Study Reveals 'Deeply Concerning' Racial Disparities In Burlington Traffic Stops

Apr 12, 2016

A new analysis by the University of Vermont and Cornell University faculty shows a pattern of racial disparities in the discretionary actions of Burlington police officers conducting traffic stops.

Black drivers made up 9.5 percent of traffic stops by Burlington Police in 2015 despite the fact that Burlington’s population is 4.5 percent black, according to the analysis by by UVM Professor Stephanie Seguino and Cornell Professor Nancy Brooks.

The racial disparities continue from there. The analysis found that Burlington police stop, search and ticket black drivers at a higher rate than white drivers.

The data covers 2012 through 2015. Seguino presented the findings to Burlington’s City Council Monday evening.

Chief Brandon del Pozo acknowledged that the data was concerning and told the council that he’s using the data and working with officers to address the issues.

Seguino said the analysis focused only on stops that were discretionary in nature, so essentially all traffic stops that weren't the officer's idea were excluded. For example, a stop based on a 911 call or a stolen vehicle notice would be excluded from the data.

Seguino noted that the stops ended in arrest at a similar rate for drivers of all races, but that Burlington police end up conducting searches on black drivers at three times the rate they search white drivers. (The Vermont State Police search black drivers at 4.6 times the rate of white drivers.)

Despite the higher frequency of searches, Seguino said police had a significantly lower “hit rate” – the rate at which searches reveal contraband – when searching black drivers.

“For example, in 2015, the rate at which white drivers were found with contraband was roughly 55 percent, compared to just 36 percent for blacks,” Seguino said. “So one way to think about that is that black drivers are being over-searched. Another way to think about it is possibly that white drivers are being under-searched. But whatever the case, there’s a different probability of being searched based on one’s race.”  

The city council asked del Pozo to sit beside Seguino and respond to the findings once her presentation was complete.

University of Vermont Professor Stephanie Seguino told Burlington's city council that an analysis found significant racial disparities in traffic stop data. Burlington Police Chief Brandon del Pozo, right, says he is working to address the issues.
Credit Taylor Dobbs / VPR

Del Pozo defended officers’ actions in each of the 52 searches of black drivers in the years 2012 through 2015 (Del Pozo began as chief in September 2015). He suggested that perhaps the officers were doing solid police work while searching blacks more than whites, and that the problem may be that white people weren’t being searched enough.

“When I read the 52 searches, the fact patterns that they put forward established probable cause for the search,” del Pozo said. “Stephanie [Seguino] pointed out I think very insightfully that it might be the case that whites are under-stopped. You know, that still doesn’t explain the disparity, but it does suggest that – if you look at all 52 searches, and they’re very detailed accounts, and they offer probable cause – that maybe we’re under-stopping whites. Because I don’t accept that the smell of marijuana emanates from black drivers’ cars more often than white drivers’ cars, percentage-wise. This is Vermont.”

Seguino said that taken together, the data don’t paint a reassuring picture of Burlington’s police.

“Black drivers are much more likely to be the recipient of [a discretionary] investigatory stop, combined with the fact that the hit rate – that is the percentage of searches that yield contraband – is much lower for blacks than it is for whites, is deeply concerning. And I think there is some evidence of targeting of black drivers. That is at least what the data are saying.”

"One way to think about that is that black drivers are being over-searched. Another way to think about it is possibly that white drivers are being under-searched. But whatever the case, there's a different probability of being searched based on one's race." - Professor Stephanie Seguino, University of Vermont

Del Pozo supported Seguino’s characterization.

“And that’s a worry,” he said. “We’re not blind to that piece of data. I think that’s a strong point the professor’s making.”

Taking the data head-on is one of the ways del Pozo told the council he is addressing the problems. Del Pozo said he has invited Seguino to present the findings to department supervisors in the coming weeks.

Del Pozo said sharing officer-specific data with his cops is one of his efforts to work towards racial parity in police work.

“What we’ve done is taken this data and coded it by officer … and the officers themselves as of last month have been able to see for the first time ever their own data and their own performance and have a chance to consciously reflect on the sum total of their car stop decisions.”

The analysis found major differences in the rate at which individual officers stop black drivers, though no one named any officers before the city council. Of all officers who made 80 or more stops, the data showed that 10 officers stopped black drivers more than 12 percent of the time. One officer pulled over black drivers more than 16 percent of the time.

Del Pozo said that achieving racial equality in policing is difficult to achieve administratively.

“No police agency can tell an individual police officer, ‘I think you need to stop fewer black motorists or more white motorists,’ or, ‘I think there’s bias in your work, I’m establishing a quota on the distribution of your car stops,’” del Pozo said. “We have to start with first making the officers aware of this disparity, and then we have to proceed to make sure, through the right type of training and the right type of constant transparency, that we account for this type of performance.”

Seguino said no policy can fix the racial inequalities on its own, but that the department’s willingness to work with the data added a helpful tool to its efforts.

“It’s very hard work,” she said. “And you put one foot in front of the other, and you continue to work to see if the steps you are taking are in fact reducing those disparities. But there’s no single strategy that’s going to get us there.”