The impact of race, gender, and demeanor on receiving leniency during traffic stops

Abstract

Research on racial disparities in traffic stops has produced mixed results, with some studies finding Black and Hispanic drivers are more likely to be ticketed than White drivers and others concluding they are not. However, there is limited research assessing whether the driver’s race, gender, or demeanor has the largest impact on receiving leniency. To fill this gap in the literature, I compare the effect of a driver’s race, gender, and demeanor on the probability of receiving leniency for traffic violations. Black and Hispanic drivers were less likely to receive leniency than White drivers. However, the driver’s demeanor had the largest impact on the probability of receiving leniency.

Keywords:

• Traffic stops
• leniency
• race
• policing
• demeanor

Acknowledgments

I would like to thank Professor Hayes, Professor Nedelec, and Professor Wooldredge at the University of Cincinnati’s Criminal Justice department for their comments and feedback on early drafts of this paper.

Disclosure statement

There are no potential conflicts of interest by the authors.

Notes

1 Respondents who stated they received both a written and verbal warning were included in this group.

2 This sample excludes respondents who were not given a reason for why they were stopped. This sample also excludes respondents who were arrested or had their vehicle searched during their most recent traffic stop. I performed a sensitivity analysis including respondents who were arrested or searched and there was no substantive difference in the results of the analyses.

3 Category 1 includes drivers stopped during the night but did not know the exact time, Category 0 includes drivers who were stopped during the day but did not know the exact time.

4 For respondents who were only stopped by one police officer, a 1 on the non-white officer variable indicates the officer was not white. For respondents who were stopped by multiple officers, a 1 on the non-white officer variable indicates at least one of the officers were non-white.

5 The full list of stops under this category are for speeding, illegal lane changes, roadside checks for drunk drivers, seatbelt violations, cellphone violations, stoplight violations, blocking traffic, driving too slow, failure to yield, following too closely, improper lane changes, suspicion of driving under the influence, police conducting an investigation, reckless or aggressive driving, suspicious activity, and distracted driving.

6 I normalized the probability weights to ensure each participant has the same odds of selection in each model.

7 The total proportion for the reason for stop is over 1 because of drivers who were stopped for their driving behavior and vehicle characteristics. In addition, all the numbers in the tables and figures are rounded up to the nearest value.

8 The average marginal effect was originally calculated on a probability scale ranging from 0 to 1. For example, the average marginal effect of complaining was -.1970 and for Black drivers was -.0616. To increase the ease of interpretation for the reader I transformed these numbers into percentages by multiplying them by 100. Making it easier to understand the effect of each independent variable on the probability of receiving leniency.

Additional information

Notes on contributors

Roderick L. Pearson

Roderick L. Pearson Jr. is a graduate student and Albert C. Yates Fellow in the Criminal Justice Department at the University of Cincinnati. He received his BS in Sociology from Florida State University and a MA in Sociology from the University of Cincinnati. His research interests include urban sociology, environmental criminology, race and ethnicity, and spatial analyses of urban phenomena. His current research examines citizens’ exposure to gun violence from 1993 to 2016, the concentration of gun violence in urban neighborhoods, and the impact of gun violence on home value appreciation.