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Exploring white-collar crime and the American dream: A partial test of institutional anomie theory
Journal of Criminal Justice 34 (2006) 227 – 235
Exploring white-collar crime and the American dream: A partial test of institutional anomie theory Andrea Schoepfer, Nicole Leeper Piquero ⁎ Department of Criminology, Law and Society, University of Florida, P.O. Box 115950, Gainesville, FL 32611-5950, United States
Abstract Institutional anomie theory (IAT) suggests that high crime rates in America can be attributed to the commitment to the goal of material success. In this regard, particular emphasis is placed on the motivations derived from the profit goal of economic institutions that dominate the American culture. To date, IAT was only applied to property and violent crime. This study used Uniform Crime Report (UCR) and Census Bureau data to examine the applicability of IAT to a form of white-collar crime, embezzlement, as defined by the UCR. Results provided mixed support for IAT. Limitations and future research directions are discussed. © 2006 Elsevier Ltd. All rights reserved.
Introduction Two distinct characteristics are believed to set the United States apart from other industrialized nations. One is the cultural emphasis on the pursuit of the American dream, which suggests that everyone in America (regardless of social-background characteristics) has an equal opportunity to become monetarily successful. This success is ultimately achieved through hard work, dedication, and persistence. The other is that the United States has an extremely high (and relatively consistent) level of crime. In order to account for these distinctions, Messner and Rosenfeld (1994) developed and introduced Institutional Anomie Theory (IAT). They suggested that the American culture was enmeshed in the idea of individual monetary success through competition for limited resources. This leads to
⁎ Corresponding author. Tel.: +1 352 392 1035; fax: +1 352 392 5065. E-mail address: [email protected] (A. Schoepfer). 0047-2352/$ - see front matter © 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jcrimjus.2006.03.008
an overemphasis on the economy to the detriment of the counter-balancing importance of noneconomic institutions such as family, education, or the need for society to take care of its citizens (i.e., polity). Messner and Rosenfeld (2001) argued that this was why the crime rate of the United States greatly exceeded that of other modern industrialized nations; other nations did not place such a strong emphasis on the economy. Rather, they placed the emphasis on noneconomic institutions, thereby creating mechanisms of social control, which lead to lower levels of crime. As a general theory of crime, IAT is designed to explain all forms of offending including white-collar crimes. At first glance it may appear that street crimes differ substantively from suite crimes, after all, research had found substantial differences in background and status characteristics between individuals convicted of street crimes and those convicted of white-collar crimes (see Benson & Kerley, 2001; Benson & Moore, 1992; Weisburd, Wheeler, Waring, & Bode, 1991); however, the cultural ethos of the American dream provides the unifying explanation. The emphasis on individual
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monetary success coupled with limited resources forces individuals to innovate or find alternative means (including illegal and unethical) to achieve the desired goal (i.e., material success). Monetary success has no ending since it is always possible to acquire more money and wealth (Messner & Rosenfeld, 2001, pp. 63–64). Therefore, monetary success does not discriminate—it is equally motivating for both the wealthy and the poor. The purpose of this study was to apply IAT to whitecollar crime, a heretofore unexamined criminal activity within the context of the theory. Following Edelhertz (1970, p. 3), white-collar crime is defined as “an illegal act or series of illegal acts committed by nonphysical means and by concealment or guile to obtain money or property, to avoid the payment or loss of money or property, or to obtain business or personal advantage.” While definitions of white-collar crime vary and are much debated, a common thread throughout the various definitions has emerged in defining a distinct category of crimes and criminals that are different from the much studied street crimes and criminals (Weisburd & Waring, 2001, p. 9). Despite the definition used, white-collar crimes tend to focus on personal or organizational gain and therefore are well suited to be studied within the IAT framework. Since IAT assumes that criminal activity relates to the pursuit of monetary success and can explain those behaviors that offer monetary rewards (i.e., instrumental crimes) (Messner & Rosenfeld, 1994), white-collar crimes should not only be able to be explained under this theoretical framework but also would expand the generalizability of the theory. Before presenting the preliminary investigation of IAT in explaining one type of white-collar crime, embezzlement, a brief overview of the development of IAT is provided, followed by a review of the empirical research on IAT to date, and then a discussion of the application of the theory to white-collar offending. Institutional anomie theory When constructing their theory, Messner and Rosenfeld (1994) were interested in explaining the exceptionally high levels of crime in the United States. Believing that crime in the United States was a byproduct of societal structure and culture, they turned to Durkheim's anomie theory and Merton's strain theory to help explain the observed high levels of crime. While Durkheim (1965) focused on societal changes, Merton focused on stable social conditions associated with high crime rates, in particular, crime rates in the United States. Merton (1968) argued that the American culture
placed more emphasis on achieving wealth and prestige than it did on the institutionalized means (i.e., hard work, perseverance) to gain access to the goal. Messner and Rosenfeld (2001, pp. 13–14) argued that Merton's focus on inequality to the access of legitimate means ignored the “broader institutional structure of society” and therefore did not “provide a fully comprehensive sociological explanation of crime in America.” Building upon Merton's work, Messner and Rosenfeld explored the relationship and implications between culture and the “broader institutional structure” of American society. IAT is a macro-level theory that assumes the interplay of the American culture and the social structure. Four distinctive values are believed to underlie the American culture. The first value, achievement, refers to setting and accomplishing goals, being successful, and increasing personal wealth. The second value, individualism, refers to America's strong belief in individual rights, freedoms, and autonomy. In other words, the belief that everyone has the ability to make it on his or her own. The third value at the core of American culture is universalism. Universalism assumes that everyone is encouraged to aspire to success regardless of the available opportunities to become successful. The final underlying cultural value is materialism, or the “fetishism” of money in which success is measured by monetary rewards. These four basic values comprise the core elements of the American dream. In sum, the American dream represents the “cultural ethos that entails a commitment to the goal of material success, to be pursued by everyone in society, under conditions of open, individual competition” (Messner & Rosenfeld, 2001, p. 5). The basic thesis of IAT is that the American dream “exerts pressures toward crime by encouraging an anomic cultural environment, an environment in which people are encouraged to adopt an ‘anything goes’ mentality in the pursuit of personal goals” (Messner & Rosenfeld, 2001, p. 61). Culture, however, is only half of the equation. The other half is comprised of the social structure. There are four basic social institutions, the building blocks of societies, relevant for explaining crime rates: economy, polity, family, and education (Messner & Rosenfeld, 2001, p. 65). The key concept of IAT is the institutional balance (or imbalance) of power across the different social institutions. High crime rates in the United States are attributed to dominance of the economy over and above the influence of other social institutions. Therefore, since the economy dominates all other social institutions in America, cultural pressures develop so that noneconomic institutions (i.e., family,
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education, and polity) cannot function effectively which leads to weak social controls and ultimately to higher levels of crime. As suggested by Chamlin and Cochran (1995, p. 421), the most important feature of IAT is the interplay or interactive effects of the economy and the other noneconomic social institutions. In other words, reductions in rates of crime can be expected when there is an improvement in economic conditions coupled with the strengthening of the other social institutions. On the other hand, the dominance of the economy can be observed when despite strengths to noneconomic social institutions, declines in the economy (e.g., high rates of poverty or unemployment) increase rates of crime. Prior empirical research on IAT To date, there were only a handful of empirical studies that directly tested institutional anomie theory (Chamlin & Cochran, 1995; Maume & Lee, 2003; Messner & Rosenfeld, 1997; Piquero & Piquero, 1998; Pratt & Godsey, 2003; Savolainen, 2000) and they overwhelmingly focused on property and violent crime to the exclusion of white-collar crime.1 Chamlin and Cochran (1995, p. 414) were the first to empirically examine IAT. They examined the effects of economic (e.g., poverty) and noneconomic measures (e.g., family, religion, and polity) on the 1980 property crime rates for each of the fifty U.S. states. They analyzed the interaction effects of economic and noneconomic measures on instrumental crime. For the most part, their findings were consistent with IAT predictions. The authors found that higher levels of church membership, higher levels of voting participation, and lower levels of the divorce-marriage ratio reduced the criminogenic effects of poverty on instrumental crime. They concluded that economic measures had no independent effect on crime; rather, it was, as expected, the interplay between economic and noneconomic institutions that increased anomie and lead to higher levels of crime within a society. Messner and Rosenfeld (1997) examined the relationship between the economy, measured as economic inequality, and the polity in relation to criminal homicide rates in forty-five modern industrialized nations. They hypothesized that homicide rates and decommodification—a measure of values and resources made available to citizens to reduce their reliance on market forces—would vary inversely. Messner and Rosenfeld found that decommodification had a direct significant negative effect on homicide rates; nations with greater decommodification scores tended to have
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lower homicide rates. They also found that the United States had a very low decommodification score and a very high homicide rate. Building upon Messner and Rosenfeld's (1997) sample size of forty-five nations, Savolainen (2000) not only added a second cross-national sample that included data from thirty-six additional countries, increasing the sample size to eighty-one, but also examined the interaction effects between economic inequality and welfare support. Savolainen found that economic inequality was a significant predictor of homicide in societies with weak institutions of social protection, thus supporting IAT. Savolainen also found that nations offering the most generous welfare programs tended to have the lowest levels of economic inequality. Piquero and Piquero (1998) examined IAT in relation to property crime rates and violent crime rates using data from each of the fifty U.S. states and Washington, D.C. Using census data for measures of the independent variables, Piquero and Piquero were the first to measure the education component of IAT. For the most part, additive effects were significant and in the expected direction for both property and violent crime models. More importantly, the interactive effects revealed that higher percentages of individuals enrolled full-time in college reduced the effect of poverty on both crime types while the polity-economy interaction was only statistically significant for violent crime rates. Maume and Lee (2003) investigated the effects of IAT by disaggregating homicide rates (e.g., total versus expressive versus instrumental) at the county level. In addition, they suggested that noneconomic institutions will not only moderate the influence of the economy on rates of crime, but will also mediate the influence of the economy on crime rates. Overall, they found limited support for the commonly used moderating hypothesis; that is, they only found one significant interaction effect that suggested the effect of economic pressure (e.g., Gini coefficient) on homicide rates (no matter how measured) was significantly weaker in counties with low levels of welfare disbursements. More support was garnered for the mediating hypothesis in that across all three models the measure of economy, the Gini coefficient of family income inequality, was reduced when the noneconomic institutions were added into the model. Pratt and Godsey (2003) highlighted the similarities of the relationships among measures of social support, economic inequality and crime across three theoretical perspectives: IAT, social support (Cullen, 1994), and macro-level general strain theory (Agnew, 1999). By
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examining cross-national homicide rates, they found direct effects for both social support, measured as amount spent on health care, and income inequality, measured as ratio of median income for richest and poorest 20 percent of the population. In addition, they found mediating effects for both social support and income inequality in that when added together in a model, the main effects for both diminished. Finally, the moderating effect of income inequality and social support suggested that the presence of high levels of social support reduced the effect of economic inequality on rates of homicide. These studies, taken as a whole, seemed to suggest partial support for IAT. Across a variety of different outcomes as well as aggregated units and utilizing various measures of noneconomic institutions, there did appear to be some support for the presumption of the importance of the economy in explaining instrumental crimes. What had not been investigated, yet had been suggested (see Chamlin & Cochran, 1995, p. 413; Messner & Rosenfeld, 2001, p. 3), was the ability of IAT to explain rates of white-collar crime. Applying institutional anomie theory (IAT) to white-collar crime The applicability of IAT to the study of white-collar crimes (like that of street crimes) derives from the motivation of offending. IAT suggests that crime in the U.S. is driven by immense pressures to succeed and profit monetarily. This sentiment is echoed by Coleman (1987, p. 416) who argues that key components of the culture of competition, or “the idea that wealth and success are central goals of human endeavor,” are important in motivating the white-collar criminal. Weisburd et al. (1991, p. 188) studied a sample of individuals convicted of white-collar crimes in federal courts and speculated (since presentence investigation reports are not ideal for examining motivation) that there was one motivation underlying the white-collar crimes of these individuals: financial need. While the motivation appeared to be from the same underlying cause, their analysis revealed two distinct paths to reach the point of feeling that subjective sense of financial need: one for the “high-risk ego gratifiers” and the other for those who exhibit the “fear of falling” (Weisburd et al., 1991, p. 189). The “high-risk ego gratifier” group was originally suggested by Weisburd et al. (1991) to be comprised of offenders who exhibit low self-control (as suggested by Gottfredson & Hirschi, 1990). The empirical evidence, however, was not supportive of the ability of low self-
control to account for white-collar offending (Benson & Moore, 1992; Simpson & Piquero, 2002). More recent research had suggested the personality trait, the desirefor-control, or the general wish to be in control over everyday life events, was better equipped to explain forms of white-collar criminality (Piquero, Exum, & Simpson, 2005). This intense need to control everyday events, including events beyond their control, coupled with the disdain for failure pushes individuals with high levels of desire-for-control to resort to crime or innovate to find ways to ensure their own success. Oftentimes, because of hubris, or an exaggerated self-confidence, individuals with high levels of desire-for-control have elevated levels of aspirations and thus seek goals impossible to attain which forces them “to do something—even if it is criminal—in order to survive, get by, and perhaps more importantly, get ahead” (Piquero et al., 2005, p. 260). The fear of falling refers to an individual's perception of economic security and the fear of losing what he or she has worked hard to obtain. In trying to understand why white-collar criminals commit crime, Wheeler (1992, pp. 112, 115) looked toward prospect theory (Kahneman & Tversky, 1979) for an explanation other than greed, defined as wanting even more than what one already has, and suggests that the logic behind the fear of falling is based on “perceptions and judgments, which are attuned to the evaluation of changes or differences rather than to the evaluation of absolute magnitudes.” As such, changes, such as gains or losses, in one's economic position judged from the reference point of one's current level of wealth are more important as a motivating factor than one's final asset position. In this regard, “losses loom larger than gains” (Wheeler, 1992, p. 115). The logic behind the fear of falling is believed to apply not only to monetary success, but is equally applicable to the loss of status and prestige (Wheeler, 1992). In a similar vein, Coleman (1987, p. 417) discusses the “fear of failure as the inevitable correlate of the demand for success” and notes that together these two concepts are “central to the motivation of economic behavior.” This is not to suggest that these two avenues, desirefor-control and fear of falling, are exclusive to whitecollar offenders but rather they are applicable to explain why a group of individuals, who presumably have a lot to lose, are likely to form the motivations for engaging in criminal and unethical behavior. In fact, Coleman (1987) suggests that the fear of failure permeates every stratum of contemporary society and therefore suggests that everyone fears being perceived as a loser. Thus, individuals motivated by the fear of losing or falling
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behind are both victims of, and contributors to, the widespread cultural pressures of monetary success, which can, and oftentimes will be achieved through both legitimate and illegitimate means.
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suggested. Studies of UCR quality had shown that “defects of the UCR had been grossly overstated” (Hirschi & Gottfredson, 1989, p. 367). Variables
Current study Building off of the IAT framework, the goal of this research was to introduce an as of yet unexamined crime type, white-collar crime, into the theoretical mix. While differences exist between street offenders and suite offenders, the underlying cultural ethos of the American dream provides the motivation for both types of offenses and offenders albeit, through different mechanisms. This study was intended to present a preliminary empirical investigation into one type of white-collar crime, embezzlement. Methods Data Since Messner and Rosenfeld did not describe ideal measures for each of the institutions, researchers were left to their own interpretation for appropriate measurement. With this in mind, the selection of variables for the current study was dictated by previous tests of IAT. Data was collected for each of the fifty U.S. states.2 The data were gathered from the Federal Bureau of Investigation's (1991) Uniform Crime Reports and the U.S. Bureau of Census' (1990) Statistical Abstracts of the United States. Census data was collected from 1990 in order to predict 1991 crime rates. The Federal Bureau of Investigation's Uniform Crime Reporting (UCR) system collects and reports arrest data on “white-collar offenses.” The UCR's measures of white-collar crime only take into account the offense itself (offense-based definitions); they do not examine corporate structure or the characteristics and occupation of the offender. The use of UCR-defined white-collar crime may be controversial. Steffensmeier (1989, p. 347) argued that “UCR data have little or nothing to do with white-collar crime” because the typical fraud and forgery offenders committed non-occupational crimes. Embezzlement offenders on the other hand, did fit the broad definition for white-collar offenses, but Steffensmeier (1989, p. 348) argued that arrests for embezzlement were so few that “the crime is relatively insignificant in terms of overall crime patterns.” In response, Hirschi and Gottfredson (1989) admitted that UCR data might be flawed but it was not worthless as Steffensmeier
Acknowledging the definitional problems with UCR defined white-collar crime, the dependent variable utilized in this research was restricted to the embezzlement rate for each state as reported in the UCR for 1991 (embezzlements per 100,000 citizens). The use of UCR measures of embezzlement was appropriate for this initial application of IAT to white-collar crime for two reasons. First, most white-collar crime scholars would agree that embezzlement theoretically fit into the broad definition of white-collar crime, referring to “crimes committed by any employee” (Steffensmeier, 1989, p. 347). Second, research on individuals convicted of white-collar crimes in federal courts seemed to suggest that embezzlement had much in common with street crimes (Hochstetler, Kerley, & Mason 2002; also see Benson & Kerley, 2001; Weisburd & Waring, 2001; Weisburd et al., 1991). Hochstetler et al. (2002, p. 8) in reviewing Benson and Kerley (2001) noted that embezzlers were similar to street offenders “in terms of age at first arrest, family background, educational attainment, residential stability, and wealth than other white-collar offenders” (Hochstetler et al., 2002, p. 8). All independent variables were gathered from the U.S. Bureau of Census (1990) statistical abstracts and measured each of the four institutions identified by IAT. Messner and Rosenfeld were specific about the functions of each institution. For example, the primary function of the family is to socialize and insulate its members by instilling the values and skills that are necessary to combat the pressures created by the American dream; therefore, IAT expects that disrupted families would be less adequate in providing these necessary elements. Following Chamlin and Cochran (1995), family is measured as the yearly divorce rate divided by the yearly marriage rate per 1,000 of population (divorce/marriage ratio). Therefore, IAT would predict that higher levels of family disruption would increase rates of crime. Education functions to provide the tools necessary to succeed later in life and to place the emphasis on knowledge rather than monetary success. Thus, IAT would predict that lower levels of education in a society would reflect a lack of commitment to the institution of education. Following Piquero and Piquero (1998), education is operationalized as the percent of the population that did not graduate from high school. IAT
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would, therefore, suggest that higher levels of high school dropouts would increase crime rates. Polity is designed to maintain public safety and measures society's ability to take care of its members. As Messner and Rosenfeld (1997, p. 394) suggested, it “reflects the quality as well as the quantity of social rights and entitlements.” Therefore, the polity is measured as the percent of registered voters who voted in the 1990 general state and local elections. Voting habits express citizens' interests in society and the ability to exercise social rights. Hence, IAT would hypothesize that higher levels of voter participation would reduce the crime rate. Finally, as IAT assumes that the American society is centered around economic wealth and success, measures of economic deprivation would express the inability of its citizens to gain access to the American dream through legitimate means. The last institution, economy, is operationalized as the percent of the population that is unemployed. Although previous IAT research measured the economy as the percent of the population below the poverty level (Chamlin & Cochran, 1995; Piquero & Piquero, 1998), this operationalization makes little sense when trying to explain rates of white-collar crime (e.g., embezzlement). While a clear theoretical and empirical link can be established between poverty and street crime (see Short, 1997 for review), the same is not true for white-collar crimes. In general, IAT would predict that higher levels of unemployment would increase rates of crime. In the context of white-collar crime outcomes, however, this prediction is just the opposite. In other words, it is expected that low levels of unemployment would increase the rate of white-collar crimes, such as embezzlement. This is because of the definitional presumption that white-collar crimes are dependent upon being employed, whereas property and violent crimes have no such dependence.
The most important characteristic of IAT is the interaction effects of the economy with the other noneconomic institutions (Chamlin & Cochran, 1995). IAT would, therefore, predict the following hypotheses: (1) lower percentages of the population without high school degrees lessens the effect of unemployment on rates of embezzlement; (2) lower divorce/marriage ratios (less divorces than marriages) lessen the effect of unemployment on embezzlement rates; (3) higher percentages of registered voters who voted lessens the effect of unemployment on embezzlement. Results Table 1 contains the descriptive information as well as bivariate correlations for all variables used in this analysis. Before proceeding to the multivariate analysis, it is worthwhile to check for problems of multicollinearity. As can be seen, none of the correlations between any of the variables exceeded .70, so multicollinearity did not appear to be a problem in the analysis (Hanushek & Jackson, 1977). Multicollinearity, however, may still be a problem in the multivariate models that include an interaction term. Since IAT asserts the interplay of the economy with other noneconomic social institutions, interaction terms were created between the economy variable and the other three noneconomic institutions. In order to minimize the potential of multicollinearity in the interaction models, the variables contributing to the interaction terms were mean-centered following the approach suggested by Jaccard, Turrisi, and Wan (1990, p. 31). Now turn to the multivariate analysis. Since the dependent variable was an aggregate crime rate, suggesting that the offense rate was low relative to the population size thus skewing the distribution, Poisson based regression models were utilized (Osgood, 2000).
Table 1 Bivariate correlations and descriptive statistics Variable 1. Crime 2. Education 3. Economy 4. Family 5. Polity Mean Std. Dev. Minimum Maximum N ⁎ p < .05. ⁎⁎ p < .01.
Embezzlement % not H.S. graduate % unemployed Divorce/marriage ratio % voted
1
2
3
4
– .305 ⁎ .098 .028 − .359
– .439 ⁎⁎ .082 −.532 ⁎⁎
– .396 ⁎⁎ − .345 ⁎
– − .142
6.191 7.948 .106 34.466 50
23.714 5.630 13.400 35.700 50
5.448 1.136 2.200 8.400 50
.489 .117 .115 .736 48
5
– 57.020 10.499 23.000 77.000 48
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Table 2 Poisson estimates for embezzlement rates 1991 Variable
Polity Education Economy Family Constant Economy⁎education Economy⁎family Economy⁎polity Pseudo R-square
Model 1
Model 2
Model 3
Model 4
Estimate
S.E.
Estimate
S.E.
Estimate
S.E.
Estimate
S.E.
− .025 ⁎⁎ .050 ⁎⁎ − .136 ⁎ .108 2.696 ⁎⁎
.006 .013 .064 .556 .660
− .025 ⁎⁎ .050 ⁎⁎ − .139 ⁎ .097 3.160 ⁎⁎ .001
.006 .013 .066 .561 .450 .007
−.025 ⁎⁎ .050 ⁎⁎ −.148 ⁎ .141 1.993 ⁎⁎
.006 .013 .067 .557 .584
−.019 ⁎⁎ .048 ⁎⁎ −.151 ⁎⁎ −.104 .632
.006 .013 .062 .555 .451
.257
.379
−.009 ⁎⁎ 0.154
.002
0.133
0.133
0.134
⁎ p < .05. ⁎⁎ p < .01.
Poisson regression was well suited for rare event data, since it recognizes the dependence of crime rates on counts of crime, in general and in this case in particular (Maume & Lee, 2003; Osgood, 2000). The estimates for the additive effects of the independent variables on embezzlement rates can be found in Model 1 of Table 2. Polity (estimate = − .025), education (estimate = .050), and economy (estimate = − .136) were the only significant variables and overall in their expected direction. This suggested that higher percentages of voter participation and higher percentages of unemployment reduced the embezzlement rate, while higher levels of the population without high school degrees increased the rate of embezzlement. Results for the interaction terms can be found in Models 2 through 4. The interaction terms for economy⁎education (Model 2) and economy⁎family (Model 3) did not attain significance in either of their respective models. In both models, the additive effects of the independent variables remained unchanged from Model 1. Model 4, however, showed that the economy⁎polity interaction (estimate = − .009) was in its negative expected (via IAT) direction and attained significance suggesting that higher levels of voter participation appeared to reduce the criminogenic effect of the economy on rates of embezzlement. As before, the additive effects remained unchanged. Discussion This article set out to examine the relationship between IAT and white-collar crime, using the UCR defined rates of embezzlement, a previously unexamined crime type in the context of the theory. Ironically, just as Sutherland (1940) argued years ago that whitecollar crime was largely ignored in criminological
research, the same can be said for researchers testing the scope of IAT. The current research attempted to fill the gap. For the most part, the results of this investigation were supportive of IAT. The additive effects indicated that higher levels of voter participation were prohibitive of embezzlement while increasing high school dropout rates exacerbated embezzlement. Additionally, the economy was predictive of embezzlement, but in a slightly different way. Here, more unemployment was associated with less and not more embezzlement. Though not necessarily as expected by IAT, this negative effect, however, would be inline for most forms of white-collar crime, including embezzlement, since employment provides the opportunity for offending. Hence, this effect makes sense within the context of this particular crime type (e.g., white-collar crime). Finally, with regard to the three interaction effects only one, economy⁎polity, was significant. The sign of this interaction implied that higher rates of polity weakened the effect of unemployment on embezzlement. As with all research, this study was not without limitations. First, IAT is a difficult theory to test, specifically because there are no clear indicators of how to measure the four social institutions. This research relied on variables that had been successfully applied in previous IAT studies of property crime and violent crime. The question raised herein was whether these same variables could also explain white-collar offending. As with the previous studies though, results showed mixed support. Also, there were no measures of anomie or culture, and the assumption was made that everyone buys into the American dream. It had been suggested that the inclusion of cultural variables might reduce the economic and noneconomic variables to nothing more than control
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variables (Piquero & Piquero, 1998). Still, like all other IAT studies, this effort did not contain a measure of culture. A second limitation lies in the interpretation of the variables. For purposes of this study, it was assumed that divorce was a measure of family disruption when in fact divorce could be interpreted as an end to family disruption. Even though it is the same variable, it is expected that the two interpretations exert opposite effects on the crime rate. The same is true when measuring economy as the unemployment rate; the results did not necessarily support the theoretical expectations of IAT, but were intuitive and inline with white-collar crime research. Perhaps, when dealing with white-collar crime, unemployment is best regarded as a measure of opportunity rather than of economic conditions. In other words, misinterpretation of what the variables actually measure creates a problem when determining support, or lack thereof, for the theory in the case of white-collar crime. Finally, the measurement of the dependent variable was a limitation to this study. As discussed earlier, there were many criticisms about using UCR data to measure white-collar crime. This was somewhat mitigated because embezzlement rates did fit the broad definition for white-collar offenses, even though there were not as many arrests. This was particularly important because according to Cressey (1953), embezzlement could be considered a quintessential white-collar crime. Future research using more accurate measures of white-collar crime is necessary to further the scope of IAT. The very nature of complex white-collar crimes and criminals, such as corporate crimes and criminals, however, may necessitate the altering of the expected effects of predictor variables. For example, because the kinds of white-collar offenders who commit complicated corporate crimes tend to be more incorporated into mainstream society than street offenders,3 these offenders may be more likely to partake in community activities such as voting, which may result in higher rates of voter participation leading to higher rates of certain types of white-collar/corporate crime, such as price fixing. This and related hypotheses await empirical confirmation. Additionally, measures of corporate offending and regulatory and civil violations should also be included. While opportunity was not specifically measured in this study, it was indirectly through measuring the economy as unemployment. Opportunity is a key component to white-collar offending, and therefore, future studies should attempt to control for it. Future research should
also focus more on appropriate institutional operationalizations that limit the possible interpretations of variables. It is much too early to declare that IAT is irrelevant for understanding white-collar crime. Instead, the results of this first application of the theory to white-collar crime showed substantively similar (i.e., mixed) results to extant empirical research on IAT. Perhaps what may be needed within the context of IAT and white-collar crime is a direct examination of culture. While culture is an important element in both IAT and white-collar crime (and more specifically corporate crime) research, the role of culture differs. IAT assumes that culture is a constant and that everyone is constantly in pursuit of monetary success. Corporate crime research, on the other hand, suggested that corporate culture matters but that it might vary across organizations (Vaughan, 1996). Therefore, by measuring culture and including it within the white-collar and corporate crime context, the theory's ability to explain this crime type may be further realized and understood. Acknowledgements The authors would like to thank Richard Hollinger and Alex Piquero for their helpful comments on earlier drafts of this article. Notes 1. Other research had discussed Messner and Rosenfeld's IAT in a more theoretical fashion. Sims (1997, p. 5) argued that Marxist criminology was the “missing link” in IAT that could better explain social and economic inequalities. Sims suggested that Messner and Rosenfeld's idea of achievement, individualism, universalism, and materialism were all results of a capitalist society in which “members are socialized to overemphasize materialism, which quite often leads to greed.” Bernburg (2002) theoretically examined IAT and concluded that although IAT diverged slightly from Merton's strain theory, they still complimented each other and could explain more together than separate. Bernburg argued that Merton emphasized imbalances within a social fabric, while Messner and Rosenfeld took a more Durkheimian approach and emphasized the nature of the social fabric itself. 2. Some may argue that because IAT is a theory of culture and structure, it should be measured at the international rather than the state level since culture is presumed to be constant within a country, particularly in capitalist societies. Messner and Rosenfeld (2001, p. 80), however, offered that the cross-national context was but one unit of aggregation failing under the purview of IAT: “We believe that the logic of our argument is compatible with observed differences in crime rates across social categories in American society.” Therefore, insofar as IAT can account for social distributions of crime within the nation, other macrosocial units should not be excluded from analysis. Prior research had found some support for the use of other macrosocial units within the U.S., such as states (Chamlin & Cochran, 1995; Piquero & Piquero, 1998) and counties (Maume & Lee, 2003), in investigating IAT.
A. Schoepfer, N.L. Piquero / Journal of Criminal Justice 34 (2006) 227–235 3. Benson and Kerley (2001) analyzed the Forst and Rhodes (n.d.) data set of individuals convicted of white-collar crimes in federal courts and found that white-collar offenders were more likely to be involved in social/community groups as well as church/religious activities than their street criminal counterparts.
References Agnew, R. (1999). A general strain theory of community differences in crime rates. Journal of Research in Crime and Delinquency, 36, 123–155. Benson, M. L., & Kerley, K. R. (2001). Life course theory and whitecollar crime. In H. N. Pontell & D. Shichor (Eds.), Contemporary issues in crime and criminal justice: Essays in honor of Gilbert Geis (pp. 121–136). Upper Saddle River, NJ: Prentice Hall. Benson, M. L., & Moore, E. (1992). Are white-collar and common offenders the same?: An empirical and theoretical critique of a recently proposed general theory of crime. Journal of Research in Crime and Delinquency, 29, 251–272. Bernburg, J. G. (2002). Anomie, social change and crime: A theoretical examination of institutional-anomie theory. British Journal of Criminology, 42, 729–742. Chamlin, M. B., & Cochran, J. K. (1995). Assessing Messner and Rosenfeld's institutional anomie theory: A partial test. Criminology, 33, 411–429. Coleman, J. W. (1987). Toward an integrated theory of white-collar crime. American Journal of Sociology, 93, 406–439. Cressey, D. R. (1953). Other people's money. Glencoe, IL: Free Press. Cullen, F. T. (1994). Social support as an organizing concept for criminology: Presidential address to the Academy of Criminal Justice Sciences. Justice Quarterly, 11, 527–559. Durkheim, E. (1965). The division of labor in society (G. Simpson, Trans.). New York: The Free Press. Edelhertz, H. (1970). The nature, impact, and prosecution of whitecollar crime. Washington, DC: U.S. Government Printing Office. Federal Bureau of Investigation. (1991). Uniform Crime Reports. Washington, DC: Criminal Justice Information Services Division. Forst, B., & Rhodes, W. (n.d.). Sentencing in eight United States district courts, 1973–1978. Codebook (Interuniversity Consortium for Political and Social Research Study No. 8622). Ann Arbor: University of Michigan. Gottfredson, M. R., & Hirschi, T. (1990). General theory of crime. Stanford, CA: Stanford University Press. Hanushek, E. A., & Jackson, J. E. (1977). Statistical methods for social scientists. San Diego, CA: Academic Press. Hirschi, T., & Gottfredson, M. (1989). The significance of white-collar crime for a general theory of crime. Criminology, 27, 359–371. Hochstetler, A., Kerley, K. R., & Mason, K. (2002). Structural predictors of embezzlement: A preliminary analysis. Journal of Crime and Justice, 25, 1–22. Jaccard, J., Turrisi, R., & Wan, C. K. (1990). Interaction effects in multiple regression. Thousand Oaks, CA: Sage. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.
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Maume, M. O., & Lee, M. R. (2003). Social institutions and violence: A sub-national test of institutional anomie theory. Criminology, 41, 1137–1172. Merton, R. K. (1968). Social theory and social structure. New York: The Free Press. Messner, S. F., & Rosenfeld, R. (1994). Crime and the American dream. Belmont, CA: Wadsworth. Messner, S. F., & Rosenfeld, R. (1997). Political restraint of the market and levels of criminal homicide: A cross-national application of institutional-anomie theory. Social Forces, 75, 1393–1416. Messner, S. F., & Rosenfeld, R. (2001). Crime and the American dream (3rd ed.). Belmont, CA: Wadsworth. Osgood, D. W. (2000). Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology, 16, 21–43. Piquero, A., & Piquero, N. L. (1998). On testing institutional anomie theory with varying specifications. Studies on Crime and Crime Prevention, 7, 61–84. Piquero, N. L., Exum, M. L., & Simpson, S. S. (2005). Integrating the desire for control and rational choice in a corporate crime context. Justice Quarterly, 22, 252–280. Pratt, T. C., & Godsey, T. W. (2003). Social support, inequality, and homicide: A cross-national test of an integrated theoretical model. Criminology, 41, 611–643. Savolainen, J. (2000). Inequality, welfare state, and homicide: Further support for the institutional anomie theory. Criminology, 38, 1021–1038. Short, J. F., Jr. (1997). Poverty, ethnicity, and violent crime. Boulder, CO: Westview Press. Simpson, S. S., & Piquero, N. L. (2002). Low self-control, organizational theory, and corporate crime. Law and Society Review, 36, 509–548. Sims, B. (1997). Crime, punishment, and the American dream: Toward a Marxist integration. Journal of Research in Crime and Delinquency, 34, 5–24. Steffensmeier, D. (1989). On the causes of “white-collar” crime: An assessment of Hirschi and Gottfredson's claims. Criminology, 27, 345–358. Sutherland, E. H. (1940). White-collar criminality. American Sociological Review, 5, 1–12. U.S. Bureau of Census. (1990). Statistical abstracts of the United States. Washington, DC: Author. Vaughan, D. (1996). The Challenger launch decision: Risky technology, culture, and deviance at NASA. Chicago: University of Chicago Press. Weisburd, D., & Waring, E. (2001). White-collar crime and criminal careers. New York: Cambridge University Press. Weisburd, D., Wheeler, S., Waring, E., & Bode, N. (1991). Crimes of the middle classes: White-collar offenders in the federal courts. New Haven, CT: Yale University Press. Wheeler, S. (1992). The problem of white-collar crime motivation. In K. Schlegel & D. Weisburd (Eds.), White-collar crime reconsidered (pp. 108–123). Boston: Northeastern University Press.
Exploring white-collar crime and the American dream: A partial test of institutional anomie theory Andrea Schoepfer, Nicole Leeper Piquero ⁎ Department of Criminology, Law and Society, University of Florida, P.O. Box 115950, Gainesville, FL 32611-5950, United States
Abstract Institutional anomie theory (IAT) suggests that high crime rates in America can be attributed to the commitment to the goal of material success. In this regard, particular emphasis is placed on the motivations derived from the profit goal of economic institutions that dominate the American culture. To date, IAT was only applied to property and violent crime. This study used Uniform Crime Report (UCR) and Census Bureau data to examine the applicability of IAT to a form of white-collar crime, embezzlement, as defined by the UCR. Results provided mixed support for IAT. Limitations and future research directions are discussed. © 2006 Elsevier Ltd. All rights reserved.
Introduction Two distinct characteristics are believed to set the United States apart from other industrialized nations. One is the cultural emphasis on the pursuit of the American dream, which suggests that everyone in America (regardless of social-background characteristics) has an equal opportunity to become monetarily successful. This success is ultimately achieved through hard work, dedication, and persistence. The other is that the United States has an extremely high (and relatively consistent) level of crime. In order to account for these distinctions, Messner and Rosenfeld (1994) developed and introduced Institutional Anomie Theory (IAT). They suggested that the American culture was enmeshed in the idea of individual monetary success through competition for limited resources. This leads to
⁎ Corresponding author. Tel.: +1 352 392 1035; fax: +1 352 392 5065. E-mail address: [email protected] (A. Schoepfer). 0047-2352/$ - see front matter © 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jcrimjus.2006.03.008
an overemphasis on the economy to the detriment of the counter-balancing importance of noneconomic institutions such as family, education, or the need for society to take care of its citizens (i.e., polity). Messner and Rosenfeld (2001) argued that this was why the crime rate of the United States greatly exceeded that of other modern industrialized nations; other nations did not place such a strong emphasis on the economy. Rather, they placed the emphasis on noneconomic institutions, thereby creating mechanisms of social control, which lead to lower levels of crime. As a general theory of crime, IAT is designed to explain all forms of offending including white-collar crimes. At first glance it may appear that street crimes differ substantively from suite crimes, after all, research had found substantial differences in background and status characteristics between individuals convicted of street crimes and those convicted of white-collar crimes (see Benson & Kerley, 2001; Benson & Moore, 1992; Weisburd, Wheeler, Waring, & Bode, 1991); however, the cultural ethos of the American dream provides the unifying explanation. The emphasis on individual
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monetary success coupled with limited resources forces individuals to innovate or find alternative means (including illegal and unethical) to achieve the desired goal (i.e., material success). Monetary success has no ending since it is always possible to acquire more money and wealth (Messner & Rosenfeld, 2001, pp. 63–64). Therefore, monetary success does not discriminate—it is equally motivating for both the wealthy and the poor. The purpose of this study was to apply IAT to whitecollar crime, a heretofore unexamined criminal activity within the context of the theory. Following Edelhertz (1970, p. 3), white-collar crime is defined as “an illegal act or series of illegal acts committed by nonphysical means and by concealment or guile to obtain money or property, to avoid the payment or loss of money or property, or to obtain business or personal advantage.” While definitions of white-collar crime vary and are much debated, a common thread throughout the various definitions has emerged in defining a distinct category of crimes and criminals that are different from the much studied street crimes and criminals (Weisburd & Waring, 2001, p. 9). Despite the definition used, white-collar crimes tend to focus on personal or organizational gain and therefore are well suited to be studied within the IAT framework. Since IAT assumes that criminal activity relates to the pursuit of monetary success and can explain those behaviors that offer monetary rewards (i.e., instrumental crimes) (Messner & Rosenfeld, 1994), white-collar crimes should not only be able to be explained under this theoretical framework but also would expand the generalizability of the theory. Before presenting the preliminary investigation of IAT in explaining one type of white-collar crime, embezzlement, a brief overview of the development of IAT is provided, followed by a review of the empirical research on IAT to date, and then a discussion of the application of the theory to white-collar offending. Institutional anomie theory When constructing their theory, Messner and Rosenfeld (1994) were interested in explaining the exceptionally high levels of crime in the United States. Believing that crime in the United States was a byproduct of societal structure and culture, they turned to Durkheim's anomie theory and Merton's strain theory to help explain the observed high levels of crime. While Durkheim (1965) focused on societal changes, Merton focused on stable social conditions associated with high crime rates, in particular, crime rates in the United States. Merton (1968) argued that the American culture
placed more emphasis on achieving wealth and prestige than it did on the institutionalized means (i.e., hard work, perseverance) to gain access to the goal. Messner and Rosenfeld (2001, pp. 13–14) argued that Merton's focus on inequality to the access of legitimate means ignored the “broader institutional structure of society” and therefore did not “provide a fully comprehensive sociological explanation of crime in America.” Building upon Merton's work, Messner and Rosenfeld explored the relationship and implications between culture and the “broader institutional structure” of American society. IAT is a macro-level theory that assumes the interplay of the American culture and the social structure. Four distinctive values are believed to underlie the American culture. The first value, achievement, refers to setting and accomplishing goals, being successful, and increasing personal wealth. The second value, individualism, refers to America's strong belief in individual rights, freedoms, and autonomy. In other words, the belief that everyone has the ability to make it on his or her own. The third value at the core of American culture is universalism. Universalism assumes that everyone is encouraged to aspire to success regardless of the available opportunities to become successful. The final underlying cultural value is materialism, or the “fetishism” of money in which success is measured by monetary rewards. These four basic values comprise the core elements of the American dream. In sum, the American dream represents the “cultural ethos that entails a commitment to the goal of material success, to be pursued by everyone in society, under conditions of open, individual competition” (Messner & Rosenfeld, 2001, p. 5). The basic thesis of IAT is that the American dream “exerts pressures toward crime by encouraging an anomic cultural environment, an environment in which people are encouraged to adopt an ‘anything goes’ mentality in the pursuit of personal goals” (Messner & Rosenfeld, 2001, p. 61). Culture, however, is only half of the equation. The other half is comprised of the social structure. There are four basic social institutions, the building blocks of societies, relevant for explaining crime rates: economy, polity, family, and education (Messner & Rosenfeld, 2001, p. 65). The key concept of IAT is the institutional balance (or imbalance) of power across the different social institutions. High crime rates in the United States are attributed to dominance of the economy over and above the influence of other social institutions. Therefore, since the economy dominates all other social institutions in America, cultural pressures develop so that noneconomic institutions (i.e., family,
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education, and polity) cannot function effectively which leads to weak social controls and ultimately to higher levels of crime. As suggested by Chamlin and Cochran (1995, p. 421), the most important feature of IAT is the interplay or interactive effects of the economy and the other noneconomic social institutions. In other words, reductions in rates of crime can be expected when there is an improvement in economic conditions coupled with the strengthening of the other social institutions. On the other hand, the dominance of the economy can be observed when despite strengths to noneconomic social institutions, declines in the economy (e.g., high rates of poverty or unemployment) increase rates of crime. Prior empirical research on IAT To date, there were only a handful of empirical studies that directly tested institutional anomie theory (Chamlin & Cochran, 1995; Maume & Lee, 2003; Messner & Rosenfeld, 1997; Piquero & Piquero, 1998; Pratt & Godsey, 2003; Savolainen, 2000) and they overwhelmingly focused on property and violent crime to the exclusion of white-collar crime.1 Chamlin and Cochran (1995, p. 414) were the first to empirically examine IAT. They examined the effects of economic (e.g., poverty) and noneconomic measures (e.g., family, religion, and polity) on the 1980 property crime rates for each of the fifty U.S. states. They analyzed the interaction effects of economic and noneconomic measures on instrumental crime. For the most part, their findings were consistent with IAT predictions. The authors found that higher levels of church membership, higher levels of voting participation, and lower levels of the divorce-marriage ratio reduced the criminogenic effects of poverty on instrumental crime. They concluded that economic measures had no independent effect on crime; rather, it was, as expected, the interplay between economic and noneconomic institutions that increased anomie and lead to higher levels of crime within a society. Messner and Rosenfeld (1997) examined the relationship between the economy, measured as economic inequality, and the polity in relation to criminal homicide rates in forty-five modern industrialized nations. They hypothesized that homicide rates and decommodification—a measure of values and resources made available to citizens to reduce their reliance on market forces—would vary inversely. Messner and Rosenfeld found that decommodification had a direct significant negative effect on homicide rates; nations with greater decommodification scores tended to have
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lower homicide rates. They also found that the United States had a very low decommodification score and a very high homicide rate. Building upon Messner and Rosenfeld's (1997) sample size of forty-five nations, Savolainen (2000) not only added a second cross-national sample that included data from thirty-six additional countries, increasing the sample size to eighty-one, but also examined the interaction effects between economic inequality and welfare support. Savolainen found that economic inequality was a significant predictor of homicide in societies with weak institutions of social protection, thus supporting IAT. Savolainen also found that nations offering the most generous welfare programs tended to have the lowest levels of economic inequality. Piquero and Piquero (1998) examined IAT in relation to property crime rates and violent crime rates using data from each of the fifty U.S. states and Washington, D.C. Using census data for measures of the independent variables, Piquero and Piquero were the first to measure the education component of IAT. For the most part, additive effects were significant and in the expected direction for both property and violent crime models. More importantly, the interactive effects revealed that higher percentages of individuals enrolled full-time in college reduced the effect of poverty on both crime types while the polity-economy interaction was only statistically significant for violent crime rates. Maume and Lee (2003) investigated the effects of IAT by disaggregating homicide rates (e.g., total versus expressive versus instrumental) at the county level. In addition, they suggested that noneconomic institutions will not only moderate the influence of the economy on rates of crime, but will also mediate the influence of the economy on crime rates. Overall, they found limited support for the commonly used moderating hypothesis; that is, they only found one significant interaction effect that suggested the effect of economic pressure (e.g., Gini coefficient) on homicide rates (no matter how measured) was significantly weaker in counties with low levels of welfare disbursements. More support was garnered for the mediating hypothesis in that across all three models the measure of economy, the Gini coefficient of family income inequality, was reduced when the noneconomic institutions were added into the model. Pratt and Godsey (2003) highlighted the similarities of the relationships among measures of social support, economic inequality and crime across three theoretical perspectives: IAT, social support (Cullen, 1994), and macro-level general strain theory (Agnew, 1999). By
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examining cross-national homicide rates, they found direct effects for both social support, measured as amount spent on health care, and income inequality, measured as ratio of median income for richest and poorest 20 percent of the population. In addition, they found mediating effects for both social support and income inequality in that when added together in a model, the main effects for both diminished. Finally, the moderating effect of income inequality and social support suggested that the presence of high levels of social support reduced the effect of economic inequality on rates of homicide. These studies, taken as a whole, seemed to suggest partial support for IAT. Across a variety of different outcomes as well as aggregated units and utilizing various measures of noneconomic institutions, there did appear to be some support for the presumption of the importance of the economy in explaining instrumental crimes. What had not been investigated, yet had been suggested (see Chamlin & Cochran, 1995, p. 413; Messner & Rosenfeld, 2001, p. 3), was the ability of IAT to explain rates of white-collar crime. Applying institutional anomie theory (IAT) to white-collar crime The applicability of IAT to the study of white-collar crimes (like that of street crimes) derives from the motivation of offending. IAT suggests that crime in the U.S. is driven by immense pressures to succeed and profit monetarily. This sentiment is echoed by Coleman (1987, p. 416) who argues that key components of the culture of competition, or “the idea that wealth and success are central goals of human endeavor,” are important in motivating the white-collar criminal. Weisburd et al. (1991, p. 188) studied a sample of individuals convicted of white-collar crimes in federal courts and speculated (since presentence investigation reports are not ideal for examining motivation) that there was one motivation underlying the white-collar crimes of these individuals: financial need. While the motivation appeared to be from the same underlying cause, their analysis revealed two distinct paths to reach the point of feeling that subjective sense of financial need: one for the “high-risk ego gratifiers” and the other for those who exhibit the “fear of falling” (Weisburd et al., 1991, p. 189). The “high-risk ego gratifier” group was originally suggested by Weisburd et al. (1991) to be comprised of offenders who exhibit low self-control (as suggested by Gottfredson & Hirschi, 1990). The empirical evidence, however, was not supportive of the ability of low self-
control to account for white-collar offending (Benson & Moore, 1992; Simpson & Piquero, 2002). More recent research had suggested the personality trait, the desirefor-control, or the general wish to be in control over everyday life events, was better equipped to explain forms of white-collar criminality (Piquero, Exum, & Simpson, 2005). This intense need to control everyday events, including events beyond their control, coupled with the disdain for failure pushes individuals with high levels of desire-for-control to resort to crime or innovate to find ways to ensure their own success. Oftentimes, because of hubris, or an exaggerated self-confidence, individuals with high levels of desire-for-control have elevated levels of aspirations and thus seek goals impossible to attain which forces them “to do something—even if it is criminal—in order to survive, get by, and perhaps more importantly, get ahead” (Piquero et al., 2005, p. 260). The fear of falling refers to an individual's perception of economic security and the fear of losing what he or she has worked hard to obtain. In trying to understand why white-collar criminals commit crime, Wheeler (1992, pp. 112, 115) looked toward prospect theory (Kahneman & Tversky, 1979) for an explanation other than greed, defined as wanting even more than what one already has, and suggests that the logic behind the fear of falling is based on “perceptions and judgments, which are attuned to the evaluation of changes or differences rather than to the evaluation of absolute magnitudes.” As such, changes, such as gains or losses, in one's economic position judged from the reference point of one's current level of wealth are more important as a motivating factor than one's final asset position. In this regard, “losses loom larger than gains” (Wheeler, 1992, p. 115). The logic behind the fear of falling is believed to apply not only to monetary success, but is equally applicable to the loss of status and prestige (Wheeler, 1992). In a similar vein, Coleman (1987, p. 417) discusses the “fear of failure as the inevitable correlate of the demand for success” and notes that together these two concepts are “central to the motivation of economic behavior.” This is not to suggest that these two avenues, desirefor-control and fear of falling, are exclusive to whitecollar offenders but rather they are applicable to explain why a group of individuals, who presumably have a lot to lose, are likely to form the motivations for engaging in criminal and unethical behavior. In fact, Coleman (1987) suggests that the fear of failure permeates every stratum of contemporary society and therefore suggests that everyone fears being perceived as a loser. Thus, individuals motivated by the fear of losing or falling
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behind are both victims of, and contributors to, the widespread cultural pressures of monetary success, which can, and oftentimes will be achieved through both legitimate and illegitimate means.
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suggested. Studies of UCR quality had shown that “defects of the UCR had been grossly overstated” (Hirschi & Gottfredson, 1989, p. 367). Variables
Current study Building off of the IAT framework, the goal of this research was to introduce an as of yet unexamined crime type, white-collar crime, into the theoretical mix. While differences exist between street offenders and suite offenders, the underlying cultural ethos of the American dream provides the motivation for both types of offenses and offenders albeit, through different mechanisms. This study was intended to present a preliminary empirical investigation into one type of white-collar crime, embezzlement. Methods Data Since Messner and Rosenfeld did not describe ideal measures for each of the institutions, researchers were left to their own interpretation for appropriate measurement. With this in mind, the selection of variables for the current study was dictated by previous tests of IAT. Data was collected for each of the fifty U.S. states.2 The data were gathered from the Federal Bureau of Investigation's (1991) Uniform Crime Reports and the U.S. Bureau of Census' (1990) Statistical Abstracts of the United States. Census data was collected from 1990 in order to predict 1991 crime rates. The Federal Bureau of Investigation's Uniform Crime Reporting (UCR) system collects and reports arrest data on “white-collar offenses.” The UCR's measures of white-collar crime only take into account the offense itself (offense-based definitions); they do not examine corporate structure or the characteristics and occupation of the offender. The use of UCR-defined white-collar crime may be controversial. Steffensmeier (1989, p. 347) argued that “UCR data have little or nothing to do with white-collar crime” because the typical fraud and forgery offenders committed non-occupational crimes. Embezzlement offenders on the other hand, did fit the broad definition for white-collar offenses, but Steffensmeier (1989, p. 348) argued that arrests for embezzlement were so few that “the crime is relatively insignificant in terms of overall crime patterns.” In response, Hirschi and Gottfredson (1989) admitted that UCR data might be flawed but it was not worthless as Steffensmeier
Acknowledging the definitional problems with UCR defined white-collar crime, the dependent variable utilized in this research was restricted to the embezzlement rate for each state as reported in the UCR for 1991 (embezzlements per 100,000 citizens). The use of UCR measures of embezzlement was appropriate for this initial application of IAT to white-collar crime for two reasons. First, most white-collar crime scholars would agree that embezzlement theoretically fit into the broad definition of white-collar crime, referring to “crimes committed by any employee” (Steffensmeier, 1989, p. 347). Second, research on individuals convicted of white-collar crimes in federal courts seemed to suggest that embezzlement had much in common with street crimes (Hochstetler, Kerley, & Mason 2002; also see Benson & Kerley, 2001; Weisburd & Waring, 2001; Weisburd et al., 1991). Hochstetler et al. (2002, p. 8) in reviewing Benson and Kerley (2001) noted that embezzlers were similar to street offenders “in terms of age at first arrest, family background, educational attainment, residential stability, and wealth than other white-collar offenders” (Hochstetler et al., 2002, p. 8). All independent variables were gathered from the U.S. Bureau of Census (1990) statistical abstracts and measured each of the four institutions identified by IAT. Messner and Rosenfeld were specific about the functions of each institution. For example, the primary function of the family is to socialize and insulate its members by instilling the values and skills that are necessary to combat the pressures created by the American dream; therefore, IAT expects that disrupted families would be less adequate in providing these necessary elements. Following Chamlin and Cochran (1995), family is measured as the yearly divorce rate divided by the yearly marriage rate per 1,000 of population (divorce/marriage ratio). Therefore, IAT would predict that higher levels of family disruption would increase rates of crime. Education functions to provide the tools necessary to succeed later in life and to place the emphasis on knowledge rather than monetary success. Thus, IAT would predict that lower levels of education in a society would reflect a lack of commitment to the institution of education. Following Piquero and Piquero (1998), education is operationalized as the percent of the population that did not graduate from high school. IAT
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would, therefore, suggest that higher levels of high school dropouts would increase crime rates. Polity is designed to maintain public safety and measures society's ability to take care of its members. As Messner and Rosenfeld (1997, p. 394) suggested, it “reflects the quality as well as the quantity of social rights and entitlements.” Therefore, the polity is measured as the percent of registered voters who voted in the 1990 general state and local elections. Voting habits express citizens' interests in society and the ability to exercise social rights. Hence, IAT would hypothesize that higher levels of voter participation would reduce the crime rate. Finally, as IAT assumes that the American society is centered around economic wealth and success, measures of economic deprivation would express the inability of its citizens to gain access to the American dream through legitimate means. The last institution, economy, is operationalized as the percent of the population that is unemployed. Although previous IAT research measured the economy as the percent of the population below the poverty level (Chamlin & Cochran, 1995; Piquero & Piquero, 1998), this operationalization makes little sense when trying to explain rates of white-collar crime (e.g., embezzlement). While a clear theoretical and empirical link can be established between poverty and street crime (see Short, 1997 for review), the same is not true for white-collar crimes. In general, IAT would predict that higher levels of unemployment would increase rates of crime. In the context of white-collar crime outcomes, however, this prediction is just the opposite. In other words, it is expected that low levels of unemployment would increase the rate of white-collar crimes, such as embezzlement. This is because of the definitional presumption that white-collar crimes are dependent upon being employed, whereas property and violent crimes have no such dependence.
The most important characteristic of IAT is the interaction effects of the economy with the other noneconomic institutions (Chamlin & Cochran, 1995). IAT would, therefore, predict the following hypotheses: (1) lower percentages of the population without high school degrees lessens the effect of unemployment on rates of embezzlement; (2) lower divorce/marriage ratios (less divorces than marriages) lessen the effect of unemployment on embezzlement rates; (3) higher percentages of registered voters who voted lessens the effect of unemployment on embezzlement. Results Table 1 contains the descriptive information as well as bivariate correlations for all variables used in this analysis. Before proceeding to the multivariate analysis, it is worthwhile to check for problems of multicollinearity. As can be seen, none of the correlations between any of the variables exceeded .70, so multicollinearity did not appear to be a problem in the analysis (Hanushek & Jackson, 1977). Multicollinearity, however, may still be a problem in the multivariate models that include an interaction term. Since IAT asserts the interplay of the economy with other noneconomic social institutions, interaction terms were created between the economy variable and the other three noneconomic institutions. In order to minimize the potential of multicollinearity in the interaction models, the variables contributing to the interaction terms were mean-centered following the approach suggested by Jaccard, Turrisi, and Wan (1990, p. 31). Now turn to the multivariate analysis. Since the dependent variable was an aggregate crime rate, suggesting that the offense rate was low relative to the population size thus skewing the distribution, Poisson based regression models were utilized (Osgood, 2000).
Table 1 Bivariate correlations and descriptive statistics Variable 1. Crime 2. Education 3. Economy 4. Family 5. Polity Mean Std. Dev. Minimum Maximum N ⁎ p < .05. ⁎⁎ p < .01.
Embezzlement % not H.S. graduate % unemployed Divorce/marriage ratio % voted
1
2
3
4
– .305 ⁎ .098 .028 − .359
– .439 ⁎⁎ .082 −.532 ⁎⁎
– .396 ⁎⁎ − .345 ⁎
– − .142
6.191 7.948 .106 34.466 50
23.714 5.630 13.400 35.700 50
5.448 1.136 2.200 8.400 50
.489 .117 .115 .736 48
5
– 57.020 10.499 23.000 77.000 48
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Table 2 Poisson estimates for embezzlement rates 1991 Variable
Polity Education Economy Family Constant Economy⁎education Economy⁎family Economy⁎polity Pseudo R-square
Model 1
Model 2
Model 3
Model 4
Estimate
S.E.
Estimate
S.E.
Estimate
S.E.
Estimate
S.E.
− .025 ⁎⁎ .050 ⁎⁎ − .136 ⁎ .108 2.696 ⁎⁎
.006 .013 .064 .556 .660
− .025 ⁎⁎ .050 ⁎⁎ − .139 ⁎ .097 3.160 ⁎⁎ .001
.006 .013 .066 .561 .450 .007
−.025 ⁎⁎ .050 ⁎⁎ −.148 ⁎ .141 1.993 ⁎⁎
.006 .013 .067 .557 .584
−.019 ⁎⁎ .048 ⁎⁎ −.151 ⁎⁎ −.104 .632
.006 .013 .062 .555 .451
.257
.379
−.009 ⁎⁎ 0.154
.002
0.133
0.133
0.134
⁎ p < .05. ⁎⁎ p < .01.
Poisson regression was well suited for rare event data, since it recognizes the dependence of crime rates on counts of crime, in general and in this case in particular (Maume & Lee, 2003; Osgood, 2000). The estimates for the additive effects of the independent variables on embezzlement rates can be found in Model 1 of Table 2. Polity (estimate = − .025), education (estimate = .050), and economy (estimate = − .136) were the only significant variables and overall in their expected direction. This suggested that higher percentages of voter participation and higher percentages of unemployment reduced the embezzlement rate, while higher levels of the population without high school degrees increased the rate of embezzlement. Results for the interaction terms can be found in Models 2 through 4. The interaction terms for economy⁎education (Model 2) and economy⁎family (Model 3) did not attain significance in either of their respective models. In both models, the additive effects of the independent variables remained unchanged from Model 1. Model 4, however, showed that the economy⁎polity interaction (estimate = − .009) was in its negative expected (via IAT) direction and attained significance suggesting that higher levels of voter participation appeared to reduce the criminogenic effect of the economy on rates of embezzlement. As before, the additive effects remained unchanged. Discussion This article set out to examine the relationship between IAT and white-collar crime, using the UCR defined rates of embezzlement, a previously unexamined crime type in the context of the theory. Ironically, just as Sutherland (1940) argued years ago that whitecollar crime was largely ignored in criminological
research, the same can be said for researchers testing the scope of IAT. The current research attempted to fill the gap. For the most part, the results of this investigation were supportive of IAT. The additive effects indicated that higher levels of voter participation were prohibitive of embezzlement while increasing high school dropout rates exacerbated embezzlement. Additionally, the economy was predictive of embezzlement, but in a slightly different way. Here, more unemployment was associated with less and not more embezzlement. Though not necessarily as expected by IAT, this negative effect, however, would be inline for most forms of white-collar crime, including embezzlement, since employment provides the opportunity for offending. Hence, this effect makes sense within the context of this particular crime type (e.g., white-collar crime). Finally, with regard to the three interaction effects only one, economy⁎polity, was significant. The sign of this interaction implied that higher rates of polity weakened the effect of unemployment on embezzlement. As with all research, this study was not without limitations. First, IAT is a difficult theory to test, specifically because there are no clear indicators of how to measure the four social institutions. This research relied on variables that had been successfully applied in previous IAT studies of property crime and violent crime. The question raised herein was whether these same variables could also explain white-collar offending. As with the previous studies though, results showed mixed support. Also, there were no measures of anomie or culture, and the assumption was made that everyone buys into the American dream. It had been suggested that the inclusion of cultural variables might reduce the economic and noneconomic variables to nothing more than control
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variables (Piquero & Piquero, 1998). Still, like all other IAT studies, this effort did not contain a measure of culture. A second limitation lies in the interpretation of the variables. For purposes of this study, it was assumed that divorce was a measure of family disruption when in fact divorce could be interpreted as an end to family disruption. Even though it is the same variable, it is expected that the two interpretations exert opposite effects on the crime rate. The same is true when measuring economy as the unemployment rate; the results did not necessarily support the theoretical expectations of IAT, but were intuitive and inline with white-collar crime research. Perhaps, when dealing with white-collar crime, unemployment is best regarded as a measure of opportunity rather than of economic conditions. In other words, misinterpretation of what the variables actually measure creates a problem when determining support, or lack thereof, for the theory in the case of white-collar crime. Finally, the measurement of the dependent variable was a limitation to this study. As discussed earlier, there were many criticisms about using UCR data to measure white-collar crime. This was somewhat mitigated because embezzlement rates did fit the broad definition for white-collar offenses, even though there were not as many arrests. This was particularly important because according to Cressey (1953), embezzlement could be considered a quintessential white-collar crime. Future research using more accurate measures of white-collar crime is necessary to further the scope of IAT. The very nature of complex white-collar crimes and criminals, such as corporate crimes and criminals, however, may necessitate the altering of the expected effects of predictor variables. For example, because the kinds of white-collar offenders who commit complicated corporate crimes tend to be more incorporated into mainstream society than street offenders,3 these offenders may be more likely to partake in community activities such as voting, which may result in higher rates of voter participation leading to higher rates of certain types of white-collar/corporate crime, such as price fixing. This and related hypotheses await empirical confirmation. Additionally, measures of corporate offending and regulatory and civil violations should also be included. While opportunity was not specifically measured in this study, it was indirectly through measuring the economy as unemployment. Opportunity is a key component to white-collar offending, and therefore, future studies should attempt to control for it. Future research should
also focus more on appropriate institutional operationalizations that limit the possible interpretations of variables. It is much too early to declare that IAT is irrelevant for understanding white-collar crime. Instead, the results of this first application of the theory to white-collar crime showed substantively similar (i.e., mixed) results to extant empirical research on IAT. Perhaps what may be needed within the context of IAT and white-collar crime is a direct examination of culture. While culture is an important element in both IAT and white-collar crime (and more specifically corporate crime) research, the role of culture differs. IAT assumes that culture is a constant and that everyone is constantly in pursuit of monetary success. Corporate crime research, on the other hand, suggested that corporate culture matters but that it might vary across organizations (Vaughan, 1996). Therefore, by measuring culture and including it within the white-collar and corporate crime context, the theory's ability to explain this crime type may be further realized and understood. Acknowledgements The authors would like to thank Richard Hollinger and Alex Piquero for their helpful comments on earlier drafts of this article. Notes 1. Other research had discussed Messner and Rosenfeld's IAT in a more theoretical fashion. Sims (1997, p. 5) argued that Marxist criminology was the “missing link” in IAT that could better explain social and economic inequalities. Sims suggested that Messner and Rosenfeld's idea of achievement, individualism, universalism, and materialism were all results of a capitalist society in which “members are socialized to overemphasize materialism, which quite often leads to greed.” Bernburg (2002) theoretically examined IAT and concluded that although IAT diverged slightly from Merton's strain theory, they still complimented each other and could explain more together than separate. Bernburg argued that Merton emphasized imbalances within a social fabric, while Messner and Rosenfeld took a more Durkheimian approach and emphasized the nature of the social fabric itself. 2. Some may argue that because IAT is a theory of culture and structure, it should be measured at the international rather than the state level since culture is presumed to be constant within a country, particularly in capitalist societies. Messner and Rosenfeld (2001, p. 80), however, offered that the cross-national context was but one unit of aggregation failing under the purview of IAT: “We believe that the logic of our argument is compatible with observed differences in crime rates across social categories in American society.” Therefore, insofar as IAT can account for social distributions of crime within the nation, other macrosocial units should not be excluded from analysis. Prior research had found some support for the use of other macrosocial units within the U.S., such as states (Chamlin & Cochran, 1995; Piquero & Piquero, 1998) and counties (Maume & Lee, 2003), in investigating IAT.
A. Schoepfer, N.L. Piquero / Journal of Criminal Justice 34 (2006) 227–235 3. Benson and Kerley (2001) analyzed the Forst and Rhodes (n.d.) data set of individuals convicted of white-collar crimes in federal courts and found that white-collar offenders were more likely to be involved in social/community groups as well as church/religious activities than their street criminal counterparts.
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