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Journal of Forensic Psychology Practice, 10:373–402, 2010 Copyright © Taylor & Francis Group, LLC ISSN: 1522-8932 print/1522-9092 online DOI: 10.1080/15228932.2010.489846
AREA REVIEW
A Critical Examination of Research on the Psychological Profiles
of White-Collar Criminals
LAURIE RAGATZ, MA and WILLIAM FREMOUW, PhD Department of Psychology, West Virginia University, Morgantown, West Virginia
This article critically reviewed 16 studies on the demographic and psychological characteristics of white-collar criminals. Some of the more-supported findings imply white-collar offenders are older, Caucasian, employed, and have a high school diploma or higher education. They also tended to be low in conscientiousness, agreeableness, and self-control. The most prominent limitations of the reviewed studies were the lack of a uniform definition of white-collar crime, not controlling for extraneous variables, and failing to control for type I error. Future research needs to explore how female white-collar offenders may be unique from male white-collar offenders. Several psychological variables (e.g., criminal thinking, psychopathy, motivations) could be investigated to further treatment practices.
KEYWORDS white-collar criminals, personality, demographic variables, criminal thinking
Bernie Madoff recently disclosed that his firm, Bernard L. Madoff Investment Securities, was a fraud. Madoff had utilized a Ponzi scheme method to con- vince investors that they were profiting from their investments in his firm. Throughout his Ponzi scheme, he used the money invested by new investors to pay old investors. Madoff’s scheme unraveled when investors started to withdraw their funds and Madoff lacked the funds to pay the investors.
Address correspondence to Laurie Ragatz, Department of Psychology, West Virginia University, 53 Campus Drive, 1124 Life Sciences Building, PO Box 6040, Morgantown, WV 26506-6040, USA. E-mail: [email protected]
373
374 L. Ragatz and W. Fremouw
His elaborate Ponzi scheme has been estimated to cost investors as much as $50 billion (Arnold, 2009; Chernoff, 2009). Kenneth Lay and John and Timothy Rigas also represent publicized white-collar criminals whose actions have cost society billions (BBC News, 2006; Bull, 2004; CNN, 2005; Crawford, 2004; Friedrichs, 2007).
The costs of white-collar crime in the United States have been substan- tial and far-reaching. Estimates of the financial costs due to white-collar crime vary, with conservative estimates around $500 billion yearly and higher esti- mates reaching as much as $1 trillion annually (Friedrichs, 2007; Potter, 2002; Schlegel, 2000). Physical costs of white-collar crime include work-related injuries, illnesses, or deaths (Friedrichs). Victims of white-collar crime can bear psychological costs, with research demonstrating white-collar crime victims may be at an increased risk for developing depression and anxi- ety (Ganzini, McFarland, and Bloom, 1990; Sharp, Shreve-Neiger, Fremouw, Kane, & Hutton, 2004).
DEFINITIONS OF WHITE-COLLAR CRIME
Definitions used to define white-collar crime have varied. Edwin H. Sutherland is credited with initially defining white-collar crime (Friedrichs, 2007; Sutherland, 1940). He believed white-collar crime represented “crime committed by a person of respectability and high social status in the course of his occupation” (Sutherland, 1949, p. 9). Sutherland has been criticized for the vagueness of his definition of white-collar crime. However, Sutherland was most interested in developing his theory of differential association (which proposed that criminal behavior is learned from both verbal and nonverbal communication with criminal others) as an explanation of white- collar criminality rather than formulating a concrete definition of white-collar crime. Furthermore, Sutherland’s definition included social status as an important and necessary element of being a white-collar criminal (Friedrichs; Geis, 1991; Sutherland, 1940).
A definitional dispute has continued to this day over what is the most suitable definition for white-collar crime. Friedrichs (2007) stresses that part of the definitional problem may stem from the vast array of terms (e.g., occu- pational crime, corporate crime, governmental crime) used to refer to crimes that may or may not be white-collar. For instance, Clinard and Quinney (1973) asserted that the term white-collar crime should be replaced by the terms occupational crime and corporate crime. They defined occupational crime as “offenses committed by individuals for themselves in the course of their occupations and the offenses of employees against their employers” (p. 188). Corporate crime was described as “offenses committed by corporate officials for their corporation and the offenses of the corporation itself” (p. 188). Clinard and Quinney’s definition is unique because it does
Profile of White-Collar Criminals 375
not confine the offender to upper-class status; instead, the offender could be of any social status. Some of the most debated facets of the definition include who represents the culprit (individual or organization), perpetrator’s motives, socioeconomic status of the offender, and types of laws violated (criminal, civil, or administrative). Most scholars in this field have agreed that white-collar crime “occurs in a legitimate occupational context; is moti- vated by the objective of economic gain or occupational success; and is not characterized by direct, intentional violence” (Friedrichs, 2007, p. 4).
Coleman (2002) provided an organized discussion of white-collar crimes by dividing them into six categories: employee theft, embezzlement, computer crime, fraud and deception, conflict of interest, and bribery-and- corruption. Employee theft included stealing material items and private information from a business. Embezzlement involved employees’ taking money from others for themselves when the money had other purposes (e.g., investment in a company). Computer crimes were carried out via the computer and fell within three categories: theft (e.g., private company information), vandalism (e.g., computer viruses), or other illegal activities (e.g., selling illegal merchandise). Fraud and deception included such illegal activities as tax evasion, falsely advertising products, filing fake insurance claims, manipulating stock values, and consumer scams. Crimes within the conflict-of-interest category often involved fraud or bribery. An example would be if physician recommended an unnecessary patient treatment and in return received a large payment for the recommendation. The bribery- and-corruption category included crimes whereby individuals of various professions (e.g., judges, political officials) received payoffs to change polit- ical policies, eliminate punishment, advance product sales, or get access to secret company information (Coleman).
PREVALENCE OF WHITE-COLLAR CRIME
White-collar crime prevalence data have been frequently gathered from var- ious government organizations, media channels, or journals. This method of data collection is problematic because different coding methods and definitions are utilized across sources (Friedrichs, 2007). The 2007 white- collar crime data from the Federal Judiciary of the U. S. Courts showed there were 994 forgery, 10,678 fraud, and 565 embezzlement cases. Fraud offenses were broken down into 18 categories. The most prominent fraud convictions included conspiracies to defraud the United States (n = 2,195), identification or information fraud (n = 1,951), false statements (n = 811), mail fraud (n = 717), tax fraud (n = 615), wire or television fraud (n = 577), and health care fraud (n = 316). Embezzlement offenses were subdivided into the following categories: bank (n = 202), postal service (n = 173), financial institutions (n = 23), and other (n = 167; National White-Collar
376 L. Ragatz and W. Fremouw
Crime Center, 2008). These statistics substantially underestimate the preva- lence of white-collar crime because they included only criminals who were prosecuted and convicted in federal courts.
PURPOSE AND DESCRIPTION OF THIS CRITICAL REVIEW
Researchers disagree on who should be attributed culpability in white-collar crime cases, with some suggesting the individual and others inferring the organization (Friedrichs, 2007). Several scholars have explored the organi- zational factors that contribute to white-collar crime (e.g., Clinard & Yeager, 1980; Shover & Hochstetler, 2002); however, this critical review will focus only on the characteristics of the individual that have been associated with white-collar criminal behavior. This study will contribute to the existing lit- erature by critically reviewing research examining the demographic (e.g., gender, age, criminal versatility) and psychological attributes (e.g., personal- ity, criminal thinking styles) of white-collar criminals. This area of research lacks previous critical analysis and is limited in scope. Due to the dispar- ity in defining white-collar crime and the vast overlap in samples utilized across studies, we chose not to conduct a meta-analysis. It is the hope that this review may assist with facilitating an innovative but yet convergent approach to the investigation of white-collar crime.
Studies were included in this review if they were empirical (exploratory, comparative, or laboratory), in the English language, and occurred within the last 30 years. The following databases were utilized for obtaining arti- cles included in this review: Ebscohost, PsychArticles PsychInfo, and Sage Criminology. Search terms utilized for finding the articles that were incorpo- rated into this review included attribute, business, characteristic, corporate, crime, criminal, demographic, deviance, embezzlement, employee, employee theft, fraud, organizational, personality, white-collar, and workplace. These search terms were used in a variety of arrangements. Reference sections for each of the included articles were also reviewed to identify additional pertinent articles that also were included in this review.
This article will begin by detailing the studies that have explored the demographic characteristics of white-collar criminals. A total of 10 stud- ies portraying the demographic attributes of white-collar offenders were included in this review (Table 1). These studies were discussed from least rigorous (exploratory studies) to most scientifically rigorous (comparative studies) in design. Exploratory studies included all studies in which scholars provided descriptive statistics on the characteristics of white-collar offenders. Comparative studies included all studies wherein the demographic char- acteristics of white-collar offenders were compared to the demographic characteristics of other offender types or non-offenders.
TA B
LE 1
D es
cr ip
tio n
o f
th e
St u d y
Sa m
p le
s an
d W
h ite
-C o lla
r C ri m
es E xp
lo re
d in
E ac
h o f
th e
St u d ie
s o n
D em
o gr
ap h ic
C h ar
ac te
ri st
ic s
In cl
u d ed
in th
e C ri tic
al R ev
ie w
A u th
o r(
s) St
u d y
sa m
p le
W h ite
-C o lla
r o ff en
se s
C o m
p ar
is o n
sa m
p le
E xp
lo ra
to ry
St u
d ie
s (n
= 6
) P o gr
eb in
, P o o le
, &
R eg
o li
(1 98
6) M
al e
an d
fe m
al e
w h ite
-c o lla
r o ff en
d er
s co
n vi
ct ed
in th
e Ju
d ic
ia l D
is tr ic
t o f C o lo
ra d o
at D
en ve
r (N
= 62
)
B an
k em
b ez
zl em
en t
N / A
D al
y (1
98 9)
; La
n gt
o n
& P iq
u er
o (2
00 7)
M al
e an
d fe
m al
e w
h ite
-c o lla
r o ff en
d er
s co
n vi
ct ed
in U
.S .
fe d er
al co
u rt s
(N =
1, 34
2)
B an
k em
b ez
zl em
en t,
in co
m e
ta x
fr au
d , p o st
al fr
au d ,
cr ed
it fr
au d , fa
ls e
cl ai
m s
an d
st at
em en
ts , b ri b er
y, an
tit ru
st ,
se cu
ri tie
s fr
au d
N / A
W ei
sb u rd
, C h ay
et , &
W ar
in g
(1 99
0) W
h ite
-c o lla
r o ff en
d er
s co
n vi
ct ed
in U
. S.
fe d er
al co
u rt s
(N =
1, 09
0)
B an
k em
b ez
zl em
en t,
in co
m e
ta x
fr au
d , p o st
al fr
au d ,
cr ed
it fr
au d , fa
ls e
cl ai
m s
an d
st at
em en
ts , b ri b er
y, an
tit ru
st ,
se cu
ri tie
s fr
au d
N / A
D o d d
(2 00
4) E m
p lo
ye es
o f a
p ac
ka ge
sh ip
m en
t co
m p an
y (N
= 10
3)
D am
ag e
to p ro
p er
ty , st
ea lin
g, w
ill fu
l d el
ay , ev
as io
n o f
fe es
, fr
au d , an
d em
b ez
zl em
en t
N / A
H o ltf
re te
r (2
00 5)
C as
es d es
cr ib
ed b y
ce rt ifi
ed fr
au d
ex am
in er
s (N
= 1,
14 2)
A ss
et m
is ap
p ro
p ri at
io n ,
co rr
u p tio
n , fr
au d u le
n t
st at
em en
ts
N / A
(C on
ti n
u ed
)
377
TA B
LE 1
(C o n tin
u ed
)
A u th
o r(
s) St
u d y
sa m
p le
W h ite
-C o lla
r o ff en
se s
C o m
p ar
is o n
sa m
p le
C om
pa ra
ti ve
St u
d ie
s (n
= 4
) W
h ee
le r,
W ei
sb u rd
, W
ar in
g, &
B o d e
(1 98
8)
M al
e an
d fe
m al
e w
h ite
-c o lla
r o ff en
d er
s co
n vi
ct ed
in U
. S.
fe d er
al co
u rt s
(n =
1, 34
2)
B an
k em
b ez
zl em
en t,
in co
m e
ta x
fr au
d , p o st
al fr
au d ,
cr ed
it fr
au d , fa
ls e
cl ai
m s
an d
st at
em en
ts , b ri b er
y, an
tit ru
st ,
se cu
ri tie
s fr
au d
M al
e an
d fe
m al
e o ff en
d er
s co
n vi
ct ed
o f p o st
al fr
au d
o r
fo rg
er y
in U
. S.
fe d er
al co
u rt s
(n =
21 0)
an d
a U
. S.
co m
m u n ity
sa m
p le
B en
so n
& M
o o re
(1 99
2) W
h ite
-c o lla
r o ff en
d er
s co
n vi
ct ed
in U
. S.
fe d er
al co
u rt s
(n =
2, 46
2)
B an
k em
b ez
zl em
en t,
in co
m e
ta x
fr au
d , fa
ls e
cl ai
m s
an d
st at
em en
ts , b ri b er
y, p o st
al fr
au d
O ff en
d er
s co
n vi
ct ed
o f d ru
g cr
im es
, p o st
al fo
rg er
y, o r
b an
k ro
b b er
y in
U . S.
fe d er
al co
u rt s
(n =
1, 98
6) M
u st
ai n e
& Te
w ks
b u ry
(2 00
2) M
al e
an d
fe m
al e
co lle
ge st
u d en
ts w
h o
ad m
itt ed
w o rk
p la
ce th
ef t (n
= 57
4)
T h ef
t o f em
p lo
ye r’ s
p ro
p er
ty M
al e
an d
fe m
al e
co lle
ge st
u d en
ts w
h o
d id
n o t ad
m it
w o rk
p la
ce th
ef t (n
= 31
4) P o o rt in
ga , Le
m m
en , &
Ji b so
n (2
00 6)
M al
e an
d fe
m al
e d ef
en d an
ts ch
ar ge
d w
ith w
h ite
-c o lla
r cr
im e
an d
re fe
rr ed
to p sy
ch ia
tr ic
fa ci
lit y
(n =
70 )
E m
b ez
zl em
en t
M al
e an
d fe
m al
e d ef
en d an
ts ch
ar ge
d w
ith n o n vi
o le
n t
th ef
t cr
im es
(i .e
., re
ta il
fr au
d , la
rc en
y, m
o to
r ve
h ic
le th
ef t)
an d
re fe
rr ed
to p sy
ch ia
tr ic
fa ci
lit y
(n =
73 )
N / A
= n o t ap
p lic
ab le
.
378
Profile of White-Collar Criminals 379
Six studies were found that discussed psychological attributes of white- collar criminals and are later depicted (Table 2). One study discussed in this area was exploratory whereas the remaining five studies discussed were comparative studies. Criteria for exploratory and comparative studies were defined earlier. Studies within each topic area (i.e., demographic or psycho- logical characteristics) were discussed in chronological order (from earliest to most recent studies), and a critique of the studies occurred after details had been provided for all studies of a specific design (e.g., comparative). The exception was that the critique of exploratory and comparative studies was combined when discussing the studies regarding the psychological char- acteristics of white-collar offenders because there was only one exploratory study to critique.
DEMOGRAPHIC CHARACTERISTICS OF WHITE-COLLAR CRIMINALS
Exploratory Studies
Pogrebin, Poole, and Regoli (1986) reviewed the U. S. probation office files of 62 individuals (62.9%, women) found guilty of bank embezzlement. Most embezzlers were Caucasian (77.4%), between the ages of 21 and 30 (59.7%), and had a high school diploma or GED (48.4%). Many embezzlers were married (45.2%) and had fewer than two children (45.2%). Furthermore, most embezzlers had worked for their employers for less than 1 year when caught (50.7%) and were in service-level positions (77.4%). The majority of embezzlers had an income of less than $10,000 (82.3%). Few embez- zlers had previous misdemeanor convictions (16.1%) or felony convictions (8.1%). Most embezzlers reported committing their crime because of mar- ital or family difficulties (59.7%), to improve their quality of life (48.4%), or to reduce monetary debt (46.8%). Embezzlers stated that their stolen funds were most often spent on individual debt (66.1%) and luxurious pur- chases (43.5%). The majority of individuals embezzled $5,000 or less (50.1%). The researchers made comparisons between those who embezzled less than $5,000 and those who embezzled more than $5,000. Chi-square analyses showed that those who embezzled more money tended to be Caucasian and in an administrative position and had a high school degree or higher education level.
Using reanalyzed data from previous studies (Wheeler, Weisburd, & Bode, 1982; Wheeler, Weisburd, Waring, & Bode, 1988), Daly (1989) explored the demographic characteristics of male and female white-collar offenders. The study sample included 1,342 defendants (12.8% women) con- victed of white-collar offenses in U. S. federal criminal courts throughout seven districts between 1976 and 1978. Sample data were obtained originally by coding information that had been contained in defendants’ presentence
TA B
LE 2
D es
cr ip
tio n
o f
th e
St u d y
Sa m
p le
s an
d W
h ite
-C o lla
r C ri m
es E xp
lo re
d in
E ac
h o f
th e
St u d ie
s o n
P er
so n al
ity A
tt ri b u te
s In
cl u d ed
in th
e C ri tic
al R ev
ie w
A u th
o r(
s) St
u d y
sa m
p le
W h ite
-C o lla
r o ff en
se s
C o m
p ar
is o n
sa m
p le
E xp
lo ra
to ry
St u
d y
(n =
1 )
D h am
i (2
00 7)
M al
e W
h ite
-c o lla
r o ff en
d er
s co
n vi
ct ed
in E n gl
an d
co u rt s
(n =
14 )
D ec
ep tio
n , fr
au d
N / A
C om
pa ra
ti ve
St u
d ie
s (n
= 5
) C o lli
n s
& Sc
h m
id t (1
99 3)
W h ite
-c o lla
r o ff en
d er
s co
n vi
ct ed
in U
. S.
fe d er
al co
u rt s
(n =
32 9)
A n tit
ru st
vi o la
tio n s,
co u n te
rf ei
tin g,
em b ez
zl em
en t,
fo rg
er y,
fr au
d , in
te rs
ta te
tr an
sp o rt at
io n
o f st
o le
n ve
h ic
le s,
m is
u se
o f p u b lic
m o n ey
, m
o n ey
la u n d er
in g,
b ri b er
y, ra
ck et
ee r
in fl u en
ce in
co rr
u p t o rg
an iz
at io
n s
W h ite
-c o lla
r em
p lo
ye es
(n =
32 0)
K o lz
(1 99
9) M
al e
an d
fe m
al e
re ta
il em
p lo
ye es
th at
ad m
itt ed
to w
o rk
p la
ce th
ef t (n
= 17
6)
T h ef
t o f em
p lo
ye r’ s
p ro
p er
ty M
al e
an d
fe m
al e
re ta
il em
p lo
ye es
th at
d id
n o t
ad m
it to
w o rk
p la
ce th
ef t
(n =
42 )
A la
le h to
(2 00
3) In
fo rm
an ts
’d es
cr ip
tio n s
o f
fr ie
n d
o r
co w
o rk
er w
h o
co m
m itt
ed a
cr im
in al
ac t at
w o rk
(n =
55 )
T ax
ev as
io n
In fo
rm an
ts ’d
es cr
ip tio
n s
o f
fr ie
n d
o r
co w
o rk
er w
h o
d id
n o t co
m m
it cr
im in
al ac
t at
w o rk
(n =
69 )
W al
te rs
& G
ey er
(2 00
4) M
al e
w h ite
-c o lla
r o ff en
d er
s w
ith n o
cr im
in al
h is
to ry
o r
a h is
to ry
o f w
h ite
-c o lla
r cr
im e
o n ly
an d
co n vi
ct ed
in U
. S.
fe d er
al co
u rt s
(n =
34 )
A n tit
ru st
vi o la
tio n s,
se cu
ri tie
s fr
au d , p o st
al an
d w
ir e
fr au
d ,
fa ls
e cl
ai m
s an
d st
at em
en ts
, cr
ed it
fr au
d , b an
k em
b ez
zl em
en t,
ta x
fr au
d ,
b ri b er
y, h ea
lth ca
re fr
au d ,
co u n te
rf ei
tin g.
M al
e o ff en
d er
s co
n vi
ct ed
o f
w h ite
-c o lla
r cr
im e
w ith
a h is
to ry
o f co
m m
o n
cr im
e (n
= 23
) an
d m
al e
o ff en
d er
s co
n vi
ct ed
o f co
m m
o n
cr im
e in
U . S.
fe d er
al co
u rt s
(n =
66 )
B lic
kl e,
Sc h le
ge l,
Fa ss
b en
d er
, &
K le
in (2
00 6)
M al
e an
d fe
m al
e w
h ite
-c o lla
r o ff en
d er
s co
n vi
ct ed
in G
er m
an st
at es
(n =
76 )
B ri b er
y, co
u n te
rf ei
tin g,
em b ez
zl em
en t,
fo rg
er y,
fr au
d , b an
kr u p tc
y fr
au d ,
sm u gg
lin g,
ta x
ev as
io n
M al
e an
d fe
m al
e b u si
n es
s m
an ag
er s
(n =
15 0)
380
Profile of White-Collar Criminals 381
investigation (PSI) reports. The definition used to define white-collar crime was adopted from Wheeler et al. (1982): “economic offenses committed through the use of some combination of fraud, deception, or collusion” (p. 642). The following offenses were considered white-collar: bank embez- zlement, postal fraud, credit fraud, false claims and statements, income tax fraud, bribery, securities fraud, and antitrust violations. The sample included 30 individuals randomly selected from each of the seven districts in each of the white-collar crime categories, with the exception that all offenders convicted of antirust and securities fraud were included in the sample.
Descriptive comparisons were made between male and female white- collar offenders convicted of bank embezzlement, postal fraud, credit fraud, and false claims (not enough women were convicted of the other four crimes to make comparisons). The majority of male offenders were Caucasian (62.0%–86.0% of offenders across the four crimes). Female offenders were slightly more likely to be Caucasian (54.0%–63.0% across three crimes), with the exception being those convicted of postal fraud (53.0% non-Caucasian vs. 47.0% Caucasian). The median age for women was younger as compared to men (bank embezzlement: 26.0 vs. 31.0; postal fraud: 30.0 vs. 41.0; credit fraud: 31.0 vs. 38.0; false claims: 32.0 vs. 38.0, respectively). Most offend- ers had a high school degree. Men who had committed white-collar crime were predominately married and had children. Women were more often not married with children. Male offenders were substantially more likely than women to have been in management or administrative position. Women were more likely to have been in clerical positions. A greater percentage of women (29.0%–37.0%) reported being motivated by the financial need of their family compared to men (14.0%–18.0%; Daly, 1989).
Weisburd, Chayet, and Waring (1990) also adopted their sample and white-collar crime definition from the Wheeler et al. (1982, 1988) studies. The sample (N = 1,090) was predominately Caucasian (78.0%), previously employed in white-collar jobs (78.0%), and had a mean age of 40.0 years. The percentage of white-collar offenders with previous convictions in each crime category consisted of 46.0% credit card fraud, 45.0% false claims, 41.0% mail fraud, 37.0% tax evasion, 26.0% securities fraud, 22.0% bank embez- zlement, and 19.0% bribery. The percentage of offenders in each of the white-collar crime categories with past incarcerations included 25.0% credit fraud, 23.0% mail fraud, 22.0% false claims, 14.0% tax evasion, 7.0% bank embezzlement, 6.0% bribery, and 4.0% securities fraud.
The researchers then explored whether the criminal histories of offend- ers looked different if a more restricted definition for white-collar offenders were used. Offenders were included if they had capital greater than $250,000, used their job to commit the crime, and had a specific title (e.g., attorney, physician) or had previously held a powerful position (e.g., management). A total of 319 offenders met these criteria. A small percent- age of these offenders demonstrated past convictions (22.0%), past felony
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convictions (10.0%), and previous incarceration (6.0%) (Weisburd et al., 1990).
Dodd (2004) utilized employee records of white-collar offenses kept by a United Kingdom package shipment business. A total of 103 employees were included in the sample because they committed one of the following occupational offenses: damaging property, stealing, willful delay, evasion of fees, fraud, and embezzlement. A smallest space analysis (SSA) plot revealed that the variables loaded onto a two-factor solution. The researcher concluded that one of the factors represented a “nothing-to-lose” offender subtype and the other factor represented the “troublemaker” offender sub- type. Troublemakers were not likely to confess, had a history of being investigated by the employee investigation unit, had previous criminal con- victions, had a partner, and demonstrated behaviors that decreased work productivity. The nothing-to-lose offenders were more likely to have had financial problems, not own a vehicle, not have a partner, to have con- fessed, and to be non-permanent staff. Both offender types were equally likely to not have children, be younger in age than 28.0, and to work at the company for less than 3.5 years (Dodd).
Holtfreter (2005) surveyed elite members of the Association of Certified Fraud Examiners (organization with the mission of decreasing fraud via research and instruction to its members) who had passed an exam qualifying them to be certified fraud examiners (CFEs). This study looked exclusively at occupational fraud offenses. The three types of fraud crimes investigated were asset misappropriation (stealing or unlawfully using company prop- erty), corruption (using position to get rewards), and fraudulent statements (lying on business papers). Surveys were distributed to a random sample of 1,000 CFEs within the United States between the years 1997 and 1998 and again between the years of 2001 and 2002. Respondents were asked to describe the most recent fraud case they had investigated. A total of 1,142 descriptions were provided.
Asset misappropriation was the most commonly described fraud offense (84.0%), followed by corruption (8.7%) and fraudulent statements (7.4%). Fraudulent statement and corruption offenders tended to be significantly older than asset misappropriation offenders (44.4 vs. 43.1 vs. 40.4, respec- tively). Offenders who previously committed fraudulent statement offenses were more likely to be male compared to other offense types. Asset mis- appropriation offenders had less education compared to other offense groups. Fraudulent statement offenders were more likely to have been in management or executive-level job positions (Holtfreter, 2005).
Another study (Langton & Piquero, 2007) used data originally gathered in Wheeler, Weisburd, and Bode (2000) to explore the differences between white-collar and non-white-collar offenders. Data were initially gathered from the PSI reports of 1,910 white-collar offenders (16.0% female) con- victed in federal courts between the years 1976 and 1978. The white-collar
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crime definition, inclusive offenses, and sampling procedures were adopted from previous studies (Weisburd, Wheeler, Waring, & Bode, 1991; Wheeler et al. 1982, 1988). The mean age of the sample was 39.7. The mean num- ber of previous arrests was 5.6. Motives for committing white-collar offenses consisted of financial gain (45.0%), business motives (32.0%), and personal gain (23.0%). Particularly unique to this study was that the eight crimes were organized into low-level (i.e., embezzlement, credit fraud, tax fraud), mid- level (i.e., bribery, mail fraud, false claims and statements), and high-level corporation crimes (i.e., antitrust, securities fraud). A multinomial logistic regression was conducted to determine which variables predicted commis- sion of low- or mid-level offenses in comparison to high-level offenses. Results demonstrated that low- and mid-level offenses were significantly predicted by decreased age, non–white race, greater number of past arrests, and financial or personal motivations.
Critique of Exploratory Studies
Problematically, all exploratory studies lacked a comparison group, which makes it impossible to determine whether white-collar offenders are dis- tinct from other types of offender (e.g., property, drug) or from white-collar professionals. Generalizability of findings is limited because of the various definitions of white-collar crime and qualifying offenses utilized across stud- ies. Some researchers chose to look at only one white-collar crime type (Holtfreter, 2005; Pogrebin et al., 1986). Holtfreter claimed to investigate only the fraud offenses of asset misappropriation, corruption, and fraudu- lent statements; however, some of these fraud offenses did not correspond with Coleman’s definition of fraud crimes. For instance, asset misappro- priation seemed to be more similar to what Coleman (2002) described as embezzlement. Holtfreter also considered corruption to be a type of fraud, but Coleman considered corruption and bribery to be a separate entity from fraud. In another study (Dodd, 2004), it was hard to discern whether several of the offenses included in the study were actually white-collar offenses (i.e., damage, willful delay, evasion of fees) because their parameters were not defined.
Three studies utilized the same definition of white-collar crime (Daly, 1989; Langton & Piquero, 2007; Weisburd et al., 1990); however, these studies also reanalyzed data from the same previous studies (Weisburd et al., 1991; Wheeler et al., 1982, 1988, 2000). These three studies focused mainly on fraud offenses, with five of the eight offenses representing a type of fraud (Daly, 1989; Langton & Piquero, 2007; Weisburd et al., 1990). Another study limitation is that all the data for these studies were gath- ered over the same time period (1976–1978), which was more than 30 years ago. Consequently, the study sample may not be representative of the
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demographic characteristics of white-collar offenders today. The sample is limited geographically because all participant information was gathered from the same seven federal districts for all three of these studies. There are 94 federal districts in the United States, and these studies included white-collar criminals in only 7% of these districts. External validity of these findings is substantially limited.
Three studies (Daly, 1989; Langton & Piquero, 2007; Weisburd et al., 1990) did not include coding information because they were secondary data analysis studies; therefore, the original study (Wheeler et al., 1982) was con- sulted for this information. Wheeler et al. (1982) mentioned that coding was completed by trained college students. Inter-rater reliability was assessed by recoding 119 of the 1,910 cases in the original study. The highest error rate reported across variables was 17.0%. Wheeler et al. (1982) failed to mention who trained the coders. Pogrebin et al. (1986) mentioned that probation officers, trained by the researchers, coded the PSI file data. Pogrebin et al. did not provide inter-rater reliability information. Not assessing for inter- rater reliability could decrease the internal validity of theses studies. Use of archival data can also be problematic because it is subject to the biases of the individual who originally did the documentation.
Holtfreter (2005) utilized informant data that included asking fraud examiners about the most recent fraud case they assisted in prosecuting. The researcher mentioned that they had compared fraud examiners’ reports with secondary sources (i.e., court or business documents) to lessen poten- tial bias that may have existed in the gathered information. However, study findings are confounded because informants could have reported on a fraud case that resulted in no conviction, civil conviction, or criminal conviction. No descriptive information was provided on the conviction received in each fraud case, and no means were utilized to control for it influence.
Some studies provided means for variables such as gender, ethnicity, or job position when frequencies would have been more informative for cate- gorical variables (Holtfreter, 2005; Langton & Piquero, 2007). Several studies provided only the percentages of individuals who demonstrated specific demographic traits, which made findings difficult to interpret because sam- ple sizes varied for different variables (Daly, 1989; Weisburd et al., 1990). Providing frequencies along with the percentages would have made the results more informative to the reader. Some variables (e.g., motives, prior convictions, and prior arrests) had missing data, so interpretation of these variables was problematic (Daly, 1989; Dodd, 2004; Weisburd et al., 1990). Dodd (2004) mentioned that data were missing for many variables included in the study. Dodd did not provide frequencies, means, or percentages for these variables; so it can be difficult to draw any conclusions from these data.
Daly (1989) claimed to make comparisons between male and female white-collar offenders, yet all data reported were descriptive. Results may have been more informative had inferential statistics been utilized (e.g.,
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chi-square analyses). Two studies (Langton & Piquero, 2007; Pogrebin et al., 1986) incorporated a multitude of statistical tests but did not take precautions to control for type I error (e.g., Bonferroni method, multivariate statisti- cal procedures). One study (Holtfreter, 2005) used t-tests and analyses of variance (ANOVA) with categorical dependent variables, which violated the assumptions of the statistical tests. Analyses suited for categorical dependent variables (e.g., chi-square analyses) would have been more appropriate.
Dodd (2004) concluded from an SSA plot that two subtypes of white- collar offenders existed (i.e., “troublemaker” and “nothing-to-lose” offender subtypes). It should be acknowledged that Dodd’s research question would have been more appropriately answered with cluster analysis instead of an SSA. Dodd’s conclusion is problematic because an SSA analysis provides only a dimensional conclusion, whereas Dodd assumed that the findings could provide a categorical conclusion (i.e., typology). Thus far, research has not demonstrated whether white-collar crime should be considered a dimen- sional or categorical structure; however, an analysis such as taxometrics could potential answer this question.
Comparative Studies
Wheeler et al. (1988) examined the difference in demographic characteristics between non-white-collar offenders (n = 210, 31.4% women), white-collar offenders (n = 1,342, 14.5% women), and a U. S. community sample. The white-collar offender sample and the definition of white-collar crime used in this study were described above in the discussion of Daly (1989). The non-white-collar offender group consisted of individuals convicted of the federal offenses of postal theft or forgery. A total of 15 offenders con- victed of postal theft and 15 offenders convicted of forgery were randomly selected from the same federal districts as the white-collar offenders. White- collar offenders were more likely to be male (85.5% vs. 68.6% vs. 48.6%, respectively); Caucasian (81.7% vs. 34.3% vs. 76.8%, respectively); and older age (40.0 vs. 30.0 vs. 30.0, respectively) when compared to non-white-collar criminals and a U. S. community sample. Non-white-collar criminals were less likely to have graduated from college (45.5%) compared to white-collar criminals (79.3%) and the U. S. community sample (69.0%). Also, white- collar offenders were more likely to have earned a college degree (27.1%) compared to non-white-collar criminals (3.9%) and the U. S. Community sample (19%). Non-white-collar crime offenders were much more likely to have previously been unemployed (56.7%) compared to white-collar offenders (5.7%) and a U. S. community sample (5.9%). A greater percent- age of white-collar offenders reported being of the Jewish faith (15.25%) compared to non-white-collar criminals (2.9%) and the U. S. community sample (2.0%).
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Benson and Moore (1992) utilized the PSI reports of male and female federal offenders convicted between the years 1973 and 1978. White-collar criminals (n = 2,462) were convicted of bank embezzlement, bribery, income tax violations, false claims and statements, or mail fraud. The non- white-collar criminals (n = 1,986) were convicted of drug crimes, postal forgery, or bank robbery. A stratified random sampling procedure was utilized, with 120 offenders selected across five districts and 40 offend- ers selected across the three smaller districts for each of the crime types. White-collar offenders were less likely to have a previous arrest compared to non-white-collar offenders. Specifically, white-collar criminals guilty of embezzlement (18.4%) were less likely to have a past arrest followed by bribery (23.6%), income tax fraud (42.1%), false claims (49.0%) and finally mail fraud (65.9%). For non-white-collar offenders, bank robbery offenders (88.4%) were most likely to have past arrests followed by postal forgery (82.6%) and drug offenders (72.2%). White-collar offenders were less likely than non-white-collar criminals to have used drugs (5.5% vs. 48.5%, respectively), received low grades in school (24.6% vs. 53.5%, respectively), previously demonstrated impaired school adjustment (21.5% vs. 45.3%, respectively), and had a drinking problem (4.2% vs. 8.3%, respectively).
The researchers created a criminal versatility index, which was based on whether the offender committed crimes in four categories (violent, property, white-collar, and minor offenses). The index was created for offenders with five previous arrests. Results of a t-test showed white-collar offenders were more likely to have been previously arrested for white-collar crimes. Non- white-collar criminals demonstrated previous arrest histories that consisted of offenses that spanned all four crime categories.
A self-report study (Mustaine & Tewksbury, 2002) looked at workplace theft among employed college students (N = 888, 53.8% women) in nine universities. Employee theft was measured by yes/no responses to the ques- tion “In the past six months have you taken property from your place of work for personal use (office supplies, software, furniture, food, etc”; p. 118). Students who reported previous workplace thefts were equally as likely as students who did not endorse past workplace theft to be white, unmarried, middle or higher socioeconomic status, live off-campus, and het- erosexual. The mean age (20.0) for both groups was similar. A regression analysis showed those who stole from work were more likely to have a his- tory of various antisocial behaviors (i.e., served prison time, broke into an automobile, stole from people, been drunk in public), many previous jobs, and were more often in cash-handling positions.
Poortinga, Lemmen, and Jibson (2006) looked at the characteristics of male and female white-collar and non-white-collar criminals referred to a psychiatric facility for evaluation between the years 1991 and 2002. All white- collar offenders in the sample were charged with embezzlement (n = 70,
Profile of White-Collar Criminals 387
52.9% women). All participant data was gathered from court evaluations. The control group consisted of non-white-collar criminals (n = 73, 39.7% women) charged with a non-violent theft crimes (i.e., retail fraud, stealing from a person, bank robbery without a weapon, vehicle theft). A ran- dom selection of non-white-collar offenders was matched to white-collar offenders based on evaluation year. White-collar offenders were signif- icantly more often previously employed (85.7% vs. 51.8%, respectively) and to have been in management position (21.9% vs. 0.0%, respectively) compared to non-white-collar offenders. White-collar offenders were less likely to have past adult convictions (41.8% vs. 76.6%, respectively) than non-white-collar criminals. A logistic regression demonstrated that not hav- ing a substance abuse disorder, being white, and having more education accounted for 37.0% of the variance in predicting likelihood of being a white-collar criminal.
Critique of Comparative Studies
Three of the comparative studies utilized similar definitions of white-collar crime (Benson & Moore, 1992; Poortinga et al., 2006; Wheeler et al., 1988). Benson and Moore used a more restrictive definition than Wheeler et al. (1988), excluding three white-collar crime types from their definition (i.e., credit fraud, antitrust, securities fraud) that had been included in Wheeler et al. (1988). Poortinga et al. (2006) attempted to include all white-collar offenders charged with one of the eight white-collar crimes specified in Wheeler et al. but were limited to including only embezzlement offend- ers due to the lack of offenders charged with the other offenses. Mustaine and Tewksbury (2002) used a self-report question about previous theft behavior to determine whether an individual engaged in employee theft. The question was very general and susceptible to impression management. Responses could have also been influenced by the fact that participants’ course instructors administered the questionnaire (containing the employee theft question). Moreover, it is not clear whether course instructors or the researchers kept the survey responses anonymous.
Three of the comparative studies (Benson & Moore, 1992; Poortinga et al., 2006; Wheeler et al., 1988) contrasted white-collar offenders to other nonviolent criminal types (i.e., postal forgery, drug offenses, bank rob- bery). All three studies attempted to match white-collar and non-white-collar offender samples on different characteristics (e.g., year of evaluation) in an effort to increase internal validity of findings. External validity of study find- ings was limited because two studies utilized the same sample (convicted federal offenders), which was gathered in the same manner (PSI reports) and during a similar time period (1973–1978). Benson and Moore (1992) gathered data from eight federal districts that were different from the eight
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federal districts Wheeler et al. (1988) utilized. Gathering sample informa- tion from different federal districts increased the generalizability of findings. One study utilized a U. S. community sample (Wheeler et al., 1988), but this was problematic because the data for this sample came from various sources (i.e., Federal Judicial Center, United States Bureau of the Census). The time period the U. S. community sample data was gathered (1983–1986) was different from the time period the federal offender sample data had been collected (1973–1978). Moreover, data were accumulated during dif- ferent years for the two samples; therefore, a population shift may have impacted study findings.
Three comparative studies utilized archival data to gather sample infor- mation (Benson & Moore, 1992; Poortinga et al., 2006; Wheeler et al., 1988). Two of these studies neglected to include descriptions of the individuals coding data, their training, and inter-rater reliability between coders (i.e., Benson & Moore, 1992; Wheeler et al., 1988). Poortinga et al. specified that only one individual coded the study data (i.e. the first author); therefore, inter-rater reliability could not and was not computed for that study. These studies obtained archival data from PSI reports, which were susceptible to the bias and selectivity of the original reporter. Due to selectivity in formu- lating PSI reports, several missing data within some variables (e.g., school performance, drinking problems) made study findings (Benson & Moore, 1992) difficult to interpret. Poortinga et al. (2006) used a nonstandard defi- nition for the variables of depression (more than two depressive symptoms previous to current criminal charge) and adjustment disorder (more than two depressive symptoms after being charged with current criminal charge). The unique definitions utilized for these variables can create problems of generalizability. Wheeler et al. (1988) developed a criminal versatility index for each offender. Based on this variable, the researchers concluded that white-collar offenders had a less diverse criminal history than non-white- collar offenders. This conclusion should be interpreted with caution because a different definition of white-collar crime (“fraud, embezzlement, corporate crime, other white-collar offenses,” p. 270) was utilized when developing the versatility index than was used for defining the present white-collar conviction.
None of the comparative studies looked at the differences between male and female white-collar and non-white-collar offenders, which confounded the results. Some of the comparative studies (Benson & Moore, 1992; Wheeler et al., 1988) provided only descriptive data on several variables (e.g., prior arrests, age), which made it difficult to conclude whether white- collar offenders were significantly different from non-white-collar offenders or non-offenders. Two studies (Mustaine and Tewksbury, 2002; Poortinga et al., 2006) utilized several inferential statistical procedures but neglected to use a Bonferroni method or multivariate statistical tests to control for type I error.
Profile of White-Collar Criminals 389
PSYCHOLOGICAL ATTRIBUTES OF WHITE-COLLAR CRIMINALS
Exploratory Study
Dhami (2007) utilized a semi-structured interview procedure to gather infor- mation regarding how white-collar offenders believed other parties (e.g., media, significant other, prison inmates, prison guards, and judiciary) viewed their criminal actions and how they viewed their criminal actions. White- collar crime was defined as “an economic crime committed through the use of fraud and/or deception by a person occupying a senior position in an organization” (p. 61). All white-collar offenders included in the final sam- ple (n = 14) were male and Caucasian. Nine offenders had committed the offense of fraud, and five offenders had committed the crime of deception. Descriptive findings demonstrated that the white-collar offenders believed prison guards, prison inmates, and their significant others (e.g., children, spouse) had positive attitudes (e.g., supportive, respectful, accepting) toward them. Conversely, white-collar offenders believed that the media and judi- ciary displayed negative attitudes (e.g., bias, punitive) toward them. Results of this study also showed that 93% of white-collar offenders believed that the crime they had been convicted of was not criminal. Some of the reasons they expressed when justifying their actions as non-criminal were no iden- tifiable victim had been injured, their crime is legal in other countries, and they were under the influence of the market.
Comparative Studies
Collins and Schmidt (1993) used a self-report survey design to look at the difference in personality characteristics between federal white-collar crim- inals (n = 329, 21.6% women) and non-criminal white-collar employees (n = 320, 53.8% women). White-collar crime was defined as a nonvi- olent act that had been committed with the intent for financial growth. The white-collar criminal act had to include an element of deception. Last, the white-collar offender had to have carried out the act in his occupa- tional position or had to have special knowledge in business or government. The study sample was predominately Caucasian and had a mean age of 49.0. The California Psychological Inventory (Gough, 1987) and the General Biodata Questionnaire; Owens, 1976) were used to measure personality. The Employment Inventory (Paajanen, 1988) was utilized to measure traits related to work performance (e.g., responsible). White-collar offenders were significantly higher in anxiety, involvement in extracurricular activity, and social extraversion. Significantly higher levels of socialization, responsibil- ity, tolerance, and performance were seen among noncriminal professionals compared to offenders.
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One study (Kolz, 1999) looked at employee theft among 218 individuals (69.3% women) working at a women’s clothing store chain. All participants completed a demographic questionnaire and the NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992). To measure past theft behavior, partici- pants responded to a yes/no question assessing whether “they had stolen any cash or merchandise from their employer in the past year” (Kolz, 1999, p. 110). Records on work attendance and supervisor interviews (conducted to assess socialization and cooperation of each employee) also were used. Study findings demonstrated 19.0% of the study sample admitted to stealing over the past year. Low levels of conscientiousness and agreeableness were found to be predictive of having admitted employee theft.
Utilizing a semi-structured interview procedure, Alalehto (2003) had 128 business professionals report on the behavior and personality traits of a friend or coworker in the construction, music, or engineering businesses. The researcher had participants describe the illegal behavior of their friend or coworker if they had “close knowledge of whether or not the person committed the economic crime, regardless of whether that person was con- victed of it” (p. 343). Participants described a friend or coworker who did not participate in illegal behaviors at work if they did not have knowledge of such activity. A total of 55 criminal white-collar offenders and 69 noncriminal white-collar professionals were described. The interview manual consisted of questions assessing six personality traits (i.e., extroversion, agreeable- ness, conceitedness, neuroticism, intellectualism, negative valency). Example interview questions used to assess these personality dimensions include “Is he dutiful or does he take each day as it comes, rather thoughtlessly, and so forth?” and “Would he rather be liked by others in all that he does or is he not bothered much by this?” (p. 353). When a participant described his friend or colleague’s attitude in response to a specific interview ques- tion, the descriptions were categorized into one of the six traits. A computer program was then used to assess the different combination of personality traits that were common among white-collar offenders and professionals. Descriptive data showed a greater number of white-collar offenders were described as extroverted (e.g., outgoing, controlling, calculating), less agree- able, and neurotic. The white-collar professionals were more agreeable and conceited (e.g., diligent, frugal, refined).
Walters and Geyer (2004) compared 34 male white-collar offenders (who had no criminal history or only a criminal history of white-collar offending) to 23 male criminally versatile white-collar offenders (with a criminal history consisting of other offense types besides white-collar) and 66 male non-white-collar criminals. Measures utilized in this study included the Psychological Inventory of Criminal Thinking Styles (PICTS; Walters, 1995; 2003); the Social Identity as a Criminal Scale (SIC; Cameron, 1999); and the Lifestyle Criminality Screening Form-Revised (LCSF-R; Walters, 1998; Walters, White, & Denney, 1991). The PICTS was a self-report measure used
Profile of White-Collar Criminals 391
to assess criminal thinking styles. The PICTS is composed of 19 subscales, but only the four factor scales were utilized in this study. The problem avoid- ance factor scale assessed the extent to which an individual has committed antisocial acts to avoid life problems. The interpersonal hostility factor scale measured the propensity for an individual to become angry with others. The self-assertion/deception factor scale measured the extent to which an individual has justified committing criminal actions. The denial of harm fac- tor subscale assessed an individual’s tendency to minimize the consequences of illegal actions. The SIC is a self-report measure that assessed the extent an individual has identified with other criminals. The measure contains three subscales (in-group ties, centrality, and in-group affect). The in-group ties subscale assessed the degree to which an individual has associated with criminals. The centrality subscale measured the extent to which an individ- ual finds group identity important. The in-group affect subscale assessed an individual’s beliefs about criminals. The LCSF-R was used to measure four criminal interpersonal subtypes and was completed by reviewing PSI reports. The four interpersonal subtype scales included irresponsibility, self- indulgence, interpersonal intrusiveness, and social rule breaking. The LCSF-R was modified for this study by eliminating arrest items.
Findings of this study demonstrated that the white-collar-crime-only group was significantly older (50.1) than the white-collar criminally versatile (43.6) and non-white-collar (41.6) offender groups. White-collar-crime-only individuals (16.0) had more years of education than white-collar criminally versatile (14.1) and non-white-collar offenders (12.4). White-collar offend- ers were predominately Caucasian compared to non-white-collar offenders. The non-white-collar offender group was primarily drug offenders (78.8%). The white-collar criminally versatile group mainly committed credit fraud (34.8%) and tax fraud (21.7%). The white-collar-crime-only group was mostly convicted of postal and wire fraud (41.2%). An ANOVA demonstrated white-collar-only offenders had significantly lower scores on the PICTS self- assertion/deception subscale compared to all other offender groups, but this difference disappeared when analyses of covariance (ANCOVAs) con- trolling for demographic factors were conducted. ANCOVA analyses showed white-collar only criminals had higher scores on the SIC in-group ties sub- scale compared to the other offender groups. Also, all offender groups were significantly different on the LCSF-R total score. Specifically, the white-collar- crime-only offenders demonstrated the lowest score followed by white-collar criminally versatile and lastly non-white-collar offenders.
Blickle, Schlegel, Fassbender, and Klein (2006) explored the differences in personality between 76 incarcerated white-collar offenders (7.9% women) and 150 business managers (37.3% women). The white-collar offenders (46.8) were older than the managers sample (44.1). White-collar offenders reported having had a mean annual income of $93,472 previous to their cur- rent incarceration. Individuals in the management sample reported a mean
392 L. Ragatz and W. Fremouw
annual income of $148,326. All respondents completed self-report measures assessing social desirability, hedonism, narcissism, and conscientiousness. Self-control was measured via an assessment in which individuals read four separate scenarios wherein cheating another individual was possible. If respondents chose to cheat, they were considered low in self-control. Self- control was also measured using the Retrospective Behavioral Self-Control scale (RBS; Marcus, 2003). A logistic regression showed higher hedonism, narcissism, conscientiousness, and lower levels of behavioral self-control predicted white-collar criminality.
Critique of Exploratory and Comparative Studies
All six studies utilized different definitions of white-collar crime, creating problems of generalizability. Dhami (2007) included in their sample of white-collar offenders only those individuals who were in higher-level man- agement positions and had committed the crimes of deception or fraud in the course of their occupation. Dhami did not operationally define fraud or deception, so it is unclear what the specific offenses were that fit within these broad offense categories. Walters and Geyer (2004) utilized a white- collar crime definition similar to Wheeler et al. (1988) but also included two additional white-collar crimes (i.e., health care fraud, counterfeiting). Collins and Schmidt’s (1993) definition of white-collar crime included several crimes (i.e., antitrust violations, counterfeiting, bribery, fraud) that overlapped with Walter and Geyer’s definition but also incorporated numerous offenses that were unique to their definition (i.e., interstate transport of stolen vehicles, misuse of public money, forgery, money laundering, racketeering influence within an organization). Blickle et al. (2006) included various crimes in their definition that were included in both Collins and Schmidt and Walters and Geyer but added the dissimilar offense of smuggling. Kolz (1999) looked only at the white-collar crime of employee theft and used one yes/no question about past stealing of workplace property to define this variable. Alalehto (2003) did not specifically provide a definition of white-collar crime but stated only that most crimes described were tax evasion–related.
Several studies utilized existing records but made no mention of who coded the data, training procedures for the coders, or inter-rater reliability. In many cases, how data were coded was ambiguous. Alalehto (2003) used a ranking system when coding data to assess whether a specific personality structures were unique to white-collar offenders and white-collar profes- sionals. Three points were provided to cases in which the individual had been described as having only one dominant personality trait. Two points were provided to cases in which the individual described had only two dominant personality traits. Last, one point was provided for cases where the individual described had more than two dominant personality traits.
Profile of White-Collar Criminals 393
The distribution of points for each personality trait was summed separately for white-collar offenders and white-collar professionals. The ranking system may have inflated the number of points provided to some personality traits. Alalehto (2003) concluded that study results could assist job recruiters, but the deficiencies of the study made this conclusion over-ambitious. Walters and Geyer (2004) was the only study to mention who coded the data (the authors) and inter-rater reliability (r = .91).
Three studies (Alalehto, 2003; Dhami, 2007; Kolz, 1999) used interview methods to collect data but did not describe the credentials or training of interviewers. Self-report and interviewing methods may have been suscepti- ble to impression management. Kolz’s (1999) study was especially prone to socially desirable responding because employee surveys contained identify- ing information. This identifying information then had to be used to match survey responses with attendance records and supervisor interview data. Blickle et al. (2006) relied exclusively on self-report data, even when other secondary sources of information were available to verify the validity of responses. Specifically, white-collar offenders were asked to self-report their current offense. The researchers did not verify that the self-reported offenses matched the offense documented in each offender’s court file. However, Blickle et al. should be applauded for making an effort to control for social desirability when conducting a logistic regression meant to predict white- collar criminality. Dhami did not detail who gathered the interview data or whether the interviewer was trained on the interviewing procedures. Moreover, similar to Blickle et al., Dhami also relied heavily on self-report data. The accuracy of demographic data is questionable as Dhami did not utilize a secondary source of information to verify this information but instead relied exclusively on data gathered during the interview.
Many researchers failed to clearly operationalize the procedures they utilized in their study. For instance, Dhami (2007) stated that offense infor- mation were gathered from the prison database but did not detail whether any demographic data were also gleaned from this database. Dhami does endorse gathering demographic information during the individual interview with offenders but does not specifically detail which variables were col- lected in this fashion. Collins and Schmidt (1993) reported demographic data for their sample but did not specify how the data were gathered (e.g., interview, self-report, archival). Alalehto’s (2003) study excluded details on several demographic variables (e.g., age, gender), which could suggest that this fundamental information was not collected. Alalehto mentioned using a semi-structured interview to gather participant information. The researcher provided some example questions from the interview but did not spec- ify which interview questions were used to assess each of the different personality attributes. Kolz (1999) failed to operationalize several variables (long breaks, cooperation, socializing excessively). Several of the mea- sures utilized in Blickle et al. (e.g., narcissism, RBS) were not efficiently
394 L. Ragatz and W. Fremouw
operationalized; therefore, the format and scoring of these measures was not apparent. Insufficiently defined variables can make replication of these studies impractical.
The Cronbach alphas were not provided for all measures utilized in the five comparative studies. Three studies included measures with very low reli- ability (Blickle et al., 2006; Collins & Schmidt, 1993; Walters & Geyer, 2004). Collins and Schmidt (1993) mentioned that the reliability for the General Biodata Questionnaire subscales ranged from .60 to .92 but did not specify which of the subscales had the low reliability of .60. The reliability of the SIC subscales (in-group ties, α = .50; centrality, α = .48; in-group affect, α = .58) in the Walter and Geyer (2004) study were extremely low. The Blickle et al. (2006) measures of behavioral control (α = .62) and narcissism (α = .52) also demonstrated low reliability. Conclusions made by researchers with these unreliable measures should be interpreted with caution.
Dhami did not include a comparison group; therefore, it is impossible to know whether white-collar offenders exhibit unique attitudes from non- white-collar offenders toward their criminal actions. Many of the comparative studies did not match white-collar offenders and non-white-collar offend- ers or non-offender samples on demographic variables. Failing to match groups on demographic characteristics could create the potential for extra- neous variables to impact results. Bias in selection of the sample may have existed due to attrition as few of these studies compared the characteristics of individuals who agreed to participate in the study with the character- istics of participants who dropped out of the study (Blickle et al., 2006; Dhami, 2007; Kolz, 1999). Collins and Schmidt (1993) matched the pro- fessional and offender groups on age and ethnicity. The groups were not matched on gender or job position. In fact, the offender sample had pre- dominately more women than the professional sample. Also, no information on past job position for the offender group was mentioned in the study. Walters and Geyer (2004) made exceptional efforts to attempt to control for demographic differences between the experimental group and comparison groups. The researchers compared white-collar offenders with no criminal history and white-collar offenders with a history of white-collar offenses on several demographic variables (i.e., age, education, sentence, race, mar- ital status). They also compared the demographic characteristics of study participants and dropouts. The researchers attempted to match study sam- ple groups on demographic variables but found this problematic because of the difference in age between offender groups. Conversely, Walters and Geyer used demographic variables as covariates to statistically control for theses differences.
Three of the studies claimed to examine the five-factor model of person- ality. Two studies (Blickle et al., 2006; Kolz, 1999) used the same personality assessment (NEO-FFI; Costa & McCrae, 1992). Blickle et al. (2006) looked only at the conscientiousness scales. Kolz (1999) looked exclusively at the
Profile of White-Collar Criminals 395
conscientiousness, neuroticism, and agreeableness scales of the NEO-FFI. Results of these studies appeared deficient as all five factors were not considered. Alalehto (2003) investigated the five-factor traits; however, sev- eral of the attributes discussed were not part of the traditional model (i.e., conceited, intellectual, positive valency, negative valency).
The Dhami (2007) and Alalehto (2003) findings seemed inconclusive as descriptive data were lacking on several variables. No inferential statis- tics were utilized by Alalehto to examine whether white-collar criminals were significantly different from non-criminal white-collar professionals on various personality dimensions. Collins and Schmidt (1993) employed a cross-validation sample and interpreted findings from the cross-validation sample. Use of a cross-validation sample decreased the likelihood of a type I error. Moreover, Collins and Schmidt demonstrated strong findings; with effect sizes in the medium to high range. Two studies (Kolz, 1999; Walters & Geyer, 2004) used multiple statistical tests. These two studies could have utilized multivariate statistical tests or a Bonferroni method to decrease the chance of making a type I error. Kolz’s (1999) findings should be interpreted with caution because the significant logistic regression accounted for only 4% of the variance in predicting white-collar criminality.
STRENGTHS AND WEAKNESSES OF THE CURRENT RESEARCH
Many of the reviewed studies used large samples. A large sample size can increase the power that is available to detect significant results and also decrease the chance of a type II error. A majority of the studies utilized the same or similar offense-based definition of white-collar crime, which can increase generalizability across studies. Many different research meth- ods were implemented across the studies (e.g., self-report, informant report, archival, interview, and scenario), and future research may want to incor- porate several of these methods into one study to increase the validity of findings.
The independent variable (i.e., white-collar criminal, non-white-collar criminal, white-collar professional) across all of the reviewed studies was a static variable, so random assignment to groups is not possible. Future studies should attempt to match experimental and comparison groups on demographic measures (e.g., gender, age, marital status) to decrease the influence these extraneous factors could have on results. Matching exper- imental and comparison groups on demographic variables may be more easily accomplished when the two groups being matched are white-collar offenders and white-collar professionals. Walters and Geyer (2004) demon- strated that matching non-white-collar criminals and white-collar criminals
396 L. Ragatz and W. Fremouw
on several demographic variables (e.g., age, marital status, race) can be difficult and may even be impractical. If matching groups on specific demo- graphic measures is not possible, statistical control of such variables should be utilized. Steps need to be taken to increase the internal validity of future studies. Assessments and variables need to be described in more detail in future works. Furthermore, several studies utilized measures with low reli- ability. It is suggested that coefficient alphas should be .65 or higher to be in the acceptably reliable range (Cohen & Swerdlik, 2005); therefore, this should be taken into account when determining which psychological mea- sures to use in the future. Archival data were regularly used in many studies, and these studies commonly used insufficient coding methods. Future stud- ies should practice appropriate coding procedures. Inferential statistics need to be more frequently utilized instead of descriptive statistics. Last, future research should make efforts to control for type I error (e.g., Bonferroni methods, multivariate statistical tests).
CONCLUSIONS FROM THE RESEARCH AND FUTURE DIRECTIONS
In sum, few conclusions can be drawn from the white-collar crime literature as it currently stands. Some of the more-supported findings imply white- collar offenders are older, white, employed, have a high school diploma or GED, are less likely to have a substance abuse problem, and less likely to have a criminal history in comparison to non-white-collar offenders (i.e., drug, postal theft, or bank robbery; Benson & Moore, 1992; Poortinga et al., 2006; Walters & Geyer, 2004; Wheeler et al., 1988). White-collar offenders also tend to be lower in conscientiousness, agreeableness, and behavioral self-control than white-collar professionals. Additionally, white-collar offend- ers reported higher levels of anxiety and extroversion than white-collar professionals (Alalehto, 2003; Blickle et al., 2006; Collins & Schmidt, 1993; Kolz, 1999).
These conclusions are unsatisfactory because the researchers frequently evaluated male and female white-collar offenders collectively. Preliminary research has suggested that female white-collar offenders may be differ- ent demographically than male white-collar offenders. Female white-collar offenders are less likely to be white, more often in clerical positions (male white-collar offenders are more often in management or administrative posi- tions), and younger in age than male white-collar offenders (Daly, 1989). Future research needs to clarify the differences and similarities between male and female white-collar offenders.
Scholars disagree on the facets that should define white-collar crimi- nals. One commonly debated characteristic of the white-collar offender is
Profile of White-Collar Criminals 397
socioeconomic status (Friedrichs, 2007). The vast majority of studies in this review did not report the socioeconomic status of the white-collar offenders included in their study sample. Two studies assessed socioeconomic status and found very different results. Pogrebin et al. (1984) found that the mean annual income for a sample of convicted embezzles was $10,000. The Blickle et al. (2006) white-collar offenders sample had a mean annual income of $93,472. Thus, the study samples utilized in these two studies were very different. The Pogrebin et al. study sample consisted of a higher percent- age of women (62.9%) compared to the Blickle et al. sample (7.9%). Blickle et al. looked at white-collar criminals convicted of various different white- collar offenses (e.g., bribery, smuggling, embezzlement), whereas Pogrebin et al. looked only at convicted embezzlers. Research needs to examine the differences in socioeconomic status that may exist between male and female white-collar offenders. Scholars could also examine whether a high socioeconomic status might be more common among white-collar offend- ers of specific crimes (e.g., bribery, insider trading). Future research could explore the socioeconomic status of white-collar offenders in comparison to white-collar professionals and non-white-collar criminals.
Comparative studies that explored the demographic characteristics of white-collar offenders regularly used non-white-collar criminals as the com- parison group. Future studies should explore how white-collar criminals differ on demographic characteristics from non-criminal white-collar profes- sionals. In contrast, comparative studies that examined the psychological characteristics of white-collar criminals often used non-criminal samples as the comparison group. Future research should look at how white- collar criminals vary on different psychological attributes compared to the non-white-collar criminal.
For the research on white-collar offenders to thrive, an accepted defini- tion of white-collar crime needs to be developed and accepted by scholars. Currently, construct validity of the concept of white-collar crime is lim- ited across studies. Many researchers have used, at least as a foundation to their definition, the offense-based definition set forth in Wheeler et al. (1982, 1988). Another approach might be to examine the characteristics of white-collar offenders that commit a specific white-collar crime. Some stud- ies have already taken this approach by looking specifically at offenders who have committed employee theft or embezzlement. Research still needs to look in depth at the characteristics of offenders of these and other types of white-collar crime (e.g., income tax evasion, insider trading, health care fraud).
Most research has looked exclusively at white-collar criminals who have been convicted under criminal laws. Future research may want to look at white-collar criminals found guilty of breaking civil or administrative laws. Research could also explore the characteristics of white-collar offenders who have not been convicted for their crimes. Convicted white-collar offenders
398 L. Ragatz and W. Fremouw
may have different demographic and psychological attributes than white- collar offenders who have not been adjudicated.
Researchers have only begun to investigate the psychological attributes of white-collar criminals. Psychopathic traits have been correlated with various antisocial acts (e.g., committing violent crimes, substance abuse; Porter, Birt, & Boer, 2001; Smith & Newman, 1990). Case study research has suggested the potential for psychopathy to also play a role in work- place criminal behavior (Babiak, 2007). Moreover, some of the characteristics of a psychopath (i.e., charming, grandiose) emulate those features which make a successful business person (Babiak, 2007). Several self-report mea- sures of psychopathy (e.g., Psychopathic Personality Inventory: Lilienfeld & Andrews, 1996; Levenson’s Self-Report of Psychopathy Scale: Levenson, Kiehl, & Fitzpatrick, 1995) exist, making it possible and promising to assess for this personality dimension in white-collar professionals. Future research should evaluate whether white-collar criminals exhibit psychopathic traits. Research should also look at how white-collar professionals and non- white-collar offenders differ from white-collar offenders on psychopathic features.
Some studies (Blickle et al., 2006; Kolz, 1999) have utilized the NEO-FFI to assess personality dimensions in white-collar offender and non-offender samples. Future research may want to use the Revised NEO Personality Inventory (NEO-PI-R; Costa & McCrae, 1992) because it is a more com- prehensive five-factor personality measure. This measure might also offer opportunity to assess for psychopathy in non-criminal samples. Previous research has demonstrated that a unique profile on the NEO-PI-R may be suggestive of having psychopathic traits (Miller & Lynam, 2003).
Criminal attitudes have been associated with recidivism by non-white- collar criminals (Simourd, 1997). Little is known about the criminal attitudes of white-collar offenders. Dhami (2007) showed that white-collar offenders utilize several excuses to minimize the severity of their actions and jus- tify their criminal behavior. Specifically, several offenders concluded their actions were noncriminal because there is no clear crime victim, their actions are unique from non-white-collar offenders, and external circum- stances were to blame for their criminal acts. One study utilized the PICTS to assess and compare the criminal attitudes of white-collar and non-white- collar offenders. This study was limited because it investigated only the four factor scales. This study neglected to look at the eight thinking style scales, two content scales, the fear-of-change scale, and the total scale score that also make up the PICTS measure (Walters, 2006). The attitudes that white-collar offenders displayed toward their criminal actions in Dhami’s study suggests that white-collar offenders might be likely to exhibit two of the eight PICTS criminal thinking styles: mollification (blame outside events for engagement in criminal behavior) and entitlement (perceived unique- ness that the individual has that justifies engaging in acts that infringe
Profile of White-Collar Criminals 399
on the rights of other people; Walters, 2006). Future research may want to investigate how these scales compare for white-collar offenders, non- white-collar offenders, and white-collar professionals. Various additional assessments exist for assessing criminal thinking (e.g., Criminal Sentiments Scale-Modified: Simourd, 1997; Texas Christian University Criminal Thinking Scales: Knight, Garner, Simpson, Morey, & Flynn, 2006) and should also be utilized in future research. Additionally, having knowledge of white-collar offenders’ criminal thinking patterns could assist with creating appropriate treatment programs for such offenders.
A handful of studies have explored the motives of white-collar offend- ers. One study suggested financial need might be the most frequent motive for women’s choice to engage in white-collar criminality (Daly, 1989). Preliminary evidence has also shown specific motives might be more preva- lent among offenders of particular offenses. For instance, business motives were more pronounced among antitrust and securities fraud offenders. Personal gain or financial need motives were more common among embez- zlement, mail fraud, and tax fraud offenders (Pogrebin et al., 1986; Langton & Piquero, 2007). Future research should continue to explore the motives of white-collar offender. This information could contribute to treatment development.
The media are inundated with stories of notorious white-collar crimi- nals. Most recent is that of Bernie Madoff, who is accused of taking more than $50 billion from investors in his securities trading firm (Chernoff, 2009). Understanding and predicting criminality among white-collar profes- sionals seems of the utmost importance, especially when the costs are so considerable.
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