VOLUME 18 ISSUE 2 PAGES 21–38 (2017)
Criminology, Criminal Justice, Law & Society
E-ISSN 2332-886X
Available online at
https://scholasticahq.com/criminology-criminal-justice-law-society/
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
Juvenile Hybrid White-Collar Delinquency:
An Empirical Examination of Various Frauds
Andrea Schoepfer,
a
Michael Baglivio,
b
Joseph Schwartz,
c
a
California State University – San Bernardino
b
G4S Youth Services
c
University of Nebraska – Omaha
A B S T R A C T A N D A R T I C L E I N F O R M A T I O N
With recent technological advances, juveniles now have more opportunities to engage in certain types of hybrid white-
collar crime such as credit card fraud, identity theft, general fraud, intellectual property crimes, and financial/bank fraud,
yet they are largely ignored in white-collar crime research due to offender- and opportunity-based definitions of the
phenomenon. Using data from the Florida Department of Juvenile Justice, the current paper examines various aspects of
this recently acknowledged variety of offending behavior among juveniles. Results indicate that significant differences
exist between hybrid white-collar delinquents and conventional crime delinquents in various factors identified in the
literature relating to deviant/criminal behavior. Theoretical implications and future research directions are also
discussed.
Article History:
Received 05 June 2016
Received in revised form
10 November 2016
Accepted 29 November 2016
Keywords:
juvenile delinquency, fraud, hybrid white-collar crime, referral
© 2017 Criminology, Criminal Justice, Law & Society and The Western Society of Criminology
Hosting by Scholastica. All rights reserved.
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 22
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
When Sutherland (1940) coined the term “white-
collar crime,” he was referring to the criminal
behavior of “respectable” upper class citizens (p. 1).
Since the original conceptualization, researchers have
moved to broader definitions that do not necessarily
delineate socioeconomic status; rather, they focus on
the opportunity to engage in the act.
1
To complicate
the matter further, there are those illegal activities
that lie just outside of the margins of traditionally
defined white-collar crimes and, as such, are often
included under the general label of white-collar crime
(e.g., computer crime, identity theft, bank fraud, etc.);
these are often referred to as “hybrid” white-collar
crimes (see Friedrichs, 2007). Although, some have
argued that the techniques utilized for engaging in
certain frauds (e.g., common theft, access to
technology) should relegate these crimes to a
conventional crime classification and that they should
no longer fall under the broad white-collar crime
umbrella (Copes & Vieraitis, 2009).
The majority of white-collar crime research has
focused on adult offenders with few exceptions
(Ruggiero, Greenberger, & Steinberg, 1982; Wright
& Cullen, 2000). This is not surprising given that
opportunities for white-collar crime have largely
resided in the adult realm, but as technology has
changed, so too have the availabilities of opportunity.
Modern computers eliminate the need for large and
expensive equipment previously required to take part
in acts of hybrid delinquency such as check fraud or
counterfeiting (Bowker, 1999). Access to technology
and high levels of anonymity contribute to the appeal
of acts that involve almost no face-to-face
confrontation and are individualistic in nature. A
juvenile can commit many forms of hybrid
delinquency at any time from the comfort of his or
her own bedroom. Even a juvenile without the
ability to legally operate a motor vehicle could, for
example, steal a large sum of money.
For the purposes of the current study, we use
Edelhertz’s (1970) offense-based definition of white-
collar crime which defines the phenomenon as “an
illegal act or series of illegal acts committed by
nonphysical means and by concealment or guile to
obtain money or property, to avoid the payment or
loss of money or property, or to obtain business or
personal advantage” (p. 3). This broad definition
overlooks the typical requirements of employment,
social status, and positions of trust, allowing us to
focus on the characteristics of the crimes rather than
the characteristics of the offenders.
Just as juveniles engage in conventional offenses
similar to adult offenders, they also possess the
ability to engage in white-collar offenses similar to
adult offenders. Utilizing data comprised of juvenile
offenders from the state of Florida, the current study
examines juvenile perpetrators of various frauds from
an offense-based orientation. Pontell and Rosoff
(2009) theorized that white-collar offenses that were
historically committed exclusively by adults also
have a place in the juvenile community as well. This
“migration” has taken place for a number of reasons,
with the majority of them closely tied to the nearly
limitless access juveniles currently have to
technology. Given the growing incidence of frauds
and identity thefts (Federal Trade Commission,
2014), it is important to study all groups in efforts to
gain a better understanding of the phenomenon. The
current study examines hybrid white-collar crimes,
but to avoid confusion with terminology, from this
point on, this paper will refer to them as “hybrid
delinquency/crime.” The identification of
characteristics related to the different types of
offending is yet another step in fully understanding
juvenile offending. It is critical that these
characteristics or “profiles” be taken into account
when examining both the juvenile justice system and
hybrid white-collar crime in general.
Literature Review
Research has shown that adult white-collar
offenders tend to be male, White, middle-aged or
older, middle to high socio-economic status,
relatively well educated, and have stable family
situations (Benson & Kerley, 2000; Benson &
Moore, 1992; Friedrichs, 2007; Weisburd, Wheeler,
Waring, & Bode, 1991). These characteristics are in
contrast to the demographic characteristics of the
typical street offender, and while they may not
always be strong, differences do exist. Yet, due to
their marginalized status, juveniles are largely
ignored in white-collar crime research, and it is
unknown to what extent the differences seen in adult
populations extend to juvenile populations.
The limited research on juvenile engagement in
white-collar crimes has focused mainly on
occupational delinquency. Ruggiero and colleagues
(1982) conducted the first empirical study of juvenile
occupational deviance. High school students holding
their first part-time job were asked how often they
engaged in various delinquent behaviors while at
work. The authors found that within their sample,
approximately 60% of first-time employees had
committed at least one deviant act within the first
nine months of employment, and nearly 24% of the
sample could be described as “relatively frequent
offenders” (p. 441). Employing the same
occupational delinquency scale, Wright and Cullen
(2000) found that juveniles who interacted with
deviant peer groups, possessed low grade point
averages, and held strong materialistic views were
23 SCHOEPFER, BAGLIVIO, & SCHWARTZ
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
more likely to engage in occupational delinquency
than their counterparts.
Although limited, there is some literature
regarding juvenile involvement in computer related
crimes that reveal motivations, such as boredom with
the simplicity of school curriculum, constant
dismissal by teachers and parents, frustration with the
high price of services due to corporate greed, and the
desire to explore and learn (The Mentor, 1986; Yar,
2005). Additionally, it would appear that juveniles
who take part in these particular activities attempt to
justify their actions by “blaming their victims” (Yar,
2005), which is also seen in the white-collar crime
literature (Copes & Vieraitis, 2009; Shover, Coffey,
& Hobbs, 2003; Piquero, Tibbetts, & Blankenship,
2005) and is in line with Sykes and Matza’s (1957)
techniques of neutralization.
Studies of perpetrators of non-corporate types of
fraud have largely focused on adult samples. Allison,
Schuck, and Lersch (2005) were among the first to
examine characteristics of adult fraud offenders
(including identity theft and credit card and check
fraud) and found that typical offenders were female,
Black, and unemployed and committed the offense
alone and were unknown to their victims. With the
exception of gender, similar results have been found
in subsequent studies (see Copes & Vieraitis, 2009,
2012). While this research is limited, there appears
to be a sizeable void in the literature in regard to
juvenile perpetrators of fraud, yet the same cannot be
said for juvenile conventional delinquents.
Many studies examining characteristics
associated with conventional delinquency have found
that specific traumatic events that occur during
childhood are typically linked to conventional
delinquency (Baer & Maschi, 2003; Baglivio et al.,
2014; Dixon, Howie, & Starling, 2004; Jensen,
Potter, & Howard, 2001; Martin, Martin, Dell, Davis,
& Guerrieri, 2008; Wolff, Baglivio, & Piquero,
2015). According to Martin and colleagues (2008),
“the most serious offenders enter the juvenile justice
system with histories that include physical and sexual
abuse, witnessing violent acts, parental substances
abuse and neglect, and numerous mental health,
developmental, and emotional issues” (p. 608).
Similar results were also found in an examination of
juvenile computer hackers (Verton, 2002). In
addition to traumatic events that occur during
childhood, conventional juvenile offenders are more
likely to be male (Cauffman, 2008; Martin et al.,
2008), non-White (Jensen et al., 2001; Mann &
Reynolds, 2006; Martin et al., 2008), and to have a
propensity toward violent or antisocial behavior
(Corbitt, 2000; Martin et al., 2008; Onwuegbuzie,
Daley, & Waytowich, 2008). Studies have also
focused on the mental health of conventional juvenile
offenders (Grisso, 2008), poor academic performance
(Jensen et al., 2001; Mann & Reynolds, 2006; Martin
et al., 2008), and low socioeconomic status (SES;
Martin et al., 2008; Stouthamer-Loeber, Loeber, Wei,
Farrington, & Wikstrom, 2002) as factors related to
delinquency. Several studies, however, have found
that SES is not significantly correlated with juvenile
offending (Alltucker, Bullis, Close, & Yovanoff,
2006; Tittle & Meier 1991).
While the literature tends to indicate that males
clearly engage in a higher frequency of serious
violent acts, as well as delinquency overall (see
Cauffman, 2008; Lenssen, Doreleijers, van Dijk, &
Hartman, 2000; Martin et al., 2008; Snyder &
Sickmund, 2006), female juvenile offending has
recently increased at a much more rapid pace than
male offending (Calhoun, Glaser, & Bartolomucci,
2001; Cauffman, 2008; Mullis, Cornille, Millis, &
Huber, 2004; Smith & Smith 2005). In fact, studies
have indicated that the female gender gap is closing
in regards to alcohol and drug related criminal acts
(e.g., driving under the influence; Putkonen,
Weizmann-Henelius, Lindberg, Rovamo, &
Hakkanen, 2009; Schwartz & Rookey, 2008) and
aggravated and simple assaults (Lauritsen & Heimer,
2008).
2
The female gender gap may even be closing
at a much more rapid pace in regards to white-collar
crime. Between 1993 and 2002, female involvement
in embezzlement increased 85% (Teicher, 2004), and
by 2005, females were responsible for 50% of all
embezzlement cases (Dodge, 2009). The National
Incident Based Reporting System (NIBRS) shows
that between 1997 and 1999, adult females were
responsible for 41% of counterfeiting and 36% of
fraud (as cited in Allison et al., 2005).
Theoretical Guidance
Pontell and Rosoff (2009) identified several
theories that may or may not be applicable to the
white-collar delinquency they describe. Among the
theories they identified, limitations in the current data
only allow us to examine elements of Gottfredson
and Hirschi’s (1990) general theory of crime and peer
associations. Although not mentioned by Pontell and
Rosoff (2009), we also examine elements of
Hirschi’s (1969) social control theory. Data
limitations do not permit us to examine each theory
in full. While not ideal, this is a first attempt at
examining the concept of juvenile hybrid white-collar
delinquency, a distinct, policy-relevant subgroup of
juvenile offenders of which limited knowledge is
available.
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 24
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
The general theory of crime has received
extensive support in studies of conventional crimes
(see Pratt & Cullen, 2000), yet it has not received
much support in the area of white-collar crime
(Benson & Moore, 1992; Piquero, Schoepfer, &
Langton, 2010; Schoepfer, Piquero, & Langton,
2014; Simpson & Piquero, 2002). The basic tenets of
the general theory of crime are that individuals with
low self-control are more likely to engage in criminal
and analogous behaviors. Individuals with low self-
control generally have a here-and-now orientation
and do not think about the consequences of their
actions. This appears to be in contrast to individuals
who engage in traditional white-collar crime as such
acts typically involve planning, skill, and patience.
Even Pontell and Rosoff (2009) are weary of general
theory’s ability to explain juvenile white-collar
delinquency. Due to the nature of the crimes and the
sample examined in the current study, we chose to
include measures of low self-control in the analysis.
Likewise, the availability of advanced technology has
greatly reduced the planning, skill, and patience
formerly required to engage in such frauds. As the
current study examines hybrid delinquencies and not
traditional white-collar crimes, we do expect
indicators of low self-control to attain statistical
significance (see Hirschi & Gottredson, 1989).
Hirschi’s (1969) social control theory suggests
that individuals who are strongly bonded to
conventional others may be less likely to engage in
crime/delinquency because they do not want to let
others down or lose what they have worked so hard
to obtain. Unlike the general theory of crime, social
control theory does focus on the consequences of
one’s actions, and this theory may be more applicable
given the use of our juvenile sample.
Although many of the crimes we examine in this
study are individualistic in nature, we still control for
delinquent peer associations. Social learning theory
(Burgess & Akers, 1966) suggests that individuals
learn how to behave/act through associations with
others, yet Pontell and Rosoff (2009) are uncertain as
to how peer associations would operate in white-
collar delinquency as peers in the technological age
are not “known” in the conventional sense. We, too,
are uncertain of the influence of peers given the
individualistic nature of the delinquency we examine,
but given the fact that these juveniles have to “learn”
the skills required to engage in these crimes, it would
stand to reason that peers would have an influence.
Data and Methods
The Florida Department of Juvenile Justice
(FDJJ) is one of the largest juvenile justice agencies
in the United States. The FDJJ implemented the
Positive Achievement Change Tool (PACT)
risk/needs assessment in 2006, which was designed
to assess juvenile offenders on dynamic risk, needs,
and strengths related to the same risk factors as
outlined in the extant “what works” literature
(Andrews & Bonta, 2003). The PACT is heavily
adapted from the validated Washington State
Juvenile Court Assessment (WSJCA), which has
been in use throughout the country since 1998
(Washington State Institute, 2004).
3
There are two versions of the PACT: the pre-
screen, with 46 items, and the full assessment,
consisting of 126 items. Both versions produce
identical overall risk to reoffend classifications, but
the full assessment provides additional information
regarding criminal history, school, leisure/free time,
employment, relationships, family/living situation,
alcohol/drugs, mental health, attitudes/behaviors,
aggression, and social skills. All youth scoring
moderate-high or high on the pre-screen or those
being considered for placement above traditional
probation supervision require a full assessment.
Only those youth assessed with the PACT full
assessment were included in the current study.
Notably, this process oversamples higher risk youth.
The FDJJ maintains a comprehensive database
(Juvenile Justice Information System) containing
information on all youth entering the system (arrests)
and all placement and risk assessment (PACT)
information of those youth. The current study uses all
PACT assessments from November 1, 2008 to
November 30, 2014, including the entire offense
history and demographics for every youth who was
referred to the Florida juvenile justice system.
4
The data extract provided records for 211,889
individual youth. The youth were responsible for
1,334,022 delinquency charges across 860,766
referrals, as many youth are referred multiple times,
and a referral may have multiple charges. A
“hybrid white-collar” crime was charged in 3,865 of
the referrals. There were 3,612 individual youth
responsible for those hybrid white-collar crime
referrals. The 3,612 youth were responsible for
35,685 individual charges total, illustrating that the
hybrid crime youth did not necessarily specialize in
committing only hybrid offenses. The prevalence
rate of ever being arrested for a hybrid offense among
juvenile offenders is 1.7% (3,612/211,889=.017),
which has heretofore been unestablished.
Additionally, there were 66,575 conventional
crime offenders assessed with the PACT full
assessment over the study period. Using random
selection, 2,064 offenders that did not have a hybrid
white-collar charge in any referral were collected.
5
The final data set contained 2,064 individual youth
who committed a hybrid white-collar offense, and
25 SCHOEPFER, BAGLIVIO, & SCHWARTZ
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
2,064 individual youth who had never been referred
for a hybrid offense, with each youth contained only
one time within the data set (total n=4,128).
Dependent Variables
As all youth in the data set have offended, in one
set of analysis, offense type is predicted. Offense
type was coded dichotomously (hybrid crimes =1;
conventional crimes =0).
6
The hybrid crimes include
general fraud, identity fraud (e.g., fake/stolen
identification, fraudulent documents, etc.),
intellectual property theft (e.g., music piracy,
software piracy, etc.), financial/bank fraud (e.g.,
forgery, passing bad checks, credit card fraud, etc.),
and computer crimes (e.g., hacking, phishing, etc.).
Conventional crimes include everything else.
Independent Variables
The majority of the independent variables were
based on self-report from the youth during the PACT
interview conducted by a juvenile probation officer
or intake screener. Consistency among probation
officers/screeners in the scoring of all items on the
PACT is enhanced by the existence of help screens
within the PACT software, which define,
operationalize, and give examples of concepts for
each item of the assessment. Variables included in
the analysis are identified in Tables 1a and 1b below.
Table 1a: Variables in the Analysis
Concepts
Attributes
Substance Abuse
0 = no use; 1= use; 2= use causes problems*
Abuse/Neglect
0=no; 1=yes
0= no placement; 1=1; 2=2; 3= 3+ placements
Mental Health History
Higher values equal more problems (range 0-3)
0=no; 1=yes
Mental Health Scale
One factor loading; Cronbach alpha = .740
Risk to Reoffend Score
Scores are associated with a matrix used by
FDJJ to determine risk to reoffend.
Higher scores = higher risk
Demographics
0=female; 1 =male
Continuous 0=White; 1=non-White
Higher scores = lower family income
Higher scores = lower GPA
* Problems defined as family conflict, disrupting education, health, interferes with keeping pro-social friends, contributes to criminal behavior,
needing increased dosages, and withdrawal problems
**Data corroborated through access to the child welfare records granted FDJJ PACT assessors
***Confirmed by a professional in the social service/healthcare field
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 26
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
Table 1b: Variables in the Analysis
Variables
Attributes
General Theory of
Crime
- Frustration tolerance (temper)
- Belief in use of physical aggression
- Impulsivity
- Goal setting****
- Punishment from parents for bad behavior
(operationalized as clear communication, timely
response, proportionate to the conduct youth
displayed)
- Scale of all 5
Higher values = less self-control
0=consistently appropriate
1=inconsistent or inappropriate
Cronbach alpha= .742
Social Control
Theory
- Belief in the value of getting an education
- Involvement in positive school activities
- Respect for authorities
- Current pro-social community ties
- Belief that laws apply to respondent
- Scale of all 5 (higher values = less social
control)
Higher values = less belief in value (range 0-2)
Higher values = less involvement (range 0-3)
Higher values = less respect (range 0-3)
Higher values = less ties (range 0-2)
Higher values = more defiance to laws (range 0-3)
Cronbach alpha = .754
Social Learning
Theory
- Current friends
0=pro-social; 1=anti-social and/or gang members
**** Assessed by the juvenile’s probation officer
Analytical Plan
Our empirical analysis was conducted in three
steps. First, bivariate correlations were examined
among the predictor variables to ensure that
multicollinearity did not exist.
7
Next, we examined
the independent t-tests to search for statistically
different means among several variables in regards to
hybrid and conventional offenders and again between
those who were referred and those who were
adjudicated for their offenses. Finally, using
binomial logistic regressions, we assessed the impact
of social control theory, the general theory of crime,
delinquent peer associations, and mental health
variables on predicting offense type.
Results
The results of the t-test examining the
differences between those juveniles who were
referred to FDJJ for hybrid delinquency and those
referred for conventional delinquency can be found in
Table 2. Each juvenile is contained within the data
only one time. Either the juvenile has been referred
for a crime that included a hybrid delinquency
offense, or the juvenile has never been referred for a
hybrid offense (conventional crime group). Several
demographic variables are significantly different.
The results suggest that those who are referred
(arrested) for hybrid delinquency tend to be
significantly older, have higher risk to reoffend
scores, are more likely to be non-White, more likely
to be female, and have lower educational
performance than those juveniles who are referred for
conventional delinquencies.
In terms of histories of abuse, neglect, and
mental health, the results indicate that juveniles
referred for hybrid offenses evidence significantly
more risk across all indicators than those juveniles
who were referred for conventional delinquency, with
the exception of income and sexual abuse history,
which are non-significant. What is interesting is that
the conventional delinquents in our sample have
significantly less negative life experiences (i.e.,
abuse, neglect, anger, trauma, substance abuse, etc.)
than those individuals who engaged in hybrid
delinquency.
27 SCHOEPFER, BAGLIVIO, & SCHWARTZ
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
Table 2: Independent T-tests for Referral Sample Only
Hybrid (n=2,064)
Conventional (n=2,064)
T-test
Measure
Mean
SD
Mean
SD
T-value
Cohen's D
Age
15.69
1.292
14.91
1.8
-16.028***
-0.499
Risk to Re-offend
2.990
1.056
2.252
1.164
-21.334***
-0.664
Race (0=white)
0.673
0.469
0.634
0.482
-2.585*
-0.134
Gender (0=female)
0.751
0.433
0.783
0.412
2.432*
0.076
Income
1.987
0.917
2.007
0.873
.730
Academic Achievement
2.542
1.399
2.372
1.284
-4.080***
-0.127
Witnessed Violence
0.705
0.456
0.629
0.483
-5.168***
-0.161
Sexual Abuse
0.049
0.216
0.047
0.213
-.218
Physical Abuse
0.184
0.387
0.151
0.358
-2.795**
-0.087
Child Welfare Placement
0.395
0.819
0.253
0.666
-6.088***
-0.19
Neglect
0.127
0.333
0.089
0.285
-3.917***
-0.122
Anger
1.131
0.886
1.001
0.920
-4.600***
-0.143
Trauma
0.282
0.575
0.220
0.516
-3.646***
-0.114
Depression
0.584
0.733
0.504
0.707
-3.588***
-0.112
Mental Health Problems
0.267
0.442
0.203
0.403
-4.820***
-0.15
Drug History
1.127
0.713
0.900
0.748
-10.010***
-0.312
Current Drug Use
0.485
0.756
0.380
0.694
-4.653***
-0.145
Current Alcohol Use
0.168
0.454
0.128
0.407
-2.958**
-0.092
Note: *=p<.05; **=p<.01; ***=p<.001
In order to control for the fact that not all
individuals who are arrested are also convicted, we
examined the same variables among a sample of
adjudicated juveniles.
8
The results of the t-tests from
the hybrid offense adjudicated sample were similar to
that of the hybrid offense referral sample with the
exception that the adjudicated youth had higher risk
to reoffend scores and more child welfare placements
outside of the home. Among the conventional
offenders, the adjudicated youth were older and had
higher risk to reoffend scores than those who were
referred for a conventional offense. This consistency
is indicative of uniformity in the juvenile system
when processing cases, regardless of the offense.
Those higher-risk juveniles with prior records and
more contact with the system are more likely to be
deemed in need of intervention. Given the
similarities between referred and adjudicated youth,
further analysis will be presented on referrals only.
The data was further disaggregated according to
gender. When examining the females only (n=962),
several significant differences arose. The results in
Table 3 indicate that among females, hybrid
delinquents tend to be older, are more likely to be
White, and have higher family income than those
females who committed conventional offenses.
Additionally, female hybrid delinquents have higher
risk to reoffend scores; have witnessed more
violence; are more likely to have experienced
physical abuse, neglect, trauma, and child welfare
placements; and have significantly more mental
health problems and substance related problems. In
terms of basic demographics (age, race, income),
female hybrid offenders mirror the demographics of
adult white-collar offenders (Friedrichs, 2007). Yet,
the female hybrid offenders have significantly more
negative life experiences than those females
committing only conventional offenses.
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 28
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
Table 3: Independent T-tests for Females Only
Hybrid (n=514)
Conventional (n=448)
T-test
Measure
Mean
SD
Mean
SD
T-value
Cohen's D
Age
15.68
1.272
14.90
1.698
-8.029***
-0.519
Risk to Re-offend
2.7977
1.15213
2.0335
1.16098
-10.225***
-0.662
Race (0=white)
.5292
.49963
.6094
.48844
2.509*
0.162
Income
1.9319
.98192
2.0647
.87868
2.214*
0.143
Academic Achievement
2.4202
1.44640
2.3371
1.21521
-0.958
Witnessed Violence
.7568
.42943
.6719
.47006
-2.910**
-0.188
Sexual Abuse
.1362
.34332
.1228
.32854
-0.617
Physical Abuse
.3482
.47688
.2344
.42408
-3.920***
-0.254
Child Welfare Placement
.6556
1.04485
.3571
.78984
-5.034***
-0.326
Neglect
.2082
.40640
.1250
.33109
-3.496***
-0.226
Anger
1.3132
.93510
1.3058
.98660
-0.12
Trauma
.4805
.70130
.3750
.66723
-2.390*
-0.155
Depression
.8852
.81915
.7813
.84154
-1.939
Mental Health Problems
.4105
.49240
.2522
.43478
-5.294***
-0.343
Drug History
1.0953
.76003
.8170
.75824
-5.673***
-0.367
Current Drug Use
.4572
.75129
.3036
.64630
-3.409**
-0.221
Current Alcohol Use
.2043
.50253
.1317
.41566
-2.451*
-0.159
Note: *=p<.05; **=p<.01; ***=p<.001
Similar results emerge when examining males
only (see Table 4). In the male only sample
(n=3,166), hybrid offense delinquents were older, had
a higher risk to reoffend score, were composed of
more non-Whites, had lower academic achievement,
were more likely to witness abuse, to have child
welfare placements, and to be neglected than those
who
committed only conventional offenses. Additionally,
male hybrid offenders evidenced higher levels of
anger, trauma, depression, mental health problems,
drug history, and current drug use. Essentially, males
who commit hybrid offenses evidenced more risk
across domains than males who were
referred/arrested for a conventional offense.
Table 4: Independent T-tests for Males Only
Hybrid (n=1,550)
Conventional (n=1,616)
T-test
Measure
Mean
SD
Mean
SD
T-value
Cohen's D
Age
15.69
1.299
14.91
1.825
-13.900***
-0.494
Risk to Re-offend
3.0535
1.01460
2.3125
1.15734
-19.179***
-0.682
Race (0=white)
.7200
.44914
.6411
.47983
-4.779***
-0.17
Income
2.0052
.89398
1.9913
.87053
-0.441
Academic Achievement
2.5826
1.38070
2.3812
1.30282
-4.217***
-0.15
Witnessed Violence
.6877
.46356
.6176
.48613
-4.157***
-0.148
29 SCHOEPFER, BAGLIVIO, & SCHWARTZ
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
Sexual Abuse
.0200
.14005
.0266
.16099
1.234
Physical Abuse
.1290
.33534
.1281
.33430
-0.079
Child Welfare Placement
.3084
.70897
.2246
.62431
-3.522***
-0.125
Neglect
.1000
.30010
.0792
.27015
-2.046*
-0.073
Anger
1.0703
.86139
.9171
.88289
-4.943***
-0.176
Trauma
.2161
.50981
.1770
.45706
-2.272*
-0.081
Depression
.4845
.67374
.4270
.64380
-2.455*
-0.087
Mental Health Problems
.2194
.41394
.1900
.39240
-2.048*
-0.073
Drug History
1.1381
.69697
.9226
.74374
-8.413***
-0.299
Current Drug Use
.4948
.75777
.4016
.70573
-3.579***
-0.127
Current Alcohol Use
.1555
.43679
.1269
.40507
-1.91
Note: *=p<.05; **=p<.01; ***=p<.001
Theoretical Indicators
Following Pontell and Rossoff’s (2009)
theoretical piece, the next step was to analyze
theoretical explanations of the different crime types.
Due to data constraints, we were limited in our
theoretical operationalization. Table 5 presents the
binomial logistic regression when examining the
referral sample to predict offense type (hybrid
offense=1). As shown, non-White youth and older
youth were always more likely to be hybrid
offenders. Females were more likely to engage in
hybrid offenses in Models 1 and 2 (general theory of
crime model and social control model, respectively).
Among the self-control measures, hybrid offenders
were 1.232 times more likely to receive
inappropriate/inadequate or inconsistent punishment
from their parents for antisocial behavior and
were 1.236 times more likely to be impulsive than the
conventional offenders. Among social control items
(Model 2), hybrid offenders had more involvement in
school activities but were more likely to believe laws
did not apply to them. After controlling for mental
health, the self-control scale did not attain
significance in Model 3. Yet, when the mental health
scale was removed from the model (results not
shown), self-control did attain significance indicating
that hybrid offenders evidenced less social control.
Model 3 indicates that hybrid crime offenders were
1.057 times more likely to have mental health issues
than the conventional crime offenders.
Table 5: Binominal Logistic Regressions for Referral Sample Predicting Offense Type (0=conventional; 1=hybrid)
Model 1 - General Theory
Model 2 - Social Control
Model 3 - Full Model
Measure
B / O.R.
C.I.
B / O.R.
C.I.
B / O.R.
C.I.
Punish
0.209** / 1.232
(1.07-1.42)
Impulse
0.212*** / 1.236
(1.13-1.36)
Frustration
-0.031 / 0.969
(0.86-1.09)
Physical Aggression
0.060 / 1.061
(0.98-1.15)
Goals
-0.070 / 0.933
(0.83-1.05)
Low Self-Control Scale
0.028 / 1.029
(1.00-1.06)
Education Value
0.133 / 1.142
(0.99-1.31)
Involved in School
Activities
-0.088** / 0.916
(0.86-0.98)
Respect for Authority
0.098 / 1.103
(0.99-1.23)
Community Ties
0.019 / 1.019
(0.92-1.13)
Belief Laws Apply
0.282*** / 1.326
(1.20-1.47)
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 30
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
Social Control Scale
0.028* / 1.029
(1.00-1.06)
Delinquent Peers
0.022 / 1.023
(0.89-1.17)
Mental Health Scale
0.055*** / 1.057
(1.04-1.07)
Race (White=0)
0.217** / 1.243
(1.09-1.42)
0.213** / 1.238
(1.01-1.42)
0.298*** / 1.347
(1.17-1.55)
Gender (Female=0)
-0.194* / 0.823
(0.71-0.96)
-0.210** / 0.810
(0.70-0.94)
-0.048 / 0.953
(0.82-1.12)
Age
0.333*** / 1.395
(1.34-1.46)
0.323*** / 1.382
(1.32-1.44)
0.349*** / 1.417
(1.36-1.48)
Constant
-5.379*** / 0.005
-5.093*** / 0.006
-5.518*** / 0.004
Nagelkerke R
2
0.098
0.104
0.112
Note: B = Beta, O.R.= odds ratio, C.I.= 95% confidence interval; *=p<.05; **=p<.01; ***=p<.001
Gender Differences
Table 6 examines the theoretical indicators in the
prediction of male hybrid offending.
Inappropriate/inconsistent punishment by parents of
delinquent behavior (O.R. 1.257), impulsivity (O.R.
1.235), belief in the use of physical aggression (O.R.
1.112), and goal setting (O.R. 0.869) significantly
predicted male hybrid offending, with those hybrid
offenders having more risk in each area. Male hybrid
offenders were older and more likely to be non-
White, which holds across all models. Among social
control
measures, only belief that laws apply to them was
significant, with hybrid offending males having
stronger beliefs that laws do not apply to them (O.R.
1.353). Model 3 highlights both the self-control scale
(O.R. 1.042) and the social control scale (O.R.
1.043), with hybrid offense youth scoring higher risk
on each scale. Additionally, male hybrid offenders
scored significantly higher on the mental health index
(O.R. 1.052) than conventional offenders. The
delinquent peers variable did not attain significance.
Table 6: Binominal Logistic Regressions for Males Only Predicting Offense Type (0=conventional; 1=hybrid)
Model 1 - General Theory
Model 2 - Social Control
Model 3 - Full Model
Measure
B / O.R.
C.I.
B / O.R.
C.I.
B / O.R.
C.I.
Punish
0.299** / 1.257
(1.07-1.48)
Impulse
0.211*** / 1.235
(1.11-1.37)
Frustration
0.015 / 1.015
(0.89-1.16)
Physical Aggression
0.106* / 1.112
(1.01-1.22)
Goals
-0.140* / 0.869
(0.76-1.00)
Low Self-Control Scale
0.042* / 1.042
(1.01-1.08)
Education Value
0.134 / 1.144
(0.98-1.34)
Involved in School
Activities
-0.04 / 0.961
(0.89-1.04)
Respect for Authority
0.093 / 1.098
(0.97-1.24)
Community Ties
0.057 / 1.058
(0.94-1.20)
Belief Laws Apply
0.302*** / 1.353
(1.20-1.52)
Social Control Scale
0.042** / 1.043
(1.01-1.08)
Delinquent Peers
-0.071 / 0.93
(0.80-1.09)
Mental Health Scale
0.051*** / 1.052
(1.03-1.07)
Race (White=0)
0.380*** / 1.462
(1.25-1.71)
0.367*** / 1.443
(1.23-1.69)
0.454*** / 1.575
(1.34-1.85)
Age
0.327*** / 1.386
(1.32-1.46)
0.319*** / 1.376
(1.31-1.45)
0.343*** / 1.410
(1.34-1.48)
Constant
-5.607 / 0.004
-5.455 / 0.004
-5.538*** / 0.004
31 SCHOEPFER, BAGLIVIO, & SCHWARTZ
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
Nagelkerke R
2
0.104
0.108
0.115
Note: B = Beta, O.R.= odds ratio, C.I.= 95% confidence interval; *=p<.05; **=p<.01; ***=p<.001
Next, the prediction of hybrid juvenile offending
for females was examined (see Table 7). For self-
control measures, only impulse control was
significant, with hybrid offenders scoring 1.234 times
higher on the impulsivity measure than the
conventional offenders. Additionally, age was
statistically significant indicating that female hybrid
offenders were older, but race was not a statistically
significant predictor of crime type, findings which
hold true across all models. Among social control
measures, hybrid delinquency females were more
involved in school activities. Examining our
theoretical indices, neither the self-control scale nor
the social control scale predicted female hybrid
offending. However, female hybrid delinquents were
significantly more likely to have gang-related or
antisocial peer associations (O.R. 1.354) and scored
higher on the mental health problems index (O.R.
1.069) than conventional female offenders.
Table 7: Binominal Logistic Regressions for Females Only Predicting Offense Type (0=conventional; 1=hybrid)
Model 1 - General Theory
Model 2 - Social Control
Model 3 - Full Model
Measure
B / O.R.
C.I.
B / O.R.
C.I.
B / O.R.
C.I.
Punish
0.139 / 1.149
(0.86-1.54)
Impulse
0.201* / 1.234
(1.02-1.49)
Frustration
-0.171 / 0.843
(0.67-1.07)
Physical Aggression
-0.061 / 0.477
(0.79-1.11)
Goals
0.165 / 1.179
(0.92-1.52)
Low Self-Control Scale
-0.007 / 0.993
(0.94-1.05)
Education Value
0.057 / 1.058
(0.78-1.43)
Involved in School
Activities
-0.240*** / 0.786
(0.69-0.90)
Respect for Authority
0.142 / 1.152
(0.94-1.41)
Community Ties
-0.093 / 0.911
(0.73-1.13)
Belief Laws Apply
0.201 / 1.222
(0.99-1.50)
Social Control Scale
-0.019 / 0.981
(0.90-1.04)
Delinquent Peers
0.303* / 1.354
(1.02-1.80)
Mental Health Scale
0.066*** / 1.069
(1.04-1.10)
Race (White=0)
-0.243 / 0.784
(0.60-1.03)
-0.225 / 0.799
(0.61-1.05)
-0.151 / 0.86
(0.66-1.13)
Age
0.348*** / 1.416
(1.29-1.55)
0.325*** / 1.384
(1.26-1.52)
0.354*** / 1.425
(1.30-1.57)
Constant
-5.282*** / 0.005
-4.494*** / 0.011
-5.511*** / 0.004
Nagelkerke R
2
0.104
0.123
0.133
Conclusion/Discussion
The results from the t-tests indicate that there are
significant differences between hybrid white-collar
and conventional crime delinquents in our sample.
While both types of delinquents did report negative
life events (e.g., abuse, neglect, anger, depression,
etc.), the hybrid delinquents appeared to have more
of these problems than the conventional crime
delinquents. This fact alone is interesting in regards
to the overall white-collar crime literature. We tend
to assume that white-collar offenders do not have the
typical negative life events that are relevant for
conventional offenders, but research has not yet
examined the early lives of adult white-collar
offenders, and therefore, these assumptions are
anecdotal only and have no basis in the literature (for
an exception on early life factors and workplace
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 32
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
deviance, see Piquero & Moffitt, 2014). Future
research should compare white-collar delinquents,
conventional delinquents, and non-offenders to see if
there is a range of negative life events associated with
the different categories of offending and non-
offending. Our results are consistent with the
literature on juvenile offenders with respect to
experiencing traumatic life events (Baer & Maschi
2003; Baglivio et al., 2014; Dixon et al., 2004; Jensen
et al., 2001; Martin et al., 2008). However, we did
not expect to find that hybrid delinquents had more
serious traumas and mental health problems. The
argument that the conventional offenders in our
sample were committing offenses akin to “kids being
kids” is underscored by the use of the full PACT
assessment that is generally restricted to the more
serious offenders. Our results suggest that the hybrid
delinquency youth have more negative/traumatic life
experiences than the conventional crime youth. This
is intriguing given that the hybrid crimes are largely
instrumental crimes, or crimes of deception, whereas
the conventional crimes are more so crimes of force
and/or expression. Perhaps it is the negative life
events that have lead these juveniles to engage in
these instrumental crimes as a way to take care of
themselves. Future research should examine the
motivations behind the different juvenile offenses.
The prevalence rate of ever being arrested for a
hybrid offense among juvenile offenders in the state
of Florida during the 2008-2014 data collection
period is 1.7% (3,612/211,889=.017), which has
heretofore been unestablished. In terms of gender
difference, of the adjudicated females in the sample
(n=301), 60.5% were adjudicated for committing a
hybrid delinquency as compared to 55.2% of males
(n=1,026 adjudicated males). While males are still
committing more crimes overall, females are
committing a higher relative proportion of hybrid
delinquencies than males, which is consistent with
the adult white-collar crime literature that suggests
females are increasing their participation in these
crime types (Dodge, 2009). This may be due to the
individualistic and non-confrontational nature of the
white-collar offenses measured in this study.
Paternalism of the system when dealing with female
criminality may provide an additional explanation as
well; females tend to be treated more harshly than
males when it comes to status offenses (Barrett,
Katsiyannis, & Zhang, 2006; Chesney-Lind, 2004;
Rhodes & Fischer, 1993), and this could be occurring
for the crimes under current investigation as well.
As measured, the theoretical implications vary
by gender. Among males, hybrid delinquents had
lower self-control and lower social control scores
than conventional offenders. Among females, hybrid
delinquents did not differ from conventional
offenders on the self-control or social control scales;
they were, however, more likely to have gang-related
or antisocial peers. This finding is consistent with
research that suggests that females tend to carry out
crimes with others (Koons-Witt & Schram, 2003;
Van Mastrigt & Farrington, 2009). Although these
frauds are generally seen as individualistic and non-
confrontational in nature, females may still be
influenced to engage in these crimes by their peers.
Future research should measure concurrent offending
among these types of frauds. Additionally, the
females in the sample that engaged in hybrid white-
collar crimes reported significantly more involvement
with prosocial school activities than the conventional
offenders.
Our results suggest that while males may be
attracted to different types of offending somewhat
equally, there appears to be different factors that may
attract females to the different crime types. This is
very important as the majority of what we know
about offending behaviors comes from samples of
male offenders. If females are increasing their
criminal participation, we need to acknowledge the
potential differences as this preliminary investigation
indicates that females are indeed different from males
when it comes to hybrid white-collar and
conventional offending.
Another important issue concerning our juvenile
sample is the age at which they are starting and the
possible length of their offending careers. Benson
and Kerley (2000) found that the average age for
white-collar offenders with prior records was 24
compared to 19 for the typical street offender.
Weisburd and Waring (2001) found that repeat white-
collar offenders in their study had longer offending
careers than the typical street offender. Taking this
information into account, the white-collar delinquents
in our sample are offending earlier and may very well
have longer offending careers than their conventional
offender counterparts (assuming they continue to
offend). It is important to note that life-course
research tends to suggest that offenders do not always
specialize in one crime type (see Piquero, Farrington,
& Blumstein, 2003), and that if specialization occurs,
it tends to happen later in adult life (Nieuwbeerta,
Blokland, Piquero, & Sweeten, 2011). The 3,612
hybrid-delinquent youth in our sample were
responsible for 35,685 individual charges total,
indicating that they may not necessarily specialize in
only hybrid offenses.
This study was not without limitations. First, our
sample consisted of juvenile offenders thus
prohibiting us from making comparisons to non-
offenders. Yet, due to the breadth of the data
utilized, we were able to examine all juvenile
offenders with a hybrid white-collar crime as the
33 SCHOEPFER, BAGLIVIO, & SCHWARTZ
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
most serious charge in the state of Florida. Second,
the use of secondary data that was not designed to
test for criminological theories also limited our
research. In addition, theories are typically used to
predict offending, whereas we utilized the theories to
examine their predictive power among different
crime types. If comparing offenders and non-
offenders, our theory results would very likely be
different. Another limitation lies with our crime
types; we did not examine traditional white-collar
crimes, but rather the hybrid forms of white-collar
crime. Regardless, our results still show significant
differences between these hybrid white-collar and
conventional offenders. If arguments that these
hybrid crimes were really just a form of conventional
crime, we would expect to find fewer differences
among the offenders, yet the results suggest that
differences between the two groups do exist. We
cannot expect juveniles to engage in traditional types
of white-collar offending due to their marginalized
status and inexperience in the workforce, but future
research should examine if differences exist among
adult conventional, white-collar, and hybrid
offenders.
Future research should also take a life-course
approach with these juveniles to examine several
facets. First, it would be interesting to see if those
who were referred but not adjudicated for white-
collar delinquency might be encouraged to offend
again. Research has suggested that cheating in
school can lead to cheating in the corporate world
(Sims, 1993). Being referred for a white-collar crime
and not being adjudicated may very well translate to
later offending if the individual internalizes the idea
that they can “get away with it.”
Although the current study suffers from some
distinct data limitations, it is the first to examine such
a large group of juveniles engaged in crimes that
have historically been classified as hybrid white-
collar crime. Overall, we found significant
differences among the hybrid white-collar and
conventional crime samples. This further emphasizes
the idea that these types of white-collar crimes are
indeed different from conventional crimes and
different explanations may be needed to fully
understand this phenomenon. The premise of white-
collar delinquency as set forth by Pontell and Rosoff
(2009) is that juveniles now possess the ability to
engage in sophisticated and elaborate criminal acts
that were formerly only committed by adults. Such
offenses become even more complex when
committed by a minor. White collar delinquency
raises issues that have not been previously examined,
which further exemplifies the need for more
empirical study.
References
Andrews, B.A., & Bonta, J. (2003). The psychology
of criminal conduct (3
rd
ed.). Cincinnati, OH:
Anderson.
Allison, S., Schuck, A., & Lersch, K. M. (2005).
Exploring the crime of identity theft: Prevalence,
clearance rates, and victim/offender
characteristics. Journal of Criminal Justice, 33,
19–29. doi:10.1016/j.jcrimjus.2004.10.007.
Alltucker, K., Bullis, M., Close, D., & Yovanoff, P.
(2006). Different pathways to juvenile
delinquency: Characteristics of early and late
starters in a sample of previously incarcerated
youth. Journal of Child and Family Studies, 15,
479–492. doi:10.1007/s10826-006-9032-2.
Baer, J., & Maschi, T. (2003). Random acts of
delinquency: Trauma and self-destructiveness in
juvenile offenders. Child and Adolescent Social
Work Journal, 20, 85–98. doi:
10.1023/A:1022812630174
Baglivio, M. T. (2009). The assessment of risk to
recidivate among a juvenile population. Journal
of Criminal Justice, 37, 596–607. doi:
10.1016/j.jcrimjus.2009.09.008.
Baglivio, M. T., Epps, N., Swartz, K., Sayedul Huq,
M., Sheer, A., & Hardt, N. (2014). The
prevalence of adverse childhood experiences
(ACE) in the lives of juvenile offenders. Journal
of Juvenile Justice, 3, 1–23.
Baglivio, M. T., & Jackowski, K. (2013). Examining
the validity of a juvenile offending risk
assessment instrument across gender and
race/ethnicity. Youth Violence and Juvenile
Justice, 11, 26–43. doi:
10.1177/1541204012440107
Baird, C., Healy, T., Johnson, K., Bogie, A., Dankert,
E. W., & Scharenbroch, C. (2013). A comparison
of risk assessment instruments in juvenile justice.
Madison, WI: National Council on Crime and
Delinquency.Retrieved from
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 34
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
https://www.nccdglobal.org/sites/default/files/pu
blication_pdf/nccd_fire_report.pdf.
Barnoski, R. (2004). Assessing risk for re-offense:
Validating the Washington State Juvenile Court
Assessment. Olympia, WA: Washington State
Institute for Public Policy.
Barrett, D.E., Katsiyannis, A., & Zhang, D. (2006).
Predictors of offense severity, prosecution,
incarceration and repeat violations for adolescent
male and female offenders. Journal of Child and
Family Studies, 15, 709–719. doi:
10.1007/s10826-006-9044-y.
Benson, M. L., & Kerley, K. R. (2000). Life course
theory and white-collar crime. In H. N. Pontell &
D. Shichor (Eds.), Contemporary issues in crime
and criminal justice: Essays in honor of Gilbert
Geis (pp.121-136). Upper Saddle River, NJ:
Prentice Hall.
Benson, M. L., & Moore, E. (1992). Are white-collar
and common offenders the same? An empirical
and theoretical critique of a recently proposed
general theory of crime. Journal of Research in
Crime and Delinquency, 29, 251–272. doi:
10.1177/0022427892029003001
Bowker, A. L. (1999). Juveniles and computers:
Should we be concerned? Federal Probation, 63,
40–44.
Burgess, R., & Akers, R. (1966). A differential
association-reinforcement theory of criminal
behavior. Social Problems, 14, 363–383.
doi:10.2307/798612
Calhoun, G., Glaser, B., & Bartolomucci, C. (2001).
The juvenile counseling and assessment model
and program: A conceptualization and
intervention for juvenile delinquency. Journal of
Counseling & Development, 79, 131–141. doi:
10.1002/j.1556-6676.2001.tb01952.x
Cauffman, E. (2008). Understanding the female
offender. The Future of Children, 18, 119–142.
doi: 10.1353/foc.0.0015
Chesney-Lind, M. (2001). What about the girls?
Delinquency programming as if gender mattered.
Corrections Today, 63, 38–45.
Chesney-Lind, M. (2004). Girls and violence: Is the
gender gap closing? (Applied Research Forum).
Retrieved from Violence Against Women Net, a
project of the National Resource Center on
Domestic Violence, website:
http://www.vawnet.org
Copes, H., & Vieraitis, L. (2009). Understanding
identity theft: Offenders’ accounts of their lives
and crimes. Criminal Justice Review, 34, 329–
349. doi: 10.1177/0734016808330589
Copes, H., & Vieraitis, L. (2012). Identity thieves
motives and methods. Boston, MA: Northeastern
University Press.
Corbitt, W. A. (2000). Violent crimes among
juveniles: Behavioral aspects. FBI Law
Enforcement Bulletin, June 2000, 18–21.
Dixon, A., Howie, P., & Starling, J. (2004).
Psychopathology in female juvenile offenders.
Journal of Child Psychology and Psychiatry, 46,
789–806.
doi: 10.1111/j.1469-7610.2004.00307.x.
Dodge, M. (2009). Women and white-collar crime.
Upper Saddle River: NJ. Pearson Education, Inc.
Edelhertz, H. (1970). The nature, impact, and
prosecution of white-collar crime. Washington,
DC: U.S. Government Printing Office.
Federal Trade Commission. (2014). Consumer
Sentinel Network Data Book for January –
December 2013. Washington DC, February
2014. Retrieved from
https://www.ftc.gov/system/files/documents/repo
rts/consumer-sentinel-network-data-book-
january-december-2013/sentinel-cy2013.pdf
Friedrichs, D. (2007). Trusted criminals: White-
collar crime in contemporary society. Belmont,
CA: Thomson.
Geis, G. (1992). White-collar crime: What is it? In K.
Schlegel & D. Weisburd (Eds.), White-collar
crime reconsidered (pp. 31–52). Boston, MA:
Northwestern University Press.
Gottfredson, M.R., & Hirschi, T. (1990). A general
theory of crime. Stanford, CA: Stanford
University Press.
35 SCHOEPFER, BAGLIVIO, & SCHWARTZ
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
Green, S. (2004). The concept of white collar crime
in law and legal theory. Buffalo Law Review, 8,
101–134. doi: 10.1525/nclr.2004.8.1.1
Grisso, T. (2008). Adolescent offenders with mental
disorders. The Future of Children, 18, 143–164.
doi: 10.1353/foc.0.0016
Hirschi, T. (1969). Causes of delinquency. Berkeley:
University of California Press.
Hirschi, T., & Gottfredson, M. (1989). The
significance of white-collar crime for a general
theory of crime. Criminology, 27, 359–371. doi:
10.1111/j.1745-9125.1989.tb01037.x
Jensen, J., Potter, C., & Howard, M. (2001).
American juvenile justice: Recent trends and
issues in youth offending. Social Policy &
Administration, 35, 48–68. doi: 10.1111/1467-
9515.00219
Koons-Witt, B., & Schram, P. (2003). The
prevalence and nature of violent offending by
females. Journal of Criminal Justice, 31, 361–
371. doi: 10.1016/S0047-2352(03)00028-X.
Lauritsen, J. L., & Heimer, K. (2008). The gender
gap in violent victimization, 1973-2004. Journal
of Quantitative Criminology, 24, 125–147. doi:
10.1007/s10940-008-9041-y.
Lenssen, S., Doreleijers, T., van Dijk, M., &
Hartman, C. (2000). Girls in detention: What are
their characteristics? A project to explore and
document the character of this target group and
the significant ways in which it differs from one
consisting of boys. Journal of Adolescence, 23,
287–303. doi: 10.1006/jado.2000.0315
Mann, E., & Reynolds, A. (2006). Early intervention
and juvenile delinquency prevention: Evidence
from the Chicago Longitudinal Study. Social
Work Research, 30, 153–167. doi:
10.1093/swr/30.3.153
Martin, D., Martin, M., Dell, R., Davis, C., &
Guerrieri, K. (2008). Profile of incarcerated
juveniles: Comparison of male and female
offenders. Adolescence, 43, 607–622.
The Mentor. (1986). The conscience of a hacker
a.k.a. “The hacker’s manifesto.” Phrack, 1(7).
Retrieved from
http://www.phrack.org/issues/7/3.html#article
Mullis, R., Cornille, T., Millis, A., & Huber, J.
(2004). Female juvenile offending: A review of
characteristics and contexts. Journal of Child
and Family Studies, 13, 205–218. doi:
10.1023/B:JCFS.0000015708.71295.2a
Nieuwbeerta, P., Blokland, A., Piquero, A., &
Sweeten, G. (2011). A life-course analysis of
offense specialization across age: Introducing a
new method for studying individual
specialization over the life course. Crime &
Delinquency, 57, 3–28. doi:
10.1177/0011128710376336.
Onwuegbuzie, A. J., Daley, C. E., & Waytowich, V.
L. (2008). A mixed methods investigation of
male juvenile delinquents’ attributions toward
violence. At Risk Issues, 14, 1–11.
Piquero, A., Farrington, D., & Blumstein, A. (2003).
The criminal career paradigm. In M. Tonry (Ed.),
Crime and justice: A review of research (Vol.
31, pp. 359–506). Chicago, IL: University of
Chicago Press.
Piquero, N.L., & Moffitt, T. (2014). Can childhood
factors predict workplace deviance? Justice
Quarterly, 31, 664–692. doi:
10.1080/07418825.2012.661446.
Piquero, N.L., Schoepfer, A., & Langton, L. (2010).
Completely out of control or the desire to be in
complete control? An examination of low self-
control and the desire-for-control. Crime and
Delinquency, 56, 627–647. doi:
10.1177/0011128708325052.
Piquero, N. L., Tibbetts, S., & Blankenship, M.
(2005). Examining the role of differential
association and techniques of neutralization in
explaining corporate crime. Deviant Behavior,
226, 159–188.
doi:10.1080/01639620590881930.
Pontell, H. N., & Rosoff, S. M. (2009). White-collar
delinquency. Crime, Law and Social Change,
51, 147–162. doi:10.1007/s10611-008-9146-0
Pratt, T. C., & Cullen, F. T. (2000). The empirical
status of Gottfredson and Hirschi’s general
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 36
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
theory of crime: A meta-analysis. Criminology,
38, 931–964. doi:10.1111/j.1745-
9125.2000.tb00911.x
Putkonen, H., Weizmann-Henelius, G., Lindberg, N.,
Rovamo, T., & Hakkanen, H. (2009). Changes
over time in homicides by women: A register-
based study comparing female offenders from
1982 to 1992 and 1993 to 2005. Criminal
Behavior & Mental Health, 18, 268–278. doi:
10.1002/cbm.711
Rhodes, J. E., & Fischer, K. (1993). Spanning the
gender gap: Gender differences in delinquency
among inner-city adolescents. Adolescence, 28,
879–889.
Ruggiero, M., Greenberger, E., & Steinberg, L. D.
(1982). Occupational deviance among adolescent
workers. Youth and Society, 13, 423–448. doi:
10.1177/0044118x82013004003
Schoepfer, A., Piquero, N. L., & Langton, L. (2014).
Low self-control versus the desire-for-control:
An empirical test of white-collar crime and
conventional crime. Deviant Behavior, 35, 197–
214. doi:10.1080/01639625.2013.834758.
Schwartz, J., & Rookey, B.D. (2008). The narrowing
gender gap in arrests: Assessing competing
explanations using self-report, traffic fatality,
and official data on drunk driving, 1980-2004.
Criminology, 46, 637–671. doi:10.1111/j.1745-
9125.2008.00121.x.
Shapiro, S. (1990). Collaring the crime, not the
criminal: Reconsidering the concept of white-
collar crime. American Sociological
Association, 55, 346–365. doi:10.2307/2095761
Shover, N., Coffey, G., & Hobbs, D. (2003). Crime
on the line: Telemarketing and the changing
nature of professional crime. British Journal of
Criminology, 43, 489−505. doi:
10.1093/bjc/azg489
Simpson, S. S., & Piquero, N. L. (2002). Low self-
control, organizational theory, and corporate
crime. Law and Society Review, 36, 509–548.
doi: 10.2307/1512161
Sims, R. (1993). The relationship between academic
dishonesty and unethical business practices.
Journal of Education for Business, 68, 207–211.
doi: 10.1080/08832323.1993.10117614
Smith, P., & Smith, W. (2005). Experiencing
community through the eyes of young female
offenders. Journal of Contemporary Criminal
Justice, 21, 364–385. doi:
10.1177/1043986205281722.
Snyder, H., & Sickmund, M. (2006). Juvenile
offenders and victims: 2006 National Report,
Chapter 3: Juvenile offenders. Washington, DC:
U.S. Department of Justice, Office of Justice
Programs, Office of Juvenile Justice and
Delinquency Prevention. Retrieved from
https://www.ojjdp.gov/ojstatbb/nr2006/download
s/NR2006.pdf
Steffensmeier, D., Schwartz, J., Zhong, H., &
Ackerman, J. (2005). An assessment of recent
trends in girls’ violence using diverse
longitudinal sources: Is the gender gap closing?
Criminology, 43, 355–406. doi: 10.1111/j.0011-
1348.2005.00011.x.
Stouthamer-Loeber, M., Loeber, R., Wei, E.,
Farrington, D., & Wikstrom, P. (2002). Risk and
promotive effects in the explanation of persistent
serious delinquency in boys. Journal of
Consulting and Clinical Psychology, 70, 111–
123. doi: 10.1037//0022-006x.70.1.111
Sutherland, E. H. (1940). White collar criminality.
American Sociological Review, 5, 1–12. doi:
10.2307/2083937
Sykes, G., & Matza, D. (1957). Techniques of
neutralization: A theory of delinquency.
American Sociological Review, 22, 664–670.
doi: 10.2307/2089195
Teicher, S. (2004, March 15). Do female execs have
cleaner hands? Christian Science Monitor.
Retrieved from http://csmonitor.com
Tittle, C. R., & Meier, R. F. (1991). Specifying the
SES/delinquency relationship by social
characteristics of contexts. Journal of Research
in Crime and Delinquency, 28, 430–455. doi:
10.1177/0022427891028004005
Van der Put, C. E., Stams, G. J .J. M., Dekovic, M.,
& van der Laan, P.H. (2012). Predictive validity
of the Washington state juvenile court pre-screen
37 SCHOEPFER, BAGLIVIO, & SCHWARTZ
Corresponding author: Andrea Schoepfer, Department of Criminal Justice, California State University – San Bernardino, San
Bernardino, CA, 92407, USA.
Email: aschoepf@csusb.edu
assessment in the Netherlands: The development
of a new scoring system. Assessment, 21, 92–
107. doi:1073191112436666.
Van Mastrigt, S., & Farrington, D. (2009). Co-
offending, age, gender, and crime type:
Implications for criminal justice policy. British
Journal of Criminology, 49, 552–573.
doi:10.1093/bjc/azp021.
Verton, D. (2002). The hacker diaries: Confessions
of teenage hackers, Berkeley, CA: McGraw-
Hill/Osborne.
Washington State Institute for Public Policy. (2004).
Assessing risk for re-offense: validating the
Washington State Juvenile Court assessment.
Olympia, WA. Retrieved from
http://www.wsipp.wa.gov/ReportFile/868
Weisburd, D., & Waring, E. (2001). White-collar
crime and criminal careers. New York, NY:
Cambridge University Press.
Weisburd, D., Wheeler, S., Waring, E., & Bode, N.
(1991). Crimes of the middle classes: White-
collar offenders in the federal courts. New
Haven, CT: Yale University Press.
Winokur-Early, K., Hand, G. A., & Blankenship, J.
L. (2012). Validity and reliability of the Florida
Positive Achievement Change Tool (PACT) risk
and needs assessment instrument: A three-phase
evaluation (validation study, factor analysis,
inter-rater reliability). Tallahassee, FL: Justice
Research Center.
Wolff, K., Baglivio, M., & Piquero, A. (2015). The
relationship between adverse childhood
experiences and recidivism in a sample of
juvenile offenders in community-based
treatment. International Journal of Offender
Therapy and Comparative Criminology.
Advanced online publication.
doi:10.1177/0306624X15613992.
Wright, J. P., & Cullen, F. T. (2000). Juvenile
involvement in occupational delinquency.
Criminology, 38, 863–896. doi: 10.1111/j.1745-
9125.2000.tb00909.x
Yar, M. (2005). Computer hacking: Just another case
of juvenile delinquency? The Howard Journal
of Criminal Justice, 44, 387–399. doi:
10.1111/j.1468-2311.2005.00383.x.
About the Authors
Andrea Schoepfer received her Ph.D. in
Criminology, Law & Society from the University of
Florida and is currently an Associate Professor at
California State University, San Bernardino. Her
research interests have centered around the study of
white-collar crime offending, victimization,
perceptions, and the application of criminological
theories to crime and deviance. She has published
numerous articles in top-tier criminology and
criminal justice journals including the Journal of
Criminal Justice, Crime & Delinquency, and Deviant
Behavior, and various other peer reviewed
publications.
Michael Baglivio is currently the Director of
Research and Program Development for G4S Youth
Services, responsible for performance measurement
and Program evaluation. For the past ten years
Michael has evaluated juvenile justice reforms in
Florida. His recent work has appeared in the Journal
of Criminal Justice, Journal of Forensic Psychiatry
and Psychology, and the Journal of Youth and
Adolescence, among other peer reviewed
publications.
Joseph Schwartz received his Ph.D. in
Criminology & Criminal Justice from Florida State
University and is currently an Assistant Professor at
the University of Nebraska, Omaha. His research
interests have centered around the study of biosocial
criminology, life-course studies, and theories of
crime and offending. He has published numerous
articles in top-tier journals including Justice
Quarterly, Criminology, and the Journal of Criminal
Justice.
JUVENILE HYBRID WHITE-COLLAR DELINQUENCY 38
Criminology, Criminal Justice, Law & Society – Volume 18, Issue 2
Endnotes
1
The authors acknowledge the controversies surrounding the definition of white-collar crime that have been
discussed at length elsewhere (see, for example, Friedrichs, 2007; Geis, 1992; Green, 2004; Shapiro, 1990).
Due to space limitations, we do not go into detail in the current paper.
2
It is important to note that not all researchers agree with the narrowing gender gap argument. Some argue that
the increase in female offending (in conventional crime) may be due to changes in definitions of violence,
changes in domestic violence policing, or changes in societal tolerance of female offending (Steffensmeier,
Schwartz, Zhong, & Ackerman, 2005; see also Chesney-Lind, 2001).
3
Empirical evaluations assessed the predictive validity of the overall risk to re-offend score of the
WSJCA/PACT, finding the risk level predicts subsequent recidivism (Baglivio, 2009; Baird et al., 2013;
Barnoski, 2004; van der Put, Stams, Dekovic, & van der Laan, 2012; Winokur-Early, Hand, & Blankenship,
2012). Results hold true for both male and female youth (Baglivio & Jackowski, 2013). PACT validation
studies specifically of youth referred to the Florida Department of Juvenile Justice have a cumulative sample
size in excess of 130,000. Reliability analyses of the PACT in Florida (Baird et al., 2013) used videotaped
interviews and an offense history file to assess reliability across raters, finding an intra-class correlation
coefficient (ICC) of .83 for the overall PACT risk level, and only 4% of items (5 items) with less than 75%
agreement with an “expert” rater.
4
A referral is equivalent to an adult arrest and does not imply adjudication, though adjudication information is
also contained in the extracted official records. All youth in Florida who are “arrested” under the age of 18 enter
the juvenile justice system.
5
Of note, the random conventional delinquency youth were compared to the excluded conventional youth on all
30 measures employed in the current study. The randomly selected youth were equivalent on 28 measures
assessed, though were less impulsive and received more appropriate punishment from parents; however, both t-
values were under 2.5, and effect sizes were very small (results not shown for brevity). At a p=.05 one would
expect two measures to differ significantly.
6
As this is an exploratory study utilizing a previously unexamined data source, the authors chose to combine all
relevant fraud and white-collar-type crime categories into one category, hybrid white-collar crime. We chose to
dichotomize this variable with conventional crimes to simplify the initial investigation of whether or not there
were significant differences between hybrid and conventional crimes. Future research should categorize the
crimes more specifically (e.g., violent, property, fraud, computer crime, identity theft, etc.) in efforts to identify
more accurate causes of specific crime categories.
7
Due to space limitations, the results of the bivariate correlations are available upon request. It is important to
note, though, that none of the variables exceeded correlations of over 0.455.
8
T-test tables comparing referrals versus adjudication for hybrid offenders and conventional offenders are
available upon request.