Am J Crim Just (2012) 37:4–18 DOI 10.1007/s12103-010-9094-y
Sympathy for the Devil: An Exploration of Federal Judicial Discretion in the Processing of White-Collar Offenders Sean Maddan & Richard D. Hartley & Jeffery T. Walker & J. Mitchell Miller
Received: 9 March 2010 / Accepted: 1 September 2010 / Published online: 29 December 2010 # Southern Criminal Justice Association 2010
Abstract Since the late 1990s, the United States has experienced a series of major corporate malfeasance events leading to the collapse of corporations such as Worldcom and Enron, predatory lending practices which devastated the nation’s real estate market and the Bernie Madoff scandal serving as prime examples. While the leading culprits in such well-publicized cases have met stiff sanctions, the common notion is that white-collar offenders are treated more leniently than street offenders by the criminal justice system. Given the scope and severity of victimization attributable to the contemporary white collar crime epidemic, the matter of sanctioning fairness and severity is of timely importance. This paper examines judicial discretion in the form of the decision to incarcerate and the length of sentences imposed for federal white collar and street level offenders. Findings inform discussion oriented around the related issues of deterrence and public safety. Keywords White-collar offenders . Judicial discretion . Federal sentencing
S. Maddan Department of Criminology, University of Tampa, 401 W. Kennedy Blvd., Tampa, FL 33606, USA e-mail:
[email protected] R. D. Hartley (*) Department of Criminal Justice, University of Texas at San Antonio, 501 W. Durango Blvd., San Antonio, TX 78207, USA e-mail:
[email protected] J. T. Walker Department of Criminal Justice, University of Arkansas, Little Rock, 2801 South University Avenue, Little Rock, AR 72204-1099, USA e-mail:
[email protected] J. M. Miller Department of Criminal Justice, University of Texas at San Antonio, 501 W. Durango Blvd., San Antonio, TX 78248, USA e-mail:
[email protected]
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Introduction Sanctioning outcomes are a primary indicator of fairness in the American criminal justice system. Second only to verdict accuracy, fairness is observed as the measure of sentencing severity equivalency across offenders representing various social groups, particularly when characterized in terms of race and social class. Because socioeconomic status and race correlate strongly with offense type, the sanctioning of white collar crime is a particular point of focus for examining institutionalized system bias. Attitudes concerning the levels of threat and harm attributable to white collar versus street crime, differences in legal and extralegal factors, and general capitalistic societal reinforcement of preferential treatment for those with class status combine to form the perception that white collar criminals receive greater leniency. While the research on disparity in sentencing has grown rapidly over the last two decades (see for example, Steffensmeier et al. 1998; Steffensmeier and Demuth 2000, 2001; Spohn and Holleran 2000; Ulmer and Johnson 2004; for extensive reviews, see Spohn 2000; The Sentencing Project 2005), focus has been primarily on the identification of influential offender characteristics, especially race/ethnicity and class, across offense types more so than on severity. This study is an attempt to readdress the issue of sentencing severity for white collar criminals relative to street criminals through observation of judicial discretion about the incarceration and sentence length decisions. After reviewing the relevant white collar crime research, multivariate analyses of these indicators generate findings informing future white collar crime research and criminal justice policy.
Review of the Literature The term “white-collar crime” originates from a seminal speech given in 1939 by Edwin Sutherland in his presidential address to the American Sociological Society. Sutherland sought a theory of crime causation that was not rooted in society’s lower classes; thus, he defined white-collar crime as “a crime committed by a person of respectability and high social status in the course of his occupation” (1983, p. 7). Sutherland proposed the term white-collar crime to indicate the offenses typically committed by people in higher socioeconomic statuses (Sutherland 1940, p. 2) however, this emphasis on the rich being white-collar criminals has led to the stereotyping of white-collar offenders as businessmen who embezzle money from large corporations (Robertson 1999, p. 24). This ideation has also led to the definitional splintering of white-collar crime. Since Sutherland coined the phrase, the definition of white-collar crime has been a source of controversy (Meier and Short 1982; Dinitz 1982; Poole and Walsh 1983; Hagan and Simon 1999) with a common point of contention being whether whitecollar crime should be oriented toward the offender or the offense (Stier 1982, p. 153). An offender-related approach examines only those individuals in the upper class, whereas an offense-related approach focuses on the nature of the crime with a breach of trust as a requisite characteristic. Thus, any individual in any class could potentially be a white-collar offender.
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Perhaps reflective of these definitional issues, Israel and Podgor (1997, p. 4) noted that there are no sets of law that deal specifically with white-collar crime; based instead on a “vast array of statutory offenses”. In the Federal Code, for example, the term “white-collar crime” does not appear at all. Additionally, as Friedrichs (1996, p. 270) noted, white-collar crime is often addressed as a civil matter instead of a criminal one, with the goal of regaining lost interests over criminal sanction. A second problem in this line of research is that white-collar crime has been quantitatively hard to measure. A primary data source of crime, the Uniform Crime Report (UCR) has virtually nothing related to white collar crime. Other sources of crime data, such as city arrest records, either have no data directly related to white collar crime or so much missing data that the numbers are not useful. Researchers (Manson 1986; Schlesinger 1987) have tried, however, to use UCR data by manipulating the definition of white-collar crime to better fit the data available. In these manipulations, exclusively white-collar crimes are either omitted or other crimes are included along with white-collar offenses. As Nagel and Hagan (1982, p. 1439) wrote, the “relevance of these definitional problems is that the particular definition of white-collar used to determine sample inclusion very much affects research seeking to compare the sanctioning of whitecollar offenders to that of nonwhite-collar offenders.” The conceptualization of white-collar crime thus becomes critical for meaningful study—nowhere more evident than in the study of the sentencing of white-collar offenders. While the wide range of criminal behavior and the great differences in type of injury caused makes comparison between white-collar offenders and street level offenders at any stage of the criminal justice system difficult, it is a subject of obvious timely significance. Research on the Sentencing of White-Collar Offenders Criminologists have examined white collar crime with both variable analysis and fieldwork research designs. Katz’s (1978) ethnographic examination of the prosecutorial decision to charge white-collar offenders, for example, illustrated that prosecutorial discretion in New York was exercised with subtlety: “There is little effective discretion not to prosecute once the case reaches the prosecutor’s office” (Katz 1978, p. 449). Prosecutors facing long investigations and prolonged court cases have added incentive to accept pleas that, with white collar cases often means halting criminal proceedings and redirecting cases to civil or administrative proceedings. Hagan et al. (1980) provided one of the first quantitative studies of the sentencing of white-collar offenders by examining white collar offenders prosecuted (9,068) and sentenced (6,562) in ten federal district courts between 1974 and 1977. By including both characteristics of the offense and the offender, they identified 31 offenses from the U.S. Code as white-collar. Different offenders were separated by a cross measure of income and education. To capture female offenders, the measure of income was thrown out. The final model placed offenders in one of four categories: 1) less educated, common criminals, 2) more educated, common criminals, 3) less educated, white-collar criminals and 4) more educated, white-collar criminals.
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Utilizing OLS regression, Hagan et al. (1980) examined the effects of criminal history, seriousness of the offense, number of charges, the presence of multiple defendants, ethnicity, sex, employment, age, bail status, the presence of a plea, a school dummy variable, and mental and physical illness of the offender upon the severity of the sentence an offender received. The severity scale of the offense included: fines, probation, imprisonment, or some form of mixed sentence. Whitecollar criminals were found to be treated much the same as common criminals, with the exception of one district explained as having a proactive white-collar prosecution strategy generating more white-collar defendants (both well and less educated) resulting in more cases and a statistically significant difference between type of offender. In general, a statistically significant disparate treatment of white-collar and common offenders was not observed and there was no disparity in the treatment of well educated and less educated white-collar offenders. Mann et al. (1980) furthered the line of inquiry into differences in sentencing between white collar and street criminals by interviewing federal judges with white collar crime cases. Their research illustrated there is at least a perception of differences regarding offender type that could produce differential treatment. Interviews indicated that judges believe that white-collar offenders are more susceptible to the general deterrent effects of sentencing strategies and prefer to punish without negatively affecting the offender’s spouse, children, employees, clients, or community relations. White-collar offenders, according to these judges, may also be better able to compensate victims and the community monetarily; thus a sentence will have a deterrent effect, “but without imposing the deprivations that would come from an extended stay in prison” (Mann et al. 1980, p. 499). Concentrating only on the outlier district from their previous work, Hagan et al. (1982) reexamined this data, instead choosing to focus upon offender income and not education. They found that income was as good a proxy of social standing as educational attainment. Further, Hagan et al. (1982) concluded that people with higher incomes were more likely to receive more lenient sentences in this one U.S. federal judicial district. Shanzenbach and Yaeger (2006) similarly found that larger fines mediate sentences for white collar offenders; those who have larger fines imposed will receive shorter sentences. Their research revealed that the imposition of fines effect could explain away some of the racial and ethnic disparities found in sentencing for white-collar offenders. Albonetti (1994, 1998, 1999) focused on judicial choice to suspend a defendant’s sentence in white-collar cases. Using Wheeler et al.’s (1982) data, she found that educational level and net worth (socioeconomic status) did not substantially predict the likelihood of receiving a suspended sentence but that pleading guilty and increased case complexity had both direct and indirect effects on decisions to suspend the sentence. This body of research shows that the status of the offender may not be as important as the status of the corporation whom the offender works for. Offenders in larger corporations (those with ample money to pay fines, and where cases were complex) received more lenient sentences. This research, however, studied sentences in the pre-guideline era. Wheeler et al. (1982) used federal sentencing data from 1976 to 1978 to examine eight identified white-collar crimes (antitrust, securities and exchange fraud, postal and wire fraud, false claims, credit and lending fraud, bank embezzlement, IRS
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fraud, and bribery). Through analytic models of the decision to imprison and the length of the sentence based on prior studies and interviews with federal judges, they found males were treated more harshly in both phases of sentencing, and a positive, statistically significant relationship existed between socioeconomic status and the severity of the sentence. Benson and Walker (1988) utilized a design akin to the Wheeler et al. (1982) study and found that social status, as measured by education and income, did not influence the decision to incarcerate in a Midwestern state. Hagan and Parker’s (1985) research on the sentencing of white-collar criminals focused on securities violations in Canada. Their analysis incorporated a sample of offenders obtained from both criminal and administrative court files supplemented with interviews of different courtroom personnel. An examination of the different levels of employees in an organization (employers, managers, petty bourgeoisie, and workers) revealed that securities offenders at the managerial level were treated more harshly than the other categories. These sentences came after incidents such as Watergate and Harbourgate and it could be argued that this class, rather than owners or employers, were “scapegoats” for the societal backlash against white-collar crime. Empirical Shortcomings The extant research on the sentencing of white-collar crime has several methodological shortcomings. First, the majority of the quantitative data analyzed to date has come from only two sources: Hagan et al. (1980) and Wheeler et al. (1982). This limits the value of the findings in that these data sets are now almost 30 years old and sentencing practices have undergone extreme change since the 1970’s when the majority of the data was collected. Second, and somewhat more problematic, is that the operationalization of whitecollar offenses are not exclusive of what would be considered white-collar. For instance, most sentencing studies consider tax evasion to be a white-collar crime, however, tax evasion occurs across the entire social strata. Thus, most offenses utilized in these studies are not “pure” white-collar or “pure” street level offenses. As Katz (1978, p. 434) warns, it is time criminologists recognized that crimes recorded in official statistics are, on the whole, comprised of both white-collar and street level offenses; some of these official crime categories are “more white-collar” than others or white-collar only in some aspects.
Research Methods Data The primary question driving this research explores the relationship between sentencing practices for white-collar offenders and street level offenders. The data used to examine this relationship was drawn from federal sentencing data made available by the United States Sentencing Commission (ICPSR) and were downloaded from the Inter-University Consortium for Political and Social Research (ICPSR). These data represent information on defendants processed in United States District Courts for embezzlement and auto theft and include variables such as the
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defendant’s criminal history and seriousness of the offense as well as extra legal variables such as race, age, and gender. The data for 1993 is studied here because it is one of the few years of federal data that includes information on the defendant’s annual income, which has been an important variable in white-collar sentencing studies as a proxy for white-collar status. Dependent Variables The primary dependent variables examined in this research include the decision to imprison the defendant (no prison = 0, prison = 1), and for those incarcerated, the number of months in prison the defendant received. We hypothesize that white-collar offenders are less likely to receive a prison sentence and that white-collar offenders, if incarcerated, will receive less time (in months) in prison than street level offenders. The dependent variable (disposition) reflects whether the defendant was given a sentence of prison or not and the variable “duration” indicates the length of sentence in months. Independent Variables The main independent variable, offense type, requires some explanation. As was illustrated in the literature review, the term white-collar crime has a myriad of definitions which has translated into various methodological designs but some common elements have included the violation of trust and an offense commission in an occupational context (Friedrichs 1996, p. 11). Focusing on embezzlement as a measure of white collar crime we isolate those who fit the characterization of white-collar offenders and compare them to those who have committed what would be characterized as street level crime. This operationalization compares embezzlers to auto thieves, which are used as a proxy for street level crime. Focusing on these two offenses facilitates an argument that the two groups are mutually exclusive; thus any differences between the two should be indicative of differences between white collar and street level crimes. This dichotomy should also allow a more accurate examination of any differential sentencing practices for white-collar and street level offenders. Embezzlers and auto thieves are good comparison groups for other reasons as well. First, each of these offenses is similar in that a victim is not physically confronted by the offender. Second, both of these offenses have the potential for similar financial losses to the victim; typically into the thousands of dollars. Finally, neither of these offenses receives extensive coverage in the media. Auto theft is rarely reported in the media, while white-collar crime typically has long periods of dormancy in the media until a major incident pushes it to the fore. For instance, the collapses of Enron and World Com have brought white-collar crime to the attention of the media. Typically following a short peak of intense coverage, media attention will return to relative obscurity. This research also controls for other variables that previous research has shown to influence sentencing outcomes. The legal characteristics in this analysis include the offender’s criminal history, the seriousness of the offense, whether or not the
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offender accepted responsibility, and whether or not the judge departed from the sentencing guidelines. The extra legal factors (social characteristics) that will be examined in this analysis are income, education, case disposition, race, age, ethnicity, and sex. Income and education have been key variables in the study of the sentencing of white-collar offenders. In this study, education is dummy coded into whether an offender had less than a high school degree, a high school degree or GED equivalent, or a university or college degree. Annual income is a continuous variable measured in dollars. The preceding variables are used as controls in the statistical analyses. Descriptive statistics for the independent variables are presented in Table 1. This study, therefore, will incorporate two separate analyses. The first examines the incarceration decision (disposition), is a dichotomous dependent variable (0 = no prison; 1 = prison) and is analyzed using logistic regression. The second examines the length of sentence (duration), is a continuous dependent variable and analyzed using OLS regression.
Table 1 Frequencies and means of variables included in analytical models Variable
Embezzlement
Auto Theft
Frequency/Mean
Frequency/Mean
Dependent Variables Sent to Prison
391 (42%)
234 (75%)
Sentence Length
4.05
14.73
Offense Level
20.63
17.56
Criminal History
2.09
2.86
Independent Variables
Responsibility Departure Disposition
No Acceptance (0)
49
39
Accepts Responsibility (1)
867
267
No Departure (0)
798
269
Departure (1)
106
35
Plea (0)
919
292
Trial (1)
20
19
$17,800
$12,000
Less than High School
58
137
High School Diploma/GED
391
101
College Degree
455
68
White
575
204
Black
261
72
Ethnicity
Hispanic
62
27
Sex
Male (0)
379
284
Female (1)
560
27
Incomea Education
Race
a
Age
35
33
N
939
311
Median annual income
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Findings Disposition Weisburd et al. (1991) suggested that researchers should not examine white-collar offenders and street level offenders within the same model. This research utilizes a new methodological design, wherein we first consider white-collar and street level offenders together, then if significant differences are found between these groups of offenders, the data will be partitioned by these two offense types and each group of offenders will be analyzed separately. Table 2 presents the logistic regression for the judicial decision to imprison. Table 2 reveals that the majority of the variables in this model are not statistically significant. Surprisingly, the variables for criminal history and offense seriousness, the variables upon which sentencing guidelines were promulgated, are not significant. Three other variables that are also surprisingly not statistically significant are race, sex, and income. Income has been one of the most oft used variables to distinguish white-collar offenders in the past; however, here it does not have a significant effect on the imprisonment decision. The variable for offense type, however, was significant; white-collar offenders received more lenient treatment from federal judges than their street level counterparts. Auto thieves were almost four times more likely to receive a prison sentence. Having a college degree and pleading not guilty were also significant predictors of the incarceration decision. Offenders who plead not guilty, went to trial and were Table 2 Logistic regression model for decision to imprison Variable
B
SE
Wald
Exp(B) 0.942
Race (1=Black)
−0.059
0.043
1.927
Ethnicity (1=Hispanic)
−0.143
0.110
1.690
0.867
Sex (1=Female)
−0.111
0.141
0.623
0.895
HS/GED (1=Diploma)
−0.267
0.229
1.360
0.766
College (1=Degree)
−0.723*
0.280
6.694
0.485
Age
0.020*
0.006
10.132
1.020
Offense Type (1=Auto Theft)
1.368*
0.183
55.937
3.929
Income
0.001
0.001
2.462
1.000
Disposition (1=Trial)
1.993*
0.547
13.302
7.341
−0.007
0.006
1.134
0.993
0.036
0.070
0.261
1.036
Departure
−0.002
0.045
0.003
0.998
Accept Responsibility
−0.058
0.072
0.651
0.943
0.214
0.536
0.159
1.239
Offense Seriousness Criminal History
Constant -2 Log likelihood
1556.862
χ2
176.006*
Nagelkerke R2 *p
