Obstructing Justice: The Association Between Prosecutorial Misconduct and the Identification of True Perpetrators

Abstract

Prosecutorial misconduct is a potential barrier to identifying true perpetrators of crimes in wrongful conviction cases. Previous theories posit that pressures on prosecutors to carry out their role as ministers of justice in an adversarial system can incentivize misconduct and disincentivize postconviction cooperation, especially regarding alleged misconduct at trial. This study empirically tests how prosecutorial misconduct at trial can affect postconviction proceedings by analyzing the relation between prosecutorial misconduct alleged at trial and the identification of a true perpetrator postconviction in DNA exoneration cases. Results demonstrate that, as predicted, prosecutorial misconduct is associated with a decrease in the odds of true perpetrator identification. This work further underscores the need for the implementation of functional policy to deter prosecutorial misconduct.

Identifying and Charging True Perpetrators in Cases of Wrongful Convictions

“…We found that prosecutorial misconduct, most severe crime type, and the race of the defendant and the victim all significantly impacted the probability of identifying a true perpetrator. In line with prior research (Weintraub, 2020), prosecutorial misconduct was found to be negatively associated with the odds of identifying a true perpetrator. As previously discussed, prosecutors historically do not cooperate with the majority of post-conviction innocence claims (Webster, 2019), especially in high-stakes cases such as those with prosecutorial misconduct (Bowman & Gould, 2020).…”

“…The consensus across this line of work is that when true perpetrators escape justice, they continue to pose a significant public safety risk by committing additional serious crimes. Only recently has research started to examine factors that influence whether or not true perpetrators are identified (Weintraub, 2020), but scholars have speculated that contributors to wrongful convictions, such as false confessions, may affect the likelihood of true perpetrator identification (Norris, Weintraub, et al, 2019). Additional work is needed to understand the factors underlying the identification of true perpetrators so that more of these individuals can be identified and a more accurate estimate of the harm they cause produced.…”

“…Specifically, we include defining characteristics of wrongful convictions cases, such as contributors to wrongful convictions (e.g., eyewitness misidentification and prosecutorial misconduct), case demographics (e.g., the most severe crime type and high volume exoneration county), and the assistance of an innocence organization (e.g., Gould, Carrano, Leo, & Young, 2013;West & Meterko, 2016). We then include additional factors that are related to criminal justice outcomes more broadly, such as race (e.g., Eberhardt et al, 2006;Viglione, Hannon, & DeFina, 2011) and the advent of DNA technology (e.g., Bowman & Gould, 2020;Weintraub, 2020). We hypothesize that, given the overarching effect of these variables in the criminal justice system, they likely impact outcomes for true perpetrator identification as well.…”

“…Prosecutorial Misconduct. 9 Following previous work (Weintraub, 2020), summaries from the Innocence Project and NRE websites were qualitatively coded for evidence of actual or alleged prosecutorial misconduct. As systemic issues often cause the identification of prosecutorial misconduct at trial to be incomplete (Davis, 2009;West, 2010;West & Meterko, 2015), data were collected in a variety of ways to ensure accuracy of the code.…”

“…To control for counties that are over-represented in cases of wrongful convictions involving DNA exonerations (The National Registry of Exonerations, 2019), we included a variable to flag high volume exoneration counties. Following previous work (Weintraub, 2020), any county which had 6 or more cases in the sample was grouped into a category of high-volume wrongful conviction cases (n = 100). These counties included: Cook, IL (n = 39); Dallas, TX (n = 26); Harris, TX (n = 9); Jefferson, LA (n = 7); New York, NY (n = 7); Cuyahoga, OH (n = 6); and Gage, NE (n = 6).…”