And justice for all? Investigators' perceptions of ... - Springer Link

Crime Law Soc Change (2008) 49:397–412 DOI 10.1007/s10611-008-9110-z

And justice for all? Investigators’ perceptions of punishment for fraud perpetrators Kristy Holtfreter & Nicole Leeper Piquero & Alex R. Piquero

Published online: 18 April 2008 # Springer Science + Business Media B.V. 2008

Abstract Despite extensive financial losses and other indicators of harm, the American public and legal professionals have historically been ambivalent toward white-collar crime. Recent research demonstrates that public perceptions of whitecollar crime and attitudes toward the punishment of white-collar offenders have become more punitive. Along these lines, a neglected area of research concerns those individuals who routinely face white-collar crimes: fraud investigators. Using data collected during the height of recent corporate scandals (2001–02), this study examines the perceptions of 663 fraud investigators and extends prior research by considering the influence of investigator characteristics, organizational context (i.e., size, setting, internal controls, and resource capacity), case characteristics (i.e., offense type, financial loss, and sanction), and offender characteristics on legal professionals’ general and specific punishment perceptions. Results indicate that organizational resources increase the likelihood of both outcomes. Additionally, the correlates of general and specific punishment perceptions are found to differ: government agency context influences general but not specific perceptions. Comparatively, the perception that fraud is increasing and a sanction that includes incarceration each have a significant, positive influence on specific punishment perceptions. Implications of these findings for future research and policy are discussed. Introduction There exists a large array of studies that have sought to assess citizen perceptions associated with crime and punishment [26, 44]. This line of research has tended to K. Holtfreter (*) Arizona State University, School of Criminology & Criminal Justice, 4701 W. Thunderbird Rd, Glendale, AZ 85306-4908, USA e-mail: [email protected] N. L. Piquero Virginia Commonwealth University, Richmond, VA, USA A. R. Piquero University of Maryland College Park, College Park, MA, USA

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focus on more serious, person-oriented offenses, and tends to find that the public perceives certain forms of crime as a real threat (sometimes increasing, other times decreasing), and that the punishment associated with the crime typically does not fit its seriousness. When the citizenry is further asked to estimate the likelihood of punishment for a specific crime as well as the range of sanctions associated with punishment for various crimes however, they provide respective estimates that depart significantly from objective reality [45]. In short, the public at large does not represent a good barometer with respect to an understanding of the applicability, range, and effectiveness of deterrence sanctions.1 Extant research on the perceptions of crime and punishment literature have further extended the knowledge base by widening the scope of crimes to include whitecollar and corporate violations [9–11, 27, 29, 32]. In addition to drawing samples from the general population [21, 31], this line of work has extended the more general research base to include a range of sampling frames including criminal justice bureaucrats [18], police officers [23, 24], and local prosecutors [3]. Although it is too early to draw any definitive summary conclusion about perceptions of crime and punishment among such crime types, this line of work tends to also suggest that representative samples do not have a complete understanding of the nature of such crimes, nor adequately understand the deterrence and punishment process regarding white-collar violations. A more general limitation associated with the afore-mentioned studies deals with their lack of attention to the determinants of sanction threat perceptions, including those associated with general and specific deterrence. The former set of perceptions deal with perceptions about the system so as not to include an individual’s specific point of view in his/her own case (e.g., perceptions associated with how the courts hand out adequate punishment to perpetrators). Although related to general deterrence perceptions, specific deterrence deals with aspects of sanction threat perceptions that deal with an individual him/herself and not with the external system (e.g., perceptions associated with punishment in a case the respondent was directly involved in). Studying the determinants of sanction threat perceptions is important because of the intimate linkage between sanctions threat perceptions and behavior. Unfortunately, empirical study of the determinants of general and specific punishment perceptions has been ill-studied and consequently remains a little understood issue in criminology [19]. As a result, this paper examines punishment perceptions associated with occupational fraud cases, cases in which an individual used his/her occupation for personal enrichment through the deliberate misuse or misapplication of the employing organization’s resources or assets. Importantly, this issue is examined with a unique sample of individuals who confront this sort of crime on a routine basis: fraud investigators. Examining deterrence perceptions among a sample of such individuals extends the extant research base in several ways. First, as noted earlier, much of the public opinion research has focused on the views of the citizenry at large. Although this has yielded important information, the generality afforded to such research may be 1

It is also the case the offenders have mixed knowledge about the risks and punishments associated with criminal offending [20, 22, 38].

Investigators’ perceptions of punishment for fraud perpetrators

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limited to the extent that they are based on media portrayals and public perceptions of high-profile cases (e.g., Enron and Tyco). Second, while recent research has examined perceptions associated with white-collar crime violations and punishment, this line of work has tended to employ samples of individuals within the criminal justice system who encounter only a limited range of white-collar offenses. By studying the perceptions of police officers and prosecutors, who handle only a handful of white-collar crimes on a regular basis, much knowledge has been gained but their insights may be limited due to the fact that the cases they handle are unique by the mere fact that these white-collar offenses came to the attention of the criminal justice system rather than being handled by an administrative regulatory body. Moreover, the most recent studies regarding the perceptions of legal professionals in the white-collar crime area were conducted nearly 20 years ago, and much has happened in the landscape of white-collar crime and punishment. Additionally, as Benson et al. [4:360] note, studies of local prosecutors’ perceptions specifically included white-collar offenses committed by individuals for personal gain. Both of these issues are addressed with recent data and cases that are largely representative of the occupational forms of white-collar crime, rather than that of corporate crime.2 Therefore, examining the perceptions of those individuals who are on the ‘front lines,’ Certified Fraud Examiners (CFEs), may provide a unique lens through which to study punishment perceptions. Third, and consistent with this previous point, recent legislative efforts (i.e., Sarbanes-Oxley [30]) enacted in response to corporate scandals have sought to prevent or deter fraud by targeting the control systems of organizations. Because the CFEs are typically responsible for administering such controls, studying their perceptions allows for a more concomitant assessment of the linkage between perceptions and behavior. Although CFEs are themselves a unique population, studying their perceptions may curtail some of the more common sample selection biases that appear in other studies because data is included from the earliest possible legal decision point: fraud detection. Lastly, a comprehensive range of variables that contain many factors that have not been available in prior deterrence-oriented research will be analyzed. Before we turn to our data analysis, we provide a brief overview of the extant research in this area.

Prior research The notion that white-collar offenses are considered less serious than street crimes is not a new idea. In fact, the earliest commentators [28, 35] on “white-collar crimes” argued that the general public was disinterested in this type of offending both in terms of criminalizing it as well as punishing it [12]. While views such as these helped make great strides in bringing white-collar crime into the lexicon of scholars, these earliest conclusions were not based on empirical data but rather upon the commentary of a few.

2

It is, of course, recognized that fraudulent statements may equate with corporate crime.

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Extant empirical research on public perceptions of white-collar crime primarily focused on the perceived seriousness rankings of these offenses compared to those of street crimes. This body of literature has been conducted on various samples including the general public as well as samples of criminal justice personnel. The earliest studies of the general population indicated that white-collar crimes were either considered not to be serious or more generally revealed overall feelings of indifference. For example, Rossi and his colleagues [29] used a block quota sampling design to interview a sample of adults living in Baltimore in the Fall of 1972. Each respondent was asked to indicate the seriousness of a list of offenses ranging on a scale of 1 (least serious) to 9 (most serious). In total 140 offenses across eleven subcategories were ranked.3 Their rankings revealed that white-collar crimes were considered less serious than most common street crimes, with the white-collar crime category ranked tenth out of eleven in terms of seriousness only ahead of public order crimes. However, more recent research revealed that white-collar offenses were indeed considered serious by the general public once the seriousness of the offense was taken into consideration [9–11]. In an early, interesting study, Schrager and Short [32] reanalyzed the Rossi et al. [29] data by differentiating between organizational and individual white-collar crimes. They found that organizational crimes which inflicted injury or death were ranked similar to some conventional crimes. In other words, the perceived seriousness of the offense was directly related to the harm produced by the act rather than by whether it was classified as a street or white-collar crime. Cullen and his colleagues [10] replicated the earlier Baltimore seriousness perception study [29] by mailing survey questionnaires to residents in Macomb, Illinois in 1979. As before, respondents were asked to rate the seriousness of a total of 140 offenses on a scale of 1 (not at all serious) to 9 (extremely serious). Their findings revealed some polarizing views regarding specific forms of white-collar crimes. There were some forms of white-collar crime (i.e., price-fixing, defrauding consumers, and income tax fraud) that the public was not at all concerned about and others (i.e., violent corporate crimes, embezzlement against a business, and government corruption) that very much were of concern to the respondents. Additionally, the seriousness rating of the white-collar crime category increased from the earlier study (e.g., [29]) from a ranking of 10 to 7. The post-Watergate sample now ranked white-collar crimes as more serious than the crime categories of crimes against police, some forms of property crime, and victimless crimes. Most recently, Rosenmerkel [27] sampled mid-Western college students and found that

3

The eleven offense categories include: (1) crimes against the person I: murder, manslaughter; (2) crimes against the person II: assault, rape, and incest; (3) crimes against the person III: all other crimes involving actual or threatened personal injury exclusive of those shown above; (4) crimes involving property I: cases in which the value of goods involved was more than $25; (5) Crimes involving property II: all other crimes involving property; (6) Selling illegal drugs: heroin, LSD, marijuana, pep pills; (7) white-collar crimes: embezzlement, income tax cheating, fraudulent business practices, etc; (8) victimless crimes: prostitution, homosexuality; (9) subversion (crimes against the state): desertion, spying for enemy; (10) crimes involving action against policemen; (11) crimes involving offenses against order: loitering, disturbing the peace.


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they ranked white-collar crimes more serious than property crimes but less serious than violent crimes. The same findings held when examining how the students ranked each of the crime types in regard to wrongfulness and harmfulness of the act. While the general public appears to view white-collar crimes at least as serious as some forms of conventional offending, some scholars have turned their attention to understanding the perceptions of crime seriousness by those who work in the criminal justice system. Overall a high degree of consensus is revealed between perceptions of the general public and those who work within the criminal justice system. McCleary et al. [18] found that their criminal justice respondents rated seriousness in terms of many more dimensions than did their sample of citizens but attribute the difference in perceptions to the formal legal education of the criminal justice bureaucrats. Police investigators were found to perceive higher seriousness ratings than did police chiefs and the general public but that the average rankings of offenses were overall similar [23, 24]. White-collar crimes were not regarded as a serious problem by local prosecutors [3, 4]. A survey of perceptions of seriousness with a sample of local prosecutors revealed that violent crimes and drug crimes had a higher priority than either white-collar or corporate crimes [3]. Overall it appears that while white-collar crimes are considered to be serious or at least as serious as some forms of street offenses by the general public, the consensus among criminal justice officials is more mixed. The current research builds upon the perceptions of seriousness literature in several ways. First, we study such perceptions among a sample of criminal justice personnel who, as of yet, have not been studied in this regard. Specifically, we examine fraud perceptions among fraud examiners, those individuals who are at the front lines of fraud detection, and who represent the first line of defense in trying to reduce white-collar offending. Such individuals are also the ones who are most qualified to identify and investigate instances of fraud. Additionally, the current study examines the determinants of punishment in terms of general and specific punishment, a key theoretical distinction in the deterrence literature.

Methods Data This study uses secondary data on occupational fraud cases, originally collected by the Association of Certified Fraud Examiners (ACFE).4 Broadly defined, occupational fraud is “the use of one’s occupation for personal enrichment through the deliberate misuse or misapplication of the employing organization’s resources or assets” [1:6]. This offense-based definition is similar to Friedrichs’ [13]

4

The ACFE is a professional organization of over 30,000 members that provides educational training and conducts research on fraud. The ACFE administers an exam that members must pass to earn the Certified Fraud Examiner (CFE) designation, which is similar to the Certified Public Accountant (CPA) exam. Additionally, there are minimum academic and professional requirements set by the ACFE Board of Regents, including a Bachelor’s degree and at least 2 years of professional experience in areas such as accounting, auditing, criminology or sociology, fraud investigation, loss prevention, or law. Members must abide by ACFE bylaws and the ACFE Code of Professional Ethics. ACFE frequently conducts surveys of its membership.

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conceptualization of occupational crime. As discussed further, the type of occupational fraud committed may vary, but all cases are distinct from corporate crime in that they “enrich individuals at the expense of the economic system” [7:320]. The primary goals of the survey were to extend previous exploratory research examining investigators’ perceptions of fraud levels and their opinions on punishment, and to obtain a better understanding of detected occupational frauds. Procedures Data collection began in April 2001 and ran through February 2002. Questionnaires were distributed by mail to 1,000 randomly selected Certified Fraud Examiners (CFEs) in the United States. A total of 971 CFEs provided usable survey responses; 663 cases involved fraud committed by employees against organizations.5 Participation in the surveys was voluntary. CFEs were offered 2 h of Continuing Professional Education (CPE) credit for their participation.6 Respondents were asked to provide detailed information on the case they most recently investigated. This restriction was placed on respondents to avoid the possibility that CFEs would reply based on their most interesting, well-known or “celebrated” case, which may result in highly skewed distributions (e.g., see [39]).7 Cases selected had to meet the following qualifications: (1) the investigation was completed; (2) the perpetrator(s) was identified; and (3) all legal proceedings, if any, were finished.8 Because not all respondents who returned the survey answered every question, missing data imputation was performed using PRELIS 2.30 (Scientific Software International, Chicago, IL).9 Respondent characteristics All survey respondents were CFEs employed in the following settings: 208 in private industry (31.4%), 192 in public corporations (29%), 192 in government agencies (24.4%), and the remaining 89 (13.4%) in nonprofit organizations. The

5

The other 308 cases also victimized organizations, but were committed by non-employees (e.g., customers or vendors). In addition to the questionnaires distributed by mail, the survey was also made available on the ACFE’s website to test the feasibility of conducting online surveys and to increase the response rate. A total of 78 CFEs completed online surveys, but only 9 responses were deemed useable to be included in the sample.

6

CPE credit contributes to the current status of a CFE’s professional license. Typical opportunities for credit include attendance at workshops or training seminars sponsored by ACFE.

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Conversely, it is possible that this restriction resulted in an oversampling of insignificant cases. According to Klepper et al. [17, p. 63], one potential problem is that some features of a case that may affect its processing cannot be observed by the researcher. Although selection bias may occur at multiple stages of the criminal justice system, it is least likely at the first stage examined here [17].

8

For cases in which civil suits were filed, a case was considered finished when a judgment was rendered. Criminal cases were considered finished at the sentencing stage (e.g., conviction, acquittal, plea bargain). Approximately 25% of cases included more than one offender. Respondents were asked to provide information on the one offender deemed to be primarily responsible for the offense. The number of offenders is controlled for in the analyses.

9

PRELIS substitutes the missing value for a specific case with a value from another case that has a very similar response pattern. According to Jöreskog and Sörbom [15], this approach is preferred over other methods (e.g., listwise or pairwise deletion).


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majority of the sample was employed as auditors (249 or 37.6%), followed by law enforcement or private investigations (194 or 29.3%), accountants (128 or 19.3%), loss prevention (50 or 7.5%), and attorneys (42 or 6.3%). This variable was coded as three dummy variables: Accountant/Auditor (1=yes; 0=no); Law Enforcement/Loss Prevention (1=yes; 0=no); and Attorney (1=yes; 0=no), with Attorney as the omitted reference category. The work histories of the respondents ranged from 1– 45 years with an average of just under 19 (18.79) years of experience. In the year prior to the survey, the median annual salary of the sample was $88,000 (range=$28,000 to $260,000, with a mean of $93,299 and standard deviation of $27,174). Regarding general perceptions about white-collar crime and punishment, the majority of respondents (63%) felt that fraud levels had increased in the 5 years prior to the survey, while a slightly lower percentage (59%) felt that fraud prevention efforts had also increased during this time period. Coding Participants were asked to provide a narrative explanation of the case, and this information was used to categorize cases based on ACFE’s extensive fraud classification system developed in their previous research.10 Cases were coded by survey respondents as three mutually exclusive types of fraud: asset misappropriation, corruption, and fraudulent statements. Examples of asset misappropriation included theft of cash or other inventory. The majority of cases (85.5% or 567) fit into this category. Examples of corruption included bribery, engaging in conflicts of interest, and acceptance of illegal gratuities. Corruption was least common, with only 38 cases reported (5.7%). The additional 58 cases (8.7% of the sample) were classified as fraudulent statements. Examples of fraudulent statements included falsification of financial statements (e.g., manipulating earnings and revenue) and non-financial statements (e.g., altering credentials or documents). These classifications are consistent with offenses considered in prior white-collar and corporate crime research, and all are trust violations (see [33].11 Additional details on occupational fraud cases, victim organizations, and perpetrators are included in the variables section. Respondent characteristics and descriptive statistics for all variables are presented in Table 1.

10

Survey data were provided to the first author in the fall of 2002. Due to confidentiality issues associated with participants’ identities, respondents’ personal demographic (e.g., age, gender, etc) and narrative data were not made available. A limited amount of qualitative data (e.g., CFE responses to open-ended questions) was provided for supplementary analyses.

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Despite conceptual disagreement over defining white-collar crime, Clinard and Quinney’s [8] distinction between corporate crime and occupational crime as the two subtypes of white-collar crime is generally accepted. The cases included in the current study fit Clinard and Quinney’s conceptualization of white-collar crime. Asset misappropriation, which described the majority of cases, clearly reflects the occupational crime subtype, while the categories of corruption and fraudulent statements might arguably be described as either corporate crime or occupational crime. Although investigators considered organizations to be the primary victims of all offenses, it is possible that some organizations benefited from specific offenses (e.g., overstating revenue) and others outside the organization (e.g., shareholders) may have been secondary victims. It is important to note here that individual offenders, not organizations, were identified as the “responsible parties.” This distinction has been accepted in previous studies of “hybrid crime” in the Savings and Loan industry [7].

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Table 1 Descriptive statistics (N=663) Variable Dependent variables General punishment Specific punishment Respondent characteristics Attorney Accountant/auditor Law enforcement Annual salary Years experience Fraud increasing Prevention increasing Organizational context Resources Government agency Nonprofit agency Privately held Public company Organizational size Internal controls Case characteristics Dollar loss (sq. Rt.) Asset misappropriation Corruption Fraudulent statements Incarceration Civil suit Perpetrator control variables Age Male Education Manager

Mean

SD

Min.

Max.

0.14 0.43

0.34 0.49

0.00 0.00

1.00 1.00

0.06 0.37 0.57 93,299.00 18.79 0.63 0.59

0.24 0.48 0.49 27,175.00 8.53 0.48 0.49

0.00 0.00 0.00 28,000.00 0.00 0.00 0.00

1.00 1.00 1.00 260,000.00 46.00 1.00 1.00

0.45 0.24 0.13 0.31 0.29 2.19 1.79

0.50 0.43 0.34 0.46 0.45 1.10 1.42

0.00 0.00 0.00 0.00 0.00 1.00 0.00

1.00 1.00 1.00 1.00 1.00 4.00 4.00

1530.00 0.85 0.06 0.09 0.26 0.06

7617.00 0.35 0.23 0.28 0.44 0.24

0.01 0.00 0.00 0.00 0.00 0.00

187,083.00 1.00 1.00 1.00 1.00 1.00

41.08 0.48 1.54 0.41

9.73 0.50 0.68 0.49

16.00 0.00 1.00 0.00

80.00 1.00 3.00 1.00

Dependent variables Two dependent variables are included in this study: general punishment perceptions and specific punishment perceptions. The first dependent variable, general punishment perceptions, is a one-item measure of respondents’ perceptions of the courts’ handling of fraud offenders. Specifically, this question asked “In your opinion, do the courts hand out adequate punishment to fraud perpetrators?” (1 = yes; 0 = no). As Table 1 shows, few respondents (14% or 91) answered affirmatively. The second dependent variable, specific punishment perceptions, asked respondents whether they felt that the perpetrator’s punishment in the current fraud case was adequate (1 = yes; 0 = no). Of the 559 who responded, approximately 43% (240) felt that the punishment was adequate. Independent variables In addition to the respondent characteristics and initial perceptions described above, a number of case characteristics, organizational context measures, and perpetrator characteristics were considered.


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Case Characteristics Several case characteristics may influence punishment perceptions. The seriousness of the offense was measured by Dollar loss, a continuous variable indicating the dollar loss from the case, which originally ranged from less than $100 to $4.2 million (a square root transformation was used to reduce the impact of extreme values). Previous research suggests that degree of harm is an important determinant of willingness to prosecute corporate crime [4]. Also considered was whether the sanction received influenced punishment perceptions. In this sample, 486 cases (73.3%) were handled officially (446 referred criminally and 40 sued civilly). A number of non-exclusive sentencing outcomes resulted from these referrals, including incarceration, fines, probation, community service, and restitution. Incarceration is a dummy variable measuring whether the perpetrator received a term of incarceration upon conviction (1 = yes; 0 = no). Incarceration represents the most serious outcome received by a perpetrator, and 163 of the 446 handled externally (33.5%) received a sentence that included incarceration. It is hypothesized that perceptions of adequate punishment will be more likely in cases that resulted in incarceration. Apart from criminal punishment, white-collar crime cases (e.g., fraud) may be routinely handled using civil remedies [16, 34]. Accordingly, a dummy variable reflecting whether a civil suit was filed against the perpetrator was included (1 = yes; 0 = no). Type of Fraud As discussed previously, investigators classified cases as three mutually exclusive types of occupational fraud. Three dummy variables were initially used to measure type of fraud: Asset Misappropriation (1 = yes; 0 = no); Fraudulent Statements (1 = yes; 0 = no); and Corruption (1 = yes; 0 = no). Organizational Context Characteristics of organizations may also influence investigators’ punishment perceptions. Type of Organization includes four dummy variables indicating whether the investigator worked for a government agency (1 = yes; 0 = no); publicly traded company (1 yes; 0 = no); privately held company (1 = yes; 0 = no); or nonprofit agency (1 = yes; 0 = no). Another important characteristic is the size of the organization, which serves as a proxy for access to resources (e.g., availability of policies and procedures to handle fraud). Organizational size measures number of employees and is coded as an ordered, categorical variable: 1=99 or less, 2=100–99, 3=1,000–9,999, and 4=10,000 or more.12 A slight majority of respondents (39%) worked for organizations with 99 or fewer employees, and the smallest victim category was 10,000 or more employees (16.6%). Organizational resources is measured with the question, “In your opinion, does your organization dedicate enough resources to preventing and detecting fraud?” (1 = yes; 0 = no). The importance of organizational resources has been documented in the research on local prosecutors’ perceptions of white-collar crime and punishment [5], as well as the handling of fraud perpetrators during the savings and loan crisis [7, 37]. Internal controls is a summary measure reflecting the availability of control mechanisms in victim organizations. Four dummy variables were summed, reflecting the presence of the following: background checks (1 = yes; 0 = no); anonymous 12

The decision to code this variable as interval, rather than continuous, was made by ACFE research staff in the survey design stage.

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fraud reporting (1 = yes; 0 = no); internal audits (1 = yes; 0 = no) or external audits (1 = yes; 0 = no). The composite measure ranged from 0–4 (Cronbach’s α=0.70), and the average number of control mechanisms was 1.8. These measures reflect procedures for detecting and/or preventing fraud, and serve as another indicator of organizations’ access to resources [42].

Control variables Perpetrator Characteristics Four characteristics of perpetrators were included as controls. Age (in years) is a continuous variable ranging from 16 to 80 (mean age= approximately 41 years). Sex is a dummy variable with male coded=1 and female coded=0. Slightly less than half of perpetrators (321 or 48.4%) were males. Manager/Executive is a dummy variable reflecting whether the perpetrator holds a managerial or executive level position (1 = yes; 0 = no). The majority of perpetrators (59%) held lower level, employee positions. Education is an ordinal variable with three categories reflecting an offender’s highest level of formal schooling: 1 = high school diploma or less (54.9%), 2 = bachelor’s degree (35.3%), and 3 = graduate degree (10.4%). The average age, education level, and gender breakdowns reported here are consistent with sample demographics from past white-collar crime research [6, 40, 41, 43].

Analytic strategy The analysis begins by examining the bivariate relationships between the variables of interest (respondent characteristics, organizational context, case characteristics, and perpetrator characteristics) and the two dependent variables (i.e., general and specific punishment perceptions). Next, two logistic regression models are estimated to determine the effects of the independent variables, net of statistical controls, on the two types of punishment perceptions.

Results Bivariate associations The significant bivariate associations between study variables are provided in Table 2. Some interesting findings are revealed. None of the respondent characteristics or perpetrator characteristics are significantly associated with either punishment perception outcome, a finding that differs from the research on perceptions of white-collar crime and punishment among the general public [14, 21, 25, 31]. Consistent with the research on local prosecutors’ opinions [5], organizational context variables and case characteristics are related to general punishment perceptions. Specifically, working for a government agency (r=0.10, p