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

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

3. Analysis of traffic stop records

4. 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?

5. Traffic stop records, by driver race

6. ‘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

7. ‘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

8. ‘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

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

10. 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

11. 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

12. Analysis of post-stop outcomes

13. 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

14. Raw data on post-stop outcomes, by driver race

15. 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

16. 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

17. Comparing post-stop outcomes among matched Black and White drivers

18. Comparing post-stop outcomes among matched Hispanic and White drivers

19. Research challenges and limitations

20. 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

21. Missing data

22. Evidence of underreporting

23. Recommendations

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

25. 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

26. Thank you.