SCIENTIA
2007
The Journal of the Honors Program
About the Author
Christina Evonne Poulos is receiving a Bachelor of Arts degree in Criminal Justice and minors
in Comprehensive Social Sciences, Business and Forensic Accounting. She is a member of Delta
Epsilon Sigma, the National Scholastic Honor Society, Alpha Phi Sigma, the National Criminal
Justice Honor Society and Kappa Gamma Pi, the National Catholic College Graduate Honor
Society and is the Vice President of the Criminal Justice Club. She has also been involved with
Volunteers in Action, Support Our Troops, and the American Red Cross. Christina has presented
research at the 2007 Pennsylvania Association of Criminal Justice Educators Conference. She
chose to pursue a Citation in Honors to challenge herself academically, enhance her
undergraduate experience and expand her course of study. Christina plans to pursue her Master
of Science Degree in Criminal Justice in the fall through Marywood University’s five-year
program. Christina would like to thank her advisor and thesis director, Dr. Broughton, for his
expertise and editing skills; her reader, Mr. Marcinek, for his perspective, support and
encouragement; and Christina Elvidge for her guidance and editing skills. Finally, Christina
would like to thank her mom, dad and sisters for their endless love and support as well as her
friends.
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A Comparative Study Of White-Collar and Blue-Collar Criminals in American Jails
Christina Evonne Poulos
Abstract
The present study compares white and blue-collar criminals in the hopes of better
understanding white-collar criminals. White-collar crime includes fraud, embezzlement, money
laundering, bribery and forgery while blue-collar crime includes robbery, burglary, theft, assault
and substance abuse. Many criminological theories exist that attempt to explain why individuals
engage in criminal activity. Such theories include Sutherland’s differential association,
Gottfredson and Hirschi’s low self-control theory, and Sampson and Laub’s age-graded theory of
informal social control. The Survey of Inmates in Local Jails from 2002, with a sample of 6,982
completed inmate interviews, will be used as the data set. Any similarities between these two
types of offenders will be discovered by analyzing the independent variable of white and bluecollar crime with the dependent variables. The dependent variables include such groups as
socio-economic, delinquent friends, arrest, and a compilation of contemporary variables such as
ever been admitted to a mental facility and ever considered suicide. This study will also include
implications for future study and researchers.
Introduction
The criminal justice system has the responsibility to deal with and to respond to crimes
committed by individuals in our society, as it has done in the past and continues to do today.
One type of crime that has increased in public awareness is white-collar crime. A few notorious
examples aiding such awareness include both the Enron and Martha Stewart scandals. The
Enron Corporation was found guilty on multiple charges including fraud, falsifying profits,
concealing debts, and lying to auditors. Enron had the ability to falsify its profits through the use
of offshore accounts, which gave the illusion of profits when, in reality, the company was in debt
to the point of near bankruptcy. Martha Stewart was found guilty of lying to federal
investigators and conspiracy of concealing her involvement in stock trading. Stock or insider
trading can be legal if properly reported; however, criminally speaking, it is when an insider
obtains non-public or private information and secretly trades stock or securities, such as bonds,
based on that information (Federal Bureau of Investigation).
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Unknown factors still exist about white-collar criminals, such as why they commit crimes
when other individuals in their position do not, and whether certain behaviors indicate such
offenders. The purpose of this study is to determine any similarities between white-collar and
blue-collar criminals in the hopes of better understanding the former. Utilizing such variables as
socio-economic class, arrest characteristics, and delinquent friends will assist in this
understanding of white-collar criminals. Such information will enable us to better understand
white-collar criminals because it is in these specific areas that white-collar and blue-collar
criminals differ the most.
Literature Review
White-collar crime is defined as “those illegal acts which are characterized by deceit,
concealment, or violation of trust and which are not dependent upon the application or threat of
physical force or violence” (Federal Bureau of Investigation). This type of crime is committed
by a person or an organization solely for money, property, or services; for the prevention of
economic loss; and/or for the procurement of either personal or business advantage. Whitecollar crime is designated as a property offense which more often than not is committed by
businessmen and individuals highly ranked in society and/or occupation. Fraud, however, as a
specific sub-type of white-collar crime, can also pertain to the street level criminal. Such types
of petty fraud consist of identity theft, tax evasion, credit card, bank and mail fraud (Federal
Bureau of Investigation).
Historically, crime was generally explained as the result of low social and economic
class, and poverty. This claim obviously proved to be problematic for white-collar crime
because white-collar criminals are from upper social and economic classes; therefore, poverty is
not a causal condition. It was suggested that if a common factor were to be found between street
criminals and those of white-collar crime, then a general theory of criminological behavior could
be determined and analyzed (Sutherland 6).
Edwin H. Sutherland is known as the father of criminology and the individual who first
identified white-collar crime in 1940. Sutherland states that white-collar crime, as well as other
types of crime, derives or generates from a process known as differential association. This
learning theory states that criminal behavior is observed and learned by individuals who see
nothing wrong with crime and are unfamiliar with individuals who consider otherwise. In other
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words, this behavior is adopted through interaction, where anyone would choose to engage in
crime if the benefits outweigh the consequences (Sutherland 234). Theories that criminals
offend due to their social status, economic status, family background, childhood, evidence of
abuse, and/or low education level are difficult to apply to white-collar crime. It is for this reason
that Sutherland’s theory of differential association is still valuable today, because differential
association is a theory that can explain crime among any type of individual regardless of their
socio-economic status.
Another pertinent theory which is applicable to white-collar crime is Gottfredson and
Hirschi’s theory of low self-control. This theory contends that criminal acts of any type are the
result of low self-control. This behavioral characteristic could explain why some individuals in
an occupational position commit criminal acts while others do not (Gottfredson & Hirschi 191).
However, researchers hypothesized that a significantly low amount of criminal activity will exist
amongst white-collar employees. The fact that white-collar employee positions commonly
require such characteristics as high self-control and responsibility promotes such a statement.
Therefore, the theory of low self-control does not appear to cause white-collar offending,
suggesting that these crimes require an independent criminological theory.
A pair of researchers, Sally Simpson and Nicole Piquero, wrote an article about the
evaluation of the theory of self-control in contention with the organizational theory. Simpson
and Piquero’s article, however, contradicts Gottfredson and Hirschi’s theory of low self-control.
They state that “the data used to test [the] research hypotheses [was] drawn from a factorial
vignette survey that was constructed to examine organizational and individual-level factors
associated with managers’ decisions to engage in corporate crime” (Simpson & Piquero 520).
They found, contrary to Gottfredson and Hirschi’s theory, that there is an insignificant
correlation between behavioral indicators of low self-control and intentions to participate in
crime. The results did show, though, that approximately 66% of the time, participants reported a
non-zero chance of participating in criminal activity. This contradicts the low self-control theory
because MBA students and executives, who make up the study’s population, are individuals with
high self-control. Therefore, while this study shows a correlation between low self-control and
criminal behavior, it is only a minor correlation (Simpson & Piquero 533).
A pair of theorists with another theory relevant to white-collar crime is Sampson and
Laub. Their age-graded theory of informal social control acknowledges the importance of
70
childhood behavior but rejects the implication that adult social factors have little relevance
(Sampson & Laub 17). In other words, this theory signifies how weak or illegitimate
attachments to society will increase antisocial behavior at any age in an individual’s life. This
theory relates to white-collar crime because it refers to the association between initial
delinquency and the commission of crime. Blue-collar criminals lack societal attachments and
engage in crime directly after their introduction to delinquency; however, white-collar criminals
desist from juvenile delinquency because of proper societal attachments and engage in crime
later on in life because of contemporary illegitimacies such as unemployment and/or marital
problems.
Sampson and Laub conducted one measurement based on the social factors of occupation
maintenance, dedication, ambition, and marital status among a group of delinquents. The
authors state that while a matching sample research design increases delinquency differences
among groups, an analysis done inside one group controls for consistent delinquency among
individuals. Matching is defined as “the procedure whereby pairs of subjects are matched on the
basis of their similarities on one or more variables, and one member of the pair is assigned to the
experimental group and the other to the control group” (Babbie 226). Therefore, Sampson and
Laub state that matching will help show differences in delinquency between groups, while it can
also show differences between individuals from one specific group. In reviewing occupation
maintenance, the authors found that 17-25 year olds with steady jobs were significantly less
likely to have major problems with alcohol and/or delinquency in their 25-32 year old adult life.
The authors also established similar findings in regard to goals of ambition and marital status in
this measurement. Delinquent individuals who had aspiring goals, such as education level, were
highly less likely to participate in deviancy, to consume alcohol, or to be arrested at both age
groups 17-25 and 25-32 (Sampson & Laub 145-7).
Similarly, Sampson and Laub also found that those individuals who had strong
commitments to their spouses were less likely to consume alcohol, to participate in delinquent
behavior, and/or to be arrested at ages 25-32. It is also important to mention that the high and
strong measurements of occupation maintenance, goals of aspiration, and marital status are all
statistically significant at the .05 level (Sampson & Laub 146-7). These results help signify how
weak attachments to society throughout an individual’s teenage years will increase such
antisocial behavior as alcohol consumption and being arrested.
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Another article relevant to white-collar crime was written by Nicole Piquero and Michael
Benson. This article regards developmental theories as applicable to the life course theory,
further proving the significance of societal attachment early in an individual’s life. To elaborate,
developmental theories for separate offenders presume that there are different causes at different
stages of a career criminal’s life (Piquero & Benson 151). A career criminal is someone who
views crime as a way of life and who repeatedly enters the criminal justice system. Also, the life
course perspective analyzes whether juveniles continue or refrain from crime as they age
(Piquero & Benson 149). Piquero and Benson state, in concurrence with Sampson and Laub,
that if proper attachments to society are not developed early in life, that they generally will not
be developed throughout adulthood. Therefore, not only do behaviors of delinquency and crime
develop, but they also increase in potency in later teenage and adult years. Therefore, regardless
of the particular stage in an individual’s life, societal attachments directly influence high or low
behavioral criminality (Piquero & Benson 152). This theory helps explain white-collar crime
because it once again illustrates how contemporary illegitimacies, such as marital problems and
unemployment, can cause participation in crime.
Weisburd, Wheeler, Waring, and Bode conducted another excellent study applicable to
white-collar crime. With measurable indicators of social influences on the behavior of whitecollar criminals, Weisburd et al., examine employment, social rank, education, financial status,
and home ownership among white-collar criminals, general criminals, and average society
members. Weisburd et al., show that 58% of general criminals are unemployed as compared to
only 7% of white-collar criminals, and that 11% of general criminals maintain employment while
a high 55% of white-collar criminals do. These percentages are not shocking considering that
white-collar criminals rely on their occupation for their offense, and blue-collar criminals tend to
be of lower social status and have difficulty maintaining steady jobs (Weisburd et al. 63).
In terms of education level, 78% of white-collar criminals are high school graduates or
have their GED, with an additional 25% being college graduates. General criminals, on the other
hand, graduated high school or earned their GED at a percentage of 46 with only 3% being
college graduates. This also is to be expected since white-collar criminals need to be well
educated for their occupational position, while more general criminals commit typically violent
and/or petty crimes that most of the time are on impulse or are executed poorly. Lastly in regard
to assets, or in this case home ownership to be more specific, the authors found that 45% of
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white-collar criminals own homes, while a mere 7% of general criminals do (Weisburd et al. 63).
These statistics assist in illustrating the differences between white-collar criminals with more
general or blue-collar criminals, which again signifies that white-collar criminals commit crimes
for reasons independent of other types of criminals.
Michael Benson’s work, Crime and the Life Course, overviews several theories and
studies of white-collar crime in an excellent fashion. Benson discusses several perspectives of
causation in specific regard to white-collar criminals. He states that different inducements of
crime may range from reviewing criminals throughout life, to social and economic conditions
that may lead an individual to participate in criminal activity. Benson discusses age and crime,
behavior continuance, antisocial behavior, and social influences on behavior. In regard to age
and crime, Benson states that, because white-collar criminals offend at approximately forty years
of age, they are entering into a life of crime at a time when other criminal types are typically
exiting, as indicated by the life course perspective (Benson 135). In relation to prior offenses,
Benson does note that a percentage of white-collar offenders do in fact have prior records. A
common perception of white-collar criminals is that they commit their offense once they have
reached a certain level or position in their occupation, or not until they have earned a sense of
trust within that organization (Benson 151).
Another pair of researchers, Glenn Walters and Matthew Geyer, reviews the criminal
history and prior offenses of white-collar criminals. The authors hoped to determine whether
inmates who solely committed white-collar crimes varied from other inmates. The group of
other inmates includes white-collar inmates with prior records of crimes other than white-collar,
and inmates who only committed crimes other than white-collar. With the use of the
Psychological Inventory of Criminal Thinking Styles measurement report, Walters and Geyer
measured variables of criminal thinking, identity, and lifestyle involvement (Walters & Geyer
266).
The findings of this study concurred with previously conducted research, stating that
there are two types of white-collar offenders: The first type is a large and specialized group
lacking prior records of crimes other than white-collar, and the second is a smaller and more
adaptable group of white-collar offenders with prior records of one or more crimes other than
white-collar. It was determined, though, by the testing scales that white-collar offenders were
73
less likely to invest in criminal thoughts, to identify with other criminals, and to show signs of a
criminal lifestyle (Walters & Geyer 275-6).
After reviewing the criminological theories and studies mentioned in the literature
review, I have developed several hypotheses for this study. The hypotheses for this study, in
regard to white-collar criminals, are that the offender would be male, white, aged thirty-five and
older, come from a more privileged upbringing, and would commit the crime in question based
on their occupational level. To elaborate, I interpreted a more privileged upbringing as including
the offender’s childhood as well as educational level.
Methods
For the purpose of this exploratory research, the Survey of Inmates in Local Jails from
the year 2002 will be used as the data set. This data was distributed by the Inter-University
Consortium for Political and Social Research (ICPSR) and was conducted by the U.S. Census
Bureau for the Bureau of Justice Statistics. This set supplies information on individuals held
nationally and awaiting trial, and convicted criminals serving time in local jails or held
temporarily for transport to state prisons. Between January and April of 2002, this survey was
conducted with computer-assisted personal interviewing; it is typically performed every 5-6
years. The sample for this survey was chosen from 3,365 jails which held 529,084 adult male
inmates, 67,464 adult female inmates, and 9,449 juvenile male and female inmates (United
States Department of Justice).
A cross-sectional study is one which is established on observations from one incident at
one time. Exploratory research, such as this study, is typically cross-sectional. This crosssectional sampling design had a two-part stratified selection process in which jails were selected
first and inmates second. Stratification is defined as the combination of units within a population
into equal groups and is typically done before sampling. Stratification significantly decreases
sampling error, because a proper study population is practically guaranteed (Babbie 205). A
total of 465 jails were chosen during the first step of the selection process. However, only 417
jails participated, because 39 declined the invitation and 9 were unable to participate. During the
second step of the selection process, a sample of male, female, and juvenile inmates were
randomly selected for participation. Out of the 7,750 inmates chosen, an amount of 6,982
interviews were conducted for the survey (United States Department of Justice).
74
Procedure
For the purpose of this analysis, the single independent variable of white-collar and bluecollar crime will be operationally defined. In defining the independent variable, offense codes
for the survey were used, which were derived from the National Corrections Reporting Program
(see Appendix 1). In this research, white-collar crime will include blackmail and extortion
(150), forgery and fraud (220), embezzlement (260), income and/or tax evasion and money
laundering (670), and bribery (673). Blue-collar or street crime will include burglary (190),
attempted burglary (191), auto theft (210), grand larceny (230), petty larceny (240), larceny
and/or theft (250), and attempted larceny and/or theft(251). Such street crimes were chosen as a
comparison with white-collar crime because they are blue-collar level property crimes (United
States Department of Justice).
To examine the problem of this exploratory research, SPSS (Statistical Package for the
Social Sciences) analyses will be conducted for further analysis. The analyses performed include
descriptive statistics, variable frequencies, cross-tabulations and Pearson chi-square tests. Cross
tabulations allow the capability to determine the relationship between two categorical or nominal
variables. Chi-square significance tests are valuable because they “identify whether a
relationship is statistically significant or a product of randomness and/or error” (Babbie 464-5).
Results
The single independent variable of this exploratory research is white-collar and bluecollar or street crime. White-collar crime will be operationally defined as blackmail, extortion,
forgery, fraud, embezzlement, income and/or tax evasion, money laundering, and bribery. Bluecollar or street crime will be operationally defined as burglary, attempted burglary, auto theft,
grand larceny, petty larceny, larceny and/or theft, and attempted larceny and/or theft. The
independent variable was established by a variable posing the question: “For what new offense
were you arrested or charged when you were admitted to jail after being on [status] (first
offense)?” (United States Department of Justice 127). Using this variable, the total number of
cases that fit the operational definitions are 117 for white-collar crime, and 288 for blue-collar
crime (United States Department of Justice).
After analyzing criminological theories and research, a total of fifty dependent variables
will be used. However, given the large number of dependent variables in this study, I have
75
arranged them into similar groups. Such arranged groups include socio-economic status,
association with delinquent friends and arrest variables, as well as a compilation of other
variables including substance abuse, mental treatment and suicide. The socio-economic group
includes such variables as education level, committing the offense due to occupation or
developed skills, income amount, and type of occupation. The association with delinquent
friends group includes such variables as friends using drugs, participating in shoplifting or
stealing property, and whether the inmate participated as well. The arrest group includes such
variables as the amount of prior offenses, types of offenses, and if the inmate was drinking
during the crime. The other group includes such variables as age when the person first drank
alcohol, if he ever received mental treatment, and if he ever thought about suicide (United States
Department of Justice).
The first dependent variable, sex, showed that 72% of inmates are male and 28% of
inmates are female, which is to be expected. The collapsed age variable showed that 20% of
inmates are 20-24 years old, 15% are 25-29 and 30-34 years old, and 14% are 35-39 years old.
The collapsed race variable shows that 37% of inmates are white, 40% are black, and 18% are
Hispanic. The highest education level prior to admission variable shows the highest percentages
ranging in ninth to twelfth grade. Twenty-nine percent of inmates have an education level of
twelfth grade, 20% have eleventh, 16% have tenth, and 11% have ninth (United States
Department of Justice).
The occupational codes variable has thirty-three choices from which the inmates can
choose, with the highest occupation being construction laborer at 10%, and the next highest
being cook at 6%. While the number of prior arrests variable ranges from one to eighty-seven,
22% have none, 17% have one, 14% have two, 10% have three, and 6% have either four or five.
The age at first arrest variable shows that the highest percentages range throughout adolescent
ages: Thirteen percent of inmates were first arrested at age eighteen, 9% at both seventeen and
sixteen years old, 8% at fifteen years old, 6% at fourteen years old, and 5% at thirteen years old.
The first offense variable, if referring to white-collar crimes specifically, shows that 0.3% of
inmates committed extortion and/or blackmail, 4.3% committed fraud and/or forgery, 0.1%
committed embezzlement, and 0.3% committed income evasion, tax evasion, and/or money
laundering (United States Department of Justice).
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The type of offense variable shows that 24% of inmates committed violent crimes, a high
of 36% committed property crimes, and 26% committed drug crimes. The controlling offense
general categories variable shows that 25% of inmates committed violent crimes, 26%
committed property crimes, 25% committed drug related crimes, and 24% violated public order.
Lastly, the collapsed criminal history variable shows that 58% of inmates have a prior sentence,
41% have no prior sentence, and 1% have no offense. These dependent variables will be
corresponded with the single independent variable, white-collar and blue-collar crime, in the
pursuit of comparing the types of previous offenses and criminals themselves (United States
Department of Justice). The corresponding variable frequencies and descriptive statistics
between the single independent and multiple dependent variables can be viewed in total in
Appendix 2. The first table, Table 1, on the next page shows cross-tabulation percentages and
Pearson chi-square significance values for the socio-economic group of variables.
Table 1
Cross-Tabulations of White-Collar and Blue-Collar Criminals (Independent Variable) and the
Socio-Economic Group (Dependent Variables)
Variable
Blue-Collar
White-Collar
Chi-Square
Female
28%
58%
.000**
Age 30 or Older
49%
59%
.000**
Married
12%
20%
.000**
Some College/Graduate
14%
21%
.000**
Have Children
49%
64%
.000**
** Correlation is significant at the .01 level
Based on the results in Table 1, white-collar criminals are more likely to be female, aged 30 or
older, married, and have either some years of college level education or are college graduates.
They also have children at a higher percentage than blue-collar criminals. All of these variables
are statistically significant at the .01 level, which means that the likelihood of these results
happening by chance or randomness is very low. The second table, Table 2, below, shows crosstabulation percentages and Pearson chi-square significance values for the delinquent friends
group of variables.
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Table 2
Cross-Tabulations of White-Collar and Blue-Collar Criminals (Independent Variable) and the
Delinquent Friends Group (Dependent Variables)
Variable
Blue-Collar
White-Collar
Chi-Square
Used Drugs
74%
59%
.000**
Damaged Property
36%
20%
.000**
Shoplifted
54%
36%
.000**
Auto Theft
33%
20%
.000**
Sold Stolen Property
39%
21%
.000**
Broke Into Homes
34%
16%
.000**
Sole/Imported/Made Drugs
39%
29%
.000**
Stole From People
17%
8%
.000**
** Correlation is significant at the .01 level
Each of these variables in Table 2 shows that blue-collar criminals are more likely than
white-collar criminals to associate with delinquent friends, which clearly explains why bluecollar criminals engage in illegitimate activities as juvenile more so than white-collar criminals.
All of these variables are also statistically significant at the .01 level. The third table, Table 3,
below, shows cross-tabulation percentages and Pearson chi-square significance values for the
contemporary group of variables.
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Table 3
Cross-Tabulations of White-Collar and Blue-Collar Criminals (Independent Variable) and the
Contemporary Group (Dependent Variables)
Variable
Blue-Collar
White-Collar
Chi-Square
Drinking During Offense
32%
25%
.02*
Drank Daily Year Prior To
41%
34%
.04*
19%
13%
.005**
Ever Considered Suicide
15%
19%
.04*
Commit Offense Due To
6%
11%
.004**
Offense
Ever Been Admitted Mental
Treatment
Educational/Occupational Skills
*Correlation is significant at the .05 level
**Correlation is significant at the .01 level
Some of the variables in this contemporary category, which were statistically significant
at either the .01 or .05 level, include drinking during offense, drinking daily year prior to offense,
ever being admitted to a mental treatment facility, ever considering suicide, and committing
offense due to educational or occupational skills. As you can see in Table 3, white-collar
criminals considered suicide and committed their offense due to educational or occupational
skills more so than blue-collar criminals. All of the cross-tabulations and Pearson chi-square
values can be viewed in total in Appendix 3.
Discussion
The results of this study seem to corroborate one of the criminological theories from the
literature review in particular. The statistically significant variables appear to be reflective of
Sampson and Laub’s age-graded theory of informal social control. To restate, this theory
acknowledges the importance of childhood behavior but rejects the implication that adult social
factors have little relevance. This theory signifies how weak or illegitimate attachments to
society will increase antisocial behavior at any age.
More than half of the dependent variables, approximately 60%, were statistically
significant with the independent variable of white-collar and blue-collar crime. Some of the
79
arrest variables that promote this are number of priors, age at first arrest, and age at first
conviction. These variables helped signify how both white-collar and blue-collar criminals
engage in criminal activity as juveniles. They further signify how there is a gap between age at
first arrest and age at first conviction for white-collar criminals, while blue-collar criminals
consistently engage in criminal activities.
Another theory less reflective in the results is Sutherland’s theory of differential
association. This learning theory states that criminal behavior is observed by individuals and is
adopted through social interaction, and that any given individual would participate in criminal
activity if the rewards outweigh the consequences. The category of delinquent friends variables
is the only category which promotes this theory among the results. However, to clarify, these
variables are only useful in predicting criminal activity among juveniles. Researchers are still
theorizing if and why only certain white-collar occupants choose to offend, while others remain
legitimate. However, the purpose of this study is to determine any comparatives between whitecollar and blue-collar criminals with respect to criminological theories and statistical data.
The dependent variables that were not statistically significant with the independent
variable are still valuable in the sense that they contradict some of my earlier hypotheses. Some
of these variables are persons lived with while growing up, living in foster care, employment and
income month prior offense, chances of theft in job, commission of offense due to job, and
business or government knowledge. I was surprised to discover that childhood living
arrangements did not affect criminality among either white-collar or blue-collar criminals.
I hypothesized that living with one parent, both parents, other relatives, or in the foster
system would have caused some criminal behaviors among jail inmates. Even more surprising
was the realization that being employed the month prior to the offense, opportunities of theft in
the job, and committing the offense due to job, business or government knowledge was not
significant. One hypothesis which I have always maintained was that white-collar criminals
offend once they have reached a certain occupational level, or that either through a position of
trust or, more commonly, with specialized knowledge, they had the ability to commit the
crime(s) in question.
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Conclusion
In summation, the purpose of this study was to determine any comparatives between
white-collar and blue-collar criminals in the hopes of better understanding the former. Yet even
with an amount of fifty dependent variables, only thirty, approximately 60%, were statistically
significant with the independent variable. The research and criminological theories, however,
did prove useful in signifying variables consistent with white-collar and blue-collar criminals.
While Sutherland’s theory of differential association portrayed how juveniles engage in criminal
activity, Sampson and Laub’s age-graded theory of informal social control corroborates best with
the results. Some of the variable categories include socio-economic, delinquent friends, arrest,
and a compilation of other variables, including substance abuse, mental treatment and suicide.
Given the fact that white-collar criminals are particularly less representative in local jails,
further research is deemed highly valuable in criminology. A more precise study composed of
fewer and more specific variables could potentially be an excellent source of white-collar
criminality. Even though this study disproved some of my hypotheses, it did corroborate
criminological theories, and hopefully will be an incentive for further research.
81
Bibliography
Babbie, Earl. The Practice Of Social Research. California: Wadsworth/Thomson Learning,
Inc.,2004.
Benson, Michael L.,. Crime and The Life Course: An Introduction. Los Angeles, California:
Roxbury Publishing Company, 2002.
Federal Bureau of Investigation. Federal Bureau of Investigation. 2007. United States
Federal Government, United States Department of Justice. 29 January 2007
<http://www.fbi.gov/homepage.htm>.
Gottfredson, Michael R., and Travis Hirschi. A General Theory of Crime. Stanford,
California: Stanford University Press, 1990.
Piquero, Nicole L., and Michael L. Benson. “White-Collar Crime And Criminal Careers.”
The Journal of Contemporary Criminal Justice 2004: 128-165.
Sampson, Robert J., and John H. Laub. Crime In The Making: Pathways And Turning Points
Through Life. Cambridge, Massachusetts: Harvard University Press, 1993.
Simpson, Sally S., and Nicole L. Piquero. “Low Self-Control, Organizational Theory, and
Corporate Crime.” Law and Society Review 2002: 509-47. Wilson Web. The H.W.
Wilson Company. Marywood University. Scranton, PA. 27 Nov. 2006
<http://www.hwwilson.com/default.cfm>.
Sutherland, Edwin H.,. White Collar Crime. New York: Holt, Rinehart and Winston, Inc.,
1949.
United States Department of Justice, Bureau of Justice Statistics. “2002 Survey of Inmates in
Local Jails.” 2002: 1-871.
Walters, Glenn D. and Matthew D. Geyer. “Criminal Thinking and Identity in Male WhiteCollar Offenders.” Criminal Justice and Behavior June 2004: 263-281. National
Criminal Justice Reference Service. U.S. Department of Justice. Marywood University.
Scranton, PA. 05 Nov. 2006 <http://www.ncjrs.gov/index.html>.
Weisburd, David, Stanton Wheeler, Elin Waring, and Nancy Bode. Crimes Of The Middle
Classes: White-Collar Offenders In The Federal Courts. New Haven: Yale University
Press, 1991.