A University of Michigan study reveals a concerning disparity: black drivers are subjected to more frequent searches during traffic stops, even when no contraband is discovered, compared to white drivers.
The study scrutinized data from a staggering 98 million traffic stops. Its findings are stark: innocent black drivers are subjected to searches approximately 3.4 to 4.5 percent of the time, whereas their white counterparts face searches at a lower rate of about 1.9 to 2.7 percent.
Pervasive bias
“We show that there’s this pervasive bias in multiple states and multiple counties across different stop and search reasons that we need to understand,” the researchers explain. “We’re not the first people to find racial bias in policing and we won’t be the last, but hopefully, this gives a clear place to intervene.”
Drawing from the extensive repository of the Stanford Open Policing Project, which collates traffic stop data from law enforcement agencies nationwide, the researchers scrutinized traffic stops spanning 14 state police departments and 11 local law enforcement agencies from 1999 to 2017.
For instance, in Durham County, North Carolina, the study unveils a troubling pattern: black drivers experience a false alarm rate ranging from 6 to 8%, whereas white drivers encounter a lower false alarm rate of 3 to 4%. This translates to approximately 11,000 innocent black drivers subjected to searches, compared to around 2,500 innocent white drivers.
“We know that there’s at least this 2% difference at most, where the two values are the closest. That’s where we can start to make these claims of bias,” the researchers explain. “The really powerful part about these data is that these findings aren’t massive—they’re not 30, 40%, they’re 2, 3, 4, 5%. But at 98 million traffic stops across 14 states, that’s still a very large and meaningful number of innocent drivers who are searched.”
Navigating trade offs
Officers must navigate complex trade-offs when deciding whether to conduct searches during traffic stops. These decisions involve weighing the likelihood of contraband within the population, the probability of discovering contraband among those selected for search, and the costs associated with errors, such as overlooking contraband or conducting unnecessary searches.
The researchers had access to three key pieces of data regarding these traffic stops: the total number of stops within a given county or state during a specified period, whether officers opted to search vehicles, and whether contraband was discovered. However, they lacked information about whether drivers who weren’t searched possessed contraband.
To address this gap, the researchers devised the Overlapping Condition Test. This method utilizes a standard statistical tool known as a 2×2 table, allowing for a comprehensive evaluation of decisions and outcomes. It relies on hit rates, which gauge officer accuracy based on contraband rates among all stopped drivers, regardless of whether they were searched, and false alarm rates, which denote the proportion of searched drivers where no contraband was found, relative to all innocent drivers stopped, regardless of search status.
Within this framework, the researchers inputted known data points regarding search decisions and contraband discoveries. They then explored various scenarios to deduce the potential outcomes for drivers who weren’t subjected to searches, thereby shedding light on the broader picture of traffic stop dynamics.
“It’s analogous to presidential elections with the electoral college. An election can be called because one candidate already has enough electoral votes to win, even though all of us haven’t been counted,” the authors conclude. “So even though there may still be missing information, the outcome of the election is set. Even if those uncounted votes went for the other candidate, one candidate has already got it in the bag.”
