New research offers evidence that black drivers are more likely to be stopped by police than their white peers.
Photo courtesy of Getty Images/Matt Gush
A new study by Ravi Shroff, an assistant professor holding joint appointments at NYU Steinhardt and NYU CUSP, and his colleagues at the Stanford Open Policing Project, found that in a dataset of nearly 100 million traffic stops across the United States, black drivers were about 20 percent more likely to be stopped than white drivers relative to their share of the residential population.
The study also found that once stopped, black drivers were searched about 1.5 to 2 times as often as white drivers, while they were less likely to be carrying drugs, guns, or other illegal contraband compared to their white peers.
Shroff and his colleagues also measured the disparity in stop rates before and after sunset. They found that black drivers made up a smaller share of those stopped at night, when it’s more difficult to discern the race of a driver, which suggests that racial bias may influence stop decisions. For example, in Texas, about 25 percent of drivers stopped right before sunset were black, compared to about 20 percent just after dusk. The analysis found the same basic pattern across all the stops in aggregate. Overall, the data showed about a 5-10 percent drop in the share of drivers stopped at night who are black.
The study, published in peer-reviewed journal Nature Human Behaviour, analyzed hundreds of millions of traffic stop records throughout the country with the goal of helping researchers and policymakers investigate and improve interactions between the police and the public.
“Our team contacted over 100 police departments and processed and released over 20 million traffic stop records, in addition to the more than 150 million state-level traffic stop records that were also simultaneously released,” said Shroff. “It was a challenging task, but we hope the public release of this extensive dataset and analysis will provide new insight into the nature of law enforcement interactions with the public.”
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Jemar Bather is a biostatistics doctoral student and a HIV/AIDS Training Grant Fellow at the Harvard T.H. Chan School of Public Health. After earning a master’s degree from NYU Steinhardt’s Program in Applied Statistics for Social Science Research, he served as a research assistant in the Department of Biostatistics at the NYU College of Global Public Health, where he built statistical models to address health disparities in breast cancer, obesity, health literacy, and patient decision-making.