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TheTreatmentEffectofSchoolExclusiononUnemployment
ARTICLEinSSRNELECTRONICJOURNAL·JANUARY2014
DOI:10.2139/ssrn.2380956
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Electronic copy available at: http://ssrn.com/abstract=2380956
The treatment effect of school exclusion onunemployment∗
Alex Sutherland†
Institute of Criminology
Manuel Eisner‡
Institute of Criminology
January 29, 2014
Abstract
Objectives: Fixed-term school exclusions are disciplinary sanctions on pupilsin response to serious aggressive or disruptive behaviour in schools. It is unclearwhether these sanctions aggravate future problems. Here we assess what impactfixed term exclusions have on later unemployment.Methods: We use data from the Longitudinal Study of Young People in Eng-land (LSYPE), a prospective cohort study of over 15,000 adolescents. We treatschool exclusion as an ‘intervention’ and apply propensity score matching to as-sess whether it has a treatment effect on unemployment.Results: We find a consistent difference between excluded and non-excludedchildren in their likelihood of being unemployed at aged 18/19. This effect rangesbetween 6-16 percentage points depending on the methodological approach taken.Conclusions: Our results suggest an independent effect of school exclusion onthe probability of being unemployed two years later, over and above numerousbaseline individual/family characteristics. To truly understand the effect exclu-sion has on young people, we suggest that high-quality cluster randomised trialsare needed.Keywords: school exclusion; unemployment; propensity score matching; multi-level modelling; cohort study.
∗We thank participants at the 2012 European Society of Criminology conference in Budapest for their commentson a presentation based on this paper. We are also grateful to Philippe Sulger and Ian White for their help and advicewith the research that lead to this article.†Institute of Criminology, Sidgwick Avenue, University of Cambridge, CB3 9DA Cambridge, UK; Phone: +44
(0)1223 746519, E-mail: as2140@cam.ac.uk.‡Institute of Criminology, Sidgwick Avenue, University of Cambridge, CB3 9DA Cambridge, UK; +44 (0)1223
335374, E-mail: mpe23@cam.ac.uk.
Electronic copy available at: http://ssrn.com/abstract=2380956
1 Introduction
In England, school exclusions are a disciplinary measure governed by the 2002 Educa-
tion Act (The Centre for Social Justice, 2011). The Act defines two types of exclusion:
Permanent exclusion means that a pupil is permanently removed from a school; fixed
period exclusions1 are exclusions from school for between one and a maximum of 45
days per school year (i.e. up to nine weeks in a given school year of 39 weeks). Across
England, substantial numbers of pupils are affected by school exclusion. In 2009/10,
there were 5,740 permanent exclusions, corresponding to 0.08% of the school popula-
tion. Fixed period exclusions are much more frequent. In 2009/10 there were 331,380
fixed period exclusions across all maintained primary, state-funded secondary and spe-
cial schools, meaning that 2.4% of the school population experience at least one fixed
period exclusion during a school year (Department for Education, 2011b).
Most school exclusions occur during secondary school, i.e. between ages 11 and 16,
with a peak during the last three years of compulsory school (i.e. Years 9-11). Amongst
these cohorts, 8.8% of male pupils and 4.1% of female pupils are excluded at least once
from school annually. In 2010/11 about 6.5% of pupils in England who were in the last
two years of compulsory education (years 10 and 11) experienced one or more fixed
period school exclusions for disciplinary reasons (Department for Education, 2011c).
These pupils are at a greatly increased risk of failing exams, not being in education,
employment or training (NEET) at ages 16-24, and having criminal convictions as
adolescents or young adults. Whilst there is evidence from a range of disciplines on the
association between exclusion and a host of negative outcomes, much of this evidence
relies on methodologies which are unsuited to establishing causal effects. In this paper
we use propensity score matching (PSM) to assess the impact of exclusion at aged
1These are also known as fixed term exclusions or ‘suspension’, all three terms are used inter-changeably throughout this paper.
1
15/16 on young people’s chance of being NEET at aged 18/19. This is important
as being NEET has significant economic costs. Coles et al. (2010:17) estimated, for
example, that the lifetime costs to the economy, the individual, their family and wider
society of a person who is NEET at ages 16-18 are about £105,000. With one-in-ten
16-18 year olds NEET at any one time in the UK — between two- and three-hundred
thousand young people — these costs are a significant burden on society.
2 Background
‘...there is little, if any, empirical evidence that school exclusions are
associated with a reduction or elimination of problematic behavior’ (Theriot
et al., 2010:13).
Despite being exposed to a series of individual, academic, socio-economic and fam-
ily risk factors, children who experience fixed period exclusions receive little specific
support. For pupils excluded for less than six days the only requirement is that schools
provide homework. For children excluded for six days or more schools must provide al-
ternative full-time education (the so-called ‘six-day rule’). Furthermore, head-teachers
arrange a reintegration interview with the parents for any child excluded at primary
school and for pupils excluded for more than five days at secondary schools.
National data show that the most frequent reasons for fixed term exclusions in the
UK were disruptive or aggressive behaviour, constituted by physical assault against a
pupil or an adult (24.2%), ‘persistent disruptive behaviour’ (23.8%) and verbal abuse
or threatening behaviour against an adult or pupil (24.9%) (Department for Education,
2011c). This pattern has been fairly consistent in recent years (see Petras et al. forth-
coming). As such, exclusions are largely responses to aggressive externalising behaviour
by children and young people at school. However, while exclusions are a disciplinary
2
tool mainly used in reaction to disruptive behaviours, headteachers have considerable
discretion about whether to exclude or not, and the length of the exclusion, as others
have noted (e.g. Macrae et al., 2003; Theriot et al., 2010). We know that, for instance,
enforcement around correct school uniform varies between schools: ‘One fundamental
factor in the decision to exclude is the ethos of the school, the discipline policies of
individual schools and the degree of tolerance maintained by different head teachers’
(Macrae et al., 2003:95). This suggests that a clearer understanding of perhaps idiosyn-
cratic differences (Galloway et al., 1985; Hayden, 2009) in school behavioural policies
and their enforcement is needed to capture, at the institutional level, what is driving
exclusion.2
2.1 Who is excluded?
Fixed term school exclusions affect some children disproportionately: Male pupils are
2.7-times more likely to be suspended from school than female pupils. Furthermore,
school exclusions affect children from poor and some minority backgrounds significantly
more often (Department for Education, 2011b). Thus, 21% of secondary-school pupils
eligible for free school meals experienced one or more fixed term exclusions in compar-
ison to 6.5% of other pupils. Similarly, pupils of ‘Caribbean’ (17.2%), ‘white and black
Caribbean’ (17.2%) and ‘Black’ (13.5%) background are considerably overrepresented,
while pupils of ‘Indian’ (2.5%) and ‘Asian’ (4.1%) background are underrepresented.
At the family level, excluded children are more likely to come from families that are
under stress, have no employment, are experiencing multiple disadvantage and where
2‘School ethos’ (AKA ‘school climate’ in the US) features heavily in discussions about thepossible criminogenic effects of school, and the extent to which school effects exist (see Rut-ter et al., 1979; Boxford, 2006). There is even a ‘National School Climate Center’ in the UShttp://www.schoolclimate.org/climate/. A principal component of climate/ethos is discipline,but to suggest that schools with ‘better’ discipline have lower levels of problem behaviour is hardlygroundbreaking. What is more interesting is understanding why school disciplinary climate varies.
3
parents themselves had experienced difficulties at school (Macrae et al., 2003). Fur-
thermore, children with special educational needs (SEN) are around eight times more
likely to be excluded than those without SEN (Department for Education, 2011b). For
example, in secondary schools across England, 12.3% of pupils with SEN who had a
statement (a document that obliges schools to provide specific help) were suspended in
2009/10. Rates of exclusion are very similar for pupils with special educational needs
but without a statement (12.0%). In comparison, only 2.8% of students without SEN
experienced school exclusion (Department for Education, 2011b). Furthermore, many
of those who end up being excluded from secondary school may have had educational
difficulties which were ‘inadequately appreciated or addressed during their years in pri-
mary education’ (Macrae et al., 2003:91). Finally, rates of fixed period and permanent
exclusions have been found to be 10-25 times higher for children with persistent mental
disorders, especially conduct disorder and hyperkinetic disorders (Meltzer et al., 2003).
Whilst there is some variation over time, patterns of exclusion do not appear to
have changed substantially since the mid-1990s. In short, it is still the same ‘sorts’
of children being excluded from school in terms of family breakdown, deprivation and
other social issues (Munn et al., 2000).
2.2 Effects of school exclusion
School exclusion has been found to be related to poor academic and occupational
outcomes (Massey, 2011; Sparkes, 1999), externalizing behavior including crime (Gra-
ham, 1988) and negative internalizing outcomes, such as self-harm (McAra and McVie,
2010). Gilbertson (1998) showed that 42% of sentenced juvenile offenders had expe-
rienced previous school exclusion. Speilhofer et al. (2009) showed that amongst those
young people who were in sustained NEET, the majority had experienced previous
prior exclusions and truancy. A recent study also suggests that approximately 50%
4
of excluded children become NEET within two years after their exclusion (Massey,
2011). Taken together these data suggest that children who are subject to temporary
or permanent school exclusion are at a much greater risk of behavioral, health-related,
occupational and educational difficulties.
While there is evidence supporting a correlation between school exclusion and later
adverse outcomes, it is currently unclear whether the disciplinary action itself has a
causal effect over and beyond the social, familial and behavioral characteristics of the
affected children. To date, studies have used analytical approaches that are unable
to reliably establish a robust link between exclusion and outcomes such as criminal
behavior. Typically, exclusion has not been the focus of these studies and is simply
a ‘risk factor’ that is ‘controlled for’ during analyses. Our aim is to use data from a
pre-existing longitudinal study to examine the short- and long-term effects of school
exclusion on a range of behavioral outcomes utilizing statistical approaches which are
more appropriate for estimating causal effects from observational data.
Theoretically, there exist (at least) three possible relationships between school ex-
clusion and being NEET. The first is that the exclusions are mere markers of levels
of problem behaviour without any genuine causal effects on subsequent behaviour. If
true, one would expect that excluded and non-excluded children with the same combi-
nation of characteristics and behaviour will not differ in later behaviour. The second
possibility is that exclusions achieve a deterrent or ‘correctional effect’ on the child
and reduce the risk of later negative developments. The third possibility is that exclu-
sions are linked to a series of unfavourable effects such as negative labelling, further
disengagement from school, exposure to criminogenic environments (Wikstrom et al.,
2012) and fewer learning opportunities - which all increase the risk of later problem
behaviours over and above the likelihood of such behaviours prior to the exclusionary
event.
5
3 Data and methods
The data used here come from the Longitudinal Study of Young People in England
(LSYPE) which is funded and run by the Department for Education (DfE). LSYPE is
a multi-site panel study of school children across England and was primarily intended
to study the progress of a cohort of young people through secondary school, further
and higher education, as well as transitions into work. A summary is given here of the
sampling strategy and attrition rates, more information about the study can be found
on the Department for Education website (www.education.gov.uk) and the LSYPE
user guide (Department for Education, 2011a). In addition, we combined data from
LSYPE with administrative data from the National Pupil Database (NPD), which is
also held by the DfE. Data were linked using anonymous Pupil Matching Reference
Numbers (PMR). We use NPD exclusion data corresponding to wave three of LSYPE
(2006) because this is the first year that these data were collated centrally by the
Department for Education.
3.1 LSYPE: sampling
Sampling for the study was achieved via a stratified random sample with dispropor-
tionate sampling for deprived schools. Schools were the primary sampling units, then
children within schools. Children from major ethnic minority backgrounds were over-
sampled at pupil level (Department for Education, 2011a). Data collection initially
took place in schools and began in 2004 when children were in Year 9 (aged 13/14),
continuing until 2010 (resulting in seven waves of data).
The initial sample for LSYPE was 15,770 children from 658 schools. There was
attrition between waves (see Table 1). Two strategies were employed by the DfE
research team to account for non-response: analytic weights and a booster sample
6
in wave four.3 Table 1 shows that there is attrition from the study from wave four
onwards, which reached nearly 40% by wave six. Data loss presents us with difficulties
- namely that analyses may be biased by selective attrition and suffer a loss of statistical
power.
Table 1: Attrition in LSYPE
Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6Surveys issued 21,000 15,678 13,525 12,410 11,793 11,225Responses 15,770 13,539 12,439 11,801 10,430 9,799Response rate 75% 86% 92% 95% 88% 87%Response rate from w1 sample 100% 86% 79% 75% 66% 62%
Note: In wave four a ‘booster’ sample consisted of 600 surveys was issued, with 352 responses (59%),taking the total sample for wave four to 16,122 but we ignore these cases in our analysis.
Of concern is missingness on the outcome variable, being NEET at aged 18/19. The
missing rate for this variable is nearly 40% (there were 9,554 out of 15,770 potential
observations, see Table A in Appendix A). When faced with attrition and missingness,
the question is whether the available data still ‘represent’ the population of interest,
i.e. whether the statistical procedure leads to valid and efficient inferences about the
population the sample is drawn from (Schafer and Graham, 2002:149). Many articles
do not acknowledge these issues instead opting to overlook missingness or otherwise
pretend it does not matter for results. We feel it is important to spell out the potential
impact missing data may have and how we deal with the issue in this paper.
3.1.1 Missing data
Our approach to missing data in this paper is to assume that data are Missing Com-
pletely At Random (MCAR) and employ listwise deletion as our approach to missing-
3The booster sample consisted of 352 new participants, taking the total possible sample in thatwave to 16,122 (see note for Table 1). We exclude observations from the booster sample becausewe base our propensity score estimation on measures gathered in earlier waves that do not containinformation from these individuals.
7
ness.4 Listwise deletion reduces to ignoring missingness, resulting in reduced sample
sizes. Whilst this is a limitation, listwise deletion is the method that is most robust to
violations of Missing At Random among independent variables in a regression analy-
sis. ‘Specifically, if the probability of missing data on any of the independent variables
does not depend on the values of the dependent variable, then regression estimates
using listwise deletion will be unbiased (if all the usual assumptions of the regression
model are satisfied)’ (Allison, 2002:6-7). This can be explained by the equivalence of a
disproportionate stratified sampling on the independent variables and a missing data
mechanism that depends only on the values of the independent variables. This is also
the case for other models such as logistic, Cox, or Poisson regressions. We estimate
models using listwise deletion mainly motivated by the fact that it is most robust to
violations of MAR among the independent variables in a regression analysis. We do
not consider other ‘simple solutions’ such as pairwise deletion, mean imputation, or,
for longitudinal data, last observation forward and baseline observation carried for-
ward imputation becuase of well-established problems with these approaches (Allison,
2002).5
3.2 LSYPE: measures
The data used in this study come from the first six waves of LSYPE, covering over
15,000 children. The outcome data (whether NEET or not) is self-reported and comes
from wave six, when the participants were 18/19 years old. The ‘treatment’ of fixed
term exclusion is measured at the stage of wave three when the children were aged
15/16 and covariates used in later analyses come from waves one and two (primarily
4We are also pursuing a separate analysis employing multiple imputation which makes use of thelongitudinal nature of the data.
5We are aware that, for instance, regression/stochastic regression imputation can yield unbiasedestimates of means, regression weights, or correlations under MAR.
8
one). Overall there were 905 fixed-term exclusions, meaning that roughly 6% of the
sample were excluded in that school year.
The tables in Appendix A report summary statistics on all variables used in the later
propensity score models along with the extent of missing data for each variable. Apart
from NEET (wave six), school exclusion (wave three), the young person’s General
Health Questionnaire (Age 12) score (wave two), and the measure on the relative
punishment for breaking school rules (wave two), all other measures come from wave
one of the study. We included variables from LSYPE which either theoretically or
empirically are associated with exclusion (and the common reasons for it) and/or the
probability of being NEET after compulsory schooling. On the basis of national data
relating to those excluded or NEET, we include measures of: prior aggression, violence
and anti-social behaviour; peer behaviour; special educational needs; gender, ethnic
and social characteristics; parenting and family relationship quality; formal involvement
with state welfare and criminal justice agencies; prior academic achievement and future
aspirations. These measures encompass the pupil, his/her family situation and the
quality of schooling. Table 2 offers a simple summary of the measures included, a
complete list of variables is given in Appendix A.
3.3 Propensity score matching
We use propensity score matching (PSM) to assess the causal effect of exclusion on
being NEET. PSM is premised on the idea of counterfactuals, i.e. what would have
happened if an individual had not received an intervention? Obviously we cannot
observe the treated and untreated versions of an individual so instead we try to find
un-treated individuals who are, in terms of the observed variables, indistinguishable
from those who were treated. From this pool of untreated individuals, we then match
9
Table 2: Summary of variables
Type Wave Example variablesDemographics 1 YP gender, ethnicity, month of birthParental characteristics 1 age of motherFamily structure 1 married, no. people in HH, no. older siblingsFamily socioeconomic status 1 housing tenure, NS-SEC class, HH incomeFamily relationships 1 quality of relationship, frequency of arguingYPs health 1&2 physical health, concentration, sleep, GHQ scoreSpecial Educational Needs 1 whether YP ever identified as having SENYP truancy or prior exclusion 1 times truant, prior temp. or perm. exclusionsYP ed. aspirations of YP/peers 1 future schooling plansYP victimisation 1 being bullied, violenceYP anti-social behaviour 1 substance use, fighting, bullyingYP prior attainment 1 YP Key Stage 2 average point scoreParental supervision 1 child whereabouts, compliance with disciplineParental satisfaction 1 with schooling, school disciplineAgency involvement 1 Criminal justice or social servicesExclusion (NPD) 3 National Pupil DatabaseNEET 6 Self-report from LSYPE
to the treated based upon the predicted probability of receiving ‘treatment’ (in this
case, being excluded from school). So if two individuals have a p=.25 chance of being
excluded based on a given set of covariates, those covariates will then not distinguish
between those who did and did not receive treatment. This approach means that two
individuals with different characteristics can be matched if their probabilities are very
close to each other. The predicted probability acts as a ‘balancing score’ between
the two groups of intervention and non-intervention, where ‘balance’ refers to equity
of factors which are relevant for both intervention assignment and the outcome of
interest.6 In short, we want the two groups to be as similar as possible except for the
intervention. It is important to note that achieving balance depends on the quality of
the covariates. Here, we use variables that closely match the characteristics of those
6Guidance on this point is inconsistent, e.g. ‘matching variables should affect both the outcomeand treatment equations’ (Blundell et al., 2003:12); versus ‘unless a variable can be excluded becausethere is a consensus that it is unrelated to outcome or is not a proper covariate, it is advisable toinclude it in the propensity score model even if it is not statistically significant’ (Rubin and Thomas,1996:253).
10
who are excluded or NEET based on national statistics: prior aggressive, violent or
anti-social behaviour; special educational needs; academic ability; family background;
ethnic and social characteristics; parenting, as well as other factors such as physical
and mental health.
The precise matching approach depends largely on the size of the pool of potential
comparison cases (Dehejia and Wahba, 2002). We apply a three-to-one matching ap-
proach with replacement and a caliper setting, meaning we try to match each treated
individual to three non-treated individuals who were indistinguishable in terms of co-
variates from the treated group. ‘With replacement’ means that non-treated individu-
als who are similar to many treated individuals can be used repeatedly, which has the
advantage of reducing bias (Stuart, 2010). The matches are then equally weighted. We
set the caliper to 0.25 of a standard deviation of the propensity score, meaning that
we are demanding matches with individuals similar in terms of propensity and thus we
can be more confident of a ‘like with like’ comparison. In the case of tied propensity
scores, we specified that ties were randomly broken.
As with any method, PSM has its limitations. The primary issue is that PSM relies
upon the explicit assumption that given a set of observed confounders, assignment to
intervention is exogenous in the sense that the assignment to the treatment or control
group and (potential) outcomes are conditionally independent (Rosenbaum and Rubin,
1983; Dehejia and Wahba, 2002). In other words, taking into account what we observe
(e.g., the demographic or criminogenic variables and by implication the propensity
score), treatment assignment is viewed ‘as random’. As with any modelling approach,
this means that PSM is only as good as the data being used and the technique is largely
premised on complete data (Stuart, 2010).
A final consideration for matching is that LSYPE data are, by the nature of the
sampling design, clustered by school. Therefore it is likely that unobserved school-
11
specific characteristics (e.g., school disciplinary policies) affect the likelihood of being
excluded and also the outcome (being NEET). Following Arpino and Mealli (2011), we
account for the hierarchical nature of the data when estimating the propensity score
by using multilevel models7 via the xtmelogit command in Stata 12 (StataCorp., 2011)
In the following sections we employ a variety of approaches, either singly or in
combination, which deal with clustering and/or confounding. We pursue different
strategies in order to assess the sensitivity of results to model specification and to
show the effect of different adjustments on results.
4 Results
We first present a ‘crude’ comparison between the means of the treated and untreated
group. This provides a basis for judging how much confounding we have adjusted for
in later models. The first set of results in Table 3, a simple t-test, shows that the
unadjusted likelihood of becoming NEET for excluded youths is about 15 percentage
points higher than those in the non-excluded group. As a next step, we present results
from a multilevel linear probability model (LPM) with NEET as the outcome.8 Here,
we are implicitly assuming that the treatment assignment is unconfoundend conditional
on the covariates we include in the regression alongside school exclusion. The set of
these covariates is the same as those we believe are important in influencing and/or
moderating the likelihood of being excluded or not and its effect on the likelihood of
7Arpino and Mealli (2011)’s simulations suggest that a dummy variable approach to account forclustering is the ‘best’ performing method to reduce potential bias in the propensity score due toomitted cluster-characteristics, and that the incidental parameter problem (Neyman and Scott, 1948)does not seem to affect the quality in terms of bias and efficiency. However, we have about 650schools and due to missingness in the data and lack of sufficient degrees of freedom, a dummy variableapproach is not feasible. Instead we opted for the ‘second-best’ approach (according to Arpino andMealli, 2011), by modelling the treatment-outcome by means of a multilevel random intercept logitmodel (Model 2 in Arpino and Mealli, 2011:1773).
8With all linear probability models presented, we use robust standard errors.
12
being NEET later on. A legend for the confounders’ is given in Appendix A. Result
(2) in Table 3 presents the result from this showing a positive but marginally non-
significant effect of exclusion on the likelihood being NEET (b 0.058, p 0.054). However,
the regression approach assumes that for each combination of covariates we have good
comparison pairs between treated and untreated individuals, which is unlikely to be
the case. We next turn to matching in order to tackle this.
We run a multilevel logistic regression with exclusion as the outcome variable to
derive the propensity score and do three-to-one matching with replacement and a
caliper set to 0.25 of a standard deviation of the propensity score, where ties are
randomly broken. The matching was successful as only one of the matching variables
was out of balance based on a t-test at the 5%-significance level.9 The results are
given as model (3) in Table 3. We report the Average Treatment Effect on the Treated
(ATT), finding a significant difference between treated and untreated groups amounting
to roughly thirteen percentage points (b 0.126; se 0.025; p ≤ .001).
As a means of checking our matching approach we also estimate models using
inverse probability weighting (IPW) which attempts to make two groups comparable
using the inverse of the propensity score as a regression weight.10 This weight generates
‘replicas’ of individuals in the treatment and control groups (referred to as ‘potential
samples’ or ‘pseudo-populations’; Williamson et al. (2012)). These serve as proxies
for samples where everyone received treatment or no-one received treatment. We then
compare the outcomes for the two potential samples. The results from the IPW are
given below. We estimated a linear probability model with NEET as the dependent
variable and exclusion as the independent variable, with weights as specified above.
Using this approach we again observe a positive relationship between being excluded
9We would expect to observe 4.7 variables (94 × 0.05) to be out of balance by chance if we hadimplemented a randomized experiment with 94 covariates.
10The weight to estimate the ATT is 1 if excluded and pscore/(1−pscore) if not (Williamson et al.,2012).
13
and being NEET, equating to roughly eight percentage points (b 0.083, p 0.14, 95% CI
2–15%).11
Table 3: Treatment effect of fixed term exclusion on the probability of being NEET
Model Result SE p 95% CI LB 95% CI UB
1. t-test 0.157a 0.021 .000 0.115 0.199
2. Multilevel Linear Probability Model 0.058a 0.030c .054 -0.001 0.117
3. Propensity Score Matching 0.126a,b 0.025 .000 0.077 0.175
4. Linear Probability Model w/IPW 0.083a 0.034c .014 0.017 0.149
a: Difference in proportion NEET at aged 18/19 between excluded and non-excluded groups.b: Average Treatment Effect on the Treated (ATT).c: Cluster robust standard errors.
5 Discussion
The relationship between school exclusion and later outcomes is rarely the direct focus
of research. Here, we have attempted to disentangle the relationship between school ex-
clusion and the later probability of educational or economic inactivity using a variety of
methods. As set out earlier in this paper, there are at least three possible relationships
between school exclusion and being NEET. The first is that exclusions are proxies for
problematic behaviour, with variation in exclusions being a function of, for example,
school-level differences in behaviour management policy/enforcement. If this were the
case then after adjustment for prior problem behaviour and other measures we would
expect to find no difference in the likelihood of later outcomes for excluded versus non-
excluded young people. The second is that exclusion might exert a corrective effect on
11Running the IPW model as a logistic model (also with cluster-robust standard errors) gives anodds-ratio of 1.74 (se .420, p .022, 95% CI 1.08–2.79).
14
those subject to it and thus reduce the later risk of negative outcomes. The third is
that exclusions are part of a series of additional barriers to acquiring social and edu-
cational capital that include labelling, school disengagement (perhaps arising from the
exclusion) and the resultant reduction in opportunities for education or employment.
Here exclusion may serve to actually increase the risk of later problems, even when
taking into account the problem behaviours which triggered the exclusion(s) in the
first place (e.g. by introducing ‘problem’ children to unsupervised ‘free time’ which is
likely to exacerbate anti-social behaviour, especially those more sensitive to situational
triggers – see Wikstrom et al., 2012).
Using a variety of models that adjust for confounding and clustering we find that
breaching school rules and being temporarily excluded from school at aged 15/16 is pos-
itively associated with being NEET at 18/19 years of age. Specifically, our results show
an average 10 percentage point difference in the likelihood of being Not in Education,
Employment or Training (NEET) at aged 18/19 between excluded and non-excluded
young people. So, for every 100 NEET young adults who were not excluded, there
would be 110 who had been excluded. This is not a large effect, but given everything
else which influences being NEET (e.g. the job market, business cycles), it is perhaps
surprising to find an effect at all.
However, it would be wrong to assert that exclusion itself solely or even directly
‘caused’ the higher proportion of NEET excludees. In our dataset, the intervention
(fixed term exclusion) took place during Year 11, at the time, the final year for com-
pulsory schooling in England. The outcome, being NEET, is observed two-to-three
years later. Being excluded in the final year of compulsory schooling could have had
a detrimental effect on exam performance due to the disruption to studying caused
by not being at school. Poor exam performance is then related to a higher likelihood
of being NEET because of the premium placed on school grades by employers and
15
education establishments (Bynner and Parsons, 2002). This is just one, fairly obvious,
factor between being excluded from school and being NEET after leaving school. But
this does not mean we should not think about the policy of exclusion or attempt to
address its potential (in)direct consequences. Given the association between exclusion
and negative consequences such as dropping out of education, delinquency and gen-
erally poor academic attainment, it seems sensible to place this policy under further
theoretical and empirical scrutiny.
6 Limitations
This study has a number of important strengths. First, it uses a large, longitudinal
and nationally representative survey/administrative dataset to tackle the question at
hand. Second, we employ a combination of methods that adjust for problems such as
clustering of observations and we explicitly focus on the estimation of causal effects of
exclusion by using an approach novel to this area of research. However, there remain
some limitations with our paper that we discuss below.
Conscious of the potential difficulty in defending the Missing Completely At Ran-
dom (MCAR) assumption with respect to being NEET, and illustrating that selection
on the dependent variable might be of concern, we were motivated to model the joint
distribution of the data and the missing data mechanism by means of a Heckman
selection model (Heckman, 1979). However, we encountered two problems with the
Heckman approach which is why we did not report the results here. First, when run-
ning the Heckman two-step model in Stata 12 (via the heckprob command) the second
stage probit model would not converge.12 Second, and more importantly, one assump-
12We also ran the model with a linear probit model at the second stage using the heckman commandwhich did converge, producing a similar point estimate and standard error for the impact of exclusionon being NEET (b 0.078; se 0.027).
16
tion for Heckman models to be plausible is that the model for predicting observing
NEET is somehow different than the one for predicting being NEET. This is the so-
called ‘exclusion restriction’ wherein we require variables that we believe affect selection
(here the observation of NEET) but not the value of NEET itself. For example, we
have run this model with ‘school exclusion’ left out of the first stage selection model,
then included in the second stage reduced model. The implicit assumption is that the
fact of exclusion does not affect the likelihood of observing NEET, but that exclusion
does affect the value of NEET itself. This assumption is made whenever the exclusion
restriction is invoked, but it seems implausible that a variable would affect observing
NEET but not the value of NEET itself.
We have also undertaken modelling of whether NEET is observed or not using those
variables included in the multivariate models. The results (not shown) demonstrate
that all between school variation in observing NEET is accounted for by these measures,
suggesting that they do a good job of capturing the selective observation of the outcome
variable. Finally, we are working on a follow-up paper that directly tackles missingness
via multiple imputation – preliminary results suggest that the estimates from imputed
models do not vary substantially from those presented here (but these are of course
subject to change).
To summarise our results: whether we use regression adjustment or matching, we
find the same positive relationship between fixed-term exclusion and the later proba-
bility of being NEET. Given that many of these results are also in the same order of
magnitude (between 6-16%) again suggests that we are finding evidence of some real
relationship.
17
7 Concluding remarks
Parsons (2005) points out that the discussion surrounding school exclusion can be
emotive and often vitriolic, with those expressing doubts about the purpose or effi-
cacy of exclusion branded as ‘soft’ on school violence or disruptive behaviour. This
is frequently coupled with rhetoric that appeals to simple ‘common-sense’ solutions to
dealing with ‘problem’ children. Little is known about school disciplinary policies in
the UK, but in the US they are typified by increasingly punitive responses to (increas-
ingly minor) misdemeanours (see Fenning et al., 2012). Evidence on the effectiveness
of school-based policies in reducing crime, drug use and victimisation is quite weak
(Mendez, 2003), with programmes typically being launched ‘in response to high-profile
events without doing a high-quality evaluation’ (Gottfredson et al., 2012:271). In the
US questions have been repeatedly raised about the efficacy of school exclusion, par-
ticularly when underpinned by a ‘zero tolerance’ approach (see e.g. Fenning et al.,
2012; Magg, 2012; Fabelo et al., 2011). Yet, with few exceptions there has been little
empirical engagement with this aspect of school discipline in the UK, which is a sur-
prise given the much publicised 15,000 hours (Rutter et al., 1979) children spend at
school.13
To conclude, there are two clear empirical questions raised by this paper and two
areas for intervention highlighted. First, studies that assess the efficacy of exclusion
are typically confounded by (or otherwise ignore) selection effects, which might lead to
an endogeneity problem. That is, it is not clear whether the effect of school discplinary
procedures control behaviour or whether school policies are introduced because of the
13Approaches that emphasise building positive school environments, the fair application and en-forcement of rules, and the use of proportionate punishments all have evidence demonstrating effec-tiveness (Gottfredson et al., 2012). There is some evidence on the efficacy on restorative justice basedapproaches to school discipline (McCluskey et al., 2011) as well as so-called School-wide Positive Be-havioral Interventions (e.g. Waasdorp et al., 2012) but see the discussion in Bear (2012) in relationto the ‘blanket’ application of behavioural policies.
18
children attending a given school (e.g. Maimon et al., 2012). In relation to school
exclusion, there is an ongoing study looking at changes to how permanent exclusions
are managed,14 but we believe there is a good case to run field experiments examining
exclusion ‘or not’, to determine the effects of fixed term exclusions (and indeed there is
anecdotal evidence that more schools now routinely try to avoid fixed term exclusions).
Second, so far we have only explored the effect of one episode of exclusion, but a
sub-sample of children go on to be excluded many times. It is of both academic and
political interest to determine whether there is a dose-response effect, either positive
or negative, in relation to school exclusion, something it would be possible to capture
via non-bipartite matching (see Guo and Fraser, 2010).
In terms of interventions, both the DfE data presented earlier and other research
(e.g. Petras et al. forthcoming) suggests that aggression is the main driver behind
exclusion. We know that aggression is a fairly stable externalising behaviour (see e.g.
Olweus, 1979), so focusing on (the antecedents of) aggression - be they individual
or otherwise - seems to be a sensible approach to minimising school disruption and
reducing exclusion. One area that seems ripe for intervention but is under-explored
is aiming to improve children’s self-control before they reach secondary school (and
whilst there) (Heckman, 2006). In an era when we need to monitor how much we eat,
what we spend, resist the many addictive substances that abound and not ‘give in’ to
the temptation of impulsive acts that can have long-lasting consequences, self-control
has never been more important to human-beings (Moffitt et al., 2011; Piquero et al.,
2010).
Finally, and more speculatively, exclusion as a policy emphasises individual respon-
sibility for one’s actions - ignoring the fact that it is often structural issues such as
deprivation that are more strongly linked to exclusion. As noted above, the poor are
14http://www.education.gov.uk/schools/pupilsupport/behaviour/exclusion/b00200074/
exclusion-trial/
19
generally those being excluded, a pattern that has not changed dramatically over time.
Following on from this, one of the strongest predictors of aggression is growing up
in poverty, so addressing structural inequalities might be an over-arching strategy to
reduce both aggression in children and their exclusion from school for aggressive acts.
20
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24
AV
ari
able
nam
es
and
desc
ripti
ve
stati
stic
s
Table
4:
Cov
aria
tes
inpro
pen
isty
scor
em
odel
s
Variable
Label
Mean
SD
Obs.
%m
issing
nee
t06
YP
self
-rep
ort
ing
NE
ET
inw
ave
6?
0.0
80.2
79,5
54
39.4
%
excl
usi
on
YP
FT
Ein
wave
3of
LS
YP
E(Y
r11,
aged
15/16)?
0.0
60.2
315,7
54
0.0
%
w1se
xyp
ad
min
:in
terv
iew
erco
de
sex
of
yp
0.5
10.5
015,4
15
2.1
%
eth
nic
3B
inary
eth
nic
ity
mea
sure
(1=
non
-wh
ite)
0.6
70.4
715,3
96
2.2
%
w1qfh
hn
oad
min
:nu
mb
erof
peo
ple
inth
eh
ou
seh
old
4.5
01.4
515,6
50
0.7
%
w1n
old
sib
hs
hr:
nu
mb
erof
old
ersi
blin
gs
that
yp
has
1.0
61.2
915,2
07
3.5
%
ow
nsh
om
eP
are
nts
ow
nh
om
e?0.6
80.4
715,5
66
1.2
%
w1in
cest
dv:
esti
mate
of
gro
ssh
ou
seh
old
inco
me
(ed
ited
)23.3
37.6
111,7
21
25.6
%
ben
efitw
1F
am
ily
claim
ing
ben
efits
?(1
=yes
)0.4
90.5
015,4
93
1.7
%
w1ca
reyp
yp
:w
het
her
yp
has
any
cari
ng
resp
on
sib
ilit
ies
wit
hin
hou
seh
old
0.0
50.2
215,3
96
2.3
%
w1agem
um
dv:
age
of
yp
’sm
oth
er41.3
45.6
315,0
43
4.5
%
w1ch
ea1h
sh
r:w
het
her
yp
has
any
lon
g-s
tan
din
gil
lnes
s,d
isab
ilit
yor
infi
rmit
y0.1
30.3
415,2
37
3.3
%
w1h
ea1m
p2
RE
CO
DE
of
w1h
ea1m
p(m
p:
gen
eral
hea
lth
inla
st12
month
s)0.1
50.3
615,4
80
1.7
%
w1se
nm
pm
p:
wh
eth
eryp
ever
iden
tifi
ed(b
yanyon
e)as
havin
gsp
ecia
led
uca
tion
al
nee
ds
0.1
90.3
915,4
37
2.0
%
w1se
rvss
mp
mp
:w
het
her
bee
nin
conta
ctw
ith
soci
al
serv
ices
ab
ou
typ
’sb
ehavio
ur
0.0
40.2
114,0
46
10.8
%
w1se
rvew
mp
mp
:w
het
her
bee
nin
tou
chw
ith
edu
cati
on
al
wel
fare
serv
ices
inla
st12
month
sb
e0.0
50.2
214,0
55
10.8
%
w1se
rvoth
mp
mp
:w
het
her
bee
nin
conta
ctw
ith
any
oth
ersi
milar
serv
ices
ab
ou
typ
’sb
ehavio
ur
0.0
50.2
214,0
47
10.8
%
w1p
olice
1m
p2
RE
CO
DE
of
w1p
olice
1m
p(m
p:
wh
eth
erp
olice
have
got
into
uch
bec
au
seof
som
eth
ing
0.0
80.2
714,0
45
10.8
%
w1qu
ald
ism
p2
RE
CO
DE
of
w1qu
ald
ism
p(m
p:
sati
sfact
ion
wit
h:
dis
cip
lin
eat
yp
’ssc
hool)
0.1
80.3
815,1
79
3.6
%
w1qu
alr
elm
pvgood
RE
CO
DE
qu
ality
of
rela
tion
ship
w/M
Pis
ver
ygood
0.6
40.4
815,4
98
1.6
%
w1hw
havey
pyp:
wh
eth
erev
erse
th
om
ework
at
sch
ool
0.9
70.1
615,4
10
2.2
%
w1ysc
hat1
dv:
you
ng
per
son
’satt
itu
de
tosc
hool
(ad
dit
ive)
34.0
47.3
015,1
82
3.6
%
w1fp
lan
16yp
yp:
wh
at
thin
km
ost
of
frie
nd
sw
ill
do
aft
eryea
r11
0.7
60.4
314,0
07
11.1
%
25
w1h
eposs
mp
mp
:likel
ihood
of
yp
goin
gin
toh
igh
ered
uca
tion
2.0
81.0
714,5
31
7.8
%
w1ab
s3m
emp
mp
:w
het
her
yp
has
ever
bee
noff
sch
ool
for
3m
onth
sor
more
0.0
30.1
714,0
50
10.8
%
w1ab
s1m
ym
pm
p:
wh
eth
eryp
has
bee
nab
sent
for
1m
onth
or
more
,in
last
12
month
s0.0
40.2
013,8
28
12.2
%
fte3
yr
FT
Ein
3yrs
pri
or
tow
1?
0.1
10.3
114,0
61
10.7
%
w1ex
pel
mp
mp
:w
het
her
yp
has
ever
bee
nex
pel
led
or
per
man
entl
yex
clu
ded
from
sch
ool
0.0
10.0
914,0
87
10.6
%
w1tr
uanty
pyp
:w
het
her
pla
yed
tru
ant
inla
st12
month
s0.1
60.3
614,3
89
8.7
%
w1tr
uant3
yp
yp
:w
het
her
pare
nts
ever
kee
pyp
off
sch
ool
0.2
10.4
114,3
74
8.8
%
get
on
mu
mR
EC
OD
Eof
w1m
get
onyp
(yp
:h
ow
wel
lget
on
wit
h(s
tep
-)m
oth
er)
0.0
20.1
514,4
30
8.4
%
qu
arr
elm
um
RE
CO
DE
of
w1m
qu
arr
eyp
(yp
:h
ow
oft
enfa
llou
tw
ith
(ste
p-)
moth
er)
0.2
20.4
114,1
29
10.3
%
w1fa
mm
ealm
p2
RE
CO
DE
of
w1fa
mm
ealm
p(m
p:
how
oft
enh
ad
fam
ily
mea
lin
last
7d
ays)
0.2
40.4
315,4
77
1.8
%
w1fa
mm
usm
p2
RE
CO
DE
of
w1fa
mm
usm
p(m
p:
how
oft
engo
ou
tto
get
her
as
afa
mily
(excl
ud
ing
shop
p0.6
20.4
915,3
18
2.8
%
w1fa
min
mp
2R
EC
OD
Eof
w1fa
min
mp
(mp
:h
ow
oft
ensp
ent
even
ing
toget
her
at
hom
eas
fam
ily)
0.1
10.3
115,3
33
2.7
%
w1p
art
imd
mp
2R
EC
OD
Eof
w1p
art
imd
mp
(mp
:w
het
her
yp
com
esb
ack
by
tim
ese
ton
nig
hts
bef
ore
sc0.1
00.3
111,7
76
25.3
%
w1p
art
mew
mp
2R
EC
OD
Eof
w1p
art
mew
mp
(mp
:w
het
her
yp
com
esb
ack
by
tim
ese
ton
frid
ay
or
satu
r0.0
90.2
912,0
87
23.3
%
w1b
ulr
cd
v:
wh
eth
eryp
bu
llie
din
any
way
inla
st12
month
s0.4
60.5
014,4
94
8.0
%
w1p
bu
lrc
dv:
wh
eth
eryp
bu
llie
din
som
ew
ay
inla
st12
month
s(p
are
nta
lre
port
)0.4
00.4
913,2
63
15.8
%
w1n
am
esyp
yp:
wh
eth
erh
ave
bee
nu
pse
tby
nam
e-ca
llin
gin
cte
xt
or
inla
st12
month
s0.2
80.4
514,5
69
7.5
%
w1m
on
eyyp
yp
:w
het
her
have
bee
nm
ad
eto
han
dover
mon
eyor
poss
essi
on
sin
last
12
month
s0.0
40.1
914,9
33
5.2
%
w1th
hit
yp
yp
:w
het
her
have
bee
nth
reate
ned
wit
hvio
len
ceby
stu
den
tsin
last
12
month
s0.1
90.3
914,8
63
5.7
%
w1ach
ityp
yp
:w
het
her
have
exp
erie
nce
dvio
len
cefr
om
stu
den
tsin
last
12
month
s0.1
70.3
814,8
97
5.4
%
w1alc
ever
yp
yp
:w
het
her
ever
had
pro
per
alc
oh
olic
dri
nk
0.4
60.5
014,5
86
7.4
%
w1ci
gn
ow
yp
yp
:w
het
her
ever
smoke
cigare
ttes
0.1
00.3
014,6
58
7.0
%
w1ca
nntr
yyp
yp
:w
het
her
ever
trie
dca
nn
ab
is0.0
90.2
814,9
43
5.1
%
w1sp
rayyp
yp
:w
het
her
ever
gra
ffitt
ied
on
walls
0.0
70.2
515,0
63
4.4
%
w1sm
ash
yp
yp
:w
het
her
ever
van
dali
sed
pu
blic
pro
per
ty0.1
00.3
014,8
60
5.7
%
w1sh
opyp
yp
:w
het
her
ever
shop
lift
ed0.1
20.3
214,8
89
5.5
%
w1fi
ghty
pyp
:w
het
her
ever
taken
part
infi
ghti
ng
or
pu
blic
dis
turb
ance
0.1
90.3
914,8
36
5.8
%
w1ri
skd
v:
nu
mb
erof
risk
fact
ors
you
ng
per
son
has
exp
erie
nce
d0.9
71.5
013,9
71
11.3
%
26
w2gh
q12sc
rd
v:
you
ng
per
son
gh
q12
score
-12
poin
tsc
ale
1.6
92.5
212,7
10
19.3
%
cvap
2ap
sks2
aver
age
poin
tsc
ore
(usi
ng
fin
egra
din
g)
for
conte
xtu
al
valu
ead
ded
.26.8
64.1
214,5
59
7.6
%
27
Mother’s highest education level Freq. Percent
degree or equivalent 1,403 8.91higher education below degree level 1,694 10.75gce a level or equiv 1,735 11.01gcse grades a-c or equiv 3,909 24.81qualifications at level 1 and below 1,292 8.2other qualifications 257 1.63no qualification 3,906 24.79Missing 1,558 9.89
Table 5: Maternal education
Family’s NS-SEC class (HH ref. person) Freq. Percent
higher managerial and professional occu 1,616 10.26lower managerial and professional occup 3,262 20.71intermediate occupations 1,018 6.46small employers and own account workers 1,752 11.12lower supervisory and technical occupat 1,576 10semi-routine occupations 1,880 11.93routine occupations 1,687 10.71never worked/long term unemployed 1,096 6.96Missing 1,867 11.85
Table 6: Family occupational class
Mother’s employment status Freq. Percent
Working FT/PT 9,651 61.26Unemployed 213 1.35Housewife 4,510 28.63Other 632 4.01Missing 748 4.75
Table 7: Maternal employment
Marital status Freq. Percent
Married 10,259 65.12Cohabiting 1,195 7.59Single parent 3,970 25.2no parents in HH 198 1.26Missing 132 0.84
Table 8: Parent(s) marital status
28
MP: satisfaction with YP’s school progress Freq. Percent
very satisfied 6,946 44.09fairly satisfied 7,035 44.66fairly dissatisfied 1,082 6.87very dissatisfied 372 2.36can’t say 63 0.4Missing 256 1.62
Table 9: Satisfaction with YP progress at school
MP: satisfaction with how much interest teachers show in YP Freq. Percent
very satisfied 6,337 40.22fairly satisfied 7,034 44.65fairly dissatisfied 1,329 8.44very dissatisfied 381 2.42can’t say 417 2.65Missing 256 1.62
Table 10: Satisfaction with teacher interest in YP
MP: overall quality of YP’s school Freq. Percent
very good 6,548 41.56fairly good 7,015 44.53Neither good nor bad 1,318 8.37fairly bad 467 2.96Missing 406 2.58
Table 11: Overall quality of school
29
How many teachers who set hwork check it’s done? Freq. Percent
Most/all teachers 12,050 76.49Some teachers 2,387 15.15None/hardly any 730 4.63Missing 587 3.73
Table 12: How many teachers check homework?
My teachers make sure we do any hwork (w1yys14yp) Freq. Percent
Most/all teachers 10,810 68.62Some teachers 3,312 21.02None/hardly any 1,002 6.36Missing 630 4
Table 13: My teachers check homework?
How often YP difficult to study in class because of disruption? Freq. Percent
or has this not been a problem at all? 2,153 13.67now and then 6,114 38.81in about half your classes 2,888 18.33less often but in more than half of the 2,115 13.43in most or all of your classes 2,075 13.17Missing 409 2.6
Table 14: Freq. class disruption?
How heavily YP punished for breaking school rules rel. to others Freq. Percentpunished more heavily than others 1,579 10.02punished less heavily than others 1,033 6.56treated much the same as anyone else 7,830 49.7never break school rules 500 3.17Missing 4,812 30.54
Table 15: Relative punishment
30
MP: how often parent’s set time for YP to be home Freq. Percent
Doesn’t go out 2,915 18.5Always/most times 11,811 74.97Sometimes/never 725 4.6Missing 303 1.92
Table 16: Curfew set by parents
MP: how often know where YP is when goes out in evening Freq. Percent
Don’t go out 2,080 13.2Sometimes/rarely/never 647 4.11Always/usually 12,756 80.97Missing 271 1.72
Table 17: Parents know where YP go?
How YP mainly spends free time Freq. Percent
at home/at friends house/with sibs 6,690 42.47out w/friends 7,218 45.82by self 1,325 8.41Other 100 0.63Missing 421 2.67
Table 18: How YP spends free time?
31
How often parents know where YP going out in evening Freq. Percent
always 9,970 63.29usually 3,556 22.57sometimes 1,077 6.84rarely (hardly ever) 337 2.14never 141 0.9Missing 673 4.27
Table 19: Freq. parents know where YP go?
Whether parents ever set curfew on school nights Freq. Percentnot allowed out/don’t go out 2,511 15.94often 9,396 59.64sometimes 2,402 15.25never 485 3.08Missing 960 6.09
Table 20: Freq. parents know where YP go?
How many times had friends round to house in last 7 days Freq. Percent
none 6,186 39.27once or twice 5,541 35.173-5 times 2,283 14.496+ times 1,366 8.67Missing 378 2.4
Table 21: Freq. friends round last week?
How many times gone out with friends in last 7 days Freq. Percent
none 3,593 22.81once or twice 5,155 32.723-5 times 3,616 22.956+ times 3,018 19.16Missing 372 2.36
Table 22: Freq. out with friends last week?
32