a job on USAJOBS that you wish to apply for, click on the job title and then click the Apply button. For questions about the vacancy, contact the Agency Contact at the bottom of the announcement. Proceed through the steps noted at the top of the USAJOBS page. You will be able to select a resume and documents from your USAJOBS Account that you can submit as a package as part of your DOL application. In the final step, once you have certified your application, click the Continue to agency site button.

STEP 3 - Complete the 2nd part of the application process (DOL). On the Department of Labor (DOL) page, create a DOL Account if you have not already and click Apply to this vacancy . Continue through the progress steps at the top of the DOL page. The 2nd progress step is where you answer the vacancy-specific questions. The 3rd progress step Documents is where you submit the required documents (only if applicable to you) specified in the Required Documents section of this vacancy.

STEP 4 - On the Review and Submit step , verify each section of your application is complete and correct. In order to submit your application, you must have a check mark next to each section listed. If there is an X, return to the appropriate section and follow the prompts. When it is ready for submission, certify your application and click the Submit Application button.

STEP 5 - Edit Application as needed by 11:59 pm Eastern Time of Closing Date by returning to USAJOBS, clicking the vacancy, then Update Application. NOTE: It is your responsibility to ensure your responses and appropriate documentation is submitted prior to the closing date. For more detailed information on applying for positions with DOL click here to view the U.S. Department of Labor, How to Apply website.

Agency contact information

Cassandra ross.

972-850-4808

[email protected]

Once your complete application is received, we will conduct an evaluation of your qualifications and refer candidates for selection consideration. Candidates will be referred to the hiring manager for further consideration and possible interviews. You will be notified of the outcome. A selection is expected to be made within 30 calendar days of the issuance date of the certificate.

For instructions on how to check the status of your application, go to USAJOBS Help Guide.

USAJOBS will no longer send status alert email notifications. You can check your application status in the TAS by logging into USAJOBS and in the Applicant Dashboard, click the hiring agency Talent Acquisition System (TAS). If the TAS does not provide application tracking information, contact the agency's point of contact on the job announcement. To verify your application is complete, log into your USAJOBS account, https://my.usajobs.gov/Account/Login, select the Application Status link, and then select the more information link for this position. The Application page will display the status of your application, the documentation received and processed, and any correspondence the agency has sent related to this application. Your uploaded documents may take several hours to clear the virus scan process. To return to an incomplete application, log into your USAJOBS account and click Update Application in the vacancy announcement. You must re-select your resume and/or other documents from your USAJOBS account or your application will be incomplete.

The Federal hiring process is set up to be fair and transparent. Please read the following guidance.

• Equal Employment Opportunity (EEO) Policy
• Reasonable accommodation policy
• Financial suitability
• Selective Service
• New employee probationary period
• Signature and false statements
• Privacy Act
• Social security number request

Required Documents

How to apply, fair & transparent.

This job originated on www.usajobs.gov . For the full announcement and to apply, visit www.usajobs.gov/job/788633900 . Only resumes submitted according to the instructions on the job announcement listed at www.usajobs.gov will be considered.

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