In 2015 Erin Harbinson, then a PhD candidate in criminal justice at the University of Cincinnati approached the U. S. Probation and Pre-Trial Services Office (PPSO) and offered to conduct a study of their risk assessment tool called the Post-Conviction Risk Assessment (PCRA). She wanted to know if the PCRA could be reliably used on people convicted of white-collar type crimes to predict their likelihood of re-offending or violating the terms of their release while under the supervision of the PPSO. With the permission of the PPSO, Harbinson used that study as the basis for her dissertation (Harbinson, Citation2017) and for a subsequent publication of its main findings (Harbinson, Benson, & Latessa, Citation2019). To complete her dissertation Harbinson had to build a massive data file that included information on a nationally representative sample of over 31,000 people who had been convicted of white-collar type crimes and who were under the supervision of the PPSO sometime between 2006 and 2014. The resulting dataset contained a wealth of information about these individuals that could be used to address questions that extended well beyond the narrow focus of Harbinson’s dissertation project. Recognizing the potential value of the dataset to advance understanding of the people who engage in white-collar crime and their treatment in the Federal judicial system, she invited a team of scholars to undertake additional analyses focusing specifically on gender and to present their results at the annual meeting of the American Society of Criminology in 2018. That session led to the three main papers in this special issue by Ruhland and Selzer, Goulette, and Benson and Harbinson.
As Harbinson (Citationthis issue) explains in detail, the sample for the study was identified using an offense-based definition of white-collar crime. That is, for the studies reported here white-collar crime was restricted to ‘economic offenses committed through the use of some combination of fraud, deception, or collusion’ (Shapiro, Citation1981). This approach is similar to the one used in the famous Yale studies (Wheeler, Weisburd, & Bode, Citation1982), but expands upon it because the sample includes additional offenses, such as environmental and workplace safety violations, that were not part of the Yale study. Indeed, the sample includes virtually all of the different types of white-collar crime that federal prosecutors pursued between 2006 and 2014. Hence, we are confident that the findings presented here have not been contaminated by any form of sample selection bias.
Although gender is one of the most important, well known, and well-studied correlates of criminal involvement, relatively little research has been conducted on how gender relates to involvement in white-collar crime. The lack of rigorous quantitative studies focusing on gender and white-collar crime is particularly notable, especially studies based on large random samples (but see, Daly, Citation1989; Holtfreter, Citation2005; Steffensmeier, Schwartz, & Roche, Citation2013 for exceptions). Accordingly, we believe that each of the studies presented here represents an important contribution to the study of both gender and white-collar crime because of the large and representative sample on which they are based and because the studies ask questions and use variables that have received almost no attention from scholars of white-collar crime. For example, Ruhland and Selzer (Citationthis issue) look into gendered differences in the demographic characteristics and community supervision experiences of the people convicted of white-collar offenses. Goulette (Citationthis issue) uses the aforementioned PCRA tool to explore the criminogenic risk factors of males and females on supervision for white-collar crime. Finally, Benson and Harbinson (Citationthis issue) use the Psychological Inventory of Criminal Thinking Styles (PICTS) to investigate whether the men and women who violate white-collar offense statutes differ in their criminal thinking styles. Unfortunately, despite their obvious theoretical and practical significance for our understanding of the etiology and control of white-collar crime, there is little quantitative research on these topics, especially research that focuses on a contemporary sample of individuals who commit white-collar offenses.
Besides addressing critical gaps in our understanding of white-collar crime and the characteristics of people who engage in it, the research presented here is also designed to address an area that has been under studied in the field of correctional rehabilitation. For the past three decades, the risk-need-responsivity (RNR) principles (Andrews, Bonta, & Hoge, Citation1990) have evolved into widely accepted practices in the research literature and in the field. Throughout the country, probation officers use risk and needs assessment instruments and supervision agencies establish policies that tie assessment to supervision practices, such as supervising people according to their risk level and linking interventions to their individual needs. However, one topic neglected in this area is how RNR applies to people who commit white-collar types of crimes. Just as some scholars have argued that the RNR principles should be studied with samples of females to understand better how RNR principles can reduce recidivism among justice involved women (Blanchette, Citation2002), a similar argument could be made for understanding how RNR applies to people who engage in white-collar types of crime. Thus, these papers also contribute to the literature regarding gender responsive assessment and corrections.
For several reasons, the content and organization of this special issue differs from what is typically found in journals. Because the three substantive studies are all based on the same dataset and because only Harbinson had permission to access and analyze the data, it was decided that it would be best to have her write a general description of the sample and methods (Harbinson, Citationthis issue). This approach avoids having repetitive sections in the substantive papers and allows the authors more time and space to focus on their respective theoretical concerns. Another difference concerns the articles by Galvin (Citationthis issue) and Ndrecka (Citationthis issue). Because the substantive studies have both theoretical and practical implications, we asked Galvin to address how the studies relate to the discipline’s broader theoretical understanding of gender and white-collar crime, and we asked Ndrecka to speculate on the practical relevance of the findings for the supervision of people sentenced for white-collar offenses in the community. Taken together, the three substantive articles and the two reaction pieces shed considerable light on a class of individuals about whom we know relatively little and who commit offenses that threaten the financial well-being of individual victims as well as the economic health of the nation in general.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Michael L. Benson
Michael L. Benson is a Professor in the School of Criminal Justice at the University of Cincinnati.
Erin Harbinson
Erin Harbinson is a Research Scholar the Robina Institute of Criminal Law and Criminal Justice at the University of Minnesota Law School.
References
- Andrews, D.A., Bonta, J., & Hoge, R.D. (1990). Classification for effective rehabilitation: Rediscovering psychology. Criminal Justice and Behavior, 17, 19–52.
- Benson, M.L., & Harbinson, E. (this issue). Gender and criminal thinking among individuals convicted on white-collar crimes.
- Blanchette, K. (2002). Classifying female offenders for effective intervention: Application of the case-based principles of risk and need. Forum on Corrections Research, 14, 31–35.
- Daly, K. (1989). Gender and varieties of white-collar crime. Criminology, 27, 769–794.
- Galvin, M.A. (this issue). Gender and white-collar crime: Theoretical issues.
- Goulette, N. (this issue). What are the gender differences in risk and needs of males and females sentenced for white-collar crimes?
- Harbinson, E. (2017). Is corrections “collar” blind?: Examining the predictive validity of a risk/needs assessment tool on white-collar offenders. (Doctoral Dissertation). Retrieved from OhioLINK. (ucin1504800469606019)
- Harbinson, E. (this issue). Investigating women and men convicted of white-collar offenses on federal community supervision: Sample and methods.
- Harbinson, E., Benson, M.L., & Latessa, E.J. (2019). Assessing risk among white-collar offenders under federal supervision in the community. Criminal Justice and Behavior, 46(2), 261–279.
- Holtfreter, K. (2005). Is occupational fraud “typical” white-collar crime? A comparison of individual and organizational characteristics. Journal of Criminal Justice, 33(4), 353–365.
- Ndrecka, M. (this issue).
- Ruhland, E., & Selzer, N. (this issue). Gender differences in white-collar offending and supervision.
- Shapiro, S.P. (1981). Thinking about white-collar crime: Matters of conceptualization and research. Washington, D.C.: National Institute of Justice.
- Steffensmeier, D.J., Schwartz, J., & Roche, M. (2013). Gender and 21st century corporate crime: Female involvement and the gender gap in Enron-era frauds. American Sociological Review, 78(3), 448–476.
- Wheeler, S., Weisburd, D., & Bode, N. (1982). Sentencing the white collar offender: Rhetoric and reality. American Sociological Review, 47, 641–659.