Stereotypical Hate Crimes and Criminal Justice Processing: A Multi-Dataset Comparison of Bias Crime Arrest Patterns by Offender and Victim Race

Abstract

Many hate crimes are not reported and even fewer hate crimes result in an arrest. This study investigates patterns of victim reporting and arrest for hate crimes in two parts. First, using data from the National Crime Victimization Survey, we find that, controlling for offense severity, hate crimes are less likely than non-bias crimes to be reported to the police and that the police are less likely to take further action for hate crimes, compared to non-hate crimes. Second, we use data from the Pennsylvania Human Relations Commission and the National Incident-Based Reporting System to compare differences between types of hate crimes in the likelihood of crime clearance. We find that those hate crimes most likely to result in arrest are those that fit the profile of a “stereotypical” hate crime: violent incidents, incidents committed by hate groups, and incidents involving white offenders and black victims.

Keywords:

• hate crime
• bias crime
• NCVS
• NIBRS

Notes

¹ The terms hate crimes and bias crimes are used interchangeably throughout this manuscript.

² FBI, https://www.fbi.gov/about-us/cjis/ucr/hate-crime/2014/tables/table-12.

³ While this research focuses on the victim decision to report a hate crime to the police, it is important to note that third parties and witnesses may also play a role in reporting to the police, both as reporters (e.g. Felson & Paré, 2005) and as advisors to the victim (e.g. Ruback, 1994).

⁴ The crimes included in the NCVS are largely street-level offenses, including rape and sexual assault, robbery, aggravated assault, simple assault, burglary, motor vehicle theft, and theft.

⁵ For a more complete discussion of the advantages and limitations of the PHRC data, please refer to Gladfelter, Lantz, and Ruback (2017).

⁶ Because the analyses include multiple years of data, we tested for significant differences in our results over time. The results were substantively and statistically similar when controlling for year, and year was not a significant predictor of the outcome measure in any of the models.

⁷ Victim race measures slightly different constructs in each data source, due to differences in measurement. In the NIBRS data, for example the other race measure is composed of Indian and Asian victims (and offenders), while the PHRC measure is much more heterogeneous (e.g. Arabic).

⁸ An additional measure of whether or not the incident was violent (1 = yes) was examined in preliminary analyses, but was excluded from the final analyses due to collinearity with weapon use and injury. The results are substantively similar when using either (a) violent or (b) weapon and injury as covariates.

⁹ In analyses not presented we examined differences between different types of hate crime, compared to other crime types. Compared to non-bias crimes, hate crimes motivated by gender were particularly unlikely to be reported to the police (p < .05).

¹⁰ Like the analysis of the NCVS, comparison of the PHRC data from the Pennsylvania Commission on Sentencing (PCS) suggests hate crimes are especially likely to drop out as they move through the criminal justice system. According to the PHRC data, there is an average of 107 instances of hate crime in Pennsylvania per year; the PCS data indicate there is an average of about 10 crimes sentenced as ethnic intimidation (Pennsylvania’s designation for hate crime) each year.

¹¹ It should be noted that, in order for an offense to be coded as exceptional clearance in the NIBRS data, an offender must have been identified and located, and probable cause must have been established. That is, the victim can refuse to proceed, or the prosecution can be declined, only if an offense would have otherwise resulted in an arrest.

Additional information

Notes on contributors

Brendan Lantz

Brendan Lantz is an assistant professor of Criminology and Criminal Justice at Florida State University. He received his MA and PhD in Criminology from The Pennsylvania State University. He was a Penn State University Graduate Fellow and a Graduate Research Fellow for the Bureau of Justice Statistics (BJS). His research interests focus on group crime and co-offending, social networks, violence, and hate crime.

Andrew S. Gladfelter

Andrew Gladfelter is an assistant professor of Sociology at William Paterson University. He received his BS in Criminal Justice and MS in Administration of Justice from Shippensburg University and his PhD in Criminology from The Pennsylvania State University. His research examines a variety of topics broadly related to victimization, including the provision of victim services, the causes and consequences of hate crime, individuals’ fear of crime, and the risk of criminal victimization and discrimination following residential moves.

R. Barry Ruback

R Barry Ruback is a professor of criminology and sociology at Penn State. He conducts research examining the predictors and effects of sentencing decisions, particularly economic sanctions, and he is the faculty consultant to the Pennsylvania Commission on Sentencing.