New Article: Can a racial justice frame help overcome opposition to automated traffic enforcement?

I'm happy to announce my latest publication with @jmbarajas @AlexaDelbosc. For my followers who know me for my work on the language of crash reporting, this is a bit of a departure. Why do I think this work is important? What did we do? Read on to find out.

First, you can download the full paper via Open Access (Free!) here: https://authors.elsevier.com/sd/article/S2590-1982(22)00056-2

As safety researchers, we know that traffic cameras can help lower speeds and save lives. But despite the well-documented promise of these tools, there is vocal opposition to cameras and few cities in the US have them.

We also know that if implemented correctly (a big IF), cameras can also help reduce racial bias and profiling by the police. And because drivers get a ticket by mail, cameras can prevent stressful—and occasionally fatal—traffic stops. ?(see excellent work by @marco_conner here and @JBWoodsProf here)

We wanted to see whether we could increase public support for cameras by re-framing them, not as a safety tool, but as a tool for racial justice. In other words, could we leverage interest in #BlackLivesMatter and #DefundThePolice to increase support for cameras? ?

Before sharing the results, I want to emphasize that it matters immensely where cameras are located and how they are managed. If done poorly, cameras can exacerbate racial bias rather than help reduce it. (See for example this excellent piece by @msanchezMIA and @indyemapolis) We discuss these issues at length in the paper.

To see if reframing could help, we ran an experiment. For some people in our survey, we described cameras as a tool for racial justice. For others, we just explained how cameras would work. Respondents who encountered the racial justice frame were more supportive of cameras than those who read a control message (71% vs. 57%).

We worried that the frame might lead to backlash among some groups (white respondents, those who approve of racial profiling, etc.) We find no evidence of backlash and even found that support for cameras increased among white respondents.

The message was most effective among Liberals, those who believe profiling occurs, and those who disapprove of the practice. These groups were already more supportive of cameras, but they became even more so after the racial justice message.

The survey reveals complicated feelings about trust in the police. Support for cameras was highest among those most AND least trusting. We suspect that the most trusting assumed police would operate the cameras, while the least trusting assumed that a different agency would operate them. To gain and maintain public support, it matters immensely who operates cameras.

The paper is sprinkled with rich written comments from our respondents (and an obligatory regression model). I hope you enjoy it as much as we enjoyed writing it.

In closing, our ultimate hope is that cities can use camera revenues to improve infrastructure to create self-enforcing streets. By working together, safety advocates and racial justice advocates can create livable streets for all.