TRAFFIC ENFORCEMENT IN SAN DIEGO, CALIFORNIA Joshua Chanin, Megan Welsh, Dana Nurge, and Stuart Henry School of Public Affairs San Diego State University

Discussion roadmap Analysis of traffic stop records Analysis of post-stop outcomes Challenges and limitations Recommendations

Analysis of traffic stop records

Key Questions To what extent is there a department-level pattern of racial disparity in the initiation of traffic stops? To what extent are racial disparities in the initiation of traffic stops evident at the patrol division-level?

Traffic stop records, by driver race

‘Veil of darkness’ approach Natural experiment used to compare racial distribution of daylight stops to those made in darkness Two key assumptions Driver race less visible at dusk, after dark If race is a factor, minority stops more likely during day

‘Veil of darkness’ approach, cont. Analysis confined to stops during “inter- twilight period” – 5:09 pm to 8:29 pm Enables comparison of daylight and darkness stop patterns by race, for example: Black drivers: daylight v. darkness stops White drivers: daylight v. darkness stops

‘Veil of darkness’ approach, cont. Strengths Obviates need for external benchmark Intuitive, cost-effective Weaknesses Relatively new approach; need for further replication Does not account for presence of ambient light, tinted windows, or other factors that may challenge assumptions

Analysis of citywide stop patterns Disparities between Black and White drivers evident in 2014 traffic stop records, but not in 2015 data or the combined dataset No such disparities found between Hispanic and White drivers over same periods

Analysis of stop patterns in SDPD’s five northern divisions Disparities between Black and White drivers evident in 2014 data and in the combined dataset, but not in 2015 No such disparities found between Hispanic and White drivers over same periods

Analysis of stop patterns in SDPD’s four southern divisions White drivers were more likely to be stopped in daylight than after dark, compared to both Black and Hispanic drivers 2014, 2015, and in the combined dataset

Analysis of post-stop outcomes

Key Question To what extent is there a department-level pattern of racial disparity in the outcome of traffic stops? Search, contraband discovery (‘hit rate’) Field interview, arrest, citation

Raw data on post-stop outcomes, by driver race

Analyzing post-stop outcomes Propensity score matching used to pair Black and Hispanic drivers with White counterparts across a set of demographic and stop-based characteristics Justification for stop Location, day, month, time of stop Driver age, gender, residency status

Propensity score matching Strengths Conservative, well-tested method Enables a more precise comparison than other approaches Weaknesses Cannot account for factors not included as part of matching process (e.g., vehicle make/model) Draws on a smaller sub-sample than other techniques

Comparing post-stop outcomes among matched Black and White drivers

Comparing post-stop outcomes among matched Hispanic and White drivers

Research challenges and limitations

Relevant data not captured Several predictive variables not captured by the current Vehicle Stop Data Card Make, model, condition of vehicle Behavior and demeanor of driver/passenger Specific location of stop Officer demographics, patrol assignment

Missing data

Evidence of underreporting

Recommendations

Recommendations Acknowledge and address existence of racial/ethnic disparities Enhance officer training and oversight Emphasize communication and transparency – both internally and with community

Recommendations, cont. Discontinue the use of traffic stop data cards Replacement system incorporate data from: CAD system Judicial records of citations and warnings issued Existing records of field interview, search, seizure, and arrest

Thank you.