THE IMPACT OF YOUTH CRIMINAL BEHAVIORON ADULT EARNINGS.docx

THE IMPACT OF YOUTH CRIMINAL BEHAVIOR
ON ADULT EARNINGS
Sam Allgood
University of Nebraska
[email protected]
David B. Mustard
University of Georgia
[email protected]
Ronald S. Warren, Jr.
University of Georgia
[email protected]
September 1999
Abstract
Individuals charged with or convicted of a criminal offense
when young complete
fewer years of schooling and accumulate less work experience
as young adults than those
with no contact as a youth with the criminal-justice system.
Because both schooling and
experience are positively correlated with earnings, having a
criminal background when
young indirectly lowers earnings as an adult. We show,
however, that – holding these
human-capital variables constant – youth criminal behavior
directly reduces subsequent
earnings as an adult.

We combine data from the 1980 wave of the National
Longitudinal Survey of
Youth, which provides detailed, self-reported information on
criminal background, with
socioeconomic and demographic variables to specify and
estimate a model of the
determinants of earnings in 1983 and 1989. The results imply
that having been convicted
prior to 1980 of a crime when young reduces 1983 earnings by
at least 12%. However,
having been charged - but not convicted - of an offense as a
youth has no statistically
significant effect on such earnings. A criminal case adjudicated
in juvenile court reduces
1983 earnings by at least 9%, while having a charge decided in
adult court lowers those
earnings by about 14%. The magnitudes of these earnings
effects persist over the
subsequent six years.
2
I. Introduction
It is well known that young people are more likely to engage in
illegal activity
than are older individuals. However, the extent to which illegal
behavior engaged in as a
youth influences adult socioeconomic outcomes is less clearly
understood. For example,

does such activity as a youth persistently affect subsequent
labor-market opportunities, or
are its effects relatively short-lived? Our paper analyzes this
relationship by estimating
the impact of youth criminal activity on adult labor-market
earnings.
Few studies have examined how youth criminal activity affects
adult labor-market
outcomes. Instead, the literature has focused on how adult
criminal activity affects adult
outcomes. Previous studies have reached conflicting
conclusions about the effect of an
adult conviction on subsequent income. Lott (1989, 1992a,
1992b) examined the earnings
of adult federal offenders, and concluded that their post-
conviction reduction in income is
statistically significant and is largest for high-income offenders.
He argued that the most
important aspect of society’s sanction against criminals is the
reduced legitimate earnings
of offenders upon their return to the labor force. Waldfogel
(1994b) also studied adult
federal offenders, and found that a first-time conviction reduced
employment

probabilities and significantly depressed legitimate income.
These effects were largest for
offenders whose pre-conviction jobs required trust.
Conversely, several studies have found that the labor-market
effects of a criminal
background are modest in magnitude and duration. Grogger
(1995), using a sample of
male arrestees from California, concluded that earnings and
employment effects are
relatively short-lived, that convictions have little effect on
earnings, and that probation
has no effect on arrestees' subsequent earnings. Waldfogel
(1994a) also addressed the
3
persistence of labor-market penalties for criminal participation
and found that prior to
their current conviction ex-offenders earned less and were less
likely to work than first-
time offenders. These earnings and employment gaps grew with
the number of prior
convictions.
Nagin and Waldfogel (1998) maintained that criminal

participation increases
observed wages shortly after conviction. They argued that
conviction reduces access to
career jobs offering stable, long-term employment, and
relegates offenders to spot-market
jobs that have higher initial pay, but do not offer stable
employment or steadily rising
wages. Consequently, a first conviction has a positive effect on
income for those under
age 25 and an increasingly negative earnings impact for
offenders over age 30. Nagin and
Waldfogel (1995) studied about 300 London offenders, and
concluded that prior
criminality has no effect on job performance, whereas a
criminal conviction increases
both job instability and pay. This result is consistent with their
other findings that
conviction increases both the income and employment
instability of young offenders.
This study is distinguished from the previous literature in two
ways. First, our
observations are drawn randomly from the young-adult
population. In contrast, other
studies have confined attention to labor-market outcomes for

offenders.1 If, however,
offenders are systematically different from non-offenders,
previous results may be
affected by this sample-selection bias. Second, the longitudinal
nature of our data allows
us to examine the extent to which labor-market penalties for
previous criminal activities
persist over workers' early careers. Most studies have examined
the effect on income for
only a few (usually no more than three) years after conviction.
However, our study
1 Grogger (1992) examined the effect of conviction on
employment, and reported results from one
regression that used data from non-offenders.
4
follows labor-market performance for at least 10 years after
data were collected on prior
contact with the criminal-justice system.
We find that individuals who were convicted of a crime as
youths experience a
12% reduction in earnings when they are young adults, holding

constant various human-
capital characteristics like education and work experience.
However, those who were
charged, but not convicted, of a criminal offense when young
suffer no reduction in
early-career earnings, ceteris paribus. Young adults who had
one or more criminal cases
adjudicated in juvenile court earned 9% less than their non-
offender counterparts, but
adjudication in adult court reduces earnings by an additional
5%. These estimated effects
are found to persist over the subsequent six years. However,
individuals who had contact
with the criminal justice system as youths also complete fewer
years of schooling and
accumulate less work experience as young adults. Because
schooling and experience
increase future earnings, these estimated partial effects of a
criminal background
underestimate its total effect on such earnings.
The paper is organized as follows. Section II describes the data.
Section III
presents the model, and discusses how we control for person-
specific heterogeneity.

Section IV reports the empirical results, and Section V
concludes.
II. Data
We use data on males from the 1980, 1984, and 1990 waves of
the National
Longitudinal Survey of Youth (NLSY), a stratified random
sample of individuals who
were between 14 and 22 years old in 1979. The 1980 wave
included a special section
5
about the respondents' self-reported participation in delinquent
and criminal activities.
This section of the survey provides detailed information about
each respondent's history
of criminal charges and convictions, the nature of any offenses
committed, and whether
adjudication of a criminal case was in juvenile or adult court.
We combine this
information with standard demographic and labor-market data to
estimate earnings
equations augmented by a variety of criminal participation
variables. The 1984 and 1990

surveys record labor-market earnings for 1983 and 1989,
respectively.
Our empirical work uses two distinct samples: one includes
individuals through
the 1984 wave of the NLSY, and the second includes
individuals through the 1990 wave.
For the first data set we omitted all individuals younger than 21
at the time of the 1984
interview, because many were still in school or just beginning
their labor-market
experiences.2 Furthermore, observations were deleted for those
reporting zero weeks of
work or zero income and those responding inappropriately.3
Finally, we deleted people
who were students during the week of the interview.4 There are
2897 respondents with
complete records for all variables of interest in 1984.
The 1990 data set was constructed by imposing the same
restrictions used to
create the 1984 data, with the exception of the age restriction.
We did not impose an age
2 We also ran, but do not report, regressions that do not impose
this restriction. The estimated

effects of the criminal-participation variables were slightly
larger in these regressions.
3 Missing observations are those defined as REFUSAL, DON’T
KNOW, INVALID SKIP, or
NONINTERVIEWS. Variables also include the code VALID
SKIPS, but this is not necessarily a missing
observation. For example, VALID SKIPS for the variables
ADLTCRT, NUMCHAR, and NUMCNVC
reflect those not charged or convicted of crimes. These valid
skips are recoded as zeros. This reduces the
sample from 12,686 to 5,400. Of those remaining, 16.7% report
having been charged with a crime and
9.9% report having been convicted.
4 This is done using a variable in the NLSY called Employment
Status Recode (R15199), which
reflects employment status during the week of the interview.
Individuals coded “Going to School” were
deleted.
6
restriction for the 1990 sample because respondents to the
survey were not of typical
school-going age. There are 3280 respondents with complete
records for all variables of
interest in 1990. The 1990 sample is larger than the 1984
sample because the age
restriction was relaxed. We adjusted 1989 income data to

constant 1983 dollars. Table 1
contains the summary statistics for the two samples.
III. Model
We estimate the model
( ) ititiiit VFCY εβββα ++++= 3201ln (1)
where itY is annual earnings in 1983 or 1989, 0iC is a set of
criminal participation
variables for each person i , as of the interview year 1980, iF is
a vector of fixed
individual characteristics, such as race, ethnicity, age and
AFQT5 score, itV is a vector of
characteristics that vary over time, such as educational
attainment, marriage, work
experience, union membership and whether one lives in a
Metropolitan Statistical Area,
and itε is the individual-specific error term.
We use four alternative measures of youthful contact with the
criminal-justice
system: (i) a dummy variable indicating whether the individual
had been charged with a
crime; (ii) a dummy variable indicating whether the individual
had been convicted of a

crime; (iii) a pair of dummy variables indicating, respectively,
whether an individual had
been charged but not convicted, and whether he had been
convicted; and (iv) a pair of
5 AFQT denotes the normalized score on the Armed Forces
Qualification Test, administered in
1980 to over 90% of the NLSY panel, and measures pre-market
skills.
7
dummy variables denoting whether an individual’s criminal case
was adjudicated in
juvenile or adult court. We estimate these four specifications
for both the 1984 and 1990
samples, and therefore report eight sets of estimates on
subsequent adult earnings.
Because characteristics that lead to high wages and employment
also reduce
participation in criminal activity, estimates that do not control
for this heterogeneity will
be biased toward finding the expected negative relationship –
that youth criminal
participation leads to lower earnings. Several papers have

attempted to control for
heterogeneity in a variety of ways. Grogger (1995) chose a
comparison group for the
California arrestees comprising his sample to control
statistically for any time-invariant,
individual-specific, unobservable characteristics. Waldfogel
(1994b) and Lott (1992a,
1992b) estimated differences between pre- and post-conviction
income as a function of
changes in criminal participation.
Unfortunately, because the NLSY records criminal participation
only in the initial
year (1980), we do not observe changes in criminal
participation, and cannot control for
unobserved heterogeneity with a fixed-effects, panel-data
model. Instead we control for
heterogeneity in two ways. First, the NLSY contains an
extensive set of demographic
variables that allow us to control for many observed individual
characteristics. One of
these variables, AFQT, is frequently omitted from earnings
regressions, and as a proxy
for ability captures much of the heterogeneity. Grogger (1995)
pursued a similar strategy

by incorporating various demographic variables, but he
excluded AFQT.6 Second, the full
model specification in (1) includes many characteristics over
which individuals have
6 Grogger also notes a problem with the NLSY arrest data –
blacks and whites have the same
number of self-reported arrests on average. In most other
samples, however, the arrest rate for blacks is
about 3 times that of whites.
8
some degree of choice–these are captured in itV above.
Because educational attainment,
marital status, and work experience are functions of criminal
activity, the indirect effect
of youth criminal activity on adult earnings is absorbed by the
coefficients on these
variables. Consequently, the estimate of 1β in the full
specification understates the total
effect of youth criminal background on adult earnings.
Our analysis is limited to young adults who reported positive
labor-market

earnings. However, both Freeman (1991) and Grogger (1992)
found that having a
criminal record when young reduces the probability of legal
employment as an adult.
Consequently, by restricting our sample to employed
individuals, we further
underestimate the total effect of youth criminal background on
adult earnings, inclusive
of its effect on employment status.
IV. Empirical Results
We begin our empirical analysis by estimating the raw,
unadjusted difference in
adult earnings between individuals who, when young, had
formal contact with the
criminal justice system (criminal charges and/or convictions)
and those who did not. This
estimated difference does not control for either fixed, pre-
market traits that affect adult
earnings (such as race or ability) or for other human-capital
variables (like schooling and
work experience) that help determine adult earnings, but also
could be affected by youth
criminal activity. We obtain this raw difference by estimating a
bivariate regression in

which the dependent variable is either 1983 or 1989 log annual
income.
Table 2 contains ordinary least-squares estimates of four
bivariate regressions
9
using 1983 log annual earnings as the dependent variable and
each of the alternative
measures of youth criminal background. Column 1 indicates that
individuals who were
charged with a crime when young (whether convicted) earned
approximately 27% less in
1983, on average, than individuals who were not criminally
charged. Of course, because
this regression does not control for observed (and unobserved)
differences in
characteristics that affect earnings, this point estimate is
equivalent to a simple
difference-in-means. The bivariate regression results reported in
column 2 imply that
young adults convicted of a crime as youths earned about 29%
less in 1983, on average,
than those who were not. As expected, the coefficient on having

been convicted is larger
than the one on having been charged reported in column 1.
Column 3 shows that those
youths who were charged but not convicted of a criminal
offense earned approximately
21% less as young adults than individuals with no criminal
charges against them, while
persons convicted of crimes when young earned about 31% less
as young adults than did
those who had no criminal convictions. In column 4, finally,
youths whose criminal
charges were adjudicated in juvenile and adult court
experienced a 27% and 26%
decrease, respectively, in 1983 earnings compared with
uncharged individuals.
Table 3 replicates the same four specifications for 1989
earnings, and shows the
same general results—the coefficients on the criminal sanction
variables are uniformly
negative and significantly different from zero. The coefficient
estimates on being charged
and convicted are slightly higher than for 1983 earnings.
An analysis of the effect of youth criminal background on adult
earnings must

assign to (observable) pre-market characteristics some of the
explanatory power for
differences in subsequent earnings between youthful offenders
and non-offenders.
10
Inherent skill (or ability or aptitude), along with ethnicity and
age, are important
determinants of labor-market earnings that are unaffected by
subsequent human-capital
investment but may be correlated with criminal behavior when
young.
Tables 4 and 5 report least-squares estimates of the effect of our
four alternative
measures of youth criminal activity, controlling for the pre-
market variables, on 1983 and
1989 earnings, respectively. The point estimate in column 1
implies that, holding
ethnicity, skill, and age constant, individuals who were charged
with a crime when young
earned almost 29% less in 1983 than those who were not. The
magnitude of the
CHARGED coefficient is smaller in this specification than in

the simple bivariate mode,
because in the latter, the estimated coefficient captures effects
on subsequent earnings
more properly attributed to the pre-market variables included
here. As expected, the
estimated coefficient on BLACK is negative and significantly
different from zero, and
implies that blacks earn about 32% less than whites, holding
pre-market skills and age
constant. However, this specification is extremely
parsimonious, and does not control for
variables such as education and work experience that are
typically included in earnings
regressions and are correlated with race. In contrast, the
estimate of the HISPANIC
coefficient is small and not significantly different from zero.
The estimated coefficients
on AFQT and AGE are positive and significantly different from
zero, as expected.
Column 2 reports the results of estimating the same
specification discussed above,
with criminal background now represented by a dummy variable
indicating whether one
was convicted of a crime as a youth. The coefficient estimate on

CONVICTED is
positive, significantly different from zero, and somewhat larger
than the estimated
coefficient on the CHARGED variable reported in column 1.
11
on the included pre-market variables are virtually identical to
those in column 1.
Of course, individuals convicted of a crime when young were
also charged with
that crime, so it is of interest to separate out the marginal effect
on earnings of having
been convicted of a youthful crime, given that one has been
charged with the crime. The
estimates in column 3 indicate that someone who was charged
but not convicted earns
about 22% less than his uncharged counterpart. However, an
individual who was charged
and subsequently convicted experienced a 34% reduction in
1983 labor-market earnings.
Therefore, the marginal impact of a prior conviction on 1983
earnings is about -11.5% [-

33.9 - (-22.4)], ceteris paribus.
Finally, the data permit us to distinguish between the
subsequent earnings effects
of a criminal charge adjudicated in juvenile court rather than in
adult court. Column 4
reports the empirical results for this specification, and shows
that individuals whose
criminal cases were handled in juvenile court earned
approximately 20% less than those
having had no contact when young with the criminal-justice
system. However, those
youths whose cases were adjudicated in adult court experienced
a 36% reduction in 1983
earnings. This large difference in coefficient estimates may
reflect one or both of the
following phenomena: (i) because of the confidentiality of
juvenile-court proceedings,
the “scarring” or “signaling” aspects of criminal charges
handled in that setting are less
than in cases dealt with in open adult court; (ii) youths who
commit crimes of such
severity that they are tried in adult court are different from their
juvenile-court
counterparts in ways that adversely affect subsequent labor-

market earnings. As before,
the 1989 results for the criminal sanction variables are very
similar to the 1983 findings.
The results reported in Tables 4 and 5 control only for
exogenous pre-market
12
variables that, along with youth criminal background, affect the
subsequent earnings of
young adults. However, the model on which these estimates are
based is an under-
specified representation of the process determining such
earnings. In particular, this
model specification excludes variables such as schooling and
work experience which
proxy human-capital investment affecting earnings as a young
adult. To redress this
shortcoming, we specify a more complete model of earnings
incorporating additional
variables that are exogenous to earnings but whose values are
determined by choices
made after adolescence.
Tables 6 and 7 report the results of this more completely

specified earnings
model. Because we include both schooling and work experience
in this regression and
use a sample of males for whom post-schooling work experience
is, on average, highly
continuous, we excluded age from the estimated regressions.
on the pre-market variables HISPANIC and AFQT are very
similar to those from the
more parsimonious specification reported in Table 4.
Interestingly, the size of the
coefficient on BLACK is reduced by almost three-fifths after
controlling for the post-
adolescence explanatory variables, suggesting considerable
heterogeneity among the
black population with respect to these additional observable
determinants of earnings.
The signs, sizes, and significance levels of the coefficients on
the additional
explanatory variables in column 1 conform to standard results
reported in the empirical
earnings literature. In particular, the coefficients on schooling
(grades completed),
married, urban residence, and union membership are positive

and significantly different
from zero. Additional weeks of work experience increase
earnings, but at a decreasing
rate. Individuals who were charged with a crime when young
earned approximately
13
11.4% less in 1983 than their non-charged counterparts, and this
adverse earnings effect
is significantly different from zero. However, the size of the
criminal-background
discount on adult earnings is lowered by about three-fifths with
the inclusion of
additional controls for observable influences on adult earnings.
We interpret this
reduction in the estimated effect of youth criminal background
to mean that a portion of
the total effect of having been charged when young with a
criminal offense is now being
attributed to variables – such as labor-market experience and
years of completed
schooling – that are affected by adolescent criminal activity. As
a consequence, the

estimated coefficient on CHARGED is a downward-biased
estimate of the true effect of a
youthful criminal charge on subsequent earnings. This
downward bias offsets to an
unknown degree the upward bias in the estimated effect
associated with any individual
heterogeneity arising from omitted (unobservable) variables that
are correlated with both
youth criminal background and adult earnings.
In column 2 the point estimate of the CONVICTED coefficient
is slightly higher
than that on CHARGED, reported in the previous column, and is
significantly different
from zero. As before, the model specification in column 3
permits us to separate the
marginal effect of being convicted when young of a criminal
offense from the effect of
having been charged but not convicted. The point estimates of
the coefficients on both
criminal-participation variables are substantially lower than
before, again suggesting that
the total effects of these variables are being attributed partly to
post-adolescent individual
characteristics that are, in turn, affected by youth criminal

behavior. The evidence from
this specification implies that an individual charged with a
crime when young
experiences about a 9% reduction in earnings as a young adult,
ceteris paribus, while the
14
marginal effect on earnings of a conviction, having been
charged, is -12.8 - (-8.8) = -
4.0%.
Column 4 reports the results of estimating the model with
dummy variables
indicating adjudication of any criminal case(s) in adult or
juvenile court. Again, the point
estimate of the coefficient on the adult-court variable is
substantially lower than the
estimated coefficient on the juvenile-court variable (-0.131
versus -0.095). Moreover, the
magnitudes of both coefficients are lower in this estimated
regression than in the more
parsimonious model reported in Table 4, as expected.
Compared with the 1983 results, the estimated effects on 1989
earnings of being

black, living in an urban area, being a union member, previous
work experience, and
being married are smaller, while the estimated return to
schooling is substantially larger.
The coefficient estimates on the variable CHARGED in column
1 across the three tables,
show essentially no difference in the magnitudes of the
estimated effects on 1983 and
1989 earnings. The point estimate of the effect on 1989 earnings
of having been
convicted is slightly higher than on 1983 earnings for each of
the model specifications.
V. Conclusion
We have used data from a stratified random sample of young
adults to estimate
the effect of youth criminal arrests, charges, and convictions on
labor-market earnings as
an adult. Individuals charged with or convicted of a criminal
offense when young have
lower adult earnings because they complete fewer years of
schooling and accumulate less
work experience than those with no contact as a youth with the
criminal-justice system.

15
However, we show that youth criminal behavior when young
also directly reduces adult
earnings, even after controlling for these human-capital
variables. Having been charged
but not convicted decreases earnings by between 5-8% and
having been convicted as a
youth permanently lowers adult earnings by at least 12%.
Adjudication in a juvenile court
lowers adult earnings by at least 9%, while having one’s case
adjudicated in an adult
court lowers earnings an additional 5%.
16
References
Freeman, Richard (1991) “Crime and the Employment of
Disadvantaged Youths.” NBER
Working Paper no. 3875.
Grogger, Jeff (1992) “Arrests, Persistent Youth Joblessness, and
Black/White
Review of Economics and Statistics, Vol. 74
(February): 100-106.

Grogger, Jeff (1995) “Effect of Arrests on the Employment and
Earnings of Young
Quarterly Journal of Economics, Vol. 110 (February): 52-71.
Lott, John R. Jr. (1989) “The Effect of Conviction on the
Legitimate Income of
Economics Letters, Vol. 34, no. 4: 381-385.
Lott, John R. Jr. (1992a) “An Attempt at Measuring the Total
Monetary Penalty from
Drug Convictions: The Importance of an Individual’s
Reputation.” Journal of
Legal Studies, Vol. 21, (January): 159-187.
Lott, John R. Jr. (1992b) “Do We Punish High-Income
Criminals Too Heavily?”
Economic Inquiry, Vol. 30, (October): 583-608.
Nagin, Daniel and Joel Waldfogel (1995) “The Effects of
Criminality and Conviction on
the Labor Market Status of Young British Offenders.”
International Review of
Law and Economics, Vol. 15 (January): 109-126.
Nagin, Daniel, and Joel Waldfogel (1998) "The Effect of
Conviction on Income Through
the Life Cycle." International Review of Law and Economics,
Vol. 18
(March): 25-40.
Waldfogel, Joel (1994a) “Does Conviction Have a Persistent
Effect on Income and
International Review of Law and Economics, Vol. 14 (March)
103-119.

Waldfogel, Joel (1994b) “The Effect of Criminal Conviction on
Income and the Trust
The Journal of Human Resources, Vol. 29, (Winter):
62-81.
17
Table 1
Summary Statistics
Variable Number Mean St. Dev. Min. Max.
1984 Data
Age 2897 23.65 1.76 21 27
Income83 2897 11,237 8,210 25 75001
Black 2897 0.23 0.42 0 1
Hispanic 2897 0.14 0.35 0 1
AFQT89 2897 45.07 29.90 1 99
SMSA 2897 0.76 0.42 0 1
Grade 2897 12.46 2.14 2 20
Married 2897 0.33 0.47 0 1
Experience (weeks) 2897 206 77 2 312
Experience2 2897 48,452 29,837 4 97344
Union Member 2897 0.21 0.41 0 1
Charged 2897 0.18 0.39 0 1
Just Charged 2897 0.09 0.29 0 1
Convicted 2897 0.11 0.31 0 1
Adult Court 2897 0.10 0.29 0 1
Juvenile Court 2897 0.09 0.28 0 1
1990 data
Age 3280

Income89 3280 17,963 12,133 40 138204
Black 3280 0.25 0.43 0 1
Hispanic 3280 0.16 0.36 0 1
AFQT89 3280 42.87 30.39 1 99
SMSA 3280 0.79 0.41 0 1
Grade 3280 12.92 2.47 3 20
Married 3280 0.52 0.50 0 1
Experience (weeks) 3280 435.31 132.20 3 624
Experience2 3280 206,963 107,009 9 389376
Union Member 3280 0.20 0.40 0 1
Charged 3280 0.14 0.35 0 1
Just Charged 3280 0.07 0.25 0 1
Convicted 3280 0.08 0.28 0 1
Adult Court 3280 0.06 0.24 0 1
Juvenile Court 3280 0.08 0.27 0 1
18
Table 2
The Effect of Criminal Participation on 1983 Wages
Bivariate Regression
(1) (2) (3) (4)
Variable Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat
Charged -0.268 -5.40
Convicted -0.288 -4.62 -0.308 -4.94
Just Charged -0.208 -3.09
Adult Court -0.263 -4.03
Juvenile Court -0.273 -3.97
Intercept 9.009 426.42 8.991 444.10 9.012 422.82 9.009 426.34
Num. of Obs. 2897 2897 2897 2897 2897 2897 2897 2897
F-Statistic
Adj. R2

Notes: Dependent variable is the natural log of 1983 income.
Standard errors are in parentheses.
Table 3
Bivariate Regression
(1) (2) (3) (4)
Charged -0.279 -6.78
Convicted -0.325 -6.33 -0.339 -6.59
Intercept 9.594 626.43 9.583 644.63 9.596 622.05 9.594 626.50
Num. of Obs. 3280 3280 3280 3280 3280 3280 3280 3280
F-Statistic
Adj. R2
19
Table 4
with Fixed Factors
(1) (2) (3) (4)
Charged -0.286 -5.99
Convicted -0.314 -5.26 -0.339 -5.64

Black -0.322 -6.50 -0.315 -6.36 -0.324 -6.56 -0.324 -6.56
Hispanic 0.030 0.54 0.028 0.50 0.026 0.47 0.026 0.47
AFQT 0.121 5.83 0.124 5.99 0.119 5.73 0.119 5.73
Age 0.119 11.27 0.119 11.24 0.120 11.39 0.120 11.39
Intercept 6.267 25.04 6.251 24.93 6.242 24.94 6.242 24.94
Num. of Obs. 2897 2897 2897 2897 2897 2897 2897 2897
F-Statistic
Adj. R2
Table 5
with Fixed Factors
(1) (2) (3) (4)
Charged -0.271 -7.11
Convicted -0.322 -6.81 -0.336 -7.08
Black -0.162 -4.73 -0.158 -4.61 -0.163 -4.76 -0.162 -4.74
Hispanic 0.061 1.61 0.059 1.54 0.058 1.53 0.061 1.59
AFQT 0.265 17.89 0.268 18.19 0.265 17.88 0.265 17.89
Age 0.047 7.94 0.045 7.69 0.047 7.93 0.0485 7.98
Intercept 8.571 64.65 8.596 64.90 8.575 64.73 8.551 63.65
Num. of Obs. 3280 3280 3280 3280 3280 3280 3280 3280
F-Statistic
Adj. R2

20
Table 6
Full Specification
(1) (2) (3) (4)
Charged -0.114 -2.73
Convicted -0.117 -2.26 -0.128 -2.46
Black -0.125 -2.81 -0.123 -2.77 -0.126 -2.82 -0.125 -2.81
Hispanic -0.024 -0.50 -0.024 -0.51 -0.025 -0.53 -0.024 -0.51
AFQT 0.124 5.42 0.123 5.36 0.123 5.40 0.124 5.43
SMSA 0.149 3.98 0.145 3.86 0.148 3.94 0.149 3.97
Grade 0.017 1.73 0.019 1.95 0.017 1.74 0.017 1.73
Married 0.325 9.40 0.324 9.38 0.325 9.40 0.325 9.40
Experience 0.012 12.47 0.012 12.44 0.012 12.48 0.012 12.47
Experience2 0.000 -6.40 0.000 -6.35 0.000 -6.41 0.000 -6.40
Union 0.282 7.18 0.284 7.25 0.282 7.18 0.281 7.18
Intercept 6.783 44.47 6.751 44.65 6.783 44.47 6.782 44.46
Num. of Obs. 2897 2897 2897 2897 2897 2897 2897 2897
F-Statistic
Adj. R2
Table 7

Full Specification
(1) (2) (3) (4)
Charged -0.117 -3.39
Convicted -0.140 -3.28 -0.145 -3.36
Black -0.091 -2.86 -0.090 -2.83 -0.091 -2.86 -0.091 -2.85
Hispanic 0.037 1.09 0.037 1.07 0.037 1.06 0.037 1.08
AFQT 0.129 7.36 0.129 7.35 0.130 7.37 0.130 7.38
SMSA 0.119 4.10 0.115 3.94 0.116 3.97 0.119 4.09
Grade 0.061 9.35 0.062 9.56 0.061 9.39 0.061 9.35
Married 0.228 9.26 0.229 9.32 0.228 9.28 0.228 9.26
Experience 0.006 11.93 0.006 11.83 0.006 11.84 0.006 11.96
Experience2 0.000 -7.58 0.000 -7.49 0.000 -7.50 0.000 -7.60
Union 0.161 5.49 0.161 5.49 0.161 5.49 0.161 5.50
Intercept 7.019 56.38 7.011 56.43 7.025 56.24 7.014 56.31
Num. of Obs. 3280 3280 3280 3280 3280 3280 3280 3280
F-Statistic
Adj. R2
U.S. Department of Justice
Office of Justice Programs
Office of Juvenile Justice and Delinquency Prevention

John J. Wilson, Acting Administrator
From the Administrator
Seriously delinquent youth often ex-
hibit other problem behaviors. Under-
standing the extent of overlap be-
tween delinquency and these other
problem behaviors is important for
developing effective prevention strat-
egies and targeted interventions.
Using data from the first 3 years of
OJJDP’s Program of Research on
the Causes and Correlates of Delin-
quency, this Bulletin examines the
co-occurrence of serious delinquency
with specific problem areas: school
behavior, drug use, mental health,
and combinations of these behaviors.
Preliminary findings show that a large
proportion of serious delinquents are
not involved in persistent drug use,
nor do they have persistent school
or mental health problems; that the
problem that co-occurs most fre-
quently with serious delinquency is
drug use; and that, for males, as the
number of problem behaviors other
than delinquency increases, so does
the likelihood that an individual will
be a serious delinquent.
These findings emphasize the impor-
tance of identifying and addressing

the unique needs of individual youth,
rather than proceeding under the as-
sumption that all offenders require
similar treatment, to most effectively
prevent and reduce serious, chronic
delinquency.
John J. Wilson
Acting Administrator
November 2000
Some studies of youth who have been
incarcerated or arrested suggest that the
overlap of these problems is substantial
(see references in Huizinga and Jakob-
Chien, 1998). However, not all youth in-
volved in illegal behaviors are arrested
or come in contact with the juvenile jus-
tice system. Understanding the extent of
overlap of these problem behaviors re-
quires studies based on representative
samples drawn from complete popula-
tions of youth, where the examination of
overlap is not limited to particular sub-
groups defined by official delinquency,
school issues, or mental health status.
However, there are only a few studies of
national or community samples that ex-
amine these issues.1
Answers to the questions posed above
are important because a large overlap
may indicate general risk factors that
prevention and intervention initiatives
should address. On the other hand, a

small overlap may indicate that preven-
tion and intervention initiatives should
be more tailored to risk factors related
to the specific problem behaviors of in-
dividual youth.
1 See, for example, Elliott and Huizinga, 1989; Elliott,
Huizinga, and Menard, 1989; Huizinga, Loeber, and
Thornberry, 1993.
Co-occurrence of
Delinquency and Other
Problem Behaviors
David Huizinga, Rolf Loeber,
Terence P. Thornberry, and Lynn Cothern
This Bulletin is part of the Office of Juve-
nile Justice and Delinquency Prevention
(OJJDP) Youth Development Series, which
presents findings from the Program of Re-
search on the Causes and Correlates of
Delinquency. Teams at the University at
Albany, State University of New York; the
University of Colorado; and the University
of Pittsburgh collaborated extensively in
designing the studies. At study sites in Roch-
ester, New York; Denver, Colorado; and
Pittsburgh, Pennsylvania, the three research
teams have interviewed 4,000 participants
at regular intervals for a decade, recording
their lives in detail. Findings to date indi-
cate that preventing delinquency requires
accurate identification of the risk factors
that increase the likelihood of delinquent
behavior and the protective factors that

enhance positive adolescent development.
This Bulletin examines the co-occurrence
or overlap of serious delinquency with
drug use, problems in school, and mental
health problems. Many youth who are seri-
ously delinquent also experience difficulty
in other areas of life. However, with the
exception of the co-occurrence of drug
use and delinquency, little is known about
the overlap of these problem behaviors
in general populations. Do most youth
who commit serious delinquent acts have
school and mental health problems? Are
most youth who have school or mental
health problems also seriously delinquent?
2
Many youth are only intermittently in-
volved in serious delinquency, violence,
or gang membership, and involvement
often lasts only a single year during ado-
lescence.2 For this reason, of greater con-
cern are youth who have a more sus-
tained involvement in delinquency, whose
involvement is often considered more
problematic and serious. Thus, this Bulle-
tin is based on research that focuses on
persistent serious delinquency and per-
sistent school and mental health prob-
lems lasting 2 years or more.
One of the few current research projects

that has adequate information to allow an
examination of the co-occurrence of per-
sistent problem behaviors in general popu-
lations is OJJDP’s Program of Research on
the Causes and Correlates of Delinquency.
The data presented in this Bulletin come
from the first 3 years of this project. The
Program of Research involves the Denver
Youth Survey, the Pittsburgh Youth Study,
and the Rochester Youth Development
Study. These studies use prospective longi-
tudinal designs, which allow examination
of developmental processes over the life
course. The projects involved more than
4,000 inner-city children and youth who, at
the beginning of the research (1987–88),
ranged in age from 7 to 15 years. Research-
ers interviewed these children and one
parent of each child in private settings at
regular intervals.
The selection of youth varied from study to
study. The Denver Youth Survey sample con-
sists of 1,527 youth (806 boys and 721 girls)
who were ages 7, 9, 11, 13, and 15 in 1987.
These respondents came from the more
than 20,000 households randomly drawn
from high-risk neighborhoods in Denver, CO.
The Pittsburgh Youth Study began by ran-
domly sampling boys who were in the first,
fourth, and seventh grades in public schools
in Pittsburgh, PA, in 1987. Through inter-
views with each boy, his parent, and his
teacher, researchers selected the 30 percent
of these boys who had the most disruptive
behavior. The final Pittsburgh sample con-

sists of 1,517 boys, including the 30 percent
who were the most disruptive; the remain-
der were randomly selected. The Rochester
Youth Development Study sample consists
of 1,000 randomly selected students who
were in the seventh and eighth grades in
public schools in Rochester, NY, in the
spring semester of the 1988 school year.
Edelbrock, 1982). In all cases, persistent
problems were problems that occurred in
at least 2 of the 3 years examined.
Prevalence of
Persistent Problem
Behavior
Most problem behaviors are intermittent
or transitory. Most youth who exhibit prob-
lem behaviors do so only during a single
year, a pattern that holds true for all of
the problems examined in this Bulletin.
The next most common pattern is 2 years,
and the third is 3 years (see table 1). This
Bulletin focuses on persistent serious de-
linquency and persistent problem behav-
ior occurring for 2 years or more.
Across all three study sites, the prevalence
of persistent problem behavior was gener-
ally consistent (see figure 1). Twenty to
thirty percent of males were serious de-
linquents; 14–17 percent were drug users;
7–22 percent had school problems; and
7–14 percent had mental health problems.
In Rochester, where a greater number of
males dropped out of school than in the

other sites, 22 percent of males had school
problems. The dropout rate for boys in
Table 1: Number of Years of Involvement in Problem Behavior
Number Percentage of Males Percentage of Females
of Years Denver Pittsburgh Rochester Denver Rochester
Serious Delinquency
0 48.6 42.4 58.3 75.3 77.5
1 27.8 28.0 21.4 19.5 17.4
2 14.7 19.7 14.0 4.2 3.9
3 9.0 10.0 6.3 1.0 1.1
Drug Use
0 66.4 61.4 69.7 72.1 68.1
1 19.4 23.5 13.9 17.3 19.7
2 7.9 9.7 9.0 6.7 7.3
3 6.3 5.3 7.5 3.9 4.9
Poor Academic Grades in School
0 80.3 80.7 86.7 85.5 86.6
1 15.6 18.0 9.3 11.0 10.8
2 3.2 1.1 3.5 3.2 2.6
3 0.9 0.2 0.5 0.2 0.0
Externalizing Behavioral Problems*
0 82.9 83.0 74.4 84.3 82.3
1 11.4 9.4 13.7 11.0 8.2
2 5.6 4.6 9.2 4.7 6.3
3 — 3.0 2.8 — 3.2

*Behavioral problems such as hyperactivity and aggression.
This measure is available for only
2 years at the Denver site.
3 These terms represent broad groupings of behavioral
problems—internalizing refers to personality or emo-
tional problems and externalizing refers to behavioral
problems such as hyperactivity and aggression.
This Bulletin summarizes the findings of
these studies to give a picture of the co-
occurrence of persistent serious delin-
quency with persistent drug use, problems
in school, mental health problems, and
combinations of these problems. For the
purposes of this Bulletin, persistent seri-
ous delinquency is defined as involvement
as an offender in serious assault or serious
property offenses in at least 2 of the 3
years examined. To avoid repetition, the
use of the term “persistent” is often omit-
ted, but it applies to all the behaviors dis-
cussed. Drug problems include the use of
marijuana, inhalants, cocaine or crack,
heroin, angel dust (PCP), psychedelics,
amphetamines, tranquilizers, or barbitu-
rates. School problems were defined as
having below-average grades (D or F) or
having dropped out of school. Mental
health problems were indicated if the per-
son was in the top 10 percent of the distri-
bution of internalizing or externalizing
symptoms3 of a subset of items from the
Child Behavior Checklist (Achenbach and

2 Elliott, Huizinga, and Morse, 1986; Huizinga,
Esbensen, and Weiher, 1994; Thornberry et al., 1993;
Esbensen and Huizinga, 1993.
3
Rochester was 18.5 percent, as compared
with 3.1 percent in Denver and 6.2 percent
in Pittsburgh. Combining the overall fig-
ures and ignoring the high dropout rate in
Rochester, roughly 25 percent of males
were serious delinquents, 15 percent were
drug users, 7 percent had school problems,
and 10 percent had mental health problems.
Females were studied in Denver and Roch-
ester, but not in Pittsburgh. Among females,
the overall figures indicated that 5 percent
were serious delinquents, 11–12 percent
were drug users, 10–21 percent had school
problems, and 6–11 percent had mental
health problems (see figure 2). A greater
proportion of males than females were
persistent serious delinquents. Gender
differences are small, however, when com-
paring drug use, problems in school, and
mental health problems at each site.
Drug Use
The results of the Program of Research on
the Causes and Correlates of Delinquency
support the robust relationship between
drug use and serious delinquent behavior
established by other researchers over the

past 25 years, although previous findings
vary in the extent of overlap and strength
of the relationship by age, drug, and tem-
poral period or decade examined. (Rele-
vant references can be found in Huizinga,
Loeber, and Thornberry, 1997, and changes
in the drugs-delinquency relationship over
time are described in Huizinga, 1997.)
The Denver, Pittsburgh, and Rochester
studies all found a statistically significant
relationship between persistent delin-
quency and persistent drug use for both
males and females (across all three sites
for males and at the two sites where fe-
males were studied) (see table 2). However,
a majority of persistent serious delinquents
were not persistent drug users, and more
than 50 percent of drug-using males and
about 20 percent of drug-using females
were persistent serious delinquents.
The data from the three studies indicat-
ed that 38 percent of serious male delin-
quents were also drug users. In Denver
and Rochester, slightly more than half of
drug users were serious delinquents, and
in Pittsburgh, 70 percent of drug users
were serious delinquents. Thus, for males,
the majority of persistent serious delin-
quents were not drug users, but the major-
ity of drug users were serious delinquents.
For females, the opposite was true. Slightly
less than half of serious delinquents in

Figure 1: Prevalence of Persistent Problem Behaviors Among
Males
Figure 2: Prevalence of Persistent Problem Behaviors Among
Females
Rochester and Denver were drug users,
while only 20 percent of drug users were
serious delinquents. Among females, there-
fore, delinquency is a stronger indicator of
drug use than drug use is an indicator of
delinquency.
Although the relationship between serious
delinquency and drug use is statistically
significant for females (at the two sites
where females were studied) and for males
across all three sites, a number of caveats
about this relationship are necessary. First,
the level of the relationship varies by site
and gender. Second, even though the rela-
tionship is robust, it cannot be assumed
that most delinquents are serious drug us-
ers. In fact, for both genders, the majority of
serious delinquents were not drug users.
Neither can it be assumed that most drug
users are serious delinquents. This relation-
ship varies by gender. Among females, for
example, most drug users were not serious
delinquents. However, among males, a ma-
jority of drug users were serious delin-
quents (70 percent in Pittsburgh). Third,
the causal nature of the relationship is not
clear. It has been argued that drugs cause
crime, that crime leads to drug use, that the

relationship is spurious (that is, crime and
drug use are related only because they are
both dependent on other factors), and that
it is reciprocal (that is, crime leads to drug
use and drug use also leads to crime). How-
ever, it is possible that each of these can
be true, depending on the population, sub-
group, or individual examined.
School Problems
A long history of research has demonstrat-
ed a relationship between school problems
Percentage
Serious
Delinquency
Drug Use
School
Problems
Mental Health
Problems
PittsburghDenver Rochester
24
30
20
14
15

17
7
8
22
7
8
14
0 10 20 30 40
Percentage
Serious
Delinquency
Drug Use
School
Problems
Mental Health
Problems
Denver Rochester
0 10 20 30
5
5
11
12

10
21
6
11
4
(poor academic performance, truancy, and
dropping out) and delinquency.4 However,
the meaning of the relationship is not fully
understood. The three sites examined here
differed substantially in the evidence each
yielded about the prevalence of school
problems.
The sites also differed in terms of the ex-
tent of co-occurrence of persistent school
problems and persistent delinquency.
For example, although not significant in
Pittsburgh, there is a statistically signifi-
cant relationship between school prob-
lems and delinquency for males in Den-
ver and Rochester. However, at these two
sites, less than half of the delinquents
had school problems and less than half
of those with school problems were de-
linquent (see table 3).
In Rochester, where the relationship is
strongest, 41 percent of male serious delin-
quents had school problems, while 35 per-
cent of those with school problems were

delinquent. These figures differed in Den-
ver, where approximately 14 percent of de-
linquent males had school problems, and
slightly less than half of those with school
problems were delinquent. In general, the
overlap is significant for males, but the ma-
jority of persistent serious delinquents did
not have school problems, and the majority
of those with persistent school problems
were not persistent serious delinquents.
The relationship is different for females.
In Rochester, where slightly more than
half of female serious delinquents also
had school problems, the relationship is
statistically significant. In Denver, only 11
percent of female serious delinquents had
school problems. Among females with
school problems, approximately 13 per-
cent in Rochester and 6 percent in Denver
were also serious delinquents.
An examination of academic failure and
dropping out of school (each examined
separately) revealed that academic failure
(grades D and F) and delinquency were sig-
nificantly related only for boys in Denver.
Dropping out was significantly related to
delinquency only in Rochester, and this re-
lationship was significant for both genders.
These findings again indicate that broad
generalizations about the relationship be-
Table 2: Co-occurrence of Persistent Serious Delinquency and
Persistent

Drug Use
Denver Pittsburgh Rochester
Males
Delinquents who are drug users (%) 33.6% 35.7% 43.6%
Drug users who are delinquents (%) 55.8 70.4 53.6
p=0.000 p=0.000 p=0.000
Females
Delinquents who are drug users (%) 45.6% NA* 48.1%
Drug users who are delinquents (%) 22.6 NA 20.0
p=0.000 p=0.000
*NA, not available.
tween persistent delinquency and other
persistent problems are unwarranted.
Even taking site differences into consider-
ation, it appears that—given the large
number of serious delinquents who were
not having school problems—serious de-
linquents should not be characterized as
having school problems, nor should those
with school problems be characterized as
persistent delinquents.
Mental Health Problems
Mental health problems among offenders
are a growing concern in light of the pub-
lic fascination with violent crimes com-
mitted by mentally ill offenders (Howells
et al., 1983; Marzuk, 1996). On the other
hand, mental illness is sometimes seen as
an excuse for criminal behavior (Szasz

and Alexander, 1968). Many juvenile of-
fenders who need screening and treatment
4 Brier, 1995; Elliott, Huizinga, and Menard, 1989; Elliott and
Voss, 1974; Fagan and Pabon, 1990; Gold and Mann, 1984;
Gottfredson, 1981; Maguin and Loeber, 1996; O’Donnell et
al., 1995; Thornberry, Esbensen, and Van Kammen, 1991;
Thornberry, Moore, and Christenson, 1985.
for mental health problems fail to receive
either (Woolard et al., 1992).
Data from the Program of Research on the
Causes and Correlates of Delinquency in-
dicated that the relationship between per-
sistent mental health problems and per-
sistent serious delinquency is statistically
significant for males at all three sites (see
table 4). For males, the presence of mental
health problems, as measured in the stud-
ies, is a better indicator of serious delin-
quency than serious delinquency is an
indicator of mental health problems. That
is, less than 25 percent of male delinquents
displayed mental health problems. On the
other hand, of those with mental health
problems, almost one-third in Rochester
and almost one-half at each of the other
two sites were serious delinquents.
The relationship is statistically signifi-
cant for females only in Rochester, where
Persistent
School Problems

Males
Delinquents who have
school problems (%) 13.9% 9.2% 40.8%
Those with school problems
who are delinquents (%) 48.9 35.3 34.7
p=0.002 p=0.374 p=0.000
Females
school problems (%) 11.3% NA* 55.3%
Those with school problems
who are delinquents (%) 5.8 NA 13.1
p=0.999 p=0.000
Note: School problems defined as poor academic grades and
dropping out combined.
*NA, not available.
5
more than half of the female serious delin-
quents in Denver display no other prob-
lems; in Rochester, the figure is roughly
40 percent for both genders. Second,
drug use, alone or in combination with
other problems, is the most common
problem for both male and female delin-

quents and provides a moderate risk of
serious delinquency.
Another way to examine combinations of
problems is by a count of problems. The
largest proportion of male serious delin-
quents (39–56 percent across all sites)
had none of the persistent problems ex-
amined in this Bulletin, followed in de-
creasing order by those having one prob-
lem (30–32 percent) and those with two or
more problems (11–31 percent) (see table
7). However, among those with problems,
as the number of problems increases, so
does the chance of being a serious delin-
quent. More than half (55–73 percent) of
those with two or more problems were
also serious delinquents.
For females, the relationship was different
and varied by site (see table 8). In Roches-
ter, more than half of female delinquents
were involved in two or more problem
behaviors; in Denver, this figure was about
11 percent. In Rochester, approximately
one-third of females with multiple problem
behaviors were serious delinquents; in Den-
ver, 15 percent were serious delinquents.
The findings about girls are thus site spe-
cific, and generalizations are unwarranted.
Summary
Serious delinquency, drug use, school
problems, and mental health problems
are most likely to be intermittent in na-
ture. For all sites, the most common tem-

poral pattern of each problem behavior
was that it occurred for only 1 year. The
next most common pattern was occur-
rence for 2 years, and then occurrence for
3 years. This Bulletin examines only per-
sistent problem behavior lasting 2 years
or more. There are some consistent find-
ings about the co-occurrence of persis-
tent serious delinquency and other per-
sistent problem behaviors across all three
sites of the Program of Research on the
Causes and Correlates of Delinquency.
First, a large proportion of persistent seri-
ous delinquents are not involved in persis-
tent drug use, nor do they have persistent
school or mental health problems. Although
a significant number of offenders have
other problems and are in need of help,
Table 5: The Overlap of Persistent Serious Offending and
Combinations of
Other Persistent Problems Among Males
Those With
Persistent Serious Persistent Problems
Delinquents Who Have Who Are Persistent
Persistent Problems Serious Delinquents*
Problem Denver Pittsburgh Rochester Denver Pittsburgh
Rochester
None 55.2% 56.4% 38.8% 16.8% 22.3% 12.1%
Drug use only 21.4 24.3 17.7 49.1 65.4 45.7
School only 4.9 2.9 7.2 30.7 19.0 15.1

Mental health
only 4.6 5.0 5.6 30.3 30.4 18.3
Drug use and
school 6.4 4.3 17.2 (78.5) (75.0) 64.3
Drug use and
mental health 4.9 5.7 3.2 (73.6) (88.9) (65.2)
School and
mental health 1.8 0.0 4.7 (66.7) (0.0) (33.2)
Drug use, school,
and mental
health 0.9 1.4 5.6 (50.0) (100.0) (50.4)
*Figures in parentheses are based on sample sizes too small to
be considered reliable. They are
presented to show consistent effects of multiple problems.
one-third of females who were serious
delinquents also had mental health prob-
lems. At the same time, only 17 percent of
those with mental health problems were
serious delinquents. This relationship is
the reverse of that seen in males. Thus, at
least in the case of Rochester, the pres-
ence of delinquency among females is a
better indicator of mental health prob-
lems than mental health problems are an
indicator of delinquency.
Combinations of
Persistent Problems
Allowing for the higher rate of school

problems in Rochester, the relationship
between persistent serious delinquency
and combinations of other persistent prob-
lem behaviors is fairly consistent across
the sites studied (see tables 5 and 6).
First, more than half of the male serious
delinquents in Denver and Pittsburgh and
Mental
Health Problems
Males
mental health problems (%) 13.0% 13.5% 21.1%
Those with mental health
problems who are delinquents (%) 46.2 45.9 31.4
p=0.005 p=0.015 p=0.019
Females
mental health problems (%) 0.0% NA* 33.7%
Those with mental health
problems who are delinquents (%) 0.0 NA 16.7
p=0.240 p=0.000
*NA, not available.

6
Table 6: The Overlap of Persistent Serious Offending and
Combinations of
Other Persistent Problems Among Females
Those With
Persistent Problems Serious Delinquents
Problem Denver Rochester Denver Rochester
None 54.4% 39.9% 3.7% 3.0%
Drug use only 34.4 3.6 22.4 3.1
School only 0.0 3.6 0.0 1.6
Mental health only 0.0 0.0 0.0 0.0
Drug use and school 11.3 21.7 ␣ (—)* 24.2
Drug use and
mental health 0.0 7.8 (—) (—)
School and mental
health 0.0 8.3 (—) (—)
Drug use, school,
and mental health 0.0 15.1 (—) (—)
*Represent estimates based on sample sizes too small to be
considered reliable.
Table 7: Number of Persistent Problems and Persistent Serious
Delinquency Among Males

Those With
Number of Persistent Problems Serious Delinquents
Problems Denver Pittsburgh Rochester Denver Pittsburgh
Rochester
0 55.2% 56.4% 38.8% 16.8% 22.3% 12.1%
1 30.9 32.1 30.5 41.4 46.9 26.1
2 or more 13.9 11.4 30.7 70.0 72.7 54.7
Table 8: Number of Persistent Problems and Persistent Serious
Delinquency Among Females
Those With
Number of Persistent Problems Serious Delinquents
Problems Denver Rochester Denver Rochester
0 54.4% 39.9% 3.7% 3.0%
1 34.4 7.3 9.6 1.6
2 or more 11.3 52.9 15.4 36.1
Fourth, while the co-occurrence of per-
sistent problems and persistent serious
delinquency is an important issue, the

findings cited above show that serious de-
linquency does not always co-occur with
other problems. For some youth, involve-
ment in serious delinquency and other
problems go together. For others, however,
involvement in serious delinquency does
not indicate the presence of other prob-
lems; conversely, a youth experiencing
other persistent problems is not neces-
sarily a persistent serious delinquent.
Fifth, the degree of co-occurrence between
persistent serious delinquency and other
persistent problems is not overwhelming,
but the size of the overlap suggests that a
large number of persistent serious delin-
quents face additional problems that
need to be addressed. Careful identifica-
tion of the configuration of problems fac-
ing individual youth is needed. This is
necessary so that delinquent youth with
serious persistent problems are treated
for those problems, and youth who do not
warrant intervention are not treated,
since such treatment may be unnecessary
or may have criminogenic effects. The
magnitude of the overlap of delinquency
and other persistent problems suggests
that not all delinquent youth require in-
terventions such as mental health ser-
vices or remedial education. Rather, at-
tention to the unique needs of individual
youth is necessary.
For Further Information
For more information on OJJDP’s Causes

and Correlates studies or to obtain copies
of other OJJDP publications, contact the
Juvenile Justice Clearinghouse (JJC) at
800–638–8736 (phone), 301–519–5600 (fax),
or www.ncjrs.org/puborder (Internet).
JJC also maintains a Causes and Correlates
of Delinquency Web page (www.ojjdp.
ncjrs.org/ccd/index.html).
References
Achenbach, T.M., and Edelbrock, C.S. 1982.
Manual for the Child Behavior Checklist and Re-
vised Child Behavior Profile. Burlington, VT: Uni-
versity of Vermont, Department of Psychiatry.
Brier, N. 1995. Predicting anti-social behavior
in youngsters displaying poor academic
achievement: A review of risk factors. Develop-
mental and Behavioral Pediatrics 16:271–276.
Elliott, D.S., and Huizinga, D. 1989. The relation-
ship between delinquent behavior and ADM
problems. In Juvenile Offenders With Serious
Drug, Alcohol and Mental Health Problems,
users; 46–48 percent of female serious
delinquents were also drug users.
Third, for males, as the number of persis-
tent problems other than delinquency
increases, so does the likelihood that an
individual will be a persistent serious de-
linquent. A combination of persistent
drug, school, and mental health problems
is a reasonably strong risk factor for per-
sistent serious delinquency.

persistent offenders as a group cannot be
characterized as having other problems.
Second, although less than half of persis-
tent offenders are persistent drug users,
the problem that co-occurs most frequently
with persistent serious delinquency (for
males and females) is persistent drug
use. Among males who were serious de-
linquents, 34–44 percent were also drug
7
edited by C. Hampton. Washington, DC: U.S.
Government Printing Office.
Elliott, D.S., Huizinga, D., and Menard, S. 1989.
Multiple Problem Youth: Delinquency, Substance
Use and Mental Health Problems. New York, NY:
Springer-Verlag.
Elliott, D.S., Huizinga, D., and Morse, B. 1986.
Self-reported violent offending: A descriptive
analysis of juvenile violent offenders and their
offending careers. Journal of Interpersonal Vio-
lence 1(4):472–514.
Elliott, D.S., and Voss, H. 1974. Delinquency and
Dropout. Lexington, MA: Lexington Books.
Esbensen, F., and Huizinga, D. 1993. Gangs,
drugs, and delinquency in a survey of urban
youth. Criminology 31(4):565–587.

Fagan, J., and Pabon, E. 1990. Contributions of
delinquency and substance use to school drop-
out among inner-city youths. Youth and Society
21(3):306–354.
Gold, M., and Mann, D.W. 1984. Expelled to a
Friendlier Place: A Study of Alternative Schools.
Ann Arbor, MI: University of Michigan Press.
Gottfredson, G.D. 1981. Schooling and delin-
quency. In New Directions in the Rehabilitation
of Criminal Offenders, edited by S.W. Martin,
L.B. Sechrest, and R. Rednez. Washington, DC:
National Academy Press, pp. 424–469.
behavior: A report of the Program of Research on
the Causes and Correlates of Delinquency. Un-
published report submitted to the Office of Juve-
nile Justice and Delinquency Prevention, 1997.
Maguin, E., and Loeber, R. 1996. Academic per-
formance and delinquency. In Crime and Jus-
tice: A Review of Research, vol. 2, edited by M.
Tonry. Chicago, IL: University of Chicago Press.
Marzuk, P.M. 1996. Violence, crime and mental
illness: How strong a link? Archives of General
Psychiatry 53:481–488.
O’Donnell, J., Hawkins, J.D., Catalano, R.F.,
Abbott, R.D., and Day, L.E. 1995. Preventing
school failure, drug use, and delinquency
among low-income children: Long-term inter-
vention in elementary schools. American Jour-
nal of Orthopsychiatry 65(1):87–100.

Szasz, T.S., and Alexander, G.J. 1968. Mental
illness as an excuse for civil wrongs. Journal of
Nervous and Mental Disease 147:113–123.
Thornberry, T.P., Esbensen, F., and Van
Kammen, W. 1991. Commitment to school and
delinquency. In Urban Delinquency and Drug
Use, edited by D. Huizinga, T.P. Thornberry,
and R. Loeber. Unpublished report submitted
to the Office of Juvenile Justice and Delin-
quency Prevention, 1991.
Thornberry, T.P., Krohn, M.D., Lizotte, A.J., and
Chard-Wierschem, D. 1993. The role of juvenile
Program of Research on the Causes and Correlates of
Delinquency
Howells, K., McEwan, M., Jones, B., and
Mathews, C. 1983. Social evaluations of mental
illness in relation to criminal behavior. British
Journal of Social Psychology 22:165–166.
Huizinga, D. 1997. Over-time changes in delin-
quency and drug use: The 1970’s to the 1990’s.
Unpublished report submitted to the Office of
Juvenile Justice and Delinquency Prevention,
September 1997.
Huizinga, D., Esbensen, F., and Weiher, A.W.
1994. Examining developmental trajectories in
delinquency using accelerated longitudinal
designs. In Cross-National Longitudinal Research
on Human Development and Criminal Behavior,
edited by E.G.M. Weitekamp and H. Kerner.

Boston, MA: Kluwer Academic Publishers.
Huizinga, D., and Jakob-Chien, C. 1998. The con-
temporaneous co-occurrence of serious and vio-
lent juvenile offending and other problem behav-
iors. In Serious and Violent Juvenile Offenders:
Risk Factors and Successful Interventions, edited
by R. Loeber and D.P. Farrington. Thousand
Oaks, CA: Sage Publications, Inc., pp. 47–67.
Huizinga, D., Loeber, R., and Thornberry, T.P.
1993. Delinquency, drug use, sex, and pregnancy
among urban youth. Public Health Reports
108(supplement):90–96.
Huizinga, D., Loeber, R., and Thornberry, T.P.
1997. The co-occurrence of persistent problem
The Program of Research on the
Causes and Correlates of Delinquency
is an example of OJJDP’s support of
long-term research in a variety of fields.
Initiated in 1986, the Causes and Cor-
relates program includes three closely
coordinated longitudinal projects: the
Pittsburgh Youth Study, directed by
Dr. Rolf Loeber at the University of
Pittsburgh; the Rochester Youth Devel-
opment Study, directed by Dr. Terence P.
Thornberry at the University at Albany,
State University of New York; and the
Denver Youth Survey, directed by Dr.
David Huizinga at the University of
Colorado. The Causes and Correlates
program represents a milestone in cri-
minological research because it consti-

tutes the largest shared-measurement
approach ever achieved in delinquency
research. From the beginning, the three
research teams have worked together
with similar measurement techniques,
thus enhancing their ability to general-
ize their findings.
Although each of the three projects has
unique features, they share several key
elements:
◆ All three are longitudinal investigations
that involve repeated contacts with the
same juveniles over a substantial por-
tion of their developmental years.
◆ In each study, researchers have con-
ducted face-to-face interviews with ado-
lescents in a private setting. By using
self-report data rather than juvenile jus-
tice records, researchers have been
able to come much closer to measuring
actual delinquent behaviors and ascer-
taining the age at onset of delinquent
careers.
◆ Multiple perspectives on each child’s
development and behavior are obtained
through interviews with the child’s pri-
mary caretaker and teachers and from
official school, police, and court records.
◆ Participants are interviewed at regular
and frequent intervals (6 or 12 months).

◆ Sample retention has been excellent.
As of 1997, at least 84 percent of the
participants had been retained at
each site, and the average retention
rate across all interview periods was
90 percent.
◆ The three sites have collaborated
to use a common measurement
package, collecting data on a wide
range of variables that make possible
cross-site comparisons of similarities
and differences.
Each project has disseminated the re-
sults of its research through a broad
range of publications, reports, and pres-
entations. In 1997, OJJDP initiated the
Youth Development Series of Bulletins
to present findings from the Causes and
Correlates program. In addition to the
present Bulletin, six other Bulletins have
been published in the Youth Develop-
ment Series: Epidemiology of Serious
Violence, Gang Members and Delin-
quent Behavior, In the Wake of Child-
hood Maltreatment, Developmental
Pathways in Boys’ Disruptive and Delin-
quent Behavior, Family Disruption and
Delinquency, and Teenage Fatherhood
and Delinquent Behavior.
PRESORTED STANDARD

POSTAGE & FEES PAID
DOJ/OJJDP
PERMIT NO. G–91
NCJ 182211Bulletin
U.S. Department of Justice
Office of Justice Programs
Office of Juvenile Justice and Delinquency Prevention
Washington, DC 20531
Official Business
Penalty for Private Use $300
gangs in facilitating delinquent behavior. Journal
of Research in Crime and Delinquency 30(1):55–87.
Thornberry, T.P., Moore, M., and Christenson,
R.L. 1985. The effect of dropping out of high
school on subsequent criminal behavior. Crimi-
nology 23(1):3–18.
Woolard, J.L., Gross, S.L., Mulvey, E.P., and
Repucci, N.D. 1992. Legal issues affecting men-
tally disordered youth in the juvenile justice
system. In Responding to the Mental Health
Needs of Youth in the Juvenile Justice System,
edited by J.J. Cocozza. Seattle, WA: National
Coalition for the Mentally Ill in the Criminal
Justice System.
Points of view or opinions expressed in this

document are those of the authors and do not
necessarily represent the official position or
policies of OJJDP or the U.S. Department of
Justice.
The Of fice of Juvenile Justice and Delin-
quency Prevention is a component of the Of-
fice of Justice Programs, which also includes
the Bureau of Justice Assistance, the Bureau
of Justice Statistics, the National Institute of
Justice, and the Office for Victims of Crime.
Acknowledgments
This Bulletin is based on “The Co-Occurrence of Persistent
Problem Behavior: A
Report of the Program of Research on the Causes and Correlates
of Delinquency”
by David Huizinga, Rolf Loeber, and Terence P. Thornberry
(unpublished report
submitted to OJJDP, October 1997).
David Huizinga, Ph.D., is a Senior Research Associate at the
Institute of Behav-
ioral Science, University of Colorado, Boulder, and Director of
the Denver Youth
Survey. Rolf Loeber, Ph.D., is Professor of Psychiatry,
Psychology, and Epidemiol-
ogy at the University of Pittsburgh, PA, and Director of the
Pittsburgh Youth Study.
Terence P. Thornberry, Ph.D., is Professor and former Dean at
the School of
Criminal Justice, University at Albany, State University of New
York, and Director
of the Rochester Youth Development Study. Lynn Cothern,
Ph.D., is a Senior

Writer-Editor for the Juvenile Justice Resource Center,
Rockville, MD.
The authors would like to thank the data collection and research
staff of the three
projects and all respondents of the three studies, without whom
this research
would not be possible.
Research for the Denver Youth Survey, the Pittsburgh Youth
Study, and the Roch-
ester Youth Development Study is supported by OJJDP under
grants 96–MU–FX–
0017, 96–MU–FX–0012, and 96–MU–FX–0014, respectively.
The Denver Youth
Survey is also supported by a grant from the National Institute
on Drug Abuse
(NIDA). The Pittsburgh Youth Study is also supported by a
grant from the National
Institute of Mental Health. The Rochester Youth Development
Study is also
supported by grants from NIDA and the National Science
Foundation.
CHAPTER 1 ||||| OVERVIEW
1
one one one one one conduct disorders:
an overview
Key messages

• Conduct disorders are the most common reason for referral of
young children to mental health services.
• The prevalence of conduct disorders in 5–10-year-olds is 6.5%
for boys and 2.7% for girls.
• Sixty-two per cent of three-year-olds with conduct disorders
were found to continue these problems
through to the age of eight.
• Children who become violent as adolescents can be identified
with almost 50% reliability as early as age
seven.
• Approximately 40–50% of children with conduct disorders
may develop antisocial personality disorder
as adults.
• The estimated annual cost per child if conduct disorder is left
untreated is £15,270.
• Five aspects of parenting which have been repeatedly found to
have a long-term association with
antisocial behaviour are: poor supervision, erratic harsh
discipline, parental disharmony, rejection of the
child, and low parental involvement in the child’s activities.
DEFINITIONS AND TERMINOLOGY
The term ‘conduct disorder’ is generally used to describe a
pattern of repeated and persistent
misbehaviour. This misbehaviour is much worse than would
normally be expected in a child of that
age. The essential feature is a persistent pattern of conduct in
which the basic rights of others and
major age-appropriate societal norms and rules are violated
(American Psychiatric Association,
2000).

Professionals and researchers use a variety of terms to describe
conduct disorders. These include
disobedient, aggressive, antisocial, challenging behaviour,
oppositional, defiant, delinquent and
conduct problems. For the purposes of this report we have
chosen to use the term ‘conduct
disorders’ to cover children who are described as having either
conduct disorder (CD) or, as is
more frequently the case in young children, oppositional defiant
disorder (ODD). For the full ICD–
10 and DSM–IV classifications for CD and ODD see Appendix
1.
Obviously there are a frequency and a severity of certain
disruptive behaviours which are expected
in young children and are considered part of ‘normal’
development, and children will usually
grow out of them. These behaviours occur as part of the child’s
developmental process; although
they may be difficult for the parents to deal with, they will not
be discussed in this report. A
number of programmes are provided by various voluntary
organisations to address less severe
behaviour problems (Smith, 1996).
PARENT-TRAINING PROGRAMMES ||||| FINDINGS FROM
RESEARCH
2
PREVALENCE
Epidemiological studies suggest that approximately half of

those who meet diagnostic mental
health criteria for CD will also meet criteria for at least one
other disorder. The most frequent
combination of problems is hyperactivity with CD, found in
about 45–70% of those with CD.
The prevalence of CD in children between the ages of 5 and 10
years is 1.7% for boys and 0.6%
for girls (Meltzer et al, 2000). Meltzer et al (2000) found the
prevalence of ODD in 5–10-year-olds
to be 4.8% for boys and 2.1% for girls. Although symptoms are
generally similar in each gender,
boys may have more confrontational behaviour and more
persistent symptoms. There are also
differences regarding gender in relation to the age of onset of
conduct disorders. Robins (1966)
found that the median age of onset for children referred to
mental health clinics with antisocial
behaviour was in the 8–10-year age range. Fifty-seven per cent
of boys had an onset before the
age of 10 years, whereas for girls the onset was mainly between
14 and 16 years of age.
LONG-TERM OUTCOMES
Conduct disorders have been described as being either those
which start in young children and
become persistent for the life course or those which emerge in
adolescence. Research has shown
that there is a particularly poor prognosis attached to early
onset, which indicates that early
treatments in these groups are essential (Moffit et al, 1996).
Early starting patterns of conduct
disorder are remarkably stable (Farrington, 1989). Richman et
al (1982) found that 62% of 3-
year-olds with conduct disorders continued these problems

through to the age of 8. Almost half
of all youths who initiated serious violent acts before the age of
11 continued this type of offending
beyond the age of 20, twice the rate of those who began their
violent careers at age 11 or 12
(Elliott, 1994).
A number of theorists have suggested there should be strong
links between disruptive and
externalising behaviours in pre-school years and externalising
behaviours in adolescents (Rutter,
1985; Loeber, 1990). The hypothesised early-onset pathway
begins with the emergence of ODD
in early pre-school years and school years and progresses to
both aggressive and non-aggressive
symptoms (e.g. lying and stealing) of conduct disorders in
middle childhood and then to the most
serious symptoms by adolescence.
The Isle of Wight study showed that children with conduct
disorders at ages 10 and 11 fared
worse at follow-up at ages 14 and 15 than children with other
problems (Graham & Rutter,
1973). Farrington (1989, 1990), in the Cambridge Study in
Delinquent Development, found half
of the most antisocial boys at ages 8–10 were still antisocial at
age 14 and 43% were still among
the most antisocial at age 18. The Conduct Problems Prevention
Research Group (1999a), which
consists of a group of American researchers involved in the Fast
Track project (described in more
detail in Chapter 5), argues that although there will be false
positives, the probability of identifying
the majority of those children who are at serious long-term risk
at school entry is high.

Loeber et al (1993) demonstrated that children who became
violent as adolescents could be
identified with almost 50% reliability as early as age 7, as a
result of their aggressive and disruptive
behaviour at home and at school. Robins (1966, 1978) noted
that it was rare to find an antisocial
adult who had not exhibited conduct disorders as a child, even
though no more than half of the
children identified as having conduct disorders go on to become
antisocial adults. Studies have
3
shown that approximately 40–50% of children with conduct
disorder go on to develop antisocial
personality disorder as adults (Robins, 1966; Loeber, 1982;
Rutter & Giller, 1983; American Academy
of Child and Adolescent Psychiatry, 1997). Children with
conduct disorders who do not go on to
develop antisocial personality disorder may develop a range of
other psychiatric disturbances,
including substance misuse, mania, schizophrenia, obsessive–
compulsive disorder, major depressive
disorder and panic disorder (Robins, 1966; Maughan & Rutter,
1998). Higher rates of violent
death have been shown to occur in young people diagnosed with
conduct disorder (Rydelius,
1988). Farrington (1995) found that, as well as developing
psychiatric problems, many children
with conduct disorder develop non-psychiatric antisocial
behaviours, which include theft, violence
to people and property, drunk driving, use of illegal drugs,

carrying and using weapons, and
group violence.
Conduct disorders in childhood have also been linked to: failure
to complete schooling; joblessness
and consequent financial dependency; poor interpersonal
relationships, particularly family break-
up and divorce. They have also been shown to lead to abuse of
the next generation of children,
thus increasing the chance of them developing conduct disorders
(Rutter & Giller, 1983; Robins,
1991).
Robins (1991) states, ‘because conduct disorder is common and
has pervasive long-range effects,
it is a very important public health problem’.
COST OF TREATING CHILDREN
The cost of conduct disorders, both in terms of the quality of
life of those who have conduct
disorders (and the people around them) and in terms of the
resources necessary to counteract
them, is high. It is therefore important that treatment for
conduct disorders is both effective and
cost-effective.
Knapp et al (1999) state that the NHS resources spent on
children with conduct disorders are
considerable. Thirty per cent of child consultations with general
practitioners are for conduct
disorders. Forty-five per cent of community child health
referrals are for behaviour disturbances,
with an even higher level at schools for children with special
needs and in clinics for children with
developmental delay, where challenging behaviour is a common

problem. Psychiatric disorders
are present in 28% of paediatric out-patient referrals.
Social services departments expend a lot of energy trying to
protect disruptive children whose
parents can no longer cope without hitting or abusing them.
Often this may include a brief time
with a foster family or the placement of the child in residential
care.
Education costs include funding special schools for emotionally
and behaviourally disturbed children,
as well as providing extra staff to support and provide special-
needs education. Law enforcement
agencies and the probation service have to detect and prevent
delinquency and bring the delinquents
to justice. The rate of unemployment and receipt of state
benefits is also high among young
people with conduct disorders (Rutter et al, 1998).
All agencies will spend considerable amounts of money in
supporting a child or young person with
conduct disorder over the life span if nothing is done to treat
the child. Knapp et al (1999)
RESEARCH
4
examined the cost of treating children diagnosed with conduct
disorder. The total direct costs for
all agencies (see Fig. 1 for a breakdown) were £8258. The
indirect costs, which included loss of

employment for some parents, additional housework and
repairs, and allowances and benefits,
were estimated to be £7012. The total cost annually per child
with conduct disorder was likely to
amount therefore to a staggering £15,270.
The House of Commons Health Committee (1997), in its report
on child and adolescent mental
health services, cited two recent outcome studies of projects in
the US aimed at improving the
behaviour of children from disadvantaged backgrounds. The two
studies also looked at the costs
saved by early intervention for conduct disorders.
••••• The Perry Pre-school Project worked with 3–4-year-olds
and looked at real-life outcomes to 19
years of age. This study found fewer delinquent acts, less use of
special education and better
peer relationships. Compared with controls, there were savings
of $14,819 per child (Barnett,
1993; Schweinhart & Weikart, 1997).
••••• The Yale Project ran a family support programme in the
pre-school years and found that at the
age of 13 years the children involved got better grades, attended
school more regularly and
had fewer behaviour problems. Compared with controls, there
were savings of $20,000 per
family in community resources expended (Seitz et al, 1985).
A consultation document for the National Assembly for Wales
(2000) explains that if the NHS
were successfully to treat a child with conduct disorder, with an
expensive investment in childhood,
this would not only save the NHS money over the person’s
lifetime, but also other public sector

Fig. 1. Annual costs (£) per child with conduct disorder.
Data from paper by Knapp et al (1999), based on a sample of 10
children.
Local authority
social services
991
Voluntary sector
56
National Health Service
2457
Local authority
education services
4754
5
organisations could save significant amounts of money in the
long run. This approach emphasises
the importance of multi-agency working.
RISK FACTORS
Conduct disorders present a significant public health problem
for both the individual and the
economy. To reduce the frequency of conduct disorders, the

first step is to recognise the risk
factors for them. These may in turn suggest the causes of
conduct disorders and help to identify
the children most likely to develop them. Risk factors for the
development of conduct disorders
may be considered in terms of child, parenting and
environmental factors. The interaction of
these factors is outlined in Fig. 2.
Child factors
TTTTTemperamentemperamentemperamentemperamentemperam
ent
Temperament refers to a number of characteristics that show
some consistency over time (Normand
et al, 1996). These characteristics appear soon after birth
(Coffman et al, 1992). A number of
studies suggest that infants assessed as having a difficult
temperament are more likely to show
problems with behaviour later on (Greenberg & Speltz, 1993;
Prior et al, 1993). A difficult
temperament may make children more likely to be the target of
parental anger, which in turn
may be linked to conduct disorders later on (Marshall & Watt,
1999). However, Wooton et al
(1997) demonstrated a possible strong relationship between
‘callous-unemotional’ temperament
and behaviour problems despite good parenting practices. The
authors concluded that these
children, with a lack of empathy, lack of guilt and emotional
constrictedness, develop conduct
disorders through causal factors distinct from other children
with conduct disorders.
GeneticGeneticGeneticGeneticGenetic

Conduct disorder is thought to differ from attention-deficit
hyperactivity disorder (ADHD) in terms
of genetic influence. For children with ADHD, the magnitude of
the genetic influences is thought
to be 60–90% (Goodman & Stevenson, 1989; Thapar et al, 1995;
Silberg et al, 1996). There is,
however, little evidence to suggest that genetic factors alone
contribute to conduct disorder.
Plomin (1994) found genetic factors accounted for half the
variation of externalising behaviour.
Genetic factors plus adverse environmental factors accounted
for more of the variation in children
with conduct disorders (Eaves et al, 1997). As Walters (1992)
states, it is very unlikely that a single
gene or even a simple genetic model can account for complex
behaviours such as conduct disorders
or criminal activity.
Physical illnessesPhysical illnessesPhysical illnessesPhysical
illnessesPhysical illnesses
Rutter et al (1970) found that children with epilepsy or other
disorders of cerebral function are at
increased risk for conduct as well as emotional disorders. Rutter
(1988) found that chronically ill
children have three times the incidence of conduct disorders
than their peers; if the chronic condition
was found to affect the central nervous system (CNS), the risk
factor rose approximately fivefold.
It has also been shown that perinatal complications such as long
labour, delivery with instruments
and asphyxia predict conduct disorders and delinquency,
although the effects of these complications
may vary with other risk factors (Mednick & Kandel, 1988;
Raine et al, 1994).

RESEARCH
6
Fig. 2. Influences on antisocial behaviour seen at home and at
school,
and how the consequences may perpetuate it. (From Spender &
Scott, 1997.)
Cognitive deficitsCognitive deficitsCognitive deficitsCognitive
deficitsCognitive deficits
A number of studies have examined the cognitive correlates of
conduct disorders in younger
children and have found that they often have delays in language
development and cognitive
functioning (Cantwell & Baker, 1991; Hinshaw, 1992).
Language problems, however, could also
be considered not to be a child factor, as many factors
associated with language development
involve the parents’ and the child’s environment. An example of
this is a study which found
mother–child interactions and the home environment to be good
predictors of language skill by
the age of three years (Bee et al, 1982).
Cognitive deficits do lead to school underachievement and this
has been found to be associated
with conduct disorder. Rutter et al (1970, 1976) in the Isle of
Wight study of 10–11-year-olds
found that a third of children with severely delayed reading
levels had conduct disorder and a

third of children with conduct disorder were severely behind in
their reading. Scott (1995)
emphasises the importance of turning around educational
underachievement in conduct-disordered
children due to cognitive deficits, as this leads to a continuing
feeling of low self-esteem in the
child. This low self-esteem and belief that they are bad (when
often the appropriate assessments
are not made and so specific reading and learning disabilities
may easily be missed) can cause
marked misery and unhappiness and, as a result, a higher
incidence of depression (Scott, 1995). It
Antisocial behaviour at school
Disruptive in class
Fights or bullies
Hostile attitude
Difficulty making friends
Difficulty making academic progress
Antisocial behaviour at home
Refuses to obey requests
Temper tantrums
Behaves in a way to annoy or anger
adults
Social context

Poverty
Unemployment
Poor neighbourhood support
Large family size
Distal parental factors
Own upbringing inadequate
Psychiatric disorder
Unsupportive partner
Social isolation
Child–parent interaction
Inconsistent discipline
High parental criticism
Low parental warmth
Mutually coercive cycles
Insecure or disorganised child
attachment pattern
Child constitution
Difficult temperament

Attention-deficit/hyperactivity
Language or reading difficulty
Bad reputation of child
in local community
Parental discouragement
and helplessness
Parental isolation
from school
Peer
rejection
Deviant
peer
group
Negative image with teacher
School
failure
7
has been suggested that academic failure is a cause rather than a
consequence of antisocial
behaviour; however, programmes that have improved the
academic skills of these children have
not achieved reductions in antisocial behaviours (Wilson &

Herrnstein, 1985). Similar results have
been found for peer rejection, despite these children having
been given social skills training (Kazdin,
1987).
Poor social skillsPoor social skillsPoor social skillsPoor social
skillsPoor social skills
Some of these children lack the social skills to maintain
friendships and may become isolated from
peer groups (Kazdin, 1995). Children engaging in problem
behaviours are thought to have
underlying distortions or deficits in their social information
processing system (Dodge & Schwartz,
1997). Dodge & Price (1994) found that aggressive children
were more likely to interpret social
cues as provocative and to respond more aggressively to neutral
situations. Children who are
aggressive or antisocial are often rejected by their peers
(Marshall & Watt, 1999). As Dishion et al
(1991) show, peer group rejection is often a prelude to deviant
peer group membership, which
reinforces deviant behaviours. It has also been found that
aggressive, antisocial children are socially
inept in their interactions with adults. They are less likely to
defer to adult authority, show politeness
and to respond in such ways as to promote further interactions
(Freedman et al, 1978).
Parenting factors
According to Carr (1999), neglect, abuse, separations, lack of
opportunities to develop secure
attachments, and harsh, lax or inconsistent discipline are among
the more important aspects of
the parent–child relationship that place youngsters at risk of

developing conduct disorders. Parenting
behaviour and parent characteristics such as depression are
among the strongest predictors of
child behaviour problems (Marshall & Watt, 1999).
Poor parenting skillsPoor parenting skillsPoor parenting
skillsPoor parenting skillsPoor parenting skills
Scott (1998) showed that five aspects of how parents bring up
their children have been found
repeatedly to have a long-term association with conduct
disorders. These are:
••••• poor supervision;
••••• erratic harsh discipline;
••••• parental disharmony;
••••• rejection of the child;
••••• low parental involvement in the child’s activities.
Such parenting appears to be a major cause of conduct disorders
in children.
Webster-Stratton & Spitzer (1991) found parents of children
with conduct disorders lack
fundamental parenting skills and exhibit fewer positive
behaviours. Their discipline involves more
violence and criticism, and they are more permissive, erratic
and inconsistent, and more likely to
fail to monitor their child’s behaviour, to reinforce
inappropriate behaviours and to ignore or
punish pro-social behaviours.

RESEARCH
8
Patterson’s work shows that parents of antisocial children are
deficient in their child-rearing skills
(Patterson, 1982; Patterson et al, 1989):
••••• they do not tell their children how they expect them to
behave;
••••• they fail to monitor the behaviour of their children to
ensure it is desirable;
••••• they fail to enforce rules promptly and clearly with
positive and negative reinforcement.
AttachmentAttachmentAttachmentAttachmentAttachment
According to the attachment model proposed by Bowlby (1969),
parental responsiveness is
conceptualised as critical to the development of self-regulation
skills. Therefore, differences in
caregiver sensitivity and the resultant bond between the parent
and infant are important factors
in later patterns of the child’s behaviour (Lyons-Ruth, 1996).
Greenberg & Speltz (1988) found
that children who had received insufficient caregiving will act
more disruptively to obtain the
attention of their parent. They have less to lose in terms of love
(Shaw & Winslow, 1997). Shaw &
Winslow (1997) examined infant attachment security and
observed the responsiveness of caregivers,

and found that the parent–infant relationship correlated with
externalising behaviour at a later
age.
Poor interactions between mother and child can influence the
child in many ways (Marshall &
Watt, 1999): the mother’s inappropriate modelling of
interactional behaviour (Bandura, 1986);
the child’s development of unrealistic goals and lack of
knowledge of social rules within relationships
with adults and peers (Goodman & Brumley, 1990); the
establishment of coercive patterns of
interaction within the parent–child relationship that are carried
forward to the peer group
(Patterson, 1986); and the impact of a lack of warmth on the
child’s self-concept (Patterson et al,
1989).
Separation and disruption of primary attachments through
neglect or abuse may also prevent
children from developing internal working models for secure
attachments.
Mental health problems in parentsMental health problems in
parentsMental health problems in parentsMental health
problems in parentsMental health problems in parents
Offord et al (1989), in their longitudinal study of single- and
two-parent families, found that
mothers with psychological distress, major depression or
alcohol problems were more than twice
as likely to have children with externalising problems directed
at others. Stein et al (1991) and
Beck (1998) found that children older than one year whose
mother is postnatally depressed display
problems such as insecure attachment, antisocial behaviour and

cognitive deficits. Depressed
mothers are highly critical of their children, find it difficult to
set limits and are often emotionally
unavailable. Hall et al (1991) report that mothers who are
depressed are more likely to perceive
their child’s behaviour as inappropriate or maladjusted.
West & Farrington (1973) report strong links between the
presence of an antisocial personality in
one or both parents and similar behaviour in the child.
Substance misuse and criminality in parentsSubstance misuse
and criminality in parentsSubstance misuse and criminality in
parentsSubstance misuse and criminality in parentsSubstance
misuse and criminality in parents
Children coming from families where parents are involved in
substance misuse or criminal activities
are at particular risk of developing conduct disorders (Patterson
et al, 1989; Frick et al, 1991).
9
Research has shown that when both parents are alcoholics this
increases the chances of children
developing ODD and CD (Earls et al, 1988). A number of
researchers suggest that a combination
of risk factors play a role in increasing behaviour problems.
Miller & Jang (1977) found that
children of alcoholics tend to come from lower-class homes
with other problems, including parental
mental illness, criminal activity, more marital breakdowns and

more welfare assistance. Parents
involved in crime may provide deviant role models for children
to imitate and substance misuse
may compromise parents’ capacity to care for their children
correctly (Carr, 1999).
TTTTTeenage parentseenage parentseenage parentseenage
parentseenage parents
Marshall & Watt (1999) highlight the research showing that
children of teenage mothers had
more conduct disorders at age 8, 10, and 12 years compared
with older mothers. However, as the
research goes on to point out, the effects of teenage pregnancy
may be due to the fact that
children with teenage mothers tend to live on lower incomes,
have absent biological fathers and
suffer from poor child-rearing practices. Fergusson & Lynskey
(1995) found maternal age, socio-
economic status, number of siblings at the time of the child’s
birth and punitive parenting practices
were all significant in the relationship between maternal age
and conduct disorders.
Marital discordMarital discordMarital discordMarital
discordMarital discord
Marital problems, as previously mentioned, are a risk factor.
Marital conflict leading to divorce can
have detrimental effects on children (Marshall & Watt, 1999).
Marital disruption is often associated
with a change in economic circumstances and adjustments to
altered living conditions; parents
may be distressed and this may affect their parenting practices.
Also, separated parents may not
agree on rules and how they should be implemented. This may

lead to a lack of communication
about discipline and in turn to inconsistent disciplinary
practices.
Some research suggests that when there is persistent conflict in
families in which the parents do
not separate, there are high levels of child behaviour problems
and poor self-esteem in children
(Marshall & Watt, 1999). In a recent study, negative marital
conflict management skills on the part
of parents (defined as the inability to collaborate and problem
solve, to communicate positively
about problems and to regulate negative affect) were a key
variable in contributing to child
conduct disorders (Webster-Stratton & Hammond, 1999).
Marital violenceMarital violenceMarital violenceMarital
violenceMarital violence
Marshall & Watt (1999) also provide evidence that marital
conflict involving physical aggression is
more upsetting to children than other forms of marital conflict.
Children exposed to marital
violence may imitate this in their relationships with others and
display violent behaviour towards
family, peers and teachers. Carr (1999) goes on to suggest
that where children are exposed to
negative emotions, their safety and security may be threatened
and therefore they may express
anger towards their parents.
AbuseAbuseAbuseAbuseAbuse
Abusive and injurious parenting practices are regarded as the
most influential risk factors for
conduct disorders (Luntz & Widom, 1994). Physically

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