In 2020, the killing of George Floyd by a Minneapolis police officer—and the deaths of other African Americans in police custody—sparked widespread protests and intensified concerns about persistent racial inequities in the criminal justice system. In recent PPIC surveys, 61 percent of California adults believe that the criminal justice system is biased against Black individuals, with 84 percent of Black Californians holding this view (Baldassare et al. 2022). And while 54 percent of California adults say police treat all racial and ethnic minorities fairly “almost always” or “most of the time,” only 18 percent of Black Californians share this opinion (Lawler and Thomas 2021).
Efforts to reduce racial inequities in people’s experiences with police are critical to improving community engagement and trust in law enforcement. To begin answering questions about the size of these racial disparities, the contexts in which they occur, and the groups of people being affected, California passed the Racial and Identity Profiling Act (RIPA) in 2015. Implementation has been rolled out in waves based on the size of the agency. Since 2018, the state’s largest law enforcement agencies have been collecting officer-perceived data on demographics and other details for all pedestrian and traffic stops, and reporting this information to the California Department of Justice, with more agencies required to report each year. By 2023, all law enforcement agencies in California will be required to collect and submit stop data.
This rich data source has already yielded important insights into racial disparities in police stops and encounters. Recent research finds that Black Californians are more than twice as likely to be searched as white Californians, but searches of Black Californians are somewhat less likely to yield contraband or evidence. While differences in jurisdiction and context significantly contribute to racial disparities in experiences with law enforcement officers, notable inequities remain even after accounting for such factors (Lofstrom et al. 2021). This research also identifies that disparities—especially between Black and white individuals—are greatest in traffic stops made by local law enforcement (i.e., police and sheriff departments, as opposed to the California Highway Patrol).
Interactions between civilians and police, whether for a traffic stop or another reason, carry inherent risks. In 2019, 799 assaults on police officers occurred during traffic pursuits and stops in California (7.6% of total assaults on law enforcement)—55 of which involved a firearm (19% of all firearm-related assaults on law enforcement). In addition, in a study of police use of force, Premkumar et al. (2021) finds that traffic and pedestrian stops account for about 15 percent of police encounters in which a civilian is seriously injured or killed. Overall, there are stark racial disparities in civilians injured during law enforcement encounters. Black Californians are about three times more likely to be seriously injured, shot, or killed by the police relative to their share of the state’s population. Concerns about these racial disparities in use of force have motivated several statewide policing reforms. In addition, a few cities in California, including Los Angeles, San Francisco and Berkeley, have proposed reforming enforcement of some traffic violations.
In this report, we build on our previous work by using RIPA data to help identify traffic stops that may deserve consideration for alternative enforcement practices. Traffic stops that could be enforced using alternative methods would be those that are unlikely to jeopardize public or road safety but that could: (1) improve safety for officers and civilians, (2) increase police efficiency, and (3) reduce racial disparities.
Since agencies have substantial differences in their primary missions, objectives, and jurisdictions, we examine stop outcomes by type of law enforcement agency (the California Highway Patrol or local law enforcement; the latter can be further separated into police and sheriff departments). Furthermore, we analyze traffic stops for both moving violations (e.g., speeding or failure to stop) and non-moving violations (e.g., improper display of a license plate, expired registration tag, or failure to maintain vehicle light equipment). Non-moving violations in particular may be an area where alternative enforcement—such as mailing the vehicle owner a “fix-it ticket” and/or citation, as proposed by San Francisco Police Chief Bill Scott—is feasible and safe.
Traffic stops that do not lead to any enforcement or discovery of contraband or evidence deserve special attention—while these incidents are a small minority of all stops, they fall disproportionately on people of color and may not be an efficient use of law enforcement officers’ time (Lofstrom et al. 2021). The “intrusiveness” of these stops, as measured by outcomes such as whether individuals were asked to step out of the vehicle, searched, detained, or handcuffed, and whether an officer aimed or used a weapon, is another critical factor that could affect the relationship between community members and law enforcement. Importantly, we also examine the extent to which stop outcomes and racial disparities vary throughout the day. Such information can be used to determine when changes in policing practices may be warranted and could help guide potential changes. If, for example, searches during certain times of the day are particularly unlikely to yield contraband or evidence, this may present an opportunity to redirect such efforts and resources to other policing tasks.
One key question is whether racial disparities in traffic stops are the result of bias or targeting on the part of law enforcement. While disparities could be driven by racial bias, other factors may also play a role. For example, vehicle condition as well as driving patterns and behavior may differ across race/ethnicity and could lead to disparities in the likelihood of being stopped for a traffic violation. To examine the potential role of racial bias in traffic stops, we employ the “veil of darkness” theory, which posits that it is more difficult for officers to ascertain a person’s race or ethnicity during dark hours. If officers are racially profiling drivers, the share of people of color in traffic stops would be lower during dark hours, compared to light hours, holding everything else constant. Using the shift to and from Daylight Saving Time, we examine if people of color are more or less likely to be stopped for a traffic violation when, for a given time of the day, sudden changes in light conditions make it easier or harder, respectively, to determine an individual’s race or ethnicity.
Note that the test of the “veil of darkness” theory only applies to the likelihood of being stopped for a traffic violation and cannot be applied to other stop outcomes, as once a stop has been made, the officer has determined (the perceived) race/ethnicity of the driver. Furthermore, while nighttime conditions make it more difficult to determine a driver’s race/ethnicity than during daytime, there are contexts and locations, such as where there are streetlights, when racial/ethnic identification may still be possible. Officers may also use type, make, and condition of a vehicle to infer race/ethnicity. If such factors are indeed correlated with race/ethnicity, racial profiling may also be possible during dark hours.
This report begins with an overview of the RIPA data on police stops. Using data from 2019 for the state’s 15 largest law enforcement agencies, we then examine traffic stops across different times of day by agency, type of traffic violation, and intrusiveness. Next, we focus on racial disparities in traffic stops across these same dimensions. We then investigate the potential role of racial bias in traffic stops. Finally, we conclude with additional considerations for policy and practice based on this research.