White-Collar Crime, Sentencing Gender Disparities Post-Booker, and Implications for Criminal Sentencing

Turner, Sarah | April 18, 2023

“White-collar crime” is an amorphous term that has yet to be conclusively defined since its first use in 1939. This category of criminal activity results in what can be characterized as either economic harm or an impediment to the government’s ability to run successfully while minimizing conflicts of interest. Sentencing of white-collar crimes came into question in the late twentieth century due to a perception that white-collar offenders were receiving much lower sentences than offenders committing more traditional crimes. Additionally, the relationship between sentencing outcomes and status characteristics like race, age, citizen status, and gender were cause for concern. Different outcomes based on demographic differences were a significant part of the impetus for sentencing reform. To address these disparities, the United States Sentencing Commission (USSC) promulgated the United States Sentencing Guidelines (Guidelines) in 1987. The purpose of the Guidelines was to provide a comprehensive, uniform sentencing scheme that would minimize nationwide sentencing disparities. Although called “Guidelines,” these pre-determined sentencing ranges were mandatory until 2005, when the Supreme Court deemed them merely advisory in United States v. Booker. The resumption of judicial discretion has potentially opened the door to new trends in sentencing disparities. This Comment will focus on analyzing the data provided by the USSC to determine if there has been a gender-based disparity in sentencing since 2005, and, if so, why. Historically, when criminally convicted, women have been sentenced much more leniently than men. The rise of women in corporate management positions seems to lend itself to the idea that there should be a rising number of women participating in and being sentenced for white-collar crimes. This Comment will investigate if this has been the case and will attempt to explain the results from nationwide data.