Why Connecticut is a model for reducing racial disparities in traffic stops—and why other states haven’t succeeded

More than 20 million drivers are stopped by police every year in the U.S. Although traffic stops are relatively routine, they can also turn deadly, particularly for minority drivers. It’s why some states have started to more closely examine racial disparities in traffic stops in an attempt to mitigate them.

For more than a decade, one state, Connecticut, has served as the gold standard for this kind of policing policy change. A recent study from Northeastern University shows how successful the state has been in reducing racial disparities in traffic stops –– and why other states have failed to follow the Constitution State’s model.

In 2011, Connecticut launched the Connecticut Racial Profiling Prohibition Project, one of the first statewide programs aimed at addressing racial disparities in traffic stops at a systemic level. Since then, the program has served as a model at the state and federal level, becoming widely known as the “Connecticut model.”

Matthew Ross, an associate professor of public policy and economics at Northeastern, has been working with Connecticut to conduct the annual racial profiling reports that are part of this program. Those analyses identify departments that have potential racial and ethnic disparities in traffic stop patterns and result in follow-up interventions with those departments that are designed to reduce disparities.

These interventions take the form of open conversations between program staff and command staff in police departments. With the traffic data as a starting point, police departments are given a detailed understanding of their traffic patterns that helps them rethink their enforcement strategies.

The most recent annual report identified 29 police departments in Connecticut with racial disparities in their traffic stop patterns. The interventions used in those departments resulted in a 23.56% decrease in Black and Hispanic drivers involved in traffic stops over the course of at least 12 months.

Notably, Ross says, the number of unsuccessful pretextual stops, “stops that basically look like fishing expeditions for other types of criminal offenses,” dropped by 42%. Stops that resulted in warnings fell by 30%; arrests declined by 31%. Meanwhile, there was no evidence that vehicle crashes or crime increased in any of the towns that had interventions.

“For a long time, largely based on anecdotal evidence, policing agencies have thought that pretextual type traffic enforcement was an effective crime fighting mechanism,” Ross says. “But the truth is that the data doesn’t necessarily support that, and the only thing that the data does support is that it has a disparate impact on racial and ethnic minorities.”

“If this isn’t actually an effective crime-fighting tool and it’s having this disparate impact, then we really ought to be reconsidering why we’re doing these things,” Ross adds.

Some communities are already taking active steps to crack down on pretextual stops. Cities like Minneapolis, Philadelphia and Los Angeles have enacted bans on pretextual stops because of how they disproportionately impact minority drivers. But Ross says this paper is one of the first to show data that proves “you can reduce pretextual traffic enforcement without having unintended consequences on accidents and crime.”

If Connecticut has been so successful, why haven’t other states followed suit? In fact, many states have adopted the Connecticut model, Ross says. They might conduct an initial analysis and release a report that highlights racial disparities in traffic stops, but Ross says they hardly ever conduct the follow-up interventions that have made Connecticut’s program successful for the long haul.

“It’s not just the analysis and the report and the advisory board,” Ross says. “It’s actually giving the department something tangible that they can do when they get identified and not using it as a tool to beat them over the head with in the media.”

Ross notes that most police departments are already collecting the traffic data that would help them make meaningful changes to their enforcement tactics. They just don’t have the capacity to analyze and use that data effectively.

“Giving them the ability to do that and reflect on it in a non-confrontational way is really low hanging fruit to be able to reduce these [disparities] and make their traffic enforcement more effective in general,” Ross says.

This story is republished courtesy of Northeastern Global News news.northeastern.edu.