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Journal of Vocational Behavior 60, 289–309 (2002)
doi:10.1006/jvbe.2001.1868, available online at http://www.idealibrary.com on
Social Exclusion and the Transition from School to Work:
The Case of Young People Not in Education,
Employment, or Training (NEET)
John Bynner and Samantha Parsons
Center for Longitudinal Studies, Institute of Education, London, United Kingdom
In the modern labor market what Cˆot´e (1996) describes as “identity capital”—comprising
educational, social, and psychological resources—is at a premium in entering and maintain-
ing employment. One consequence is the extension of education and training while young
people acquire the qualifications and skills that will enhance their employability. In accor-
dance with the perspective of life span developmental psychology, this places particular
pressure on those young people growing up in disadvantaged circumstances and lacking
support, especially when attempting to negotiate the transition from school to work. A par-
ticular policy concern in Britain has been directed at those young people who leave full-time
education at the minimum age of 16 and then spend a substantial period not in education,
employment, or training (NEET). This article reports the result of analyzing longitudinal
data, collected for a subsample of the 1970 British Birth Cohort Study surveyed at age 21, to
model the relationship of NEET status to earlier educational achievement and circumstances
and to assess the added difficulties NEET poses in relation to the building of adult identity
capital. It is concluded that although poor educational achievement is the major factor in
entering NEET, inner city living for boys and lack of parental interest in their education for
girls are also important. For young men the consequences of NEET lie mainly in subse-
quent poor labor market experience. For young women, the majority of whom are teenage
mothers, the damaging effects of NEET extend to the psychological domain as well. It is
concluded that effective counseling targeted at high risk groups, along the lines of the new
UK “ConneXions” service, are needed to help young people avoid the damaging effects of
NEET and make a successful transition to adult life. C
2002 Elsevier Science (USA)
Key Words: transition to work; human capital; social capital; identity capital; training;
unemployment; education; labor market; qualifications; teenage motherhood.
It is well established that the social and economic context of youth transitions
is critically important in determining their shape and their outcomes for different
groups. These effects, operating across the life course and from one generation to
the next, draw attention to the need to study interactions between developmental
processes and the social context in which they take place. Life span developmental
psychology offers a set of perspectives for doing this (Super, 1980; Vondracek,
Address correspondence and reprint requests to John Bynner, Centre for Longitudinal Studies, Insti-
tute of Education, 20 Bedford Way, London WC1H 0AL, United Kingdom. E-mail: jb@cls.ioe.ac.uk.
289
0001-8791/02 $35.00
C
2002 Elsevier Science (USA)
All rights reserved.
290 BYNNER AND PARSONS
Lerner, & Schulenberg, 1986; Savickas, 1985; Blustein et al., 1997; Bynner, 1998;
Crocket & Silbereisen, 2000; Silbereisen, 1994).
Contexts are changing over time so different cohorts of young people will expe-
rience their effects differently. Life course theory (Brooks-Gunn, Phelps, & Elder,
1991; Elder, 1974, 1991; Crockett & Silbereisen, 2000) underlines the point that,
regardless of social origin, young people in successive cohorts face different sets
of obstacles and opportunities when constructing their own life courses. One facet
of social change that has been noted by numerous commentators is a prolongation
over the past 20 years or so of the transition from school to work and the increased
complexity encountered in passing through it (Jones & Wallace, 1992; Banks et al.,
1992; Bynner, Chisholm, & Furlong, 1997). Predictability of life course “trajecto-
ries”originating in certain locations in the social structure to particular outcomes
in the labor market “opportunity structure”available locally (Roberts, 1984) gives
way to the individualized life course in which personal agency is of paramount
importance in the “negotiation”of the transition that has to be undertaken (Evans &
Heinz, 1994; Crocket & Silbereisen, 2000). In what has been described as the “risk
society”(Beck, 1986) there is increasing uncertainty about the choices to make and
increasing probability that the wrong ones will lead to inferior life chances. But
despite the loosening of structural constraints, as some writers have been at pains to
stress, much of the old determinacy remains: Individualization is still bounded by
class, gender, and ethnicity (Furlong & Cartmel, 1997; Roberts, Clark, & Wallace,
1994; Breen & Goldthorpe, 2001). The concentrations of disadvantage identified
with location in the social structure continue to be reproduced from one generation
to the next.
Under the conditions of the risk society certification and the skills acquired
through kinds of employment experience become increasingly important in main-
taining a position in the adult labor market. Those who do not have these “human
capital”attributes (Becker, 1975), deemed important by employers, face difficul-
ties not only in entering employment but in sustaining any kind of fulfilling career.
Categorized in the United States as the “high risk category of non-college bound
youth”(Worthington & Juntunen, 1997), such young people often find themselves
on the margins of the labor market, moving between various short-term unskilled
jobs and unemployment; young women frequently exit early from the labor market
to pursue the alternative route of motherhood (Bynner, Ferri, & Shepherd, 1997;
Coles, 2000). Such polarization between the “haves”and the “have-nots”in terms
of human capital is increasingly characterized as social exclusion for a substantial
minority from mainstream adult life. Apart from patchy employment prospects,
subsequent consequences may include difficult relationships, lack of social and po-
litical participation, poor physical and mental health, drug abuse, and criminality
(Robins & Rutter, 1990; Atkinson & Hills, 1997).
Although human capital, as embodied in skills and qualifications, serves as
some kind of insurance against social exclusion, may not on its own be sufficient
to sustain a fulfilling adult life. In addition to the need for social support net-
works, or “social capital”(Coleman, 1998), and family know-how, or “cultural
SOCIAL EXCLUSION AND THE TRANSITION 291
capital”(Bordieu & Passeron, 1977), biological and health factors may also play
a part, of which low birth weight has been identified in some studies as significant
(Wadsworth, 1991; Silva & Stanton, 1996). According to Cˆot´e (1996, 1997) there
has also been an increasing premium placed by employers on the possession of
“identity capital.”This embraces the three forms of capital just described and a
range of psychological attributes. Its active form may be seen as manifested in the
personal agency that enables individuals to “navigate”their way into and through
the modern labor market (Evans & Heinz, 1994; Evans & Furlong, 1997). A lack of
such attributes typically originates in a childhood marked by disadvantaged family
circumstances and family values that place little emphasis on educational achieve-
ment (Bynner, 1998). In the British context, there has been particular concern
about young people who have suffered these difficulties and who are consequently
described as “Status Zero,”“Generation X,”“Getting Nowhere,”and “Off Regis-
ter”(Williamson, 1997; Pearce & Hillman, 1998; Bynner, Ferri, & Shepherd,
1997; Bentley & Gurumurthy, 1999). The common theme of all of these catego-
rizations is disengagement, particularly from the labor market and the means of
entering it through education or training. In Britain the group who have attracted
particular attention from policy-makers are those who, during the critical period
of the late teens, spend a substantial amount of time outside any form of educa-
tion, employment, or training (NEET). A major report from the UK Government’s
Social Exclusion Unit was devoted exclusively to the problems of this group and
a new policy of counseling and support for these young people was formulated,
“ConneXions,”to help them achieve successful transitions to adulthood (Social
Exclusion Unit, 1999).
Two questions arise about such young people. First, what characterizes those
who enter NEET? Are they the group who have simply failed to do well at school
and therefore drop out of all organized activity at the first opportunity or are there
other things that are distinctive about them which put them on an even weaker
opportunity route? Second, is the experience of NEET no more than a tempo-
rary staging post on a life course marred by disadvantage and failure or does the
experience in itself constitute a disabling condition or identity capital deficit in
its own right, making subsequent adjustment to the demands of adult life signifi-
cantly more difficult? This second, stronger view of NEET is that failure to gain
the critical work experience and job training after leaving school is permanently
damaging not only with respect to employment, but also in making a satisfac-
tory adjustment to adult life. In the British context, particularly, employers expect
young school-leavers to gain experience in occupationally useful ways (Bynner &
Roberts, 1991). Failure to do so marks the young person as an employment risk.
Identifying a problematic transition is not the same as defining it. For the pur-
poses of understanding both the origins of NEET and its consequences for subse-
quent adult statuses, it was necessary to have an operational definition that would
capture as precisely as possible the attributes of such youth. In this article, we
use longitudinal data from the 1970 British Birth Cohort Study (BCS70) to opera-
tionalize NEET and to model entry into it and exit from it into statuses in adult life.
292 BYNNER AND PARSONS
More precisely we want to use the data to assess the penalty in identity capital terms
attached to NEET status in the teens, over and above the penalty attached to lack
of qualifications and other disadvantaging factors in young people’s early lives.
METHODS
Data Source
BCS70 comprises a sample of all individuals born in Britain during the week
5–11 April 1970 (n=16,761), who have been followed up subsequently to adult
life. Information has been collected from a variety of sources, including inter-
views with parents, teachers, and medical professionals, together with educational
tests and self-completion questionnaires. In addition to the initial survey at birth,
follow-up surveys have taken place at ages 5, 10, 16, and 26 and most recently
at age 30. In 1991, at age 21, a representative 10% sample (n=1,623) of the
original birth cohort was also followed up in a study of basic skills (Ekinsmyth &
Bynner, 1994). In addition to assessing cohort members’competence in literacy
and numeracy and establishing their current employment, housing, family life,
and health statuses, this 1991 subsample survey also contained occupational his-
tory data, comprising a month-by-month record of relevant statuses—education,
employment, and training—back to age 16. It therefore provided exactly the data
needed for identifying NEET status.
BCS70 was also an appropriate dataset with which to investigate NEET for
other reasons. In 1986, when the cohort reached age 16 and were able to leave
school, about 50% of the 16-year-old population were leaving full-time education.
This compares with 70% leaving school at age 16 in 1976. By the end of the
1980s, the proportion of school children leaving at the minimum age had reduced
to about one-third. In consequence, in the 1970 cohort, we see a group of young
men and women whose opportunities when leaving school at age 16 have become
symptomatic of the situation for young people in Britain ever since. Instead of
work, they encountered youth training as embodied in the government’s national
scheme (YTS) or unemployment (Dolton et al., 1999). In 1986, YTS lasted 2 years;
by 1988 all benefits for unemployed young people between the ages of 16 and 18
were removed to “encourage”them to engage in youth training or stay on in
education; but unemployment was still a preferred option for some. The cohort’s
experience therefore exemplified the new world, in which young people found
themselves, of increasing pressure to enter training, go back to education, or take
on any kind of job. Labor market inactivity—including that connected with teenage
motherhood—was becoming increasingly stigmatized as an unacceptable option.
Variables
The variables for inclusion in the models reflected the postulated origins of
NEET in terms of the different elements of capital formation and its identity
capital outcomes. Table 1a lists the variables representing antecedent influences
hypothesized as leading to NEET, including highest qualification achieved by age
16. Table 1b then lists the postulated outcomes of NEET.
SOCIAL EXCLUSION AND THE TRANSITION 293
TABLE 1a
Antecedents of NEET Status between Ages 16 and 18
Variable information Values
CM birth weight low (birth)
Measured in ounces, converted to grams 0 =2515 g or more
1=under 2515 g
CM family social class (birth)
Registrar General’s Classification (RGSC) based
on father’s occupation or mother’s occupation
if “no father”or father information missing
0=nonmanual or skilled manual
1=semiskilled or unskilled manual
Parent(s) did not read to CM (age 5)
CM parent asked who in the family read to CM 0 =mother and/or father read to CM
1=mother and father do not read to CM
Parent(s) had no interest in CM education (age 10)
Composite score from information given by CM 0 =very interested
teacher: (a) if parents had met the teacher or 1 =low/no interest
(b) showed interest in CM education
CM has no hobbies/interests (age 10)
Parent answered whether CM did any of 13 listed 0 =top 3 quartile ranges
spare time activities, coded as follows: often 1=bottom quartile range.
(2), sometimes (1), or never/hardly ever (0).
Scores were aggregated and grouped
Inner city neighborhood (age 10)
Parent selected from a list the best description for 0 =rural, village, outskirts of town, other
the neighborhood where the family lived 1 =inner urban, council estate
CM receives free school meals (age 10)
Parent reported if CM received free school meals 0 =no
1=yes
Family receives state benefits excluding pensions and
child benefit (age 10)
Parent checked all benefits that any member of 0=no
the immediate family received 1 =yes
CM cognitive ability low (age 10)
This was measured by performance in two tests. 0 =top 3 quartile ranges
Scores were aggregated and grouped. 1 =bottom quartile rang.
The Edinburgh Reading Test: a shortened version
of this test of word recognition was used after
consultation with its authors (Godfrey,
Thompson, & Unit, 1978). The shortened test
contained 67 items examining vocabulary,
syntax, sequencing, comprehension, and
retention.
Friendly Maths Test: the lack of an appropriate
mathematics test for 10-year-olds led to the
development of a special test for the BCS70
cohort. It consisted of a total of 72
multiple-choice questions and covered in
essence the rules of arithmetic, number skills,
fractions, measures in a variety of forms,
algebra, geometry, and statistics.
294 BYNNER AND PARSONS
TABLE 1a—Continued
Variable information Values
No or minimum qualifications at age 16
(age 21)
BCS70 were one of the last cohorts to sit the 0 =O-Levels grade A–C or CSE grade 1
two-tiered examination structure of Ordinary 1 =CSE grades 2–5
Level (O-Levels) or Certificate of Secondary 2 =no formal qualifications
Education (CSE) qualifications. O-Level
grades range from A to E, with A to C being
pass grades. CSE grades range from 1 to 6,
with grade 1 being equivalent to O-Level grade
C and grades 2–5 being lower level passes.
CM self-reported all qualifications they held at
age 21 and the age they achieved them; these
were converted to a scale of “highest
qualification achieved”
Note. CM=cohort member; text in brackets =the survey in which the variable was measured;
Code 0 =reference category.
Antecedents
Variables are labeled to indicate the direction of the postulated influence in
precipitating NEET, e.g., no qualifications or family in financial difficulties. They
comprise physical characteristics (low birth weight), family circumstances at age
10 (including inner city neighborhood and receipt of state benefits and free school
meals), cultural capital of the home (manual social class and parents showed little
or no interest in cohort member’s education), educational achievement (combined
reading and math score at age 10 in the lowest quartile range, few hobbies of any
kind at age 10, and no qualifications at age 16).
Outcomes
The variables taken to signify identity capital comprise occupational and marital
status, self-assessed physical health, mental health (as measured by the Malaise
Inventory, Rutter et al., 1970, designed to assess depression), and self-appraisal
(fatalism, lack of a sense of control, dissatisfaction with life, life problems).
Analytic Approach
The analysis was carried out in three stages.
Stage 1 comprised the operationalization of NEET based on the BCS70 21-
year occupational history data. To identify more precisely the distinguishing char-
acteristics of NEET young people we restricted analysis to those who had left
school at the minimum age of 16 and were not in full-time education in Jan-
uary 1987 (n=930,470 boys and 460 girls). This was to reduce as much pos-
sible the confounding effects of educational achievement with NEET; i.e., those
young people pursuing the academic route to A levels and higher are by definition
SOCIAL EXCLUSION AND THE TRANSITION 295
TABLE 1b
Postulated Outcomes of NEET Status between Ages 16 and 18
Variable Information Values
Employed
Whether CM was in full-time or part-time 0 =other
employment at age 21 1 =employed
NEET
Whether CM was not employed, in training or 0 =employed, in training or education
education at age 21 1 =other
Ever married or cohabited by 21
CM reported if they were or ever had lived 0 =no
with a partner or had been married by age 21 1 =yes
General Health poor at 21
CM reported if they had been in excellent, 0 =excellent or good
good, fair, or poor health in the 12 months prior 1 =okay or poor
to interview
Depressed at 21
CM had their psychological well-being 0 =okay
assessed by use of the Malaise Inventory
(Rutter, et al., 1970). Twenty-four yes/no
questions elicited whether feelings of anxiety
and depression were currently being
experienced. A “depressed”score is assigned
if “yes”is answered to 8 or more questions
1=depressed
Fatalistic attitude
CM opinion on three statements relating to 0 =bottom 3 quartile ranges
employment and job opportunities: 1 =top quartile range
Getting a job today is just a matter of chance.
Success at work is just a matter of luck.
Getting on at work depends on others.
Opinion was graded on a 5-point scale ranging
from strongly disagree to strongly agree. The
average score over the three questions was
measured, with a high score representing a
fatalistic attitude.
Dissatisfaction with Life: does CM get what
want out of life?
CM had to chose which statement comes 0 =I usually gets what want out of life
closest to their own view 1 =I never really gets what want out of life
Lack of Control: does CM feel they have control
over what happens in life?
CM had to chose which statement comes 0 =I usually have free choice and control over
closest to their own view my life
1=Whatever I do has no real effect on what
happens to me
Problems in life: can CM run life as they want to?
CM had to chose which statement comes 0 =Usually I can run my life more or less as I
closest to their own view want to
1=I usually find life’s problems just too much
for me
Note. CM =cohort member; Code 0 =reference category.
296 BYNNER AND PARSONS
engaged in education over the period 16–18 and are therefore by definition
non-NEET.
Stage 2 used a logistic regression model to assess separately for young men
and young women the variables that predicted the status of NEET. The model was
built up in two steps; the first with highest qualification at 16 excluded and the
second with highest qualification included. The idea was to test whether inclusion
of school leaving qualifications eliminated the effects of earlier circumstances and
achievement. That is to say we wanted to determine whether the possible influ-
ence of these variables on NEET operated entirely through highest qualification
achieved or whether as features of social, cultural, and biological capital they con-
tinued to have an independent effect on NEET status and its possible consequences
for identity capital formation.
Stage 3 again used a logistic regression model to assess separately for young
men and young women the effect of NEET status on the various outcomes. The
model was built up in a number of steps: each of the outcome variables was
first predicted from NEET status alone; second, from NEET status plus high-
est qualification; and finally, from NEET status plus highest qualification plus
all the variables used to predict NEET. Under this last condition of maximum
statistical control, if NEET status continues to predict the various outcomes we
can conclude that the experience of NEET has a distinct effect on the various
identity capital outcomes. This influence is over and above that of lack of quali-
fications and the other potential influences with which the NEET effect might be
confounded.
The logistic regression model used here always involved the prediction of a
binary outcome variable, e.g., “NEET/Not NEET”and “employed/not employed,”
in terms of a set of antecedent (or predictor) variables. The results are reported as
relative odds or odds ratios for each category of each predictor variable compared
with the odds ratio for a reference category, which in this analysis is by definition
1. Odds ratios greater than 1 signify a positive relationship between category
membership and the outcome and odds ratio less than 1, a negative relationship.
Thus for prediction of NEET status from the three categories of the qualifications
variable “O level grade A-C/CSE grade 1 (or higher) qualifications,”“CSE grade
2–5,”and “no qualifications,”with the reference category set at “O level grade
A-C/CSE grade 1 (or higher) qualifications,”we might expect the category “no
qualifications”to attract an odds ratio substantially greater than 1. As a criterion
for establishing the statistical significance of the difference between a given odds
ratio and 1 we set p<.05 but also noted odds ratios, which fell just outside this
range, i.e., up to p<.10.
Missing Value Imputation
The longitudinal data on which the modeling was based contained much missing
data. To maintain a comparable sample size of 930 cases across all analyses missing
values were imputed. The method employed in the SPSS statistical package, MVA,
displays and tabulates the patterns of missing data to establish whether the data
SOCIAL EXCLUSION AND THE TRANSITION 297
are missing at random. Data can be categorical or quantitative for each variable.
The program then estimates means, standard deviation, covariances, and correla-
tions using multiple-regression or expectation-maximization (EM) methods. We
adopted the latter. This assumes that the pattern of missing values conforms to that
of the observed data, i.e., is nonrandom.
To obtain the MVA data, a data set was constructed that contained all vari-
ables from birth to age 10 that discriminated between NEET and non-NEET. The
complete list is displayed in the Appendix, Table A1.
DEFINING NEET
As NEET status reflects the dynamics of young people’s lives it has to be
defined longitudinally, i.e., it must represent a minimum period of time outside
education, training, and employment as opposed to being in one or more of them
over the same period. However, the precise boundaries for this experience are not
obvious. Many young people leave full-time education at the end of the summer
term (June/July)—having passed the minimum leaving age of 16—to work over
the summer period and then return to education in the autumn. Others do not
make up their minds until they have had a term away from school, returning in
the following January. Some move between education training and short-term jobs
interspersed with unemployment. Another complication is part-time employment,
which many young people mix with education or unemployment. In the case of
young women with children (teenage mothers), part-time work is often mixed with
child-care. Child-care itself is obviously an occupation, but because it involves,
for some, complete exit from the labor market, it may also be seen as aligned to
NEET. Accordingly the study focused on the education, employment, and training
activities of the 1970 cohort during the 24 months from January 1987 to December
1988 inclusive, i.e., January 1987 was taken as the start date instead of September
1986 to allow for a “settling down”period.
Numerous exploratory analyses were carried out on the data to determine which
cutoff points produced the strongest discrimination between those young people
categorized as NEET as opposed to non-NEET. The final decision was to define
NEET as “6 months or more during the ages 16–18 outside education, employment,
or training.”This contrasts with the category in education employment or training
for all of the 24 months between ages 16 and 18 and leaves a missing period of
18–24 months where the status is unclear. For the purposes of the analysis that
follows, the latter two categories were combined as “non-NEET.”Two versions
of the classification were tested, one with part-time jobs classified as employment
and one with part-time jobs classified as unemployment.
Table 2 shows the proportions of young men and women classified by their NEET
status with and without inclusion of part-time work in NEET. Eleven percent of
the total sample experienced 6 months or more with no education, employment,
or training over the ages 16–18, comprising 7% young men and roughly twice as
many, 14%, young women. The higher proportion of young women partly reflects
the status of those who were out of the labor market through having children and
298 BYNNER AND PARSONS
TABLE 2
Grouped Distribution by Number of Months in Education, Full-Time or Part-Time Employment or
Training between January 1987 and December 1988
With part-time work excluded With part-time work included
from NEET in NEET
All % Males Females All Males Females
NEET:
0 to 17 months education, 10 7 14 15 10 19
employment or training
18–24 months education, 12 12 11 14 14 13
employment or training
EET:
24 months, education, 73 81 75 72 76 68
employment or training
n930 470 460 930 470 460
not actively seeking work, i.e., some of these NEET young women were pursuing
the alternative full-time career of motherhood.
The first of these two classifications produced the stronger discrimination, i.e.,
part-time work is best not treated as a feature of NEET. Thus the teenage moth-
ers placed in the NEET category were not engaged in any form of full-time or
part-time employment for at least 6 months of the designated period. Notably our
specification of NEET corresponds quite closely to the definition used in the pio-
neering study of “Status Zero youth,”which also used a period of 6 months out of
the labor market as indicating lack of engagement (Istance, Rees, & Williamson,
1994; Williamson, 1997).
Predicting NEET Status between 16 and 18
Tables 3 and 4 give the odds ratios for young men and young women and the
sample as a whole in the prediction of NEET.
The figures in the “All”column give the overall profile of NEET status young
people. They were likely to have low birth weight and to have grown up in inner
city public housing estates in homes marked by poverty (free school meals and
state benefits) and lacking cultural capital (parents not reading to the children and
lacking interest in their children’s education). Although cognitive ability at age 10
did not appear to be involved, when highest qualification at age 16 was taken into
account, a strong effect of educational achievement was evident. Young people
with no qualifications were six times as likely to be in NEET status as those with
“Olevel”or above qualifications.
The separate analysis for young men and young women showed similarities and
some striking differences in the odds ratios. Thus although the low birth weight
effect was evident for both sexes parents’reading at age 5 only featured for the
young men and parents’interest in education at age 10 only for young women. The
particularly notable sex difference was for two features of material disadvantage:
SOCIAL EXCLUSION AND THE TRANSITION 299
TABLE 3
Predicting NEET: Odds Ratios
Predictors All Young men Young women
Part 1: Without highest qualification at 16
RGSC IV or V =0 1.30 1.45 1.00
Low birthweight =02.50 2.71∗2.39∗
Parents did not read to child =51.68 2.56 1.31
Free School Meals or State Benefits =10 1.89 1.00 2.55
Inner City or Council Estate =10 2.01 3.84 1.47
Low cognitive ability =10 1.11 1.18 1.13
Few hobbies or interests =10 1.08 0.52 1.46
Little parental interest =10 1.61 0.98 2.28
Part 2: With highest qualification at 16
RGSC IV or V =0 1.32 1.25 1.16
Low birthweight =02.45 2.95∗2.15
Parents did not read to child =51.52 2.55 1.17
Free School Meals or State Benefits =10 1.59 0.79 2.20
Inner City or Council Estate =10 2.03 4.03 1.48
Low cognitive ability =10 0.83 1.10 0.72
Few hobbies or interests =10 1.10 0.54 1.44
Little parental interest =10 1.26 0.70 1.75∗
Highest qualification: CSE =16 1.82 0.96 2.72
Highest qualification: none =16 5.84 9.32 6.21
Note. Age at which data were collected is indicated at the end of each variable description. Bold
types signifies statistical significance at p<.05; an asterisk signifies statistical significance at p<.10.
inner city housing and family poverty. For boys inner city housing had a large effect
(odds ratio =3.84), whereas for girls family poverty appeared to matter more (odds
ratio =2.55). When highest qualification at 16 was brought into the model the odds
ratios for girls were reduced and in the case of low birth weight and lack of parental
interest in children’s education reduced to statistical insignificance. For boys the
reductions were generally smaller or actually increased. In the case of inner city
living the odds ratio rose from 3.84 to 4.03, showing the centrality of geographical
location to boys’experience of NEET.
For both sexes highest qualification again had the highest odds ratio of all the
predictors, 9.32 for boys and 6.21 for girls, showing the dominance of educational
achievement in young people’s life chances. But notably many other factors in
early life experience also remained significant independently of qualifications,
suggesting that family circumstances are also an important influence. The lack of
effects for manual social class and for low cognitive ability were unexpected, but
almost certainly reflect the relative homogeneity of school leavers with respect to
these characteristics compared with the others that the analysis embraced.
Table 4 summarizes the results of modeling the impact of NEET status on the
identity capital outcomes. The table shows separately for young men and young
women the NEET status odds ratio for each outcome; first for NEET alone, second
300 BYNNER AND PARSONS
TABLE 4
Predicting the outcomes of NEET: Odds Ratios
Predictors
Young men Young women
NEET with NEET with
NEET with CSE or no quals NEET with CSE or no quals
CSE or controls +early CSE or controls +early
no quals experience no quals experience
Outcomes at 21 NEET controls controls NEET controls controls
NEET21 4.46 3.59 3.32 7.76 5.83 5.32
Full-Time or Part-Time .24 .32 .34 .13 .17 .19
Employment
Married/cohabiting .92 .85 .76 4.00 3.23 3.09
Poor general heath 1.73 1.55 1.45 1.38 1.08 1.00
Malaise 3.23 2.12 2.20 1.81∗1.76∗1.69
Fatalistic attitude 2.50 1.95 1.85 2.25 1.70 1.56
Dissatisfaction 2.34 1.92∗1.66 3.51 2.93 2.96
with life
Lack of control 2.65 1.77 1.41 4.20 3.36 3.47
over life
Problems with life 1.52 .87 .81 4.13 3.18 3.79
Note. Bold signifies statistical significance, p<.05; an asterisk signifies statistical significance at
p<.10.
with qualification level added as a control, and third with qualification level and
the set of early experience variables used as controls to predict NEET. The full
results giving the odds ratios for all the variables in the models are supplied in the
Appendix, Tables A1 and A2.
The results support the hypothesis that NEET status has a negative effect on the
adult outcomes associated with identity capital formation, particularly for young
women. For young men the effects of NEET status in the late teens could be seen
mainly through poor labor market performance, especially though the continuation
of NEET status itself at age 21. These effects were sustained at a slightly reduced
level when controls for qualifications were included in the model and were reduced
again (marginally) when the wider set of early experience variables were added
in as well. Thus young men who had experienced NEET were over three times
as likely as those who had avoided NEET to not be in education, employment, or
training at age 21, taking account of qualifications and early life experiences. The
odds ratio for NEET was even higher for the young women remaining at 5.3 when
all the controls were applied. But this may well be because the young women in
the NEET group with one or two children had particular difficulties in reentering
the labor market, returning to education, or in undertaking training.
Further adverse consequences of NEET for young men were restricted to lack of
full-time and part-time employment. Although such other hypothesized outcomes
as depression and fatalistic attitudes, dissatisfaction with life, lack of a sense of con-
trol, and experiencing problems in life all had significant odds ratios in the model
SOCIAL EXCLUSION AND THE TRANSITION 301
without controls, when controls were applied these odds ratios reduced in size,
failing to maintain statistical significance in the model with maximum controls.
Notably the variables that replaced them as significant predictors of NEET were
lack of qualifications and inner city residence and childhood poverty (Appendix,
Table A1).
For young women the picture was rather different, with NEET’s effects not only
sustained in relation to labor market outcomes, but also extending to early marriage
or cohabiting, feelings of dissatisfaction with life, lack of a sense of control, and
experiencing problems in life. NEET maintained statistically significant odds ra-
tios for all of these outcomes even in the model with maximum controls. It seemed
that these young women were suffering particular problems to which their earlier
NEET experience had contributed directly. As for boys, the other factors impli-
cated included lack of qualifications inner city residence and childhood poverty
(Appendix, Table A2).
DISCUSSION AND CONCLUSIONS
The analysis enables us to identify the key characteristics that separate young
people with NEET status from others. For young people who leave education at
the minimum age of 16, capital in the home, as reflected in parent’s not reading to
child (boys at age 5) and lack of parental interest in child’s education (girls at age
10) predict NEET. For boys, living in the inner-city is also significant; whereas for
girls, family poverty (e.g. free school meals) matters. Notably these effects persist
even when highest qualification achieved at 16 is taken into account, suggesting
that the components of identity capital derived from family circumstances and
experience add to, rather than operate through, educational achievement in driving
some young people toward NEET. The role of inner city housing estate residence
for boys gives particularly striking endorsement to the problematic nature of this
experience for boys’life chances (e.g., Power & Tunstall, 1994). For girls the
significance of educational interest in the home (or rather lack of it) appears to
push them along a path which, for many in the NEET category, is identified with
early motherhood (cf. Griffin, 1985; Wallace, 1987).
The difficulty in assessing NEET as a distinct category for girls needs to be
acknowledged. Numbers were too small to separate NEET girls into two groups:
those who were looking after a baby or babies at home and those who had yet to
become parents. The latter are clearly closest to the boys in relation to labor market
status, but were too few in number to investigate separately in this study. Further
work on a larger longitudinal data set will be needed to investigate the differences
between them. However, the centrality of child bearing in the construction of female
careers (Wallace, 1987; Hakim, 1996; Evans & Heinz, 1994) suggests that young
women’s“dropping out”through pregnancy has a certain functional equivalence
to young men’s disengagement from education employment and training. Young
women who drop out without becoming pregnant are probably very similar to the
young mothers in most other respects.
The consequences of NEET status in early adulthood point again to the differ-
ences in the lives of men and women and the paths they take to social exclusion.
302 BYNNER AND PARSONS
The dominance of poor labor market experience as the main outcome associated
with NEET for young men does query whether NEET status does damage their
identity capital formation in Cˆot´e’s broad sense of the term (1996) rather than
just its human capital component. The experience of NEET simply compounds
a history of educational failure, reducing prospects of employment or for acquir-
ing human capital through education or training even further. In this sense NEET
experience, unaccompanied by other factors, may well be not much more than a
staging post on the downward path to the bottom of the labor market and social ex-
clusion. For young women the NEET experience appears to impact on other facets
of identity as well. The association of NEET with negative psychological states,
including (self-reported) lack of a sense of control over life and problems and
dissatisfaction with life, points perhaps to more fundamental damage occurring.
And this is at a time when, in terms of educational achievement and progress in the
middle to higher echelons of the labor market, women’s prospects have never been
better (Hakim, 1996; Bynner and Parsons, 1997; Arnot et al., 1999). Perhaps it is
their powerlessness to take advantages of these opportunities that underpins these
NEET young women’s negative feelings about themselves. On the other hand,
again we need to qualify such a conclusion on methodological grounds. Women
are more willing to express their feelings about themselves openly than men, so
young NEET men’s lack of acknowledgment of psychological difficulties does not
rule out their existence.
We set the task at the beginning of this analysis of attempting to identify the
category of experience over the ages 16–18, which was both characterized by lack
of education, employment, and training and predictive of later social exclusion
outcomes at 21. We settled on a category of NEET experience of 6 months or more
over the ages 16–18 not in education, employment, or training. When part-time
employment was also excluded from the definition of this status we had identi-
fied a category of young people whose subsequent lives were clearly marked by
difficulty. These signs of social exclusion included poor labor market experience,
depression, early parenting, and poor housing. In the case of men, engagement in
the labor market was likely to be marginal with much experience of unemploy-
ment. In the case of women an early career at home looking after children was
more likely. The results underline the importance of taking the social context and
changes in it into account in studies of the transition from school to work and
vocational choice, as argued in the “Life Span”and “Life Course”perspectives in
developmental psychology (Super, 1980; Vondracek, Learner, & Schulenberg,
1986; Savickas, 1985; Blustein et al., 1997; Bynner, 1999; Silbereisen, 1994; Elder,
1991; Brooks-Gunn, Phelps, & Elder, 1991; Elder, 1974; Crockett & Silbereisen,
2000).
The cohort born in Great Britain in 1970 faced exceptional difficulties in making
a successful transition to work. In the context of a disappearing youth labor market,
and considerable uncertainty about the means of maximizing job opportunities in
the future, these young people faced the choice whether to staying on in education
or leave, and if they left, whether to take any job or training scheme on offer or
wait for something better to turn up. Some were lucky with the choices they made;
SOCIAL EXCLUSION AND THE TRANSITION 303
others without qualifications or work experience faced an increasingly uncertain
future. Those boys growing up in impoverished inner city areas, often lacking good
schools and housing, and those girls growing up in families without educational
commitment, more frequently than others drifted into the NEET status, which
disadvantaged their prospects even further. Given that ever-more complex forms
of identity capital are likely to define employability in the future (Cˆot´e 1996,
1997), the social context in which such capital is acquired becomes increasingly
important. Clearly the ages 16–18 represent a critical stage in the lives of such
young people which underlines the importance of professional intervention to
move their careers off the exclusion path toward fulfilling occupations. If, through
the help of career counselors and others, they can land secure jobs with training
and prospects of career progression, then a secure future for them may still be
assured (Worthington & Juntunen, 1997).
However, lack of qualifications will continually be a problem if the jobs NEET
young people enter terminate. For those who spend a substantial part of the pe-
riod not engaged in employment and who do not or cannot take the opportu-
nity to engage in education or training either, the future may be bleak. The lack
of physical amenities, educational resources, and employment opportunities that
also frequently characteries the inner city neighborhoods, in which many of these
young people grow up, exacerbates their difficulties even further (cf. Dolton et
al., 1999). Clearly, these young people have not been excluded from training and
employment altogether. In fact, under the definition we used, up to 18 months
of the period had been spent in doing one of these. But probably what charac-
terized these experiences was the lack of a genuine base for employability. This
makes the case for investment in an education and training infrastructure that
will keep opportunities open. It also underlines the need for much stronger com-
mitment on the part of employers to ensuring that first jobs, as well as training
experiences, are all seen as part of an educational progression into proper adult
work.
The British system, with its variety of routes to skilled employment, offers,
through training, a pale shadow of the much stronger systems of vocational prepa-
ration in evidence in other European countries (Heinz, 1990; Rose, 1991; Bynner
& Roberts, 1991; Evans, 2000). In the United States staying on to age 18 to gradu-
ate 12th Grade is the norm rather than the exception, though concerns in the United
States with dropouts and the need for new models of vocational preparation have
a striking resonance with these results (Hamilton & Hamilton, 1999). The modern
apprenticeship now promoted by the British government and targeted at all early
school leavers goes some of the way toward the German apprenticeship model,
combining work-based training with off-site vocational education (DES, 1991).
But despite high hopes for modern apprenticeship, it is unlikely that it will em-
brace all young people over the ages 16–18 and already there are signs that many
young people who embark on it do not complete it: only 40% get a vocational
qualification from their apprenticeship.
There is clearly a long way to go still in terms of the strengthening of early labor
market experience in employability directions than the current systems in many
304 BYNNER AND PARSONS
countries allow. Effective counseling services and educational investment become
ever more vital as the means of bridging the gap.
APPENDIX A
Variables Used in the Imputation of Missing Values
At birth
CM birthweight
CM mother smoking habit during pregnancy
Age CM mother at her first birth
Age CM mother and father left full-time education
Marital status of CM parents
Social class (based on father’s occupation or mother’s occupation if “no father”or father information
missing)
Age 5
CM living with natural/adopted mother
CM living with natural/adopted father
Parents’divorced/separated
Number of family moves since birth
Housing tenure: home ownership, rented accommodation, tied property, etc.
Overcrowded livingaccommodation: ratio of number people in house to number of rooms (excluding
kitchen and bathroom)
CM ever been in Local Authority care
CM ever separated from mother for 1 month or more
Presence of a long-term illness in the household
CM father experienced unemployment in the last 12 months
CM attended preschool or nursery
CM parents read to CM
CM father figure helped mother with domestic duties and childcare responsibilities
Behavior adjustment [measured on the Rutter (home) scale, a modified version of the Rutter “A”
Scale (Rutter et al., 1970)]
Cognitive development [measured by CM performance in the Copying Designs Test (a test to obtain
some assessment of the child’s perceptuomotor ability) and the Human Figure Drawing Test (a
modified version of the Draw-a-Man test originally devised by Goodenough, 1926, and later
developed by Harris, 1963)
Age 10
CM family in receipt of state benefits
CM has free school meals
CM ever been in care
Presence of a long-term family illness in the household
CM living with natural/adopted father
CM Parents divorced/separated
Overcrowded livingaccommodation: ratio of number people in house to number of rooms (excluding
kitchen and bathroom)
Housing tenure: home ownership, rented accommodation, tied property, etc.
Number of family moves since birth
Description of neighborhood CM family lived in (inner city, suburbs, rural, etc.)
Parent(s) interest in CM education
Parent(s) education aspirations for CM: wanted them to leave ft education at 16, pursue post-16
education, etc.
SOCIAL EXCLUSION AND THE TRANSITION 305
Number of CM’s hobbies and interests
Behavior adjustment [measured on the Rutter (home) scale, a modified version of the Rutter “A”
Scale (Rutter et al., 1970)]
Cognitive development [measured by CM performance in The Edinburgh Reading Test (a shortened
version of this test of word recognition was used after consultation with its authors, Godfrey,
Thompson, Unit, 1978, which examined vocabulary, syntax, sequencing, comprehension, and
retention) and the Friendly Maths Test (a special mathematics test developed for the BCS70
cohort covering the rules of arithmetic, number skills, fractions, measures in a variety of forms,
algebra, geometry, and statistics)]
Age 16 (from age 21 data set)
Highest qualification: each CM listed all qualifications they held at age 21 and the age they attained the
qualification. A “highest qualification”variable was then derived from all the public examinations
a CM had passed at age 16. As the BCS70 cohort was one of the last to experience the two-tiered
examination structure of Ordinary Levelexaminations (O-Levels) and the less academic Certificate
of Secondary Education (CSE) examinations. Some CMs also sat General Certificate of Secondary
Education (GCSE) examinations, the examination system which was to replace O-Levels and CSE
examinations.
Note. CM =cohort member.
TABLE A1
Prediction of Adult Outcomes from NEET—Odds Ratios for Young Men
Not get No Not run
FT or Married/ Poor Fatalistic what want control life as
PT emp Neet 21 cohab health Malaise attitude out of life over life want to
NEET 0.24 4.46 0.92 1.73 3.23 2.50 2.34 2.65 1.52
NEET 0.32 3.59 0.85 1.55 2.12 1.95 1.92 1.77 0.87
Hq16-cse 0.72 1.45 0.93 1.07 1.57 2.15 1.38 4.25 1.72
Hq16-none 0.28 2.78 1.30 1.50 5.01 3.35 2.28 9.14 6.14
NEET 0.34 3.32 0.76 1.45 2.20 1.85 1.66 1.41 0.81
Hq16-cse 0.81 1.31 0.79 0.96 1.62 2.05 1.40 4.70 1.99
Hq16-none 0.36 2.17∗1.00 1.42 5.99 2.72 2.16 9.94 6.77
RGSC 0.87 1.33 1.02 1.04 0.53 1.10 1.03 1.06 0.76
IV or V 0
Low Birth 0.73 1.76 0.44 0.58 0.57 1.09 2.14∗4.78 3.36∗
weight 0
Parents read to 1.03 1.08 1.24 1.01 1.66 1.11 1.22 1.24 0.84
child 5
FSM or State 0.62∗1.67∗1.42 0.88 0.74 1.02 0.77 0.79 0.41∗
Benefits 10
Inner City or 0.79 1.16 1.27 1.19 0.90 1.11 1.49∗1.66 1.35
Council 10
Cognitive 0.86 1.06 1.29 1.75 1.23 0.86 0.69 0.72 0.62
ability 10
Few hobbies 10 0.77 1.24 0.82 0.83 0.62 0.66 1.03 1.25 0.64
Little parental 0.85 1.08 1.15 0.75 0.90 1.92 1.59∗1.20 2.11∗
interest 10
Note. Bold type signifies statistical significance, p<.05; figures marked with an asterisk signify
statistical significance, p<.10.
306 BYNNER AND PARSONS
TABLE A2
Prediction of Adult Outcomes from NEET—Odds Ratios for Young Women
Not get
what No Not run
FT or Married/ Poor Fatalistic want out control life as
PT emp Neet 21 cohab health Malaise attitude of life over life want to
NEET 0.13 7.76 4.00 1.38 1.81∗2.25 3.51 4.20 4.13
NEET 0.17 5.83 3.23 1.08 1.76∗1.70 2.93 3.36 3.18
Hq16-cse 0.49 2.18 1.34 1.40 1.00 1.41 1.22 2.38 1.87
Hq16-none 0.26 3.89 2.77 2.54 1.15 2.91 2.20 2.10 2.65∗
NEET 0.19 5.32 3.09 1.00 1.69 1.56 2.96 3.47 3.79
Hq16-cse 0.62∗1.74 1.30 1.22 0.98 1.21 1.11 2.58 1.85
Hq16-none 0.39 2.64 2.58 2.12 1.20 2.69 2.09∗2.45 3.13
RGSC 0.80 1.44 1.00 1.13 1.03 0.99 1.37 0.93 1.17
IV or V 0
Low birth 1.14 0.97 1.08 0.54 1.23 0.81 0.99 1.23 1.13
weight 0
Parents read 0.95 1.06 1.03 1.12 1.10 1.08 1.23 0.60 1.62
to child 5
FSM or State 0.60 1.53∗1.56 1.58 1.29 1.59∗0.98 1.07 0.76
Benefits 10
Inner City or 0.89 1.25 0.81 1.12 2.14 2.36 1.37 1.52 1.10
Council 10
Cognitive 0.56 1.73 1.04 1.27 0.97 1.54 1.31 0.89 1.15
ability 10
Few hobbies 10 0.76 1.32 0.72 1.24 0.72 0.87 1.64 0.64 1.29
Little parental 0.98 0.98 1.15 0.92 0.59 0.56∗0.54 0.83 0.34
interest 10
Note. Bold type signifies statistical significance, p<.05; figures marked with an asterisk signify
statistical significance, p<.10.
APPENDIX B
Comparison Categories for Variables in Logistic Regressions
Personal Attributes
1. Started nursery/school after age 4 vs Started nursery/school by age 4
2. More than 5 days absent from school at age 10 vs No days absent from school at age 10
3. No qualifications at 16 vs O-Level/CSE grade 1 or NVQ2 qualifications at 16
4. Low grade qualifications at 16 (CSE grades 2 to 5/NVQ1) vs O-Level/CSE grade 1 or NVQ2
qualifications at 16
The BCS70 cohort was the last to sit the two-tiered system of O-Level (Ordinary Level) and CSE
(Certificate of Secondary Education) examinations at age 16. O-Level exams are graded A–E, with
grade C being the lowest pass. CSE exams are graded 1–5, with grade 1 being deemed equivalent to
an O-Level grade C pass. The National Vocational Qualification (NVQ) level system attempt place all
academic and vocational qualifications within one system. NVQ levels range from NVQ1 to NVQ6,
with NVQ5 being equivalent to degree level qualifications.
SOCIAL EXCLUSION AND THE TRANSITION 307
Socioeconomic Characteristics of Family
1. Father in manual occupation at birth/no father vs father in nonmanual occupation at birth
2. Overcrowded accommodation at age 10 (more than 1 person per room) vs Accommodation at
age 10 with up to 1 person per room
3. Living in an inner city environment at age 10 vs Living in outskirts of town or a rural environment
at age 10
4. Few household goods in the home at 5 vs Average of above number of household goods in the
home at 5
Of a list of household possessions such as television, telephone, washing machine, and so on, the
variable was split into cohort members with less than average and those with the average and above
number of goods. Scores ranged from 0 to 7, with 5 being the average number.
5. Father not in regular paid work at 10 vs Father in regular paid work at 10
6. Gross family income less than £100 per week vs Gross family income £100 or more per week
Education of Mother/Educational Aspirations of Parents for Cohort
Member (CM)
1. Mother not staying at school past end of compulsory education (controlling for changes to the
end of compulsory education by accounting for age of mother) vs Mother experienced some form of
extended education
2. Mother not having any qualifications vs Mother with some qualifications
3. Mother having little interest in CM education at 10 vs Mother with interest in CM education
4. Parents were unsure/did not want CM to continue training after they left school at 16
5. Parents wanted CM to continue training after they left school at 16.
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Received: October 1, 2001
