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Date: April 29, 2024 Mon
Time: 11:13 pm
Time: 11:13 pm
Results for traffic policing
1 results foundAuthor: Sequino, Stephanie Title: Driving While Black and Brown in Vermont Summary: Vermont is perceived to be a political outlier in the United States. It was the first state to outlaw slavery in 1777. And in our more recent history, Vermont was one of the first states to legalize civil unions and to push (unsuccessfully) for a single payer health care system. When it comes to race relations, it is assumed that Vermont is equally liberal and as result, racial bias towards people who are Black and Hispanic, evident in other parts of the country, should largely be absent here. This paper investigates that assumption. In particular, the authors analyze police traffic stop data to assess the extent, if any, of racial disparities in policing. This task is made possible by legislation passed in the Vermont House that required police departments to begin to collect traffic stop data by race as of September 2014. Our goal in this study is to examine whether the treatment of Black and Hispanic drivers differs significantly from that of White and Asian drivers. We compiled the dataset used to conduct this analysis from data sent to us by individual law enforcement agencies. A number of agencies were unable to deliver the full dataset because they lacked administrative support to extract the data and provide it in a form that was analyzable. County sheriff's departments were less likely than municipal police agencies to be able to provide the requisite data. Some agencies collected only a few of the required categories of data, and in cases where essential data were missing, results from these agencies could not be included. Finally, in many cases, the collection of race data was incomplete, such that there are a large number of police stops with missing information. Also, the starting date of data collection varied between agencies. In total, this report is based on data from 29 police agencies. These include 24 out of 44 municipal police agencies. Because our dataset covers the largest towns and cities in Vermont, however, it represents 78% of the total population policed by all municipal police agencies. In addition, the dataset includes three out of 14 county sheriff departments as well as the Vermont State Police and University of Vermont police. In this study, we conducted analysis at four levels: 1) the state, 2) counties, 3) local police agencies, and 4) within-agency at the officer level where data by officer was provided. At the state level, we evaluated racial disparities for 2015, the only year for which we have complete data for all 29 police agencies. In addition, we evaluated racial disparities by county, based on aggregating the 29 agencies included in our data set, also for 2015. The state and county analyses are of particular relevance in understanding the experience of traffic policing from the perspective of Black and Hispanic drivers in Vermont. Finally, we evaluate results for each agency separately using all years for which data was provided. Agency-level data reveal differences in patterns of policing across the state. Due to the limited data available from some of the smaller agencies, we could only make statistically reliable inferences on racial disparities for a restricted set of indicators. Nevertheless, we provide a full summary of traffic stops and outcomes by race for individual agencies in Appendix A, Table A4. Our study is based on an analysis of several indicators, no single one of which is evidence of racial bias. Rather, the collective results from all of the indicators provide the basis for our summary assessment. The indicators in this study include: 1) stop rates by race compared to racial shares of the population; 2) males as a share of stops by race, 3) the proportion of drivers by race receiving citations; 4) racial differences in arrest rates; 5) racial differences in search rates; and 6) the percentage of searches that yield contraband (the "hit" rate). Where an agency has provided data, we also offer an agency-level analysis of stop behavior by officer. Among our main findings are the following: - Ticket rates: At the state level, Black and Hispanic drivers are more likely to receive a citation once stopped than are White or Asian drivers. - Arrest rates: At the state level, the Black arrest rate is almost double the White arrest rate. At the agency level, disparities differ. For example, at the high end, Black drivers stopped by Rutland police are 2.6 times more likely to be arrested than White drivers, subsequent to a discretionary stop (excluding arrests on warrant), and in Williston, 2.3 times more likely. - Search and "hit" rates: At the state level, Black drivers are four times more likely to be searched, subsequent to a stop, than White drivers. Hispanics also experience elevated search rates compared to Whites; they are almost three times more likely to be searched. Asian drivers are less likely to be searched than White (or Black and Hispanic) drivers. In contrast to these search rates, Black and Hispanic drivers are less likely than White or Asian drivers to be found with contraband that leads to a citation or an arrest. Officers would appear to have a lower threshold of evidence for searching vehicles with Black and Hispanic drivers. This suggests a problem of over-searching of Black and Hispanic drivers as compared to a possible under-searching of White and Asian drivers. Variations exist at the agency level. However, only a few agencies have sufficient data to make statistically reliable inferences on racial differences in hit rates. Among those that do (Burlington, Rutland, Vermont State Police), hit rates of Black drivers are lower than of White drivers. Hits that result in arrests - indicative of more serious contraband - occur also at a lower rate for Black drivers than White drivers for all of three of these agencies as well as Williston. - Stop rates: Black and Hispanic drivers are stopped at a higher rate than their share of the population while White and Asian drivers are stopped at rates that are below their population shares. Stop rate disparities are often subject to criticism because researchers typically lack precise measures of the driving population. We have sought to overcome that by using accident data on the race of not-at-fault drivers. Also, most of our indicators of racial disparities are based on post-stop outcomes, which do not rely on estimates of the driving population. - Male drivers are more likely to be stopped than female drivers, regardless of race/ethnicity. But the racial disparities in male shares of stops are notably large. At the agency level, for example, in Middlebury, among Black drivers stopped, 88% are male, while among White drivers stopped, 62% are male. Overall, Black and Hispanic males comprise a larger share of stopped drivers in their racial/ethnic group than do White males, suggesting a possibility that Black and Hispanic males, in particular, are targets of heightened police scrutiny. - Officer stop rates of Black drivers: Twelve agencies provided traffic stop data by officer, allowing us to calculate within-agency disparities in stop rates. The results indicate that the disparity in Black/White stop rates at the agency level cannot, in general, be attributed to the behavior of just a few officers. The data indicate that this behavior is common to many officers, perhaps suggesting more pervasive cultural norms within agencies that contribute to disparities. Of note, in Brandon Police Department, 67% of officers stop Black drivers at a rate that is 50% greater than their share of the population. A sizeable share of officers in Bennington, Manchester, Middlebury, and Winooski also stop Black drivers at rates higher than expected, given population shares. In addition to disparities in stop rates by race by officer, we also found evidence of variation by officer in the completeness of their legally required data reporting. - Data quality: Missing data is a concern. Some agencies were not able to respond to our request for data. Moreover, many agencies have high rates of missing data in key categories. For example, in St. Albans Police Department, race was not recorded in 29% of stops, and in Addison County Sheriff Department, race was missing in 17% of traffic stop incident reports. Missing data undermines efforts to accurately assess the degree of racial disparities in traffic policing Details: Unpublished paper, 2017. 79p. Source: Internet Resource: Accessed April 15, 2019 at: https://www.uvm.edu/giee/pdfs/SeguinoBrooks_PoliceRace_2017.pdf Year: 2017 Country: United States URL: https://www.uvm.edu/giee/pdfs/SeguinoBrooks_PoliceRace_2017.pdf Shelf Number: 155406 Keywords: Racial DisparitiesRacial Profiling in Law EnforcementStop and SearchTraffic Policing |