1. Developing a Data Driven Approach to Address Racial Profiling

2. Quick Facts On average, Connecticut law enforcement agencies conduct approximately 600,000 traffic stops a year. Traffic stops are the most common encounter police have with the public. On average, approximately 25 racial profiling complaints are investigated annually in Connecticut. In 2012, only 27 law enforcement agencies were collecting and submitting traffic stop information to the African American Affairs Commission.

3. Overview of Alvin W. Penn Law 1999: Connecticut enacts The Alvin W. Penn Racial Profiling Prohibition Act (Public Act 99-198) that prohibits any law enforcement agency from stopping, detaining, or searching any motorist when the stop is motivated solely by considerations of the race, color, ethnicity, age, gender or sexual orientation (Connecticut General Statutes Sections 54- 1l and 54-1m). 2012: Responsibility for implementation changed to the Office of Policy and Management (OPM) and established the Racial Profiling Prohibition Advisory Board. 2013: The law was expanded to include all agencies with the power to conduct a motor vehicle stop and added new information to be collected. It also changed the reporting requirements.

4. New Law Took Effect on October 1, 2013 106 Agencies are required to collect data on all traffic stops and electronically report to a centralized database on a monthly basis. Multiple electronic data collection and reporting options were designed and offered to law enforcement agencies. State law requires an analysis of data on an annual basis. State law also required the development of an on-line database to be available to the public.

5. http://ctrp3.ctdata.org

7. Annual Analysis of Data Guiding Principles for Statistical Analysis Principle 1: Acknowledge that statistical evaluation is limited to finding racial and ethnic disparities that are indicative of racial and ethnic bias but that, in the absence of a formal procedural investigation, cannot be considered comprehensive evidence. Principle 2: Apply a holistic approach for assessing racial and ethnic disparities in Connecticut policing data by using a variety of approaches that rely on well-respected techniques from existing literature. Principle 3: Outline the assumptions and limitations of each approach transparently so that the public and policy makers can use their judgment in drawing conclusions from the analysis.

8. Characteristics of Traffic Stop Data Aggregate Traffic Stops by Month of the Year

9. Characteristics of Traffic Stop Data Aggregate Traffic Stops by Time of Day

10. Characteristics of Traffic Stop Data Volume of traffic stops vary across departments. For every 1,000 CT residents, 211 are stopped. Newtown (452) and Berlin (413) stop the highest number of residents per 1,000. Shelton (19) and Waterbury (21) stop the lowest number of residents per 1,000.

11. Characteristics of Traffic Stop Data Race and EthnicityGenderResidencyAge White73.1% Male63.9%Connecticut Resident87.2% 16 to 208% 21 to 3030% Black13.5%31 to 4019% All Other Races1.8% Female36.1%Nonresident12.8% 41 to 5019% 51 to 6014% Hispanic11.7% Older than 618% Statewide Driver Characteristics

12. Characteristics of Traffic Stop Data Classification of StopBasis for Stop Motor Vehicle Violation88.0%Speeding26.9% Equipment Violation9.8%Registration9.4% Investigatory2.2%Cell Phone9.0% Outcome of StopDefective Lights8.9% Uniform Arrest Report0.9%Misc. Moving Violation7.5% Misdemeanor Summons5.5%Traffic Control Signal6.7% Infraction Ticket47.7%Stop Sign5.8% Written Warning17.9%Seatbelt4.1% Verbal Warning26.4%Display of Plates2.9% No Disposition1.6%Suspended License1.3% Vehicles Searched2.9%All Other17.4% Statewide Stop Characteristics

13. Characteristics of Traffic Stop Data Stops for defective lights, excessive window tint, or a display of plate violation are considered to have more Officer discretion. o Statewide average of 12.9% for these violations o 62 departments exceeded the statewide average. Wethersfield (33%) South Windsor (31.7%) Clinton (31.6%) Newington (31%) Torrington (30.8%)

14. Characteristics of Traffic Stop Data 47.7% of all stops result in an infraction ticket Department NameTotal StopsInfraction Ticket Highest Municipal Departments Danbury6,18282.3% Meriden3,20970.2% Derby3,72568.6% Department of Motor Vehicle2,31766.5% Trumbull2,97464.2% Hartford8,25461.9% Branford6,89159.1% Bridgeport4,71759.1% Greenwich8,04158.4% Norwalk7,90056.4% Highest State Police Troops Non-Troop State Police15,63685.9% Troop F25,61777.7% Troop G27,50677.1% Troop H18,79073.2% Troop C27,82670.7%

15. Characteristics of Traffic Stop Data 44.3% of all stops result in a warning Department NameTotal StopsResulted in Warning Highest Municipal Departments Putnam2,30892.9% Middlebury26692.9% Suffield55687.2% Portland16086.9% Plainfield1,24084.0% West Haven3,86582.6% Plymouth2,61082.2% Thomaston94282.0% Guilford2,71181.9% Redding2,53781.0% Highest State Police Troops Troop B6,15942.3% Troop L13,79040.0% Troop D16,66233.0% Troop A23,66728.6% Troop K21,78727.4%

16. Characteristics of Traffic Stop Data Less than 1% of all traffic stops result in an arrest Department NameTotal StopsArrests New London1,5247.3% West Hartford8,2215.9% Waterbury1,7425.3% Canton1,7514.3% Wallingford9,1783.7% Hartford8,2543.4% Plainfield1,2402.6% Groton Town6,2522.5% New Haven11,1592.4% Farmington4,5252.1%

17. Characteristics of Traffic Stop Data 2.9% of all traffic stops result in a vehicle search Department NameTotal StopsResulted in Search Highest Municipal Departments Waterbury1,74228.8% Bridgeport4,71711.1% Milford4,3589.7% New London1,5248.5% West Hartford8,2218.2% Derby3,7258.2% Middletown3,7008.1% Norwalk7,9008.0% Yale University1,0507.5% New Haven11,1597.5% Highest State Police Troops Troop A23,6672.3% Troop H18,7902.2% Troop L13,7902.1% Troop I13,6701.7% Troop G27,5061.6%

18. Descriptive Statistics and Intuitive Measures 4 Intuitive Measures were used: Statewide Average Comparison Estimated Driving Population Resident Stops Peer Groups

19. Descriptive Statistics and Intuitive Measures Department Name Statewide Average Estimated Driving Population Resident Population Peer Group Total MBHMBHMBHMBH Tier 1 WethersfieldXXXXXXX X X9 HamdenXX XX XX XX 8 ManchesterXX XX XX XX 8 New BritainX XX XX XX X8 StratfordXX XX XX XX 8 WaterburyX XX XX XXX8 East Hartford XX XX XXX7 Tier 2 MeridenX X X XX X6 New Haven XX XX XX 6 NewingtonX XX X X X6 Norwich XX XX XX 6 Windsor XX XX XX 6

20. Veil of Darkness If racial bias is driven by the ability of officers to observe the race of drivers before making a stop, then we should observe a statistical disparity between the rate of minority stops occurring in daylight vs. darkness. o Developed by Jeffery Grogger (U. Chicago) and Greg Ridgeway (U. Penn and NIJ) in 2006 o Restricts sample to intertwilight window o Control statistically for a number of factors that could change risk-set Time of the day, day of the week, state traffic volume, police department, time of day*department fixed effects, day of the week*department fixed effects, and volume*department o Estimates are for several minority definitions o Considered by CERC/IMRP to be the strongest and most accurate test

21. KPT Hit Rate Analysis If drivers and motorists behave rationally and optimize behavior, in equilibrium they are expected to have equal hit rates across races i.e. guilt/searches. o Developed by Knowles (IZA) Persico (NYU) and Todd (U. Penn) in 2001 o Utilizes only post stop data and restricts sample to discretionary searches o Estimated across several minority definitions and compared to control group o Has known shortcomings but can be used to confirm other tests

22. Summary of Findings Statewide Results o A total of 13.5 % of motorists stopped during the analysis period were observed to be Black. A comparable 11.7 % of stops were of motorists from a Hispanic descent. The results from the Veil of Darkness analysis indicated that minority stops were more likely to have occurred during daylight hours than at night. The statistical disparity provides evidence in support of the claim that certain officers in the state are engaged in racial profiling during daylight hours when motorist race and ethnicity is visible. o The results from the post-stop analysis confirm that the disparity carries through to post-stop behavior for Hispanics. It is important to note that it is specific officers and departments that are driving these statewide trends

23. Summary of Findings Departmental Results The results from the Veil of Darkness indicated that minority motorists, across all racial and ethnic categories, were more likely to have been stopped during daylight as opposed to darkness hours. The analysis using the Veil of Darkness produced sufficiently strong results to make a determination that these results indicate the presence of a significant racial and ethnic disparity for: o Groton Town o Granby o Waterbury o State Police Troop C o State Police Troop H

24. Summary of Findings Departmental Results The results from the post-stop analys is indicated that minority motorists, as compared to their Caucasian counterparts, were being searched more frequently relative to the rate at which they were found with contraband. The results of the post-stop analysis produced sufficiently strong results to make a determination that these results indicate the presence of a significant racial and ethnic disparity for: o Waterbury o State Police Troop C

25. Summary of Findings 12 Departments were identified using 4 the descriptive measures. o 7 Departments exceeded the disparity threshold levels in at least 3 of the 4 benchmarks as well as a majority of the 12 possible measures. These departments will be reviewed further by the project staff. Wethersfield Hamden Manchester New Britain Stratford Waterbury East Hartford o 5 Departments exceeded the disparity threshold levels in at least 2 of the 4 benchmarks as well as 6 of 12 measures. These departments will be monitored to determine if changes relative to the benchmarks indicate the need for further analysis. Meriden New Haven Newington Norwich Windsor

26. Results of April 2015 Analysis As a result of our first statewide analysis, we identified 11 departments with significant racial and ethnic disparities that warranted additional analysis. Forums were held in each town identified to discuss with the results with the community. Several meetings have been held with law enforcement administrators. Fair and Impartial Policing Training has been conducted to over 1,000 officers since the report was published.

27. Follow-Up Analysis Officer Level Analysis Mapping Traffic Stop Data

28. Mapping Traffic Stop Data for Follow- Up Analysis

29. Public Use of Data Several media outlets have used the traffic stop data to develop their own analysis o “Police being kinder, gentler to drivers in Connecticut”Police being kinder, gentler to drivers in Connecticut o “Who gets off with a warning after a traffic stop in Connecticut”Who gets off with a warning after a traffic stop in Connecticut o “What time of day drivers get ticketed most in Connecticut”What time of day drivers get ticketed most in Connecticut o “Interactive: Racial Profiling, By Town”Interactive: Racial Profiling, By Town o “Data: Minority Motorists Still Pulled Over, Ticketed at Higher Rates than Whites”Data: Minority Motorists Still Pulled Over, Ticketed at Higher Rates than Whites