Citation
Streets versus suites

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Title:
Streets versus suites public perceptions about the seriousness of white-collar crime
Creator:
Van Antwerp, Victoria
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English
Physical Description:
1 electronic file : ;

Subjects

Subjects / Keywords:
White collar crimes ( lcsh )
Jurors -- Attitudes ( lcsh )
Jurors -- Attitudes ( fast )
White collar crimes ( fast )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Abstract:
The following research explores public perceptions of white-collar crime. Participants were asked to complete a two-page survey, inquiring about their perceptions of. Six white-collar crime scenarios and six street crime scenarios. Surveys were distributed to jury participants after they had been dismissed from jury. Participants were asked to read the crime scenarios and then judge the offense and offender on: seriousness, appropriate punishment for the offender, greed, remorse, and stress. Analyze revealed that public opinion on white-collar crimes has shifted. Overall, the public perceives white-collar crime to be just as serious as street crime, if not more serious. Change in public perception about white-collar crime may stem from the media focus on high profile incidents such as Enron, World Com, Martha Stewart's insider trading, and Bernard Madoff's Ponzi scheme.
Bibliography:
Includes bibliographical references.
Thesis:
Criminal justice
General Note:
School of Public Affairs
Statement of Responsibility:
by Victoria Van Antwerp.

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Source Institution:
|University of Colorado Denver
Holding Location:
|Auraria Library
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All applicable rights reserved by the source institution and holding location.
Resource Identifier:
861753513 ( OCLC )
ocn861753513

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STREFTS VFRSt J S St JITLS: Pt J BI .1(. PFR( TPTI< )NS ABOl JT TIIF SFRIOl JSNFSS OF \\'II ITL-COI.I.AR CRIMI: lw Victoria Van Antwerp R.A in Sociology. t l niYcrsity of ( 'olorado Boulder. cum laude. 2009 B.A. in Women Studies. t l nivcrsity of ( 'olorado Boulder. 2009 A thesis submitted to the University of Colorado Denver in partial fulfillment of the requirements for the degree of Master of Criminal .Justice 2011

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' h,-\ "icl\lri; t \';111 :\111\\CI'p :\II rc,..;n,cd.

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This thesis for the Master of Criminal Justice degree by Victoria Van Antwerp has been approved by Callie M. Rennison, Ph.D. Tracie Keesee, Ph.D. Date

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Van Antwerp, Victoria (M.C.J.) Suites versus Streets: Public Perceptions about the Seriousness of White-Collar Crime Thesis directed by A ssociate Professor Mary J. Dod g e ABSTRAC T The following research explore s public perceptions of white-collar crime. Participants were asked to complete a two-page survey inquiring about their perceptions of. Six white-collar crime scenarios and six street crime scenarios. Surveys were distributed to jury participants after they had been dismi ss ed from jury Participants were asked to read the crime scenarios and then judge the offense and offender on: seriousness appropriate punishment for the offender greed remorse and stress. Analyze revealed that public opinion on white-collar crimes has shifted. Overall the public perceives white-collar crime to be just as serious as street crime if not more serious. Change in public perception about white-collar crime may s tem from the media focus on hi g h profile incid e nts such a s E nron World C om Martha Stewart's insider trading and Bernard Mad oft" s Ponzi scheme. This abstract accurately represents the content of the candidate s thesis. I recommend its publication. Signed t odge

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DEDICATION I dedicate my thesis to my family Mary Dodge and Wyatt Kennedy. Each of you holds a special place in my heart and in this thesis process. My family has given me an appreciation of learning and taught me the values of perseverance, dedication, and determination ; without these values and your unconditional support, this thesis would not have been possible. Mary, I could never begin to thank you for everything you hav e done for me without you none of this would have been possible. You truly inspire me! Wyatt, thank you for your unfaltering support and understanding while I completed this thesis I could not have done it without your support understanding and encouragement.

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ACKNOWLEDGEMENT My deepest thanks to my advisor, Mary Dodge, your support, expertise, and contribution to my research made this thesis possible. You always went above and beyond, and I can never tell you how grateful I am for all your help. I also want to thank Callie Rennison and Tracie Keesee for all your valuable participation and insights throughout this process. Callie, I could not have produced the results I did without your guidance and instruction on appropriate testing procedures. I am truly appreciative of all the time you spend with me to create an amazing polished product. Tracie, you taught me how to look through several different lenses, without those lenses, this process would have been a view without multiple perspectives I cannot begin to tell all of you how much I appreciate all the time, and dedication you showed to my research process and project. You three truly helped make this process easier, and helped create a great thesis. I am incredibly grateful for the judicial staff in the El Paso County Courthouse, who aided in gathering participants for the research study, approving the research study to take place within the El Paso County courthouse, and allowed me to sequester jurors within the courthouse during the research period. Particularly, the Jury Commissioner, Mr. Dennis McKinney, for continually helping gather research participants for me, and for helping get the study approved through the proper channels in El Paso County. Assistant Jury Commissioner's Leilani Hendrick and Michelle Flesher are also owed many thanks for their assistance, and support during the research process. Lastly, the principal investigator is indebted to the individuals of El Paso County who chose to participate in the research study. Without their help in this research process, this research would not have been possible. I also owe a thank you to my family. To my parents, Ken and Kathy Van Antwerp, thank you for believing in me, and encouraging me to finish this thesis out, even when I didn't believe in myself. Grandpa, you were always there to listen to me read out loud my articles, I can' t tell you how much it helped to have an extra ear to listen to me babble on. Uncle Bob, thanks for your unconditional support, and for listening to my stories about data collecting. Yetti, thanks for giving up two weeks of your time to help me out with my research. I could never have gotten through the research as quickly as I did without all your help. You also were an awesome help doing data entry! Thanks so much everyone for everything. To my great friends: Carolyn Caputo, Derek Bauer, David Dean, Dana Reynolds, Donessa Gaspar, Jack Thorpe, Rachel Freeman, Razan Naqeeb, and Tim Jones, thank

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you for reminding me that you need to have fun, and laugh even in the most stressful times. I would also like to thank the School of Public Affairs staff and faculty. Particularly the staff and faculty that helped me through parts of this process: Antoinette Sandoval, Brendan Hardy, Christopher Weible, Dawn Savage, Jen Gartner Kaylene McCrum, Lisa Carlson, and Rob Drouillard. Each of you encouraged me, inquired how the thesis process was going and helped me through some part of the process. I am grateful for everything you did for me during this process, and for the help you give the students of the School of Public Affairs. Each of you helps make SPA the best place to be! Wyatt you were truly a rock through this thesis. I cannot even begin to put in words how much your support meant to me. You were always willing to pick-up the slack and encourage me when I felt this was just impossible. Your unfaltering belief in me meant more to me than I can ever begin to tell you! Thank you for everything my dear!

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LIST OF TABLES Table 4.1 Descriptive Statistics for the dependent variables (Ponzi Scheme Embezzlement, Auto Theft) ...... ........................................... 29 4.2 Descriptive Statistics for the dependent variables (Burglary, Corporate Crime and Prostitution) .......................................... .. ...... . . ....... .. 30 4.3 Descriptive statistics percentage means and standard deviations for sample, n=900 ............................................................................. 31 4.4 Regression analyses for Ponzi crimes ......................................... 34 4.5 Regression analyses for Embezzlement crimes .................. ........... 3 7 4.6 Regression analyses for AutoTheft crimes ........ .. ...................... ... 41 4. 7 Regression analyses for Burglary crimes ...................................... 44 4.8 Regression analyses for corporate crimes .. .................................. 4 7 4.9 Regression analyses for prostitution crimes ................................... 50 4.10 Binary Logistic Regression analyses for White Collar and Street Crimes .... 52 VIII

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TABLE OF CONTENTS Tables .. .............................. ................................................. . .... ..... viii Preface .............................. ............................................ ........... ... ix CHAPTER 1. INTRODUCTIO l ................ . ................ . .......................... I 2. LITERAT U R E REVIEW ... ....... .. .. . . ...................... . . .... .... 3 Public Perception o f White Collar Crime .... ... ............ ... . ...... .4 Gender and White-Collar Crime ........................ .................. 9 Public Perception o n Sentencin g ........................ .. . .......... ... 1 3 3. R ESEA RCH HYPOTHESIS .................................................. 21 4. METHODOLO G Y ......... .. ... . . .. ................ ........ ... ... .... ....... 22 Participant s ................................ .. ... . ............................. 22 Survey Ins trument .............................................. . .......... 22 Analytic Technique ................................... ....................... 2 7 5. R ESUL T S ... . ................................ . ........ .. .. ......... ... ... ... 28 Ordinary Least Square Analyses ........ ............... ... ... ............. 31 Bin ary Logistic R egression Analysi s .. ... .................. ... .......... 51 6. DISC USSION AND ........ ............ .................. 53 APPENDIX A Q UESTIONNAIRES B. H UMAN S U BJECTS APPRO\.AL R EFE R ENCES ............................................. ............................ ..... 57 VII

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PREFACE 'A criminal is a person with predatory instincts who has not sufficient capital to form a corporation.' Clarence Darrow IX

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CHAPTER I INTRODUCTION In 1939, at the American Sociological Society convention, Edwin Sutherland's presidential address introduced and defined white-collar crime. According to Sutherland ( 1949), white-collar crime constituted "a crime committed by a person of respectability and high social status in the course of his occupation" (p. 9). Currently, many definitions of white-collar crime exist that focus on different offenses and offenders. The National White Collar Crime Center's definition, for example, is far more inclusive: white-collar crime includes any "planned illegal or unethical acts of deception, committed by an individual or organization, usually during the course of legitimate occupational activity by persons of high or respectable status for personal or organizational gain that violates fiduciary responsibility or public trust" (M. Dodge, personal communication, January 30, 2011 ). The labeling of white-collar crime has changed the way social scientists, economists, and businesses conduct and look at management and practices. Public sentiment appeared to be one of indifference before recent, high-profile white-collar crime scandals such as Enron's collapse, Martha Stewart's insider trading scandal, Bernard Madoffs Ponzi scheme, and WorldCom's dissolution. These 1

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major white-collar crime incidents unleashed a frenz y of media attention that may have altered public ideas about the nature and seriousness of occupational and corporate crime. Changes in public perceptions about white-collar crime are the impetus for this research. This study is designed to examine public opinions about the seriousness of white-collar crime. In order to fully examine the public's perceptions of white-collar crime, this article examines prior literature on elite crime presents original survey research, and lastly, explores policy implications and future research. 2

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CHAPTER 2 LITERATURE REVIEW When picturing a criminal, individuals rarely think of a man with his white-collar or a woman with her pearl necklace who sits in a position of corporate authority. Instead, individuals picture the hooded figures in the dark alleyway waiting to prey on their next victim. The stereotypical offender is perceived to be the "dangerous" street criminal. This common perception ignores professionals and corporate executives who engage in illegal and unethical behavior. In today s media savvy culture, scandalous information is rarely concealed for long periods of time. Daily the public will tum on their televisions computers, or open a newspaper and read about an incident of white-collar crime. Despite recent publicity focusing on white-collar crime, few individuals actually understand the intricacies of the offenses and their impact on society. Media coverage often fails to fully explore the harms and costs of white-collar crime. The lack of understanding of the damages these elite criminals and crimes cause often shield upper echelon criminals from full blame and distort public perceptions. Victimization by white-collar offenders includes individuals and, in some cases, entire communities. In fact, tax payers often pay for the financial misdeeds of illegal actions by banks and government employees. Scholarly attention has resulted in a higher level of scrutiny of white-collar crime to help de-3

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mystifying the definition of white-collar crime, the costs of elite crime to society, and the malice behind these crimes. Unlike other areas of criminological research, white-collar crime is a fairly new area of study that has yet to be fully explored and researched. Due to white-collar crimes infancy in the scholarly field, research on the topic of elite crime is sparse and sporadic. Particularly, public perception of white-collar crime is underdeveloped in the literature. This literature review offers an overview of empirical explorations of white-collar crime and public perceptions, addresses issues of gender within whitecollar crime, and examines public perception and sentencing. In sum, several whitecollar crime issues emerge that deserve further research. First, current comparisons of current public perceptions of seriousness of white-collar crime versus traditional street crime are needed. Second, virtually no information is available that examines differences in perception between male and female offenders. Third, opinions about the motivations and actions of the offenders have yet to be explored. Fourth, scant information exists about different views of appropriate levels of punishment for street versus white-collar crime. Public Perception Of White-Collar Crime "Althoughjinanciallossesfrom white-collar crime continue to exce e d those a_[ street crime, the criminal justice system has traditionally focus e d on the latter" (Hol(freter VanSlyke, Bratton & Gertz 2008 p. 50) 4

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Within certain areas of criminology public perception research is vast, while in other areas, research is sparse. Perhaps the reason public perception on white-collar crime research is scant, is the lack of attention paid to elite deviance until more recent high profile scandals were featured in the media. Public perception of elite deviance is an area of research that has little literature available; specifically, opinions about the seriousness of white-collar crime. The various studies that have addressed the perceived seriousness of elite deviance have shown mixed results of just how the public perceives white-collar crime. Rossi, Waite, Bose, and Berk ( 1974) conducted one of the first studies on public perceptions of white-collar crime. Using a variety of crime scenarios, Rossi et al. noted several important discoveries about how individuals rank crime and the seriousness of various criminal acts. Crimes against persons were perceived as more serious than crimes where no harm against people was committed. White-collar crimes, which are not typically seen as being directly harmful to victims, were seen as less serious than other street crimes. Rossi et al. (1974) also discovered various demographic groups viewed crime differently. Their study found that Blacks tended to rate crime more seriously than Whites. Females viewed crimes more seriously than their male counterparts (Rossi et al., 1974). Younger individuals saw crime as a more serious issue than older individuals. Socio-economic status also influenced the ways individuals viewed the 5

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crime specifically, individuals flom lower socio-economic households had higher seriousness ratings for the crimes overall compared to their higher socio-economic counterparts. The influence of socio-economic class was particularly noticeable with males. Lastly, education affected the way people viewed crime seriousness. Rossi et a!. reported that the lower an individual's educational level, the more serious these individuals felt the crimes were, as opposed to the more highly educated individuals who viewed crime as less serious. Understanding how these various demographics affect individual's perception of crime seriousness opened the doors for scholars to understand how the pub! ic perceives issues differently based upon their various backgrounds. Rossi et al. s study gave future scholars an initial framework for understanding how the public views white-collar crime. Rosenmerkel (200 1) attempted to replicate Rossi's 197 4 study measuring the seriousness of white-collar crime in relation to other crimes. Using a survey instrument, Rosenmerkel (200 1) examined eight white-collar offenses, six property offenses and seven violent offenses. He found that white-collar crime was rated as less serious than almost all other crimes; more specifically, elite deviance was believed to be less serious than property or violent crimes. However, when elite deviance was evaluated in the categories of harmfulness, seriousness, and wrongfulness white-collar crime was ranked between property and violent crime (Rosenmerkel, 2001 ). The study suggests that when ranking white-collar crime offenses individuals use harmfulness over the wrongfulness of the offense when 6

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rating the seriousness of elite deviance Lastly, the results showed that harm produced by the crime was more important than the seriousness when determining the wrongfulness of the elite deviance. Unlike the Rossi et al. (1974) study Rosenmerkel' s was unable to reproduce the same results in terms oftheir demographic, though the latter used college students as participants While Rossi et al. (1974) and Rosen merkel s (200 I) studies found that the public did not perceive white-collar crime to be serious or as serious as other crimes Piquero, Carmichael, and Piquero (2008) found mixed results that contradict the findings. In terms of seriousness Piquero et al. discovered that elite deviance was perceived to be more serious in four of the six crime categories. When all six categories were compared, between 14 and 21 percent of the total sample respondents believed that elite deviance was as equally serious as the street crimes scenarios. In Piquero et al. 's (2008) study results largel y suggested that a majority of individuals perceived white-collar crime to be just as serious as street crime if not more serious in some cases. This finding suggests that earlier views about white collar crime being less serious are changing and that possible recent attention to elite deviance by the media has raised awareness. When asked about how resources should be allocated 65o/o of the respondents belie v ed that an equal amount of resources should be spent on dealing with street crime and white-collar crime. O v erall it appears that the public is becoming les s tolerant of elite deviance. 7

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Other notable variables in the Piquero et al. (2008) study included certain demographics that heavily influenced how individuals viewed the seriousness of white-collar crime; these demographic variables include age and education. Older individuals were more likely to see white-collar crimes as equally serious as the street crimes whereas younger individuals were less likely to see white-collar crime as being equally serious. Individuals who had college education also perceived whitecollar crime to be equally serious to street crime. Piquero et al. suggest that individuals with college educations have more exposure to problems and costs associated with white-collar crime. Lastly, the researchers found that sex and marital status did not have as much influence on individual's perceptions of seriousness of street and white-collar crime as previously believed. While these studies offer some insight into the public s perception on whitecollar crime, they offer few definitive answers. Since Rossi et al. s (1974) ground breaking study and Piquero et al. s (2008) study greater media and scholarly attention has been paid to elite deviance. Therefore, one might conclude that the extra media attention and empirical work has brought attention to the issue of elite deviance that did not exist prior to the 1970s. However, no definitive answers within the literature are offered as to the public attitudes shift in elite deviance, or even if the public's overall perception of white-collar crime has changed. 8

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Gender and White-Collar Crime White-collar crime has been researched by men and about men with little recognition qj'the roles women play as victims and perpetrators of elite deviance (Dodge, 2009, p. 1) Typically, the white-collar offender is a male who is educated and holds a position of authority or power within the corporate arena (Vande Walle, 2002). Most accepted definitions of white-collar crime are based upon the identity of the offender, who is still typically seen as a male. Dodge (2009) believes that "occupational marginality is the primary reason for the low number of women who participate in elite deviance, and low-status employment positions have circumscribed their efforts to engage in crimes associated with power and prestige" (p. 14). Today, women's role within elite deviance is evolving, and changing; "the role of women in white-collar crime has been discounted as crimes of the powerless, and the rare cases in which women held positions of authority and engaged in illegalities have limited definitive conclusions about their actions and the nature of their crimes," (Dodge, 2009, p. 24). Women's role in elite deviance will continue to evolve as their participation in the economic and corporate world expand. Historically, men have dominated the criminological research and the theories behind deviance; while women have typically been neglected from research arenas involving criminality. When women have been acknowledged within criminological 9

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research, they are seen as: biologically different, unnatural, abnormal, or crazy (see e.g., Cohen, 1955; Pollak, 1950). Historical assumptions about female criminality within criminological research have tainted gender perspectives, and served as a basis for patronizing and demeaning woman. The historical assumptions about female criminality created attitudes that women were unworthy of study. During the 1960s and 1970s, female criminality began to be looked at in a different lens by scholars, and regarded as something that warranted attention; the older theories about female criminals as inherently different were no longer seen as acceptable for explaining female criminality (Adler, 1975; Simon, 1975). While the 1960s and 1970s changed the way scholars were examining women's deviance, the old attitudes and outlooks of women's deviance were sti II prevalent. Feminist scholars have attempted to explain female deviance differently than their male counterparts (Cullen & Agnew, 2003; Daly & Chesney-Lind; 1988). Many feminist theories for deviance are shaped by the social movements that have transformed ideas about women's roles in society (Dodge, 2009). When discussing elite deviance, gendered terms are rarely used since men have mostly dominated the corporate, economic, and political worlds. Dodge (2009) explains that the overrepresentation of males in elite deviance is tied to the position and opportunity available to men, which traditionally has not been available to women. Limited opportunity to engage in elite deviance has kept women out of the corporate, economic, and political arenas (Adler, 1975) 10

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Though excluded in large part from these arenas, women have been victimized by white-collar crime. The victimization and vulnerability of women to white-collar crime is especially evident in the Victorian society; lack of knowledge about business matters and limited access to reliable sources of financial information left women highly vulnerable to abuses of white-collar crime during the 19th Century (Robb, 2006). Robb noted: "considerable evidence exists that women were sought out as victims by frauds and embezzlers who well understood their vulnerability" (p. 1 062). Shareholding was viewed by many women as an investment that offered them a way to make money, yet, according to Robb, this type of capital investment still left women open to abuses by the economic system. Throughout the 19th Century, middle-class women organized movements to seek greater political participation, better educational opportunities, and economic independence from men (Robb, 2006). The first wave of feminist organizing the women's movements in England and America highly criticized the economic system that marginalized and left women vulnerable to exploitations (Robb, 2006). Feminists looking to change women's positions and opportunities within society were met with fierce opposition from traditionalist who countered that allowing females into the foundations of society would destroy the purity of women by exposing women to the corruption of the market (Robb, 2006). The discourse of women's appropriate place in society and exclusion from the marketplace continued throughout the Victorian era; according to Robb, it was feared that by allowing women "outside the protective, or restraining, 11

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influences of the home, women might prove even more reckless than men" (p. 1 066). The view that women would be corrupted by the economic sector held for a large portion of the 19th century. For centuries, women continued to struggle for equality in the economic sector. It was not until the passage of the Equal Protection Clause of the Fourteenth Amendment, and the 1964 Civil Rights Act that women started entering the workforce in mass numbers, and in positions that were equal to their male counterparts (Dodge, 2009). In 1972, the Equal Employment Opportunity Act as applied to Title VII of the Civil Rights Act passed, which tore down the social, and legal structures that once allowed discrimination to occur based on sex, religion, race, and national origin within a workplace. As women entered the workforce, female scholars began looking at the expanded opportunities for women in the economic sector and how criminal activity might change (Adler, 1975; Simon, 1975). Freda Adler (1975) began exploring the issues of female criminality as a whole, and female criminality within white-collar crime. Adler argued that, "the higher rates for female deviancy was based on increased opportunities and decreased social controls" (Dodge, 2009, p. 1 0). These decreased social control and increased opportunities allowed women to compete with their male counterparts; however, with these opportunities came the increased opportunity for women to engage in the criminality similar to their male counterparts. Adler was not the only feminist scholar 12

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to predict the rise in female criminality. Rita James Simon ( 1975) also predicted that as opportunities arose, women would become involved in white-collar crime. While there was some consensus among feminist scholars that as women's opportunities increase so would their deviance, there was controversy as to where females fit into the male dominated world of white-collar crime. Public Perception on Sentencing "In the end, the public shows a tendency to be punitive and progressive, wishing the correctional system to achieve the diverse missions of doing justice, protecting public safety and reforming the wayward" (Cullen, Fisher, and Applegate 2000, p. 1). It is important to address the issue of public perceptions on sentencing. An examination of prior literature on the public perceptions of appropriate sentences for elite and street deviants provides a framework for understanding public viewpoints on street and white-collar crime. Prior research on punishment delineates two distinct categories: demographic factors that influence sentencing opinions and issues on sanctioning white-collar offenders versus street crimes. Demographic Factors that Influence Sentencing Options: Holtfreter, VanSlyke, Bratton, and Gertz (2008) specifically addressed public perceptions about apprehending and punishing white-collar offenders and street criminals. Holtfreter et al. found that a majority of respondents felt that violent 13

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offenders were more likely to be caught and receive harsher punishments for their crimes compared to white-collar criminals They also found that there were various demographics that affected the way members of the public perceived sentencing of offenders. Gender of respondents affected how individuals felt about the sentencing of various types of offenders. Females were significantly more likely to believe that white-collar criminals had an equal or greater chance of being caught and receiving a harsher punishment if caught than street criminals In addition females were significantly less likely to support funding for fighting white-collar crime compared to street crime. Similarly, Schoepfer, Carmichael, and Piquero (2007) found that females were less likely to believe that street crime should be punished more severely than white-collar crime. Income represented another significant predictor as to how individuals viewed the likelihood of an individual being apprehended and punished for white-collar crime. Individuals who made over $50 000 annually were significantl y less likely to believe that elite deviants would be caught and punished for their misdeeds (Holtfreter et al. 2008). Income paralleled with education in punishment and apprehension beliefs, individuals with more education were less likel y to believe that elite deviants would be caught and apprehended for their crimes (Holtfreter et al., 2008; Schoepfer et al., 2007). However, Schoepfer et al. (2007) found that individuals in higher income and educational brackets were less likely to believe that white-collar criminals would r e ceive a more severe punishment when apprehended. 14

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Ideology also played a role in how individuals perceived the likelihood of elite deviance being apprehended and punished for their crimes. Individuals who identified themselves as conservative or moderate were more likely than individuals who identified themselves as liberal to believe that white-collar offenders had an equal or greater chance of being punished (1loltfrctcr et al., 2008). 1 lowever, when examining Schoepfer et al. 's (2007) study, conservatives were less likely to believe robbery and the white-collar crime of fraud. would be equally likely to receive harsh punishments. While conservative to moderate individuals believed in punishing white-collar criminals. there was some disagreement as to whether political ideology influences harsher punishment. Geographic location also innuenced whether or not individuals perceived elite deviance as receiving punishment. Holtfreter et al. (2008) found that individuals who lived in urban settings were less likely to believe that white-collar criminals would be sanctioned. In contrast, Schoepfer et al. (2007) found that city dwellers were more likely to believe that white-collar criminals should be punished more severely than to believe that street crimes and white-collar crimes should be equally punished. In addition to geographic location homeownership also innuenced whether or not individuals believed elite deviants would be punished. Overall, homeowners were more likely to want elite deviants punished at an equal or harsher level to violent offenders (Holtfreter et al., 2008). Lastly race was found to have little to no effect on 15

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the opinions about whether or not elite deviants should be punished more harshly than street criminals. Issues Sanctioning White-Collar Criminals In the pa s t the general public has failed to pay attention to white-collar crime; henc efo rth, a major attitude s hift needed to occur for the public to become favorable towards prosecuting e lite deviants. Three distinct chronological shifts occurred within the public mindset that changed support for punishing white-collar criminals (Cullen, Hartman & Jonson 2009). The first chronological period is referred to as the inattention period; this time is often referred to as any decade prior to the 1970s (Cullen et a!., 2009). During the inattention period the public was unaware of the dangers of elite deviance political power was able to deflect criminal law, and company officials power and authority prevented offenders from being seen as a common criminal (Cullen eta!., 2009). Characteristic of this period, according to Cullen eta!., was how white-collar criminals lived well beyond the means of the criminal law and prosecution ofthese high powered individuals was unattainable. The inattention period was also characterized by public ignorance and apathy towards upper world criminality ; without the public's intimate knowledge about the dangers of elite deviance, white-collar criminals were able to continue their deviance without uproar from the general public (Cullen eta!., 2009). During the 1970s attitudes within the public began to shift. 16

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The second period is labeled the rising attention period. After 1970s, attention began to focus on white-collar crime. The Civil Rights movement of the 1960s focused on equal justice for all, it was through the movements for equal justice that elite deviants were suddenly not seen as untouchable by the justice system (Cullen et a!., 2009). During the rising attention period elite deviance became a common term in magazines, newspapers, and was reported on by the media (Cullen eta!., 2009). The attention period brought around a significant shift in the public attitude, whitecollar criminals were now being seen as criminals rather than elites in society and worthy of punishment. The final period is denoted as the "bad guy period." Since 2000, white-collar criminals have been labeled as "bad guys," and deserving of punishment (Cullen et a!., 2009). Cullen et a!. (2009) noted: As its prevalence and the magnitude of its harm was publicized, the public became aware of white-collar crime and critical of offenders in white-collars. Confidence in businesses and in other institutions declined, while concern for equal justice escalated. This confluence created a special problem for the government. For the state to protect its own declining legitimacy it had to show a concerned public that it was not beholden to corporate interests. It had to prove that it understood the need for victims to be accorded total justice. As a result, the state created space for the expanded use of the criminal law against white-collar miscreants. In doing so, it revealed that crime occurred across classes and that no offender was above the law (p. 38) The shift in labeling white-collar criminals as "bad guys" came from a large public push that labeled these elite offenders as criminals and not above the law. 17

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Currently, more attention is paid to white-collar criminals. Previous research has examined the area of public perception of sentencing elite deviants and suggests that the public favors harsh sentencing of white-collar offenders. Some research even suggests that the public feels that white-collar criminals should be punished as severely as street criminals. Understanding public opinion on sentencing of whitecollar offenders offers insight into whether or not elite deviance is viewed as a serious ISSUe. One research study found that over 80 percent of the subjects felt that whitecollar criminals had been treated too leniently and needed to be punished just as severely as street criminals (Cullen, Mathers Clark, & Cullen, 1983 ). Certain types of white-collar criminality were more likely to be seen as serious specifically if the offense involved physical harm or individual violations of trust that would normally be disapproved by society. However, participants were less troubled by the issue of price fixing, false advertising, or other types of corporate illegalities. Despite that the public was not as bothered by corporate illegalities as they were white-collar crimes that showed direct harm, subjects were willing to still hand out criminal penalties to white-collar offenders, even if the crimes were deemed relatively minor. Cullen et al. (1983) found that 73 percent of the subjects believe that stiffer jail sentences may make white-collar criminals re-think their calculated efforts to commit deviance. The researchers concluded that subjects felt elite deviants were being treated too leniently by the justice system and should pay more harshly for their crimes. 18

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Outside of formal sanctions from the judicial system there are informal sanctions. Benson ( 1989) speculated that class position influences both the formal and informal sanctions that occur when white-collar criminals are sentenced in and out of the courtroom. Depending on the individual's social standing, there is certain stigma attached to the sentence. Benson speculated that for less powerful white-collar offenders there is more of a stigma attached to their criminal sentence than more powerful elite deviant. Overall, social class determined the informal sanctions individual elite offenders received; according to Bensen, elite working class offenders are more likely to suffer from their conviction than those in a higher social standing. The legal stigma handed down during convictions is more likely to discredit workers and lower-class elite criminals than higher authority criminals (Benson, 1989). In addition, although higher elite deviants commit some of the most serious offenses, they are the least likely to lose their jobs or positions of trust because of a criminal conviction. Benson concluded that informal sanctioning may be more influenced by class structures than the social control of the law. Individuals of higher social standing are more likely to receive less social stigma for their deviance than their lower social standing counterparts who are more susceptible to the social stigma that comes from a formal or criminal conviction. Unlike other scholars, Podgor (2007) presents a different viewpoint on the challenges of sentencing white-collar offenders. She maintains that the typical white-collar offender is likely to receive more prison time than an individual who has 19

PAGE 30

committed a violent crime; in addition, most of these elite deviants are first time offenders. She argues that since elite deviants have further to fall, they stand to lose more from their criminal conviction. Lastly, Podgor contends that individuals who have been sentenced and convicted of white-collar crime can seldom return to the jobs or positions of authority. Consequently, recidivism rates are fairly low compared to other crimes. While Podgor's argument is interesting some may view it as being sympathetic to the elite deviants who often cause more damage to society than a basic street criminal. However, the opposing viewpoints are important in order to fully understand the challenges of sentencing white-collar criminals. 20

PAGE 31

CHAPTER 3 RESEARCH HYPOTHESIS The current research project is designed to explore public perception s of white-collar crime versus street crime. Of focus is the role that offenders gender plays in these perceptions. To stud y this several dependent v ariables are used: perceptions of seriousness, punishment, greed remorse and stress. The research also explores perceptions of motivations and offender beha v i or. The null hypothe ses and research hypotheses are presented below: H0 : Perceptions of white-collar versus s treet crime are con sis tent for male and female offenders. H 1 : People view crimes by females as more serious compared to offenses committed by males. 21

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Participants CHAPTER FO U R METHODOLOGY Data were collected in El Paso County, Colorado between Februar y 7 and February 28 2011 Participants were indi v idual s w ho had been summoned for jury duty. Nine-hundred individuals volunteered to complete a survey and were informed of their rights as research participants. Participants completed a two-page survey which asked about their opinions on one of 12 crime scenarios. All participants took part in the study after they had been di s mi sse d from jury dut y to avo id influencin g th e outcomes of cases on the court docket. In a ddition, particip a nt s we re given a brief definition of white-collar crime, which can be seen as a limitation to the result s of thi s current study. Lastly, due to the research e r being a White female, th ese re s ults ma y have come out differentl y if the research e r ide ntifi e d as another sex and racia l/e thnic identity. Survey Instrument The survey contained 13 que s tion s (see A ppendix A) There we re 12 different crime scenarios. Six original scenario s were created, three sce narios involved street crimes and three involved white-collar crim e committed b y either a male or female 22

PAGE 33

offender. All crimes within the survey in s truments involved no direct harm to the scenario v ictim s The researcher chose th ese var iou s crimes since there was no direct human harm to the participants to better survey whether or not research participants saw white-collar crime or stree t crime as more serious. Following each scenario, five questions explored public perceptions of elements of the crime scenarios Re spo ndent s were asked to rate the seriousness of the crime, t y pe of punishment de served for the crime committed, the remorse felt by the defendant, how much greed was respo n s ible for his/her actions, and to what extent stress was responsible for the individual committing their crime. Eight demographic questions asked participants to identify: if they had been a victim of white-collar crime, if they had been a victim of street crime, their gender, their race/ethnicity, age in years, highest level of education attained, current marital status, and current employment status. Street Crimes As previously s tated part of this research design was to measure public's perception of the seriousness of common st reet crimes. The goal of the survey instrument was to make the street crimes used in the vignettes understandable to the general population, and to ensure that the types of crimes were common enough that the general population would comprehend what the vignettes were asking. The street crime scenarios included auto theft burglar y, and prostitution 23

PAGE 34

Research subjects were asked to evaluate the following scenarios: For the last ten years Bob Wilson / Jan e Wilson has been unemployed and has supported himse(f/herse(f by stealing cars. Bob / Jane targets cars that have high Blue Book value and expensive unattended items in plain view. The police catch Bob / Jane in the act of auto theft, and charge him / her with auto theft. In order to avoid a trial he / she pleads guilty to auto theft charges. For the last ten years, Bob Wilson/Jane Wilson, has been unemployed and has supported himseljlherself by burglarizing homes. Bob / Jane targets houses that are unoccupied and steals money jewelry and electronics. The police catch Bob / Jane in the act and he lr;;he is charged with burglary. To avoid a trail he / she confesses to the burglary. For the last ten years Bob Wilson / Jane Wilson has been unemployed and has supported himse(f/herse(f as a prostitute. The police do a sting on the area of town which is known for prostitution and catch Bob/ Jane engaging in prostitution. In order to avoid a trail he / she pleads guilty to prostitution charges. Once survey respondents finished reading the scenario, they were asked to rate seriousness of the crime on an eight-point Likert scale, where 1 =not very serious and 8 = extremely serious. The second question explored the punishment they felt the offender deserved. The response categories for this question were closed ended and included: probation, monetary fine, jail, prison, and other. If respondents chose "other," they were asked to specify their ideas about punishment. Respondents were asked to rate how much remorse, greed, and stress they believed the offender felt on an eight-point Likert scale, where 1 ="not very" and 8 ="extremely." 24

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White-Collar Crimes The goal of this research design is to measure the public's perception of the seriousness of white-collar crime compared to street crime The three white-collar crime scenarios involved corporate crime embezzlement, and a Ponzi scheme. Except for embezzlement, the white-collar crime scenarios were modeled after recent high profile white-collar crimes that featured in the media The Ponzi scheme closely mirrored the case of Bernard Madoffs Ponzi scheme that cheated people out of millions of dollars. The corporate crime scenario used in the survey instrument was modeled after the Enron scandal. Respondents were asked to evaluate the following scenarios: For the past ten years, Bob/Jan e Wilson has worked as CEO for a multibillion-dollar energy exploration company. Durin g the last five years, the company began counting mone y that had not been collected from clients as revenue. These actions allowed the co mpan y to inflate their profits by $2 50 million. In order to avoid a trail, Bob / Jane confesses to e ngaging in corporate crime. For the last ten years, Bob /Ja ne Wilson has workedfor a l ega l firm as an office manager where he /s h e handl es the firm's mon e tary transactions For the last five years Bob / Jane ha s taken money/rom the bank deposits The law firm started an investigation and found money was missing. Bob / Jane was arrested and charged with embezzlement. To avoid a trial h e / she confessed to the embezzlement. For the past ten years, Jane / Bob Wilson has worked as a financial advisor where she / he advised her / hi s clients to put their money into her / his new Zoom IV account. Jane / Bob uses her/his new investor's money to pay off her/his old inv es tors in this Zoom IV account in this large Pon z i scheme She /He ha s also stolen large amounts of money directlyfrom h e r / his clients as we ll as the business accounts The 25

PAGE 36

police investigat e her l hisfinancialfzrm regarding the missing financial fund.r., and conclude that Jan e / Bob rook money f rom her his investors In order to avoid a trail she he confesses to the P on z i scheme. Demographics The last section on the su r vey instrument included eight demographic questions. These question s asked if the s urvey respondent has b een victimized by white-collar crime, if the survey r espondent has been a victim of a street o r \\ hite collar crime gender, race. age. highe s t level of education achie, ed current marital status, and current employment status. Every dem og r aphic except f o r age had designated response categories vvhich were de sig ned to be mutually exclusive and exhaustive. The five racial categories on the suney instrument included: Black Hispanic. White. Asian. and other: the other category had an open ended response space where individuals could fill in the racial category that they as in a more specific manner. Age was the only category that did not ha\ e designated response categories for the respondents to choose from: instead. participants were asked to identify their exact age in years in the open-ended response category Resp ondents were asked to choose which option best described their highest lev-el of education completed using an ordinal measure: les s than an eighth grade education. a high school diploma or GED. some college but no degree. four-year degree or a Bache lor s a degree. an Advanced degree ( Ph.D . or J.D.). '-26

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The final two demographic questions asked about marital and employment status. For marital status respondents were asked to choose from the following closed response categories: married, never married, widowed, divorced, and separated. Lastly respondents were asked to identify their current employment status. Respondents were given two closed ended response categories to choose from: unemployed or employed. While the employment status options did not include retired, retired individuals were considered by the researcher to be unemployed since they are not currently participating in the workforce. Analytic Technique Two analytic techniques were used to address the research questions. The first analytic technique, ordinary least squares regression (OLS) was performed on the four independent variables of Seriousness Remorse Greed, and Stress. OLS is appropriate for these regressions given that the dependent variables noted are continuous in nature. The second analytic technique binary logistical regression is used for the final dependent variables given they are binary. Punishment was divided into two categories severe punishment (jail/prison) or not severe punishment (monetary fine / probation). 27

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CHAPTER 5 RESULTS Before addressing the research question, this section describes the sample. Table one presents descriptive statistics for all dependent variables used in the analysis. Results show that with one exception, respondents viewed the selected crimes as very serious. The exception to this was prostitution which was seen as less than moderately serious. Findings also demonstrate that respondents perceived that offenders in the scenarios felt very little remorse for the crimes portrayed. 28

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Table 1: Descriptive Statistics for the dependent variables (Ponzi Scheme, Embezzlement, Auto Theft) Ponzi Scheme Embezzlement Auto Theft Seriousness of crime Seriousness of crime Seriousness of crime Mean 7.05 Mean 6.70 Mean Standard 1.06 Standard 1.30 Standard deviation deviation deviation Remorse of offender Remorse of offender Remorse of offender Mean 2.92 Mean 3.76 Mean Standard 1.89 Standard 1.80 Standard deviation deviation deviation Greed of offender Greed of offender Greed of offender Mean 7 .15 Mean 6.30 Mean Standard 1 20 Standard 1.64 Standard deviation deviation deviation Stress of offender Stress of offender Stress of offender Mean 4.54 Mean 4.54 Mean Standard 2.30 Standard 2.30 Standard deviation deviation deviation 6.33 1.37 2.70 1.65 5.50 2.16 4.49 2.19 Punishment for offender Punishment for offender Punishment for offender Severe 92 0% Severe 76.1% Severe 87.6% punishment punishment punishment Less severe 8 0% Less severe 23.9% Less severe 12.4% punishment punishment punishment Scales for Seriousness, Remorse, Greed, and Stress were measured using an 8 point Likert Scale I = Not very .... 8 = Extremely .. .. Punishment was coded as a binary variable (0 and I) 0 = Less Serious Punishment I = Severe Punishment 29

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Table 2: Descriptive Statistics for the dependent variables (Burglary Corporate Crime and Prostitution) Burglary Corporate Crime Prostitution Seriousness of crime Seriousness of crime Seriousness of crime Mean 6.48 Mean 6.45 Mean 4.77 Standard 1.23 Standard 1.53 Standard 1.92 deviation deviation deviation Remorse of offender Remorse of offender Remorse of offender Mean 3.00 Mean 3.26 Mean 3.13 Standard 1.80 Standard 1.81 Standard 1.81 deviation deviation deviation Greed of offender Greed of offender Greed of offender Mean 5 36 Mean 6.74 Mean 3.42 Standard 2.02 Standard 1.54 Standard 2.02 deviation deviation deviation Stress of offender Stress of offender Stress of offender Mean 4.53 Mean 4.42 Mean 5.01 Standard 2.14 Standard 2.10 Standard 2.15 deviation deviation deviation Punishment for offender Punishment for offender Punishment for offender Severe 90.1% Severe 69 5 % Severe 29 3 % punishment punishment puni s hment Less severe 9.9% Less s evere 30 5 % Less severe 70 7 % punishment punishment punishment Scales for Seriousness, Remorse, Greed, and Stress were measured using an 8 point Likert Scale I= Not very .... 8 = Extremely .. Punishment was coded as a binary variable (0 and I ) 0= Less Serious Punishment I = Severe Punishment The next table offers descriptive for all other v ari a bles in the analysis About half of the respondents were female (53.2o/o) most were white (81.6o/o) most were characterized by some college but no degree (35o/o). Further, most were married 30

PAGE 41

(63.4o/o), middle aged (mean age of 45 years old) and employed (74.0o/o). The next section focuses on the research questions using regression analysis. Table 3: Descriptive statistics, percentage, means and standard deviations for sample, n =900 Independent variable Emglo)!_ment status Gender of respondent Unemployed 26.0% Male 46.8% Employed 74.0% Female 53.2% I Victim o[white collar crime in the east Respondent Characteristics Yes 16.2% Race o[_Resf2.ondent I Black 5.1% Victim o[street crime in the 12.ast Hispanic (any race) 8.5% Yes 25.3% White 81.6% I Asian 1.8% Age o[Respondent Other 3.0% Mean 45.41 Standard deviation 14.80 Education level o[_ ResQondent I Less than 8th grade 0.5% Marital status o[_Reseondent High school diploma or GED 16.7% Married 63.4% Some college, no degree 35.0% Never married 18.4% Bachelor's degree 28.0% Widowed 1.8% Master's degree 15.7% Divorced 14.2% Advanced degree 4.1% Separated 2.2% Ordinary Least Squares Analyses Ordinary Least Squares Regression was employed for the initial models. Specifically, this section offers findings for the dependent variables of seriousness remorse, greed, and stress. The findings for crimes are offered in the following order: ponzi schemes, embezzlement, auto theft, burglary corporate crime and prostitution. 31

PAGE 42

Ponzi Crimes Seriousness as the Dependent Variable The first regression focused on Ponzi schemes used "seriousness" as the dependent variable. An OLS regression addressing this relationship indicates several significant findings. First, the research hypothesis was supported as male respondents were significantly more likely to view a Ponzi scheme as more serious than were female respondents (b = .185, p = .093). Respondents did not view gender of the offender as a significant predictor of the seriousness of the Ponzi scheme (b = .270, p=.l37). The model is characterized by a good fit as 19.2 percent of the variation in seriousness is explained for by the variables. Remorse as the Dependent Variable The next regression addressing Ponzi schemes used "remorse" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of whether individuals viewed the remorse felt by the criminal (b = .420, p =.ll 1 ). In contrast, the offender's gender was a significant predictor of whether the public viewed a Ponzi criminal as remorseful. Specifically, male offenders were thought to be more remorseful than female offenders (b=.032, p=.009). The model is characterized by a good fit as 26.7 percent of the variation in remorsefulness is explained for by the variables. 32

PAGE 43

(/reed us the I kf!<'lldcnr l 'orioh/c Tlw nc\. t ng Pon;. i used "greed"
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Table 4: Regression analyses for Ponzi crimes Ordinary Least Squares (OLS) Seriousness Remorse Greed Stress Variable s b SE p-Beta b SE p-Beta b SE p-Beta b SE p-Beta value value value value Independe nt Variable Gender o f Respondent [ Mal e 0 185 0 170 + 0.093 0 279 0.420 0 3 1 3 0 Ill 0 182 0 147 0 195 0.452 0 066 .395 390 3 1 3 085 Offe nder's Gender Male o f fende r 0 270 0 177 0 137 0 1 3 1 0 032 0 326 0 009 0 921 -0 3 1 9 0 187 + 0 090 -0.143 294 .406 .470 064 Responde nt C hara c teristic Marital Statu s Never m a rried -0.265 0 247 0 106 0 286 0 126 0474 -0 026 0 .791 -0.417 0271 0 127 -0. 148 .333 566 .558 057 Widowe d 0 751 0 984 + 0 065 0.447 0 258 l 788 0 0 1 2 0 886 0 309 1 080 0 775 0 024 2 099 2 232 .349 079 Divorce -0. 050 0 246 -0. 018 0 .841 0.356 0.459 + 0 068 0.440 0 Ill 0 270 0 682 0 036 1 .597 559 .005 254 Separated 0 757 0 526 0 130 0 153 0 667 0 959 + 0 061 0.488 -0.533 0 .577 0.358 0 081 2 887 l 192 017 2 1 5 Employed 0 192 0 2 1 5 + 0 079 0 .375 0 .864 0.393 0 .189 0 030 0 .215 0 236 0 .365 -0 079 737 .489 135 1 3 1 Victim of WhiteCollar Crime 0 000 0 000 0 006 0 946 0 000 0 000 0 008 0 921 0 00 0 000 0 130 -0 1 2 8 000 000 836 018 Victim o f S treet Crime 0 078 0 .186 0 037 0 675 -0771 0 346 -0. 190 0 028 0 028 0 .204 0 892 0 0 1 2 606 .426 157 -123 Race Black 0.59 1 0.45 1 -0. 113 0 192 -0.572 0 819 + -0 059 0.486 -0.625 0.495 0 209 0 .106 998 1 023 .331 083 His panic 0 294 0.308 + 0 .081 0 .341 l 344 0 560 0 .199 0 018 -0.538 0.338 0 .114 -0. 133 936 698 .183 .112 1-A sian 342 .081 0 079 0703 0 010 0 9 1 0 1 230 1 278 + 0 .081 0 338 -0.563 0 .771 0467 -0.061 1 .522 1.596 Other Race -0.161 0 692 -0 020 0 .817 4 089 l 256 0 268 0 .001 2 889 0 760 0 000 -0314 l 077 1.569 .494 057 E ducation 0 027 0 077 0 .031 0 730 -0. 0 1 2 0 .143 -0 007 0 934 -0.147 0 085 + 0 086 -0. 153 .172 .177 .332 087 Age 0 .021 0 007 0 30 1 0 003 -0 041 0 0 1 3 -0. 318 0 002 0 004 0 008 0 634 0 047 048 016 003 -.30 I Cons tant 5 .081 0 .645 ---0 000 5 669 1 .203 --0 000 8.403 0 708 0 000 ---6 003 1474 000 ----* p < .05 R-Squared = .192; SEE .943 R-Squared = 267 ; SEE 1.710 R-Squared = 227 ; SEE 1 .035 R-Squarcd = 236: SEE 2 1 3 7 + p < .lO -_l__ -J l j j j j j j j j j .. ----34

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Embezzlement Seriousness as the Dependent Variable The first regression focused on embezzlement used "seriousness as the dependent variable. An OLS regression addressing this relationship indicates several significant findings. First, the research hypothesis was not supported as male respondents were not significantly more likely to view embezzlement as more serious than were female respondents (b=.006 p = .979) Respondents did not vi e w gender of the offender as a significant predictor of the seriousness of the embezzlement (b=.090, p=.667). The model is characterized by a poor fit as 10.6 percent of the variation in seriousness is explained for by the variables. Remorse as the Dependent Variable The second regression addressing embezzlement used remorse as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of whether individuals viewed remorse of criminal (b = -.538 p = .l 03). Offender s gender was not a significant predictor of whether the public viewed an embezzler's as remorseful. Male embezzlers were thought to be no more remorseful than female embezzlers (b = -.426, p=.173). The model is characterized by a good fit as 22.2 percent of the variation in remorsefulness is explained for by the variables. 35

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Greed as the Dependent Variable The third regression addressing embezzlement used greed as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of how individuals viewed the greed of embezzlers (b = -.300 p =. 320). Offender s gender wa s not a significant predictor of whether the public viewed an embezzler as greedy Specifically, male embezzlers were thought to be no more greedy than female embezzlers (b = -.163 p=.570). The model is characterized by a fair fit as 14.8 percent of the variation in greediness is explained for by the variables Stress as the Dep e ndent Variable The last regression addressing embezzlement used stress as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of how individuals viewed the stress of the embezzlers (b = .096 p = .804). In addition, the offender s gender was not a significant predictor of whether the public viewed the embezzler s actions as stress related. Male offenders were thought to be no more stress induced than female offenders (b = -.502 p = .217). The mod e l is ch a racterized b y a poor fit as 9 percent of the variation in stress is explained for by the variables. 36

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Tabl e 5: Re g r ess ion ana l yses for E mbezzlem ent crimes Ordin ary Leas t S quare s ( OLS) S eri o u snes s Remo r se G r ee d Stres s Variable s b SE p-B e t a b SE p B eta B SE p -Beta b SE p-B e t a v alu e value value v alue Ind e p ende nt Variabl e Gender of Res ponden t I Ma l e .006 2 1 7 979 002 538 327 103 -148 300 300 320 -093 096 383 804 .023 Offende r's Ge n de r \ Ma l e offender 090 208 .667 038 -.426 .311 173 .117 -163 286 570 -051 .502 .404 217 1 2 1 Respond e nt C h arac t eristic Marita l S t atus Neve r m arried -169 289 561 058 258 .422 .543 .058 -184 .40 I 647 .045 1 6 4 532 758 .032 Wid owe d 1.119 859 195 .115 2 869 1 256 .024 194 .895 1.165 444 .068 218 1.541 888 .013 Divo r c e .041 319 .897 .012 .454 497 363 082 -.4 78 .46 1 301 097 249 609 .684 .040 Separa t e d 572 1 207 637 -042 -1.166 1.763 . 510 056 1 876 1 637 .254 101 3 .683 2 163 + .091 1 5 7 Emp l oved 182 .251 .471 067 .794 376 037 190 .142 .346 .682 038 -048 .459 9 1 6 010 V i c tim o f W hiteCollar Crime 000 000 .391 .080 000 000 820 020 000 000 852 017 000 000 300 101 Vict i m of S tree t C rime .000 .000 664 039 000 000 366 .078 000 000 296 .094 000 000 709 -035 Race -Black 909 .524 085 -172 .694 .773 371 086 7 1 8 0 1 6 .244 -396 .952 .678 .044 + 1.755 His panic .463 322 .153 .131 .489 4 7 1 302 .091 -173 .437 .694 036 .566 579 330 .093 Asian .434 7 1 5 .545 -054 1 .284 1 049 .223 1 06 .204 .971 834 0 1 9 -092 1 .285 943 -007 O t her Race 0 1 5 486 975 -003 .502 7 1 3 483 .06 2 .553 .661 405 077 -764 8 7 5 384 -084 Educat ion 032 104 758 030 .278 158 + .081 164 343 143 0 1 8 -231 059 1 90 756 .031 Age .01 2 008 ,,, .).) .156 0 1 0 012 .393 084 -003 O i l 797 -.026 -028 0 1 5 + .067 199 Co n s t a n t 5 .825 802 000 ---7 838 1.18 1 000 ----8 .267 1.082 .000 ----5 .600 1.448 000 ----* p
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Auto The[t Seriousness as the Dependent Variable The first regression focused on auto theft using "seriousness" as the dependent variable. An OLS regression addressing this relationship indicates several significant findings. First the research hypothesis was unsupported as male respondents were not significantly more likely to view an auto theft as more serious than female respondents (b=.206 p = .418). However respondents did view gender of the offender as a significant predictor of the seriousness of the auto theft (b = .500 p=.054). Male offenders were seen as being more serious than female offenders. The model is characterized by a decent fit as 12.6 percent of the variation in seriousness is explained for by the variables. Remorse as the Dependent Variable The next regression addressing auto theft used remorse" as the dependent variable. An OLS regression addressing this dependent variable indicated that respondent's gender is not a significant predictor of whether individuals viewed the auto thieves as remorseful (b= -.133 p = .661 ). Also, the offender s gender was not a significant predictor of whether the public viewed a auto thief as remorseful. Specifically, male offenders were thought to be no more remorseful than female 38

PAGE 49

offenders (b = -.415 p =.l79). The model is characterized by a decent fit as 14.8 percent of the variation in remorsefulness is explained for by the variables. Greed as the Dependent Variable The third regression addressing auto theft used greed" as the dependent variable. An OLS regression addressing the dependent variable of greed indicates that respondent's gender is not a significant predictor of how individuals viewed greed of the auto thieves (b = 072, p = .858). Offender's gender was not a significant predictor of whether the public viewed an auto thief as greedy. Specifically, male auto thieves were thought to be no more greedy than female auto theives (b = .387 p = .342) The model is characterized by a poor fit as 7 2 percent of the variation in greediness is explained for by the variables. Stress as the Dependent Variable The final regression addressing auto theft used "stress" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent s gender is not a significant predictor of how individuals viewed the criminals stress level (b = -.173, p = .673). In addition the offender s gender was not a significant predictor of whether the public viewed the auto thiefs actions as stress related. Male offenders were thought to be no more stress induced than female 39

PAGE 50

offenders (b= -.415, p=.320). The model is characterized by a poor fit as 10.9 percent of the variation in stress is explained for by the variables 40

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Tabl e 6 : R eg r ess ion an a l yses for A utoTheft crim es Ordina r y Leas t Squares (O L S ) Se riou s ness R e m o r se Gree d Stress Variables b SE p-Beta b S E p B e t a b S E p Beta b SE p-B e t a valu e v alu e value v alu e Inde p ende nt V ariabl e G e n d e r o f Resp o n d ent J Ma l e 206 253 .418 075 133 .303 .661 0 4 1 072 401 858 0 1 7 -173 .407 673 0 4 0 Offende r' s G ende r Male o ff ende r 500 2 5 6 + 054 1 8 1 .415 3 0 7 179 126 387 .40 6 3 4 2 093 -.41 5 .41 6 3 2 0 096 R e spond e nt C har ac t eris ti c I I Marit a l Status Nev e r m arrie d .331 .384 .391 .089 185 .441 .676 0 4 2 .685 .583 2-12 123 785 608 199 1 3 4 Widowed .351 8 4 6 6 7 9 037 .499 I 192 676 0 3 7 365 1.31 9 7 8 3 026 -.490 I .34 1 7 1 5 .033 Div o r c e 194 388 618 0-17 -.455 .458 3 2 3 .09 4 8 1 6 606 1 8 0 133 036 6 1 6 .954 006 Se p a r a t e d 2 1 5 6 .685 0 0 2 2 9 2 695 8 1 0 3 9 2 0 8 0 .499 1 069 641 .045 9 4 6 1087 386 I 083 E m p l o y e d 244 .274 3 7 4 0 8 5 0 4 0 .32-1 9 0 2 -.01 2 229 .426 593 053 .42 1 .43 7 .337 0 9 4 V i c tim o f Whit e Colla r C rime 332 306 I 280 .098 939 .362 I 0 1 1 237 .23-1 .-17 4 622 0 4 7 260 .49 1 .597 0 4 9 Vic tim o f Street C rime 1 9 7 2 8 8 .496 060 256 .345 .-160 .065 -1 2 8 .456 779 -.025 278 .46 4 550 054 Race Black .281 .4 7 5 .555 .05 3 485 .563 .391 077 230 710 7 4 7 0 3 0 -768 .725 292 0 9 7 His p anic 167 .443 7 0 7 .034 .335 522 .523 058 .323 .690 .641 -044 .060 .702 932 008 A s i a n -1 4 8 848 862 0 1 6 1 098 1 003 275 098 1005 1.324 .449 071 1 306 1346 334 089 O t h e r Race .429 1 .054 685 037 007 1 246 995 .001 1 638 1 6 4 6 .322 .094 2 238 1 673 1 8 3 125 E ducat i o n 2 1 5 1 2 6 + .091 167 310 1 5 0 0 4 1 202 027 197 .891 014 1 5 9 2 0 1 .433 079 A g e 001 009 892 014 020 Oil 067 1 9 2 0 1 7 014 .229 131 009 0 1 5 561 062 Co n s t ant 5 564 9 1 8 000 3 9 3 4 1 050 000 ----6 304 1.388 0 0 0 ----6 .801 1420 000 --* p < .05 R Squa r e d = .126; S E E 1.36 5 R-Squa r e d = 148; SEE R-Squ a r e d = 072: SEE R S quar e d = 109: SEE 1 6 1 2 2 1 3 1 2 166 + p<.I O I I I I 41

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Burglary Seriousness a s the D epe ndent Variable The first regression focused on burglary used "seriousness" as the dependent variable. An OLS regression addressing this relationship indicates several significant findings. First, the research hypothesis was not supported as male respondents were not significantly more likely to view a burglary as more serious than female respondents (b= -.123 p=.575). Respondents did not view gender of the offender as a significant predictor of the seriousness of burglar y (b = .004 p=. 985). The model is characterized by a decent fit as 13.8 percent of the variation in seriousness is explained for by the variables. Remorse as the Dependent Variable The next regression addressing burglary used "remorse" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of whether individuals viewed remorse of criminal (b = .384 p =.21 0). In contrast the offender s gender was a significant predictor of whether the public viewed a burglar as remorseful. Specifically male burglars were thought to be more remorseful than female burglarrs (b=.548, p = .066). The model is characterized by a good fit as 20.4 percent of the variation in remorsefulness is explained for by the variables. 42

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Greed as the Dependent Variable The next regression addressing burglary used "greed" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of whether individuals viewed greed of criminal (b= -.013 p = .971 ). Also the offender s gender was not a significant predictor of whether the public viewed a burglar as greedy Male offenders were not thought to be more greedy than female offenders (b= -.030 p=.932). The model is characterized by a mediocre fit as 13.5 percent of the variation in greediness is explained for by the variables. Stress as the Dependent Variable The last regression addressing burglary used "stress" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of how individuals viewed the stress of the criminal (b=.562 p=.135). In addition, the offender s gender was not a significant predictor of how the public viewed the burglar s actions as stress related Male burglars were thought to be no more stress induced than female burglars (b = .334, p = .358). The model is characterized by a decent fit as 15. 8 percent ofthe variation in stress is explained for by the v a riables 43

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Table 7: R egression analyses for Burglar y crimes Ordinary Least Squares (OLS) Seriou sness Remors e G reed St ress Variables b SE p-Beta b SE p-Beta b SE p-Beta b SE p-Beta value value value value Inde p ende nt Variabl e Gende r o f Responden t \ Ma l e 123 .2 1 9 575 050 384 305 .210 1 08 0 1 3 358 97 1 003 562 374 135 133 Offende r' s Gende r Ma l e off ende r 004 .2 1 3 985 002 .548 .296 + .066 154 030 346 932 007 334 .36 1 .358 079 Respondent C haracteristi c Marita l Status Never married -1019 336 003 .293 .428 460 354 085 -I 303 538 0 1 7 233 519 562 357 088 Widowed 693 897 442 .081 .466 1 243 708 038 -.410 1453 778 030 2 127 1 5 1 8 164 1 46 Divorce 162 286 572 049 .435 403 282 090 186 .480 700 034 288 .501 567 050 Separated 658 1 .240 596 045 977 1.71 8 570 046 3 1 0 2 008 878 0 1 3 2 .275 2 098 .280 09 1 1-:mpl oycd 003 .255 990 .001 268 354 .450 065 746 .41 6 + 075 160 050 .435 909 0 I 0 Victim of W hit e-Collar Crime 000 000 + 098 145 000 000 388 .07 1 000 000 709 032 000 000 839 017 Victim of Street C rime 000 000 125 1 36 000 000 1 46 -124 000 000 I 065 -.165 000 000 + .071 -160 R ace Black 526 .43 2 .226 -I 09 674 599 .262 097 345 .70 I 624 044 .436 732 553 053 lli s pan i c 0 1 7 .41 5 968 .003 1 848 575 002 .266 177 672 792 023 .671 702 .341 .OR2 /\ s ian 043 1 502 977 003 1.21 0 2 082 562 057 2 787 2.433 .254 118 1 2 4 2 2 542 626 049 Ot h er Race -11 26 886 .206 107 26 7 1 227 828 0 1 8 856 1 .434 552 .051 2 563 1.498 + 090 144 Education 123 1 06 .251 1 06 -366 1 46 0 1 4 .2 1 8 176 1 7 1 307 094 ISO 179 .403 076 /\ ge 022 009 0 1 5 .243 007 0 1 3 566 055 003 0 1 5 842 020 042 0 1 5 007 .2W Co n s tant 7.42 1 826 000 2 .721 I 145 0 1 9 -----7 698 1 .338 000 ---4.496 1398 .002 ----* p < .OS R-Squa r ed = 1 38 ; S I:E 1 2 1 9 R-Squared = 204; SEE 1 689 R-Squared 1 35; su: R-Squared = 158; SIT 1 973 2 062 + p<.I O I I I I I I I I I I I I I I 44

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Corporate Crime Seriousness as the Dependent Variable The first regression focused on corporate crimes used "seriousness as the dependent variable. An OLS regression addressing this relationship indicates several significant findings. First the research hypothe s is was not supported as male respondents were not significantly more likely to view a corporate crime as more serious than female respondents (b = .2 18, p = .458). Respondents did not view gender of the offender as a significant predictor of the seriousness of corporate crime (b=.084, p = 762). The model is characterized by a poor fit as only 10.1 percent of the variation in seriousness is explained for by the variables. Remorse as the Dependent Variable The second regression addressing corporate crime used remorse as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of how individuals viewed the corporate criminals remorse (b = -.121 p = 709). In contrast. the offender's gender was a significant predictor of whether the public viewed a corporate criminal as remorseful. Specifically, male offenders were thought to be less remorseful than female offenders (b = -.538 p = .087). The model is characterized by a good fit as 21 percent of the variation in remorsefulne ss is explained for by the variables. 45

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Greed as the Dependent Variable The third regression addressing corporate crime used "greed" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of how individuals viewed the greed of corporate criminals (b= -.248 p = .405). Also the offender's gender was not a significant predictor of whether the public viewed a corporate criminal as greedy. Specifically, male offenders were thought to be no more greedy than female offenders (b=.447, p=.119). The model is characterized by a decent fit as 11.9 percent of the variation in greediness is explained for by the variables. Stress as the Dependent Variable The last regression addressing corporate crime used "stress" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of how individuals viewed the stress of the criminal (b=.021, p = .358). In addition the offender s gender was not a significant predictor of whether the public viewed the corporate criminal's actions as stress related. Male offenders were not thought to be more stress induced than female offenders (b = .264 p = .488). The model is characterized by a moderately decent fit as 17.8 percent of the variation in stress is explained for by the variables. 46

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Table 8: Regre ssi on analyses for corporate crimes Ordinary L eas t Squares (OLS) S eriou s ness Remorse Greed S tress Variables b SE p-B e ta b SE p-Beta b SE p-Beta b SE p -Beta value value va lue value Inde p en d e nt Variabl e Gende r of Resp ondent \ Male 2 1 8 292 458 073 1 2 1 324 709 034 248 297 .405 -081 .021 395 958 005 Offende r' s Gender Ma l e offe nder 084 278 .762 028 538 311 + 087 153 .447 285 .119 .146 264 380 488 .063 Re.\ponde nt C haracteristi c Marita l S tatu s Neve r married -.441 454 .333 .113 520 .503 304 .113 355 .460 .442 089 379 6 1 2 537 070 Widowed 1475 1.553 344 085 2 000 I 720 247 098 I 133 I 575 .4 73 .064 3 .663 2 090 + .082 152 D ivorce 299 396 451 075 077 .445 .862 0 1 6 018 .408 .965 004 -1.154 .551 .038 202 Sep a r a ted -1 755 .825 .035 200 .528 9 1 5 565 -051 -2 235 837 009 249 3 307 I. I 1 2 004 .271 Emp l oye d 365 337 .281 .110 .325 .376 388 083 5 1 3 344 1 3 9 152 313 .458 .496 -068 Victim of White-Collar Crime 000 000 6 1 9 064 .001 000 006 34 1 000 000 967 -005 000 000 376 .110 Victim of Street Crime .011 .335 .975 .003 2 1 4 372 566 .051 .043 340 900 0 1 2 184 .452 .685 037 Race 131ack 137 797 .864 0 1 7 -396 .883 654 043 580 808 475 072 464 I 073 .666 042 His panic 799 442 + 074 173 I 119 490 024 206 -027 .449 .953 006 924 596 124 144 Asian -I 07 1.099 923 009 3 .794 1 218 .002 .261 311 1.115 .781 .025 2 276 1 .480 127 133 Othe r Race .655 1 652 692 .053 4 7 1 7 1 830 Oil .325 1324 1.675 .431 .105 3 128 2 227 .163 182 -Educatio n 155 137 260 .117 085 152 .575 055 129 1 3 9 354 096 165 189 382 088 Age .001 0 1 2 964 006 0 1 7 014 207 145 -.01 3 0 1 2 308 124 009 0 1 7 .586 -064 Co n s tant 7300 1 057 000 ----2 234 1.171 059 ----8.415 1.072 000 ----5.790 1 .438 000 ---* p <05 R-Sq uared =.I 0 I ; SEE 1 .504 R-Squ ared = 2 1 0 ; SEE 1.667 R-Squ a red = .119 ; SEE 1.526 R-S quared = 178; SEE 2 025 + p<.I O l l l l l l l l l l l I I I 47

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Prostitution Seriousness as the Dependent Variable The first regression focused on prostitution used seriousness as the dependent variable. An OLS regression addressing this relationship indicates several significant findings. First the research hypothesis was supported as male respondents were significantly more 1 ikely to view prostitution as less serious than were female respondents (b = 654, p = .057). Respondents did not vi e w gender of the offender as a significant predictor of the seriousness of prostitution (b = .20 1 p = .546). The model is characterized by a decent fit as 12.8 percent of the variation in seriousness is explained for by the variables. Remorse as the Dependent Variable The next regression addressing prostitution used "remorse" as the dependent variable. An OLS regression addressing this dependent v ariable indicate s that respondent's gender is not a significant predictor of whether individuals viewed remorse of the prostitutes (b = -.435 p =.l76). In addition the offender' s gender was not a significant predictor of whether the public v iewed a prostitute as remorseful. Specifically, male prostitutes were thought to be no more remorseful than female prostitutes (b =-.415, p = .181 ). The model i s characterized b y an average fit as 13.5 percent of the variation in remorsefulness is explained for b y the variables. 48

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Greed as the Dependent Variable The next regression addressing prostitution used "greed" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor or whether individuals viewed greed of criminal (b = .236, p=.495). Similarly, the offender s gender was also not a significant predictor of whether the public viewed a prostitutes actions as greedy. Specifically, male offenders were not thought to be more greedy than female offenders (b= -.009, p=.979). The model is characterized by a good fit as 21.8 percent of the variation in greediness is explained for by the variables. Stress as the Dependent Variable The last regression addressing prostitution used "stress" as the dependent variable. An OLS regression addressing this dependent variable indicates that respondent's gender is not a significant predictor of how individuals viewed stress of criminal (b= -.144, p=. 714 ). Additionally the offender's gender was not a significant predictor of whether the public viewed the prostitutes actions as stress related. Male prostitutes were thought to be no more stress induced than female prostitutes (b= .184, p = .629). The model is characterized b y a poor fit as 9. 7 percent of the variation in stress is explained for by the variables. 49

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Tabl e 9: R eg re s sion anal yses for pro s titution crim es Ordinary L e a s t Squares (OL S ) Se riousness Remors e G reed S tress Variables b SE p-B e ta b S E p-B e ta b SE p-Beta b SE p-B e t a v alue v alue value valu e Inde p ende nt V ariabl e Gender o f Res p onden t I Ma l e 654 .341 + 057 166 -.435 3 1 9 176 -120 236 345 .495 057 .144 .391 .714 -033 Offende r' s Gende r Mal e offender .201 332 546 052 -.415 308 1 8 1 -11 7 009 335 979 -002 184 380 .629 -043 Re s pond e nt C hara c t eristic Mar i t a l S tatus Never marrie d -.433 506 394 -087 .442 .469 347 096 -1.469 .508 004 -276 988 576 + .088 176 Widowed -1.145 9 3 3 .222 -109 .43 7 859 6 1 2 046 .228 937 808 .021 672 1.061 .527 0 5 8 Divo rce 1 .035 .496 0 3 9 178 059 .469 90 1 011 8 1 6 .498 104 134 I 068 .565 + .061 166 Separated 039 1 168 974 .003 .492 1 346 7 1 5 .033 I 893 1 177 110 134 1 062 1334 .428 0 7 1 Emplo yed 119 .400 .767 026 323 377 393 076 .395 .40 2 328 .081 088 458 848 -017 Victim of White-C olla r C rime 000 000 6 1 6 .061 000 000 .281 -095 000 000 765 -025 000 000 889 0 1 2 V i ctim of Street Crim e 000 000 .708 .044 158 .327 .628 -042 -.421 361 .245 -095 .209 411 .61 2 .045 I Race B lack -1.021 811 210 106 359 .747 .632 -041 -1 168 8 1 3 154 11 6 928 I 022 366 080 H i s panic .595 834 .477 .062 .065 .779 934 .007 -I. 183 8 3 6 160 -1 1 7 .494 948 .603 046 A s ian .725 1.142 .527 .054 -I. 888 1 049 + 074 154 730 1.145 .525 052 .821 I 298 528 -055 O ther Race 6 1 3 .764 .424 -069 -1.0 1 0 707 156 .124 .842 .772 .278 090 -1.203 874 1 7 1 122 Educa tion 094 167 .574 048 312 154 .044 176 .521 166 .002 256 103 188 .585 .048 Age 0 I 0 0 1 4 .469 076 -020 0 1 3 1 1 9 165 -035 0 1 4 .01 4 247 -022 0 1 6 174 146 Co n s tant 5 .293 1.319 000 ----3 .772 1 .223 .003 -6.1 6 1 1308 000 --6 0 1 5 1 485 000 ---* p < 05 R Squared = 128; SEE 1.90 R Squa red = 1 35; SEE 1.746 R -Squared = 218: SEE R-Squared = 097; SEE 1.904 2 158 + p<.IO I I I I 50

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Binary Logistic Regression Analysis This section focuses on the dependent variable of "punishment". Unlike the dependent variables used earlier in this thesis, this dependent variable is measured as a dichotomy. For this reason, binary logistic regressions are utilized to address the research questions. In addition, unlike the previous sections, these analyses focus on the aggregation of white collar crimes (i.e., Ponzi schemes, embezzlement, and corporate crime), and street crimes (i.e., burglary, auto theft and prostitution). White Collar Crime The first logistic regression model focuses on the influence of respondent's gender on the severity of the punishment given to the offender in the scenarios. Findings in Table 9 indicate that respondent's gender is a significant predictor of the severity of the punishment given to the offender (b= -.505, p = .090). In contrast, the offender's gender is not a significant predictor of the dependent variables (b=440, p= .143 ). In particular, female offenders were not considered to have received less severe punishment than their male counterparts. Street Crime The second logistic regression model focuses on the influence of respondent's gender on the severity of the punishment given to the offender in the various street crime scenarios. Table 9 shows the findings from this regression that indicate that the 51

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respond ent's gend e r was not a significant predictor of th e severity of punishment g iven t o th e offend e r (b=.359, p = l 53) In add iti o n findings revealed that the offender's gend e r i s not a signific a nt predictor of th e dependent variabl e ( b = .226, p = .370). Tabl e 10: Binary Logistic Regre ss i o n a n a lyses for White Collar a nd Street Crimes White Co llar C rime s Street C rimes Variables b SE p-AOR b SE p-AOR value value Indep e ndent V ariable Gender of Res p o n dent Ma l e 505 298 -1-. 090 603 .359 .252 I 53 1 .433 Offender's Gender Ma l e offender 440 .30 I 1 4 3 1 .553 226 252 .370 1.254 R espondent C hara c teristic Marita l Status N ever married 303 .423 .47 3 .738 -.647 .350 + 065 .523 Widowed 1 9.486 20034 999 290200322 810 700 .247 .445 Divorce .113 .486 8 1 7 1.119 5 II 390 190 1 .667 Sepa r ated -2.033 .988 040 1 3 1 -.234 756 .757 .792 E mployed -.376 .286 189 .686 Victim of White. 1 30 .452 .773 1 1 39 .477 .32 8 .146 .621 Colla r C rim e Victim of St r eet C rim e 509 379 179 .60 1 .405 272 .137 1 500 Race B l ack 863 .642 179 .422 6 1 2 482 205 .542 .527 795 872 1 265 .651 052 3.542 HiSQ_anic -.137 1 7602 999 484896420 -.153 .81 8 .852 858 A s ian 1 9 999 -1. 5 I I .838 + 07 1 .22 1 -.834 .956 .383 .434 Ot h er Race .18 1 .830 .360 125 .004 1.434 E ducat ion -.186 139 Age 002 .012 888 .998 -.0 1 3 0 I 0 .182 987 Co n s t a nt 3.103 1.54 3 044 22.263 198 1.179 .866 1 .220 p < 05 + p < .IO 52

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CHAPTER 6 DISCUSSION AND CONCLUSIONS The findings in this research disagree with previous studies that discovered street crime is viewed by the public as more serious than white-collar crime. In the current study, Ponzi and embezzlement schemes were seen as more serious than any of the street crimes including burglary, auto theft. and prostitution. Overall prostitution was seen as the least serious offense. The data suggest that public perceptions on the seriousness of white-collar crime have changed, perhaps because of increased media attention. The media coverage also may have resulted in more public exposure about the costs and consequences of white-collar crime. The high number of substantial financial damages incurred as a result of Ponzi schemes in the last five years certainly may have contributed to these changed viewpoints. Perceptions of how male and female respondents vievved street ve rsus white-collar crimes varied between genders and type of crime. Males are more likel y to believe that street level crimes are more deserving of punishment compared to females. In contrast, men were more likely than women to believe that white-collar offenders should receive less severe punishment. These differences may be attributable to the characteristics of the sample: that is. working-class males may see their own schemas and actions in the vvorkplace as more 'similar to white-collar offenders. Lack of findings within this current study may be attributed to the lack of 53

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variability within the variables, which is the reason for lack of relationships within the findings sections. Future research should attempt to account for this public shift in perceptions of white-collar crime and may include surveys and interviews that can account for the shift in opinion. It may be important, for example, to ask respondents where they get their information about white-collar crime and how often they hear about such offenses in the media. Asking these types of questions may help more fully account for the change of the public's perception of seriousness of white-collar crime. Future research may also consider asking the religious orientation of survey respondents. Data suggest that individuals who identified as Hispanic viewed offenders as more remorseful. Asking religious orientation of respondents might aid in understanding if this variable is influencing how individuals view the remorse of criminal offenders. Different religious cultures may sway individuals' views on seriousness, remorse, greed, stress, and punishment. Another future research recommendation is asking the political affiliation and political ideology of survey respondents. An individual's political affiliation may heavily influence the way individuals perceive the various crime scenarios. Republicans may see crime as deserving of different punishment than Democrats or Independent affiliated individuals. In addition, political ideology would let the researcher understand how individuals that have different ideological thought process 54

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perceive various crimes. If an individual identifies themselves as conservative, they may be more likely to punish and perceive crime differently than an individual who identities themselves as a liberal thinker. Lastly, due to the geographic location in which this research took place, asking survey respondents to identify whether or not they are currently serving in the military or have served in the military in the past. Since El Paso County has such a military density, identifying past and present military background would help establish if there is a relationship between crime perception and military background. In addition, research could establish if military background is linked to other important independent variables that establish relationships in how individuals perceive crime and crime seriousness. There are several caveats associated with the current research. First though the sample was a random sample from a jury pool subjects may well have self selected to participate based on any number of characteristics. The large sample size, however, assists in minimizing self-selection problems and extending generalizations. Second, the crime scenarios may have lacked adequate depth in the descriptions of the offenders' behaviors and outcomes. Third, participants may have been unclear of the nature of the exact offenses, particularly for the corporate crime. While the current research failed to find significance of views between male and female offenders on individuals perception of how they view the various crimes, 55

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the results offered other ins ight into how individuals percei v e crime. This research unlike previous studies suggests that the public view s white-collar crime as serious. The most distinctive piece of information thi s research offers i s in s ight into the changing awareness of white-collar crime and ho w the public s perception of the once greatly un-dealt with elite de v iance is no w chan g ing 56

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REFERENCES Adler, F. ( 1975). Sisters in crime: The rise of the new lemale criminal. New York: McGraw-Hill. Benson, M.L. ( 1989). The influence of class position on the formal and informal of white-collar offenders. The Sociological Quarterly. 30(3), 465-Cohen, A. ( 1955). Delinquent boys. New York: Free Press. Cullen, F.T. & Agnew, R. (2003). Criminological theory : Past to present (2nd ed). Los Angeles, CA: Roxbury Publishing. Cullen, F.T., Fisher, B.S., & Applegate, B.K. (2000). Public opinion about punishment and corrections. Crime and Justice 27 1-79. Cullen, F.T., Hartman, J.L., & Jonson, C.L. (2009). Bad guys: why the public support punishing white-collar offenders. Crime Law Social Change 51, 31-44 Cullen, F.T., Mathers, R.A., Clark, G.A. & Cullen, J.B. (1983). Public support for punishing white-collar crime: blaming the victim revisited? Journal of Criminal Justice, 11, 481-493. Daly, K. & Chesney-Lind, D R. ( 1988). Feminism and criminology. Justice Quarterly 5, 497-535. Dodge, M. (2009). Women and white-collar crime. New Jersey: Pearson Education Inc. Holtfreter, K., VanSlyke, S., Bratton, J., & Gertz M. (2008). Public perception of white-collar crime and punishment. Journal o.l Criminal Justice 36, 50-60. Piquero, N.L., Carmichael, S. & Piquero A.R. (2008). Assessing the perceived seriousness of white-collar and street crimes. C rime and Delinquency 54(2) 291-312. Podgor, E.S. (2007). The challenge of white collar sentencing, The Journal o.l Criminal Law and Criminology 97(3), 731-759. Pollak, 0. (1950). The criminality o.l women. New York: A.S. Barnes. 57

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Robb G. (2006). Women and white-collar crime. Brit. J. Criminal, 46 1058-1072. Rosenmerkel S.P. (200 1 ). Wrongfulness and harmfulness as components of seriousness of white-collar offenses. Journal o.f Contemporary Criminal Justice, 17(4), 308-327. Rossi, P H., Waite E., Bose, C.E., & Berk R.E. (1974). The seriousness of crimes: nonnative structure and individual differences American Sociological Review, 39, 224-237. Schoepfer, A., Carmichael, S., & Piquero, N .L. (2007). Do perceptions of punishment vary between white-collar and street crime ? Journal of Cri minal Justice, 35, 151-163. Simon, R.J. (1975). Women and crime. Lexington MA: Lexington Books. Sutherland E.H. (1949). White collar crime. New York : Dryden Press. Vande Walle G. (2002). The collar makes the difference: Masculine criminology and its refusal to recognize markets as criminogenic. Crime, Law, & Social Change, 37 277-291. 58