By Stephanie Seguino, Nancy Brooks, Pat Autilio
This study has three goals. The first it to analyze police behavior as regards race and traffic policing. The second is to evaluate police compliance with the law requiring the collection and reporting of traffic stop data. And the third is to evaluate the effectiveness of the legislation in generating robust data collection on race and traffic policing that is relatively user friendly for analysis by community stakeholders. With regard to the first goal, we examine the data for evidence of racial disparities in several areas: racial shares and magnitudes of stops as well as racial disparities in stop rates, reasons for stops, arrest rates, search rates, and contraband “hit” rates. We also examine trends to determine whether racial disparities fall over time, particularly in response to the legalization of cannabis in July 2018. Our study is based on more than 800,000 traffic stops and 79 Vermont law enforcement agencies. The study includes a number of agencies that had not reported their data in time for our earlier study (Seguino and Brooks 2017). In addition to providing a statewide overview of racial disparities, we compare policing patterns as well as racial disparities across agencies, and separately for municipal law enforcement agencies, sheriff’s departments, and the Vermont State Police. We report raw data on all agencies in our sample, and these results can be found in the appendix. We have been careful to include in the body of the report that follows agency-level statistics only for those agencies that have the minimum number of observations by race (typically, 10 or more). Our main findings are as follows: • The Black and Hispanic shares of stopped drivers exceed their shares of the estimated driving population. The data indicate Black drivers were over-stopped by between 3% to 81%, depending on the measure of the driving population used. Hispanic drivers were over-stopped by 26%. The shares of stops of all other racial groups are at or below their share of the driving population. These numbers represent a statewide average, and obscure wide variation at the agency level. We provide detailed agency-level data in the report, which show that approximately 45 agencies over-stopped Black drivers by more than 25%. • The stop rate per 1,000 residents is very high in Vermont (255 drivers stopped per 1,000 residents) compared to the national average of 86 per 1,000 residents. This overall average obscures notable racial disparities in stop rates. The statewide white stop rate per 1,000 white residents is 256 compared to 459 stops of Black drivers per 1,000 Black residents. The Black stop rate is about 80% higher than the white stop rate and matches the upper bound described above since one of our measures of the driving population of an area is its number of residents. • These averages also obscure the wide variation in stop rates per 1,000 residents. Of particular concern are agencies with large racial disparities in stop rates that also significantly over-stop relative to the national average. For example, in Bennington the overall stop rate per 1,000 residents is estimated to be 659 and Black drivers are over-stopped from 55% to 335% depending on the measure of driving population. • Black and Hispanic drivers were ticketed at a higher rate than white drivers, and Black drivers were also more likely to be given multiple tickets per stop. Our ability to report accurately on ticket rates is limited by data quality concerns as some agencies only report a single outcome per stop even when more than one outcome occurred, such as multiple tickets. • White drivers were more likely to be stopped for moving violations than Black drivers. Black drivers were more likely to experience a stop for vehicle equipment violations. We are concerned that this type of stop may be more investigatory and pretextual than moving violations. Stops that are investigatory/pretextual, based on suspicion of illegal activity rather than observable behavior or evidence, are more susceptible to officer racial bias than stops based on other reasons, such as a moving violation or suspicion of Driving While Impaired (DWI). Several experts have recommended banning this type of stop, which could help to reduce not only racial stop rate disparities but also search disparities. (A November 2019 ruling by the Oregon Supreme Court has banned this increasingly controversial policing practice). • The arrest rate of Black drivers is roughly 70% greater than that of white drivers. The Hispanic-white arrest rate disparity is even larger, with the arrest rate of Hispanic drivers 90% greater than the white arrest rate. Some agency-level disparities were much wider. In Brattleboro, Black drivers’ arrest rate is 400% greater than the white rate; in Colchester, 185% times greater. • Black drivers are about 3.5 times more likely to be searched subsequent to a stop than white drivers and Hispanic drivers are searched at a rate that is 3.9 times greater than that of white drivers. Asian drivers are less likely to be searched than white drivers. Again, some agencies exhibited much wider disparities than the state average. In Brattleboro, Black drivers are almost 9 times more likely to be searched than white drivers; in Shelburne, 4.4 times greater; in South Burlington; 3.9 times greater; in Vergennes, 3.8 times greater; in Burlington, 3.6 times greater; and in Rutland, 3.45 times greater. • Black, Hispanic, and Asian drivers were less likely to be found with contraband than white drivers. The lower hit rate (that is, the percentage of searches that yield contraband) of drivers of color is widely regarded as providing evidence the police rely on a lower bar of evidence to search drivers of color than white drivers, suggesting possible racial bias in the decision to search. In a second test (a logit analysis) for racial bias in searches, we find that the race of the driver continues to be strongly correlated with the officer’s decision to search a vehicle, even after controlling for other factors that may influence the officer’s decision to search a vehicle. • We find that searches based on reasonable suspicion (a lower threshold of evidence than probable cause) have lower hit rates for all racial groups. And, the gap between the (higher) white hit rates and (lower) hit rates for people of color increases. Just as with investigatory/pretextual stops, searches based on reasonable suspicion are more prone to racial bias. With regard to trends over time: • From 2015 to 2019, the number of traffic stops has increased for all racial groups. Sheriff’s Departments registered an 86.4% increase in traffic stops over this time period, compared to a statewide average for all agencies of 39.7%. • Racial disparities in the increase in number of traffic stops are notable. While stops of white drivers increased by 44.6% over this time period, Black stops increased 72.5%; Asian stops, 66.7%; and Hispanic stops, 120.3%. • The share of stops that are investigatory/pretextual, including vehicle equipment stops, increased for all racial groups, but increases were greatest for Black drivers—so much so that by 2019, about one third of all stops of Black drivers were included in this category, up from 23% in 2016. For Hispanics, the increases in the share of such stops was even greater, rising from 18.0% in 2015 to 27.5% in 2019. • Racial disparities in arrest rates have also widened since 2014. The widening gap is due to a decline in the white arrest rate from 2018 to 2019 rather than an increase in the Black arrest rate. • Search rates declined for all racial groups after cannabis legalization but by 2019, the Black search rate continued to be 3 times greater than the white rate. Legalization of cannabis, in other words, did not have a substantial impact on the Black-white search rate disparity. The Hispanic search rate disparity widened from 2018 to 2019 with Hispanic drivers 2.6 times more likely to be searched than white drivers by 2019. • Hit rates have decreased for searches that result in any outcome (warning, ticket, or arrest) but the arrest-worthy hit rate rose slightly from 20.3% to 24.9% from 2018 to 2019. As search rates have fallen, searches appear to be somewhat more productive with regard to those that lead to an arrest but are somewhat less productive overall. Increasing hit rates suggests greater efficiency in policing decisions regarding searches, and clearly, less negative impact on drivers for whom searches are often traumatic experiences. Regarding data quality, our main findings are: • Data quality has improved for some but not all agencies over time. There continues to be a lack of compliance with the legislation requiring race data collection during traffic stops. Missing data on all of the outcomes of a stop, when stops have more than one outcome, date and time of stop, and stops IDs also hinders analysis. • Particularly worrisome is the large number of stops missing race of driver, the main concern of traffic stop data collection. One way to put into perspective the quantity of missing data is to compare the share of stops missing race of driver to the percentage of stops that are of BIPOC drivers. Given the low percentages of people of color in Vermont, even a small amount of missing race data can distort results. For more than a dozen agencies, the percentage of stops missing race of driver is at least double the percentage of stops that are reported to be of BIPOC drivers. At a minimum, this leads to low quality data and the accuracy of results from those agencies. It also violates the spirit of the legislation requiring race data collection. • The legislation has not been sufficiently precise or comprehensive in delineating the data to be collected. Police chiefs have interpreted the meaning of various components of the legislation differently, and thus do not follow a uniform method of reporting data. Some categories of data that would be useful—and are already collected—were not stipulated in the legislation. Law enforcement agencies have as a result declined to share those data. These findings suggest the need to revise the legislation on traffic stop race data collection in order to insure complete data that is uniformly submitted so that it can be analyzed without excessive difficulty.
Burlington, VT: University of Vermont, 2021. 159p.
