STATE OF CONNECTICUT TRAFFIC STOP DATA ANALYSIS AND FINDINGS, 2015-16 NOVEMBER 2017 www.ctrp3.org AUTHORS Matthew B. Ross, Ph.D. Economic and Statistical Consultant James Fazzalaro Project Manager, Connecticut Racial Profiling Prohibition Project Institute for Municipal and Regional Policy Central Connecticut State University Ken Barone Project Manager Institute for Municipal and Regional Policy Central Connecticut State University Jesse Kalinowski Economic and Statistical Consultant This report was written by the Institute for Municipal and Regional Policy (IMRP) at Central Connecticut State University with the help of Matthew B. Ross and Jesse Kalinwoski who applied the statistical tests known as the “Veil of Darkness”, “Synthetic Control”, and “KPT Hit Rate.” A special thanks to Tyler Lublin a student at Central Connecticut State University who helped to compile the data, and complete many of the tables for this report. TABLE OF CONTENTS Forward ................................................................................................................................................................................................ viii Executive Summary of Findings .................................................................................................................................................... xi E.1: The Methodological Approach of the Analysis ........................................................................................................... xi E2: Traffic Stop Analysis and Findings, 2015-16 ............................................................................................................ xiii E.3: Traffic Stop Analysis and Findings, 2013-16 ......................................................................................................... xvii Note to the Reader.......................................................................................................................................................................... xxiv Background ............................................................................................................................................................................................. 1 Part I: Description of Methodology Used in Analysis ........................................................................................... 3 I.A: Methodological Approach Underlying the Analysis ....................................................................................................... 4 I.B: Descriptive Statistics and Intuitive Measures ................................................................................................................... 6 I.B (1): Problems with Approaches Using Traditional Benchmarks ........................................................................... 6 I.B (2): Statewide Average Comparison.................................................................................................................................. 9 I.B (3): Estimated Driving Population Comparison ........................................................................................................... 9 I.B (4): Resident Only Stop Comparison .............................................................................................................................. 12 I.B (5): Conclusions from the Descriptive Comparisons ............................................................................................... 13 I.C: Veil of Darkness .......................................................................................................................................................................... 14 I.C (1): Constructing the Inter-Twilight Sample ............................................................................................................... 16 I.D. Synthetic Control Model .......................................................................................................................................................... 18 I.D (1): Constructing the Synthetic Control ........................................................................................................................ 18 I.E. KPT Hit-Rate Model ................................................................................................................................................................... 21 I.E (1): Constructing the Hit-rate test ................................................................................................................................... 21 Part II: Traffic Stop Analysis and Findings, 2015-16 ......................................................................................... 23 II.A: Characteristics of Traffic Stop Data .................................................................................................................................. 24 II.B: Descriptive Statistics and Intuitive Measures .............................................................................................................. 34 II.B (1): Statewide Average Comparison ............................................................................................................................. 34 II.B (2): Estimated Driving Population Comparison....................................................................................................... 38 II.B (3): Resident Only Stop Comparison ............................................................................................................................ 40 II.B (4): Conclusions from the Descriptive Comparisons ............................................................................................. 42 II.C: Analysis of Traffic Stops, Veil of Darkness ..................................................................................................................... 44 II.C. (1): Annual State-Level Results for the Veil of Darkness, 2015-16 ................................................................. 44 II.C. (2): Annual State-Level Robustness for the Veil of Darkness, 2015-16 ........................................................ 45 II.C. (3): Annual Department-Level Results for the Veil of Darkness, 2015-16................................................... 46 II.D. Analysis of Traffic Stops, Synthetic Control................................................................................................................... 49 II.D. (1): Annual Department-Level Synthetic Control Analysis, 2015-16 ............................................................ 49 II.E. Analysis of Vehicular Searches, KPT Hit-Rate ............................................................................................................... 52 II.E (1): Annual State-Level hit-rate Analysis, 2015-16 ................................................................................................ 52 II.E (2): State-Level Robustness for hit-rate analysis, 2015-16 ................................................................................. 53 II.E (3): Annual Department-Level hit-rate Analysis, 2015-16 .................................................................................. 53 II.F: Findings from the 2015-2016 Analysis ........................................................................................................................... 56 II.F (1): Aggregate Findings for Connecticut 2015-2016.............................................................................................. 56 II.F (2): Veil of Darkness Analysis Findings, 2015-2016 .............................................................................................. 56 II.F (3): Descriptive Statistics and Intutive Measure Findings, 2015-2016 .......................................................... 58 II.F (4): Follow-Up Analysis ...................................................................................................................................................... 59 Part III: Traffic Stop Analysis and Findings, 2013-16 ....................................................................................... 62 III.A: Characteristics of Traffic Stop Data ................................................................................................................................. 63 III.B: Descriptive Statistics and Intuitive Measures ............................................................................................................. 72 III.B (1): Statewide Average Comparison............................................................................................................................ 72 III.B (2): Estimated Driving Population Comparison ..................................................................................................... 75 III.B (3): Resident Only Stop Comparison ........................................................................................................................... 77 III.B (4): Conclusions from the Descriptive Comparisons ............................................................................................ 79 III.C: Analysis of Traffic Stops, Veil of Darkness .................................................................................................................... 81 III.C. (1): Three-year State-Level Results for the Veil of Darkness, 2013-16 ....................................................... 81 III.C. (2): Three-year State-Level Robustness for the Veil of Darkness, 2013-16............................................... 82 III.C (3): Three-year Department-Level Results for the Veil of Darkness, 2013-16 .......................................... 85 III.D. Analysis of Traffic Stops, Synthetic Control ................................................................................................................. 88 III.D. (1): Three-Year Department-Level Synthetic Control Analysis, 2013-16 .................................................. 88 III.E. Analysis of Vehicular Searches, KPT Hit-RaTE............................................................................................................ 92 III.E (1): Three-Year State-Level Hit-rate Analysis, 2013-16...................................................................................... 92 III.E (2): Three-year State-Level Robustness for hit-rate analysis, 2013-16 ....................................................... 93 III.E (3): Three-Year Department-Level Hit-rate Analysis, 2013-16 ....................................................................... 94 III.F. Findings from the 2013-2016 Analysis .......................................................................................................................... 97 III.F (1): Aggregate Findings for Connecticut 2013-2016 ............................................................................................ 97 III.F (2): Veil of Darkness Analysis Findings, 2013-2016 ............................................................................................. 97 III.F (3): Descriptive Statistics and Intuitive Measure Findings, 2013-2016 .................................................... 100 III.F (4): Follow-Up Analysis.................................................................................................................................................. 103 Technical Appendix........................................................................................................................................................................ 104 FORWARD Racial profiling sends the dehumanizing message to our citizens that they are judged by the color of their skin and harms the criminal justice system by eviscerating the trust that is necessary if law enforcement is to effectively protect our communities. US Department of Justice June 17, 2003 Racial profiling is commonly understood as the practice of using the race or ethnicity of an individual as a factor in decision making outside of specific suspect descriptions. Although racial profiling has historic roots in sanctioned government actions, in today’s America there is a general consensus that it is not only misguided, but harmful to both our country as a whole and the particular relationships between law enforcement and minority communities. As with many laws, it is often a high profile event that sparks action. Upon a 1998 Department of Justice investigation into the activities of the New Jersey State Police, then President Clinton directed federal agencies to begin collecting data on race and ethnicity on those stopped or searched by federal agents. Shortly thereafter federal, state and local laws and administrative actions banning racial profiling, especially in traffic stops, became commonplace. Aside from banning the use of profiling, these efforts are usually coupled with mandates to collect and analyze data – with an initial emphasis on traffic stops. The underlying belief in this approach is that the conversation on profiling will move from an individual to a collective understanding of police practices and therefore allow all stakeholders to adopt measures to address any highlighted findings. Since their inception, these collection and analysis methods usually focus on the amount of disparities in stops, not whether profiling exists. The methods have become more nuanced as they are informed by past practices. Yet to date, there is no one method that all stakeholders routinely agree adequately addresses the issue. This often leads to arguments as to the legitimacy of a particular report’s findings and has the effect of keeping all parties from moving past the simple question as to whether disparities exist. Notwithstanding, twenty years since the first racial profiling laws went on the books, there remains general consensus and a heightened urgency that something must be done to rectify relationships between police and minority community members. Connecticut’s racial profiling law, the Alvin W. Penn Act, follows along this national historical arc. First enacted in 1999, its genesis was the highly publicized traffic stop of then State Senator Alvin W. Penn. In addition to banning racial profiling, the law mandated data collection and a study to be produced by the State’s Attorney. In 2003 the legislature reinstated the law’s mandates for data collection and report submission, and moved the administrative oversight of the project to the African American Affairs Commission. A subsequent study was not produced and the law and collection process was generally overlooked until a 2012 US DOJ investigation into patterns and practices of discriminatory policing within the East Haven Police Department. Following the highly publicized incidents in East Haven, policymakers returned focus to the Alvin W. Penn Act. After significant deliberation, the main changes in the newly revised Penn Act were threefold: 1) the mandate of electronic submission of data by police agencies; 2) the shift in administrative oversight to the Office of Policy of Management; and 3) the creation of the CT Racial Profiling Prohibition Project Advisory viii Board. In addition, with the assistance of a federal funded grant, the law garnered resources to assure implementation. Since first gathering in 2012, the Advisory Board has strived to bring together diverse stakeholders to chart a transparent, inclusive and data-driven path towards better relationships between police and community members. These participants bring a variety of perspectives to the conversation and include members from Connecticut state government, state and local police, researchers, and civil rights advocacy groups. Through multiple meetings, public forums and individual conversations, members have come to a much greater understanding of each other’s beliefs and backgrounds in an effort to gain consensus as to how best to move forward with implementing the Penn Act. This collective action has allowed Connecticut to take a much more comprehensive approach at addressing the issue of how to implement a racial profiling law. Through a deliberative decision making process, advisory board members agreed to create a statewide analytical tool to effectively screen out the departments with the highest disparities. From there, a process was outlined to gather and publish information that would allow both police departments and the public to understand why these disparities exist. The findings in this year’s report are another important step towards fostering a transparent dialogue between law enforcement and the public at large in Connecticut. In addition to an analysis of an additional full year of statewide traffic stop data (October 2015 to September 2016), this report also contains an analysis of three years of traffic stop data from October 2013 to September 2016. Taking advantage of the full aggregate three-year sample is valuable as it allows for the analysis of departments which have a small annual sample. Further, the larger overall sample within individual departments also allows for the inclusion of a more rigorous set of controls in many of the statistical tests. This report is evidence that Connecticut remains well positioned to lead the nation in addressing the issue of racial profiling and increasing trust between the public and law enforcement. That achievement is possible through the participation and cooperation of the Racial Profiling Prohibition Project Advisory Board members. The information contained in this report strengthens the foundation for an evolving dialogue around this important issue. Connecticut’s data-driven approach allows the conversation to move beyond anecdotal and position-based views on the issue. An atmosphere of open-mindedness, empathy, and honesty remains necessary to successfully engage in a conversation about how to ensure fairness in the criminal justice system that will ultimately lead to sustained police legitimacy and a safer, more just society. Over the years, thousands of police officers have laid down their lives for their fellow citizens while hundreds of thousands more have been injured while protecting their communities. The nation owes all of those officers, as well as those who are still on patrol today, an enormous debt of gratitude. At the same time, it is also clear that the history of policing has also had darker periods. There have been times when law enforcement officers, because of the laws enacted by federal, state, and local governments, have been the face of oppression for far too many of our fellow citizens. In the past, the laws adopted by our society have required police officers to perform many unpalatable tasks, such as ensuring legalized discrimination or even denying the basic rights of citizenship to many of our fellow Americans…. ix …While we obviously cannot change the past, it is clear that we must change the future. We must move forward together to build a shared understanding. We must forge a path that allows us to move beyond our history and identify common solutions to better protect our communities. International Association of Chiefs of Police President Terrence Cunningham October 17, 2016 I’d like to thank the Advisory Board and the broader community in recognition of the work that’s been done in Connecticut. Our state has set a national standard for addressing this difficult issue. Our efforts have been made possible by the concerns and interests of the general public and law enforcement. Sincerely, Bill Dyson Advisory Board Chair x EXECUTIVE SUMMARY OF FINDINGS The Alvin W. Penn Racial Profiling Prohibition Act (Public Act 99-198) was first enacted in 1999 in the State of Connecticut. The law prohibits any law enforcement agency in the state from stopping, detaining, or searching motorists when the stop is motivated solely by considerations of the race, color, ethnicity, age, gender, or sexual orientation of that individual (Connecticut General Statutes Sections 54-1l and 54-1m). In 2012 and 2013, in response to the US Justice Department’s documentation of racial profiling by members of the East Haven Police Department, the Connecticut General Assembly made several changes to the law in an effort to ensure its effective implementation. In accordance with these changes, police agencies began collecting data pertaining to all traffic stops on October 1, 2013. In 2012, the Racial Profiling Prohibition Project Advisory Board was established to advise the Office of Policy and Management (OPM) in adopting the law’s standardized methods and guidelines. The Institute for Municipal and Regional Policy (IMRP) at Central Connecticut State University was tasked to help oversee the design, evaluation, and management of the racial profiling study mandated by Public Act No. 12-74 and Public Act No. 13-75, “An Act Concerning Traffic Stop Information.” The project staff worked with the state’s Criminal Justice Information System (CJIS) to develop a system to collect consistent and universal traffic stop information and submit it to CJIS electronically on a monthly basis. In Connecticut, there are a total of 93 municipal police departments: 29 departments employing more than 50 officers, 50 employing between 20 and 50 officers, and 14 with fewer than 20 officers. State police are comprised of 11 distinct troops. Although there are an additional 80 jurisdictions that do not have organized police departments and are provided police services by the state police, either directly or through provision of resident troopers, these stops were categorized with their overarching state police troops. Additionally, a total of 13 special agencies have the authority to conduct traffic stops. As per section 54-1m of the Connecticut General Statutes, the IMRP is required to submit an annual report analyzing traffic stops records for all police departments in Connecticut. This is the third report published by the IMRP and presents the results from an analysis in two parts, (1) a study of the 560,000 traffic stops conducted during the 12-month study period from October 1, 2015 through September 30, 2016 and (2) a study of the more than 1,755,000 traffic stops conducted over the first three years of this initiative from October 1, 2013 to September 30, 2016. E.1: THE METHODOLOGICAL APPROACH OF THE ANALYSIS Assessing racial disparities in policing data has been used for the last two decades as a policy tool to evaluate whether there exists the possibility that racial bias is occurring within a given jurisdiction. Although there has always been widespread public support for the equitable treatment of individuals across racial demographics, recent national headlines have brought this issue to the forefront of American consciousness and created a national debate about policing practices. The statistical evaluation of policing data in Connecticut is one important step towards developing a transparent dialogue between law enforcement and the public at large. As such, it is the goal of this report to present the results of that evaluation in the most transparent and unbiased manner possible. The research strategy underlying the statistical analysis presented in this report was developed with three guiding principles in mind. Each principle was considered throughout the research process and when selecting the appropriate results to display publicly. A better understanding of these principles helps to xi frame the results presented in the technical portions of the analysis. In addition, by presenting these principles at the onset of the report, readers have a better context to understand the framework of the approach. 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. Six distinct analytical tools were used to evaluate whether racial and ethnic disparities are present in the Connecticut policing data. The three techniques contained in Part I, Section I.B. are descriptive in nature and should be viewed with a degree of caution.1 These techniques are, however, extremely useful in helping to identify irregularities in the data and create a context that helps to better understand the results of more advanced statistical techniques. The three descriptive analytical tools applied in the analysis are presented in both the one year and three year analysis of data. In addition to the descriptive measures, researchers also apply a method referred to as the Veil of Darkness to assess the existence of racial and ethnic disparities in stop data. The Veil of Darkness is a statistical technique that was developed by Jeffery Grogger and Greg Ridgeway (2006) and published in the Journal of the American Statistical Association. The Veil of Darkness examines a restricted sample of stops occurring during the “inter-twilight window” and assesses relative differences in the ratio of minority to non-minority stops that occur in daylight as compared to darkness. The assumption of this technique is that if police officers are profiling motorists, they are more likely to do so during daylight hours when race and ethnicity are more easily discernible. This analytical approach is considered to be the most rigorous and broadly applicable of all the tests presented in this report. Another analytical tool used is the synthetic control analysis that has the same intuitive appeal as traditional population-based benchmarks but remains grounded in rigorous statistical theory. A synthetic control is a unique benchmark constructed for each individual department using various stop-specific and town-level demographic characteristics as captured through inverse propensity score weighting. The synthetic control is then used to assess the effect of treatment on an outcome variable(s). In the present context, treatment is defined as a traffic stop made by a specific municipal police department and the outcome variable(s) indicates whether a motorist is a racial or ethnic minority. Lastly, researchers apply an analysis of hit-rates using the classic approach developed by Knowles, Persico and Todd (2001). Although some criticism has risen concerning the technique, it contributes to an understanding of post-stop police behavior in Connecticut. 1 The justification behind this cautionary note is presented in Part I, Section I.A xii E2: TRAFFIC STOP ANALYSIS AND FINDINGS, 2015-16 E.2A: Findings from the Analysis of Policing Data, 2015-2016 A total of 14.7% of motorists stopped during the analysis period were observed to be Black. A comparable 13.1% of stops were of motorists of Hispanic descent. The results presented in the state-level Veil of Darkness analysis provide strong evidence that a disparity exists in the rate of minority traffic stops by both municipal and State Police departments in the 2015 to 2016 sample. The level of significance remains relatively consistent for both groups when the sample is reduced to only moving violations. This, we conclude that these results are relatively robust and that the State Police disparity is likely driving much of the overall statewide disparity. The results from the post-stop analysis confirm that the disparity carries through to post-stop behavior across all racial and ethnic groups. In aggregate, Connecticut police departments exhibit a strong tendency to be less successful in motorist searches across all minority groups. Again, it is impossible to clearly link these observed disparities to racial profiling as these differences may be driven by any combination of policing policy, heterogeneous enforcement patterns, or individual officer behavior. Although there is evidence of a disparity at the state level, it is important to note that it is likely that specific departments are driving these statewide trends. In an effort to better identify the source of these racial and ethnic disparities, each analysis was repeated at the department level. The departments that were identified as having a statistically significant disparity are likely to be having the largest effect on the statewide results. Although it is possible that specific officers within departments that were not identified may be engaged in racial profiling, if these behaviors existed, they were not substantial enough to influence the department level results. It is also possible that a small number of individual officers within the identified departments are driving the department level results. The six municipal departments and one state police troop identified to exhibit a statistically significant racial or ethnic disparity include: Berlin The Berlin municipal police department was observed to have made 25.6 percent minority stops of which 13.3 percent were Hispanic and 9.4 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that black and Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 3.4 times larger than the odds during darkness. The odds that a Hispanic motorist was stopped during daylight was 1.7 times larger than during darkness. These results were statistically significant at the 99 and 95 percent level respectively and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Similarly, the synthetic control revealed a disparity in the rate in which both black and Hispanic motorists were stopped that was statistically significant at the 95 and 99 percent level respectively. Meriden The Meriden municipal police department was observed to have made 46.9 percent minority stops of which 31.6 percent were Hispanic and 14.2 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that black motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 2.6 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of xiii a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Similarly, the synthetic control revealed a disparity in the rate that Hispanic motorists were stopped which was statistically significant at the 99 percent level respectively. Monroe The Monroe municipal police department was observed to have made 16 percent minority stops of which 7.5 percent were Hispanic and 7 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.7 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. The hit-rate for white nonHispanic motorists was 42.9 percent while that for black motorists was 8.3 percent and that differences was statistically significant at the 95 percent level. Newtown The Newtown municipal police department was observed to have made 16.2 percent minority stops of which 7.1 percent were Hispanic and 7 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 2.3 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Norwich The Norwich municipal police department was observed to have made 39.2 percent minority stops of which 14.9 percent were Hispanic and 20.6 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.6 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Ridgefield The Ridgefield municipal police department was observed to have made 19.2 percent minority stops of which 11.3 percent were Hispanic and 5 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 2.5 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Troop B The State Police Troop B was observed to have made 11.9 percent minority stops of which 4.7 percent were Hispanic and 5 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist xiv was stopped during daylight was 2 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. In addition to the six municipal police departments and one state police troop identified to exhibit statistically significant racial or ethnic disparities in the VOD analysis, five departments were identified using the descriptive tests. The descriptive tests are designed as a screening tool to identify the jurisdictions where consistent disparities that exceed certain thresholds have appeared in the data. They compare stop data to three different benchmarks: (1) statewide average, (2) the estimated driving population, and (3) resident-only stops. Although it is understood that certain assumptions have been made in the design of each of the three measures, it is reasonable to believe that departments with consistent data disparities that separate them from the majority of other departments should be subject to further review and analysis with respect to the factors that may be causing these differences. The five municipal departments identified to exhibit a significant racial or ethnic disparity using the descriptive measures include: Wethersfield The Wethersfield municipal police department was observed to have made 48.4 percent minority stops of which 28.1 percent were Hispanic and 18.7 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in all three benchmark areas as well as in all nine possible measures. Wethersfield received a disparity score of 8.5 out of a possible nine points, indicating consistently significant racial and ethnic disparities in traffic stops. Similarly, the synthetic control revealed a disparity in the rate in which Hispanic motorists were stopped that was statistically significant at the 99 percent level. East Hartford The East Hartford municipal police department was observed to have made 69.2 percent minority stops of which 27.9 percent were Hispanic and 39.6 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in all three benchmark areas as well as in six of the nine possible measures. East Hartford received a disparity score of 6.0 out of a possible nine points. Stratford The Stratford municipal police department was observed to have made 53.4 percent minority stops of which 19.8 percent were Hispanic and 31.2 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in all three benchmark areas as well as in six of the nine possible measures. Stratford received a disparity score of 6.0 out of a possible nine points. Darien The Darien municipal police department was observed to have made 32.3 percent minority stops of which 18.4 percent were Hispanic and 11.4 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in two of the three benchmark areas as well as in five of the nine possible measures. Darien received a disparity score of 4.5 out of a possible nine points. xv Trumbull The Trumbull municipal police department was observed to have made 37.4 percent minority stops of which 14.2 percent were Hispanic and 20.7 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in two of the three benchmark areas as well as in five of the nine possible measures. Trumbull received a disparity score of 4.5 out of a possible nine points. E.2B: Conclusions and Next Steps The entirety of the initial 2015-2016 statewide traffic stop data analysis as presented in this report is utilized as a screening tool by which the Advisory Board and project staff can focus resources on those departments displaying the greatest level of disparities in their respective stop data. As noted previously, racial and ethnic disparities in any traffic stop analysis do not, by themselves, provide conclusive evidence of racial profiling. Statistical disparities do, however, provide significant evidence of the presence of idiosyncratic data trends that warrant further analysis. By conducting in-depth follow-up analyses on the departments identified through the screening process, the public has a better understanding as to why and how disparities exist. This transparency is intended to assist in achieving the goal of increasing trust between the public and law enforcement. Therefore, an in-depth follow-up analysis will be conducted for the following departments based on our analytical results for traffic stops performed from October 1, 2015 through September 30, 2016: (1) Berlin, (2) Monroe, (3) Newtown, (4) Norwich, (5) Ridgefield, (6) Darien, and (7) Troop B. None of these seven departments have been identified in previous reports. As in previous years, police administrators from these departments will be invited to be an integral part of the follow-up analysis. In addition to being identified with racial and ethnic disparities in this study, five departments were identified with racial and ethnic disparities in previous reports. Some of these departments warrant limited additional analysis, while others do not. An explanation for each department has been provided below: East Hartford was identified in both the Year 1 (Traffic Stop Data Analysis and Findings, 2013-14) and Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) studies. An in-depth follow-up analysis, with recommendations, was conducted following the Year 1 study. East Hartford’s racial and ethnic disparities have remained fairly consistent in each of the annual studies. Based on the results of the previous follow-up analysis and our further understanding of traffic stop enforcement in East Hartford, we do not believe a full follow-up analysis is necessary. However, the department should continue to review and monitor traffic enforcement policies to evaluate the disproportionate effect they could be having on minority drivers. They should also continue to take steps to assure that its minority community is fully engaged in the process of understanding why the allocation of enforcement resources are made and what outcomes are being achieved. Meriden was identified in the Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) study. An in-depth follow-up analysis, with recommendations, was conducted following the Year 2 study. However, Meriden was not previously identified with statically significant racial and ethnic disparities in the VOD methodology. Based on the results of the previous follow-up analysis and our further understanding of traffic enforcement in Meriden, we do not believe a full follow-up analysis is necessary. However, based on the new disparities identified using the VOD methodology, we will conduct a limited analysis to verify our previous conclusions. xvi Stratford was identified in both the Year 1 (Traffic Stop Data Analysis and Findings, 2013-14) and Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) studies. An in-depth follow-up analysis, with recommendations, was conducted following the Year 1 study. Stratford’s racial and ethnic disparities have remained fairly consistent in each of the annual studies. Based on the results of the previous follow-up analysis and our further understanding of traffic enforcement in Stratford, we do not believe a full follow-up analysis is necessary. However, the department should continue to review and monitor traffic enforcement policies to evaluate the disproportionate effect they could be having on minority drivers. The department should also continue to take steps to assure that its minority community is fully engaged in the process of understanding why the allocation of enforcement resources are made and what outcomes are being achieved. Trumbull was identified in the Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) study. An in-depth follow-up analysis, with recommendations, was conducted following the Year 2 study. Trumbull’s racial and ethnic disparities have remained fairly consistent in each of the annual studies Based on the results of the previous follow-up analysis and our further understanding of traffic stop enforcement in Trumbull, we do not believe a full follow-up analysis is necessary. The department should continue to review its traffic enforcement policies to evaluate the extent to which they may have a disproportionate effect, particularly with respect to black drivers. Wethersfield was identified in both the Year 1 (Traffic Stop Data Analysis and Findings, 2013-14) and Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) studies. An in-depth follow-up analysis, with recommendations, was conducted following both the Year 1 and Year 2 studies. Notwithstanding, the town’s racial and ethnic disparities have increased each subsequent year. Based on the results of the two previous follow-up analyses, we do not believe a third follow-up analysis will provide any additional information that would significantly alter our understanding of the factors influencing disparities in their traffic stop data. We recommend that the Connecticut Racial Profiling Prohibition Advisory Board review previous years’ findings and provide guidance for appropriate next steps. E.3: TRAFFIC STOP ANALYSIS AND FINDINGS, 2013-16 E.3A: Findings from the Analysis of Policing Data, 2013-16 A total of 14.1% of motorists stopped during the analysis period were observed to be Black. A comparable 12.5% of stops were of motorists of Hispanic descent. The results presented in the state-level Veil of Darkness analysis provide strong evidence that a disparity exists in the rate of minority traffic stops by both municipal and State Police departments in the combined 2013 to 2016 sample. Throughout, the disparity persists through the inclusion of both municipal departments as well as officer fixed-effects. Further, the level of significance grows across all specifications when the sample is restricted to moving violations. One overarching observation is that the largest and most persistent disparities driving the VOD results statewide are likely coming from the State Police. Not only are these results strong across all specifications and robustness checks with a high degree of confidence, but the large overall sample size means that they exert more influence on the overall average effect for the mixed sample. The results from the post-stop analysis confirm that the disparity carries through to post-stop behavior across all racial and ethnic groups. In aggregate, Connecticut police departments exhibit a strong tendency to be less successful in motorist searches across all minority groups. Again, it is impossible to clearly link these observed disparities to xvii racial profiling as these differences may be driven by any combination of policing policy, heterogeneous enforcement patterns, or individual officer behavior. Although there is evidence of a disparity at the state level, it is important to note that it is likely that specific departments are driving these statewide trends. In an effort to better identify the source of these racial and ethnic disparities, each analysis was repeated at the department level. The departments that were identified as having a statistically significant disparity are likely to be having the largest effect on the statewide results. Although it is possible that specific officers within departments that were not identified may be engaged in racial profiling, if these behaviors existed, they were not substantial enough to influence the department level results. It is also possible that a small number of individual officers within the identified departments are driving the department level results. The six municipal departments and four state police troop identified to exhibit a statistically significant racial or ethnic disparity include: Ansonia The Ansonia municipal police department was observed to have made 29.8 percent minority stops of which 12.7 percent were Hispanic and 16.1 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the intertwilight window, the odds that a Hispanic motorist was stopped during daylight was 1.4 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Groton Town The Groton Town municipal police department was observed to have made 24 percent minority stops of which 8.7 percent were Hispanic and 12.6 percent were black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that black motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 1.6 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that Groton Town was identified with a VOD disparity in the initial 12 month study that covered stops between October 1, 2013 and September 30, 2014. The department was not identified with statistically significant disparities in subsequent annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the initial 12 month study. The aggregate three-year KPT hit-rate analysis also indicated a statistically significant disparity for Hispanic motorists. The hit-rate for white non-Hispanic motorists was 62.3 percent while that for Hispanic motorists was 42.4 percent and that difference was statistically significant at the 95 percent level. Madison The Madison municipal police department was observed to have made 8.2 percent minority stops of which 4.1 percent were Hispanic and 2.8 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 2.5 times larger than the odds during xviii darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Monroe The Monroe municipal police department was observed to have made 13.9 percent minority stops of which 6.7 percent were Hispanic and 5.9 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.5 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that Monroe was identified with a VOD disparity in the year three study presented in Part II of this report. The department was not identified with statistically significant disparities in the first two annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the most recent study period. The aggregate three-year KPT hit-rate analysis also indicated a statistically significant disparity for black motorists. The hit-rate for white non-Hispanic motorists was 50 percent while that for black motorists was 16.7 percent and that difference was statistically significant at the 99 percent level. New Milford The New Milford municipal police department was observed to have made 14.8 percent minority stops of which 8.9 percent were Hispanic and 4.2 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.8 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that New Milford was identified with a VOD disparity in second annual analysis that covered stops between October 1, 2014 and September 30, 2015. The department was not identified with statistically significant disparities in the first analysis or this most recent 12-month study. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the second year study. Norwich The Norwich municipal police department was observed to have made 38.3 percent minority stops of which 14.2 percent were Hispanic and 19.7 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the intertwilight window, the odds that a Hispanic motorist was stopped during daylight was 1.3 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that Norwich was identified with a VOD disparity in the year three study presented in Part II of this report. The department was not identified with statistically significant disparities in the first two annual studies. Therefore, it is reasonable that the average effect of a threeyear analysis would show a disparity which is largely driven by data from the most recent study period. xix The aggregate three-year KPT hit-rate analysis also indicated a statistically significant disparity for Hispanic motorists. The hit-rate for white non-Hispanic motorists was 44.1 percent while that for Hispanic motorists was 32.7 percent and that difference was statistically significant at the 95 percent level. State Police Troop C The State Police Troop C was observed to have made 24 percent minority stops of which 7.5 percent were Hispanic and 9.5 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that black and Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 1.3 times larger than the odds during darkness. The odds that a Hispanic motorist was stopped during daylight was 1.28 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that Troop C was identified with a VOD disparity in the initial 12 month study that covered stops between October 1, 2013 and September 30, 2014. The Troop was not identified with statistically significant disparities in subsequent annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the initial 12 month study. The aggregate three-year KPT hit-rate analysis also indicated a statistically significant for Hispanic motorists. The hit-rate for white non-Hispanic motorists was 44.8 percent while that for Hispanic motorists was 27.2 percent and that difference was statistically significant at the 99 percent level. State Police Troop G The State Police Troop G was observed to have made 49.3 percent minority stops of which 20.7 percent were Hispanic and 24.1 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.2 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. The hit-rate for white nonHispanic motorists was 37.2 percent while that for black motorists was 28.1 percent and Hispanic motorists was 25.6 percent. Those differences were statistically significant at the 99 percent level. Similarly, the synthetic control revealed a disparity in the rate in which Hispanic motorists were stopped that was statistically significant at the 95 percent level. State Police Troop H The State Police Troop H was observed to have made 44.8 percent minority stops of which 16.3 percent were Hispanic and 24 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that black motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 1.2 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Similarly, the synthetic control revealed a disparity in the rate in which black motorists were stopped that was statistically significant at the 99 percent level. However, it is important to note that Troop H was identified with a VOD disparity in the first and second year studies. The Troop was not identified with statistically significant disparities in the most xx recent 12 month analysis. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the first and second year studies. State Police Troop K The State Police Troop K was observed to have made 21.4 percent minority stops of which 8.5 percent were Hispanic and 9.9 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.4 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Similarly, the synthetic control revealed a disparity in the rate in which Hispanic motorists were stopped that was statistically significant at the 99 percent level. In addition to the six municipal police departments and four state police troops identified to exhibit statistically significant racial or ethnic disparities in the VOD analysis, seven departments were identified using the descriptive tests. The descriptive tests are designed as a screening tool to identify the jurisdictions where consistent disparities that exceed certain thresholds have appeared in the data. They compare stop data to three different benchmarks: (1) statewide average, (2) the estimated driving population, and (3) resident-only stops. Although it is understood that certain assumptions have been made in the design of each of the three measures, it is reasonable to believe that departments with consistent data disparities that separate them from the majority of other departments should be subject to further review and analysis with respect to the factors that may be causing these differences. The seven municipal departments identified to exhibit a significant racial or ethnic disparity using the descriptive measures include: Wethersfield The Wethersfield municipal police department was observed to have made 49 percent minority stops of which 28.9 percent were Hispanic and 18.6 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in all nine possible measures. Wethersfield received a disparity score of 8.5 out of a possible nine points, indicating consistently significant racial and ethnic disparities in traffic stops. Similarly, the synthetic control revealed a disparity in the rate in which Hispanic motorists were stopped that was statistically significant at the 99 percent level. Wethersfield was identified with significant racial and ethnic disparities in all three annual reports. Therefore, it is unsurprising that the department would be identified with statistically significant disparities in a three-year aggregate analysis. Stratford The Stratford municipal police department was observed to have made 50.9 percent minority stops of which 18.5 percent were Hispanic and 30.9 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in six of the nine possible measures. Stratford received a disparity score of 6.0 out of a possible nine points. Stratford was identified with significant racial and ethnic disparities in all three annual reports. Therefore, it is unsurprising that the department would be identified with statistically significant disparities in a three-year aggregate analysis. xxi East Hartford The East Hartford municipal police department was observed to have made 65.9 percent minority stops of which 26.7 percent were Hispanic and 37.6 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in six of the nine possible measures. East Hartford received a disparity score of 6.0 out of a possible nine points. The hit-rate for white nonHispanic motorists was 50.9 percent while that for Hispanic motorists was 41 percent and that difference was statistically significant at the 95 percent level. East Hartford was identified with significant racial and ethnic disparities in all three annual reports. Therefore, it is unsurprising that the department would be identified with statistically significant disparities in a three-year aggregate analysis. New Britain The New Britain municipal police department was observed to have made 60.8 percent minority stops of which 41.8 percent were Hispanic and 17.7 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in five of the nine possible measures. New Britain received a disparity score of 5.0 out of a possible nine points. New Britain was identified with significant racial and ethnic disparities in the first and second year studies. The department was not identified with statistically significant disparities in the most recent 12 month analysis. Therefore, it is reasonable that the average effect of a three-year aggregate analysis would show a disparity which is largely driven by data from the first and second year studies. Hamden The Hamden municipal police department was observed to have made 43.9 percent minority stops of which 8.8 percent were Hispanic and 34.1 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in five of the nine possible measures. Hamden received a disparity score of 5.0 out of a possible nine points. Hamden was identified with a disparity using the descriptive measures in the initial 12 month study that covered stops between October 1, 2013 and September 30, 2014. The department was not identified with statistically significant disparities in subsequent annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the initial 12 month study. Manchester The Manchester municipal police department was observed to have made 42 percent minority stops of which 15 percent were Hispanic and 23.8 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in five of the nine possible measures. Manchester received a disparity score of 5.0 out of a possible nine points. Similarly, the synthetic control revealed a disparity in the rate in which Black motorists were stopped that was statistically significant at the 99 percent level. Manchester was identified with a disparity using the descriptive measures in the initial 12 month study that covered stops between October 1, 2013 and September 30, 2014. The department was not identified with statistically significant disparities in subsequent annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the initial 12 month study. xxii Trumbull The Trumbull municipal police department was observed to have made 36.8 percent minority stops of which 15.3 percent were Hispanic and 19.2 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in two of the three benchmark areas as well as in five of the nine possible measures. Trumbull received a disparity score of 4.5 out of a possible nine points. Trumbull was identified with a disparity using the descriptive measures in the Year 2 study and the most recent study presented in Part II of this report. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the year 2 and year 3 studies. E.3B: Conclusions and Next Steps The entirety of the statewide traffic stop data analysis as presented in this report is utilized as a screening tool by which the Advisory Board and project staff can focus resources on those departments displaying the greatest level of disparities in their respective stop data. As noted previously, racial and ethnic disparities in any traffic stop analysis do not, by themselves, provide conclusive evidence of racial profiling. Statistical disparities do, however, provide significant evidence of the presence of idiosyncratic data trends that warrant further analysis. By conducting in-depth follow-up analyses on the departments identified through the screening process, the public has a better understanding as to why and how disparities exist. This transparency is intended to assist in achieving the goal of increasing trust between the public and law enforcement. Based on our analytical results for traffic stops conducted from October 1, 2013 through September 30, 2016 there were 13 municipal police departments and two state police troops identified with significant racial and ethnic disparities. A full in-depth follow-up analysis will be conducted only for those departments that have not been identified in any of the previous annual studies. Those departments are: (1) Ansonia, (2) Madison, (3) Troop G, and (4) Troop K. For the 11 remaining municipal police departments, it is reasonable that the average effect of a threeyear aggregate analysis would show a disparity which is largely driven by data from previous studies in which the departments were already identified. A full follow-up analysis was previously conducted for nine of the 11 departments (East Hartford, Groton Town, Hamden, Manchester, New Britain, New Milford, Stratford, Trumbull, and Wethersfield). Monroe and Norwich were identified in the annual analysis presented in Part II of this report. A full follow-up analysis will be conducted for both these departments as a result of that analysis. xxiii NOTE TO THE READER The majority of the 106 law enforcement agencies in Connecticut collect traffic stop information electronically immediately following the traffic stop. However, there are seven departments that collect information using paper forms. In these cases the officer completes a paper form following each stop and submits it to the department’s records division. The information is then manually entered into an electronic system for transmission to the state data portal. In reviewing the data submitted between October 1, 2015 and September 30, 2016, the project staff became concerned by the significant decrease in stops submitted by three departments that use a paper form system (Bridgeport, Hartford, and Middletown). Therefore, an audit was conducted to ensure that all traffic stops were being collected and submitted to the state. The nature of paper data collection makes it challenging to audit. Our audit consisted of reviewing information recorded in police dispatch logs to see if the information matched what was reported in the traffic stop data system. In addition, we also requested copies of all paper forms to determine the accuracy of the data being reported. Our review found that an indeterminate number of traffic stops were not reported by the Bridgeport, Hartford, and Middletown police departments between October 1, 2015 and September 30, 2016. We were unable to determine the exact number of unreported stops for each department, but based on our review, we believe that the number exceeded 1,000 stops for each department. As a result of our review, we met with each department to better understand if they have an adequate system in place to ensure that all stops are properly recorded. None of the three departments had a system in place to ensure that officers complete a form for each stop. We recommended that as long as a paper data collection system is the primary method of recording traffic stop information that departments develop an oversight system which would ensure that all stops are properly recorded and submitted. We have recommended that these departments review the standard operating procedures of the New London Police Department as a model system to replicate. In New London, at the end of each shift a supervisor must sign each traffic stop form from officers and verify that a form has been completed for each stopped called-in to dispatch. In addition, each form includes an area for a computer aided dispatch number which makes it possible to identify the stop in the dispatch log when conducting a review. These basic oversight protocols should ensure that the majority of traffic stops are properly recorded by the officer and submitted to the state. xxiv BACKGROUND First enacted in 1999, Connecticut's anti-racial profiling law entitled, the Alvin W. Penn Racial Profiling Prohibition Act (Public Act 99-198), 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 of that individual (Connecticut General Statutes Sections 54-1l and 541m). In 2012 and 2013, the Connecticut General Assembly made several changes to this law to create a system to address racial profiling concerns in Connecticut. In 2012, the Racial Profiling Prohibition Project Advisory Board was established to advise OPM in adopting the law’s standardized methods and guidelines. The Institute for Municipal and Regional Policy (IMRP) at Central Connecticut State University was tasked to help oversee the design, evaluation, and management of the racial profiling study mandated by PA 12-74 and PA 13-75, “An Act Concerning Traffic Stop Information.” The IMRP worked with the advisory board and all appropriate parties to enhance the collection and analysis of traffic stop data in Connecticut. Through September 30, 2013, police agencies collected traffic stop information based on requirements outlined in the original 1999 Alvin W. Penn law. Beginning October 1, 2013, police agencies had to submit traffic stop data for analysis under the new methods outlined by the Office of Policy and Management (OPM), as required by the amended racial profiling prohibition law. The law also authorized the OPM secretary to order appropriate penalties (i.e., the withholding of state funds) when municipal police departments, the Department of Emergency Services and Public Protection (DESPP), and other police departments fail to comply. The National Highway Traffic and Safety Administration (NHTSA) provided resources for this project through a grant administered by the Connecticut Department of Transportation. The Racial Profiling Prohibition Project Advisory Board and the project staff have been meeting since May 2012 in an effort to outline a plan to successfully implement the requirements of the 2012 and 2013 legislation. The focus of the project’s early phase was to better understand traffic stop data collection in other states. After an extensive review of best practices, working groups were formed and met monthly to discuss the different aspects of the project. These working groups included Data and System, Public Awareness, and Training work groups. The full advisory board held more than 20 meetings and the working groups met approximately 50 times. The advisory board and IMRP also worked with law enforcement officials to create a data collection system that is efficient, not burdensome to the police collecting it, and provides information that is easy to work with when it is submitted. Police agencies in Connecticut vary in their levels of sophistication and technological capacity with respect to how they collect and report data. The project staff worked with the state’s Criminal Justice Information System (CJIS) to develop a system to collect consistent and universal traffic stop information and submit it to CJIS electronically on a monthly basis. The IMRP developed and maintains a project website (www.ctrp3.org) that informs the public of the advisory board’s activities, statewide informational forums, and related news items on racial profiling. The website includes meeting agendas and minutes, press releases, and links to register for events. The website is updated weekly. In addition to the project website, the IMRP partnered with the Connecticut Data Collaborative to publish all traffic stop data on a quarterly basis. The public can download the information in its original form or view summary tables for easy use. A full set of analytical tools will be available for more advanced users who are interested in data analysis. 1 Although much of the initial focus of this project was to develop a standardized method for data collection and analysis, there are other important components. The initiatives include a public awareness and education campaign, effective training for officers and departments, and a rigorous complaint process. Information about all of these initiatives is provided on the project website. These initiatives collectively represent different tools available for education and the prevention of racial profiling in policing. These tools were implemented in the hope of building and enhancing trust between communities and law enforcement in Connecticut. In February 2014, the U.S. Department of Justice, Community Oriented Policing Services Division, sponsored a train-the-trainer program in Connecticut on “Fair and Impartial Policing (FIP).” The FIP program was established to train police officers and supervisors on fair and impartial policing by understanding both conscious and unconscious bias. This program was offered to police agencies throughout the state over the next year. Lastly, a major component of addressing concerns about the possibility of racial profiling in Connecticut is bringing law enforcement officials and community members together to discuss relationships between police and the community. The project staff has conducted several public forums throughout the state to bring these groups together and will continue these dialogues in the foreseeable future. They serve as an important tool to inform the public of their rights and the role of law enforcement in serving their communities. 2 PART I: DESCRIPTION OF METHODOLOGY USED IN ANALYSIS I.A: METHODOLOGICAL APPROACH UNDERLYING THE ANALYSIS Assessing racial disparities in policing data has been used for the last two decades as a policy tool to evaluate whether racial bias exists within a given jurisdiction. Although there has always been widespread public support for the equitable treatment of individuals of all races, recent national headlines have brought this issue to the forefront of American consciousness and prompted a contentious national debate about policing policy. The statistical evaluation of policing data in Connecticut is an important step towards developing a transparent dialogue between law enforcement and the public. As such, this report’s goal is to present the results of that evaluation in a transparent and unbiased manner. As an increasing number of jurisdictions have passed laws mandating the collection of policing data, researchers have become involved in the process by providing new and increasingly sophisticated analytical techniques. Prior to the development of these empirical methods, traditional policing data assessments relied principally on population-based benchmarks. Although population-based benchmarks are still frequently applied in practice because of their intuitive appeal and inherent cost-effectiveness, these test statistics cannot withstand strict scrutiny. In an effort to achieve the goal of a transparent and unbiased evaluation, the analysis in this report applies a series of sophisticated econometric tests as the primary diagnostic mechanism. The research strategy underlying this statistical analysis was developed with consideration to three guiding principles. Each principle served as an important foundation for the research process, particularly when selecting the appropriate results to disseminate to the public. A better understanding of these principles helps to frame the results in the technical portions of the analysis. Further, presenting these principles at the outset of the report provides readers with the appropriate context to understand our overall approach. 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. This report is organized to lead the reader through a host of descriptive and statistical tests that vary in their assumptions and level of scrutiny. The intent behind this approach is to apply multiple tests as a screening filter for the possibility that any one test (1) produces false positive results or (2) reports a false negative. The analysis begins by first presenting the descriptive statistics from the Connecticut policing data along with several intuitive measures that evaluate racial and ethnic disparities. These intuitive measures are considered less stringent tests, but provide a useful context for viewing the data. 4 The next section analyzes racial and ethnic disparities in the rate of motor vehicle stops by applying a wellrespected methodology colloquially known as the “Veil of Darkness.” The fifth method illustrates the application of the synthetic control analysis that has the same intuitive appeal as traditional populationbased benchmarks but remains grounded in rigorous statistical theory. The last section assesses post-stop behavior, particularly the incidence of vehicular searches. We conclude the report by summarizing our analysis of disparities in the rate of motor vehicle stops and post-stop behavior at the state and department-levels. The findings presented in the conclusion draw from each of our evaluation mechanisms and identify only those departments where statistically significant racial and ethnic disparities across multiple tests are observed. In short, we move forward with the overall goal of identifying the statistically significant racial and ethnic disparities in Connecticut policing data. A variety of statistical tests are applied to the data in the hope of providing a comprehensive approach based on the lessons learned from academic and policy applications. Our explanations of the mechanisms and assumptions that underlie each of the tests are intended to provide policymakers and the public with enough information to assess the data and draw their own conclusions from the findings. Finally, we emphasize the message that any statistical test is only truly capable of identifying racial and ethnic disparities. Such findings provide a mechanism to indicate possible racial profiling but they cannot, without further investigation, provide sufficient evidence that racial profiling exists. 5 I.B: DESCRIPTIVE STATISTICS AND INTUITIVE MEASURES This section presents the methodology used in comparison between the department-level data and the state average, and describes two benchmarks (Estimated Driving Population and Resident Population) that enhance existing population-based methods. Although any one of these benchmarks cannot provide by itself a rigorous enough analysis to draw conclusions regarding racial profiling, if taken together they do server to highlight those jurisdictions where disparities are significant enough to justify further analysis. Although bias could be one possible explanation for such disparities, there are also other possibilities including idiosyncrasies of policing practices. As will be discussed in more detail, any benchmark approach contains implicit assumptions that must be recognized and understood. These benchmarks help to provide additional context to compare and contrast our findings using more advanced econometric methods explained later in this report. I.B (1): PROBLEMS WITH APPROACHES USING TRADITIONAL BENCHMARKS A traditional approach to evaluating racial and ethnic disparities in policing data has been to apply population-based benchmarks. Although these benchmarks vary in their construction, the general methodology is consistent. Typically, the approach amounts to using residential data from the U.S Census Bureau to compare with the rate of minority traffic stops in a given geographic jurisdiction. In recent years, researchers have refined this approach by adjusting the residential census data to account for things like commuter sheds, access to vehicles, and differences over time. The population-based benchmark is an appealing approach for researchers and policymakers both because of its ease of implementation and intuitive interpretation. There are, however, numerous implicit assumptions that underlie the application of these benchmarks and are seldom presented in a transparent manner. The goal of this analysis is to evaluate racial and ethnic disparities in the Connecticut policing data using (1) intuitive measures that compare the data against uniformly applied benchmarks and (2) sophisticated econometric techniques that compare the data against itself without relying on benchmarks. The goal of this section is to clearly outline the assumptions that often accompany traditional benchmarks. We do, however, present two nontraditional benchmarks in this chapter that develop a more convincing approximation and can be used to descriptively assess the data. By presenting these benchmarks alongside our more econometric methods, we provide the context for our findings. In addition, the descriptive data presents jurisdictional information in cases where samples may be too small to provide statistically meaningful results from the more stringent tests. Although there are a number of examples, the most prominent application of a population-based benchmark is a study by the San Jose Police Department (2002) that received a great deal of criticism. A more recent example is a report by researchers from Northeastern University (McDevitt et al. 2014) using Rhode Island policing data. Although adjusted and unadjusted population-based benchmarks can be intuitively appealing, they have drawn serious criticism from academics and policymakers alike because of the extent to which they are unable to account for all of the possible unobserved variables that may affect the driving population in a geography at any given time (Walker 2001; Fridell 2004; Persico and Todd 2004; Grogger and Ridgeway 2006; Mosher and Pickerill 2012). In an effort to clarify the implicit assumptions that underlie these approaches, an informal discussion of each is presented. 6 The implicit assumption that must be made when comparing the rate of minority stops in policing data to a population-based (or otherwise constructed) benchmark include the following. Destination Commuter Traffic The application of population-based benchmarks does not account for drivers who work but do not live in a given geography. Again, the application of population-based benchmarks implicitly assumes that the demographic distribution of destination commuter traffic, on average, matches the population-based benchmark. This assumption is trivial for geographies with low levels of industrial or commercial development where destination commuter traffic is small. On the other hand, areas with a high level of industrial or commercial development attract workers from neighboring geographies and this assumption becomes more tenuous. This differential impact creates a non-random distribution of error across geographies. While this shortcoming is impossible to avoid using population-based analysis, McDevitt et al. (2004) made a notable effort to adjust static residential population demographics by creating an “estimated driving populations” for jurisdictions in Rhode Island. Pass-through Commuter Traffic A small but not insubstantial amount of traffic also comes from pass-through commuters. Although most commuter traffic likely occurs via major highways that form the link between origin and destination geographies, the commuter traffic in some towns likely contains a component of drivers who do not live or work in a given geography but must travel through the area on their way to work. As in the previous case, the application of a population-based benchmark must implicitly assume that the demographic distribution of these drivers matches the population-based benchmark. The distribution of error associated with this assumption is, again, very likely non-random. Specifically, it seems likely that a town’s proximity to a major highway may impact the level of pass-through commuter traffic from geographies further away from the major highway and, as a result, affect the magnitude of the potential error. Unfortunately, little useful data exists to quantify the extent to which this affects any particular jurisdiction. Alternatives that survey actual traffic streams are prohibitively expensive and timeconsuming to conduct on a statewide basis and, unfortunately, are subject to their own set of implicit assumptions that can affect distribution of error. Recreational Traffic Surges in recreational traffic are not accounted for in evaluation methods that utilize population-based benchmarks. In order to apply population-based benchmarks as a test statistic, it must be implicitly assumed that the demographic distribution of recreational traffic, on average, matches the populationbased benchmark. Although these assumptions are not disaggregated as with commuter traffic above, this assumption must apply to both destination and pass-through commuter traffic. Although the assumption is troublesome on its face, it becomes more concerning when considering the distribution of the associated error during specific seasons of the year. Specifically, recreational traffic likely has a differential effect across both geographic locations and over time. Differential Exposure Rates The exposure rate can be defined as the cumulative driving time of an individual on the road. The application of a population-based benchmark must implicitly assume that exposure rates are, on average, equivalent across demographic groups. Although exposure rates may differ based on cultural factors like 7 driving behavior, there are also many more factors that play an important role. An example might be the differences in age distribution across racial demographics. If a specific minority population is, on average, younger, and younger drivers have a greater exposure rate than older drivers; then one might falsely attribute a racial or ethnic disparity across these groups when there is simply a different exposure to law enforcement. Although census-based estimation methods exist to apply these demographically based exposure differences to a given population, they are best suited to situations where a single or very limited number of jurisdictions must be analyzed. Temporal Controls The lack of temporal controls in population-based benchmarks does not account for differences in the rate of stops across different times and days in the week. Assuming, that the above four assumptions hold and the population-based benchmark is representative of the demographic distribution of the driving population, then temporal controls are not an issue. However, if any of these assumptions do not hold, the lack of temporal controls may further magnify potential bias. Imagine that we believe the only assumption pertaining to exposure rates is invalid. It seems plausible that younger drivers are more likely to drive on weekend evenings than older drivers. If more stops were being made on weekend evenings than during the week and, as described above, minority groups were more prevalent in younger segments of the population, we might observe a racial or ethnic disparity simply because population-based benchmarks do not allow us to control for these temporal differences in policing patterns. When one or more of the implicit assumptions associated with a population-based benchmark is violated, it can become a biased test statistic of racial disparities in policing data. Furthermore, since the source and direction of any such bias are unknown, it is impossible to determine if the bias is positive or negative, thus creating the potential for both type one (false positive) and two error (false negative). Further, the bias also is likely to be non-random across different geographies within the state. It might be that the bias disproportionately impacts urban areas compared to rural areas, tourist destinations compared to nontourist destinations, geographies closer to highways, or based on similar policing patterns. The question then becomes: If the assumptions inherent in population-based benchmarks make them less than ideal as indicators of possible bias, why include them in a statewide analysis of policing data? One answer is that excluding them as part of a multi-level analysis guarantees only that when others inevitably use these measures as a way to interpret the data, it is highly likely to be done inappropriately. Comparing a town’s stop percentages to its residential population may not be a good way to draw conclusions about its performance but, in the absence of better alternatives, it inevitably becomes the default method for making comparisons. Providing an enhanced way to estimate the impact commuters have on the driving population and primarily analyzing the stops made during the periods of the day when those commuters are the most likely to be a significant component of the driving population improves that comparison. Another answer to the question is that the population-based and other benchmarks are not used as indicators of bias, but rather as descriptive indicators for understanding each town’s data. Since the purpose of this study is to uniformly apply a set of descriptive measures and statistical tests to all towns in order to identify possible candidates for more targeted analysis, having a broad array of possible applicable measures enhances the robustness of the screening process. Relying solely on benchmarking to accomplish this would not be effective, but using these non-statistical methods to complement and enhance the more technical evaluation results in a report that examines the data from many possible angles. 8 The third answer to the question is that the benchmarks and intuitive measures developed for this study can be useful in cases where an insufficient sample size make it difficult to draw meaningful conclusions from the formal statistical tests. The descriptive measures can serve a supportive role in this regard. I.B (2): STATEWIDE AVERAGE COMPARISON Although it is relatively easy to compare individual town stop data to the statewide average, this can be misleading if done without regard to differences in town characteristics. If, for example, the statewide average for a particular racial category of drivers stopped was 10% and the individual data for two towns was 18% and 38% respectively, a superficial comparison of both towns to the statewide average might suggest that the latter town, at 38%, could be performing less satisfactorily. However, that might not actually be the case if the town with the higher stop percentage also had a significantly higher resident population of driving age people than the statewide average. It is important to establish a context within which to make the comparisons when using the statewide average as a descriptive benchmark. Comparing town data to statewide average data is frequently the first thing the public does when trying to understand and assess how a police department may be conducting traffic stops. Although these comparisons are inevitable and have a significant intuitive appeal, the reader is cautioned against basing any conclusions about the data exclusively upon this measure. In this section, a comparison to the statewide average is presented alongside the context necessary to understand the pitfall of interpreting these statistics on face value. The method chosen to make the statewide average comparison is as follows:     The towns that exceeded the statewide average for the three racial categories being compared to the state average were selected. The amount that each town’s stop percentage exceeded the state average stop percentage was determined. The amount that each town’s resident driving age population exceeded the state average for the racial group being measured was determined. The net differences in these two measures were determined and used to assess orders of magnitude differences in these factors. While it is clear that a town’s relative proportion of driving age residents in a racial group is not, in and of itself, capable of explaining differences in stop percentages between towns, it does provide a simple and effective way to establish a baseline for all towns from which the relative differences between town stop numbers become more apparent. To provide additional context, two additional factors were identified: (1) if the town shares a border with one or more towns whose age 16 and over resident population for that racial group exceeds the state average and (2) the percentage of nonresident drivers stopped for that racial group, in that town. I.B (3): ESTIMATED DRIVING POPULATION COMPARISON Adjusting “static” residential census data to approximate the estimated driving demographics in a particular jurisdiction provides a more accurate benchmark method than previous census-based approaches. At any given time, nonresidents may use any road to commute to work or travel to and from entertainment venues, retail centers, tourist destinations, etc. in a particular town. It is impossible to account for all driving in a community at any given time, particularly for the random, itinerant driving trips 9 sometimes made for entertainment or recreational purposes. However, residential census data can be modified to create a reasonable estimate of the possible presence of many nonresidents likely to be driving in a given community because they work there and live elsewhere. This methodology is an estimate of the composition of the driving population during typical commuting hours. Previously, the most significant effort to modify census data was conducted by Northeastern University’s Institute on Race and Justice. The institute created the estimated driving population (EDP) model for traffic stop analyses in Rhode Island and Massachusetts. A summary of the steps used in the analysis is shown below in Table 1. Table I.B.1: Northeastern University Institute on Race and Justice Methodology for EDP Models in Rhode Island and Massachusetts Step 1 Step 2 Step 3 Step 4 Identify all the communities falling within a 30 mile distance of a given target community. Determine the racial and ethnic breakdown of the resident population of each of the communities in the contributing pool. Modify the potentially eligible contributing population of each contributing community by factoring in (a) vehicle ownership within the demographic, (b) numbers of persons within the demographic commuting more than 10 miles to work, and (c) commuting time in minutes. The modified number becomes the working estimate of those in each contributing community who may possibly be traveling to the target community for employment. Using four factors, (a) percentage of state employment, (b) percentage of state retail trade, (c) percentage of state food and accommodation sales, and (d) percentage of average daily road volume, rank order all communities in the state. Based on the average of all four ranking factors, place all communities in one of four groups thus approximating their ability to draw persons from the eligible nonresident pool of contributing communities. Determine driving population estimate for each community by combining resident and nonresident populations in proportions determined by which group the community falls into as determined in Step 3. (Range: 60% resident/40% nonresident for highest category communities to 90% resident/10% nonresident for lowest ranking communities) Although the EDP model created for Rhode Island and Massachusetts is a significant improvement in creating an effective benchmark, limitations of the census data at the time required certain assumptions to be made about the estimated driving population. They used information culled from certain transportation planning studies to set a limit to the towns they would include in their potential pool of nonresident commuters. Only those towns located within a 30 minute driving time of a target town were included in the nonresident portion of the EDP model. This approach assumed only those who potentially could be drawn to a community for employment, and did not account for how many people actually commute. Retail, entertainment, and other economic indicators were used to rank order communities into groups to determine the percentage of nonresident drivers to be included in the EDP. A higher rank would lead to a higher percentage of nonresidents being included in the EDP. Since development of the Rhode Island and Massachusetts model, significant enhancements were made to the U.S. Census Bureau data. It is now possible to get more nuanced estimates of those who identify their employment location as somewhere other than where they live. Since the 2004 effort by Northeastern University to benchmark Rhode Island and Massachusetts’ data, the Census Bureau has developed new tools that can provide more targeted information that can be used to create a more useful estimated driving population for analyzing weekday daytime traffic stops. 10 The source of this improved data is a database known as the LEHD Origin-Destination Employer Statistics (LODES). LEHD is an acronym for “Local Employer Household Dynamics” and is a partnership between the U.S. Census Bureau and its partner states. LODES data is available through an online application called OnTheMap operated by the Census Bureau. The data estimates where people work and where workers live. The partnership’s main purpose is to merge data from workers with data from employers to produce a collection of synthetic and partially synthetic labor market statistics including LODES and the Quarterly Workforce Indicators. Under the LEHD Partnership, states agree to share Unemployment Insurance earnings data and the Quarterly Census of Employment and Wages data with the Census Bureau. The LEHD program combines the administrative data, additional administrative data, and data from censuses and surveys. From these data, the program creates statistics on employment, earnings, and job flows at detailed levels of geography and industry. In addition, the LEHD program uses this data to create workers' residential patterns. The LEHD program is part of the Center for Economic Studies at the U.S. Census Bureau. It was determined that the data available through LODES, used in conjunction with data available in the 2010 census, could provide the tools necessary to create an advanced EDP model. The result was the creation of an individualized EDP for each of the 169 towns in Connecticut that reflects, to a certain extent, the estimated racial and ethnic demographic makeup of all persons identified in the data as working in the community but residing elsewhere. Table 2 shows the steps in this procedure. Table I.B.2: Central Connecticut State University Institute for Municipal and Regional Policy Methodology for EDP Model in Connecticut Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 For each town, LODES data was used to identify all those employed in the town but residing in some other location regardless of how far away they lived from the target community. ACS* five-year average estimated data was used to adjust for individuals commuting by some means other than driving, such as those using public transportation. For all Connecticut towns contributing commuters, racial and ethnic characteristics of the commuting population were determined by using the jurisdictions’ 2010 census demographics. For communities contributing more than 10 commuters who live outside of Connecticut, racial and ethnic characteristics of the commuting population were determined using the jurisdictions’ 2010 census demographics. For communities contributing fewer than 10 commuters who live outside of Connecticut, racial and ethnic characteristics of the commuting population were determined using the demographic data for the county in which they live. The numbers for all commuters from the contributing towns were totaled and represent the nonresident portion of the given town’s EDP. This was combined with the town’s resident driving age population. The combined nonresident and resident numbers form the town’s complete EDP. To avoid double counting, those both living and working in the target town were counted as part of the town’s resident population and not its commuting population. *American Community Survey, U.S. Census Bureau Structured in this way, each town’s EDP should reflect an improved estimate of the racial and ethnic makeup of the driving population who might be on a municipality’s streets at some time during a typical weekday/daytime period. The more sophisticated methodology central to the LODES data should make 11 this EDP, even with its inherent limitations, superior to previous uses of an EDP model. To an extent, it mirrors the process used by the Census Bureau to develop from ACS estimates the commuter-adjusted daytime populations (estimates of changes to daytime populations based on travel for employment) for minor civil divisions in several states, including Connecticut. This type of data is subject to a margin of error based on differing sample sizes and other factors. For the estimated daytime populations the Census Bureau calculated for 132 Connecticut communities, it reported margins of error ranging from 1.1% (Bridgeport) to 9.6% (East Granby). The average margin of error for all 132 towns was 3.7%. It is important to understand that the EDPs used in this report are a first attempt to use this tool in assessing traffic stop data. Much of the data used to create the EDPs comes from the same sources the Census Bureau used to create its commuter-adjusted daytime population estimates so it is reasonable to expect a similar range in the margins of error in the EDP. While the limitations of the model must be recognized, its value as a new tool to help understand some of the traffic stop data should not be dismissed. It represents a significant improvement over the use of resident census demographics as an elementary analytical tool and can hopefully be improved as the process of analyzing stop data progresses. It was determined that a limited application of the EDP can be used to assess stops that occur during typical morning and evening commuting periods, when the nonresident workers have the highest probability of actually being on the road. Traffic volume and populations can change significantly during peak commuting hours. For example, Bloomfield has a predominately Minority resident population (61.5%). According to OnTheMap, 17,007 people work in Bloomfield, but live somewhere else and we are estimating that about 73% of those people are likely to be white. The total working population exceeds the driving age resident population of 16,982 and it is reasonable to assume that the daytime driver population would change significantly due to workers in Bloomfield. According to the ACS Journey to Work survey, 73% of Connecticut residents travel to work between 6:00am and 10:00am. The census currently does not have complete state level data on residents’ travel from work to home. In the areas where evening commute information is available, it is consistently between the hours of 3:00pm and 7:00pm. In addition to looking at census information to understand peak commuting hours, the volume of nonresident traffic stops in several Connecticut communities was also reviewed, based on our theory that the proportion of nonresidents stopped should increase during peak commuting hours. The only traffic stops included in this analysis were stops conducted Monday through Friday from 6:00am to 10:00am and 3:00pm to 7:00pm (peak commuting hours). Due to the margins of error inherent in the EDP estimates, we established a reasonable set of thresholds for determining if a department shows a disparity in its stops when compared to its EDP percentages. Departments that exceed their EDP percentages by greater than 10 percentage points in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic, were identified in our tier one group. In addition, departments that exceeded their EDP percentage by more than five but less than 10 percentage points were identified in our tier two group for this benchmark if the ratio of the percentage of stops for the target group compared to the baseline measure for that group also was 1.75 or above (percentage of stops divided by benchmark percentage equals 1.75 or more) in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, or (3) Hispanic. I.B (4): RESIDENT ONLY STOP COMPARISON Some questioned the accuracy of the estimated driving population. As a result, we have limited the next part of the analysis to stops involving only residents of the community and compared them to the community demographics based on the 2010 decennial census for residents age 16 and over. 12 While comparing resident-only stops to resident driving age population eliminates the influence out-oftown drivers on the roads at any given time may be having on a town’s stop data, the mere existence of a disparity is not in and of itself significant unless it does so by a significant amount. Such disparities may exist for several reasons including high police presence on high crime areas. Therefore, we established a reasonable set of thresholds for determining if a department shows a significant enough disparity in its resident stops compared to its resident population to be identified. Departments with a difference of 10 percentage points or more between the resident stops and the 16+ resident population in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic, were identified in our tier one group. In addition, departments that exceeded their resident population percentage by more than five but less than 10 percentage points were identified in our tier two group for this benchmark if the ratio of the percentage of resident stops for the target group compared to the baseline measure for that group also was 1.75 or above(percentage of stopped residents divided by resident benchmark percentage equals 1.75 or more) in any of three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic. I.B (5): CONCLUSIONS FROM THE DESCRIPTIVE COMPARISONS The descriptive tests outlined in the above sections are designed to be used as a screening tool to identify those jurisdictions with consistent data disparities that exceed certain thresholds. The tests compare stop data to three different benchmarks: (1) statewide average, (2) the estimated driving population, and (3) resident-only stops that each cover three driver categories: Black, Hispanic, and Minority. Town data is then measured against the resulting total of nine descriptive measures for evaluation purposes. Although the design of each of the three measures is based on certain assumptions, it is reasonable to conclude that departments that consistently show data disparities separating them from the significant majority of other departments can be recommended for further review and analysis to determine the potential cause for these differences. However, the descriptive benchmarks will also be viewed in conjunction with the other more rigorous statistical tests. 13 I.C: VEIL OF DARKNESS Alternative methods to traditional benchmarking approaches have become increasingly popular because they do not require as restrictive a set of assumptions. The most notable of these approaches draws from an article published in the Journal of the American Statistical Association by Jeffrey Grogger and Greg Ridgeway (Grogger and Rdigeway 2006). The article details a unique and statistically sound methodology for testing racial disparities in the rate of minority traffic stops. The central assumption of this methodology, which has become known as the “Veil of Darkness” (VOD), is that police officers have a more difficult time determining the race and ethnicity of a driver in darkness. In daylight, police are better able to observe race and ethnicity ex-ante. Thus, officers inclined to racially profile motorists are marginally better able to do so during periods of darkness. To control for inherent differences between daylight and darkness, the test relies on quasi-random variation in the timing of sunset and includes a number of control variables. The VOD method evaluates whether there exist statistically significant disparities in the likelihood that a stopped motorists is a minority during daylight relative to darkness. Grogger and Ridgeway (2006) illustrate that under certain conditions this odds-ratio is equivalent to the odds that a minority motorist is stopped during daylight relative to darkness. Satisfying these conditions relies critically on quasi-random variation in the timing of sunset to evaluate the existence of racial disparities thus controlling for differences in day and night motorist behavior and police enforcement activity. As noted, identification comes from the idea that police officers are better able to detect the race and ethnicity of a motorist before making a stop during daylight hours. If they are inclined to exhibit discriminatory behavior, they will be better able to do so in the presence of daylight. The advantage of the VOD methodology relative to population-based benchmarks is that it does not require any assumptions about the underlying risk-set of motorists, just that it does not vary in response to changes in visibility. Further, the framework allows for differential rates of traffic stops to exist across races and the potential for differences in guilt and driving behavior. Let the parameter 𝐾𝑖𝑑𝑒𝑎𝑙 capture the true level of disparate treatment for minority group m relative to majority group w: 𝐾𝑖𝑑𝑒𝑎𝑙 = 𝑃(𝑆 𝑉′, 𝑚)𝑃(𝑆 𝑉, 𝑚) 𝑃(𝑆 𝑉′, 𝑤)𝑃(𝑆 𝑉, 𝑤) (1) The parameter captures the odds that a minority motorist is stopped during perfect visibility (V’) relative to those in complete darkness (V). The parameter 𝐾𝑖𝑑𝑒𝑎𝑙 = 1 in the absence of discrimination and 𝐾𝑖𝑑𝑒𝑎𝑙 > 1 when minority motorists face adverse treatment. Applying Baye’s rule to Equation 1 such that: 𝐾𝑖𝑑𝑒𝑎𝑙 = 𝑃(𝑚 𝑉′, 𝑆)𝑃(𝑤 𝑉, 𝑆) 𝑃(𝑚 𝑉)𝑃(𝑤 𝑉′) ∗ 𝑃(𝑤 𝑉′, 𝑆)𝑃(𝑚 𝑉, 𝑆) 𝑃(𝑤 𝑉)𝑃(𝑚 𝑉′) (2) 14 The first term in 𝐾𝑖𝑑𝑒𝑎𝑙 is the ratio of the odds that a stopped motorist is a minority during daylight relative to the same odds in darkness. Unlike Equation 1 which detailed data on roadway demography, the odds ratio in Equation 2 can be estimated using data on stop outcomes. The second term in 𝐾𝑖𝑑𝑒𝑎𝑙 is a measure of the relative risk-set of motorists on the roadway which captures any differences in the demographic composition of motorists associated with visibility. The second term will be equal unity if the composition of motorists is uncorrelated with solar visibility. Assuming that the risk-set of motorists is uncorrelated with variation in solar visibility, a test statistic for 𝐾𝑖𝑑𝑒𝑎𝑙 is then simply: 𝐾𝑣𝑜𝑑 = 𝑃(𝑚 𝑆, 𝛿 = 1)𝑃(𝑤 𝑆, 𝛿 = 0) 𝑃(𝑤 𝑆, 𝛿 = 1)𝑃(𝑚 𝑆, 𝛿 = 0) (3) Since we do not have continuous data on visibility, the variable 𝛿 is a binary indicator representing daylight. The test statistic 𝐾𝑣𝑜𝑑 will be greater than or equal to the parameter 𝐾𝑖𝑑𝑒𝑎𝑙 and exceed unity if the following conditions hold: 1) 𝐾𝑖𝑑𝑒𝑎𝑙 > 1 ; The true parameter shows that there is a racial or ethnic disparity in the rate of minority police stops. 2) 𝑃(𝑉 𝛿 = 0) < 𝑃(𝑉 𝛿 = 1) ; Darkness reduces the ability of officers to discern the race and ethnicity of motorists. 3) 𝑃(𝑚 𝑉)𝑃(𝑤 𝑉′) 𝑃(𝑤 𝑉)𝑃(𝑚 𝑉′) = 1 ; The relative risk-set is constant across the analysis window. Estimating the test statistic 𝐾𝑣𝑜𝑑 does not provide a quantitative measure for evaluating disparate treatment in policing data but does qualitatively identify the presence of disparate treatment. More concretely, the VOD identifies the presence of a racial or ethnic disparity if the test statistic 𝐾𝑣𝑜𝑑 is greater than one. Given the restrictive nature of the test statistic, it is reasonable (but not conclusive) to attribute the existence of this disparity to racially biased policing practices. Assuming that the assumptions outlined above hold, Equation 4 can be estimated using a logistic regression in the following form: 𝑙𝑛 ( 𝑃(𝑚 𝛿) ) = 𝛽0 + 𝛿 + 𝜇 1 − 𝑃(𝑚 𝛿) (4) In practice, it is unlikely that the third assumption (a constant relative risk-set) will hold without including additional controls in Equation 4. Thus, we amend Equation 4 by including controls for time of day (indicators capturing 15 minute intervals), day of week, and statewide daily traffic stop volume. In estimates using data from all departments across the state, we also include department fixed-effects. The aggregate three-year sample also allows us to include officer fixed-effects. 15 I.C (1): CONSTRUCTING THE INTER-TWILIGHT SAMPLE The VOD analysis requires that periods of darkness and daylight be properly identified. Following Grogger and Ridgeway (2006), the analysis is restricted to stops made within the inter-twilight window- that is, the time between the earliest sunset and latest end to civil twilight. As is shown in Figure 1, civil twilight is defined as the period when the sun is between zero and six degrees below the horizon and where its luminosity is transitioning from daylight to darkness. The motivation for limiting the analysis to the intertwilight window is to help control for possible differences in the driving population. Figure I.C.1: Diagram of Civil Twilight and Solar Variation In this analysis we rely primarily on a combined inter-twilight window that includes traffic stops made at both dawn and dusk. The dawn inter-twilight window is constructed from astronomical data and occurs in the morning hours. The dusk inter-twilight window, on the other hand, is constructed from the same astronomical data but occurs in the evening hours. The combined inter-twilight window relies on a sample that is created by pooling these timeframes and including an additional control variable that identifies the period. The inter-twilight window was identified by attaching astronomical data from the United States Naval Observatory (USNO) to the traffic stop data. As discussed previously, past applications of the VOD have focused on single large urban geographies and have had no need to consider the possibilities of differential astronomical impacts. The definition for both the dawn and dusk inter-twilight windows was amended to accommodate cross-municipal variation by utilizing data from the easternmost (Sterling, CT) and westernmost (Stamford, CT) points available in the USNO data. The USNO data was merged with the policing data and used to identify the presence of darkness. Again, the presence of darkness was the primary explanatory variable used to identify the presence of racial disparities in the Connecticut policing data. As a result, any observation in the data that occurred during twilight on any given day were dropped. The twilight period varied on a daily basis throughout the year 16 and was also identified using the USNO data. Twilight was defined in the dawn inter-twilight window as the time between the daily eastern start of civil twilight and western sunrise. Similarly, twilight was defined in the dusk inter-twilight window as the time between the daily eastern sunset and western end to civil twilight. The full delineation of the policing data is displayed graphically in Figure 2. Figure I.C.2: Delineation of Inter-twilight windows 17 I.D. SYNTHETIC CONTROL MODEL Traditional approaches that rely on population-based benchmarks to evaluate policing data must make a variety of very strong assumptions about the underlying risk-set of motorists. These approaches, despite their flaws, are intuitively appealing because they offer tangible descriptive measures of racial and ethnic disparities. This section presents the results of a synthetic control analysis that has the same intuition as traditional population-based benchmarks but remains grounded in rigorous statistical theory. A synthetic control is a unique benchmark constructed for each individual department using various stop-specific and town-level demographic characteristics as captured through inverse propensity score weighting. The synthetic control is then used to assess the effect of treatment on an outcome variable(s). In the present context, treatment is defined as a traffic stop made by a specific municipal police department and the outcome variable(s) indicates whether a motorist is a racial or ethnic minority.2 In observational studies, as opposed to randomized control trials, it is difficult to estimate the causal effect of treatment. The difficulty emerges because assignment to treatment occurs on a non-random basis and is often confounded with other variables. Regression analysis can accurately estimate the effect of treatment if all possible factors driving treatment are available to the analyst and the model is specified correctly. In reality, however, there are both observed as well as unobserved variables that confound the effect of treatment. These confounding variables create bias that hides the true impact of treatment on the outcome variable. As a result, it becomes difficult to disentangle the effect of treatment from compositional differences in the observed and unobserved variables. The problem of estimating treatment effects arises because unobserved variables affect both selection into treatment and outcome. Weighting the observations by the inverse of the propensity score ensures that the distribution of observable characteristics is consistent between the synthetic control and the department of interest. As long as these observed variables are predictive of unobserved confounders, inverse propensity score weighting allows for an unbiased estimate of the effect of treatment on the outcome variable. In the present context, constructing a synthetic control using inverse propensity score weights allows for an assessment of the whether specific departments are disproportionately stopping minority motorists. This methodology follows a rich and extensive literature spanning the fields of statistics, economics, and public policy. The application of similar methodologies to policing data have recently entered the criminal justice literature through notable applications by McCaffrey et al. (2004), Ridgeway (2006), Ridgeway and MacDonald (2009), and Saunders et al. (2014). I.D (1): CONSTRUCTING THE SYNTHETIC CONTROL Rosenbaum and Rubin (1983) characterize the propensity score as the probability of assignment to treatment conditional on pretreatment variables. The key insight is that conditional on this scalar function, assignment to treatment will be independent of the outcome variable. Simply put, given some observed pretreatment variables, it is possible to identify the conditional probability of treatment. Correctly adjusting for this conditional probability allows for the bias associated with observed covariates to be statistically controlled. If these observed covariates are correlated with unobserved variables, these In the proceeding methodological discussion the details of the estimation procedure are presented as if a single treatment effect were estimated using a single outcome variable. However, the estimates were constructed for each municipal department using four different outcome variables. 2 18 confounding factors will also be controlled for statistically. This methodology allows for a causal interpretation of the difference between outcomes associated with treatment and control. Hirano and Imbens (2001) note that a useful adjustment is to weight observations according to their propensity scores. This adjustment effectively creates a balanced sample among treatment and control observations. Conveniently, when the estimate of interest is the treatment effect on the treated, only potential control observations need to be weighted. In this context, the weight that balances the sample and removes bias associated with pretreatment confounding factors is exactly the inverse of the propensity score. Ridgeway and MacDonald (2009) and Saunders et al. (2014) apply this technique in the context of policing data by matching the joint distribution of a particular officer’s stop features to those by other officers. The analysis proceeds by extending this technique for the purposes of developing synthetic controls of municipal police departments using microdata on police stops in combination with U.S. Census Bureau data on demographic and employment characteristics. We begin using the dataset of k demographic and employment characteristics for county subdivision j in Connecticut. This set of variables also contains characteristics including: the racial and ethnic composition of the town, age and gender demographics, population size, land area, population density, housing characteristics, commuter patterns, employment in retail and entertainment sectors, and the aggregate racial and ethnic composition of all contiguous towns. We then applied principal components analysis to reduce dimensionality and assure orthogonality. Components were selected using Guttman-Kaiser’s stopping rule, which suggests only keeping those with an Eigen value of 1.2 or larger. Formally, the i'th loading factor is simply: arg 𝑚𝑎𝑥 2 𝑤(𝑖) = ‖𝑤‖ = 1 {∑𝑘[𝑤 ∙ 𝑥𝑗 ] }. (5) Indices were then constructed for each component satisfying Guttman-Kaiser’s stopping rule where: 𝑦𝑗,(𝑖) = ∑ 𝑤(𝑖) 𝑥𝑗 (6) 𝑘 Next, we attach the components capturing residential demographic and economic characteristics to the traffic stop data. We then conduct a second principal components analysis using variables from the traffic stop data itself, again to reduce dimensionality and ensure orthogonality. Traffic stop characteristics include time of the day, day of the week, month, department traffic stop volume, officer traffic stop volume, and type of traffic stop. We then estimate propensity scores for each j department using a logistic regression of the form: 𝑙𝑛 ( 𝐹(𝑗) ) = 𝛽0 + ∑ 𝑦𝑗,(𝑖) 1 − 𝐹(𝑗) 𝑖 (7) Propensity score 𝑝𝑗 are used to construct weights that are equal to one for the department of interest (i.e. the treatment group) and equal to 𝑝𝑗 ⁄(1 − 𝑝𝑗 ) for stops made in all other departments. Applying a 19 propensity score weight to stops made by other departments in the state creates a synthetic control group with a comparable distribution of stop-specific and town-level characteristics. The propensity score and resulting weight for those stops with characteristics that are drastically different than stops made by the department of interest will approach zero. As a result, the synthetic control will consist of the stops that are similar, in terms of stop-specific and town-level characteristics, to those made by the department of interest. The construction of a synthetic control group using propensity scores allows the comparison to reflect the average treatment effect on the treated and abstract from potential bias in so far as the observable covariates control for selection into treatment. Hirano and Imbens (2001) extend the weighting framework to what Robins and Ritov (1997) refer to as doubly robust estimation. That is, including additional covariates to a semi-parametric least-squares regression model enables capture of a more precise estimate of the treatment effect. It is shown in both of these discussions that such an estimator is consistent if either of the models is specified correctly. Ridgeway and MacDonald (2009) further extend the doubly robust propensity score framework to policing data. Specifically, the authors look at whether the department of interest deviates from the synthetic control along the outcome dimension. Here, we provide estimates with and without so called doublyrobust estimation of treatment effects. Treatment effects are estimated using a logistic regression of the form: 𝑙𝑛 ( 𝑝𝑗 𝐹(𝑚) )=( ) (𝛽0 + 𝑡(𝑗) + ∑ 𝑦𝑗,(𝑖) ) 1 − 𝐹(𝑚) 1 − 𝑝𝑗 𝑖 (8) If a particular department is designated as a treatment to a group of stops, it follows that the outcome of interest would be motorist race. The question is then simply, does the intervention by a particular department result in a relatively higher stop rate of minority motorists, controlling for all observable factors? Combining inverse propensity score weighting with regression analysis allows for a more precise answer to this question. In the circumstance where the synthetic control and individual department do not perfectly match along all dimensions of stop features, there is potential for bias in any comparison, especially if those features by which they differentiate relate to a motorist’s race. Doubly robust estimation helps to remove this source of potential bias by controlling for these features, resulting in a much more accurate department effect. The share of minority motorists stopped within a department was evaluated through a direct comparison with a unique synthetic control. 20 I.E. KPT HIT-RATE MODEL Analysis conducted using post-stop variables has historically been seen as favorable to benchmarks because it does not rely on any assumptions about the underlying risk-set. The focus on post-stop analysis has, however, decreased since the VOD was developed to accomplish these same feats with pre-stop data. The disadvantage of post-stop analysis is the small sample size when considering vehicular searches. In many cases, one is unable to estimate the model at the department-level because of this issue. Knowles, Persico, and Todd (2001) present a behavior-based model for testing and identifying disparate treatment in police searches. The model incorporates rational motorist behavior, with respect to driving with contraband, and optimal officer response. The testable implication derived from this model is that the equilibrium search strategy, in the absence of group bias, will result in an equalization of the rate of contraband that is found relative to the total number of searches (i.e. the hit-rate) across motorist groups. Knowles et al. (2001) outline a testable hypothesis and use a nonparametric test, the Pearson 𝛸 2 test, to evaluate their hypothesis. Since its initial presentation in the Journal of Political Economy, the test outlined by Knowles et al. that has subsequently become known as a test of the hit-rate test, has been applied widely across the nation. I.E (1): CONSTRUCTING THE HIT-RATE TEST The logic of the hit-rate test follows from a simplified game theoretic exposition. In the absence of disparate treatment, the costs of searching different groups of motorists are equal. Police officers make decisions to search in an effort to maximize their expectations of finding contraband. The implication being that police will be more likely to search a group that has a higher probability of carrying contraband, i.e. participate in statistical discrimination. In turn, motorists from the targeted demography understand this aspect of police behavior and respond by lowering their rate of carrying contraband. This iterative process continues within demographic groups until, in equilibrium, it is expected that an equalization of hit-rates across groups is found. Knowles et al. introduce disparate treatment via search costs incurred by officers that differ across demographic groups. An officer with a lower search cost for a specific demographic group will be more likely to search motorists from that group. The result of this action will be an observable increase in the number of targeted searches for that group. As above, the targeted group will respond rationally and reduce their exposure by carrying less contraband. Eventually, the added benefit associated with a higher probability of finding contraband in the non-targeted group will offset the lower cost of search for that group. As a result, one would expect the hit-rates to differ across demographic groups in the presence of disparate treatment. Knowles et al. (2001) developed a theoretical model with testable implications that can be used to evaluate statistical disparities in the rate of searches across demographic groups. Following Knowles et al. an empirical test of the null hypothesis (that no racial or ethnic disparity exists) in Equation 9 is presented. 𝑃(𝐻 = 1 𝑚, 𝑆) = 𝑃(𝐻 = 1 𝑆 ) ∀ 𝑟, 𝑐 (9) 21 Equation 9 computes the probability of a search resulting in a hit across different demographic groups. If the null hypothesis was true and there was no racial or ethnic disparity across these groups, one would expect the hit-rates across minority and non-minority groups to reach equilibrium. As discussed previously, this expectation stems from a game-theoretic model where officers and motorists optimize their behaviors based on knowledge of the other party’s actions. In more concrete terms, one would expect motorists to lower their propensity to carry contraband as searches increase while officers would raise their propensity to search vehicles that are more likely to have contraband. Essentially, the model allows for statistical discrimination but finds if there is bias-based discrimination. An important cautionary note about hit-rate tests related to an implicit infra-marginality assumption. Specifically, several papers have explored generalizations and extensions of the framework and found that, in certain circumstances, empirical testing using hit-rate tests can suffer from the infra-marginality problem as well as differences in the direction of bias across officers (see Antonovics and Knight 2004; Anwar and Fang 2006; Dharmapala and Ross 2003). Knowles and his colleagues responded to these critiques with further refinements of their model that provide additional evidence of its validity (Persico and Todd 2004). Although the results from a hit-rate analysis help contextualize post-stop activity within departments, the results should only be considered as supplementary evidence. 22 PART II: TRAFFIC STOP ANALYSIS AND FINDINGS, 2015- 16 23 II.A: CHARACTERISTICS OF TRAFFIC STOP DATA This section examines general patterns of traffic enforcement activities in Connecticut for the study period of October 1, 2015 to September 30, 2016. Statewide and agency activity information can be used to identify variations in traffic stop patterns to help law enforcement and local communities understand more about traffic enforcement. Although some comparisons can be made between similar communities, we caution against comparing agencies’ data in this section of the report. Please note that the tables included in this report present information from only a limited number of departments. Complete tables for all agencies are included in the technical appendix. In Connecticut, more than 560,000 traffic stops were conducted during the 12-month study period. Almost 63% of the total stops were conducted by the 93 municipal police departments, 36% of the total stops were conducted by state police, and the remaining 1% of stops were conducted by other miscellaneous policing agencies. Figure II.A.1 shows the aggregate number of traffic stops by month along with each demographic category. As can be seen below, the volume of traffic stops has a seasonal variation pattern. However, the proportion of minority stops remained relatively consistent across the year. Figure II.A.1: Aggregate Traffic Stops by Month of the Year 70000 Aggregate Traffic Stops 60000 50000 40000 30000 20000 10000 Black Hispanic Sep-16 Aug-16 Jul-16 Jun-16 May-16 Apr-16 Mar-16 Feb-16 Jan-16 Dec-15 Nov-15 Oct-15 0 All Other Stops Figure II.A.2 displays traffic stops by time of day for the entire analysis period. As can be seen from the figure, the total volume of traffic stops fluctuates significantly across different times of the day. The highest hourly volume of traffic stops in the sample occurred from five to six in the evening and accounted for 7.3% of all stops. It is not surprising that the volume of traffic stops increases between these hours as this is a peak commuting time in Connecticut. The lowest volume of traffic stops occurred between four and five in the morning and continued at a suppressed level during the morning commute. The low level of traffic stops during the morning commute is likely due to an interest in maintaining a smooth flow of 24 traffic during these hours. Discretionary traffic stops might be less likely to be made during these hours relative to others in the sample. The evening commute, in contrast to the morning commute, represents a period when a significant proportion of traffic stops are made. The surge seen between the hours of four and seven at night represents the most significant period of traffic enforcement. In aggregate, stops occurring between these hours represented 19.5% of total stops. Interestingly, there seems to be a significant correlation between the proportion of minority stops and the overall volume of stops. In particular, the share of Hispanic and Black stops increase when the total volume of stops increase. Figure II.A.2: Aggregate Traffic Stops by Time of Day 45000 40000 35000 30000 25000 20000 15000 10000 5000 Black Hispanic 11-12:00 AM 10-11:00 PM 9-10:00 PM 8-9:00 PM 7-8:00 PM 6-7:00 PM 5-6:00 PM 4-5:00 PM 3-4:00 PM 2-3:00 PM 1-2:00 PM 12-1:00 PM 11-12:00 PM 10-11:00 AM 9-10:00 AM 8-9:00 AM 7-8:00 AM 6-7:00 AM 5-6:00 AM 4-5:00 AM 3-4:00 AM 2-3:00 AM 1-2:00 AM 12-1:00 AM 0 All Other Stops Figure II.A.3 illustrates the average number of traffic stops by month for municipal police agencies and the state police. The data illustrates a fairly stable pattern of municipal traffic stop enforcement with the average number of traffic stops ranging from 276 to 451 each month for each agency. State police traffic stops are less stable by month relative to the municipal departments and range from a low of 1096 to a high of 1675. This may be due to the nature of state police traffic enforcement activity that fluctuates for a variety of reasons including enforcement campaigns around the holidays. 25 Figure II.A.3: Average Number of Traffic Stops by Month for Police Agencies 1800 1600 1400 1200 1000 800 600 400 200 State Police Troop Sep-15 Aug-15 Jul-15 Jun-15 May-15 Apr-15 Mar-15 Feb-15 Jan-15 Dec-14 Nov-14 Oct-14 0 Municipal Police The level of and reason for traffic stop enforcement varies greatly across agencies throughout the state for a number of reasons. For example, some enforcement is targeted to prevent accidents in dangerous areas, combat increased criminal activity, or respond to complaints from citizens. Those agencies with active traffic units produce a higher volume of traffic stops. The rate of traffic stops per 1,000 residents in the population helps to compare the stop activity between agencies. The five municipal police agencies with the highest stop rate per 1,000 residents are Wilton, New Canaan, Ridgefield, Orange, and Old Saybrook. Conversely, Middlebury, Shelton, Portland, Wolcott and Bridgeport have the lowest rate of stops per 1,000 residents. Table II.A.1 shows the distribution of stops for the highest and lowest level of enforcement per 1,000 residents for police agencies. 26 Table II.A.1: Municipal Police, Highest and Lowest Rates of Traffic Stops Town Name Connecticut 16+ Population* Traffic Stops Stops per 1,000 Residents 2,825,946 558,036 197 Municipal Departments with the Highest Rate of Traffic Stops Wilton 12,973 6,020 464 New Canaan 14,138 6,445 456 Ridgefield 18,111 7,979 441 Orange 11,017 4,295 390 Old Saybrook 8,330 3,142 377 Ansonia 14,979 5,110 341 Berlin 16,083 5,257 327 Monroe 14,918 4,625 310 Waterford 15,760 4,874 309 Westport 19,410 5,964 307 Municipal Departments with the Lowest Rate of Traffic Stops Middlebury 5,843 59 10 Shelton 32,010 740 23 Portland 7,480 199 27 Wolcott 13,175 376 29 Bridgeport** 109,401 3,118 29 Waterbury 83,964 3,208 38 Middletown** 38,747 1,616 42 Meriden 47,445 2,055 43 Stratford 40,980 1,957 48 Hartford** 93,669 4,505 48 * The population 16 years of age and older was obtained from the United States Census Bureau 2010 Decennial Census. **Bridgeport, Middletown, and Hartford did not report an indeterminate number of traffic stops. Please see the note to the reader on page xvi. Table II.A.2 presents some basic demographic data on persons stopped in Connecticut between October 1, 2015 and September 30, 2016. Nearly two-thirds (63.1%) of drivers stopped were male and the vast majority of drivers (85.3%) were Connecticut residents. Of the stops conducted by police departments other than state police, 89.2% were Connecticut residents. Of the stops made by state police, 78.3% were Connecticut residents. About one-third (38%) of drivers stopped were under the age of 30 compared to 27 23% over 50. The vast majority of stops in Connecticut were White Non-Hispanic drivers (69.2%);14.7% were Black Non-Hispanic drivers; 13.1% were Hispanic drivers; and 3.0% were Asian/Pacific Islander NonHispanic and American Indian/Alaskan Native Non-Hispanic drivers. Table II.A.2: Statewide Driver Characteristics Race and Ethnicity White Gender All Other Races 63.1% Connecticut Resident 85.3% 14.7% 16 to 20 8.2% 21 to 30 29.7% 31 to 40 20.7% 41 to 50 17.8% 51 to 60 14.5% Older than 61 8.9% 3.0% Female Hispanic Age 69.2% Male Black Residency 13.1% 36.9% Nonresident 14.7% Table II.A.3 presents data on the characteristics of the traffic stops in the state. Most traffic stops were made for a violation of the motor vehicle laws (89%) as opposed to a stop made for an investigatory purpose. The most common violation drivers were stopped for was speeding (28.5%). After a driver was stopped, almost half (45.3%) were given a ticket while most of the remaining drivers received some kind of a warning (47.5%). The rate of tickets versus warnings differs greatly among communities and is a topic that is discussed later in this report. Statewide, less than 1% of traffic stops resulted in a Uniform Arrest Report and only 3.0% of stops resulted in a vehicle search. 28 Table II.A.3: Statewide Stop Characteristics Classification of Stop Motor Vehicle Violation Equipment Violation Investigatory Outcome of Stop Uniform Arrest Report Misdemeanor Summons Infraction Ticket Written Warning Verbal Warning No Disposition Vehicles Searched 89.4% 8.8% 1.8% 0.9% 4.8% 45.3% 16.1% 31.4% 1.6% 3.0% Basis for Stop Speeding Cell Phone Registration STC Violation Defective Lights Misc. Moving Violation Traffic Control Signal Stop Sign Seatbelt Display of Plates All Other 28.5% 9.5% 9.1% 8.5% 8.4% 8.3% 6.9% 6.7% 3.8% 2.5% 7.8% In addition to the difference in the volume of traffic stops across communities, agencies stopped drivers for a number of different reasons. Police record the statutory reason for stopping a motor vehicle for every stop. Those statutes are then sorted into 15 categories from speeding to registration violation to stop sign violation. For example, all statutory violations that are speed related are categorized as speeding. Although speeding is the most often cited reason for stopping a motor vehicle statewide, the results vary by jurisdiction. Table II.A.4 shows the top 10 departments where speeding (as a percentage of all stops) was the most common reason for the traffic stop. Table II.A.4: Highest Speeding Stop Rates across All Departments Department Name Ledyard Suffield Simsbury Easton Portland New Milford Enfield Guilford Redding Ridgefield Total Stops 1,300 1,336 3,868 712 199 2,791 7,904 4,270 2,023 7,979 Speeding Violations 67.9% 60.8% 56.9% 55.9% 55.3% 54.9% 53.5% 53.0% 52.4% 52.3% The average municipal police department stops for speeding violations was 28.3% compared to the state police average of 32.4%. Due to the nature of state police highway operations, it is reasonable that its average for speeding is higher. In Ledyard, Suffield, Simsbury, Easton, Portland, New Milford, Enfield, Guilford, Redding, Ridgefield, Groton Long Point, and Wolcott, more than 50% of the traffic stops were for speeding violations. On the other hand, Yale University, Western Connecticut State University and the State Capitol Police stopped drivers for speeding less than 5% of the time. The three special police agencies (Yale, WCSU, and State Capitol Police) have limited jurisdiction and it is reasonable that they are not stopping a high percentage of drivers for speeding violations. Registration violations have been cited as a low discretion reason for stopping a motor vehicle, particularly due to the increased use of license plate readers to detect registration violations. Statewide, 9.1% of all traffic stops are for a registration violation. Table II.A.5 presents the top 10 departments with the highest percentage of stops for registration violations. 29 Table II.A.5: Highest Registration Violation Rates across All Departments Department Name Branford North Branford Troop L Trumbull Watertown Troop G Troop B West Haven Troop A Redding Total Stops 4,435 1,089 11,017 2,340 1,698 21,411 8,094 6,127 19,136 2,023 Registration Violations 28.3% 23.1% 21.1% 19.0% 17.3% 16.9% 16.7% 16.7% 15.9% 15.8% The Connecticut Department of Transportation and the National Highway Safety Administration work together every year to fund a variety of different driver safety campaigns. Some of the campaigns that we are most familiar with include: “Click it or Ticket,” “Drive Sober or get Pulled Over,” and “Move Over.” Each year law enforcement agencies receive federal grants to fund targeted traffic safety campaigns. Over the past few years there has been an increase in federal funding for distracted driver campaigns. This past year, Connecticut continued to see a significant increase in distracted driving related traffic stops. Stops as the result of a cell phone violation are the second most common reason for stopping a driver. Statewide, 9.5% of all stops were the result of a cell phone violation and this rate varies across departments. Table II.A.6 presents the top 10 departments with the highest percentage of stops for cell phone violations. Table II.A.6: Highest Cell Phone Violation Rates across All Departments Department Name Hamden Danbury Middlebury West Hartford Stamford Berlin Bridgeport* Westport Norwalk Brookfield Total Stops 3,767 5,907 59 9,079 5,519 5,257 3,118 5,964 4,191 2,299 Cell Phone Violations 41.9% 41.2% 28.8% 28.3% 27.1% 25.3% 24.7% 24.5% 22.1% 19.8% Bridgeport did not report an indeterminate number of traffic stops. Please see the note to the reader on page xvi. Some Connecticut residents have expressed concern about the stops made for violations that are perceived as more discretionary in nature; therefore potentially making the driver more susceptible to possible police bias. Those stops are typically referred to as pretext stops and might include stops for defective lights, excessive window tint, or a display of plate violation each of which, though a possible violation of state law, leaves the police officer with considerable discretion with respect to actually making the stop. A statewide combined average for stopping drivers for any of these violations is 12.3%. Sixtytwo municipal police departments exceeded that statewide average. The departments with the highest percentage of stops conducted for these violations are Newington (34.9%), Plymouth (34.7%), Torrington (33.7%), UCONN (33.2%), and Middletown (31.3%). 30 In communities with a larger proportion of stops due to these violations, it is recommended that the departments be proactive in discussing the reasons for these stops with members of the community and examine for themselves whether or not such stops produce disparate enforcement patterns. Many have argued that it is difficult for police to determine the defining characteristics about a driver prior to stopping and approaching the vehicle. Similar to variations found across departments for the reason for the traffic stop, there are variations that occur with the outcome of the stop. These variations illustrate the influence that local police departments have on the enforcement of state traffic laws. Some communities may view infraction tickets as the best method to increase traffic safety, while others may consider warnings to be more effective. This analysis should help police departments and local communities understand their level and type of traffic enforcement when compared to other communities. Almost half (45%) of drivers stopped in Connecticut received an infraction ticket, while 47% received either a written or verbal warning. Individual jurisdictions varied in their post-stop enforcement actions. Danbury issued infraction tickets in 68% of all traffic stops, which is the highest in the state. Eastern Connecticut State University only issued infraction tickets in 2.3% of all traffic stops, which is the lowest rate in the state. For state police, officers not assigned to a troop issued the highest infractions (88%) and Troop L issued the lowest number of infractions (46%). Table II.A.7 presents the highest infraction rates across all departments. Table II.A.7: Highest Infraction Rates across All Departments Department Name Danbury Bridgeport* Norwalk Meriden New Haven Hartford* Derby Branford Stamford Hamden CSP Headquarters Troop F Troop C Troop H Troop G Total Stops Highest Municipal Departments 5,907 3,118 4,191 2,055 19,099 4,505 3,021 4,435 5,519 3,767 Highest State Police Troops 11,486 22,009 21,804 17,932 21,411 Infraction Ticket 67.6% 61.9% 59.7% 58.6% 56.6% 56.0% 54.9% 54.3% 52.9% 52.6% 87.8% 78.9% 74.2% 73.4% 71.5% *Bridgeport and Hartford did not report an indeterminate number of traffic stops. Please see the note to the reader on page xvi. On the other hand, Eastern Connecticut State University issued warnings 95% of the time (the highest rate) and Danbury issued warnings 28% of the time (the lowest rate). For state police, Troop L issued the highest percentage of warnings (43%) and the group of officers not assigned to a troop issued the lowest percentage of warnings (7.2%). Table II.A.8 presents the highest warning rates across all departments. 31 Table II.A.8: Highest Warning Rates across All Departments Department Name Eastern CT State University Redding Middlebury Portland Torrington Putnam Plainfield Suffield Weston Central CT State University Troop L Troop B Troop D Troop K Troop A Total Stops Highest Municipal Departments 128 2,023 59 199 6,527 1,094 1,740 1,336 491 2,092 Highest State Police Troops 11,017 8,094 14,877 17,769 19,136 Resulted in Warning 95.3% 92.8% 91.5% 91.0% 89.8% 87.8% 87.2% 87.0% 87.0% 86.3% 43.0% 37.0% 30.9% 29.0% 27.0% Statewide, less than 1% of all traffic stops resulted in the driver being arrested. As with infraction tickets and warnings, municipal departments varied in the percentage of arrests associated with traffic stops. The Wallingford Police Department issued the most uniform arrest reports from a traffic stop, with 4.6% of all stops resulting in an arrest. West Hartford, Waterbury and Hartford arrested more than 3% of all drivers stopped. The variation in arrest rates for state police is much smaller across troop levels. Table II.A.9 presents the highest arrest rates across all departments. Table II.A.9: Highest Arrest Rates across All Departments Department Name Wallingford West Hartford Waterbury Hartford* New London Groton Town Stratford Middletown* Meriden East Haven Total Stops 8,980 9,079 3,208 4,505 4,120 4,431 1,957 1,616 2,055 3,512 Arrests 4.6% 3.4% 3.3% 3.0% 3.0% 2.7% 2.6% 2.0% 1.9% 1.8% *Middletown and Hartford did not report an indeterminate number of traffic stops. Please see the note to the reader on page xvi. Rarely do traffic stops in Connecticut result in a vehicle being searched. During the study period, only 3.0% of all traffic stops resulted in a search. Although searches are rare in Connecticut, they do vary across jurisdictions and the data provides information about enforcement activity throughout the state. When they search a vehicle, officers must report the supporting legal authority, and whether contraband was found. Forty-five departments exceeded the statewide average for searches, but the largest disparity was found in Waterbury (16.6%), Stratford (13.6%), and Middletown (10.4%). Of the remaining departments, 17 searched vehicles more than 5% of the time, 26 searched vehicles between 3% and 5% of the time, and the remaining departments searched vehicles less than 2% of the time. No state police troops exceeded 32 the statewide average for searches. The highest search rate was in Troop L (2.3%). Table II.A.10 presents the highest search rates across all departments. Table II.A.10: Highest Searches Rates across All Departments Department Name Waterbury Stratford Middletown* Bridgeport* Vernon Yale University Danbury Wallingford Derby Trumbull Troop L Troop G Troop H Troop C Troop A Total Stops Highest Municipal Departments 3,208 1,957 1,616 3,118 4,104 380 5,907 8,908 3,021 2,340 Highest State Police Troops 11,017 21,411 17,932 21,804 19,136 Resulted in Search 16.6% 13.6% 10.4% 9.8% 9.4% 9.2% 8.5% 7.9% 7.9% 7.5% 2.3% 2.2% 2.1% 2.1% 1.9% Bridgeport and Middletown did not report an indeterminate number of traffic stops. Please see the note to the reader on page xvi. 33 II.B: DESCRIPTIVE STATISTICS AND INTUITIVE MEASURES The descriptive statistics and benchmarks presented in this section are an excellent first step to understand patterns in Connecticut policing data. Although these simple statistics present an intriguing story, conclusions should not be drawn from these measures. The three statistical tests of racial and ethnic disparities in the policing data are based solely on the policing data itself and rely on the construction of a theoretically derived identification strategy and a natural experiment. These results have been applied by academic and police researchers in numerous areas across the country and are generally considered to be the most current and relevant approaches to assessing policing data. II.B (1): STATEWIDE AVERAGE COMPARISON In this section there are identifications for each of the three categories (Black, Hispanic, and Minority) in the towns for which the statewide average comparison indicated the largest distances between the net stop percentage and net resident population using 10 or more points as a threshold. Tables showing the calculations for all of the towns, rather than just those showing distance measures of more than 10 points, can be found in the Appendix to this report. Readers should note that this section focuses entirely on towns that exceeded the statewide average for stops in these racial groups. Comparison of Black Drivers to the State Average For the study period from October 1, 2015 through September 30, 2016, the statewide percentage of drivers stopped by police who were identified as Black was 14.6%. A total of 27 departments stopped a higher percentage of Black drivers than the state average, 10 of which exceeded the statewide average by more than 10 percentage points. The statewide average for Black residents (16+) is 9.1%. Of the 27 towns that exceeded the statewide average for Black drivers stopped, 17 also have Black resident populations (16+) that exceeded the statewide average. After the stop and resident population percentages were adjusted using the method described above, a total of six towns were found to have a relative distance between their net Black driver stop percentage and net Black population percentage of more than 10 points. These were Stratford, Orange, Trumbull, East Hartford, and Woodbridge. Table II.B.1 shows the data for these six towns. Results for all departments can be found in the Appendix of this report. Each of the six towns has at least one contiguous town with a resident Black population that exceeds the state average. Stratford and Trumbull border Bridgeport; Woodbridge borders three such towns (New Haven, Hamden, and Ansonia); and Orange borders New Haven and West Haven. In three of the six towns—Orange, Trumbull, and Woodbridge-- more than 90% of the Black drivers who were stopped were not residents of the town. The statewide average for stopped Black drivers who were not residents of the town in which they were stopped was 56%. 34 Table II.B.1: Statewide Average Comparisons for Black Drivers for Selected Towns Municipal Department Black Stops Stratford Orange Trumbull East Hartford Woodbridge Wethersfield Connecticut 31.2% 19.4% 20.7% 39.6% 18.6% 18.7% 14.6% Difference Between Town and State Average 16.6% 4.8% 6.1% 25.0% 4.0% 4.1% 0.0% Black Residents Age 16+ 12.8% 1.3% 2.9% 22.5% 1.9% 2.8% 9.1% Difference Between Town and State Average 3.6% -7.8% -6.2% 13.4% -7.2% -6.4% 0.0% Distance Between Net Differences 13.0% 12.6% 12.4% 11.6% 11.2% 10.4% NA Nonresident Black Stops 61.1% 98.6% 93.8% 46.3% 98.3% 80.6% 56.0% Comparison of Hispanic Drivers to the Statewide Average For the study period from October 1, 2015 through September 30, 2016, the statewide percentage of drivers stopped by police who were identified as Hispanic was 13%. A total of 30 towns stopped a higher percentage of Hispanic drivers than the state average, ten of which exceeded the statewide average by more than 10 percentage points. Six of the 30 departments exceeded the statewide average by 1.5 percentage points of less. The statewide Hispanic resident population (16+) is 11.9%. The ratio of stopped Hispanic drivers to Hispanic residents (16+) on a statewide basis was slightly higher (13.0% Hispanic drivers’ stopped/11.9% Hispanic residents). Of the 30 towns that exceeded the statewide average for Hispanic drivers stopped, 16 also have Hispanic resident populations (16+) that exceeded the statewide average, although Stratford’s Hispanic population exceeded the average by only 0.01%. After the stop and resident population percentages were adjusted using the method described above, a total of five towns were found to have a relative distance between their net Hispanic driver stop percentage and net Hispanic population percentage of more than 10 points. The five towns were Wethersfield, Darien, Newington, Wolcott, and Wilton. The Berlin Police Department fell just below the 10-point threshold. Table II.B.2 shows the data for the towns named above. All agency data can be found in the Appendix of this report. All five towns that have a relative difference between their net Hispanic driver stop percentage and net Hispanic population percentage of more than 10 points have at least one contiguous town with a resident Hispanic population (16 +) that exceeds the state average. Each of the following three towns borders two such towns: Wethersfield (Hartford and East Hartford), Darien (Stamford and Norwalk), and Newington (Hartford and New Britain); and the following two towns border one such town: Wolcott (Waterbury) and Wilton (Norwalk). In three of the top five towns- Darien, Wolcott and Wilton- more than 90% of the Hispanic drivers stopped were not residents of the town. The nonresident stop rate for Hispanic drivers in Newington was 85%. The statewide average for stopped Hispanic drivers who were not residents of the town in which they were stopped was 57.8%. 35 Table II.B.2: Statewide Average Comparisons for Hispanic Drivers for Selected Towns Municipal Department Wethersfield Darien Newington Wolcott Wilton Connecticut Hispanic Stops 28.1% 18.4% 20.7% 16.5% 14.0% 13.0% Difference Between Town and State Average 15.1% 5.4% 7.7% 3.4% 1.0% 0.0% Hispanic Residents Age 16+ 7.1% 3.5% 6.4% 2.8% 2.7% 11.9% Difference Between Town and State Average -4.8% -8.4% -5.5% -9.1% -9.2% 0.0% Distance Between Net Differences 19.9% 13.8% 13.2% 12.5% 10.1% NA NonResidents Hispanic Stops 72.5% 96.7% 84.5% 93.6% 94.4% 57.8% Comparison of Minority Drivers to the State Average The final category involves all drivers classified as “Minority.” This Minority category includes all racial classifications except for white drivers. Specifically it covers Blacks, Hispanics, Asian/Pacific Islander, American Indian/Alaskan Native, and Other Race classifications included in the census data. For the study period from October 1, 2015 through September 30, 2016, the statewide percentage of stopped drivers who were identified as Minority was 30.6%. A total of 30 towns stopped a higher percentage of Minority drivers than the state average, 17 of which exceeded the state average by more than 10 percentage points. The statewide average for Minority residents (16+) was 25.2%. Of the 30 towns that exceeded the statewide average for Minority drivers stopped, 20 also have Minority resident populations (16 +) that exceeded the statewide average. After the stop resident population percentages were adjusted using the method described above, a total of 13 towns were found to have a relative distance between their net Minority driver stop percentage and net Minority driving age population percentage of more than 10 points. Table II.B.3 shows the data for these 13 towns. The complete data for all towns can be found in the Appendix to this report. All but three of the towns have at least one contiguous town with a resident Minority driving age population that exceeds the state average, including West Hartford and Woodbridge with three such towns. Wethersfield, Newington, Trumbull, Orange, Berlin and Darien border two such towns. East Hartford, Wolcott, Stratford, Wilton, and Fairfield border one such town. Ten of the 13 towns reported more than 80% of the stops of Minority drivers involved nonresidents. East Hartford reported approximately 45% nonresidents among the Minority drivers stopped which was the lowest of the 13. The statewide average for stopped Minority drivers who were not residents of the town in which they were stopped was 57.2%. 36 Table II.B.3: Statewide Average Comparisons for Minority Drivers for Selected Towns Municipal Department Wethersfield Stratford Trumbull Darien Orange Newington Fairfield Wolcott Berlin Wilton West Hartford East Hartford Woodbridge Connecticut Minority Stops 48.4% 53.4% 37.4% 32.3% 34.7% 37.6% 30.8% 25.5% 25.6% 27.6% 39.9% 69.2% 29.9% 30.6% Difference Between Town and State Average 17.8% 22.8% 6.8% 1.7% 4.1% 7.0% 0.2% -5.1% -5.1% -3.0% 9.3% 38.6% -0.7% 0.0% Minority Residents Age 16+ 12.5% 27.2% 6.8% 7.2% 10.8% 14.5% 10.0% 5.4% 5.8% 8.1% 21.8% 51.6% 12.8% 25.2% Difference Between Town and State Average -12.8% 2.0% -13.3% -18.1% -14.5% -10.7% -15.2% -19.8% -19.5% -17.1% -3.4% 26.4% -12.4% 0.0% Distance Between Net Differences 30.6% 20.8% 20.1% 19.7% 18.6% 17.7% 15.4% 14.7% 14.4% 14.2% 12.8% 12.2% 11.7% NA NonResidents Minority Stops 75.5% 62.1% 92.8% 95.1% 96.9% 84.1% 92.8% 90.6% 91.4% 93.3% 85.4% 45.4% 95.2% 57.2% Special Police Departments This section briefly discusses the data from those special police departments whose stop data exceeded the statewide averages for Black, Hispanic, or Minority drivers. It is important to note that currently there is no effective method for benchmarking the data from these special departments due to their operations’ unique characteristics. However, since many of these departments are situated in urban environments, the population demographics for the municipalities which host them can serve as a proxy benchmark, provided it is viewed with caution. Conclusions should not be drawn for these departments until appropriate benchmarks have been determined. In the following six special departments, stops for Black drivers exceeded the statewide average: (1) State Capitol Police (22.5%), (2) Central Connecticut State University (18.7%), (3) Department of Motor Vehicle (17.1%), (4) Mashantucket Pequot Police (14.9%), (5) Southern Connecticut State University (59.9%), and (6) Yale University (35.0%). The State Capitol Police made only 222 stops and the Mashantucket Pequot Police made 215 stops which is marginal with respect to yielding valid percentage distributions. The remaining four agencies made a sufficient number of stops to yield valid percentage distributions. With regard to Hispanic drivers, four special departments exceeded the statewide average for Hispanic stops: (1) Western Connecticut State University (35.0%), (2) State Capitol Police (18.5%), (3) Central Connecticut State University (16.3%), and (4) Yale University (13.4%). Western Connecticut State University did not conduct a sufficient number of stops to yield a valid percentage. Yale University exceeded the statewide average by an insignificant amount (less than 0.5%) and none of the agencies yielded disparities when applied to the host town’s population. Lastly, six special departments exceeded the statewide average for all Minority stops: (1) Central Connecticut State University (37.4%), (2) Southern Connecticut State University (72.4%), (3) Yale University (55.3%), (4) State Capitol Police (44.1%), (5) Western Connecticut State University (45.0%), and (6) Mashantucket Pequot Police (36.7%). Western Connecticut State University did not conduct a 37 significant number of stops to yield a valid percentage. When compared to the demographics of the host town the results show no disparities. While several special departments exceeded the statewide stop average for drivers in one or more of the three demographic categories, only the stops made by the Southern Connecticut State University (SCSU) police department involving Black drivers is worth noting. While this data shows a disparity above the 10point threshold applied to municipal departments when using the New Haven demographics as a proxy benchmark, it should be viewed differently due to the relatively small number of stops made by SCSU and the comparison to the New Haven demographic data. This finding is consistent with the results of last year’s analysis. It is suggested that the SCSU data involving Black stops continue to be monitored and that the department review its data to determine any factors that may be influencing these numbers. II.B (2): ESTIMATED DRIVING POPULATION COMPARISON The EDP analysis was confined to the 93 municipal police departments in Connecticut. There are 80 municipalities in Connecticut that either (1) do not have their own departments and rely upon the state police for their law and traffic enforcement services or (2) have one or more resident state troopers who either provide their police services or supervise local constables or law enforcement officers. Most of these communities are smaller and located in Connecticut’s more rural areas. Once the state police stops made on limited access highways were removed from the data, we found that these towns generally had too few stops during the 6am to 10am and 3pm to 7pm periods to yield meaningful comparisons. Consequently, these towns were not considered appropriate candidates for the EDP analysis. The only traffic stops included in this analysis were stops conducted Monday through Friday from 6:00am to 10:00am and 3:00pm to 7:00pm (peak commuting hours). Overall, when compared to their respective EDP, 74 departments had a disparity between the Minorities stopped and the proportion of non-whites estimated to be in the EDP. For many of these departments (30) the disparity was very small (less than five percentage points). In the remaining 19 communities, the disparity was negative, meaning that more whites were stopped than expected in the EDP numbers. However, the negative disparities were also very small in most communities. There were 86 departments with a disparity for Black drivers stopped and 66 departments with a disparity for Hispanic drivers stopped when compared to the respective EDPs. Due to the margins of error inherent in the EDP estimates, we established a reasonable set of thresholds for determining if a department shows a disparity in its stops when compared to its EDP percentages. Departments that exceed their EDP percentages by greater than 10 percentage points in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic, were identified in our tier one group. In addition, departments that exceeded their EDP percentage by more than five but less than 10 percentage points were identified in our tier two group for this benchmark if the ratio of the percentage of stops for the target group compared to the baseline measure for that group also was 1.75 or above (percentage of stops divided by benchmark percentage equals 1.75 or more) in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, or (3) Hispanic. 38 Table II.B.4: Highest Ratio of Stops to EDP (Tier I) Department Name Number of Stops Wethersfield East Hartford Darien New Britain Trumbull Windsor Stratford Hartford Wolcott New Haven Fairfield Newington Manchester West Hartford Orange 791 3,419 1,354 2,162 710 1,457 385 1,777 168 8,350 4,171 1,343 4,486 3,344 1,485 East Hartford Windsor Hartford Wethersfield New Haven Trumbull Bloomfield Hamden Stratford Bridgeport Manchester Norwich 3,419 1,457 1,777 791 8,350 710 1,055 2,012 385 1,175 4,486 1,385 Wethersfield New Britain Darien 791 2,162 1,354 Stops EDP Minority (All Non-White) 43.2% 16.6% 66.3% 40.0% 35.3% 15.9% 56.6% 38.9% 35.5% 18.2% 49.7% 33.2% 43.6% 27.9% 64.7% 50.1% 22.6% 8.2% 60.6% 46.3% 29.9% 17.5% 30.5% 19.0% 37.6% 26.7% 35.0% 24.1% 30.4% 19.5% Black 37.1% 17.0% 35.2% 20.1% 35.8% 21.6% 18.1% 4.9% 35.6% 22.6% 18.2% 5.9% 43.0% 31.2% 27.7% 16.1% 23.6% 12.1% 37.7% 26.5% 21.1% 9.9% 18.3% 7.5% Hispanic 23.5% 8.7% 39.0% 26.0% 20.0% 8.0% Absolute Difference Ratio 26.6% 26.2% 19.4% 17.7% 17.3% 16.5% 15.8% 14.7% 14.4% 14.3% 12.4% 11.5% 11.0% 10.9% 10.9% 2.60 1.66 2.22 1.45 1.95 1.50 1.57 1.29 2.77 1.31 1.71 1.60 1.41 1.45 1.56 20.1% 15.2% 14.2% 13.2% 13.0% 12.3% 11.9% 11.6% 11.5% 11.2% 11.2% 10.8% 2.19 1.76 1.66 3.68 1.57 3.09 1.38 1.72 1.95 1.42 2.13 2.43 14.9% 13.0% 12.0% 2.71 1.50 2.50 39 Table II.B.5: High Ratio of Stops to EDP (Tier II) Department Name Redding Portland Plymouth Number of Stops 694 38 600 Orange Darien Fairfield Ledyard Woodbridge Middletown South Windsor Groton Town North Haven Derby Watertown Avon Vernon Cromwell Newington 1,485 1,354 4,171 431 586 308 1,296 1,028 1,125 838 601 232 867 385 1,343 Wolcott Newington Redding Trumbull New Canaan New Milford Ridgefield 168 1,343 694 710 2,305 1,059 3,076 Stops EDP Minority (All Non-White) 16.9% 7.6% 13.2% 7.0% 10.5% 4.6% Black 16.2% 6.3% 12.8% 3.6% 14.1% 5.3% 13.0% 4.3% 13.3% 4.8% 17.9% 9.7% 12.8% 5.8% 12.1% 5.5% 12.4% 6.3% 12.8% 6.7% 8.7% 3.0% 9.1% 3.5% 10.6% 5.3% 10.9% 5.6% 10.6% 5.5% Hispanic 13.7% 4.3% 17.4% 8.9% 10.7% 4.0% 14.8% 8.3% 12.0% 6.4% 11.4% 6.2% 11.8% 6.7% Absolute Difference Ratio 9.3% 6.2% 5.9% 2.23 1.88 2.28 9.9% 9.2% 8.9% 8.7% 8.5% 8.1% 7.1% 6.6% 6.2% 6.1% 5.6% 5.6% 5.3% 5.3% 5.1% 2.58 3.58 2.68 3.05 2.79 1.84 2.22 2.21 1.98 1.90 2.85 2.61 2.00 1.94 1.91 9.4% 8.5% 6.7% 6.5% 5.6% 5.2% 5.1% 3.16 1.96 2.67 1.78 1.88 1.83 1.77 II.B (3): RESIDENT ONLY STOP COMPARISON Overall, when compared to the census, 69 departments stopped more Minority resident drivers than white drivers. Again, the disparity for many of these departments was very small. In the remaining 24 communities, the disparity was negative, meaning that more whites were stopped than expected based on the population numbers. However, the negative disparities were also very small in most communities. Almost all departments (88 of 93) had a disparity for Black drivers stopped and 55 departments had a disparity for Hispanic drivers stopped when compared to the resident driving age population. Departments with a difference of 10 percentage points or more between the resident stops and the 16+ resident population in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic, were identified in our tier one group. In addition, departments that exceeded their resident population percentage by more than five but less than 10 percentage points were identified in our tier two group for this benchmark if the ratio of the percentage of resident stops for the target group compared to the baseline measure for that group also was 1.75 or above(percentage of stopped residents divided by resident benchmark percentage equals 1.75 or more) in any of three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic. 40 Table II.B.6: Highest Ratio of Resident Population to Resident Stops (Tier I) Department Name Number of Residents East Hartford Willimantic Wethersfield New Britain Windsor Bloomfield Waterbury Stratford Norwich New Haven New London Meriden Derby Danbury Manchester Middletown Hamden Vernon Bristol Cheshire Norwalk 40,229 20,176 21,607 57,164 23,222 16,982 83,964 40,980 31,638 100,702 21,835 47,445 10,391 64,361 46,667 38,747 50,012 23,800 48,439 21,049 68,034 Windsor Bloomfield East Hartford New Haven Norwich Waterbury Hamden Stratford Manchester Middletown Norwalk Vernon 23,222 16,982 40,229 100,702 31,638 83,964 50,012 40,980 46,667 38,747 68,034 23,800 Willimantic Wethersfield Danbury New Britain Meriden 20,176 21,607 64,361 57,164 47,445 Resident Minority Stops Resident Stops Minority (All Non-White) 51.63% 3,832 75.21% 34.55% 1,165 57.85% 12.47% 1,052 35.27% 45.00% 4,709 66.51% 43.92% 1,878 62.94% 61.51% 1,049 80.46% 48.10% 2,177 65.96% 27.20% 888 44.59% 29.09% 3,043 45.97% 62.82% 11,123 79.28% 43.57% 1,786 59.80% 34.86% 1,430 50.56% 20.56% 499 35.67% 38.64% 1,330 52.26% 27.95% 5,598 41.55% 23.49% 1,509 36.91% 30.92% 1,539 44.05% 14.05% 1,700 26.29% 12.71% 2,273 24.68% 8.62% 4,288 19.85% 40.80% 1,720 50.93% Black 32.20% 1,878 52.40% 54.76% 1,049 74.83% 22.52% 3,832 42.30% 32.16% 11,123 49.39% 8.96% 3,043 26.06% 17.37% 2,177 34.08% 18.28% 1,539 34.70% 12.76% 888 26.80% 10.15% 5,598 23.78% 11.68% 1,509 23.79% 13.13% 1,720 24.01% 4.70% 1,700 15.18% Hispanic 28.88% 1,165 50.04% 7.10% 1,052 22.91% 23.25% 1,330 38.42% 31.75% 4,709 46.74% 24.86% 1,430 36.92% Residents Difference Ratio 23.58% 23.30% 22.80% 21.51% 19.02% 18.95% 17.86% 17.40% 16.88% 16.46% 16.23% 15.70% 15.12% 13.62% 13.60% 13.42% 13.14% 12.24% 11.97% 11.22% 10.13% 1.46 1.67 2.83 1.48 1.43 1.31 1.37 1.64 1.58 1.26 1.37 1.45 1.74 1.35 1.49 1.57 1.42 1.87 1.94 2.30 1.25 20.20% 20.07% 19.79% 17.23% 17.10% 16.71% 16.42% 14.05% 13.62% 12.11% 10.88% 10.48% 1.63 1.37 1.88 1.54 2.91 1.96 1.90 2.10 2.34 2.04 1.83 3.23 21.16% 15.80% 15.17% 14.99% 12.06% 1.73 3.22 1.65 1.47 1.49 41 Table II.B.7: High Ratio of Resident Population to Resident Stops (Tier II) Department Name Number of Residents Enfield Portland Clinton 33,218 7,480 10,540 Derby Cheshire Wethersfield Ledyard East Windsor Ansonia Groton City* Enfield Bristol Danbury Groton Town Cromwell 10,391 21,049 21,607 11,527 9,164 14,979 7,960 33,218 48,439 64,361 31,520 11,357 Bristol Cheshire 48,439 21,049 Resident Minority Stops Resident Stops Minority (All Non-White) 8.65% 6,291 17.88% 4.63% 75 12.00% 6.12% 2,288 12.28% Black 6.03% 499 15.23% 1.27% 4,288 9.63% 2.75% 1,052 10.74% 3.10% 386 10.88% 5.96% 206 13.59% 9.74% 2,009 17.37% 7.70% 440 15.00% 2.63% 6,291 9.51% 3.24% 2,273 10.07% 6.42% 1,330 12.86% 6.07% 1,706 12.49% 3.69% 431 9.51% Hispanic 7.65% 2,273 13.46% 2.35% 4,288 7.98% Residents Difference Ratio 9.23% 7.37% 6.16% 2.07 2.59 2.01 9.20% 8.36% 7.99% 7.78% 7.63% 7.63% 7.30% 6.87% 6.84% 6.43% 6.42% 5.82% 2.52 7.56 3.91 3.51 2.28 1.78 1.95 3.61 3.11 2.00 2.06 2.58 5.81% 5.63% 1.76 3.39 II.B (4): CONCLUSIONS FROM THE DESCRIPTIVE COMPARISONS The descriptive tests outlined in the above sections are designed to be used as a screening tool to identify those jurisdictions with consistent data disparities that exceed certain thresholds. The tests compare stop data to three different benchmarks: (1) statewide average, (2) the estimated driving population, and (3) resident-only stops that each cover three driver categories: Black, Hispanic, and Minority. Town data is then measured against the resulting total of nine descriptive measures for evaluation purposes. In order to weight the disparities within the descriptive benchmarks, any disparity greater than 10 percentage points for a measure was given a weight of one (1) point. Any disparity of more than five, but less than 10 percentage points accompanied by a disparity ratio of 1.75 or above was given a weight of 0.5 points. Therefore, a department could score no more than nine (9) total points. Table III.B.8 identifies the 10 towns with significant disparities divided into two tiers. The first tier includes the five jurisdictions whose stop data was found to exceed the disparity threshold levels in at least two of the three benchmark areas and a weighted total score of 4.5 or more. This designation warrants additional study to further review the data and attempt to understand the factors that may be causing these differences. It is also recommended that these departments, as well as those included in the second tier of the table, evaluate their own data to try and better understand any patterns. The second tier of Table II.B.8 shows the five departments that exceeded the disparity threshold in two of the three benchmark areas, but only scored a four (4) out of a possible nine (9) points. In all of these departments there were disparities in at least two of the three benchmark areas. All of the departments 42 that were identified in the descriptive analysis with benchmark disparities and the actual values that exceeded the threshold level are included in the Appendix of the report. Table II.B.8: Departments with the Greatest Number of Disparities Relative to Descriptive Benchmarks Department Name Statewide Average M B H Estimated Driving Population M B Resident Population H M B Point Total H Tier 1 Wethersfield 30.6 10.4 East Hartford 12.2 Stratford 20.8 Darien 19.7 Trumbull 20.1 19.9 26.6 13.2 11.6 26.2 13.0 13.8 12.4 14.9 22.8 8.0 15.8 8.5 20.1 23.6 19.8 6.0 15.8 11.5 17.4 14.1 6.0 19.4 9.2 12.0 4.5 17.3 12.3 6.5 4.5 Tier 2 New Britain 17.7 Manchester 11.0 11.2 13.6 13.6 4.0 New Haven 14.3 13.0 16.5 17.2 4.0 11.5 5.1 16.5 15.2 Newington Windsor 17.7 13.2 13.0 21.5 15.0 8.5 4.0 4.0 19.0 20.2 4.0 Note 1: M=Minority, B=Black, H=Hispanic (Numbers of 10 or above yield one point, numbers less than 10 equal 0.5 points) 43 II.C: ANALYSIS OF TRAFFIC STOPS, VEIL OF DARKNESS II.C. (1): ANNUAL STATE-LEVEL RESULTS FOR THE VEIL OF DARKNESS, 2015-16 Table II.C.1 presents the results from the VOD applied at the state-level during the combined inter-twilight window for the most recent year of data collection beginning in October 2015 and ending in September 2016. These results were estimated using Equation 4 with the standard errors being clustered at the department-level. The estimates include controls for time of day, day of week, dusk inter-twilight window, statewide stop volume, and department fixed-effects. The estimates again use four definitions of minority status relative to white non-Hispanics and are annotated accordingly. As shown below, estimation using annual sample indicates a statistically significant disparity for Hispanic motorists as well as the combined sample black and Hispanic motorists. Table II.C.1: Logistic Regression of Minority Status on Daylight with Department Fixed-Effects, All Traffic Stops 2015-16 LHS: Minority Status Daylight Non-Caucasian Black Hispanic Black or Hispanic Coefficient -0.014 -0.006 0.077** 0.043* Standard Error (0.029) (0.033) (0.032) (0.023) 136,464 132,008 130,271 151,973 Effective Sample Size Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department fixed-effects. Note 3: Sample includes all traffic stops made during the inter-twilight window from October 2015 to September 2016. Table II.C.2 presents results for the municipal and State Police subsamples departments during the combined inter-twilight window. As before, the results include controls for time of day, day of week, dusk inter-twilight window, statewide stop volume, and department fixed-effects. Standard errors are clustered at the requisite department-level. As shown in the topmost panel, the results for municipal police departments indicate a marginally significant disparity for Hispanic motorists alone. The lower panel contains results for the aggregate sample of State Police troops which shows a high level of statistical significance for the black alone sample, Hispanic alone, and combined sample. As discussed in the context of the three-year analysis, the State Police disparity does seem to be driving most of the effect observed in the full sample. 44 Table II.C.2: Logistic Regression of Minority Status on Daylight, Municipal and State Police Traffic Stops 2015-16 LHS: Minority Status Non-Caucasian Black Hispanic Black or Hispanic Municipal Departments Daylight Coefficient -0.032 -0.020 0.069* 0.030 Standard Error (0.038) (0.043) (0.041) (0.030) 87,876 85,464 84,152 99,995 0.054* 0.100*** 0.120*** -0.044 -0.033 -0.038 -0.033 46,716 44,775 44,463 49,960 Effective Sample Size Daylight Coefficient State Police Troops 0.034 Standard Error Effective Sample Size Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department fixed-effects. Note 3: Sample includes all traffic stops made by municipal departments during the inter-twilight window from October 2015 to September 2016. As mentioned, these estimates aggregate all traffic stops in the state and should be considered an average effect across all departments from 2015 to 2016. Although the results from this section find a statistically significant disparity in the rate of minority traffic stops in Connecticut, these results do not identify the geographic source of this variation or rule out the possibility of issues within specific departments. The results of a department-level analysis are presented in a later section and better identify the source of specific department-wide disparities. II.C. (2): ANNUAL STATE-LEVEL ROBUSTNESS FOR THE VEIL OF DARKNESS, 2015-16 This section presents a robustness check on the initial specifications conducted at the state-level using a restricted sample of moving violations. Table II.C.3 presents results applied at the state-level during the combined inter-twilight window to a subsample of moving violations. As before, these results were estimated using Equation 4 with the standard errors being clustered at the department-level. The estimates include controls for time of day, day of week, dusk inter-twilight window, statewide stop volume, and department fixed-effects. As shown below, estimation using this restricted sample indicates a statistically significant disparity for both the combined black and Hispanic group as well as the Hispanic alone group. Table II.C.3: Logistic Regression of Minority Status on Daylight with Department Fixed-Effects, All Moving Violations 2015-16 LHS: Minority Status Daylight Coefficient Standard Error Effective Sample Size Non-Caucasian Black Hispanic Black or Hispanic 0.012 0.029 0.386*** 0.065** (0.035) (0.034) (0.13) (0.026) 82,634 79,560 78,049 89,732 Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department fixed-effects. Note 3: Sample includes moving violations made during the inter-twilight window from October 2015 to September 2016. 45 Table II.C.4 presents the results for subsamples of municipal departments and State Police troops during the combined inter-twilight window. As before, the results include controls for time of day, day of week, dusk inter-twilight window, and statewide stop volume. In the topmost panel, the results for municipal police departments indicate a marginally significant disparity for Hispanic motorists alone. The lower panel contains results for the aggregate sample of State Police troops which shows a highly significant disparity for the Hispanic alone and combine black and Hispanic sample. As noted in previous sections, the results for State Police appear to be driving the overall statewide effect. Table II.C.4: Logistic Regression of Minority Status on Daylight, Municipal and State Police Traffic Stops All Moving Violations 2015-16 LHS: Minority Status Non-Caucasian Black Hispanic Black or Hispanic Municipal Departments Daylight Coefficient -0.013 0.02 0.066* 0.05 Standard Error -0.285 -0.045 -0.039 -0.035 49,771 48,314 47,473 55,157 Coefficient State Police Troops 0.118 0.084* 0.111** 0.164*** Standard Error -0.077 -0.049 -0.045 -0.048 31,723 30,163 29,576 33,348 Effective Sample Size Daylight Effective Sample Size Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department fixed-effects. Note 3: Sample includes all traffic stops made by municipal departments during the inter-twilight window from October 2015 to September 2016. The results presented in the state-level analysis provide strong evidence that a disparity exists in the rate of minority traffic stops by both municipal and State Police departments in the 2015 to 2016 sample. The level of significance remains relatively consistent for both groups when the sample is reduced to only moving violations. Thus, we conclude that these results are relatively robust and that the State Police disparity is likely driving much of the overall statewide disparity. In the preceding section, the test will be applied to individual municipal departments and State Police troops using the 2015-16 data. II.C. (3): ANNUAL DEPARTMENT-LEVEL RESULTS FOR THE VEIL OF DARKNESS, 201516 As before, Equation 4 is estimated independently for each municipal department and State Police troop. Each set of estimates includes a vector of town-specific fixed-effects for time of day, day of week, year, dusk inter-twilight window, and statewide stop volume. Here, we identify all departments found to have a disparity that is statistically significant at the 95 percent level in either the Hispanic or Black alone minority group. The full set of results can be found in Appendix Table II.C.5.1 of the Appendix while results restricting the sample to moving violations are in Appendix Table II.C.5.2. Again, we annotate departments that did not withstand the scrutiny of the more rigorous moving violation specification. Table II.C.5 presents the results from estimating the VOD test statistic for individual departments using the 2015-16 sample. There were 10 municipal departments and one State Police troops found to have a disparity that was statistically significant at the 95 percent level in the black or Hispanic categories. As noted, the disparities in these departments did not all persist through more restrictive specifications with only 46 moving violation. In total, the disparity only persisted for six municipal departments and one State Police troop. Table II.C.5: Logistic Regression of Minority Status on Daylight for Select Departments, All Traffic Stops 2015-16 Department Berlin Meriden Monroe New Haven+ Newtown Norwich Old Saybrook+ VOD Estimate Non-Caucasian Black Hispanic Black or Hispanic Coefficient 1.101*** 1.224*** 0.518** 0.715*** SE (0.317) (0.37) (0.237) (0.209) ESS 1,291 1,232 1,327 1,474 Coefficient 1.009** 0.958** 0.209 0.305 SE (0.445) (0.447) (0.29) (0.264) ESS 332 328 443 501 Coefficient -0.440* -0.344 0.527** 0.133 SE (0.25) (0.277) (0.261) (0.197) ESS 1,393 1,347 1,353 1,489 Coefficient 0.063 0.069 0.206*** 0.122* SE (0.071) (0.072) (0.077) (0.065) ESS 6,073 5,961 4,678 8,076 Coefficient 0.064 0.205 0.820*** 0.520** SE (0.281) (0.338) (0.265) (0.217) ESS 1,493 1,457 1,482 1,614 Coefficient -0.331** -0.237 0.443*** 0.072 SE (0.146) (0.153) (0.17) (0.123) ESS 1,648 1,592 1,510 1,861 Coefficient 0.995** 1.184*** -0.268 0.177 SE (0.437) (0.442) (0.378) (0.302) ESS 868 734 942 1,009 0.524* 0.285 0.905*** 0.685*** SE (0.3) (0.367) (0.288) (0.234) ESS 1,681 1,533 1,740 1,841 Coefficient 0.632 1.155 2.320** 1.457** SE (0.702) (1.156) (1.061) (0.737) ESS 261 183 113 294 Coefficient 0.543*** 0.473** 0.194 0.296** SE (0.188) (0.196) (0.161) (0.133) ESS 2,060 2,032 2,171 2,394 Coefficient 0.186 -0.021 0.701** 0.362 SE (0.29) (0.357) (0.305) (0.24) ESS 1,916 1,790 1,797 2,014 Coefficient Ridgefield Stonington+ Wallingford+ CSP Troop B Note 1: The coefficients are presented along with robust standard errors. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. 47 Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), and volume. Note 3: Sample includes all traffic stops made during the inter-twilight window from October 2015 to September 2016. + Results are not robust across subsequent specifications. The results presented in the state-level analysis provide strong evidence that a disparity exists in the rate of minority traffic stops in each of the departments in Table II.C.5. As noted previously, only a select number of these persisted through the additional robustness checks contained in the Appendix. Although it is impossible to determine whether the robustness checks invalidated the findings in Table II.C.5 or whether they simply created power issues by reducing the overall sample size. Again, we note that it is impossible to clearly link the observed disparities to racial profiling as these differences may be driven by any combination of policing policy, heterogeneous enforcement patterns, or individual officer actions. 48 II.D. ANALYSIS OF TRAFFIC STOPS, SYNTHETIC CONTROL II.D. (1): ANNUAL DEPARTMENT-LEVEL SYNTHETIC CONTROL ANALYSIS, 2015-16 As before, each individual municipal police department and State Police troop was examined by weighting observations with inverse propensity scores estimated from Equation 7 and treatment effects are estimated using Equation 8. We identify all departments found to have a disparity that is statistically significant at the 95 percent level in either the Hispanic or Black alone minority group. The full set of results for all departments can be found in Table II.D.1.1 of the Appendix. Although we do not use doublyrobust estimation here, Appendix Table II.D.1.2 contains results with this more rigorous specification. Note that significantly more departments are identified in these estimates than those using doubly-robust estimation which indicates that in some departments, the results fail on balance. Thus, we present results here for departments identified using the less rigorous specification but only confidently identify those that withstand the more rigorous approach. Table II.D.1 presents the results from estimating treatment effects of individual departments relative to their requisite synthetic control using most recent year of traffic stop data. There were 23 municipal departments and three State Police troops observed to have a statistically significant at the 95 percent level for black or Hispanic motorists. As noted, the disparities in these departments did not persist through the more rigorous doubly-robust estimation. In total, there were eight municipal departments and one State Police troops that withstood doubly-robust estimation. As noted previously, only a select number of these persisted through the additional robustness checks contained in the Appendix. Although it is impossible to determine whether these robustness checks invalidated the findings in Table II.D.1 or whether a balanced synthetic control is simply not able to be created, we annotate the results for those departments and caution against any undue interpretation. Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2015-16 Department Estimate Non-Caucasian Black Hispanic Black or Hispanic 0.053 0.122** 0.605*** 0.437*** SE (0.051) (0.058) (0.056) (0.043) ESS 119,471 119,471 119,471 119,471 Coefficient -1.257*** -1.447*** 50.029*** -1.122*** SE (0.065) (0.073) (0.057) (0.049) ESS 118,565 118,565 118,565 118,565 Coefficient 1.240*** 1.350*** 0.716*** 0.710*** SE (0.035) (0.035) (0.064) (0.036) ESS 91,547 91,547 91,547 91,547 Coefficient 0.037 0.081 1.027*** 1.459*** SE (0.068) (0.072) (0.111) (0.064) ESS 53,160 53,160 53,160 53,160 Coefficient Berlin+ Bethel+ Bloomfield Cromwell+ 49 Department Estimate Non-Caucasian Black Hispanic Black or Hispanic 0.033 0.140 0.233** 0.199** SE (0.099) (0.107) (0.118) (0.086) ESS 351,048 351,048 351,048 351,048 Coefficient 0.466*** 0.394*** -0.387*** 0.065 SE (0.076) (0.084) (0.107) (0.07) ESS 57,145 57,145 57,145 57,145 -0.458*** -0.432*** 0.648*** 0.219*** SE (0.061) (0.063) (0.049) (0.045) ESS 351,048 351,048 351,048 351,048 Coefficient 0.431*** 0.495*** 5.101*** 0.258*** SE (0.06) (0.061) (0.079) (0.054) ESS 103,475 103,475 103,475 103,475 Coefficient -0.847*** -0.814*** 5.872*** 3.074*** SE (0.049) (0.051) (0.045) (0.035) ESS 104,913 104,913 104,913 104,913 Coefficient 0.805*** 2.452*** -0.664*** -0.837*** SE (0.073) (0.084) (0.11) (0.087) ESS 34,553 34,553 34,553 34,553 Coefficient 0.395*** 0.434*** 4.279*** 0.153*** SE (0.041) (0.043) (0.046) (0.036) ESS 79,410 79,410 79,410 79,410 1.777*** 7.204*** 4.012*** -0.479*** SE (0.09) (0.093) (0.092) (0.069) ESS 96,896 96,896 96,896 96,896 Coefficient 0.332*** 0.254*** 1.038*** -4.428*** SE (0.055) (0.06) (0.065) (0.044) ESS 169,531 169,531 169,531 169,531 Coefficient 0.902*** 10.340*** 0.893*** 0.323*** SE (0.137) (0.146) (0.139) (0.105) ESS 108,174 108,174 108,174 108,174 Coefficient -0.080 1.819*** 1.766*** -0.568*** SE (0.11) (0.071) (0.089) (0.095) ESS 119,046 119,046 119,046 119,046 0.037 0.002 10.242*** -0.646*** SE (0.103) (0.105) (0.057) (0.102) ESS 128,834 128,834 128,834 128,834 Coefficient Groton City Ledyard+ Coefficient Meriden Middletown Naugatuck+ New Milford+ Orange Coefficient Plymouth+ Rocky Hill+ Shelton+ Simsbury+ Coefficient Stratford+ 50 Department Trumbull+ Estimate Non-Caucasian Black Hispanic Black or Hispanic Coefficient 0.324*** 0.408*** 0.692*** 0.618*** SE (0.098) (0.104) (0.137) (0.09) ESS 155,526 155,526 155,526 155,526 0.234 0.268 0.826*** 0.246 SE (.) (.) (0.054) (.) ESS 139,197 139,197 139,197 139,197 Coefficient -0.156*** -0.089** 0.259*** 0.118*** SE (0.037) (0.039) (0.036) (0.029) ESS 351,048 351,048 351,048 351,048 Coefficient -0.274*** 5.761*** -4.366*** 0.112 SE (0.088) (0.086) (0.088) (0.075) ESS 62,431 62,431 62,431 62,431 Coefficient -0.113** -0.026 0.815*** 0.548*** SE (0.048) (0.05) (0.046) (0.04) ESS 68,568 68,568 68,568 68,568 -35.100*** 0.374** -0.794*** -1.001*** SE (0.154) (0.161) (0.177) (0.131) ESS 57,989 57,989 57,989 57,989 Coefficient -0.024 0.038 0.596*** 0.204* SE (0.156) (0.166) (0.142) (0.123) ESS 351,048 351,048 351,048 351,048 Coefficient 1.411*** 1.211*** 2.122*** 1.786*** SE (0.449) (0.466) (0.693) (0.418) ESS 54,479 54,479 54,479 54,479 Coefficient 0.237*** 0.288*** 0.155*** 0.284*** SE (0.026) (0.028) (0.031) (0.023) ESS 198,126 198,126 198,126 198,126 15.579*** -0.377*** 24.948*** -0.240*** SE (0.035) (0.043) (0.036) (0.032) ESS 198,126 198,126 198,126 198,126 Coefficient Vernon+ Wallingford Watertown+ Wethersfield Coefficient Winsted+ Wolcott CSP Troop G+ CSP Troop I Coefficient CSP Troop L+ Note 1: The coefficients are presented along with robust standard errors. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: Propensity scores were estimated using principal components analysis of traffic stop characteristics as well as Census data selected using the Kaiser-Guttman stopping rule. Traffic stop characteristics include time of the day, day of the week, month, department traffic stop volume, officer traffic stop volume, and type of traffic stop. Census demographics for both the primary and border towns include retail employment, entertainment employment, commuting population, vacant housing, rental housing, median earnings, population density, gender, age, race, and ethnicity. Note 3: Sample includes all traffic stops made by the primary department and an inverse propensity score weighted sample of all other departments from October 2013 to September 2016. + Results are not robust across subsequent specifications. 51 II.E. ANALYSIS OF VEHICULAR SEARCHES, KPT HIT-RATE II.E (1): ANNUAL STATE-LEVEL HIT-RATE ANALYSIS, 2015-16 The analysis begins by aggregating all search data for Connecticut by demography and performing the non-parametric test of hit-rates. The rate that searches, defined as both consent and other searches, that end in contraband being found for white non-Hispanic motorists is compared to each minority subgroup. Table II.E.1 presents hit-rates for all searches in Connecticut by demography for the 2015 to 2016 sample of searches. The results of this test can be seen in Table II.E.1 for four distinct minority definitions. As seen below, the rate of successful searches for white non-Hispanic motorists was 39.9 percent from 2015 to 2016. Relative to white non-Hispanic motorists, the hit-rate for each of the four minority subgroups was lower and ranged from 34 to 34.3 percent. The differences in hit-rates for each group was statistically significant at the 99 percent level. In aggregate, Connecticut police departments exhibit a strong tendency to be less successful in motorist searches across all minority groups. Table II.E.1: Chi-Square Test of Hit-Rate, All Consent and Other Searches 2015-16 Variable: White Non-White Black Hispanic Black or Hispanic Hit-Rate 39.9% 34.1%*** 34.0%*** 34.3%*** 34.2%*** N/A 37.69 37.638 27.797 48.287 7,384 4,026 3,870 2,869 6,602 Chi^2 ESS Note: Sample includes all consent and probable cause searches from October 2015 to September 2016. Table II.E.2 provides the results of a hit-rate analysis for the aggregate municipal department and State Police subgroups. The hit-rate in municipal departments for white non-Hispanic motorists was 39.3 percent from 2015 to 2016. Relative to white non-Hispanic motorists, the hit-rate for each of the four minority subgroups was lower and ranged from 34.2 to 35.2 percent. These differences were statistically significant at the 99 percent level. Similarly, the aggregate hit-rate for all State Police was 42.2 percent for white non-Hispanic motorist. Relative to white non-Hispanic motorists, the hit-rate for each of the four minority subgroups was lower and ranged from 30.9 to 34.3 percent. As before, each minority group had a lower rate of successful searches that were again statistically significant at the 99 percent level. Table II.E.2: Chi-Square Test of Hit-Rate, Municipal and State Police Consent and Other Searches 2015-16 Variable: White Non-White Black Hispanic Black or Hispanic Municipal Departments 39.3% 34.2%*** 34.2%*** 35.2%*** 34.7%*** N/A 22.111 21.523 11.272 24.707 ESS 5,584 3,167 3,058 2,287 5,257 Hit-Rate 42.2% 30.9%*** 33.0%*** Hit-Rate Chi^2 Chi^2 ESS State Police Troops 34.3%*** 33.8%*** N/A 14.341 15.526 22.428 25.763 1,704 808 766 551 1,268 Note: Sample includes all consent and probable cause searches from October 2015 to September 2016. 52 II.E (2): STATE-LEVEL ROBUSTNESS FOR HIT-RATE ANALYSIS, 2015-16 Table II.E.3 presents a robustness check on the initial specifications conducted at the state-level using a restricted sample of consent searches, i.e. excluding other searches. In this more restrictive subsample, the rate of successful searches for white non-Hispanic motorists was 30.9 percent from 2015 to 2016. Across each of the minority subgroups, the rate of successful searches was significantly lower ranging from 20.9 to 22.5 percent. The differences in hit-rates was statistically significant at the 99 percent level. The results of this robustness check confirm the initial set of estimates using both probable cause and consent searches. Table II.E.3: Chi-Square Test of Hit-Rate, All Consent Searches 2015-16 Variable: Hit-Rate Chi^2 ESS White 30.9% Non-White 21.2%*** Black 20.9%*** Hispanic 22.5%*** Black or Hispanic 21.6%*** N/A 54.048 56.111 31.645 67.112 2,996 1,828 1,767 1,305 3,009 Note: Sample includes all consent searches from October 2015 to September 2016. Table II.E.4 presents a robustness check on the subgroups of municipal departments and State Police using a more restrictive sample of consent searches. As seen below, the rate of successful searches made by municipal departments for white non-Hispanic was 29.9 percent from 2015 to 2016. Relative to white non-Hispanic motorists, the hit-rate for each of the four minority subgroups was lower and ranged from 19.5 to 22 percent. The difference in the rate of successful searches for each of these groups was statistically significant at the 99 percent level. For the State Police subgroup, the rate of successful searches for white non-Hispanic motorists was 32.1 percent. Relative to this group, the rate of successful searches for each minority subgroup was lower and ranged from 22.9 to 25.7 percent. As before, the difference in hit-rates was statistically significant at the 95 percent level. Table II.E.4: Chi-Square Test of Hit-Rate, Municipal and State Police Consent Searches 2015-16 Variable: White Non-White Black Hispanic Black or Hispanic Municipal Departments Hit-Rate Chi^2 ESS Hit-Rate 29.9% 19.7%*** 19.5%*** 22.0%*** 20.4%*** N/A 44.592 46.164 21.163 51.881 2,050 1,399 1,361 1,006 2,330 22.9%** 24.9%** 32.1% State Police Troops 25.7%** 25.5%** Chi^2 N/A 5.315 5.496 8.96 9.479 ESS 901 404 384 293 651 Note: Sample includes all consent searches from October 2015 to September 2016. II.E (3): ANNUAL DEPARTMENT-LEVEL HIT-RATE ANALYSIS, 2015-16 In this subsection, differences in hit-rates are estimated independently for each municipal department and State Police troop in 2015 to 2016. As before, we identify all departments found to have a disparity that is statistically significant at the 95 percent level in either the Hispanic or Black alone minority group. 53 The full set of results can be found in Appendix Table II.E.5.1 of the Appendix while results restricting the sample to just consent searches are in Appendix Table II.E.5.2. As in previous sections, we annotate departments that did not withstand the scrutiny of the more rigorous consent search specification. Table II.E.5 presents the results from estimating the hit-rate test for individual departments. There were six municipal departments and four State Police troops found to have a disparity in the hit-rate of minority motorists relative to white non-Hispanic motorists which was statistically significant at the 95 percent level. As noted, the disparity in these departments did not persist through more restrictive specifications that limited the sample to consent searches. In total, the disparity persisted through one municipal departments and one State Police troops. Table II.E.5: Chi-Square Test of Hit-Rate in Select Departments, All Consent and Probable Cause Searches 2015-16 East Hartford+ Hartford+ Monroe+ Newington+ Vernon+ West Hartford CSP Troop A+ White Non-White Black Hispanic Black or Hispanic 55.7% 46.7% 46.7% 40.8%** 44.3%** N/A 2.543 2.56 5.294 4.562 115 259 255 125 377 White Non-White Black Hispanic Black or Hispanic 35.7% 12.8%** 12.8%** 18.6% 15.6%* N/A 3.843 3.843 1.756 3.297 14 47 47 43 90 White Non-White Black Hispanic Black or Hispanic 42.9% 8.3%** 8.3%** 50% 21.1% N/A 4.878 4.878 0.139 2.697 42 12 12 8 19 White Non-White Black Hispanic Black or Hispanic 49.3% 20.9%*** 23.1%*** 38.1% 30.9%** N/A 9.182 7.281 1.355 5.464 73 43 39 42 81 White Non-White Black Hispanic Black or Hispanic 64.1% 49.3%** 49.3%** 48.8%* 49.5%** N/A 4.958 4.868 3.452 6.454 223 71 69 41 109 White Non-White Black Hispanic Black or Hispanic 78.5% 57.3%*** 58.3%*** 60.3%*** 59.3%*** N/A 17.968 15.574 14.912 23.116 321 103 96 121 216 White Non-White Black Hispanic Black or Hispanic 42.7% 20.3%*** 19.7%*** 32% 26%*** N/A 12.225 12.441 2.583 10.078 192 79 76 75 146 54 CSP Troop G CSP Troop K+ CSP Troop L+ White Non-White Black Hispanic Black or Hispanic 35.9% 25.4%** 23.7%** 23.8%** 24%** N/A 4.25 5.667 4.406 6.466 131 205 194 122 308 White Non-White Black Hispanic Black or Hispanic 44.7% 27.3%** 22.4%*** 27.6%* 25%*** N/A 5.005 7.558 2.89 8.136 141 55 49 29 76 White Non-White Black Hispanic Black or Hispanic 44.6% 53.6% 50% 20.8%** 36.7% N/A 0.794 0.272 4.914 0.969 184 28 26 24 49 Note: Sample includes all consent and probable cause searches from October 2015 to September 2016. 55 II.F: FINDINGS FROM THE 2015-2016 ANALYSIS This section represents a summary of the findings from the one year analysis of traffic stops conducted October 1, 2015 to September 30, 2016. II.F (1): AGGREGATE FINDINGS FOR CONNECTICUT 2015-2016 A total of 14.7% of motorists stopped during the analysis period were observed to be Black. A comparable 13.1% of stops were of motorists of Hispanic descent. The results presented in the state-level Veil of Darkness analysis provide strong evidence that a disparity exists in the rate of minority traffic stops by both municipal and State Police departments in the 2015 to 2016 sample. The level of significance remains relatively consistent for both groups when the sample is reduced to only moving violations. This, we conclude that these results are relatively robust and that the State Police disparity is likely driving much of the overall statewide disparity. Again, it is impossible to clearly link these observed disparities to racial profiling as these differences may be driven by any combination of policing policy, heterogeneous enforcement patterns, or individual officer behavior. The results from the post-stop analysis confirm that the disparity carries through to post-stop behavior across all racial and ethnic groups. In aggregate, Connecticut police departments exhibit a strong tendency to be less successful in motorist searches across all minority groups. II.F (2): VEIL OF DARKNESS ANALYSIS FINDINGS, 2015-2016 Although there is evidence of a disparity at the state level, it is important to note that it is likely that specific departments are driving these statewide trends. In an effort to better identify the source of these racial and ethnic disparities, each analysis was repeated at the department level. The departments that were identified as having a statistically significant disparity are likely to be having the largest effect on the statewide results. Although it is possible that specific officers within departments that were not identified may be engaged in racial profiling, if these behaviors existed, they were not substantial enough to influence the department level results. It is also possible that a small number of individual officers within the identified departments are driving the department level results. The six municipal departments and one state police troop identified to exhibit a statistically significant racial or ethnic disparity include: Berlin The Berlin municipal police department was observed to have made 25.6 percent minority stops of which 13.3 percent were Hispanic and 9.4 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that black and Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 3.4 times larger than the odds during darkness. The odds that a Hispanic motorist was stopped during daylight was 1.7 times larger than during darkness. These results were statistically significant at the 99 and 95 percent level respectively and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Similarly, the synthetic control revealed a disparity in the rate in which both black and Hispanic motorists were stopped that was statistically significant at the 95 and 99 percent level respectively. 56 Meriden The Meriden municipal police department was observed to have made 46.9 percent minority stops of which 31.6 percent were Hispanic and 14.2 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that black motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 2.6 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Similarly, the synthetic control revealed a disparity in the rate that Hispanic motorists were stopped which was statistically significant at the 99 percent level respectively. Monroe The Monroe municipal police department was observed to have made 16 percent minority stops of which 7.5 percent were Hispanic and 7 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.7 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. The hit-rate for white nonHispanic motorists was 42.9 percent while that for black motorists was 8.3 percent and that differences was statistically significant at the 95 percent level. Newtown The Newtown municipal police department was observed to have made 16.2 percent minority stops of which 7.1 percent were Hispanic and 7 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 2.3 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Norwich The Norwich municipal police department was observed to have made 39.2 percent minority stops of which 14.9 percent were Hispanic and 20.6 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.6 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Ridgefield The Ridgefield municipal police department was observed to have made 19.2 percent minority stops of which 11.3 percent were Hispanic and 5 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 2.5 times larger than the odds during darkness. 57 These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Troop B The State Police Troop B was observed to have made 11.9 percent minority stops of which 4.7 percent were Hispanic and 5 percent were Black motorists from October 2015 to September 2016. The annual VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 2 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. II.F (3): DESCRIPTIVE STATISTICS AND INTUTIVE MEASURE FINDINGS, 2015-2016 In addition to the six municipal police departments and one state police troop identified to exhibit statistically significant racial or ethnic disparities in the VOD analysis, five departments were identified using the descriptive tests. The descriptive tests are designed as a screening tool to identify the jurisdictions where consistent disparities that exceed certain thresholds have appeared in the data. They compare stop data to three different benchmarks: (1) statewide average, (2) the estimated driving population, and (3) resident-only stops. Although it is understood that certain assumptions have been made in the design of each of the three measures, it is reasonable to believe that departments with consistent data disparities that separate them from the majority of other departments should be subject to further review and analysis with respect to the factors that may be causing these differences. It is also worth noting that other departments were identified with racial and ethnic disparities when compared to one or more of the descriptive measures. It would be beneficial for departments with smaller disparities to evaluate their own data to better understand the reasons for any relevant patterns The five municipal departments identified to exhibit a significant racial or ethnic disparity using the descriptive measures include: Wethersfield The Wethersfield municipal police department was observed to have made 48.4 percent minority stops of which 28.1 percent were Hispanic and 18.7 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in all three benchmark areas as well as in all nine possible measures. Wethersfield received a disparity score of 8.5 out of a possible nine points, indicating consistently significant racial and ethnic disparities in traffic stops. Similarly, the synthetic control revealed a disparity in the rate in which Hispanic motorists were stopped that was statistically significant at the 99 percent level. East Hartford The East Hartford municipal police department was observed to have made 69.2 percent minority stops of which 27.9 percent were Hispanic and 39.6 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in all three benchmark areas as well as in six of the nine possible measures. East Hartford received a disparity score of 6.0 out of a possible nine points. 58 Stratford The Stratford municipal police department was observed to have made 53.4 percent minority stops of which 19.8 percent were Hispanic and 31.2 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in all three benchmark areas as well as in six of the nine possible measures. Stratford received a disparity score of 6.0 out of a possible nine points. Darien The Darien municipal police department was observed to have made 32.3 percent minority stops of which 18.4 percent were Hispanic and 11.4 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in two of the three benchmark areas as well as in five of the nine possible measures. Darien received a disparity score of 4.5 out of a possible nine points. Trumbull The Trumbull municipal police department was observed to have made 37.4 percent minority stops of which 14.2 percent were Hispanic and 20.7 percent were Black motorists from October 2015 to September 2016. The descriptive analysis indicated that the department exceeded the disparity threshold level in two of the three benchmark areas as well as in five of the nine possible measures. Trumbull received a disparity score of 4.5 out of a possible nine points. In addition to these five departments, a total of eight municipal departments and one state police troop were identified with statistically significant disparities in the synthetic control analysis. Identification in this test is not, in and of itself, sufficient to be identified for further analysis in the absence of significant results in any of the other five tests. II.F (4): FOLLOW-UP ANALYSIS The entirety of the initial 2015-2016 statewide traffic stop data analysis as presented in this report is utilized as a screening tool by which the Advisory Board and project staff can focus resources on those departments displaying the greatest level of disparities in their respective stop data. As noted previously, racial and ethnic disparities in any traffic stop analysis do not, by themselves, provide conclusive evidence of racial profiling. Statistical disparities do, however, provide significant evidence of the presence of idiosyncratic data trends that warrant further analysis. By conducting in-depth follow-up analyses on the departments identified through the screening process, the public has a better understanding as to why and how disparities exist. This transparency is intended to assist in achieving the goal of increasing trust between the public and law enforcement. Therefore, an in-depth follow-up analysis will be conducted for the following departments based on our analytical results for traffic stops performed from October 1, 2015 through September 30, 2016: (1) Berlin, (2) Monroe, (3) Newtown, (4) Norwich, (5) Ridgefield, (6) Darien, and (7) Troop B. None of these seven departments have been identified in previous reports. As in previous years, police administrators from these departments will be invited to be an integral part of the follow-up analysis. In addition to being identified with racial and ethnic disparities in this study, five departments were identified with racial and ethnic disparities in previous reports. Some of these departments warrant 59 limited additional analysis, while others do not. An explanation for each department has been provided below: East Hartford was identified in both the Year 1 (Traffic Stop Data Analysis and Findings, 2013-14) and Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) studies. An in-depth follow-up analysis was conducted following the Year 1 study. East Hartford’s racial and ethnic disparities have remained fairly consistent in each of the annual studies. Based on the results of the previous follow-up analysis and our further understanding of traffic stop enforcement in East Hartford, we do not believe a full follow-up analysis is necessary. However, the department should continue to review and monitor traffic enforcement policies to evaluate the disproportionate effect they could be having on minority drivers. They should also continue to take steps to assure that its minority community is fully engaged in the process of understanding why the allocation of enforcement resources are made and what outcomes are being achieved. Meriden was identified in the Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) study. An in-depth follow-up analysis was conducted following the Year 2 study. However, Meriden was not previously identified with statically significant racial and ethnic disparities in the VOD methodology as they were in this study. Based on the results of the previous follow-up analysis and our further understanding of traffic stop enforcement in Meriden, we do not believe a full follow-up analysis is necessary. However, based on the new disparities identified in the VOD study, we will conduct a limited analysis to verify our previous conclusions. Stratford was identified in both the Year 1 (Traffic Stop Data Analysis and Findings, 2013-14) and Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) studies. An in-depth follow-up analysis was conducted following the Year 1 study. Stratford’s racial and ethnic disparities have remained fairly consistent in each of the annual studies. Based on the results of the previous follow-up analysis and our further understanding of traffic stop enforcement in Stratford, we do not believe a full follow-up analysis is necessary. However, the department should continue to review and monitor traffic enforcement policies to evaluate the disproportionate effect they could be having on minority drivers. They should also continue to take steps to assure that its minority community is fully engaged in the process of understanding why the allocation of enforcement resources are made and what outcomes are being achieved. Trumbull was identified in the Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) study. An in-depth follow-up analysis was conducted following the Year 2 study. Trumbull’s racial and ethnic disparities have remained fairly consistent in each of the annual studies Based on the results of the previous follow-up analysis and our further understanding of traffic stop enforcement in Trumbull, we do not believe a full follow-up analysis is necessary. They should continue to review its traffic enforcement policies to evaluate the extent to which they may have a disproportionate effect, particularly with respect to black drivers. Wethersfield was identified in both the Year 1 (Traffic Stop Data Analysis and Findings, 2013-14) and Year 2 (Traffic Stop Data Analysis and Findings, 2014-15) studies. An in-depth follow-up analysis, with recommendations, was conducted following both the Year 1 and Year 2 studies. Notwithstanding, the town’s racial and ethnic disparities have increased each subsequent year. Based on the results of the two previous follow-up analyses, we do not believe a third follow-up analysis will provide any additional information that would significantly alter our understanding of the factors influencing disparities in their traffic stop data. We recommend that the Connecticut Racial Profiling Prohibition Advisory Board review previous years’ findings and provide guidance for appropriate next steps. 60 Although further analysis is important, another major component of addressing racial profiling in Connecticut is bringing law enforcement officials and community members together in an effort to build trust by discussing relationships between police and the community. Along with Advisory Board members, the project staff has conducted several public forums throughout the state to bring these groups together and will continue these dialogues into the foreseeable future. Through its ongoing work with OPM in implementing the Alvin Penn Act, the IMRP is committed to utilizing both data and dialogue to enhance relationships between the police and community. 61 PART TRAFFIC STOP ANALYSIS AND FINDINGS, 2013- 16 62 III.A: CHARACTERISTICS OF TRAFFIC STOP DATA This section examines general patterns of traffic enforcement activities in Connecticut for the study period of October 1, 2013 to September 30, 2016. Statewide and agency activity information can be used to identify variations in traffic stop patterns to help law enforcement and local communities understand more about traffic enforcement. Although some comparisons can be made between similar communities, we caution against comparing agencies’ data in this section of the report. Please note that the tables included in this report present information from only a limited number of departments. Complete tables for all agencies are included in the technical appendix. In Connecticut, more than 1,755,000 traffic stops were conducted during the 36-month study period. Almost 61% of the total stops were conducted by the 92 municipal police departments, 37% of the total stops were conducted by state police, and the remaining 2% of stops were conducted by other miscellaneous policing agencies. Figure III.A.1 shows the average number of traffic stops between October 2013 and September 2016 by month along with each demographic category. As can be seen below, the volume of traffic stops has a seasonal variation pattern. Figure III.A.1: Average Traffic Stops by Month of the Year 70,000 Average Traffic Stops 60,000 50,000 40,000 30,000 20,000 10,000 Black Hispanic September August July June May April March February January December November October 0 All Other Stops Figure III.A.2 displays the total number of traffic stops by month for each of the three years in the study period. Traffic stop patterns by month don’t appear to significantly change from one year to the next. The small variation in the volume of traffic stops during the spring months is likely the result of when federallyfunded enforcement campaigns were conducted. 63 Figure III.A.2: Traffic Stops by Month of the Year 80000 70000 60000 50000 40000 30000 20000 10000 0 13-14 14-15 15-16 Figure III.A.3 displays the average traffic stops by time of day between October 2013 and September 2016. As can be seen from the figure, the total volume of traffic stops fluctuates significantly across different times of the day. The highest hourly volume of traffic stops in the sample occurred from five to six in the evening and accounts for an average of 6.9% of all stops. It is not surprising that the volume of traffic stops increases between these hours as this is a peak commuting time in Connecticut. The lowest volume of traffic stops occurred between four and five in the morning and continued at a suppressed level during the morning commute. The low level of traffic stops during the morning commute is likely due to an interest in maintaining a smooth flow of traffic during these hours. Discretionary traffic stops might be less likely to be made during these hours relative to others in the sample. The evening commute, in contrast to the morning commute, represents a period when a significant proportion of traffic stops are made. The surge seen between the hours of four and seven at night represents the most significant period of traffic enforcement. In aggregate, stops occurring between these hours represented 18.6% of total stops. Interestingly, there seems to be a significant correlation between the proportion of minority stops and the overall volume of stops. In particular, the share of Hispanic and Black stops increase when the total volume of stops increase. 64 Figure III.A.3: Average Traffic Stops by Time of Day 45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 Black Hispanic 11-12:00 AM 10-11:00 PM 9-10:00 PM 8-9:00 PM 7-8:00 PM 6-7:00 PM 5-6:00 PM 4-5:00 PM 3-4:00 PM 2-3:00 PM 1-2:00 PM 12-1:00 PM 11-12:00 PM 10-11:00 AM 9-10:00 AM 8-9:00 AM 7-8:00 AM 6-7:00 AM 5-6:00 AM 4-5:00 AM 3-4:00 AM 2-3:00 AM 1-2:00 AM 12-1:00 AM 0 All Other Stops Figure III.A.4 displays the number of traffic stops by time of day for each of the three years between October 2013 and September 2016. Traffic stop patterns by time of day don’t appear to significantly change from one year to the next. The slight variation in the 13-14 data between Midnight and 4:00 a.m. is the result of data errors that were resolved in the first year of data collection. Figure III.A.4: Traffic Stops by Time of Day 50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 13-14 14-15 15-16 65 Tables III.A.1, III.A.2, and III.A.3 presents some basic demographic data on persons stopped in Connecticut between October 1, 2013 and September 30, 2016. Nearly two-thirds (63.4%) of drivers stopped were male and the vast majority of drivers (87%) were Connecticut residents. Of the stops conducted by municipal police departments, 91% were Connecticut residents. Of the stops made by state police, 79% were Connecticut residents. About one-third (38%) of drivers stopped were under the age of 30 compared to 22% over 50. The vast majority of stops in Connecticut were White Non-Hispanic drivers (70.9%);14.1% were Black Non-Hispanic drivers; 12.5% were Hispanic drivers; and 2.5% were Asian/Pacific Islander NonHispanic and American Indian/Alaskan Native Non-Hispanic drivers. Table III.A.1: Race and Ethnicity of Drivers by Year Race/Ethnicity White Black All Other Races Hispanic 2013-2014 73.1% 13.5% 1.8% 11.7% 2014-2015 70.6% 14.1% 2.8% 12.5% 2015-2016 69.2% 14.7% 3.0% 13.1% 3 Yr. Combined 70.9% 14.1% 2.5% 12.5% 2014-2015 63.2% 36.8% 2015-2016 63.1% 36.9% 3 Yr. Combined 63.4% 36.6% 2014-2015 2015-2016 3 Yr. Combined 8% 30% 20% 19% 14% 8% Table III.A.2: Gender of Drivers by Year Gender Male Female 2013-2014 63.9% 36.1% Table III.A.3: Age of Drivers by Year Age 16 to 20 21 to 30 31 to 40 41 to 50 51 to 60 Older than 61 2013-2014 8% 30% 19% 19% 14% 8% 8% 30% 21% 19% 14% 8% 8% 30% 21% 18% 15% 9% Table III.A.4 presents data on the characteristics of the traffic stops in the state for all three years combined. Most traffic stops were made for a violation of the motor vehicle laws (88.5%) as opposed to a stop made for an investigatory purpose. The most common violation drivers were stopped for was speeding (27.3%). After a driver was stopped, almost half (46.5%) were given a ticket while most of the remaining drivers received some kind of a warning (45.9%). The rate of tickets versus warnings differs greatly among communities and is a topic that is discussed later in this report. Statewide, less than 1% of traffic stops resulted in a Uniform Arrest Report and only 2.9% of stops resulted in a vehicle search. 66 Table III.A.4: Statewide Stop Characteristics Classification of Stop Motor Vehicle Violation Equipment Violation Investigatory Outcome of Stop Uniform Arrest Report Misdemeanor Summons Infraction Ticket Written Warning Verbal Warning No Disposition Vehicles Searched 88.5% 9.5% 2.0% 0.9% 5.2% 46.5% 16.6% 29.3% 1.6% 2.9% Basis for Stop Speeding Cell Phone Registration Defective Lights STC Violation Misc. Moving Violation Traffic Control Signal Stop Sign Seatbelt Display of Plates All Other 27.3% 9.8% 9.6% 8.7% 8.2% 8.0% 6.9% 6.2% 4.0% 2.7% 8.6% Law Enforcement agencies stop drivers for a number of different reasons. Police record the statutory reason for stopping a motor vehicle for every stop. Those statutes are then sorted into 15 categories from speeding to registration violation to stop sign violation. For example, all statutory violations that are speed related are categorized as speeding. Although speeding is the most often cited reason for stopping a motor vehicle statewide, the results vary by jurisdiction. The average municipal police department stops for speeding violations was 24% compared to the state police average of 32%. Due to the nature of state police highway operations, it is reasonable that its average for speeding is higher. In Portland, Suffield, New Milford, Ridgefield, and Newtown, more than 50% of the traffic stops were for speeding violations. On the other hand, Yale University, Eastern Connecticut State University and the State Capitol Police stopped drivers for speeding less than 5% of the time. The three special police agencies (Yale, ECSU, and State Capitol Police) have limited jurisdiction and it is reasonable that they are not stopping a high percentage of drivers for speeding violations. Table III.A.5 shows the top 10 departments where speeding (as a percentage of all stops) was the most common reason for the traffic stop. Table III.A.5: Highest Speeding Stop Rates across All Departments Department Name Portland Suffield New Milford Ridgefield Newtown Simsbury Easton Southington Guilford Redding Total Stops 537 3.164 10.735 23.058 24.587 10.450 1.720 14.321 9.935 6.502 Speeding Violations 62.4% 61.7% 57.5% 50.9% 50.7% 49.8% 49.6% 48.2% 48.0% 47.0% Registration violations have been cited as a low discretion reason for stopping a motor vehicle, particularly due to the increased use of license plate readers to detect registration violations. Statewide, 9.6% of all traffic stops are for a registration violation. Table III.A.6 presents the top 10 departments with the highest percentage of stops for registration violations. 67 Table III.A.6: Highest Registration Violation Rates across All Departments Department Name North Branford Branford Trumbull CSP Troop L Watertown Stratford CSP Troop D Farmington Greenwich CSP Troop A Total Stops 3,431 16,351 8,190 36,248 4,756 8,057 48,663 14,942 21,143 62,347 Registration Violations 25.9% 25.6% 23.7% 19.8% 17.5% 17.4% 17.4% 16.4% 16.3% 16.0% The Connecticut Department of Transportation and the National Highway Safety Administration work together every year to fund a variety of different driver safety campaigns. Some of the campaigns that we are most familiar with include: “Click it or Ticket,” “Drive Sober or get Pulled Over,” and “Move Over.” Each year law enforcement agencies receive federal grants to fund targeted traffic safety campaigns. Over the past few years there has been an increase in federal funding for distracted driver campaigns. Stops as the result of a cell phone violation are the second most common reason for stopping a driver. Statewide, 9.8% of all stops were the result of a cell phone violation and this rate varies across departments. Table III.A.7 presents the top 10 departments with the highest percentage of stops for cell phone violations. Table III.A.7: Highest Cell Phone Violation Rates across All Departments Department Name Danbury Middlebury Brookfield West Hartford Bridgeport Westport Hamden Hartford Wolcott Berlin Total Stops 17,401 502 7,548 25,939 13,438 18,526 14,061 18,646 1,554 17,684 Cell Phone Violations 37.6% 26.9% 25.3% 23.4% 22.4% 21.6% 20.9% 20.5% 19.9% 19.2% Some Connecticut residents have expressed concern about the stops made for violations that are perceived as more discretionary in nature; therefore potentially making the driver more susceptible to possible police bias. Those stops are typically referred to as pretext stops and might include stops for defective lights, excessive window tint, or a display of plate violation each of which, though a possible violation of state law, leaves the police officer with considerable discretion with respect to actually making the stop. A statewide combined average for stopping drivers for any of these violations is 12.9%. Sixtyone police departments exceeded that statewide average. Table III.A.8 presents the top 10 departments with the highest percentage of stops for these high discretion violations. 68 Table III.A.8: Highest Equipment Violation Rates across All Departments Department Name Total Stops Equipment Violations* Newington 16,964 34.8% Torrington 20,578 33.5% Wethersfield 13,159 31.6% South Windsor 10,285 30.9% University of Connecticut 7,476 30.3% Middletown 8,576 30.1% Plymouth 6,618 29.4% Plainville 11,742 28.4% Windsor 16,778 27.8% West Haven 15,848 27.2% *Equipment violations have been categorized as defective light, display of plate, and window tint violations. In communities with a larger proportion of stops due to these violations, it is recommended that the departments be proactive in discussing the reasons for these stops with members of the community and examine for themselves whether or not such stops produce disparate enforcement patterns. Many have argued that it is difficult for police to determine the defining characteristics about a driver prior to stopping and approaching the vehicle. Similar to variations found across departments for the reason for the traffic stop, there are variations that occur with the outcome of the stop. These variations illustrate the influence that local police departments have on the enforcement of state traffic laws. Some communities may view infraction tickets as the best method to increase traffic safety, while others may consider warnings to be more effective. This analysis should help police departments and local communities understand their level and type of traffic enforcement when compared to other communities. Almost half (46.5%) of drivers stopped in Connecticut received an infraction ticket, while 45.9% received either a written or verbal warning. Individual jurisdictions varied in their post-stop enforcement actions. Danbury issued infraction tickets in 75% of all traffic stops, which is the highest in the state. Middlebury only issued infraction tickets in 3% of all traffic stops, which is the lowest rate in the state. For state police, officers not assigned to a troop issued the highest infractions (86%) and Troop L issued the lowest number of infractions (47%). Table III.A.9 presents the top 10 municipal police departments and top five State Police Troops with the highest percentage of stops that result in an infraction. 69 Table III.A.9: Highest Infraction Rates across All Departments Department Name Danbury Meriden Hartford Derby Bridgeport Norwalk Trumbull Branford New Haven Greenwich CSP Headquarters CSP Troop F CSP Troop G CSP Troop H CSP Troop C Total Stops Highest Municipal Departments 17,401 7,964 18,646 9,545 13,438 17,413 8,190 16,351 43,076 21,143 Highest State Police Troops 42,418 72,523 74,391 56,262 76,490 Infraction Ticket 75.4% 64.1% 64.1% 63.6% 62.2% 58.8% 58.6% 58.5% 54.2% 53.9% 86.1% 78.3% 75.1% 73.3% 72.4% On the other hand, Eastern Connecticut State University issued warnings 93% of the time (the highest rate) and Danbury issued warnings 20% of the time (the lowest rate). For state police, Troop L issued the highest percentage of warnings (42%) and the group of officers not assigned to a troop issued the lowest percentage of warnings (9%). Table III.A.10 presents the top 10 municipal police departments and top five State Police Troops with the highest percentage of stops that result in a warning. Table III.A.10: Highest Warning Rates across All Departments Department Name Eastern CT State University Putnam Middlebury Plainfield Portland Suffield Torrington Redding Thomaston Guilford CSP Troop L CSP Troop B CSP Troop D CSP Troop K CSP Troop A Total Stops Highest Municipal Departments 499 4,451 502 4,674 537 3,164 20,578 6,502 2,190 9,935 Highest State Police Troops 36,248 22,465 48,663 58,366 62,347 Resulted in Warning 93.0% 91.6% 90.2% 86.2% 86.0% 85.3% 84.8% 84.5% 83.1% 83.1% 42.0% 40.1% 30.9% 28.2% 27.9% Statewide, less than 1% of all traffic stops resulted in the driver being arrested. As with infraction tickets and warnings, departments varied in the percentage of arrests associated with traffic stops. The West 70 Hartford Police Department issued the most uniform arrest reports from a traffic stop, with 4.5% of all stops resulting in an arrest. Waterbury, Wallingford, and New London arrested more than 3% of all drivers stopped. The variation in arrest rates for state police is much smaller across troop levels. Table III.A.11 presents the highest arrest rates across all departments. Table III.A.11: Highest Arrest Rates across All Departments Department Name West Hartford Waterbury Wallingford New London Hartford Yale Groton Town Canton Farmington Putnam Total Stops 25,939 7,358 28,202 7,143 18,646 2,511 16,582 4,561 14,942 4,451 Arrests 4.5% 4.3% 4.2% 4.2% 3.0% 2.8% 2.5% 2.3% 1.9% 1.9% Rarely do traffic stops in Connecticut result in a vehicle being searched. During the study period, only 2.9% of all traffic stops resulted in a search. Although searches are rare in Connecticut, they do vary across jurisdictions and the data provides information about enforcement activity throughout the state. When they search a vehicle, officers must report the supporting legal authority, and whether contraband was found. Forty-three departments exceeded the statewide average for searches, but the largest disparities were found in Waterbury (20%), Bridgeport (10%), and Stratford (9%). Of the remaining departments, 19 searched vehicles more than 5% of the time, 21 searched vehicles between 3% and 5% of the time, and the remaining departments searched vehicles less than 3% of the time. No state police troops exceeded the statewide average for searches. Table III.A.12 presents the highest search rates across all departments. Table III.A.12: Highest Searches Rates across All Departments Department Name Waterbury Bridgeport Stratford Derby Yale Milford Middletown Vernon West Hartford Danbury CSP Troop A CSP Troop L CSP Troop H CSP Troop C CSP Troop G Total Stops Highest Municipal Departments 7,358 13,438 8,057 9,545 2,511 10,313 8,576 11,503 25,939 17,401 Highest State Police Troops 62,347 36,248 56,262 76,490 74,391 Resulted in Search 20.0% 9.7% 9.1% 8.4% 8.4% 8.3% 8.2% 7.7% 7.4% 7.2% 2.3% 2.3% 2.2% 1.9% 1.7% 71 III.B: DESCRIPTIVE STATISTICS AND INTUITIVE MEASURES The descriptive statistics and benchmarks presented in this section are an excellent first step to understand patterns in Connecticut policing data. Although these simple statistics present an intriguing story, conclusions should not be drawn from these measures. The three statistical tests of racial and ethnic disparities in the policing data are based solely on the policing data itself and rely on the construction of a theoretically derived identification strategy and a natural experiment. These results have been applied by academic and police researchers in numerous areas across the country and are generally considered to be the most current and relevant approaches to assessing policing data. III.B (1): STATEWIDE AVERAGE COMPARISON In this section there are identifications for each of the three categories (Black, Hispanic, and Minority) in the towns for which the statewide average comparison indicated the largest distances between the net stop percentage and net resident population using 10 or more points as a threshold. Tables showing the calculations for all of the towns, rather than just those showing distance measures of more than 10 points, can be found in the Appendix to this report. Readers should note that this section focuses entirely on towns that exceeded the statewide average for stops in these racial groups. Comparison of Black Drivers to the State Average For the study period from October 1, 2013 through September 30, 2016, the statewide percentage of drivers stopped by police who were identified as Black was 14.1%. A total of 28 departments stopped a higher percentage of Black drivers than the state average, 10 of which exceeded the statewide average by more than 10 percentage points. The statewide average for Black residents (16+) is 9.1%. Of the 28 towns that exceeded the statewide average for Black drivers stopped, 17 also have Black resident populations (16+) that exceeded the statewide average. After the stop and resident population percentages were adjusted using the method described above, a total of seven towns were found to have a relative distance between their net Black driver stop percentage and net Black population percentage of more than 10 points. These were Stratford, Woodbridge. Orange, Trumbull, Wethersfield, Hamden, and East Hartford. Table III.B.1 shows the data for these seven towns. Results for all departments can be found in the Appendix of this report. Each of the seven towns has at least one contiguous town with a resident Black population that exceeds the state average. Stratford and Trumbull border Bridgeport; Hamden borders New Haven; Woodbridge borders three such towns (New Haven, Hamden, and Ansonia); Orange borders New Haven and West Haven; Wethersfield borders Hartford; and East Hartford borders Hartford. In three of the seven towns—Orange, Trumbull, and Woodbridge-- more than 90% of the Black drivers who were stopped were not residents of the town. The statewide average for stopped Black drivers who were not residents of the town in which they were stopped was 57.4%. 72 Table III.B.1: Statewide Average Comparisons for Black Drivers for Selected Towns Municipal Department Black Stops Stratford Woodbridge Orange Trumbull Wethersfield Hamden East Hartford Connecticut 30.9% 20.0% 18.4% 19.2% 18.6% 34.1% 37.6% 14.1% Difference Between Town and State Average 16.8% 5.9% 4.3% 5.1% 4.5% 20.0% 23.5% 0.0% Black Residents Age 16+ 12.8% 1.9% 1.3% 2.9% 2.8% 18.3% 22.5% 9.1% Difference Between Town and State Average 3.6% -7.2% -7.8% -6.2% -6.4% 9.16% 13.4% 0.0% Distance Between Net Differences Nonresident Black Stops 13.2% 13.1% 12.1% 11.3% 10.9% 10.8% 10.1% NA 61.7% 96.8% 98.1% 92.8% 89.2% 54.6% 46.3% 57.4% Comparison of Hispanic Drivers to the Statewide Average For the study period from October 1, 2013 through September 30, 2016, the statewide percentage of drivers stopped by police who were identified as Hispanic was 12.5%. A total of 30 towns stopped a higher percentage of Hispanic drivers than the state average, nine of which exceeded the statewide average by more than 10 percentage points. Seven of the 30 departments exceeded the statewide average by 1.5 percentage points of less. The statewide Hispanic resident population (16+) is 11.9%. The ratio of stopped Hispanic drivers to Hispanic residents (16+) on a statewide basis was slightly higher (12.5% Hispanic drivers’ stopped/11.9% Hispanic residents). Of the 30 towns that exceeded the statewide average for Hispanic drivers stopped, 16 also have Hispanic resident populations (16+) that exceeded the statewide average, although Stratford’s Hispanic population exceeded the average by only 0.01%. After the stop and resident population percentages were adjusted using the method described in Part I, a total of three towns were found to have a relative distance between their net Hispanic driver stop percentage and net Hispanic population percentage of more than 10 points. The three towns were Wethersfield, Newington, and Darien. The Berlin and Wilton police departments fell just below the 10point threshold. Table III.B.2 shows the data for the towns named above. All agency data can be found in the Appendix of this report. All three towns that have a relative difference between their net Hispanic driver stop percentage and net Hispanic population percentage of more than 10 points have at least one contiguous town with a resident Hispanic population (16 +) that exceeds the state average. Each of the three towns borders two such towns: Wethersfield (Hartford and East Hartford), Darien (Stamford and Norwalk), and Newington (Hartford and New Britain). In all three towns more than 85% of the Hispanic drivers stopped were not residents of the town. The statewide average for stopped Hispanic drivers who were not residents of the town in which they were stopped was 57.9%. 73 Table III.B.2: Statewide Average Comparisons for Hispanic Drivers for Selected Towns Municipal Department Wethersfield Newington Darien Connecticut Hispanic Stops 28.9% 21.0% 16.7% 12.5% Difference Between Town and State Average 16.5% 8.6% 4.3% 0.0% Hispanic Residents Age 16+ 7.1% 6.4% 3.5% 11.9% Difference Between Town and State Average -4.8% -5.5% -8.4% 0.0% Distance Between Net Differences NonResidents Hispanic Stops 21.3% 14.1% 12.7% NA 86.6% 85.5% 94.9% 57.9% Comparison of Minority Drivers to the State Average The final category involves all drivers classified as “Minority.” This Minority category includes all racial classifications except for white drivers. Specifically it covers Blacks, Hispanics, Asian/Pacific Islander, American Indian/Alaskan Native, and Other Race classifications included in the census data. For the study period from October 1, 2013 through September 30, 2016, the statewide percentage of stopped drivers who were identified as Minority was 29.1%. A total of 32 towns stopped a higher percentage of Minority drivers than the state average, 17 of which exceeded the state average by more than 10 percentage points. The statewide average for Minority residents (16+) was 25.2%. Of the 32 towns that exceeded the statewide average for Minority drivers stopped, 21 also have Minority resident populations (16 +) that exceeded the statewide average. After the stop resident population percentages were adjusted using the method described above, a total of 17 towns were found to have a relative distance between their net Minority driver stop percentage and net Minority driving age population percentage of more than 10 points. Table III.B.3 shows the data for these 17 towns. The complete data for all towns can be found in the Appendix to this report. Ten towns reported more than 85% of the stops of Minority drivers involved nonresidents. New Britain reported approximately 22% nonresidents among the Minority drivers stopped which was the lowest of the departments. The statewide average for stopped Minority drivers who were not residents of the town in which they were stopped was 57.9%. 74 Table III.B.3: Statewide Average Comparisons for Minority Drivers for Selected Towns Municipal Department Wethersfield Trumbull Newington Stratford Darien Orange Fairfield Berlin Woodbridge Wilton New Britain West Hartford Waterford South Windsor East Hartford Manchester Wolcott Connecticut Minority Stops 49.11% 36.80% 38.23% 50.90% 30.55% 33.73% 30.01% 24.44% 31.00% 25.33% 60.80% 37.36% 25.42% 29.54% 65.91% 41.97% 19.30% 30.6% Difference Between Town and State Average 20.03% 7.72% 9.15% 21.82% 1.47% 4.65% 0.93% -4.64% 1.92% -3.75% 31.72% 8.28% -3.66% 0.46% 36.83% 12.89% -9.78% 0.0% Minority Residents Age 16+ 12.47% 11.91% 14.51% 27.20% 7.17% 10.75% 10.00% 5.76% 12.82% 8.09% 45.00% 21.79% 9.85% 14.60% 51.63% 27.95% 5.43% 25.2% Difference Between Town and State Average -12.76% -13.32% -10.72% 1.97% -18.06% -14.48% -15.23% -19.47% -12.41% -17.14% 19.77% -3.44% -15.38% -10.63% 26.40% 2.72% -19.80% 0.0% Distance Between Net Differences 32.79% 21.04% 19.87% 19.85% 19.53% 19.13% 16.16% 14.83% 14.32% 13.39% 11.95% 11.73% 11.72% 11.09% 10.44% 10.17% 10.02% NA NonResidents Minority Stops 87.2% 91.4% 84.4% 63.3% 94.4% 96.1% 92.1% 92.8% 94.6% 93.4% 22.2% 84.7% 88.5% 80.0% 45.5% 53.6% 84.9% 57.9% III.B (2): ESTIMATED DRIVING POPULATION COMPARISON The only traffic stops included in this analysis were stops conducted Monday through Friday from 6:00am to 10:00am and 3:00pm to 7:00pm (peak commuting hours). Overall, when compared to their respective EDP, 72 departments had a disparity between the Minorities stopped and the proportion of non-whites estimated to be in the EDP. For many of these departments (40) the disparity was very small (less than five percentage points). In the remaining 20 communities, the disparity was negative, meaning that more whites were stopped than expected in the EDP numbers. However, the negative disparities were also very small in most communities. There were 85 departments with a disparity for Black drivers stopped and 64 departments with a disparity for Hispanic drivers stopped when compared to the respective EDPs. Due to the margins of error inherent in the EDP estimates, we established a reasonable set of thresholds for determining if a department shows a disparity in its stops when compared to its EDP percentages. Departments that exceed their EDP percentages by greater than 10 percentage points in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic, were identified in our tier one group. In addition, departments that exceeded their EDP percentage by more than five but less than 10 percentage points were identified in our tier two group for this benchmark if the ratio of the percentage of stops for the target group compared to the baseline measure for that group also was 1.75 or above (percentage of stops divided by benchmark percentage equals 1.75 or more) in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, or (3) Hispanic. 75 Table III.B.4: Highest Ratio of Stops to EDP (Tier I) Department Name Number of Stops Wethersfield East Hartford New Britain Stratford Trumbull Darien Windsor New Haven Newington Fairfield Meriden Hartford Waterbury West Hartford Woodbridge Orange Norwich Manchester Hamden 3,622 10,239 6,468 1,573 2,866 3,631 5,462 15,368 4,354 9,276 2,789 7,798 2,462 8,882 2,175 4,234 5,786 6,903 5,482 East Hartford Windsor New Haven Hartford Stratford Woodbridge Hamden Bloomfield Wethersfield Norwich Trumbull Manchester Waterbury 10,239 5,462 15,368 7,798 1,573 2,175 5,482 4,921 3,622 5,786 2,866 6,903 2,462 Wethersfield New Britain 3,622 6,468 Stops EDP Minority (All Non-White) 44.73% 16.60% 64.38% 40.04% 58.15% 38.88% 45.90% 27.87% 35.31% 18.23% 31.20% 15.92% 47.71% 33.16% 60.36% 46.32% 31.92% 18.98% 29.98% 17.52% 43.89% 31.44% 61.61% 50.07% 51.02% 40.14% 34.97% 24.14% 27.86% 17.31% 29.97% 19.51% 35.10% 24.65% 37.03% 26.68% 39.69% 29.50% Black 36.62% 16.95% 35.54% 20.06% 37.47% 22.60% 35.25% 21.57% 25.05% 12.10% 17.66% 4.77% 28.73% 16.09% 43.04% 31.15% 16.68% 4.91% 18.53% 7.52% 16.78% 5.87% 20.56% 9.92% 24.70% 14.34% Hispanic 26.53% 8.66% 40.83% 26.03% Absolute Difference Ratio 28.12% 24.34% 19.26% 18.03% 17.08% 15.29% 14.55% 14.04% 12.94% 12.46% 12.44% 11.54% 10.88% 10.83% 10.56% 10.46% 10.45% 10.35% 10.20% 2.69 1.61 1.50 1.65 1.94 1.96 1.44 1.30 1.68 1.71 1.40 1.23 1.27 1.45 1.61 1.54 1.42 1.39 1.35 19.67% 15.48% 14.88% 13.69% 12.94% 12.88% 12.64% 11.89% 11.77% 11.01% 10.91% 10.64% 10.36% 2.16 1.77 1.66 1.63 2.07 3.70 1.79 1.38 3.40 2.46 2.86 2.07 1.72 17.87% 14.80% 3.06 1.57 76 Table III.B.5: High Ratio of Stops to EDP (Tier II) Department Name Number of Stops Wolcott Redding Easton 662 2,587 611 Orange Fairfield Darien South Windsor Derby West Hartford Newington Windsor Locks 4,234 9,276 3,631 3,456 2,690 8,882 4,354 2,353 Darien Newington Trumbull Redding Easton Berlin Wolcott 3,631 4,354 2,866 2,587 611 6,396 662 Stops EDP Minority (All Non-White) 16.62% 8.18% 15.04% 7.55% 13.58% 7.50% Black 15.40% 6.26% 14.27% 5.27% 11.54% 3.57% 13.40% 5.76% 13.23% 6.72% 13.74% 7.64% 11.14% 5.53% 12.75% 7.15% Hispanic 17.21% 7.99% 17.71% 8.90% 16.36% 8.33% 9.39% 3.99% 8.84% 3.49% 11.66% 6.57% 9.37% 4.34% Absolute Difference Ratio 8.44% 7.49% 6.08% 2.03 1.99 1.81 9.14% 9.00% 7.97% 7.64% 6.52% 6.09% 5.61% 5.60% 2.46 2.71 3.23 2.33 1.97 1.80 2.02 1.78 9.22% 8.81% 8.04% 5.40% 5.34% 5.10% 5.03% 2.15 1.99 1.97 2.36 2.53 1.78 2.16 The above EDP analysis was confined to the 92 municipal police departments in Connecticut. There are 80 municipalities in Connecticut that either (1) do not have their own departments and rely upon the state police for their law and traffic enforcement services or (2) have one or more resident state troopers who either provide their police services or supervise local constables or law enforcement officers. Most of these communities are smaller and located in Connecticut’s more rural areas. Once the state police stops made on limited access highways were removed from the data, we found that these towns generally had too few stops during the 6am to 10am and 3pm to 7pm periods to yield meaningful comparisons. Consequently, these towns were not considered appropriate candidates for the EDP analysis. III.B (3): RESIDENT ONLY STOP COMPARISON Overall, when compared to the census, 70 departments stopped more Minority resident drivers than white drivers. Again, the disparity for many of these departments was very small. In the remaining 22 communities, the disparity was negative, meaning that more whites were stopped than expected based on the population numbers. However, the negative disparities were also very small in most communities. Almost all departments (91 of 92) had a disparity for Black drivers stopped and 53 departments had a disparity for Hispanic drivers stopped when compared to the resident driving age population. Departments with a difference of 10 percentage points or more between the resident stops and the 16+ resident population in any of the three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic, were identified in our tier one group. In addition, departments that exceeded their resident population percentage by more than five but less than 10 percentage points were identified in our tier two group for this benchmark if the ratio of the percentage of resident stops for the target group compared to the baseline measure for that group also was 1.75 or above(percentage of stopped residents 77 divided by resident benchmark percentage equals 1.75 or more) in any of three categories: (1) Minority (all race/ethnicity), (2) Black non-Hispanic, and (3) Hispanic. Table III.B.6: Highest Ratio of Resident Population to Resident Stops (Tier I) Department Name Number of Residents New Britain East Hartford Bloomfield Waterbury Meriden Windsor New Haven Willimantic Stratford Norwich Wethersfield Derby Manchester Hamden New London Middletown Vernon Bristol Norwalk 57,164 40,229 16,982 83,964 47,445 23,222 100,702 20,176 40,980 31,638 21,607 10,391 46,667 50,012 21,835 38,747 23,800 48,439 68,034 Bloomfield Windsor New Haven East Hartford Hamden Waterbury Norwich Stratford Manchester Middletown Norwalk 16,982 23,222 100,702 40,229 50,012 83,964 31,638 40,980 46,667 38,747 68,034 New Britain Willimantic Danbury Meriden Wethersfield 57,164 20,176 64,361 47,445 21,607 Resident Minority Stops Resident Stops Minority (All Non-White) 45.00% 14,520 67.10% 51.63% 11,572 73.44% 61.51% 4,595 80.76% 48.10% 5,330 67.22% 34.86% 5,538 53.43% 43.92% 5,971 61.71% 62.82% 24,705 79.85% 34.55% 4,674 51.56% 27.20% 3,423 43.97% 29.09% 9,766 45.71% 12.47% 2,950 28.10% 20.56% 1,560 34.87% 27.95% 9,787 41.76% 30.92% 6,036 44.25% 43.57% 3,301 56.44% 23.49% 4,825 35.40% 14.05% 4,685 25.46% 12.71% 7,595 23.98% 40.80% 8,232 51.28% Black 54.76% 4,595 75.26% 32.20% 5,971 51.85% 32.16% 24,705 51.62% 22.52% 11,572 41.27% 18.28% 6,036 36.05% 17.37% 5,330 33.70% 8.96% 9,766 24.51% 12.76% 3,423 27.87% 10.15% 9,787 23.45% 11.68% 4,825 24.33% 13.13% 8,232 24.54% Hispanic 31.75% 14,520 48.44% 28.88% 4,674 44.09% 23.25% 4,831 37.38% 24.86% 5,538 37.87% 7.10% 2,950 17.22% Residents Difference Ratio 22.10% 21.82% 19.25% 19.13% 18.57% 17.79% 17.04% 17.01% 16.77% 16.62% 15.63% 14.32% 13.81% 13.33% 12.87% 11.91% 11.41% 11.27% 10.48% 1.49 1.42 1.31 1.40 1.53 1.41 1.27 1.49 1.62 1.57 2.25 1.70 1.49 1.43 1.30 1.51 1.81 1.89 1.26 20.49% 19.65% 19.46% 18.76% 17.77% 16.32% 15.55% 15.12% 13.30% 12.66% 11.41% 1.37 1.61 1.60 1.83 1.97 1.94 2.74 2.19 2.31 2.08 1.87 16.68% 15.21% 14.13% 13.01% 10.12% 1.53 1.53 1.61 1.52 2.42 78 Table III.B.7: High Ratio of Resident Population to Resident Stops (Tier II) Department Name Number of Residents Enfield Clinton 33,218 10,540 Vernon Groton City* Derby Ansonia Meriden Cromwell Wethersfield Bristol Enfield Groton Town Cheshire 23,800 7,960 10,391 14,979 47,445 11,357 21,607 48,439 33,218 31,520 21,049 Bristol 48,439 Resident Minority Stops Resident Stops Minority (All Non-White) 8.65% 13,065 15.88% 6.12% 4,746 11.36% Black 4.70% 4,685 14.41% 7.70% 2,243 17.21% 6.03% 1,560 14.55% 9.74% 5,611 17.95% 7.80% 5,538 14.68% 3.69% 2,932 10.44% 2.75% 2,950 8.95% 3.24% 7,595 9.15% 2.63% 13,065 8.31% 6.07% 6,363 11.72% 1.27% 7,773 6.39% Hispanic 7.65% 7,595 13.77% Residents Difference Ratio 7.23% 5.24% 1.84 1.86 9.71% 9.51% 8.52% 8.21% 6.88% 6.75% 6.20% 5.91% 5.68% 5.65% 5.12% 3.07 2.23 2.41 1.84 1.88 2.83 3.26 2.83 3.16 1.93 5.02 6.12% 1.80 III.B (4): CONCLUSIONS FROM THE DESCRIPTIVE COMPARISONS The descriptive tests outlined in the above sections are designed to be used as a screening tool to identify those jurisdictions with consistent data disparities that exceed certain thresholds. The tests compare stop data to three different benchmarks: (1) statewide average, (2) the estimated driving population, and (3) resident-only stops that each cover three driver categories: Black, Hispanic, and Minority. Town data is then measured against the resulting total of nine descriptive measures for evaluation purposes. In order to weight the disparities within the descriptive benchmarks, any disparity greater than 10 percentage points for a measure was given a weight of one (1) point. Any disparity of more than five, but less than 10 percentage points accompanied by a disparity ratio of 1.75 or above was given a weight of 0.5 points. Therefore, a department could score no more than nine (9) total points. Table III.B.8 identifies the 14 towns with significant disparities divided into two tiers. The first tier includes the five jurisdictions whose stop data was found to exceed the disparity threshold levels in at least two of the three benchmark areas and a weighted total score of 4.5 or more. This designation warrants additional study to further review the data and attempt to understand the factors that may be causing these differences. It is also recommended that these departments, as well as those included in the second tier of the table, evaluate their own data to try and better understand any patterns. The second tier of Table III.B.8 shows the seven departments that exceeded the disparity threshold in two of the three benchmark areas, but only scored a four (4) out of a possible nine (9) points. In all of these departments there were disparities in at least two of the three benchmark areas. All of the departments that were identified in the descriptive analysis with benchmark disparities and the actual values that exceeded the threshold level are included in the Appendix of the report. 79 Table III.B.8: Departments with the Greatest Number of Disparities Relative to Descriptive Benchmarks Department Name Statewide Average M B H Estimated Driving Population M B H Resident Population M B H Point Total Tier 1 Wethersfield 32.8% 10.9% Stratford 19.9% East Hartford 10.4% New Britain 12.0% Hamden 21.3% 28.1% 11.8% 13.2% 18.0% 10.1% 24.3% 10.8% Manchester 10.2% Trumbull 21.0% 15.6% 6.2% 12.9% 16.8% 15.1% 6 19.7% 21.8% 18.8% 6 19.3% 11.3% 17.9% 14.8% 22.1% 10.1% 16.7% 8.5 5 10.2% 12.6% 13.3% 17.8% 5 10.4% 10.6% 13.8% 13.3% 5 17.1% 10.9% 8.0% 4.5 Tier 2 Norwich 10.5% 11.0% 15.3% 8.0% 14.0% 14.9% 12.9% 5.6% Waterbury 10.9% 10.4% 19.1% 16.3% 4 Windsor 14.6% 15.5% 17.8% 19.7% 4 10.6% 12.9% Darien 19.5% 12.7% New Haven Newington Woodbridge 19.9% 14.3% 14.1% 13.1% 16.6% 15.6% 9.2% 4 4 17.0% 19.5% 8.8% 4 4 4 Note 1: M=Minority, B=Black, H=Hispanic (Numbers of 10 or above yield one point, numbers less than 10 equal 0.5 points) 80 III.C: ANALYSIS OF TRAFFIC STOPS, VEIL OF DARKNESS III.C. (1): THREE-YEAR STATE-LEVEL RESULTS FOR THE VEIL OF DARKNESS, 2013-16 Table III.C.1 presents the results from the VOD applied at the state-level during the combined intertwilight window. These results were estimated using Equation 4 (Part I, Section I.C.) with the standard errors clustered at the department-level. The estimates include controls for time of day, day of week, year, dusk inter-twilight window, statewide stop volume, and department fixed-effects. The estimates relied on four definitions of minority status relative to white non-Hispanics and are annotated accordingly. As shown below, estimation using the three-year aggregate sample indicates a statistically significant disparity for Hispanic motorists as well as the combined black and Hispanic sample. Table III.C.1: Logistic Regression of Minority Status on Daylight with Department Fixed-Effects, All Traffic Stops 2013-16 LHS: Minority Status Daylight Coefficient Standard Error Effective Sample Size Non-Caucasian Black Hispanic Black or Hispanic 0.015 0.004 0.061*** 0.043*** (0.021) (0.020) (0.021) (0.015) 423,510 411,488 405,477 469,896 Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department fixed-effects. Note 3: Sample includes all traffic stops made during the inter-twilight window from October 2013 to September 2016. Table III.C.2 presents the results for the subsample of all municipal police departments during the combined inter-twilight window from 2013-16. As before, the results include controls for time of day, day of week, year, dusk inter-twilight window, and statewide stop volume. The top panel includes fixed-effects for departments while the bottom panel utilizes the richness of the three-year sample by including officer fixed effects. Standard errors are clustered at the requisite fixed-effect level, i.e. department or officer. As shown below, the results indicate a marginally significant disparity for Hispanic motorists alone. Although this disparity is marginal, it persists through the inclusion of the high dimensional set of officer fixed-effects. Table III.C.2: Logistic Regression of Minority Status on Daylight, Municipal Traffic Stops 2013-16 LHS: Minority Status Non-Caucasian Black Hispanic Black or Hispanic Department Fixed-Effects Daylight Coefficient -0.025 -0.026 0.039* 0.017 Standard Error (0.019) (0.022) (0.023) (0.016) 259,880 253,454 249,092 294,118 Effective Sample Size Officer Fixed-Effects Daylight Coefficient -0.025 -0.026 0.039* 0.017 Standard Error (0.019) (0.022) (0.023) (0.016) 259,880 253,454 249,092 294,118 Effective Sample Size 81 Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department or officer fixed-effects. Note 3: Sample includes all traffic stops made by municipal departments during the inter-twilight window from October 2013 to September 2016. Table III.C.3 presents for the subsample of all State Police troops during the combined inter-twilight window. As before, the results include controls for time of day, day of week, year, dusk inter-twilight window, and statewide stop volume. The top panel includes fixed-effects for troops while the bottom panel includes officer fixed effects with standard errors clustered at the requisite fixed-effect level. Across all of the specifications with department fixed-effects, the results indicate a significant disparity. Only the disparity observed in the combined sample of black and Hispanic motorists persists through the inclusion of officer fixed-effects. However, it is clear that the statewide results from Table III.C.1 are being driven primarily by this disparity both because of the high level of significance as well as the large share of the overall sample represented by State Police. Table III.C.3: Logistic Regression of Minority Status on Daylight, State Police Traffic Stops 2013-16 LHS: Minority Status Non-Caucasian Black Hispanic Black or Hispanic Department Fixed-Effects Daylight Coefficient 0.223*** 0.108*** 0.132*** 0.113*** Standard Error (0.047) (0.021) (0.044) (0.021) 157,211 151,902 150,828 168,748 Effective Sample Size Officer Fixed-Effects Daylight Coefficient Standard Error Effective Sample Size -0.278 -0.371 -0.192 0.109*** (10.197) (0.718) (0.121) (0.015) 152,275 147,132 146,101 163,405 Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department or officer fixed-effects. Note 3: Sample includes all traffic stops made by municipal departments during the inter-twilight window from October 2013 to September 2016. As mentioned, these estimates aggregate all traffic stops across multiple departments and years. As such, they should be considered an average effect across all departments and over a three year period from 2013 to 2016. Although the results from this section find a statistically significant disparity in the rate of minority traffic stops in Connecticut, these results do not identify the geographic source of that disparity. The results of a department-level analysis are presented in a later section and better identify the source of specific department-wide disparities. III.C. (2): THREE-YEAR STATE-LEVEL ROBUSTNESS FOR THE VEIL OF DARKNESS, 2013-16 This section presents robustness checks on the initial specification using a more restrictive subsample of traffic stops. Analysis using all violations may suffer from bias driven by specific violations that are correlated with visibility and minority status. To see why this might be a problem, imagine that minority 82 motorists are more likely to have a head or taillight out and that these violations are only observable to police officers during darkness. In that instance, comingling these equipment violations with other violations might make us more likely to observe more minorities stopped at night, thus biasing the results downward. In contrast, if minority motorists are more likely to talk on their cellphone or not wear a seatbelt and those violations are more easily observed during daylight, the results would be biased upwards. Since both of these scenarios seem reasonable and it is unclear the net direction of the bias, a reasonable robustness check is to limit the sample of traffic stops to moving violations. Table III.C.4 presents the results at the state-level during the combined inter-twilight window with only moving violations. As before, these results were estimated using Equation 4 (Part I, Section I.C.) with the standard errors being clustered at the department-level. The estimates include controls for time of day, day of week, year, dusk inter-twilight window, statewide stop volume, and department fixed-effects. As shown below, estimation using the three-year sample indicates a statistically significant disparity across all demographic groups. These results indicate that the previous set of estimates using all traffic stops may have been biased such that the magnitude and significance were underestimated. Table III.C.4: Logistic Regression of Minority Status on Daylight with Department Fixed-Effects, All Moving Violations 2013-16 LHS: Minority Status Daylight Coefficient Standard Error Effective Sample Size Non-Caucasian Black Hispanic Black or Hispanic 0.086*** 0.077*** -0.165*** 0.063*** (0.028) (0.026) (0.041) (0.018) 218,164 211,049 206,472 236,399 Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department fixed-effects. Note 3: Sample includes moving violations made during the inter-twilight window from October 2013 to September 2016. Table III.C.5 presents the results from the subsample of moving violations made only by municipal police departments during the combined inter-twilight window. As before, the results include controls for time of day, day of week, year, dusk inter-twilight window, and statewide stop volume. The top panel includes fixed-effects for departments while the bottom panel includes officer fixed effects with standard errors clustered at the requisite level. As shown below, the results indicate a significant disparity for the specification that includes both black and Hispanic motorists. The disparity persists through the inclusion of the high dimensional set of officer fixed-effects. Given that minority motorists make up a small relative portion of the estimation sample, the combined black and Hispanic contains the most such stops. Thus, it is not surprising that this sample is observed to be the most likely to identify the disparity as the others may suffer from a power issue. 83 Table III.C.5: Logistic Regression of Minority Status on Daylight, Municipal, All Moving Violations 2013-16 LHS: Minority Status Non-Caucasian Black Hispanic Black or Hispanic Department Fixed-Effects Daylight Coefficient Standard Error Effective Sample Size 0.042 0.051 0.045 0.051*** (0.028) (0.367) (0.036) (0.018) 136,140 132,506 129,732 149,722 Officer Fixed-Effects Daylight Coefficient -0.019 0.008 -0.103 0.090*** Standard Error (0.033) (0.034) (434.644) (0.017) 120,324 117,116 114,756 131,721 Effective Sample Size Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department or officer fixed-effects. Note 3: Sample includes all traffic stops made by municipal departments during the inter-twilight window from October 2013 to September 2016. Table III.C.6 presents the results from the subsample of moving violations for all State Police departments during the combined inter-twilight window. As before, the results include controls for time of day, day of week, year, dusk inter-twilight window, and statewide stop volume. The top panel includes fixed-effects for departments while the bottom panel includes officer fixed effects with standard errors clustered at the requisite level. As before, the disparity for the combined black and Hispanic sample persists through this more restrictive sample. Again, note that minority motorists make up a small relative portion of the estimation sample, the combined black and Hispanic contains the most such stops. Thus, it is not surprising that this sample is observed to be the most likely to identify the disparity as the others may suffer from a power issue. Table III.C.6: Logistic Regression of Minority Status on Daylight, State Police All Moving Violations 2013-16 LHS: Minority Status Non-Caucasian Black Hispanic Black or Hispanic Department Fixed-Effects Daylight Coefficient 0.243*** 0.163*** 0.153*** 0.136*** Standard Error (0.048) (0.034) (0.042) (0.029) 78,560 75,238 73,779 82,913 Effective Sample Size Officer Fixed-Effects Daylight Coefficient Standard Error Effective Sample Size 0.044 -0.294 -0.305* 0.115*** (0.057) (5.936) (0.164) (0.023) 75,736 72,532 71,083 79,881 Note 1: The coefficients are presented along with standard errors clustered at the department-level. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), volume, and department or officer fixed-effects. Note 3: Sample includes moving violations made by state police during the inter-twilight window from October 2013 to September 2016. 84 The results presented in the state-level analysis provide strong evidence that a disparity exists in the rate of minority traffic stops by both municipal and State Police departments in the combined 2013 to 2016 sample. Throughout, the disparity persists through the inclusion of both municipal departments as well as officer fixed-effects. Further, the level of significance grows across all specifications when the sample is restricted to moving violations. In the preceding section, the test will be applied to individual municipal departments and State Police troops. III.C (3): THREE-YEAR DEPARTMENT-LEVEL RESULTS FOR THE VEIL OF DARKNESS, 2013-16 The analysis presented at the state-level shows that a statistically significant disparity exists in the rate of minority traffic stops in daylight relative to darkness. That analysis does not further investigate disparities occurring within specific police departments. The analysis presented in this section seeks to better identify the source of the observed aggregate disparity and to further investigate individual police departments. Each individual municipal police department and State Police troop is examined independently by estimating the effect of visibility during the combined inter-twilight window. Equation 4 (Part I, Section I.C.) is estimated separately for each municipal department and state police troop. Thus, each set of estimates includes a vector of town-specific fixed-effects for time of day, day of week, year, dusk intertwilight window, and statewide stop volume. Here, we identify all departments found to have a disparity that is statistically significant at the 95 percent level in either the Hispanic or Black alone minority group. The full set of results are contained in Table III.C.7.1 of the Appendix. Although we do not include officer fixed or restrict the sample to moving violations here, Appendix Tables III.C.7.2, III.C.7.3 and III.C.7.4 contain results with more rigorous specifications. As discussed in detail below, we annotate departments that did not withstand the scrutiny of these more rigorous specifications. Table III.C.7 presents the results from estimating the VOD test statistic for individuals departments using the combined 2013-16 sample. There were 12 municipal departments and five State Police troops found to have a disparity that was statistically significant at the 95 percent level in the black or Hispanic categories. Two of these municipal departments (Groton Town and New Milford) and two of the State Police troops (Troop C and H) were identified in previous year’s reports. As noted, the disparity for all departments in Table III.C.7 did not persist through all of the robustness checks that included officer fixedeffects, the moving violation subsample, and the combination of these specifications. In total, the disparity only persisted through these robustness checks for six municipal departments and four State Police troops: Ansonia, Groton Town, Madison, Monroe, New Milford, Norwich, Troop C, Troop G, Troop H, and Troop K. Although the coefficient estimate here are a three-year average effect from 2013-16, the persistence of the results through a rigorous set of robustness checks and large overall sample of stops warrants serious consideration. 85 Table III.C.7: Logistic Regression of Minority Status on Daylight for Select Departments, All Traffic Stops 2013-16 Department VOD Estimate Non-Caucasian Black Hispanic Black or Hispanic 0.086 0.058 0.319*** 0.186** SE (0.098) (0.102) (0.108) (0.081) ESS 4,168 4,091 3,962 4,813 Coefficient -0.100 0.140 1.789** 0.574 SE (0.367) (0.47) (0.744) (0.381) ESS 777 662 584 758 0.758 0.578 38.003*** 0.998 SE (0.849) (0.981) (6.171) (0.958) ESS 237 180 170 249 Coefficient 0.433*** 0.473*** 0.129 0.322*** SE (0.132) (0.143) (0.162) (0.114) ESS 3,192 3,112 2,999 3,412 Coefficient -0.061 0.231 0.899** 0.563** SE (0.297) (0.384) (0.35) (0.258) ESS 2,241 2,160 2,129 2,318 Coefficient 0.131* 0.194** 0.045 0.131* SE (0.076) (0.082) (0.096) (0.069) ESS 5,563 5,322 4,796 6,250 Coefficient -0.143 -0.043 0.378** 0.168 SE (0.155) (0.172) (0.172) (0.127) ESS 4,173 4,106 4,147 4,423 Coefficient 0.230 0.129 0.581*** 0.412** SE (0.223) (0.247) (0.209) (0.166) ESS 2,718 2,621 2,826 2,940 -0.319*** -0.215* 0.292** 0.027 SE (0.123) (0.129) (0.145) (0.105) ESS 2,779 2,664 2,505 3,103 Coefficient -0.219 -0.232 0.558*** 0.068 SE (0.14) (0.154) (0.206) (0.13) ESS 2,532 2,434 2,300 2,700 Coefficient -0.059 -0.048 0.244** 0.097 SE (0.112) (0.122) (0.112) (0.092) ESS 3,172 3,010 3,217 3,869 Coefficient -0.071 -0.069 0.421*** 0.324** SE (0.228) (0.239) (0.14) (0.131) ESS 1,260 1,232 1,813 1,954 Coefficient Ansonia Avon+ Coefficient East Hampton+ Groton Town Madison Manchester+ Monroe New Milford Coefficient Norwich South Windsor+ Stamford+ Willimantic+ 86 Department CSP Troop B+ CSP Troop C CSP Troop G CSP Troop H CSP Troop K VOD Estimate Non-Caucasian Black Hispanic Black or Hispanic Coefficient 0.285* 0.191 0.397** 0.285** SE (0.163) (0.181) (0.164) (0.126) ESS 5,902 5,824 5,857 6,135 Coefficient 0.315*** 0.233*** 0.250*** 0.240*** SE (0.056) (0.07) (0.074) (0.053) ESS 22,611 21,455 21,218 22,931 Coefficient 0.161*** 0.108* 0.177*** 0.137*** SE (0.058) (0.061) (0.062) (0.049) ESS 12,888 12,298 12,121 15,287 Coefficient 0.168** 0.156** 0.140 0.144** SE (0.071) (0.076) (0.085) (0.063) ESS 10,338 9,872 9,194 11,596 Coefficient 0.111 0.114 0.300*** 0.207*** SE (0.079) (0.089) (0.085) (0.065) ESS 13,566 13,239 13,397 14,409 Note 1: The coefficients are presented along with robust standard errors. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: All specifications include controls for time of the day, day of the week, analysis year, inter-twilight window (i.e. morning and night), and volume. Note 3: Sample includes all traffic stops made during the inter-twilight window from October 2015 to September 2016. + Results are not robust across subsequent specifications. As noted previously, only a select six of the 12 municipal departments and four of the five State Police troops in Table III.C.7 persisted through the additional robustness checks contained in the Appendix. We conclude that for these departments and State Police troops, there is strong evidence that a disparity exists in the rate of minority traffic stops made during high visibility conditions. For the departments which had a disparity in Table III.C.7 but where that disparity did not persist through the robustness checks, it is impossible to determine whether these more restrictive specifications invalidated the findings or whether they simply created power issues by reducing the overall sample. Thus, we annotate the results for those departments but caution against any undue interpretation about the fact that these results did not withstand more rigorous estimation. One overarching observation is that the largest and most persistent disparities driving the results statewide are likely coming from the State Police. Not only are these results strong across all specifications and robustness checks with a high degree of confidence, but the large overall sample size means that they exert more influence on the overall average effect for the mixed sample. Again, it is impossible to clearly link these observed disparities to racial profiling as these differences may be driven by any combination of policing policy, heterogeneous enforcement patterns, or individual officer behavior. 87 III.D. ANALYSIS OF TRAFFIC STOPS, SYNTHETIC CONTROL III.D. (1): THREE-YEAR DEPARTMENT-LEVEL SYNTHETIC CONTROL ANALYSIS, 201316 Each individual municipal police department and State Police troop was examined independently by weighting observations with inverse propensity scores estimated using Equation 7 (Part I, Section I.D.). Treatment effects were estimated using Equation 8 (Part I, Section I.D.) for individual departments and State Police troops across four demographic subgroups relative to white non-Hispanics. As before, we identify all departments found to have a disparity that is statistically significant at the 95 percent level in either the Hispanic or Black alone minority group. The full set of results for all departments can be found in Table III.D.1.1 of the Appendix. Although we do not use doubly-robust estimation here, Appendix Table III.D.1.2 contains results with this more rigorous specifications. Note that significantly more departments are identified in these estimates than those using doubly-robust estimation which indicates that in some departments, the results fail on balance. Thus, we present results here for departments identified using the less rigorous specification but only confidently identify those that withstand the more rigorous approach. Table III.D.1 presents the results from estimating treatment effects of individual departments relative to their requisite synthetic control using the combined 2013-16 sample. There were 20 municipal departments and five State Police troops found to have a disparity that was statistically significant at the 95 percent level in the black or Hispanic categories. As noted, the disparities in these departments did not all persist through more restrictive modeling specifications that utilize doubly-robust estimation. In total, there were seven municipal departments and two State Police troops that withstood this more rigorous estimation procedure. Although the coefficient estimate here are a three-year average effect from 201316, the persistence of the results and large overall sample of stops warrants consideration. Table III.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department Estimate Non-Caucasian Black Hispanic Black or Hispanic 13.452*** -0.263*** 0.507*** 0.134*** SE (0.023) (0.032) (0.032) (0.024) ESS 271,655 271,655 271,655 271,655 Coefficient -1.192*** -1.348*** 3.063*** 157.379*** SE (0.038) (0.044) (0.031) (0.026) ESS 258,671 258,671 258,671 258,671 Coefficient 1.331*** 1.425*** 1.603*** 0.786*** SE (0.019) (0.019) (0.032) (0.019) ESS 411,921 411,921 411,921 411,921 Coefficient Berlin+ Bethel Bloomfield+ 88 Department Estimate Non-Caucasian Black Hispanic Black or Hispanic -0.521*** 4.287*** -1.042*** -1.981*** SE (0.049) (0.059) (0.042) (0.035) ESS 474,222 474,222 474,222 474,222 Coefficient 0.122*** 0.163*** 14.777*** -0.172*** SE (0.041) (0.043) (0.061) (0.037) ESS 134,719 134,719 134,719 134,719 Coefficient 5.834*** 11.669*** -0.319*** -0.933*** SE (0.108) (0.124) (0.087) (0.074) ESS 314,421 314,421 314,421 314,421 -18.852*** -0.216*** 5.004*** -0.120*** SE (0.025) (0.042) (0.03) (0.032) ESS 445,837 445,837 445,837 445,837 Coefficient 0.678*** 0.647*** 0.204*** 0.550*** SE (0.019) (0.02) (0.023) (0.017) ESS 397,469 397,469 397,469 397,469 Coefficient 0.372*** 0.451*** -0.236*** 0.208*** SE (0.028) (0.029) (0.039) (0.025) ESS 391,994 391,994 391,994 391,994 Coefficient 0.230*** 0.193*** 0.190*** 0.221*** SE (0.035) (0.037) (0.041) (0.03) ESS 421,729 421,729 421,729 421,729 Coefficient -0.522*** -0.480*** 10.999*** 0.998*** SE (0.026) (0.027) (0.025) (0.019) ESS 317,853 317,853 317,853 317,853 Coefficient 0.348*** 0.358*** 0.098*** 0.291*** SE (0.028) (0.03) (0.033) (0.024) ESS 254,968 254,968 254,968 254,968 Coefficient 0.195*** 0.048 0.704*** 2.939*** SE (0.028) (0.031) (0.035) (0.025) ESS 387,545 387,545 387,545 387,545 Coefficient 0.497*** 4.877*** 1.038*** 0.263*** SE (0.082) (0.089) (0.086) (0.065) ESS 349,842 349,842 349,842 349,842 Coefficient 0.842*** 0.949*** 0.640*** 1.071*** SE (0.024) (0.024) (0.029) (0.022) ESS 346,525 346,525 346,525 346,525 Coefficient Brookfield+ Cromwell+ Easton+ Coefficient Glastonbury+ Manchester Middletown+ Milford Naugatuck Orange Rocky Hill+ Shelton+ Stratford+ 89 Department Trumbull+ Watertown Wethersfield Windsor Locks+ Winsted+ CSP Troop F CSP Troop G+ CSP Troop H+ CSP Troop I Estimate Non-Caucasian Black Hispanic Black or Hispanic Coefficient 0.430*** 0.581*** 0.583*** 0.702*** SE (0.027) (0.028) (0.031) (0.023) ESS 330,636 330,636 330,636 330,636 Coefficient -1.235*** 0.740*** 2.727*** 0.258*** SE (0.052) (0.053) (0.057) (0.041) ESS 300,904 300,904 300,904 300,904 Coefficient -0.199*** -0.172*** 1.175*** 0.633*** SE (0.026) (0.027) (0.027) (0.022) ESS 220,004 220,004 220,004 220,004 Coefficient 0.419*** 0.425*** -0.301*** 0.098*** SE (0.031) (0.033) (0.051) (0.028) ESS 151,341 151,341 151,341 151,341 Coefficient 0.368*** 3.922*** 2.272*** 0.521*** SE (0.096) (0.101) (0.111) (0.082) ESS 163,929 163,929 163,929 163,929 Coefficient 0.030 0.073 0.169** 0.124** SE (0.06) (0.057) (0.073) (0.05) ESS 652,950 652,950 652,950 652,950 Coefficient 1584.850 0.584* 0.943** 193.480 SE (.) (0.328) (0.439) (.) ESS 180,269 180,269 180,269 180,269 Coefficient 0.260*** 0.239*** 0.166 0.261*** SE (0.084) (0.088) (0.104) (0.075) ESS 639,239 639,239 639,239 639,239 Coefficient 0.291*** 0.349*** 0.187*** 0.338*** SE (0.015) (0.016) (0.019) (0.014) ESS 652,945 652,945 652,945 652,945 0.260 0.197 0.725*** 0.436** SE (0.198) (0.228) (0.233) (0.178) ESS 378,779 378,779 378,779 378,779 Coefficient CSP Troop K+ Note 1: The coefficients are presented along with robust standard errors. A coefficient concatenated with * represents a p-value of .1, ** represents a p-value of .05, and *** represents a p-value of .01 significance. Note 2: Propensity scores were estimated using principal components analysis of traffic stop characteristics as well as Census data selected using the Kaiser-Guttman stopping rule. Traffic stop characteristics include time of the day, day of the week, month, department traffic stop volume, officer traffic stop volume, and type of traffic stop. Census demographics for both the primary and border towns include retail employment, entertainment employment, commuting population, vacant housing, rental housing, median earnings, population density, gender, age, race, and ethnicity. Note 3: Sample includes all traffic stops made by the primary department and an inverse propensity score weighted sample of all other departments from October 2013 to September 2016. + Results are not robust across subsequent specifications. As noted previously, only a select number of these persisted through the additional robustness check contained in the Appendix. Although it is impossible to determine whether these robustness checks 90 invalidated the findings in Table III.D.1 or whether a balanced synthetic control is simply not able to be created, we annotate the results for those departments and caution against any undue interpretation. Again, it is impossible to clearly link the observed disparities to racial profiling as these differences may be driven by any combination of policing policy, heterogeneous enforcement patterns, or individual officer actions. 91 III.E. ANALYSIS OF VEHICULAR SEARCHES, KPT HITRATE III.E (1): THREE-YEAR STATE-LEVEL HIT-RATE ANALYSIS, 2013-16 The analysis begins by aggregating all search data for Connecticut by demography and performing the non-parametric test of hit-rates. The rate that searches, defined as both consent and other searches, that end in contraband being found for white non-Hispanic motorists is compared to each minority subgroup. The results of this test can be seen in Table III.E.1 for four distinct minority definitions. As seen below, the rate of successful searches for white non-Hispanic motorists was 34.5 percent from 2013 to 2016. Relative to white non-Hispanic motorists, the hit-rate for each of the four minority subgroups was lower and ranged from 25.1 to 25.6 percent. The differences in hit-rates for each group was statistically significant at the 99 percent level. In aggregate, Connecticut police departments exhibit a strong tendency to be less successful in motorist searches across all minority groups. Table III.E.1: Chi-Square Test of Hit-Rate, Municipal and State Police, Consent and Other Searches 2013-16 Variable: White Non-White Black Hispanic Black or Hispanic Hit-Rate 34.5% 25.1%*** 25.2%*** 25.6%*** 25.3%*** N/A 384.039 366.398 270.229 498.15 25,911 14,586 14,049 10,412 23,939 Chi^2 ESS Note: Sample includes all consent and other searches from October 2013 to September 2016. Table III.E.2 provides the results of a hit-rate analysis for the aggregate municipal department and State Police subgroups. The hit-rate in municipal departments for white non-Hispanic motorists was 32.4 percent from 2013 to 2016. Relative to white non-Hispanic motorists, the hit-rate for each of the four minority subgroups was lower and ranged from 23.4 to 24.3 percent. These differences were statistically significant at the 99 percent level. Similarly, the aggregate hit-rate for all State Police was 42.2 percent for white non-Hispanic motorist. Relative to white non-Hispanic motorists, the hit-rate for each of the four minority subgroups was lower and ranged from 21.6 to 23.2 percent. As before, each minority group had a lower rate of successful searches that were again statistically significant at the 99 percent level. Table III.E.2: Chi-Square Test of Hit-Rate, All Consent and Probable Cause Searches 2013-16 Variable: White Non-White Black Hispanic Black or Hispanic Municipal Departments 32.4% 23.4%*** 23.5%*** 24.3%*** 23.8%*** N/A 299.567 279.462 190.665 363.49 ESS 20,318 11,935 11,513 8,594 19,727 Hit-Rate 42.2% 31.6%*** 32.3%*** Hit-Rate Chi^2 Chi^2 ESS State Police Troops 33.2%*** 33.0%*** N/A 57.307 58.187 61.782 95.024 5,345 2,506 2,403 1,746 4,007 92 Note: Sample includes all consent and probable cause searches from October 2013 to September 2016. III.E (2): THREE-YEAR STATE-LEVEL ROBUSTNESS FOR HIT-RATE ANALYSIS, 2013-16 This section presents a robustness checks on the initial specifications conducted at the state-level using a restricted sample of consent searches, i.e. excluding other searches. In this more restrictive subsample, the rate of successful searches for white non-Hispanic motorists was 30.8 percent from 2013 to 2016. Across each of the minority subgroups, the rate of successful searches was significantly lower ranging from 21 to 22 percent. The differences in hit-rates was statistically significant at the 99 percent level. The results of this robustness check confirm the initial set of estimates using both probable cause and consent searches. Table II.E.3: Chi-Square Test of Hit-Rate, All Consent Searches 2013-16 Variable: White Non-White Black Hispanic Black or Hispanic Hit-Rate 30.8% 21.0%*** 21.1%*** 22.0%*** 21.4%*** N/A 166.15 161.617 107.751 212.771 9,542 5,349 5,181 3,946 8,921 Chi^2 ESS Note: Sample includes all consent searches from October 2013 to September 2016. Table III.E.4 presents a robustness check on the subgroups of municipal departments and State Police using a more restrictive sample of consent searches. As seen below, the rate of successful searches made by municipal departments for white non-Hispanic was 30.1 percent from 2013 to 2016. Relative to white non-Hispanic motorists, the hit-rate for each of the four minority subgroups was lower and ranged from 19.9 to 21.7 percent. The difference in the rate of successful searches for each of these groups was statistically significant at the 99 percent level. For the State Police subgroup, the rate of successful searches for white non-Hispanic motorists was 31.9 percent. Relative to this group, the rate of successful searches for each minority subgroup was lower and ranged from 22.3 to 24.3 percent. As before, the difference in hit-rates was statistically significant at the 99 percent level. Table III.E.4: Chi-Square Test of Hit-Rate, Municipal and State Police Consent Searches 2013-16 Variable: White Non-White Black Hispanic Black or Hispanic Municipal Departments Hit-Rate Chi^2 ESS Hit-Rate Chi^2 ESS 30.1% 19.9%*** 19.9%*** 21.7%*** 20.6%*** N/A 136.616 131.925 73.664 161.116 6,680 4,024 3,926 2,964 6,763 22.3%*** 23.4%*** 31.9% State Police Troops 24.2%*** 24.3%*** N/A 24.218 22.81 31.718 42.194 2,767 1,258 1,196 961 2,078 Note: Sample includes all consent searches from October 2013 to September 2016. 93 III.E (3): THREE-YEAR DEPARTMENT-LEVEL HIT-RATE ANALYSIS, 2013-16 In this subsection, differences in hit-rates are estimated independently for each municipal department and State Police troop. Here, we identify all departments found to have a disparity that is statistically significant at the 95 percent level in either the Hispanic or Black alone minority group. The full set of results can be found in Table III.E.5.1 of the Appendix while results restricting the sample to just consent searches are in Appendix Table III.E.5.2. As in previous sections, we annotate departments that did not withstand the scrutiny of the more rigorous consent search specification. Table III.E.5 presents the results from estimating the hit-rate test for individual departments using the 2013-2016 sample. There were 15 municipal departments and five State Police troops found to have a disparity in the hit-rate of minority motorists relative to white non-Hispanic motorists which was statistically significant at the 95 percent level. As noted, the disparity in these departments did not persist through more restrictive specifications that limited the sample to consent searches. In total, the disparity persisted through four municipal departments and four State Police troops. Table III.E.5: Chi-Square Test of Hit-Rate in Select Departments, All Consent and Probable Cause Searches 2013-16 Cheshire East Hartford+ Glastonbury+ Groton Town+ Milford+ Monroe+ White Non-White Black Hispanic Black or Hispanic 51.6% 34.9%* 34.9%* 22.9%*** 29.9%*** N/A 3.777 3.777 9.511 9.893 159 43 43 35 77 White Non-White Black Hispanic Black or Hispanic 50.9% 45.8% 45.9% 41%** 43.9%** N/A 1.906 1.798 5.475 3.998 267 546 540 283 813 White Non-White Black Hispanic Black or Hispanic 58.7% 51.6% 50.8% 43.9%** 48.2%* N/A 1.012 1.194 4.138 3.434 254 62 59 57 112 White Non-White Black Hispanic Black or Hispanic 62.3% 56.3% 56.5% 42.4%** 51.1%* N/A 0.691 0.631 4.442 2.939 159 64 62 33 90 White Non-White Black Hispanic Black or Hispanic 43.4% 34.4%** 34.9%** 33.3%* 33.8%** N/A 4.419 3.855 3.578 6.599 403 192 189 108 293 White Non-White Black Hispanic Black or Hispanic 50% 20%** 16.7%*** 50% 31.4%* N/A 6.129 6.920 108 20 18 3.677 18 35 94 Newington+ North Haven Norwich+ Plainville+ Vernon+ Wallingford+ Waterbury West Hartford Westport+ CSP Troop A White Non-White Black Hispanic Black or Hispanic 34.3% 21.6%** 22%** 31.4% 27.8% N/A 5.784 5.122 0.336 2.333 207 116 109 156 263 White Non-White Black Hispanic Black or Hispanic 39.3% 19.4%** 19.4%** 26.1% 22.4%** N/A 4.692 4.692 1.409 4.799 107 36 36 23 58 White Non-White Black Hispanic Black or Hispanic 44.1% 38.8% 38.7% 32.7%** 36.8%** N/A 1.897 1.943 5.910 4.618 424 273 266 147 400 White Non-White Black Hispanic Black or Hispanic 42.1% 31%* 30.5%** 49.1% 40.9% N/A 3.648 3.880 1.893 0.071 466 84 82 116 193 White Non-White Black Hispanic Black or Hispanic 61.9% 47.9%*** 48.9%*** 50%** 49.6%*** N/A 9.151 7.704 4.865 10.147 512 146 141 98 238 White Non-White Black Hispanic Black or Hispanic 51.8% 49.1% 49.4% 39.8%*** 43.8%*** N/A 0.656 0.487 16.564 10.477 1036 279 265 399 658 White Non-White Black Hispanic Black or Hispanic 58.6% 37%*** 37.4%*** 30.7%*** 34.5%*** N/A 16.135 15.501 22.626 24.603 152 200 198 137 330 White Non-White Black Hispanic Black or Hispanic 70.5% 51.4%*** 51.8%*** 55%*** 53.8%*** N/A 35.298 32.158 30.667 49.128 1161 259 245 371 608 White Non-White Black Hispanic Black or Hispanic 42.2% 32.9%** 32.1%** 40% 34.3%* N/A 4.024 4.603 0.110 3.452 325 173 162 70 230 White Non-White Black Hispanic Black or Hispanic 40.6% 31.3%*** 30.3%*** 34%* 31.7%*** N/A 8.507 10.465 3.738 10.818 623 367 357 300 640 95 CSP Troop C CSP Troop F CSP Troop G+ CSP Troop I White Non-White Black Hispanic Black or Hispanic 44.8% 42.3% 43.6% 27.2%*** 36.6%*** N/A 0.490 0.105 19.885 7.758 880 253 234 191 418 White Non-White Black Hispanic Black or Hispanic 51.7% 29.1%*** 28.9%*** 35.3%** 31.8%*** N/A 17.373 17.214 5.948 17.730 302 117 114 68 176 White Non-White Black Hispanic Black or Hispanic 37.2% 28.7%*** 28.1%*** 25.6%*** 27.3%*** N/A 7.491 8.491 10.158 12.156 422 498 477 270 721 White Non-White Black Hispanic Black or Hispanic 42.8% 28.9%*** 29.2%** 38.8% 32.4%** N/A 6.701 6.272 0.413 4.682 173 149 144 103 238 Note 1: Sample includes all consent and probable cause searches from October 2013 to September 2016. Note 2: The test was only estimated when the combined sample of white and minority motorists exceeded 30 searches. + Results are not robust across subsequent specifications. 96 III.F. FINDINGS FROM THE 2013-2016 ANALYSIS This section represents a summary of the findings from the aggregate three-year analysis conducted in the previous sections of this report. III.F (1): AGGREGATE FINDINGS FOR CONNECTICUT 2013-2016 A total of 14.1% of motorists stopped during the analysis period were observed to be Black. A comparable 12.5% of stops were of motorists of Hispanic descent. The results presented in the state-level Veil of Darkness analysis provide strong evidence that a disparity exists in the rate of minority traffic stops by both municipal and State Police departments in the combined 2013 to 2016 sample. Throughout, the disparity persists through the inclusion of both municipal departments as well as officer fixed-effects. Further, the level of significance grows across all specifications when the sample is restricted to moving violations. One overarching observation is that the largest and most persistent disparities driving the VOD results statewide are likely coming from the State Police. Not only are these results strong across all specifications and robustness checks with a high degree of confidence, but the large overall sample size means that they exert more influence on the overall average effect for the mixed sample. Again, it is impossible to clearly link these observed disparities to racial profiling as these differences may be driven by any combination of policing policy, heterogeneous enforcement patterns, or individual officer behavior. The results from the post-stop analysis confirm that the disparity carries through to post-stop behavior across all racial and ethnic groups. In aggregate, Connecticut police departments exhibit a strong tendency to be less successful in motorist searches across all minority groups. III.F (2): VEIL OF DARKNESS ANALYSIS FINDINGS, 2013-2016 Although there is evidence of a disparity at the state level, it is important to note that it is likely that specific departments are driving these statewide trends. In an effort to better identify the source of these racial and ethnic disparities, each analysis was repeated at the department level. The departments that were identified as having a statistically significant disparity are likely to be having the largest effect on the statewide results. Although it is possible that specific officers within departments that were not identified may be engaged in racial profiling, if these behaviors existed, they were not substantial enough to influence the department level results. It is also possible that a small number of individual officers within the identified departments are driving the department level results. The six municipal departments and four state police troop identified to exhibit a statistically significant racial or ethnic disparity include: Ansonia The Ansonia municipal police department was observed to have made 29.8 percent minority stops of which 12.7 percent were Hispanic and 16.1 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the intertwilight window, the odds that a Hispanic motorist was stopped during daylight was 1.4 times larger than 97 the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Groton Town The Groton Town municipal police department was observed to have made 24 percent minority stops of which 8.7 percent were Hispanic and 12.6 percent were black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that black motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 1.6 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that Groton Town was identified with a VOD disparity in the initial 12 month study that covered stops between October 1, 2013 and September 30, 2014. The department was not identified with statistically significant disparities in subsequent annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the initial 12 month study. The aggregate three-year KPT hit-rate analysis also indicated a statistically significant disparity for Hispanic motorists. The hit-rate for white non-Hispanic motorists was 62.3 percent while that for Hispanic motorists was 42.4 percent and that difference was statistically significant at the 95 percent level. Madison The Madison municipal police department was observed to have made 8.2 percent minority stops of which 4.1 percent were Hispanic and 2.8 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 2.5 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Monroe The Monroe municipal police department was observed to have made 13.9 percent minority stops of which 6.7 percent were Hispanic and 5.9 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.5 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that Monroe was identified with a VOD disparity in the year three study presented in Part II of this report. The department was not identified with statistically significant disparities in the first two annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the most recent study period. The aggregate three-year KPT hit-rate analysis also indicated a statistically significant disparity for black motorists. The hit-rate for white non-Hispanic motorists was 50 percent while that for black motorists was 16.7 percent and that difference was statistically significant at the 99 percent level. 98 New Milford The New Milford municipal police department was observed to have made 14.8 percent minority stops of which 8.9 percent were Hispanic and 4.2 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.8 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that New Milford was identified with a VOD disparity in second annual analysis that covered stops between October 1, 2014 and September 30, 2015. The department was not identified with statistically significant disparities in the first analysis or this most recent 12-month study. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the second year study. Norwich The Norwich municipal police department was observed to have made 38.3 percent minority stops of which 14.2 percent were Hispanic and 19.7 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the intertwilight window, the odds that a Hispanic motorist was stopped during daylight was 1.3 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that Norwich was identified with a VOD disparity in the year three study presented in Part II of this report. The department was not identified with statistically significant disparities in the first two annual studies. Therefore, it is reasonable that the average effect of a threeyear analysis would show a disparity which is largely driven by data from the most recent study period. The aggregate three-year KPT hit-rate analysis also indicated a statistically significant disparity for Hispanic motorists. The hit-rate for white non-Hispanic motorists was 44.1 percent while that for Hispanic motorists was 32.7 percent and that difference was statistically significant at the 95 percent level. State Police Troop C The State Police Troop C was observed to have made 24 percent minority stops of which 7.5 percent were Hispanic and 9.5 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that black and Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 1.3 times larger than the odds during darkness. The odds that a Hispanic motorist was stopped during daylight was 1.28 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. However, it is important to note that Troop C was identified with a VOD disparity in the initial 12 month study that covered stops between October 1, 2013 and September 30, 2014. The Troop was not identified with statistically significant disparities in subsequent annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the initial 12 month study. 99 The aggregate three-year KPT hit-rate analysis also indicated a statistically significant for Hispanic motorists. The hit-rate for white non-Hispanic motorists was 44.8 percent while that for Hispanic motorists was 27.2 percent and that difference was statistically significant at the 99 percent level. State Police Troop G The State Police Troop G was observed to have made 49.3 percent minority stops of which 20.7 percent were Hispanic and 24.1 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.2 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. The hit-rate for white nonHispanic motorists was 37.2 percent while that for black motorists was 28.1 percent and Hispanic motorists was 25.6 percent. Those differences were statistically significant at the 99 percent level. Similarly, the synthetic control revealed a disparity in the rate in which Hispanic motorists were stopped that was statistically significant at the 95 percent level. State Police Troop H The State Police Troop H was observed to have made 44.8 percent minority stops of which 16.3 percent were Hispanic and 24 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that black motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a black motorist was stopped during daylight was 1.2 times larger than the odds during darkness. These results were statistically significant at the 95 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Similarly, the synthetic control revealed a disparity in the rate in which black motorists were stopped that was statistically significant at the 99 percent level. However, it is important to note that Troop H was identified with a VOD disparity in the first and second year studies. The Troop was not identified with statistically significant disparities in the most recent 12 month analysis. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the first and second year studies. State Police Troop K The State Police Troop K was observed to have made 21.4 percent minority stops of which 8.5 percent were Hispanic and 9.9 percent were Black motorists from October 2013 to September 2016. The aggregate three-year VOD analysis indicated a statistically significant disparity in the rate that Hispanic motorists were stopped during daylight relative to darkness. Within the inter-twilight window, the odds that a Hispanic motorist was stopped during daylight was 1.4 times larger than the odds during darkness. These results were statistically significant at the 99 percent level and robust to the inclusion of a variety of controls, officer fixed-effects, and a restricted sample of moving violations. Similarly, the synthetic control revealed a disparity in the rate in which Hispanic motorists were stopped that was statistically significant at the 99 percent level. III.F (3): DESCRIPTIVE STATISTICS AND INTUITIVE MEASURE FINDINGS, 2013-2016 In addition to the six municipal police departments and four state police troop identified to exhibit statistically significant racial or ethnic disparities in the VOD analysis, seven departments were identified using the descriptive tests. The descriptive tests are designed as a screening tool to identify the 100 jurisdictions where consistent disparities that exceed certain thresholds have appeared in the data. They compare stop data to three different benchmarks: (1) statewide average, (2) the estimated driving population, and (3) resident-only stops. Although it is understood that certain assumptions have been made in the design of each of the three measures, it is reasonable to believe that departments with consistent data disparities that separate them from the majority of other departments should be subject to further review and analysis with respect to the factors that may be causing these differences. The seven municipal departments identified to exhibit a significant racial or ethnic disparity using the descriptive measures include: Wethersfield The Wethersfield municipal police department was observed to have made 49 percent minority stops of which 28.9 percent were Hispanic and 18.6 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in all nine possible measures. Wethersfield received a disparity score of 8.5 out of a possible nine points, indicating consistently significant racial and ethnic disparities in traffic stops. Similarly, the synthetic control revealed a disparity in the rate in which Hispanic motorists were stopped that was statistically significant at the 99 percent level. Wethersfield was identified with significant racial and ethnic disparities in all three annual reports. Therefore, it is unsurprising that the department would be identified with statistically significant disparities in a three-year aggregate analysis. Stratford The Stratford municipal police department was observed to have made 50.9 percent minority stops of which 18.5 percent were Hispanic and 30.9 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in six of the nine possible measures. Stratford received a disparity score of 6.0 out of a possible nine points. Stratford was identified with significant racial and ethnic disparities in all three annual reports. Therefore, it is unsurprising that the department would be identified with statistically significant disparities in a three-year aggregate analysis. East Hartford The East Hartford municipal police department was observed to have made 65.9 percent minority stops of which 26.7 percent were Hispanic and 37.6 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in six of the nine possible measures. East Hartford received a disparity score of 6.0 out of a possible nine points. The hit-rate for white nonHispanic motorists was 50.9 percent while that for Hispanic motorists was 41 percent and that difference was statistically significant at the 95 percent level. East Hartford was identified with significant racial and ethnic disparities in all three annual reports. Therefore, it is unsurprising that the department would be identified with statistically significant disparities in a three-year aggregate analysis. New Britain The New Britain municipal police department was observed to have made 60.8 percent minority stops of which 41.8 percent were Hispanic and 17.7 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in five of the nine possible measures. 101 New Britain received a disparity score of 5.0 out of a possible nine points. New Britain was identified with significant racial and ethnic disparities in the first and second year studies. The department was not identified with statistically significant disparities in the most recent 12 month analysis. Therefore, it is reasonable that the average effect of a three-year aggregate analysis would show a disparity which is largely driven by data from the first and second year studies. Hamden The Hamden municipal police department was observed to have made 43.9 percent minority stops of which 8.8 percent were Hispanic and 34.1 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in five of the nine possible measures. Hamden received a disparity score of 5.0 out of a possible nine points. Hamden was identified with a disparity using the descriptive measures in the initial 12 month study that covered stops between October 1, 2013 and September 30, 2014. The department was not identified with statistically significant disparities in subsequent annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the initial 12 month study. Manchester The Manchester municipal police department was observed to have made 42 percent minority stops of which 15 percent were Hispanic and 23.8 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in all three benchmark areas as well as in five of the nine possible measures. Manchester received a disparity score of 5.0 out of a possible nine points. Similarly, the synthetic control revealed a disparity in the rate in which Black motorists were stopped that was statistically significant at the 99 percent level. Manchester was identified with a disparity using the descriptive measures in the initial 12 month study that covered stops between October 1, 2013 and September 30, 2014. The department was not identified with statistically significant disparities in subsequent annual studies. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the initial 12 month study. Trumbull The Trumbull municipal police department was observed to have made 36.8 percent minority stops of which 15.3 percent were Hispanic and 19.2 percent were Black motorists from October 2013 to September 2016. The aggregate three-year descriptive analysis indicated that the department exceeded the disparity threshold levels in two of the three benchmark areas as well as in five of the nine possible measures. Trumbull received a disparity score of 4.5 out of a possible nine points. Trumbull was identified with a disparity using the descriptive measures in the Year 2 study and the most recent study presented in Part II of this report. Therefore, it is reasonable that the average effect of a three-year analysis would show a disparity which is largely driven by data from the year 2 and year 3 studies. In addition to these seven departments, others were identified with racial and ethnic disparities when compared to one or more of the descriptive measures. It would be beneficial for departments with smaller disparities to evaluate their own data to better understand the reasons for any relevant patterns. A total of seven departments were identified with statistically significant disparities in the synthetic control analysis. Identification in this test is not, in and of itself, sufficient to be identified for further analysis in the absence of significant results in any of the other five tests. 102 III.F (4): FOLLOW-UP ANALYSIS The entirety of the statewide traffic stop data analysis as presented in this report is utilized as a screening tool by which the Advisory Board and project staff can focus resources on those departments displaying the greatest level of disparities in their respective stop data. As noted previously, racial and ethnic disparities in any traffic stop analysis do not, by themselves, provide conclusive evidence of racial profiling. Statistical disparities do, however, provide significant evidence of the presence of idiosyncratic data trends that warrant further analysis. By conducting in-depth follow-up analyses on the departments identified through the screening process, the public has a better understanding as to why and how disparities exist. This transparency is intended to assist in achieving the goal of increasing trust between the public and law enforcement. Based on our analytical results for traffic stops conducted from October 1, 2013 through September 30, 2016 there were 13 municipal police departments and two state police troops identified with significant racial and ethnic disparities. A full in-depth follow-up analysis will be conducted only for those departments that have not been identified in any of the previous annual studies. Those departments are: (1) Ansonia, (2) Madison, (3) Troop G, and (4) Troop K. For the 11 remaining municipal police departments, it is reasonable that the average effect of a threeyear aggregate analysis would show a disparity which is largely driven by data from previous studies in which the departments were already identified. A full follow-up analysis was previously conducted for nine of the 11 departments (East Hartford, Groton Town, Hamden, Manchester, New Britain, New Milford, Stratford, Trumbull, and Wethersfield). Monroe and Norwich were identified in the year 3 annual analysis presented in Part II of this report. A full follow-up analysis will be conducted for both these departments as a result of the year 3 analysis. Although further analysis is important, a major component of addressing racial profiling in Connecticut is bringing law enforcement officials and community members together in an effort to build trust by discussing relationships between police and the community. The project staff has conducted several public forums throughout the state to bring these groups together and will continue these dialogues into the foreseeable future. They serve as an important tool to inform the public of their rights and the role of law enforcement in serving their communities. Through its ongoing work with OPM in implementing the Alvin Penn Act, the IMRP is committed to working with all law enforcement agencies to make improvements that will lead to enhanced relationships between the police and community. 103 TECHNICAL APPENDIX All tables in the technical appendix are identified by the section and table number where they can be found in the report. A complete listing is provided below. Part II Appendix: Traffic Stop Analysis and Findings, 2015-16 Table II.A.1: Rate of Traffic Stops per 1,000 Residents (Sorted Alphabetically) Table II.A.4: Basis for Stop (Sorted by % Speeding) Table II.A.5: Basis for Stop (Sorted by % Registration Violation) Table II.A.6: Basis for Stop (Sorted by % Cell Phone Violation) Table II.A.7: Outcome of Stop (Sorted by % Infraction Ticket) Table II.A.8: Outcome of Stop (Sorted by % Warnings) Table II.A.9: Outcome of Stop (Sorted by % Arrest) Table II.A.10: Number of Searches (Sorted by % Search) Table II.B.1: Statewide Average Comparison for Black Drivers (Sorted Alphabetically) Table II.B.2: Statewide Average Comparison for Hispanic Drivers (Sorted Alphabetically) Table II.B.3: Statewide Average Comparison for Minority Drivers (Sorted Alphabetically) Table II.B.4/II.B.5 a: Ratio of Minority EDP to Minority Stops (Sorted Alphabetically) Table II.B.4/II.B.5 b: Ratio of Black EDP to Black Stops (Sorted Alphabetically) Table II.B.4/II.B.5 c: Ratio of Hispanic EDP to Hispanic Stops (Sorted Alphabetically) Table II.B.6/II.B.7 a: Ratio of Minority Resident Pop. to Minority Resident Stops (Sorted Alphabetically) Table II.B.6/II.B.7 b: Ratio of Black Resident Population to Black Resident Stops (Sorted Alphabetically) Table II.B.6/II.B.7 c: Ratio of Hispanic Resident Pop. to Hispanic Resident Stops (Sorted Alphabetically) Table II.B.8: Departments with Disparities Relative to Descriptive Benchmarks (Values) Table II.C.5.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops Table II.C.5.2: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops Table II.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops Table II.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2015-2016 Table II.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches Part III Appendix: Traffic Stop Analysis and Findings, 2013-16 Table III.A.5: Basis for Stop (Sorted by % Speeding) Table III.A.6: Basis for Stop (Sorted by % Registration Violation) Table III.A.7: Basis for Stop (Sorted by % Cell Phone Violation) Table III.A.8: Basis for Stop (Sorted by % Equipment Violation) Table III.A.9: Outcome of Stop (Sorted by % Infraction Ticket) Table III.A.10: Outcome of Stop (Sorted by % Warnings) Table III.A.11: Outcome of Stop (Sorted by % Arrest) Table III.A.12: Number of Searches (Sorted by % Search) Table III.B.1: Statewide Average Comparison for Black Drivers (Sorted Alphabetically) 104 Table III.B.2: Statewide Average Comparison for Hispanic Drivers (Sorted Alphabetically) Table III.B.3: Statewide Average Comparison for Minority Drivers (Sorted Alphabetically) Table III.B.4/III.B.5 a: Ratio of Minority EDP to Minority Stops (Sorted Alphabetically) Table III.B.4/III.B.5 b: Ratio of Black EDP to Black Stops (Sorted Alphabetically) Table III.B.4/III.B.5 c: Ratio of Hispanic EDP to Hispanic Stops (Sorted Alphabetically) Table III.B.6/III.B.7 a: Ratio of Minority Resident Pop. to Minority Resident Stops (Sorted Alphabetically) Table III.B.6/III.B.7 b: Ratio of Black Resident Population to Black Resident Stops (Sorted Alphabetically) Table III.B.6/III.B.7 c: Ratio of Hispanic Resident Pop. to Hispanic Resident Stops (Sorted Alphabetically) Table III.B.8: Departments with Disparities Relative to Descriptive Benchmarks (Values) Table II.C.7.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches Table III.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 105 PART II APPENDIX: TRAFFIC STOP ANALYSIS AND FINDINGS, 2015-16 Table II.A.1: Rate of Traffic Stops per 1,000 Residents (Sorted Alphabetically) 2015-2016 Town Name State of CT Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton* Guilford Hamden Hartford Ledyard* Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford 2010 16 and Over 2015-2016 Stops per Stops per 1,000 Census Pop. Traffic Stops Resident Residents 2,825,946 557,746 0.20 197 14,979 5,110 0.34 341 13,855 907 0.07 65 16,083 5,257 0.33 327 14,675 2,861 0.19 195 16,982 3,263 0.19 192 23,532 4,435 0.19 188 109,401 3,118 0.03 29 48,439 5,080 0.10 105 12,847 2,299 0.18 179 7,992 1,292 0.16 162 21,049 5,251 0.25 249 10,540 2,441 0.23 232 9,779 1,940 0.20 198 11,357 1,553 0.14 137 64,361 5,907 0.09 92 14,004 3,106 0.22 222 10,391 3,021 0.29 291 10,255 547 0.05 53 40,229 7,620 0.19 189 24,114 3,512 0.15 146 9,164 907 0.10 99 5,553 712 0.13 128 33,218 7,904 0.24 238 45,567 8,817 0.19 193 20,318 5,507 0.27 271 26,217 4,413 0.17 168 8,716 807 0.09 93 46,370 5,937 0.13 128 31,520 5,837 0.18 185 17,672 4,270 0.24 242 50,012 3,767 0.08 75 93,669 4,505 0.05 48 11,527 1,300 0.11 113 14,073 4,106 0.29 292 46,667 12,267 0.26 263 47,445 2,055 0.04 43 5,843 59 0.01 10 38,747 1,616 0.04 42 43,135 5,569 0.13 129 14,918 4,625 0.31 310 25,099 4,843 0.19 193 57,164 6,734 0.12 118 14,138 6,445 0.46 456 100,702 19,099 0.19 190 21,835 4,120 0.19 189 21,891 2,791 0.13 127 Table II.A.1: Rate of Traffic Stops per 1,000 Residents (Sorted Alphabetically) 2015-2016 Town Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Wilton Winchester Windham Windsor Windsor Locks Wolcott Woodbridge 2010 16 and Over 2015-2016 Stops per Stops per 1,000 Census Pop. Traffic Stops Resident Residents 24,978 5,071 0.20 203 20,171 5,229 0.26 259 11,549 1,089 0.09 94 19,608 3,203 0.16 163 68,034 4,191 0.06 62 31,638 6,183 0.20 195 8,330 3,142 0.38 377 11,017 4,295 0.39 390 11,918 1,740 0.15 146 14,605 3,470 0.24 238 9,660 1,943 0.20 201 7,480 199 0.03 27 7,507 1,094 0.15 146 6,955 2,023 0.29 291 18,111 7,979 0.44 441 16,224 3,566 0.22 220 13,260 3,702 0.28 279 32,010 740 0.02 23 17,773 3,868 0.22 218 20,162 3,475 0.17 172 34,301 4,790 0.14 140 98,070 5,519 0.06 56 15,078 2,819 0.19 187 40,980 1,957 0.05 48 10,782 1,336 0.12 124 6,224 542 0.09 87 29,251 6,527 0.22 223 27,678 2,340 0.08 85 23,800 4,104 0.17 172 36,530 8,980 0.25 246 83,964 3,208 0.04 38 15,760 4,874 0.31 309 18,154 1,698 0.09 94 49,650 9,079 0.18 183 44,518 6,127 0.14 138 7,255 491 0.07 68 19,410 5,964 0.31 307 21,607 3,122 0.14 144 12,973 6,020 0.46 464 9,133 724 0.08 79 20,176 2,460 0.12 122 23,222 5,497 0.24 237 10,117 2,496 0.25 247 13,175 376 0.03 29 7,119 1,585 0.22 223 Table II.A.4: Basis for Stop (Sorted by % Speeding) 2015-2016 Department Name Ledyard* Suffield Simsbury Easton Portland New Milford Enfield Guilford Redding Ridgefield Groton Long Point Wolcott CSP Headquarters Southington Windsor Locks Newtown Old Saybrook Thomaston Madison Waterford Cheshire Granby Seymour Groton City Central CT State Unviversity Bethel Coventry Putnam Windsor Canton Troop B East Hampton Norwich Weston Troop E Troop H Rocky Hill Troop C East Windsor Troop I Brookfield Troop G Woodbridge Department of Motor Vehicle Southern CT State University Monroe Total 1,300 1,336 3,868 712 199 2,791 7,904 4,270 2,023 7,979 132 376 11,486 4,790 2,496 5,229 3,142 542 4,106 4,874 5,251 807 3,702 1,274 2,092 2,861 1,940 1,094 5,497 1,292 8,094 547 6,183 491 19,183 17,932 3,566 21,804 907 13,415 2,299 21,411 1,585 1,867 666 4,625 Speed Defective Display of Equipment Moving Related Cell Phone Registration Lights Plates Violation Violation 67.85% 60.78% 56.88% 55.90% 55.28% 54.93% 53.48% 53.02% 52.40% 52.34% 51.52% 51.33% 50.20% 49.37% 46.23% 46.11% 45.32% 45.20% 43.62% 42.86% 42.22% 41.26% 40.76% 40.03% 39.77% 39.74% 39.64% 38.85% 38.31% 37.38% 35.73% 35.65% 35.44% 35.03% 34.90% 33.83% 33.65% 33.56% 32.08% 31.59% 31.54% 31.14% 31.04% 30.80% 30.78% 30.40% 1.54% 2.69% 9.75% 1.83% 7.04% 3.05% 2.56% 6.65% 1.78% 15.06% 12.88% 12.50% 8.31% 9.06% 7.49% 9.45% 10.03% 0.37% 5.19% 4.97% 11.29% 17.35% 5.46% 6.67% 4.92% 13.04% 9.69% 6.31% 3.27% 11.30% 2.82% 8.96% 9.14% 3.05% 3.13% 5.54% 10.82% 3.32% 6.39% 5.55% 19.83% 7.70% 19.62% 11.03% 5.86% 9.56% 1.92% 0.75% 1.37% 4.49% 3.02% 4.87% 5.23% 1.71% 15.77% 4.79% 3.03% 1.06% 3.01% 4.38% 1.92% 7.96% 4.23% 2.21% 8.38% 2.36% 4.42% 2.48% 2.73% 1.81% 1.86% 6.05% 3.25% 1.01% 3.64% 2.71% 16.68% 12.07% 2.13% 5.30% 11.74% 6.93% 5.92% 11.20% 8.71% 9.59% 3.31% 16.92% 9.78% 8.84% 1.05% 9.58% 7.10% 11.75% 7.16% 3.09% 2.01% 12.22% 7.91% 12.58% 6.62% 6.14% 1.52% 6.38% 0.75% 9.39% 8.61% 8.09% 13.88% 16.05% 7.14% 16.21% 12.76% 9.79% 10.37% 14.99% 13.29% 7.10% 8.25% 20.57% 18.61% 12.54% 5.08% 9.87% 18.02% 4.48% 3.93% 1.82% 15.06% 3.76% 11.58% 2.72% 13.92% 2.09% 8.90% 1.39% 9.61% 12.71% 0.54% 0.00% 1.09% 0.84% 2.51% 1.29% 2.51% 0.84% 0.30% 0.08% 0.00% 1.60% 1.15% 1.19% 0.48% 1.72% 0.22% 3.14% 0.61% 5.42% 2.57% 2.11% 1.67% 0.39% 2.15% 0.80% 0.62% 3.29% 1.24% 0.23% 1.73% 2.56% 2.85% 0.41% 0.80% 1.26% 2.13% 1.36% 0.99% 0.95% 0.61% 1.19% 4.35% 1.93% 0.15% 2.70% 0.06% 0.00% 0.03% 0.42% 0.50% 0.25% 0.52% 0.02% 0.00% 0.01% 0.00% 0.53% 0.10% 0.23% 0.20% 0.17% 0.22% 0.00% 0.68% 0.41% 0.08% 0.00% 0.24% 0.24% 0.10% 0.31% 0.57% 0.37% 0.15% 0.31% 0.28% 0.18% 0.24% 0.00% 0.17% 0.04% 0.11% 0.17% 0.22% 0.05% 0.13% 0.02% 0.32% 1.18% 0.00% 0.26% Other Seatbelt Stop Sign 3.77% 9.54% 0.23% 12.80% 0.82% 0.37% 4.96% 1.60% 1.58% 5.48% 4.49% 1.69% 7.04% 2.01% 0.00% 5.45% 4.41% 1.11% 5.92% 1.61% 7.01% 3.16% 1.43% 2.27% 4.99% 4.20% 2.87% 1.64% 3.03% 2.62% 0.00% 3.03% 3.03% 3.72% 2.60% 0.80% 7.11% 2.31% 14.93% 4.53% 1.32% 5.24% 3.37% 3.89% 5.49% 8.34% 1.66% 1.05% 5.76% 2.16% 0.51% 10.89% 7.01% 0.37% 7.72% 2.19% 2.95% 8.86% 3.82% 0.59% 7.39% 1.09% 3.35% 9.79% 0.87% 3.72% 5.00% 1.73% 2.84% 2.75% 3.06% 4.47% 5.54% 6.12% 3.49% 3.60% 1.89% 2.03% 9.95% 3.45% 7.11% 6.22% 2.56% 5.48% 7.24% 1.55% 4.33% 9.83% 7.89% 0.70% 5.65% 3.35% 3.14% 12.80% 3.29% 1.83% 9.43% 3.41% 1.83% 5.50% 27.90% 0.00% 10.92% 3.76% 2.77% 16.48% 6.43% 2.01% 7.15% 1.74% 1.04% 6.05% 3.19% 2.03% 9.15% 2.54% 5.62% 14.54% 2.18% 1.91% 9.05% 1.57% 0.74% 17.05% 3.15% 2.90% 4.42% 4.42% 2.90% 15.96% 11.68% 1.71% 5.41% 3.30% 7.96% 12.43% 2.66% 1.64% 1.15% 5.01% 7.99% 14.89% 6.53% 3.22% 3.83% 9.70% 9.44% 6.60% 24.24% 4.00% 0.38% 8.33% 13.38% 9.01% 10.95% 4.98% 10.52% 0.96% 7.62% 4.71% 20.45% 19.78% 3.49% 18.94% 6.19% 2.29% 5.06% 7.97% 3.83% 2.74% 4.77% 14.87% 1.86% 0.71% 13.74% 2.74% 8.82% 2.40% 10.92% 0.42% 3.03% 1.71% 0.15% 11.59% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 1.69% 0.22% 0.18% 0.56% 1.01% 0.36% 0.85% 0.09% 1.04% 0.11% 0.76% 3.20% 0.74% 0.48% 0.64% 0.73% 1.46% 2.77% 0.71% 2.75% 1.50% 0.62% 0.49% 1.41% 0.43% 0.45% 1.49% 0.46% 0.58% 0.46% 1.62% 2.01% 1.00% 0.41% 1.45% 1.54% 0.70% 1.10% 3.86% 1.36% 0.39% 2.10% 2.52% 0.64% 1.35% 0.76% 0.38% 0.45% 0.18% 4.49% 0.00% 0.29% 0.48% 0.75% 0.20% 0.76% 0.00% 0.00% 8.20% 0.44% 0.32% 1.03% 1.15% 0.00% 7.77% 0.39% 0.15% 0.37% 1.11% 0.08% 7.60% 0.24% 6.80% 0.18% 0.31% 0.70% 17.53% 0.18% 0.71% 0.41% 21.06% 19.87% 0.45% 29.45% 0.44% 24.08% 0.13% 11.72% 4.23% 4.34% 0.60% 0.71% 1.85% 4.27% 6.93% 1.12% 12.56% 7.42% 6.78% 7.70% 0.30% 6.17% 0.00% 4.30% 1.12% 5.59% 7.29% 4.28% 3.98% 6.64% 1.58% 8.78% 4.09% 6.57% 6.81% 4.24% 10.99% 4.65% 2.37% 12.16% 14.55% 7.74% 1.46% 7.31% 10.76% 2.44% 2.32% 1.52% 7.04% 1.04% 8.27% 1.79% 7.87% 1.61% 3.53% 4.07% 32.28% 3.87% 0.00% 0.00% 0.03% 0.56% 0.50% 0.18% 0.14% 0.02% 0.05% 0.13% 0.00% 0.53% 0.53% 0.23% 0.04% 0.33% 0.00% 0.00% 0.22% 0.39% 0.44% 0.00% 0.16% 0.00% 0.05% 0.17% 0.36% 0.18% 0.15% 0.08% 0.43% 0.18% 0.02% 0.20% 0.75% 0.98% 0.22% 0.51% 0.88% 1.03% 0.00% 1.67% 0.50% 1.02% 1.20% 0.15% 2.38% 0.07% 0.28% 0.14% 0.00% 0.97% 1.19% 0.05% 0.05% 0.51% 0.00% 7.45% 1.15% 0.21% 0.64% 0.08% 0.13% 0.37% 0.73% 1.23% 1.03% 0.37% 0.19% 0.08% 0.19% 0.98% 0.26% 0.09% 1.02% 0.15% 0.68% 0.37% 0.26% 0.00% 0.45% 1.02% 0.22% 0.52% 0.44% 0.27% 0.00% 0.34% 0.44% 3.70% 0.30% 0.97% Table II.A.4: Basis for Stop (Sorted by % Speeding) 2015-2016 Department Name Stonington Clinton New Canaan Derby Plainfield Troop K Troop A Troop L Watertown North Branford Fairfield Greenwich Wilton Westport Troop F Bristol Darien Orange East Hartford Naugatuck Bloomfield Ansonia Groton Town Farmington Glastonbury Troop D North Haven Wethersfield Shelton Meriden Plainville Torrington Hartford New Haven Middlebury Berlin University of Connecticut Willimantic Plymouth Branford South Windsor Vernon New London Avon Winsted Cromwell Total 2,819 2,441 6,445 3,021 1,740 17,769 19,136 11,017 1,698 1,089 8,817 5,937 6,020 5,964 22,009 5,080 3,106 4,295 7,620 4,843 3,263 5,110 4,431 5,507 4,413 14,877 3,203 3,122 740 2,055 3,470 6,527 4,505 19,099 59 5,257 3,219 2,460 1,943 4,435 3,475 4,104 4,120 907 724 1,553 Speed Defective Display of Equipment Moving Related Cell Phone Registration Lights Plates Violation Violation Other 30.12% 29.91% 29.87% 29.63% 29.37% 29.35% 28.79% 28.48% 28.45% 28.37% 28.31% 27.99% 27.46% 27.08% 26.99% 26.99% 26.43% 26.12% 26.10% 26.06% 25.87% 25.52% 25.50% 25.31% 24.22% 24.17% 23.07% 22.39% 22.03% 21.46% 21.10% 20.93% 19.87% 19.24% 18.64% 18.57% 18.48% 17.52% 16.98% 16.53% 16.49% 16.40% 15.75% 15.44% 14.78% 14.55% 7.45% 2.80% 3.65% 7.74% 2.25% 1.97% 4.80% 0.63% 4.20% 1.09% 5.62% 1.99% 6.09% 4.42% 4.02% 2.63% 1.35% 8.95% 6.15% 1.01% 4.47% 14.64% 3.47% 0.44% 1.96% 0.45% 2.06% 2.36% 3.04% 3.03% 2.66% 8.48% 1.45% 9.82% 1.68% 1.00% 1.38% 10.29% 5.45% 6.11% 1.38% 1.29% 4.60% 2.56% 1.87% 3.65% 1.51% 0.78% 1.95% 3.94% 10.36% 3.87% 2.56% 6.21% 3.30% 0.96% 13.24% 0.14% 10.75% 4.53% 1.82% 4.70% 2.08% 0.43% 5.15% 3.51% 10.84% 4.27% 6.78% 6.78% 1.50% 2.30% 6.43% 0.93% 9.31% 3.33% 4.94% 5.51% 4.44% 1.87% 0.98% 9.90% 2.83% 1.88% 4.93% 3.76% 14.00% 0.00% 4.28% 11.33% 4.64% 3.09% 9.61% 4.92% 11.50% 16.68% 2.01% 4.98% 5.47% 3.74% 8.13% 5.33% 10.79% 13.81% 12.13% 24.46% 3.10% 11.61% 6.99% 16.97% 13.25% 13.07% 5.85% 11.59% 5.20% 14.00% 19.26% 2.61% 11.68% 4.77% 7.70% 14.79% 12.82% 3.78% 15.87% 7.16% 28.81% 25.30% 4.85% 14.47% 9.16% 11.93% 10.76% 3.27% 13.16% 0.77% 3.73% 16.74% 12.10% 2.38% 10.61% 7.45% 0.86% 11.61% 15.92% 21.10% 17.26% 23.14% 7.08% 10.44% 8.90% 3.97% 11.63% 12.44% 5.92% 6.64% 12.06% 3.04% 1.78% 1.59% 10.70% 15.24% 8.32% 14.10% 15.77% 10.22% 8.65% 5.50% 11.50% 1.07% 1.51% 5.33% 6.78% 5.82% 2.64% 7.32% 5.40% 28.30% 9.70% 3.73% 2.04% 3.64% 4.01% 10.75% 10.61% 17.37% 17.61% 5.10% 21.32% 2.49% 2.90% 7.31% 3.83% 5.51% 6.92% 7.68% 19.09% 9.10% 2.37% 7.68% 11.72% 12.29% 2.61% 10.53% 12.38% 14.36% 13.70% 12.71% 12.94% 3.24% 9.62% 13.42% 7.70% 6.08% 17.41% 29.14% 5.79% 6.68% 3.39% 10.84% 29.42% 16.18% 15.23% 4.74% 17.15% 19.15% 8.54% 27.23% 17.40% 13.65% 0.64% 4.18% 4.25% 3.08% 1.72% 2.53% 2.14% 3.89% 7.07% 2.11% 2.43% 3.30% 1.78% 2.90% 1.05% 1.87% 9.27% 4.61% 3.11% 2.79% 4.01% 2.29% 1.70% 0.98% 1.20% 1.34% 1.90% 7.85% 3.38% 1.27% 5.45% 3.97% 4.55% 4.67% 0.00% 2.66% 2.45% 0.93% 13.59% 0.63% 8.37% 4.09% 1.14% 1.10% 7.73% 1.93% 0.25% 0.41% 0.14% 0.10% 0.23% 0.26% 0.11% 0.81% 0.06% 0.64% 0.15% 0.34% 0.42% 0.10% 0.28% 0.14% 0.10% 0.16% 0.09% 0.23% 0.18% 0.35% 0.11% 0.33% 0.25% 0.23% 0.22% 0.19% 0.54% 0.44% 0.14% 0.37% 0.27% 0.05% 0.00% 0.04% 0.96% 0.41% 0.36% 0.18% 0.35% 0.83% 0.44% 0.00% 0.69% 0.06% 9.97% 13.31% 5.49% 5.96% 21.03% 6.45% 14.11% 6.84% 4.71% 11.48% 6.03% 8.34% 12.46% 5.18% 7.94% 5.39% 5.57% 4.52% 3.14% 7.66% 7.08% 8.94% 18.21% 13.15% 7.07% 6.57% 4.68% 12.65% 12.84% 6.86% 8.50% 3.88% 6.66% 1.91% 0.00% 6.66% 12.36% 8.54% 7.31% 4.96% 5.70% 19.47% 5.85% 20.29% 10.50% 11.20% Seatbelt Stop Sign 5.25% 6.23% 4.59% 11.42% 12.87% 3.90% 1.12% 2.03% 12.07% 5.42% 4.51% 12.16% 5.15% 9.94% 1.64% 7.56% 5.47% 2.33% 4.99% 13.26% 12.11% 17.85% 5.55% 5.85% 9.77% 3.43% 6.40% 5.45% 6.08% 10.12% 5.01% 20.27% 15.03% 9.13% 54.24% 5.35% 17.65% 8.74% 10.76% 7.80% 8.98% 12.43% 20.15% 8.05% 5.11% 4.51% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 2.38% 0.45% 0.78% 5.06% 1.09% 1.16% 2.07% 3.39% 0.88% 4.04% 3.21% 0.49% 0.53% 0.52% 0.99% 2.20% 0.68% 1.49% 9.95% 0.31% 0.74% 0.86% 2.03% 1.31% 3.85% 1.89% 3.75% 5.96% 2.30% 5.50% 1.76% 0.49% 4.11% 2.08% 1.69% 1.22% 0.53% 3.25% 1.13% 1.94% 1.73% 0.97% 1.84% 0.77% 3.45% 1.67% 2.77% 1.11% 0.39% 1.36% 0.00% 27.59% 13.42% 12.76% 0.94% 0.46% 1.07% 2.54% 0.17% 2.23% 35.94% 0.87% 8.08% 4.14% 1.05% 1.07% 2.73% 0.04% 0.59% 1.58% 0.43% 25.44% 1.40% 1.09% 2.03% 1.22% 0.00% 0.52% 2.69% 0.42% 0.00% 3.21% 0.65% 1.46% 0.26% 0.52% 0.29% 1.24% 3.35% 0.55% 4.14% 0.06% 5.68% 7.17% 8.38% 6.29% 3.91% 0.99% 1.49% 0.78% 5.48% 5.33% 9.38% 6.69% 7.76% 8.85% 1.01% 10.94% 7.15% 16.39% 4.70% 9.62% 23.32% 8.63% 9.66% 6.86% 6.21% 1.24% 11.55% 7.66% 12.70% 9.68% 7.81% 12.78% 12.65% 24.58% 13.56% 16.05% 2.21% 7.36% 3.81% 14.81% 9.24% 12.26% 17.91% 8.05% 12.71% 16.74% 0.11% 0.37% 0.28% 0.26% 0.11% 0.66% 1.57% 0.81% 0.24% 1.01% 0.57% 1.20% 0.40% 0.07% 0.51% 0.79% 0.10% 0.54% 0.56% 0.17% 0.18% 0.27% 0.38% 0.29% 0.25% 0.67% 0.59% 0.10% 0.00% 0.83% 0.46% 0.09% 0.51% 0.41% 0.00% 0.38% 0.03% 0.41% 0.05% 0.32% 0.12% 0.10% 0.07% 0.11% 0.00% 0.13% 0.28% 0.82% 1.89% 2.18% 0.17% 0.41% 0.39% 1.40% 0.59% 0.00% 0.45% 1.11% 1.36% 1.16% 0.47% 0.37% 1.26% 1.12% 6.72% 0.64% 1.10% 0.55% 1.15% 0.11% 0.34% 0.85% 0.59% 4.00% 0.68% 0.97% 1.53% 0.20% 1.84% 3.24% 1.69% 0.10% 0.40% 0.77% 5.51% 1.04% 0.26% 1.36% 1.07% 0.00% 0.14% 0.26% Table II.A.4: Basis for Stop (Sorted by % Speeding) 2015-2016 Department Name Stamford East Haven West Hartford Manchester Wallingford Trumbull Middletown Newington Milford Danbury Stratford Waterbury Norwalk West Haven Bridgeport New Britain Eastern CT State University Hamden Yale University State Capitol Police Western CT State University Total 5,519 3,512 9,079 12,267 8,980 2,340 1,616 5,071 2,778 5,907 1,957 3,208 4,191 6,127 3,118 6,734 128 3,767 380 222 20 Speed Defective Display of Equipment Moving Related Cell Phone Registration Lights Plates Violation Violation 14.11% 13.35% 13.23% 13.12% 12.48% 12.22% 12.19% 11.18% 11.05% 10.90% 10.88% 10.75% 10.47% 9.30% 9.08% 7.26% 7.03% 6.69% 1.84% 0.00% 0.00% 27.09% 9.34% 28.30% 11.99% 15.41% 11.50% 3.53% 2.64% 16.27% 41.21% 5.16% 18.39% 22.14% 7.82% 24.73% 13.22% 1.56% 41.86% 4.47% 2.25% 5.00% 2.03% 8.26% 11.50% 9.11% 8.64% 19.02% 7.86% 14.55% 2.81% 10.68% 13.64% 8.60% 11.67% 16.66% 4.30% 7.92% 0.00% 9.85% 6.84% 0.90% 0.00% 6.25% 12.04% 6.62% 12.23% 13.41% 14.02% 21.91% 27.65% 8.42% 6.30% 14.31% 4.58% 6.92% 18.98% 3.66% 10.72% 20.31% 4.38% 4.74% 28.38% 0.00% 1.56% 6.06% 2.95% 3.71% 5.42% 8.63% 8.35% 3.27% 4.36% 1.08% 5.01% 4.05% 2.03% 4.88% 2.95% 4.01% 0.78% 1.65% 1.58% 0.90% 0.00% 0.09% 0.46% 0.24% 0.16% 0.67% 0.30% 0.31% 0.91% 0.11% 0.17% 0.10% 1.28% 0.84% 0.91% 0.40% 0.45% 0.78% 0.21% 0.53% 0.00% 0.00% Other Seatbelt Stop Sign 5.82% 6.60% 6.36% 6.69% 3.53% 1.03% 11.44% 2.46% 2.73% 5.11% 1.30% 14.92% 7.85% 4.11% 7.15% 4.96% 4.36% 1.92% 10.09% 3.90% 2.17% 10.92% 2.72% 0.65% 6.08% 24.48% 4.43% 4.18% 2.57% 0.25% 11.14% 5.72% 3.27% 9.26% 3.18% 5.83% 7.61% 5.25% 3.84% 6.06% 3.70% 1.99% 7.89% 2.81% 8.11% 7.60% 3.22% 4.17% 4.69% 7.03% 0.78% 4.14% 5.57% 0.48% 6.84% 26.05% 0.26% 14.41% 2.70% 1.35% 10.00% 55.00% 0.00% 5.18% 26.03% 4.42% 10.93% 12.31% 6.97% 15.72% 9.82% 8.60% 6.38% 11.65% 7.14% 8.90% 17.01% 12.51% 23.95% 56.25% 4.88% 1.05% 5.41% 30.00% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 0.20% 3.25% 2.83% 2.40% 3.18% 2.05% 3.28% 2.41% 2.34% 0.69% 6.18% 6.67% 1.91% 0.69% 1.60% 3.18% 0.78% 3.37% 4.21% 0.00% 0.00% 1.18% 0.85% 1.62% 0.64% 0.23% 0.81% 0.50% 0.04% 0.72% 1.19% 0.56% 6.02% 6.32% 0.33% 3.08% 0.09% 0.00% 4.35% 0.00% 0.45% 0.00% 20.49% 4.21% 9.40% 11.58% 7.77% 11.24% 8.54% 9.76% 9.86% 12.38% 10.02% 11.44% 9.23% 9.12% 15.07% 9.96% 0.00% 12.21% 40.53% 42.79% 0.00% 0.25% 0.80% 0.50% 0.53% 0.03% 0.51% 0.93% 0.45% 0.22% 1.22% 0.61% 0.56% 1.79% 0.42% 0.80% 0.50% 0.00% 0.19% 1.05% 0.45% 0.00% 2.79% 4.10% 1.77% 2.27% 1.33% 1.50% 0.74% 3.02% 0.25% 0.80% 1.74% 2.24% 1.07% 2.12% 3.01% 3.74% 0.00% 0.16% 0.00% 0.00% 0.00% Table II.A.5: Basis for Stop (Sorted by % Registration) 2015-2016 Department Name Branford North Branford Troop L Trumbull Watertown Troop G Troop B West Haven Troop A Redding North Haven Farmington Newington Troop D Stratford Bristol Stonington East Hampton East Hartford Troop E Norwalk Troop F Troop K West Hartford Plainville Troop C Cromwell Groton Town Danbury New Canaan Greenwich Wethersfield Hamden Woodbridge South Windsor Troop I Monroe Manchester Wilton Department of Motor Vehicle East Windsor Shelton Wallingford Waterbury Madison Glastonbury Total 4,435 1,089 11,017 2,340 1,698 21,411 8,094 6,127 19,136 2,023 3,203 5,507 5,071 14,877 1,957 5,080 2,819 547 7,620 19,183 4,191 22,009 17,769 9,079 3,470 21,804 1,553 4,431 5,907 6,445 5,937 3,122 3,767 1,585 3,475 13,415 4,625 12,267 6,020 1,867 907 740 8,980 3,208 4,106 4,413 Speed Defective Display of Equipment Moving Registration Related Cell Phone Lights Plates Violation Violation 28.30% 23.14% 21.10% 19.02% 17.26% 16.92% 16.68% 16.66% 15.92% 15.77% 15.77% 15.24% 14.55% 14.10% 13.64% 12.44% 12.10% 12.07% 12.06% 11.74% 11.67% 11.63% 11.61% 11.50% 11.50% 11.20% 10.75% 10.70% 10.68% 10.61% 10.44% 10.22% 9.85% 9.78% 9.70% 9.59% 9.58% 9.11% 8.90% 8.84% 8.71% 8.65% 8.64% 8.60% 8.38% 8.32% 16.53% 28.37% 28.48% 12.22% 28.45% 31.14% 35.73% 9.30% 28.79% 52.40% 23.07% 25.31% 11.18% 24.17% 10.88% 26.99% 30.12% 35.65% 26.10% 34.90% 10.47% 26.99% 29.35% 13.23% 21.10% 33.56% 14.55% 25.50% 10.90% 29.87% 27.99% 22.39% 6.69% 31.04% 16.49% 31.59% 30.40% 13.12% 27.46% 30.80% 32.08% 22.03% 12.48% 10.75% 43.62% 24.22% 11.93% 5.33% 3.74% 11.50% 8.13% 7.70% 2.82% 7.82% 5.47% 1.78% 11.68% 14.00% 2.64% 2.61% 5.16% 11.61% 9.61% 8.96% 13.25% 3.13% 22.14% 3.10% 4.98% 28.30% 12.82% 3.32% 16.74% 5.20% 41.21% 11.50% 13.81% 4.77% 41.86% 19.62% 10.76% 5.55% 9.56% 11.99% 12.13% 11.03% 6.39% 7.70% 15.41% 18.39% 5.19% 19.26% 4.74% 5.51% 7.31% 14.02% 3.83% 2.09% 5.08% 18.98% 2.90% 6.62% 9.62% 12.71% 27.65% 3.24% 14.31% 7.68% 10.61% 9.87% 2.61% 3.93% 6.92% 2.37% 2.49% 6.62% 17.41% 3.76% 13.65% 13.70% 6.30% 17.61% 7.68% 13.42% 4.38% 8.90% 17.15% 2.72% 12.71% 12.23% 19.09% 1.39% 11.58% 7.70% 13.41% 4.58% 7.14% 12.94% 0.63% 2.11% 3.89% 8.63% 7.07% 1.19% 1.73% 4.88% 2.14% 0.30% 1.90% 0.98% 3.27% 1.34% 5.01% 1.87% 0.64% 2.56% 3.11% 0.80% 2.03% 1.05% 2.53% 2.95% 5.45% 1.36% 1.93% 1.70% 1.08% 4.25% 3.30% 7.85% 1.65% 4.35% 8.37% 0.95% 2.70% 3.71% 1.78% 1.93% 0.99% 3.38% 5.42% 4.05% 0.61% 1.20% 0.18% 0.64% 0.81% 0.30% 0.06% 0.02% 0.28% 0.91% 0.11% 0.00% 0.22% 0.33% 0.91% 0.23% 0.10% 0.14% 0.25% 0.18% 0.09% 0.17% 0.84% 0.28% 0.26% 0.24% 0.14% 0.17% 0.06% 0.11% 0.17% 0.14% 0.34% 0.19% 0.21% 0.32% 0.35% 0.05% 0.26% 0.16% 0.42% 1.18% 0.22% 0.54% 0.67% 1.28% 0.68% 0.25% Other Seatbelt Stop Sign 4.96% 4.44% 1.87% 11.48% 6.15% 1.01% 6.84% 4.02% 2.63% 4.96% 4.36% 1.92% 4.71% 1.35% 8.95% 17.05% 3.15% 2.90% 5.65% 3.35% 3.14% 6.06% 3.70% 1.99% 14.11% 6.09% 4.42% 4.99% 4.20% 2.87% 4.68% 2.56% 6.21% 13.15% 1.51% 0.78% 10.92% 2.72% 0.65% 6.57% 10.36% 3.87% 11.14% 5.72% 3.27% 5.39% 2.66% 8.48% 9.97% 7.45% 2.80% 12.80% 3.29% 1.83% 3.14% 1.38% 10.29% 10.92% 3.76% 2.77% 7.61% 5.25% 3.84% 7.94% 3.04% 3.03% 6.45% 5.62% 1.99% 11.44% 2.46% 2.73% 8.50% 1.82% 4.70% 6.05% 3.19% 2.03% 11.20% 4.64% 3.09% 18.21% 1.87% 3.65% 4.18% 2.57% 0.25% 5.49% 2.25% 1.97% 8.34% 3.47% 0.44% 12.65% 3.30% 0.96% 4.14% 5.57% 0.48% 4.42% 4.42% 2.90% 5.70% 0.98% 9.90% 14.54% 2.18% 1.91% 12.43% 2.66% 1.64% 5.11% 1.30% 14.92% 12.46% 1.96% 0.45% 15.96% 11.68% 1.71% 9.15% 2.54% 5.62% 12.84% 13.24% 0.14% 7.85% 4.11% 7.15% 9.26% 3.18% 5.83% 7.72% 2.19% 2.95% 7.07% 1.95% 3.94% 7.80% 5.42% 2.03% 6.97% 12.07% 0.42% 3.83% 17.01% 1.12% 9.44% 6.40% 5.85% 9.82% 3.43% 11.65% 7.56% 5.25% 2.74% 4.99% 1.86% 8.90% 1.64% 3.90% 4.42% 5.01% 2.74% 4.51% 5.55% 6.38% 4.59% 12.16% 5.45% 4.88% 3.03% 8.98% 2.40% 11.59% 10.93% 5.15% 1.71% 8.82% 6.08% 12.31% 7.14% 10.52% 9.77% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 1.94% 4.04% 3.39% 2.05% 0.88% 2.10% 1.62% 0.69% 2.07% 1.04% 3.75% 1.31% 2.41% 1.89% 6.18% 2.20% 2.38% 2.01% 9.95% 1.45% 1.91% 0.99% 1.16% 2.83% 1.76% 1.10% 1.67% 2.03% 0.69% 0.78% 0.49% 5.96% 3.37% 2.52% 1.73% 1.36% 0.76% 2.40% 0.53% 0.64% 3.86% 2.30% 3.18% 6.67% 0.71% 3.85% 0.52% 0.46% 12.76% 0.81% 0.94% 11.72% 17.53% 0.33% 13.42% 0.20% 1.40% 1.58% 0.04% 25.44% 0.56% 0.87% 2.77% 0.18% 1.05% 21.06% 6.32% 35.94% 27.59% 1.62% 0.00% 29.45% 0.06% 0.59% 1.19% 0.39% 2.54% 1.09% 4.35% 4.23% 0.29% 24.08% 0.71% 0.64% 0.17% 4.34% 0.44% 2.03% 0.23% 6.02% 7.77% 0.43% 14.81% 5.33% 0.78% 11.24% 5.48% 1.61% 1.46% 9.12% 1.49% 0.30% 11.55% 6.86% 9.76% 1.24% 10.02% 10.94% 5.68% 7.31% 4.70% 2.32% 9.23% 1.01% 0.99% 9.40% 7.81% 1.04% 16.74% 9.66% 12.38% 8.38% 6.69% 7.66% 12.21% 3.53% 9.24% 1.79% 3.87% 11.58% 7.76% 4.07% 8.27% 12.70% 7.77% 11.44% 1.58% 6.21% 0.32% 1.01% 0.81% 0.51% 0.24% 1.67% 0.43% 0.42% 1.57% 0.05% 0.59% 0.29% 0.45% 0.67% 0.61% 0.79% 0.11% 0.18% 0.56% 0.75% 1.79% 0.51% 0.66% 0.50% 0.46% 0.51% 0.13% 0.38% 1.22% 0.28% 1.20% 0.10% 0.19% 0.50% 0.12% 1.03% 0.15% 0.53% 0.40% 1.02% 0.88% 0.00% 0.03% 0.56% 0.22% 0.25% 1.04% 0.00% 1.40% 1.50% 0.59% 0.34% 0.68% 2.12% 0.39% 0.05% 0.59% 0.11% 3.02% 0.85% 1.74% 0.37% 0.28% 0.37% 6.72% 0.45% 1.07% 0.47% 0.41% 1.77% 1.53% 0.52% 0.26% 1.15% 0.80% 1.89% 1.11% 4.00% 0.16% 0.44% 0.26% 0.27% 0.97% 2.27% 1.36% 3.70% 0.44% 0.68% 1.33% 2.24% 0.73% 0.34% Table II.A.5: Basis for Stop (Sorted by % Registration) 2015-2016 Department Name East Haven Newtown New Britain Middletown Derby Willimantic Fairfield Troop H Yale University Middlebury Orange Bethel Darien Rocky Hill Berlin Meriden Plymouth New Haven Weston Enfield New Milford Ridgefield Easton Cheshire Southington Bridgeport Old Saybrook Winsted Westport Vernon Avon Windsor Brookfield Coventry Naugatuck Groton Long Point Portland CSP Headquarters Milford Seymour Canton University of Connecticut Granby Clinton Waterford Thomaston Total 3,512 5,229 6,734 1,616 3,021 2,460 8,817 17,932 380 59 4,295 2,861 3,106 3,566 5,257 2,055 1,943 19,099 491 7,904 2,791 7,979 712 5,251 4,790 3,118 3,142 724 5,964 4,104 907 5,497 2,299 1,940 4,843 132 199 11,486 2,778 3,702 1,292 3,219 807 2,441 4,874 542 Speed Defective Display of Equipment Moving Registration Related Cell Phone Lights Plates Violation Violation 8.26% 7.96% 7.92% 7.86% 7.45% 7.32% 7.08% 6.93% 6.84% 6.78% 6.64% 6.05% 5.92% 5.92% 5.82% 5.50% 5.40% 5.33% 5.30% 5.23% 4.87% 4.79% 4.49% 4.42% 4.38% 4.30% 4.23% 4.01% 3.97% 3.73% 3.64% 3.64% 3.31% 3.25% 3.04% 3.03% 3.02% 3.01% 2.81% 2.73% 2.71% 2.64% 2.48% 2.38% 2.36% 2.21% 13.35% 46.11% 7.26% 12.19% 29.63% 17.52% 28.31% 33.83% 1.84% 18.64% 26.12% 39.74% 26.43% 33.65% 18.57% 21.46% 16.98% 19.24% 35.03% 53.48% 54.93% 52.34% 55.90% 42.22% 49.37% 9.08% 45.32% 14.78% 27.08% 16.40% 15.44% 38.31% 31.54% 39.64% 26.06% 51.52% 55.28% 50.20% 11.05% 40.76% 37.38% 18.48% 41.26% 29.91% 42.86% 45.20% 9.34% 9.45% 13.22% 3.53% 16.68% 14.47% 10.79% 5.54% 4.47% 28.81% 16.97% 13.04% 6.99% 10.82% 25.30% 14.79% 9.16% 7.16% 3.05% 2.56% 3.05% 15.06% 1.83% 11.29% 9.06% 24.73% 10.03% 3.73% 24.46% 3.27% 0.77% 3.27% 19.83% 9.69% 13.07% 12.88% 7.04% 8.31% 16.27% 5.46% 11.30% 4.85% 17.35% 4.92% 4.97% 0.37% 12.04% 8.09% 10.72% 21.91% 5.10% 16.18% 6.92% 1.82% 4.74% 3.39% 12.29% 7.10% 11.72% 15.06% 10.84% 6.08% 15.23% 6.68% 4.48% 7.91% 12.22% 6.14% 3.09% 12.76% 9.39% 3.66% 13.88% 17.40% 9.10% 19.15% 27.23% 18.61% 13.92% 8.25% 10.53% 1.52% 2.01% 0.75% 8.42% 10.37% 12.54% 29.42% 9.79% 17.37% 16.21% 16.05% 6.06% 1.72% 4.01% 8.35% 3.08% 0.93% 2.43% 1.26% 1.58% 0.00% 4.61% 0.80% 9.27% 2.13% 2.66% 1.27% 13.59% 4.67% 0.41% 2.51% 1.29% 0.08% 0.84% 2.57% 1.19% 2.95% 0.22% 7.73% 2.90% 4.09% 1.10% 1.24% 0.61% 0.62% 2.79% 0.00% 2.51% 1.15% 4.36% 1.67% 0.23% 2.45% 2.11% 4.18% 5.42% 3.14% 0.46% 0.17% 0.45% 0.31% 0.10% 0.41% 0.15% 0.04% 0.53% 0.00% 0.16% 0.31% 0.10% 0.11% 0.04% 0.44% 0.36% 0.05% 0.00% 0.52% 0.25% 0.01% 0.42% 0.08% 0.23% 0.40% 0.22% 0.69% 0.10% 0.83% 0.00% 0.15% 0.13% 0.57% 0.23% 0.00% 0.50% 0.10% 0.11% 0.24% 0.31% 0.96% 0.00% 0.41% 0.41% 0.00% 6.69% 8.34% 7.60% 10.09% 5.96% 8.54% 6.03% 16.48% 6.84% 0.00% 4.52% 3.60% 5.57% 7.15% 6.66% 6.86% 7.31% 1.91% 5.50% 5.92% 5.45% 1.64% 5.48% 7.39% 4.53% 7.89% 5.76% 10.50% 5.18% 19.47% 20.29% 7.24% 9.05% 9.95% 7.66% 0.00% 7.04% 7.11% 6.08% 5.00% 9.83% 12.36% 9.79% 13.31% 8.86% 10.89% Other Seatbelt Stop Sign 3.53% 1.03% 1.66% 1.05% 3.22% 4.17% 3.90% 2.17% 4.80% 0.63% 9.31% 3.33% 4.47% 14.64% 6.43% 2.01% 26.05% 0.26% 6.78% 6.78% 1.68% 1.00% 1.89% 2.03% 1.45% 9.82% 1.74% 1.04% 1.50% 2.30% 10.75% 4.53% 4.94% 5.51% 10.84% 4.27% 27.90% 0.00% 1.61% 7.01% 4.41% 1.11% 3.03% 2.62% 4.49% 1.69% 1.09% 3.35% 1.32% 5.24% 2.81% 8.11% 2.16% 0.51% 4.28% 11.33% 2.06% 2.36% 2.83% 1.88% 14.00% 0.00% 1.55% 4.33% 1.57% 0.74% 3.45% 7.11% 5.45% 6.11% 3.03% 3.03% 2.01% 0.00% 2.31% 14.93% 24.48% 4.43% 1.73% 2.84% 7.89% 0.70% 6.43% 0.93% 0.87% 3.72% 3.65% 7.74% 3.82% 0.59% 7.01% 0.37% 26.03% 9.01% 23.95% 15.72% 11.42% 8.74% 4.51% 0.71% 1.05% 54.24% 2.33% 18.94% 5.47% 13.74% 5.35% 10.12% 10.76% 9.13% 14.87% 3.83% 3.22% 6.60% 14.89% 7.62% 8.33% 12.51% 10.95% 5.11% 9.94% 12.43% 8.05% 5.06% 10.92% 6.19% 13.26% 24.24% 6.53% 0.38% 8.60% 20.45% 7.97% 17.65% 4.71% 6.23% 0.96% 4.98% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 3.25% 0.73% 3.18% 3.28% 5.06% 3.25% 3.21% 1.54% 4.21% 1.69% 1.49% 0.45% 0.68% 0.70% 1.22% 5.50% 1.13% 2.08% 0.41% 0.85% 0.36% 0.11% 0.56% 1.50% 0.48% 1.60% 1.46% 3.45% 0.52% 0.97% 0.77% 0.58% 0.39% 1.49% 0.31% 0.76% 1.01% 0.74% 2.34% 0.49% 0.46% 0.53% 0.62% 0.45% 2.75% 2.77% 0.85% 1.03% 0.09% 0.50% 1.36% 1.46% 1.07% 19.87% 0.00% 0.00% 4.14% 0.24% 8.08% 0.45% 3.21% 1.22% 0.26% 0.42% 0.41% 0.48% 0.29% 0.76% 4.49% 0.15% 0.44% 3.08% 1.15% 4.14% 2.23% 1.24% 0.55% 0.31% 0.13% 6.80% 1.07% 0.00% 0.00% 8.20% 0.72% 1.11% 0.70% 0.65% 0.37% 1.11% 0.39% 0.00% 4.21% 4.28% 9.96% 8.54% 6.29% 7.36% 9.38% 1.52% 40.53% 13.56% 16.39% 4.65% 7.15% 7.04% 16.05% 9.68% 3.81% 24.58% 2.44% 6.78% 7.42% 6.17% 1.12% 4.09% 5.59% 15.07% 3.98% 12.71% 8.85% 12.26% 8.05% 14.55% 7.87% 2.37% 9.62% 0.00% 12.56% 1.12% 9.86% 6.81% 7.74% 2.21% 6.57% 7.17% 8.78% 6.64% 0.80% 0.33% 0.50% 0.93% 0.26% 0.41% 0.57% 0.98% 1.05% 0.00% 0.54% 0.17% 0.10% 0.22% 0.38% 0.83% 0.05% 0.41% 0.20% 0.14% 0.18% 0.13% 0.56% 0.44% 0.23% 0.80% 0.00% 0.00% 0.07% 0.10% 0.11% 0.15% 0.00% 0.36% 0.17% 0.00% 0.50% 0.53% 0.22% 0.16% 0.08% 0.03% 0.00% 0.37% 0.39% 0.00% 4.10% 0.08% 3.74% 0.74% 2.18% 0.77% 0.45% 1.02% 0.00% 1.69% 1.12% 0.98% 1.26% 0.22% 0.10% 0.97% 5.51% 3.24% 0.00% 1.19% 0.97% 0.51% 0.14% 1.03% 0.21% 3.01% 0.13% 0.14% 1.16% 1.36% 0.00% 1.02% 0.00% 0.26% 0.64% 0.00% 0.00% 1.15% 0.25% 0.19% 0.15% 0.40% 0.37% 0.82% 1.23% 0.37% Table II.A.5: Basis for Stop (Sorted by % Registration) 2015-2016 Department Name Norwich New London Stamford Windsor Locks Ledyard* Central CT State Unviversity Groton City Bloomfield Guilford Ansonia Hartford Simsbury Torrington Wolcott Southern CT State University Putnam State Capitol Police Plainfield Suffield Eastern CT State University Western CT State University Total 6,183 4,120 5,519 2,496 1,300 2,092 1,274 3,263 4,270 5,110 4,505 3,868 6,527 376 666 1,094 222 1,740 1,336 128 20 Speed Defective Display of Equipment Moving Registration Related Cell Phone Lights Plates Violation Violation 2.13% 2.04% 2.03% 1.92% 1.92% 1.86% 1.81% 1.78% 1.71% 1.59% 1.51% 1.37% 1.07% 1.06% 1.05% 1.01% 0.90% 0.86% 0.75% 0.00% 0.00% 35.44% 15.75% 14.11% 46.23% 67.85% 39.77% 40.03% 25.87% 53.02% 25.52% 19.87% 56.88% 20.93% 51.33% 30.78% 38.85% 0.00% 29.37% 60.78% 7.03% 0.00% 9.14% 13.16% 27.09% 7.49% 1.54% 4.92% 6.67% 5.85% 6.65% 11.59% 15.87% 9.75% 3.78% 12.50% 5.86% 6.31% 2.25% 2.01% 2.69% 1.56% 5.00% 18.02% 8.54% 6.25% 8.61% 7.10% 13.29% 14.99% 12.38% 12.58% 14.36% 5.79% 7.16% 29.14% 6.38% 9.61% 20.57% 28.38% 21.32% 11.75% 20.31% 0.00% 2.85% 1.14% 1.56% 0.48% 0.54% 2.15% 0.39% 4.01% 0.84% 2.29% 4.55% 1.09% 3.97% 1.60% 0.15% 3.29% 0.90% 1.72% 0.00% 0.78% 0.00% 0.24% 0.44% 0.09% 0.20% 0.06% 0.10% 0.24% 0.18% 0.02% 0.35% 0.27% 0.03% 0.37% 0.53% 0.00% 0.37% 0.00% 0.23% 0.00% 0.78% 0.00% Other 9.43% 3.41% 5.85% 4.93% 5.82% 6.60% 3.37% 3.89% 3.77% 9.54% 5.54% 6.12% 2.75% 3.06% 7.08% 1.38% 3.16% 1.43% 8.94% 4.60% 6.66% 5.15% 4.96% 1.60% 3.88% 2.08% 3.72% 2.60% 5.41% 3.30% 6.22% 2.56% 14.41% 2.70% 21.03% 4.20% 12.80% 0.82% 4.69% 7.03% 10.00% 55.00% Seatbelt Stop Sign 1.83% 3.76% 6.36% 5.49% 0.23% 3.49% 4.47% 1.29% 2.27% 2.56% 3.51% 1.58% 0.43% 0.80% 7.96% 5.48% 1.35% 1.09% 0.37% 0.78% 0.00% 4.77% 20.15% 5.18% 13.38% 1.15% 3.49% 19.78% 12.11% 9.70% 17.85% 15.03% 7.99% 20.27% 4.00% 0.15% 2.29% 5.41% 12.87% 5.01% 56.25% 30.00% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 1.00% 1.84% 0.20% 0.64% 1.69% 0.43% 1.41% 0.74% 0.09% 0.86% 4.11% 0.18% 0.49% 3.20% 1.35% 0.46% 0.00% 1.09% 0.22% 0.78% 0.00% 0.71% 3.35% 1.18% 0.32% 0.38% 7.60% 0.08% 2.73% 0.75% 0.04% 2.69% 0.18% 0.52% 0.00% 0.60% 0.18% 0.45% 0.00% 0.45% 0.00% 0.00% 10.76% 17.91% 20.49% 7.29% 1.85% 10.99% 4.24% 23.32% 7.70% 8.63% 12.65% 6.93% 12.78% 4.30% 32.28% 12.16% 42.79% 3.91% 4.27% 0.00% 0.00% 0.02% 0.07% 0.25% 0.04% 0.00% 0.05% 0.00% 0.18% 0.02% 0.27% 0.51% 0.03% 0.09% 0.53% 1.20% 0.18% 0.45% 0.11% 0.00% 0.00% 0.00% 0.26% 1.07% 2.79% 0.64% 2.38% 0.19% 0.08% 1.10% 0.05% 0.55% 1.84% 0.28% 0.20% 7.45% 0.30% 0.09% 0.00% 0.17% 0.07% 0.00% 0.00% Table II.A.6: Basis for Stop (Sorted by % Cell Phone) 2015-2016 Department Name Hamden Danbury Middlebury West Hartford Stamford Berlin Bridgeport Westport Norwalk Brookfield Woodbridge Glastonbury Waterbury Granby Orange Cromwell Derby Milford Hartford Wallingford Ridgefield Meriden Willimantic Farmington Greenwich East Hartford New Britain New London Naugatuck Bethel Groton Long Point Plainville Wolcott Wilton Manchester Branford North Haven Bristol Ansonia New Canaan Trumbull Canton Cheshire Department of Motor Vehicle Rocky Hill Fairfield Total 3,767 5,907 59 9,079 5,519 5,257 3,118 5,964 4,191 2,299 1,585 4,413 3,208 807 4,295 1,553 3,021 2,778 4505 8,980 7,979 2,055 2,460 5,507 5,937 7,620 6,734 4,120 4,843 2,861 132 3,470 376 6,020 12,267 4,435 3,203 5,080 5,110 6,445 2,340 1,292 5,251 1,867 3,566 8,817 Speed Defective Display of Equipment Moving Cell Phone Related Registration Lights Plates Violation Violation 41.86% 41.21% 28.81% 28.30% 27.09% 25.30% 24.73% 24.46% 22.14% 19.83% 19.62% 19.26% 18.39% 17.35% 16.97% 16.74% 16.68% 16.27% 15.87% 15.41% 15.06% 14.79% 14.47% 14.00% 13.81% 13.25% 13.22% 13.16% 13.07% 13.04% 12.88% 12.82% 12.50% 12.13% 11.99% 11.93% 11.68% 11.61% 11.59% 11.50% 11.50% 11.30% 11.29% 11.03% 10.82% 10.79% 6.69% 10.90% 18.64% 13.23% 14.11% 18.57% 9.08% 27.08% 10.47% 31.54% 31.04% 24.22% 10.75% 41.26% 26.12% 14.55% 29.63% 11.05% 19.87% 12.48% 52.34% 21.46% 17.52% 25.31% 27.99% 26.10% 7.26% 15.75% 26.06% 39.74% 51.52% 21.10% 51.33% 27.46% 13.12% 16.53% 23.07% 26.99% 25.52% 29.87% 12.22% 37.38% 42.22% 30.80% 33.65% 28.31% 9.85% 10.68% 6.78% 11.50% 2.03% 5.82% 4.30% 3.97% 11.67% 3.31% 9.78% 8.32% 8.60% 2.48% 6.64% 10.75% 7.45% 2.81% 1.51% 8.64% 4.79% 5.50% 7.32% 15.24% 10.44% 12.06% 7.92% 2.04% 3.04% 6.05% 3.03% 11.50% 1.06% 8.90% 9.11% 28.30% 15.77% 12.44% 1.59% 10.61% 19.02% 2.71% 4.42% 8.84% 5.92% 7.08% 4.38% 6.30% 3.39% 6.62% 6.25% 10.84% 3.66% 9.10% 6.92% 13.92% 8.90% 12.94% 4.58% 9.79% 12.29% 13.65% 5.10% 8.42% 5.79% 13.41% 6.14% 6.08% 16.18% 12.71% 7.68% 2.61% 10.72% 8.54% 10.53% 7.10% 1.52% 17.41% 6.38% 19.09% 12.23% 4.74% 9.62% 7.68% 14.36% 17.61% 14.02% 12.54% 12.76% 1.39% 15.06% 6.92% 1.65% 1.08% 0.00% 2.95% 1.56% 2.66% 2.95% 2.90% 2.03% 0.61% 4.35% 1.20% 4.05% 2.11% 4.61% 1.93% 3.08% 4.36% 4.55% 5.42% 0.08% 1.27% 0.93% 0.98% 3.30% 3.11% 4.01% 1.14% 2.79% 0.80% 0.00% 5.45% 1.60% 1.78% 3.71% 0.63% 1.90% 1.87% 2.29% 4.25% 8.63% 0.23% 2.57% 1.93% 2.13% 2.43% 0.21% 0.17% 0.00% 0.24% 0.09% 0.04% 0.40% 0.10% 0.84% 0.13% 0.32% 0.25% 1.28% 0.00% 0.16% 0.06% 0.10% 0.11% 0.27% 0.67% 0.01% 0.44% 0.41% 0.33% 0.34% 0.09% 0.45% 0.44% 0.23% 0.31% 0.00% 0.14% 0.53% 0.42% 0.16% 0.18% 0.22% 0.14% 0.35% 0.14% 0.30% 0.31% 0.08% 1.18% 0.11% 0.15% Other Seatbelt Stop Sign 4.14% 5.57% 0.48% 4.18% 2.57% 0.25% 0.00% 6.78% 6.78% 11.44% 2.46% 2.73% 5.82% 6.60% 6.36% 6.66% 1.50% 2.30% 7.89% 2.81% 8.11% 5.18% 2.06% 2.36% 7.61% 5.25% 3.84% 9.05% 1.57% 0.74% 4.42% 4.42% 2.90% 7.07% 1.95% 3.94% 9.26% 3.18% 5.83% 9.79% 0.87% 3.72% 4.52% 1.68% 1.00% 11.20% 4.64% 3.09% 5.96% 4.80% 0.63% 6.08% 24.48% 4.43% 6.66% 5.15% 3.51% 7.85% 4.11% 7.15% 1.64% 3.03% 2.62% 6.86% 10.75% 4.53% 8.54% 9.31% 3.33% 13.15% 1.51% 0.78% 8.34% 3.47% 0.44% 3.14% 1.38% 10.29% 7.60% 3.22% 4.17% 5.85% 4.93% 3.76% 7.66% 5.45% 6.11% 3.60% 1.89% 2.03% 0.00% 3.03% 3.03% 8.50% 1.82% 4.70% 3.72% 2.60% 0.80% 12.46% 1.96% 0.45% 5.11% 1.30% 14.92% 4.96% 4.44% 1.87% 4.68% 2.56% 6.21% 5.39% 2.66% 8.48% 8.94% 4.60% 2.56% 5.49% 2.25% 1.97% 4.96% 4.36% 1.92% 9.83% 7.89% 0.70% 7.39% 1.09% 3.35% 15.96% 11.68% 1.71% 7.15% 1.74% 1.04% 6.03% 4.47% 14.64% 4.88% 6.38% 54.24% 4.42% 5.18% 5.35% 12.51% 9.94% 8.90% 10.92% 3.03% 9.77% 7.14% 4.71% 2.33% 4.51% 11.42% 8.60% 15.03% 12.31% 6.60% 10.12% 8.74% 5.85% 12.16% 4.99% 23.95% 20.15% 13.26% 18.94% 24.24% 5.01% 4.00% 5.15% 10.93% 7.80% 6.40% 7.56% 17.85% 4.59% 6.97% 7.97% 7.62% 1.71% 13.74% 4.51% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 3.37% 0.69% 1.69% 2.83% 0.20% 1.22% 1.60% 0.52% 1.91% 0.39% 2.52% 3.85% 6.67% 0.62% 1.49% 1.67% 5.06% 2.34% 4.11% 3.18% 0.11% 5.50% 3.25% 1.31% 0.49% 9.95% 3.18% 1.84% 0.31% 0.45% 0.76% 1.76% 3.20% 0.53% 2.40% 1.94% 3.75% 2.20% 0.86% 0.78% 2.05% 0.46% 1.50% 0.64% 0.70% 3.21% 4.35% 1.19% 0.00% 1.62% 1.18% 3.21% 3.08% 2.23% 6.32% 0.13% 4.23% 0.43% 6.02% 0.37% 4.14% 0.06% 1.36% 0.72% 2.69% 0.23% 0.76% 1.22% 1.46% 1.58% 2.54% 1.05% 0.09% 3.35% 1.07% 0.24% 0.00% 0.00% 0.00% 0.17% 0.64% 0.52% 1.40% 0.87% 0.04% 0.39% 0.81% 0.70% 0.15% 4.34% 0.45% 1.07% 12.21% 12.38% 13.56% 9.40% 20.49% 16.05% 15.07% 8.85% 9.23% 7.87% 3.53% 6.21% 11.44% 6.57% 16.39% 16.74% 6.29% 9.86% 12.65% 7.77% 6.17% 9.68% 7.36% 6.86% 6.69% 4.70% 9.96% 17.91% 9.62% 4.65% 0.00% 7.81% 4.30% 7.76% 11.58% 14.81% 11.55% 10.94% 8.63% 8.38% 11.24% 7.74% 4.09% 4.07% 7.04% 9.38% 0.19% 1.22% 0.00% 0.50% 0.25% 0.38% 0.80% 0.07% 1.79% 0.00% 0.50% 0.25% 0.56% 0.00% 0.54% 0.13% 0.26% 0.22% 0.51% 0.03% 0.13% 0.83% 0.41% 0.29% 1.20% 0.56% 0.50% 0.07% 0.17% 0.17% 0.00% 0.46% 0.53% 0.40% 0.53% 0.32% 0.59% 0.79% 0.27% 0.28% 0.51% 0.08% 0.44% 1.02% 0.22% 0.57% 0.16% 0.80% 1.69% 1.77% 2.79% 0.10% 3.01% 1.16% 1.07% 0.00% 0.44% 0.34% 2.24% 0.37% 1.12% 0.26% 2.18% 0.25% 1.84% 1.33% 0.51% 0.97% 0.77% 0.11% 1.11% 6.72% 3.74% 1.07% 0.64% 0.98% 0.00% 1.53% 7.45% 1.36% 2.27% 1.04% 0.59% 0.37% 0.55% 1.89% 1.50% 0.15% 1.03% 3.70% 0.22% 0.45% Table II.A.6: Basis for Stop (Sorted by % Cell Phone) 2015-2016 Department Name South Windsor Old Saybrook Simsbury Coventry Stonington Monroe Newtown East Haven Plymouth Norwich Southington East Hampton CSP Headquarters Watertown West Haven Shelton Troop G Windsor Locks New Haven Portland Darien Groton City Guilford East Windsor Putnam Southern CT State University Bloomfield Troop I Troop H Troop A Seymour North Branford Groton Town Madison Stratford Western CT State University Troop K Waterford Central CT State Unviversity Clinton University of Connecticut Wethersfield Yale University Torrington Troop L Winsted Total 3,475 3,142 3,868 1,940 2,819 4,625 5,229 3,512 1,943 6,183 4,790 547 11,486 1,698 6,127 740 21,411 2,496 19,099 199 3,106 1,274 4,270 907 1,094 666 3,263 13,415 17,932 19,136 3,702 1,089 4,431 4,106 1,957 20 17,769 4,874 2,092 2,441 3,219 3,122 380 6,527 11,017 724 Speed Defective Display of Equipment Moving Cell Phone Related Registration Lights Plates Violation Violation 10.76% 10.03% 9.75% 9.69% 9.61% 9.56% 9.45% 9.34% 9.16% 9.14% 9.06% 8.96% 8.31% 8.13% 7.82% 7.70% 7.70% 7.49% 7.16% 7.04% 6.99% 6.67% 6.65% 6.39% 6.31% 5.86% 5.85% 5.55% 5.54% 5.47% 5.46% 5.33% 5.20% 5.19% 5.16% 5.00% 4.98% 4.97% 4.92% 4.92% 4.85% 4.77% 4.47% 3.78% 3.74% 3.73% 16.49% 45.32% 56.88% 39.64% 30.12% 30.40% 46.11% 13.35% 16.98% 35.44% 49.37% 35.65% 50.20% 28.45% 9.30% 22.03% 31.14% 46.23% 19.24% 55.28% 26.43% 40.03% 53.02% 32.08% 38.85% 30.78% 25.87% 31.59% 33.83% 28.79% 40.76% 28.37% 25.50% 43.62% 10.88% 0.00% 29.35% 42.86% 39.77% 29.91% 18.48% 22.39% 1.84% 20.93% 28.48% 14.78% 9.70% 4.23% 1.37% 3.25% 12.10% 9.58% 7.96% 8.26% 5.40% 2.13% 4.38% 12.07% 3.01% 17.26% 16.66% 8.65% 16.92% 1.92% 5.33% 3.02% 5.92% 1.81% 1.71% 8.71% 1.01% 1.05% 1.78% 9.59% 6.93% 15.92% 2.73% 23.14% 10.70% 8.38% 13.64% 0.00% 11.61% 2.36% 1.86% 2.38% 2.64% 10.22% 6.84% 1.07% 21.10% 4.01% 17.15% 13.88% 7.16% 8.25% 10.61% 12.71% 8.09% 12.04% 15.23% 18.02% 9.39% 9.87% 0.75% 3.83% 18.98% 7.70% 2.09% 8.61% 6.68% 2.01% 11.72% 14.99% 12.58% 11.58% 20.57% 9.61% 12.38% 2.72% 1.82% 2.90% 10.37% 5.51% 13.70% 7.14% 14.31% 0.00% 2.49% 16.21% 13.29% 17.37% 29.42% 13.42% 4.74% 29.14% 7.31% 17.40% 8.37% 0.22% 1.09% 0.62% 0.64% 2.70% 1.72% 6.06% 13.59% 2.85% 1.19% 2.56% 1.15% 7.07% 4.88% 3.38% 1.19% 0.48% 4.67% 2.51% 9.27% 0.39% 0.84% 0.99% 3.29% 0.15% 4.01% 0.95% 1.26% 2.14% 1.67% 2.11% 1.70% 0.61% 5.01% 0.00% 2.53% 5.42% 2.15% 4.18% 2.45% 7.85% 1.58% 3.97% 3.89% 7.73% 0.35% 0.22% 0.03% 0.57% 0.25% 0.26% 0.17% 0.46% 0.36% 0.24% 0.23% 0.18% 0.10% 0.06% 0.91% 0.54% 0.02% 0.20% 0.05% 0.50% 0.10% 0.24% 0.02% 0.22% 0.37% 0.00% 0.18% 0.05% 0.04% 0.11% 0.24% 0.64% 0.11% 0.68% 0.10% 0.00% 0.26% 0.41% 0.10% 0.41% 0.96% 0.19% 0.53% 0.37% 0.81% 0.69% 5.70% 5.76% 4.96% 9.95% 9.97% 12.43% 8.34% 6.69% 7.31% 9.43% 4.53% 12.80% 7.11% 4.71% 6.06% 12.84% 17.05% 3.37% 1.91% 7.04% 5.57% 2.75% 3.16% 9.15% 6.22% 5.41% 7.08% 14.54% 16.48% 14.11% 5.00% 11.48% 18.21% 7.72% 11.14% 10.00% 6.45% 8.86% 5.54% 13.31% 12.36% 12.65% 6.84% 3.88% 6.84% 10.50% Other Seatbelt Stop Sign 0.98% 9.90% 2.16% 0.51% 1.60% 1.58% 3.45% 7.11% 7.45% 2.80% 2.66% 1.64% 1.66% 1.05% 3.53% 1.03% 4.94% 5.51% 3.41% 1.83% 1.32% 5.24% 3.29% 1.83% 2.31% 14.93% 1.35% 8.95% 3.70% 1.99% 13.24% 0.14% 3.15% 2.90% 3.89% 5.49% 10.84% 4.27% 2.01% 0.00% 1.45% 9.82% 3.06% 4.47% 1.43% 2.27% 2.54% 5.62% 2.56% 5.48% 3.30% 7.96% 1.38% 1.29% 2.18% 1.91% 6.43% 2.01% 6.09% 4.42% 1.73% 2.84% 6.15% 1.01% 1.87% 3.65% 2.19% 2.95% 5.72% 3.27% 55.00% 0.00% 5.62% 1.99% 3.82% 0.59% 6.12% 3.49% 3.65% 7.74% 6.43% 0.93% 3.30% 0.96% 26.05% 0.26% 2.08% 0.43% 4.02% 2.63% 4.28% 11.33% 8.98% 10.95% 7.99% 6.19% 5.25% 11.59% 9.01% 26.03% 10.76% 4.77% 8.33% 2.74% 0.38% 12.07% 17.01% 6.08% 0.42% 13.38% 9.13% 6.53% 5.47% 19.78% 9.70% 8.82% 2.29% 0.15% 12.11% 2.40% 0.71% 1.12% 20.45% 5.42% 5.55% 10.52% 11.65% 30.00% 3.90% 0.96% 3.49% 6.23% 17.65% 5.45% 1.05% 20.27% 2.03% 5.11% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 1.73% 1.46% 0.18% 1.49% 2.38% 0.76% 0.73% 3.25% 1.13% 1.00% 0.48% 2.01% 0.74% 0.88% 0.69% 2.30% 2.10% 0.64% 2.08% 1.01% 0.68% 1.41% 0.09% 3.86% 0.46% 1.35% 0.74% 1.36% 1.54% 2.07% 0.49% 4.04% 2.03% 0.71% 6.18% 0.00% 1.16% 2.75% 0.43% 0.45% 0.53% 5.96% 4.21% 0.49% 3.39% 3.45% 0.29% 1.15% 0.18% 6.80% 2.77% 0.71% 1.03% 0.85% 0.26% 0.71% 0.44% 0.18% 8.20% 0.94% 0.33% 2.03% 11.72% 0.32% 0.42% 0.00% 8.08% 0.08% 0.75% 0.44% 0.18% 0.60% 2.73% 24.08% 19.87% 13.42% 1.11% 0.46% 0.59% 7.77% 0.56% 0.00% 27.59% 0.39% 7.60% 1.11% 0.65% 1.09% 0.00% 0.52% 12.76% 4.14% 9.24% 3.98% 6.93% 2.37% 5.68% 3.87% 4.28% 4.21% 3.81% 10.76% 5.59% 7.31% 1.12% 5.48% 9.12% 12.70% 1.61% 7.29% 24.58% 12.56% 7.15% 4.24% 7.70% 8.27% 12.16% 32.28% 23.32% 1.79% 1.52% 1.49% 6.81% 5.33% 9.66% 1.58% 10.02% 0.00% 0.99% 8.78% 10.99% 7.17% 2.21% 7.66% 40.53% 12.78% 0.78% 12.71% 0.12% 0.00% 0.03% 0.36% 0.11% 0.15% 0.33% 0.80% 0.05% 0.02% 0.23% 0.18% 0.53% 0.24% 0.42% 0.00% 1.67% 0.04% 0.41% 0.50% 0.10% 0.00% 0.02% 0.88% 0.18% 1.20% 0.18% 1.03% 0.98% 1.57% 0.16% 1.01% 0.38% 0.22% 0.61% 0.00% 0.66% 0.39% 0.05% 0.37% 0.03% 0.10% 1.05% 0.09% 0.81% 0.00% 0.26% 0.13% 0.28% 0.26% 0.28% 0.97% 0.08% 4.10% 5.51% 0.26% 0.21% 0.37% 1.15% 0.59% 2.12% 0.68% 0.34% 0.64% 3.24% 0.00% 1.26% 0.08% 0.05% 0.44% 0.09% 0.30% 1.10% 0.27% 1.02% 0.39% 0.19% 0.00% 1.15% 0.73% 1.74% 0.00% 0.41% 1.23% 0.19% 0.82% 0.40% 4.00% 0.00% 0.20% 1.40% 0.14% Table II.A.6: Basis for Stop (Sorted by % Cell Phone) 2015-2016 Department Name Middletown Troop C Windsor Vernon Troop E Troop F Weston New Milford Troop B Suffield Newington Troop D Enfield State Capitol Police Plainfield Easton Redding Eastern CT State University Ledyard* Avon Thomaston Total 1,616 21,804 5,497 4,104 19,183 22,009 491 2,791 8,094 1,336 5,071 14,877 7,904 222 1,740 712 2,023 128 1300 907 542 Speed Defective Display of Equipment Moving Cell Phone Related Registration Lights Plates Violation Violation 3.53% 3.32% 3.27% 3.27% 3.13% 3.10% 3.05% 3.05% 2.82% 2.69% 2.64% 2.61% 2.56% 2.25% 2.01% 1.83% 1.78% 1.56% 1.54% 0.77% 0.37% 12.19% 33.56% 38.31% 16.40% 34.90% 26.99% 35.03% 54.93% 35.73% 60.78% 11.18% 24.17% 53.48% 0.00% 29.37% 55.90% 52.40% 7.03% 67.85% 15.44% 45.20% 7.86% 11.20% 3.64% 3.73% 11.74% 11.63% 5.30% 4.87% 16.68% 0.75% 14.55% 14.10% 5.23% 0.90% 0.86% 4.49% 15.77% 0.00% 1.92% 3.64% 2.21% 21.91% 3.76% 18.61% 19.15% 3.93% 2.37% 4.48% 12.22% 5.08% 11.75% 27.65% 3.24% 7.91% 28.38% 21.32% 3.09% 6.62% 20.31% 7.10% 27.23% 16.05% 8.35% 1.36% 1.24% 4.09% 0.80% 1.05% 0.41% 1.29% 1.73% 0.00% 3.27% 1.34% 2.51% 0.90% 1.72% 0.84% 0.30% 0.78% 0.54% 1.10% 3.14% 0.31% 0.17% 0.15% 0.83% 0.17% 0.28% 0.00% 0.25% 0.28% 0.00% 0.91% 0.23% 0.52% 0.00% 0.23% 0.42% 0.00% 0.78% 0.06% 0.00% 0.00% Other 10.09% 3.90% 6.05% 3.19% 7.24% 1.55% 19.47% 2.83% 10.92% 3.76% 7.94% 3.04% 5.50% 27.90% 5.45% 4.41% 5.65% 3.35% 12.80% 0.82% 10.92% 2.72% 6.57% 10.36% 5.92% 1.61% 14.41% 2.70% 21.03% 4.20% 5.48% 4.49% 4.99% 4.20% 4.69% 7.03% 3.77% 9.54% 20.29% 14.00% 10.89% 7.01% Seatbelt Stop Sign 2.17% 2.03% 4.33% 1.88% 2.77% 3.03% 0.00% 1.11% 3.14% 0.37% 0.65% 3.87% 7.01% 1.35% 1.09% 1.69% 2.87% 0.78% 0.23% 0.00% 0.37% 15.72% 2.74% 5.06% 12.43% 1.86% 1.64% 14.87% 3.22% 3.83% 5.01% 9.82% 3.43% 3.83% 5.41% 12.87% 14.89% 9.44% 56.25% 1.15% 8.05% 4.98% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 3.28% 1.10% 0.58% 0.97% 1.45% 0.99% 0.41% 0.36% 1.62% 0.22% 2.41% 1.89% 0.85% 0.00% 1.09% 0.56% 1.04% 0.78% 1.69% 0.77% 2.77% 0.50% 29.45% 0.31% 1.24% 21.06% 35.94% 0.41% 0.29% 17.53% 0.45% 0.04% 25.44% 0.48% 0.45% 0.00% 4.49% 0.20% 0.00% 0.38% 0.55% 0.00% 8.54% 1.04% 14.55% 12.26% 2.32% 1.01% 2.44% 7.42% 1.46% 4.27% 9.76% 1.24% 6.78% 42.79% 3.91% 1.12% 0.30% 0.00% 1.85% 8.05% 6.64% 0.93% 0.51% 0.15% 0.10% 0.75% 0.51% 0.20% 0.18% 0.43% 0.00% 0.45% 0.67% 0.14% 0.45% 0.11% 0.56% 0.05% 0.00% 0.00% 0.11% 0.00% 0.74% 0.52% 1.02% 1.36% 0.45% 0.47% 0.00% 0.97% 0.68% 0.07% 3.02% 0.85% 1.19% 0.00% 0.17% 0.14% 0.05% 0.00% 2.38% 0.00% 0.37% Table II.A.7: Outcome of Stop (Sorted by % Infraction Ticket) 2015-2016 Department Name CSP Headquarters Troop F Troop C Troop H Troop G Troop I Troop E Danbury Troop A Troop K Department of Motor Vehicle Troop D Bridgeport Norwalk Meriden New Haven Hartford Derby Branford Southern CT State University Troop B Stamford Hamden New London Trumbull Manchester East Hartford Greenwich Groton Long Point Troop L Waterbury Fairfield Western CT State University West Hartford Wolcott Darien Bristol Granby Woodbridge Ridgefield Groton City Watertown Orange North Branford New Britain Berlin Glastonbury North Haven Farmington Westport Wallingford Newtown Coventry Ledyard* East Windsor Cromwell Naugatuck South Windsor N Infraction 11,486 87.84% 22,009 78.93% 21,804 74.22% 17,932 73.43% 21,411 71.47% 13,415 71.11% 19,183 68.24% 5,907 67.60% 19,136 66.02% 17,769 65.09% 1,867 62.88% 14,877 62.51% 3,118 61.90% 4,191 59.70% 2,055 58.64% 19,099 56.64% 4,505 56.03% 3,021 54.95% 4,435 54.30% 666 54.20% 8,094 53.71% 5,519 52.91% 3,767 52.64% 4,120 50.95% 2,340 49.40% 12,267 49.37% 7,620 48.11% 5,937 47.47% 132 46.21% 11,017 46.21% 3,208 42.21% 8,817 40.09% 20 40.00% 9,079 39.80% 376 39.40% 3,106 39.28% 5,080 37.95% 807 37.55% 1,585 37.48% 7,979 36.56% 1,274 35.64% 1,698 34.98% 4,295 34.20% 1,089 33.88% 6,734 33.58% 5,257 33.21% 4,413 32.68% 3,203 32.53% 5,507 31.47% 5,964 30.70% 8,980 29.47% 5,229 29.22% 1,940 29.02% 1,300 28.62% 907 27.78% 1,553 27.11% 4,843 26.78% 3,475 26.10% UAR Mis. Sum. 1.09% 2.73% 0.11% 2.70% 0.34% 2.72% 1.75% 5.46% 0.77% 5.85% 0.33% 4.64% 0.44% 5.33% 1.20% 2.71% 0.54% 4.77% 0.43% 3.87% 0.00% 4.71% 0.38% 5.21% 1.15% 4.81% 1.29% 6.04% 1.85% 10.07% 0.77% 6.18% 3.02% 10.37% 0.30% 9.33% 0.23% 5.25% 1.05% 7.96% 0.62% 6.05% 0.82% 4.53% 0.35% 3.37% 2.99% 4.90% 0.38% 7.61% 0.55% 5.37% 1.63% 13.16% 0.39% 2.39% 0.00% 1.52% 0.74% 6.37% 3.30% 17.36% 0.66% 5.50% 0.00% 10.00% 3.37% 4.45% 0.80% 9.30% 0.90% 5.70% 1.61% 5.83% 0.12% 7.56% 0.19% 9.97% 0.13% 1.84% 0.78% 3.85% 0.53% 5.06% 0.35% 6.10% 0.28% 5.79% 1.68% 6.99% 0.40% 4.26% 0.48% 6.68% 0.53% 7.74% 1.73% 8.59% 0.44% 2.82% 4.61% 6.16% 0.25% 2.77% 0.10% 11.03% 0.23% 6.38% 1.21% 8.27% 0.64% 4.96% 0.64% 1.47% 0.63% 4.63% Written Warning 2.05% 4.93% 8.38% 4.62% 2.48% 5.61% 4.96% 0.17% 4.37% 8.59% 7.93% 8.26% 1.14% 1.41% 3.94% 7.40% 6.26% 0.13% 0.07% 29.28% 25.22% 0.58% 2.42% 10.34% 4.44% 4.23% 10.87% 17.53% 41.67% 9.96% 3.62% 0.73% 20.00% 3.03% 27.40% 16.58% 39.02% 31.10% 11.80% 46.32% 11.54% 45.82% 1.54% 32.69% 0.58% 33.82% 31.23% 2.03% 2.22% 32.68% 3.26% 16.52% 22.27% 47.85% 20.18% 14.75% 21.25% 2.47% Verbal No Warning Disposition 5.18% 1.12% 12.19% 1.14% 12.95% 1.39% 12.39% 2.35% 17.67% 1.77% 16.82% 1.48% 19.15% 1.88% 27.39% 0.93% 22.61% 1.68% 20.42% 1.60% 22.34% 2.14% 22.59% 1.06% 30.30% 0.70% 30.04% 1.53% 23.80% 1.70% 28.21% 0.80% 22.97% 1.35% 34.26% 1.03% 36.01% 4.15% 6.91% 0.60% 11.80% 2.61% 38.50% 2.66% 39.63% 1.59% 29.83% 1.00% 36.11% 2.05% 39.06% 1.42% 24.00% 2.23% 30.55% 1.67% 9.85% 0.76% 33.07% 3.65% 31.27% 2.24% 50.74% 2.28% 30.00% 0.00% 48.16% 1.20% 22.10% 1.00% 36.93% 0.61% 9.33% 6.26% 23.42% 0.25% 39.05% 1.51% 14.17% 0.98% 46.78% 1.41% 12.78% 0.82% 56.88% 0.93% 21.21% 6.15% 55.98% 1.19% 24.60% 3.71% 27.58% 1.36% 54.32% 2.84% 53.77% 2.23% 32.49% 0.87% 54.89% 1.61% 50.64% 0.59% 34.79% 2.78% 15.92% 1.00% 40.90% 1.65% 47.65% 4.89% 49.29% 0.58% 64.55% 1.61% Table II.A.7: Outcome of Stop (Sorted by % Infraction Ticket) 2015-2016 Department Name Ansonia Bethel New Canaan Yale University Shelton Newington Monroe Brookfield Plainville Norwich Willimantic New Milford Madison Winsted Stonington Rocky Hill Windsor Groton Town Enfield Windsor Locks Southington East Haven Stratford Canton Middletown Milford Simsbury Wilton Bloomfield Cheshire University of Connecticut Vernon East Hampton Seymour West Haven Guilford Easton Old Saybrook Wethersfield Plymouth Thomaston Waterford State Capitol Police Central CT State University Clinton Weston Avon Suffield Putnam Plainfield Portland Torrington Redding Middlebury Eastern CT State University N Infraction 5,110 25.95% 2,861 25.38% 6,445 24.93% 380 24.74% 740 24.59% 5,071 24.57% 4,625 24.54% 2,299 24.40% 3,470 24.18% 6,183 23.84% 2,460 23.25% 2,791 23.18% 4,106 22.92% 724 22.10% 2,819 21.67% 3,566 21.37% 5,497 20.57% 4,431 20.45% 7,904 20.43% 2,496 20.23% 4,790 19.83% 3,512 19.28% 1,957 18.80% 1,292 18.65% 1,616 18.25% 2,778 17.93% 3,868 17.53% 6,020 17.28% 3,263 16.86% 5,251 16.15% 3,219 16.09% 4,104 15.77% 547 15.72% 3,702 14.34% 6,127 12.86% 4,270 12.79% 712 12.64% 3,142 12.54% 3,122 12.04% 1,943 11.94% 542 11.62% 4,874 11.55% 222 10.36% 2,092 10.28% 2,441 10.00% 491 9.37% 907 8.82% 1,336 7.26% 1,094 6.58% 1,740 6.09% 199 6.03% 6,527 5.93% 2,023 4.94% 59 3.39% 128 2.34% UAR Mis. Sum. 1.17% 3.13% 0.35% 1.89% 0.16% 2.42% 0.79% 7.89% 0.41% 4.59% 0.45% 5.64% 0.15% 3.89% 0.57% 1.39% 0.84% 3.63% 1.13% 6.16% 1.34% 7.68% 0.36% 5.23% 0.68% 1.46% 0.97% 6.22% 1.03% 4.43% 0.98% 2.64% 0.18% 3.73% 2.69% 4.99% 0.39% 2.63% 0.56% 3.33% 0.04% 3.22% 1.79% 8.03% 2.55% 8.84% 0.62% 3.48% 2.04% 9.78% 1.73% 5.18% 0.21% 2.09% 0.33% 3.82% 1.53% 5.09% 0.72% 3.39% 0.50% 2.64% 1.66% 5.75% 0.00% 8.23% 0.62% 2.49% 0.83% 2.87% 0.12% 1.83% 0.00% 3.09% 0.64% 3.85% 1.67% 9.10% 0.67% 1.65% 0.37% 4.61% 1.05% 4.19% 0.00% 3.60% 0.19% 2.44% 1.15% 4.18% 0.00% 1.83% 1.21% 1.76% 0.00% 5.46% 1.74% 3.56% 1.09% 5.46% 0.00% 3.02% 0.18% 2.47% 0.05% 1.24% 0.00% 3.39% 0.78% 1.56% Written Warning 0.25% 49.91% 2.96% 41.58% 1.76% 64.07% 34.68% 21.53% 0.84% 59.13% 5.37% 40.74% 50.83% 21.82% 1.14% 10.32% 5.68% 30.08% 66.55% 44.67% 64.97% 2.45% 0.51% 7.35% 17.20% 25.38% 29.21% 36.26% 57.00% 72.79% 28.46% 61.82% 73.67% 3.19% 4.00% 81.22% 69.10% 69.29% 1.54% 4.07% 14.94% 34.39% 1.35% 3.11% 76.12% 30.14% 15.10% 41.99% 49.54% 2.99% 41.21% 28.71% 34.85% 5.08% 15.63% Verbal No Warning Disposition 68.57% 0.92% 21.95% 0.52% 68.07% 1.46% 24.21% 0.79% 65.54% 3.11% 3.83% 1.44% 35.16% 1.58% 51.15% 0.96% 68.93% 1.59% 9.23% 0.50% 59.67% 2.68% 27.66% 2.83% 23.21% 0.90% 45.86% 3.04% 68.75% 2.98% 64.13% 0.56% 69.04% 0.80% 41.43% 0.36% 9.77% 0.23% 30.01% 1.20% 11.67% 0.27% 66.43% 2.02% 66.38% 2.91% 66.80% 3.10% 50.62% 2.10% 48.02% 1.76% 50.59% 0.36% 40.98% 1.33% 18.42% 1.10% 6.82% 0.13% 51.85% 0.47% 13.96% 1.05% 2.01% 0.37% 79.25% 0.11% 77.92% 1.52% 3.82% 0.23% 12.22% 2.95% 13.24% 0.45% 73.51% 2.15% 78.28% 3.40% 67.71% 0.74% 47.27% 1.56% 84.23% 0.45% 83.17% 0.81% 8.56% 0.00% 56.82% 1.83% 67.92% 5.18% 44.99% 0.30% 38.30% 0.27% 84.25% 0.11% 49.75% 0.00% 61.05% 1.65% 57.93% 0.99% 86.44% 1.69% 79.69% 0.00% Table II.A.8: Outcome of Stop (Sorted by % Warning) Department Name Eastern CT State University Redding Middlebury Portland Torrington Putnam Plainfield Suffield Weston Central CT State University State Capitol Police Guilford Clinton Avon Thomaston Old Saybrook Seymour Plymouth West Haven Waterford Easton University of Connecticut Simsbury Cheshire Wilton Southington Enfield Vernon East Hampton Bloomfield Wethersfield Windsor Windsor Locks Rocky Hill Canton Madison Milford Brookfield Bethel Groton Town New Canaan Naugatuck Stonington Monroe Plainville East Haven Ansonia New Milford Norwich Newington Middletown Winsted Shelton Newtown South Windsor Stratford Yale University Westport Total 128 2,023 59 199 6,527 1,094 1,740 1,336 491 2,092 222 4,270 2,441 907 542 3,142 3,702 1,943 6,127 4,874 712 3,219 3,868 5,251 6,020 4,790 7,904 4,104 547 3,263 3,122 5,497 2,496 3,566 1,292 4,106 2,778 2,299 2,861 4,431 6,445 4,843 2,819 4,625 3,470 3,512 5,110 2,791 6,183 5,071 1,616 724 740 5,229 3,475 1,957 380 5,964 Warning 95.31% 92.78% 91.53% 90.95% 89.77% 87.84% 87.24% 86.98% 86.97% 86.28% 85.59% 85.04% 84.68% 83.02% 82.66% 82.53% 82.44% 82.35% 81.92% 81.66% 81.32% 80.30% 79.81% 79.60% 77.24% 76.64% 76.32% 75.78% 75.69% 75.42% 75.05% 74.71% 74.68% 74.45% 74.15% 74.04% 73.40% 72.68% 71.86% 71.51% 71.03% 70.53% 69.88% 69.84% 69.77% 68.88% 68.83% 68.40% 68.36% 67.90% 67.82% 67.68% 67.30% 67.16% 67.02% 66.89% 65.79% 65.17% No UAR Mis. Sum. Infraction Disposition 0.78% 1.56% 2.34% 0.00% 0.05% 1.24% 4.94% 0.99% 0.00% 3.39% 3.39% 1.69% 0.00% 3.02% 6.03% 0.00% 0.18% 2.47% 5.93% 1.65% 1.74% 3.56% 6.58% 0.27% 1.09% 5.46% 6.09% 0.11% 0.00% 5.46% 7.26% 0.30% 0.00% 1.83% 9.37% 1.83% 0.19% 2.44% 10.28% 0.81% 0.00% 3.60% 10.36% 0.45% 0.12% 1.83% 12.79% 0.23% 1.15% 4.18% 10.00% 0.00% 1.21% 1.76% 8.82% 5.18% 0.37% 4.61% 11.62% 0.74% 0.64% 3.85% 12.54% 0.45% 0.62% 2.49% 14.34% 0.11% 0.67% 1.65% 11.94% 3.40% 0.83% 2.87% 12.86% 1.52% 1.05% 4.19% 11.55% 1.56% 0.00% 3.09% 12.64% 2.95% 0.50% 2.64% 16.09% 0.47% 0.21% 2.09% 17.53% 0.36% 0.72% 3.39% 16.15% 0.13% 0.33% 3.82% 17.28% 1.33% 0.04% 3.22% 19.83% 0.27% 0.39% 2.63% 20.43% 0.23% 1.66% 5.75% 15.77% 1.05% 0.00% 8.23% 15.72% 0.37% 1.53% 5.09% 16.86% 1.10% 1.67% 9.10% 12.04% 2.15% 0.18% 3.73% 20.57% 0.80% 0.56% 3.33% 20.23% 1.20% 0.98% 2.64% 21.37% 0.56% 0.62% 3.48% 18.65% 3.10% 0.68% 1.46% 22.92% 0.90% 1.73% 5.18% 17.93% 1.76% 0.57% 1.39% 24.40% 0.96% 0.35% 1.89% 25.38% 0.52% 2.69% 4.99% 20.45% 0.36% 0.16% 2.42% 24.93% 1.46% 0.64% 1.47% 26.78% 0.58% 1.03% 4.43% 21.67% 2.98% 0.15% 3.89% 24.54% 1.58% 0.84% 3.63% 24.18% 1.59% 1.79% 8.03% 19.28% 2.02% 1.17% 3.13% 25.95% 0.92% 0.36% 5.23% 23.18% 2.83% 1.13% 6.16% 23.84% 0.50% 0.45% 5.64% 24.57% 1.44% 2.04% 9.78% 18.25% 2.10% 0.97% 6.22% 22.10% 3.04% 0.41% 4.59% 24.59% 3.11% 0.25% 2.77% 29.22% 0.59% 0.63% 4.63% 26.10% 1.61% 2.55% 8.84% 18.80% 2.91% 0.79% 7.89% 24.74% 0.79% 0.44% 2.82% 30.70% 0.87% Table II.A.8: Outcome of Stop (Sorted by % Warning) Department Name Willimantic Ledyard* Cromwell East Windsor Ridgefield Glastonbury Watertown Berlin Orange Groton City Wallingford Coventry New Britain North Haven Farmington Granby North Branford Darien Groton Long Point Fairfield West Hartford Woodbridge Western CT State University Wolcott Bristol Greenwich Manchester Troop L Hamden Trumbull New London Stamford Troop B Southern CT State University Branford New Haven Waterbury East Hartford Derby Norwalk Bridgeport Troop D Department of Motor Vehicle Hartford Troop K Meriden Danbury Troop A Troop E Troop I Troop C Troop G Troop F Troop H CSP Headquarters Total 2,460 1,300 1,553 907 7,979 4,413 1,698 5,257 4,295 1,274 8,980 1,940 6,734 3,203 5,507 807 1,089 3,106 132 8,817 9,079 1,585 20 376 5,080 5,937 12,267 11,017 3,767 2,340 4,120 5,519 8,094 666 4,435 19,099 3,208 7,620 3,021 4,191 3,118 14,877 1,867 4,505 17,769 2,055 5,907 19,136 19,183 13,415 21,804 21,411 22,009 17,932 11,486 Warning 65.04% 63.77% 62.40% 61.08% 60.50% 58.80% 58.60% 58.42% 58.42% 58.32% 58.15% 57.06% 56.56% 56.35% 55.98% 54.52% 53.90% 53.51% 51.52% 51.47% 51.18% 50.85% 50.00% 49.50% 48.35% 48.09% 43.30% 43.02% 42.05% 40.56% 40.17% 39.08% 37.02% 36.19% 36.08% 35.61% 34.88% 34.87% 34.39% 31.45% 31.44% 30.85% 30.26% 29.23% 29.01% 27.74% 27.56% 26.99% 24.10% 22.44% 21.33% 20.14% 17.12% 17.01% 7.23% No UAR Mis. Sum. Infraction Disposition 1.34% 7.68% 23.25% 2.68% 0.23% 6.38% 28.62% 1.00% 0.64% 4.96% 27.11% 4.89% 1.21% 8.27% 27.78% 1.65% 0.13% 1.84% 36.56% 0.98% 0.48% 6.68% 32.68% 1.36% 0.53% 5.06% 34.98% 0.82% 0.40% 4.26% 33.21% 3.71% 0.35% 6.10% 34.20% 0.93% 0.78% 3.85% 35.64% 1.41% 4.61% 6.16% 29.47% 1.61% 0.10% 11.03% 29.02% 2.78% 1.68% 6.99% 33.58% 1.19% 0.53% 7.74% 32.53% 2.84% 1.73% 8.59% 31.47% 2.23% 0.12% 7.56% 37.55% 0.25% 0.28% 5.79% 33.88% 6.15% 0.90% 5.70% 39.28% 0.61% 0.00% 1.52% 46.21% 0.76% 0.66% 5.50% 40.09% 2.28% 3.37% 4.45% 39.80% 1.20% 0.19% 9.97% 37.48% 1.51% 0.00% 10.00% 40.00% 0.00% 0.80% 9.30% 39.40% 1.00% 1.61% 5.83% 37.95% 6.26% 0.39% 2.39% 47.47% 1.67% 0.55% 5.37% 49.37% 1.42% 0.74% 6.37% 46.21% 3.65% 0.35% 3.37% 52.64% 1.59% 0.38% 7.61% 49.40% 2.05% 2.99% 4.90% 50.95% 1.00% 0.82% 4.53% 52.91% 2.66% 0.62% 6.05% 53.71% 2.61% 1.05% 7.96% 54.20% 0.60% 0.23% 5.25% 54.30% 4.15% 0.77% 6.18% 56.64% 0.80% 3.30% 17.36% 42.21% 2.24% 1.63% 13.16% 48.11% 2.23% 0.30% 9.33% 54.95% 1.03% 1.29% 6.04% 59.70% 1.53% 1.15% 4.81% 61.90% 0.70% 0.38% 5.21% 62.51% 1.06% 0.00% 4.71% 62.88% 2.14% 3.02% 10.37% 56.03% 1.35% 0.43% 3.87% 65.09% 1.60% 1.85% 10.07% 58.64% 1.70% 1.20% 2.71% 67.60% 0.93% 0.54% 4.77% 66.02% 1.68% 0.44% 5.33% 68.24% 1.88% 0.33% 4.64% 71.11% 1.48% 0.34% 2.72% 74.22% 1.39% 0.77% 5.85% 71.47% 1.77% 0.11% 2.70% 78.93% 1.14% 1.75% 5.46% 73.43% 2.35% 1.09% 2.73% 87.84% 1.12% Table II.A.9: Outcome of Stop (Sorted by % UAR) Department Name Wallingford West Hartford Waterbury Hartford New London Groton Town Stratford Middletown Meriden East Haven Troop H Putnam Milford Farmington New Britain Wethersfield Vernon East Hartford Bristol Bloomfield Willimantic Norwalk Avon East Windsor Danbury Ansonia Bridgeport Clinton Norwich Plainfield CSP Headquarters Southern CT State University Waterford Stonington Rocky Hill Winsted Darien Plainville West Haven Stamford Wolcott Yale University Groton City Eastern CT State University New Haven Troop G Troop L Cheshire Madison Plymouth Fairfield Cromwell Naugatuck Old Saybrook South Windsor Seymour Canton Troop B N 8,980 9,079 3,208 4,505 4,120 4,431 1,957 1,616 2,055 3,512 17,932 1,094 2,778 5,507 6,734 3,122 4,104 7,620 5,080 3,263 2,460 4,191 907 907 5,907 5,110 3,118 2,441 6,183 1,740 11,486 666 4,874 2,819 3,566 724 3,106 3,470 6,127 5,519 376 380 1,274 128 19,099 21,411 11,017 5,251 4,106 1,943 8,817 1,553 4,843 3,142 3,475 3,702 1,292 8,094 UAR Mis. Sum. Infraction 4.61% 6.16% 29.47% 3.37% 4.45% 39.80% 3.30% 17.36% 42.21% 3.02% 10.37% 56.03% 2.99% 4.90% 50.95% 2.69% 4.99% 20.45% 2.55% 8.84% 18.80% 2.04% 9.78% 18.25% 1.85% 10.07% 58.64% 1.79% 8.03% 19.28% 1.75% 5.46% 73.43% 1.74% 3.56% 6.58% 1.73% 5.18% 17.93% 1.73% 8.59% 31.47% 1.68% 6.99% 33.58% 1.67% 9.10% 12.04% 1.66% 5.75% 15.77% 1.63% 13.16% 48.11% 1.61% 5.83% 37.95% 1.53% 5.09% 16.86% 1.34% 7.68% 23.25% 1.29% 6.04% 59.70% 1.21% 1.76% 8.82% 1.21% 8.27% 27.78% 1.20% 2.71% 67.60% 1.17% 3.13% 25.95% 1.15% 4.81% 61.90% 1.15% 4.18% 10.00% 1.13% 6.16% 23.84% 1.09% 5.46% 6.09% 1.09% 2.73% 87.84% 1.05% 7.96% 54.20% 1.05% 4.19% 11.55% 1.03% 4.43% 21.67% 0.98% 2.64% 21.37% 0.97% 6.22% 22.10% 0.90% 5.70% 39.28% 0.84% 3.63% 24.18% 0.83% 2.87% 12.86% 0.82% 4.53% 52.91% 0.80% 9.30% 39.40% 0.79% 7.89% 24.74% 0.78% 3.85% 35.64% 0.78% 1.56% 2.34% 0.77% 6.18% 56.64% 0.77% 5.85% 71.47% 0.74% 6.37% 46.21% 0.72% 3.39% 16.15% 0.68% 1.46% 22.92% 0.67% 1.65% 11.94% 0.66% 5.50% 40.09% 0.64% 4.96% 27.11% 0.64% 1.47% 26.78% 0.64% 3.85% 12.54% 0.63% 4.63% 26.10% 0.62% 2.49% 14.34% 0.62% 3.48% 18.65% 0.62% 6.05% 53.71% Written Warning 3.26% 3.03% 3.62% 6.26% 10.34% 30.08% 0.51% 17.20% 3.94% 2.45% 4.62% 49.54% 25.38% 2.22% 0.58% 1.54% 61.82% 10.87% 39.02% 57.00% 5.37% 1.41% 15.10% 20.18% 0.17% 0.25% 1.14% 76.12% 59.13% 2.99% 2.05% 29.28% 34.39% 1.14% 10.32% 21.82% 16.58% 0.84% 4.00% 0.58% 27.40% 41.58% 11.54% 15.63% 7.40% 2.48% 9.96% 72.79% 50.83% 4.07% 0.73% 14.75% 21.25% 69.29% 2.47% 3.19% 7.35% 25.22% Verbal No Warning Disposition 54.89% 1.61% 48.16% 1.20% 31.27% 2.24% 22.97% 1.35% 29.83% 1.00% 41.43% 0.36% 66.38% 2.91% 50.62% 2.10% 23.80% 1.70% 66.43% 2.02% 12.39% 2.35% 38.30% 0.27% 48.02% 1.76% 53.77% 2.23% 55.98% 1.19% 73.51% 2.15% 13.96% 1.05% 24.00% 2.23% 9.33% 6.26% 18.42% 1.10% 59.67% 2.68% 30.04% 1.53% 67.92% 5.18% 40.90% 1.65% 27.39% 0.93% 68.57% 0.92% 30.30% 0.70% 8.56% 0.00% 9.23% 0.50% 84.25% 0.11% 5.18% 1.12% 6.91% 0.60% 47.27% 1.56% 68.75% 2.98% 64.13% 0.56% 45.86% 3.04% 36.93% 0.61% 68.93% 1.59% 77.92% 1.52% 38.50% 2.66% 22.10% 1.00% 24.21% 0.79% 46.78% 1.41% 79.69% 0.00% 28.21% 0.80% 17.67% 1.77% 33.07% 3.65% 6.82% 0.13% 23.21% 0.90% 78.28% 3.40% 50.74% 2.28% 47.65% 4.89% 49.29% 0.58% 13.24% 0.45% 64.55% 1.61% 79.25% 0.11% 66.80% 3.10% 11.80% 2.61% Table II.A.9: Outcome of Stop (Sorted by % UAR) Department Name Brookfield Windsor Locks Manchester Troop A North Haven Watertown University of Connecticut Glastonbury Newington Troop E Westport Troop K Shelton Berlin Enfield Greenwich Trumbull Troop D Thomaston New Milford Bethel Orange Hamden Troop C Wilton Troop I Derby North Branford Newtown Ledyard* Branford Simsbury Central CT State University Woodbridge Torrington Windsor New Canaan Monroe Ridgefield Granby Guilford Troop F Coventry Redding Southington Department of Motor Vehicle East Hampton Easton Groton Long Point Middlebury Portland State Capitol Police Suffield Western CT State University Weston N 2,299 2,496 12,267 19,136 3,203 1,698 3,219 4,413 5,071 19,183 5,964 17,769 740 5,257 7,904 5,937 2,340 14,877 542 2,791 2,861 4,295 3,767 21,804 6,020 13,415 3,021 1,089 5,229 1,300 4,435 3,868 2,092 1,585 6,527 5,497 6,445 4,625 7,979 807 4,270 22,009 1,940 2,023 4,790 1,867 547 712 132 59 199 222 1,336 20 491 UAR Mis. Sum. Infraction 0.57% 1.39% 24.40% 0.56% 3.33% 20.23% 0.55% 5.37% 49.37% 0.54% 4.77% 66.02% 0.53% 7.74% 32.53% 0.53% 5.06% 34.98% 0.50% 2.64% 16.09% 0.48% 6.68% 32.68% 0.45% 5.64% 24.57% 0.44% 5.33% 68.24% 0.44% 2.82% 30.70% 0.43% 3.87% 65.09% 0.41% 4.59% 24.59% 0.40% 4.26% 33.21% 0.39% 2.63% 20.43% 0.39% 2.39% 47.47% 0.38% 7.61% 49.40% 0.38% 5.21% 62.51% 0.37% 4.61% 11.62% 0.36% 5.23% 23.18% 0.35% 1.89% 25.38% 0.35% 6.10% 34.20% 0.35% 3.37% 52.64% 0.34% 2.72% 74.22% 0.33% 3.82% 17.28% 0.33% 4.64% 71.11% 0.30% 9.33% 54.95% 0.28% 5.79% 33.88% 0.25% 2.77% 29.22% 0.23% 6.38% 28.62% 0.23% 5.25% 54.30% 0.21% 2.09% 17.53% 0.19% 2.44% 10.28% 0.19% 9.97% 37.48% 0.18% 2.47% 5.93% 0.18% 3.73% 20.57% 0.16% 2.42% 24.93% 0.15% 3.89% 24.54% 0.13% 1.84% 36.56% 0.12% 7.56% 37.55% 0.12% 1.83% 12.79% 0.11% 2.70% 78.93% 0.10% 11.03% 29.02% 0.05% 1.24% 4.94% 0.04% 3.22% 19.83% 0.00% 4.71% 62.88% 0.00% 8.23% 15.72% 0.00% 3.09% 12.64% 0.00% 1.52% 46.21% 0.00% 3.39% 3.39% 0.00% 3.02% 6.03% 0.00% 3.60% 10.36% 0.00% 5.46% 7.26% 0.00% 10.00% 40.00% 0.00% 1.83% 9.37% Written Warning 21.53% 44.67% 4.23% 4.37% 2.03% 45.82% 28.46% 31.23% 64.07% 4.96% 32.68% 8.59% 1.76% 33.82% 66.55% 17.53% 4.44% 8.26% 14.94% 40.74% 49.91% 1.54% 2.42% 8.38% 36.26% 5.61% 0.13% 32.69% 16.52% 47.85% 0.07% 29.21% 3.11% 11.80% 28.71% 5.68% 2.96% 34.68% 46.32% 31.10% 81.22% 4.93% 22.27% 34.85% 64.97% 7.93% 73.67% 69.10% 41.67% 5.08% 41.21% 1.35% 41.99% 20.00% 30.14% Verbal No Warning Disposition 51.15% 0.96% 30.01% 1.20% 39.06% 1.42% 22.61% 1.68% 54.32% 2.84% 12.78% 0.82% 51.85% 0.47% 27.58% 1.36% 3.83% 1.44% 19.15% 1.88% 32.49% 0.87% 20.42% 1.60% 65.54% 3.11% 24.60% 3.71% 9.77% 0.23% 30.55% 1.67% 36.11% 2.05% 22.59% 1.06% 67.71% 0.74% 27.66% 2.83% 21.95% 0.52% 56.88% 0.93% 39.63% 1.59% 12.95% 1.39% 40.98% 1.33% 16.82% 1.48% 34.26% 1.03% 21.21% 6.15% 50.64% 0.59% 15.92% 1.00% 36.01% 4.15% 50.59% 0.36% 83.17% 0.81% 39.05% 1.51% 61.05% 1.65% 69.04% 0.80% 68.07% 1.46% 35.16% 1.58% 14.17% 0.98% 23.42% 0.25% 3.82% 0.23% 12.19% 1.14% 34.79% 2.78% 57.93% 0.99% 11.67% 0.27% 22.34% 2.14% 2.01% 0.37% 12.22% 2.95% 9.85% 0.76% 86.44% 1.69% 49.75% 0.00% 84.23% 0.45% 44.99% 0.30% 30.00% 0.00% 56.82% 1.83% Table II.A.10: Number of Searches(Sorted by % Search) 2015-2016 Searches Department Name Waterbury Stratford Middletown Bridgeport Vernon Yale University Danbury Wallingford Derby Trumbull Wolcott Norwich East Hartford West Hartford New Britain Wethersfield Milford Clinton New Haven Norwalk New London Glastonbury University of Connecticut Newington East Haven Willimantic West Haven Plainville Stamford Meriden Suffield Naugatuck South Windsor Plymouth Thomaston Wilton Winsted Enfield Ansonia Shelton Groton City Plainfield Watertown Darien Waterford North Haven Rocky Hill Farmington Manchester Ledyard Old Saybrook Bloomfield Portland Westport Bristol Woodbridge Troop L Groton Town Stops 3,208 1,957 1,616 3,118 4,104 380 5,907 8,980 3,021 2,340 376 6,183 7,620 9,079 6,734 3,122 2,778 2,441 19,099 4,191 4,120 4,413 3,219 5,071 3,512 2,460 6,127 3,470 5,519 2,055 1,336 4,843 3,475 1,943 542 6,020 724 7,904 5,110 740 1,274 1,740 1,698 3,106 4,874 3,203 3,566 5,507 12,267 1,300 3,142 3,263 199 5,964 5,080 1,585 11,017 4,431 N 531 267 168 306 384 35 500 710 238 175 28 449 505 579 421 195 169 137 1,040 216 202 205 144 222 152 106 261 146 231 84 54 193 135 73 19 209 25 269 173 24 41 54 52 95 146 95 104 156 337 35 84 86 5 149 121 37 254 99 % 16.55% 13.64% 10.40% 9.81% 9.36% 9.21% 8.46% 7.91% 7.88% 7.48% 7.45% 7.26% 6.63% 6.38% 6.25% 6.25% 6.08% 5.61% 5.45% 5.15% 4.90% 4.65% 4.47% 4.38% 4.33% 4.31% 4.26% 4.21% 4.19% 4.09% 4.04% 3.99% 3.88% 3.76% 3.51% 3.47% 3.45% 3.40% 3.39% 3.24% 3.22% 3.10% 3.06% 3.06% 3.00% 2.97% 2.92% 2.83% 2.75% 2.69% 2.67% 2.64% 2.51% 2.50% 2.38% 2.33% 2.31% 2.23% Table II.A.10: Number of Searches(Sorted by % Search) 2015-2016 Searches Department Name East Hampton Troop G Windsor Locks Troop H Berlin Troop C New Milford Windsor Fairfield Monroe Seymour Troop A Newtown Southern CT State University Greenwich Troop E Middlebury Troop B Troop D Cromwell Brookfield Orange Coventry Hartford North Branford Troop K Granby State Capitol Police East Windsor New Canaan Cheshire Avon Madison Torrington Troop I Branford Canton Weston Troop F Bethel Eastern CT State University Ridgefield Southington Putnam Hamden Guilford Simsbury CSP Headquarters Redding Stonington Easton Central CT State University Department of Motor Vehicle Groton Long Point Western CT State University Stops 547 21,411 2,496 17,932 5,257 21,804 2,791 5,497 8,817 4,625 3,702 19,136 5,229 666 5,937 19,183 59 8,094 14,877 1,553 2,299 4,295 1,940 4,505 1,089 17,769 807 222 907 6,445 5,251 907 4,106 6,527 13,415 4,435 1,292 491 22,009 2,861 128 7,979 4,790 1,094 3,767 4,270 3,868 11,486 2,023 2,819 712 2,092 1,867 132 20 N 12 469 53 380 110 455 57 111 171 89 71 367 100 12 106 336 1 136 249 25 37 66 29 67 16 244 11 3 12 80 65 11 43 68 138 42 12 4 177 23 1 54 31 7 24 26 23 57 10 10 2 4 3 0 0 % 2.19% 2.19% 2.12% 2.12% 2.09% 2.09% 2.04% 2.02% 1.94% 1.92% 1.92% 1.92% 1.91% 1.80% 1.79% 1.75% 1.69% 1.68% 1.67% 1.61% 1.61% 1.54% 1.49% 1.49% 1.47% 1.37% 1.36% 1.35% 1.32% 1.24% 1.24% 1.21% 1.05% 1.04% 1.03% 0.95% 0.93% 0.81% 0.80% 0.80% 0.78% 0.68% 0.65% 0.64% 0.64% 0.61% 0.59% 0.50% 0.49% 0.35% 0.28% 0.19% 0.16% 0.00% 0.00% Table II.B.1: Statewide Average Comparisons for Black Drivers (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City** Groton Long Point** Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Black Stops 16.54% 7.94% 9.44% 7.17% 53.14% 5.41% 40.10% 9.74% 4.44% 4.49% 10.00% 2.66% 3.76% 14.36% 8.97% 11.43% 15.76% 4.02% 39.62% 9.71% 15.44% 3.79% 9.63% 14.13% 9.81% 7.57% 4.21% 8.46% 14.60% 3.03% 12.95% 2.18% 31.19% 41.31% 14.10% 2.95% 23.97% 14.16% 5.08% 23.51% 13.21% 6.96% 9.11% 18.30% 7.18% 39.69% 17.31% 5.05% 13.80% 6.96% 3.21% 13.18% 20.35% 20.62% 2.90% 19.35% 2.87% 7.41% Difference Between Town and State Average 1.94% -6.66% -5.16% -7.43% 38.54% -9.19% 25.50% -4.86% -10.16% -10.11% -4.60% -11.94% -10.84% -0.24% -5.63% -3.17% 1.16% -10.58% 25.02% -4.89% 0.84% -10.81% -4.97% -0.47% -4.79% -7.03% -10.39% -6.15% 0.00% -11.57% -1.65% -12.42% 16.59% 26.71% -0.50% -11.65% 9.37% -0.44% -9.52% 8.91% -1.39% -7.64% -5.50% 3.70% -7.42% 25.09% 2.71% -9.55% -0.80% -7.64% -11.39% -1.43% 5.75% 6.02% -11.70% 4.75% -11.73% -7.19% Black Difference NonResidents Difference Between Between Net Resident Age 16+ Town and State Average Differences Black Stops 9.74% 0.62% 1.32% 58.70% 1.41% -7.71% 1.05% 94.44% 0.65% -8.47% 3.31% 93.15% 1.74% -7.38% -0.05% 86.83% 54.76% 45.64% -7.10% 54.73% 1.76% -7.36% -1.83% 76.67% 31.82% 22.70% 2.80% 11.84% 3.24% -5.88% 1.02% 53.74% 1.05% -8.07% -2.09% 76.47% 0.00% -9.12% -0.99% 96.55% 1.27% -7.85% 3.25% 21.33% 0.00% -9.12% -2.82% 9.23% 0.79% -8.33% -2.51% 86.30% 3.69% -5.43% 5.19% 81.61% 6.42% -2.70% -2.93% 67.74% 0.00% -9.12% 5.95% 96.34% 6.03% -3.09% 4.25% 84.03% 1.10% -8.02% -2.56% 81.82% 22.52% 13.40% 11.62% 46.31% 2.47% -6.65% 1.76% 80.65% 5.96% -3.16% 4.00% 80.00% 0.00% -9.12% -1.69% 96.30% 2.63% -6.49% 1.52% 21.42% 1.73% -7.39% 6.92% 94.46% 2.20% -6.92% 2.12% 90.74% 1.80% -7.32% 0.28% 81.74% 0.92% -8.20% -2.19% 76.47% 2.03% -7.09% 0.94% 85.46% 7.70% -1.42% 1.42% 64.52% 0.00% -9.12% -2.45% 100.00% 6.07% -3.05% 1.40% 62.89% 0.70% -8.42% -4.00% 74.19% 18.28% 9.16% 7.43% 54.55% 35.80% 26.68% 0.03% 3.44% 3.10% -6.02% 5.52% 77.05% 0.49% -8.63% -3.02% 87.60% 10.15% 1.03% 8.34% 54.76% 7.80% -1.32% 0.88% 37.80% 0.00% -9.12% -0.40% 100.00% 11.68% 2.56% 6.35% 5.53% 2.23% -6.89% 5.50% 85.29% 1.32% -7.80% 0.16% 90.37% 4.11% -5.01% -0.49% 57.60% 10.67% 1.55% 2.14% 29.87% 1.06% -8.06% 0.64% 88.98% 32.16% 23.04% 2.05% 27.53% 15.18% 6.06% -3.35% 40.25% 1.69% -7.43% -2.11% 63.12% 2.99% -6.13% 5.33% 87.71% 0.68% -8.44% 0.80% 97.80% 1.33% -7.79% -3.60% 80.00% 2.91% -6.21% 4.78% 92.42% 13.13% 4.01% 1.74% 51.58% 8.96% -0.16% 6.18% 37.80% 0.00% -9.12% -2.58% 84.62% 1.31% -7.81% 12.56% 98.56% 0.96% -8.16% -3.57% 46.00% 2.73% -6.39% -0.81% 78.60% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table II.B.1: Statewide Average Comparisons for Black Drivers (Sorted Alphabetically) 2015-2016 Department Name Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Black Stops 5.92% 6.03% 3.11% 4.50% 4.99% 9.93% 6.19% 6.62% 5.35% 15.63% 5.57% 18.99% 2.91% 31.22% 4.79% 4.43% 5.00% 20.73% 15.50% 9.97% 29.18% 10.59% 8.36% 15.09% 27.37% 2.85% 9.81% 18.67% 7.64% 9.39% 43.91% 13.66% 4.28% 7.45% 18.61% Difference Between Town and State Average -8.68% -8.57% -11.49% -10.10% -9.61% -4.67% -8.41% -7.98% -9.25% 1.03% -9.03% 4.39% -11.69% 16.62% -9.81% -10.17% -9.60% 6.13% 0.90% -4.63% 14.58% -4.01% -6.24% 0.49% 12.77% -11.75% -4.79% 4.07% -6.96% -5.22% 29.31% -0.94% -10.32% -7.15% 4.01% Black Difference NonResidents Difference Between Between Net Resident Age 16+ Town and State Average Differences Black Stops 0.00% -9.12% 0.44% 86.09% 1.87% -7.25% -1.32% 58.33% 1.17% -7.95% -3.55% 23.53% 0.00% -9.12% -0.98% 95.60% 0.77% -8.35% -1.26% 93.72% 3.77% -5.35% 0.68% 73.16% 2.25% -6.87% -1.54% 78.17% 2.07% -7.05% -0.93% 73.47% 1.46% -7.66% -1.59% 78.26% 3.68% -5.44% 6.47% 84.53% 1.34% -7.78% -1.25% 81.65% 12.86% 3.74% 0.65% 45.32% 0.82% -8.30% -3.39% 79.27% 12.76% 3.64% 12.98% 61.05% 1.40% -7.72% -2.09% 93.75% 0.00% -9.12% -1.05% 87.50% 2.12% -7.00% -2.60% 38.34% 2.90% -6.22% 12.35% 93.81% 4.70% -4.42% 5.32% 59.43% 1.34% -7.78% 3.15% 86.59% 17.37% 8.25% 6.33% 20.73% 2.29% -6.83% 2.82% 90.31% 1.24% -7.88% 1.64% 87.32% 5.65% -3.47% 3.96% 88.47% 17.70% 8.58% 4.19% 53.25% 1.25% -7.87% -3.88% 92.86% 1.22% -7.90% 3.11% 95.56% 2.75% -6.37% 10.44% 80.62% 4.08% -5.04% -1.92% 56.91% 1.01% -8.11% 2.90% 96.64% 32.20% 23.08% 6.23% 59.24% 4.27% -4.85% 3.91% 82.99% 1.04% -8.08% -2.24% 45.16% 1.53% -7.59% 0.44% 89.29% 1.94% -7.18% 11.19% 98.31% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table II.B.2: Statewide Average Comparisons for Hispanic Drivers (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City** Groton Long Point** Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Hispanic Stops 13.64% 5.29% 13.30% 12.44% 8.03% 7.58% 29.44% 13.09% 10.87% 2.71% 8.74% 8.52% 5.41% 5.54% 29.59% 18.42% 14.63% 2.74% 27.90% 16.51% 7.28% 9.83% 7.76% 14.13% 8.90% 8.52% 2.48% 18.07% 12.56% 5.30% 9.75% 3.37% 9.11% 28.79% 7.78% 4.41% 14.90% 31.63% 8.47% 11.32% 10.01% 7.52% 11.63% 39.69% 10.60% 23.11% 21.60% 11.07% 20.65% 7.06% 3.31% 9.21% 20.81% 14.91% 5.38% 12.48% 4.25% Difference Between Hispanic Difference Between Difference Town and State Residents Town and State Between Net Non-Resident Average Age 16+ Average Differences Hispanic Stops 0.64% 14.03% 2.12% -1.48% 61.41% -7.71% 2.76% -9.15% 1.45% 89.58% 0.30% 2.67% -9.24% 9.53% 93.71% -0.56% 6.65% -5.26% 4.70% 76.69% -4.97% 4.78% -7.13% 2.16% 82.06% -5.42% 3.45% -8.46% 3.04% 76.79% 16.34% 36.20% 24.29% -7.95% 10.24% 0.09% 7.65% -4.26% 4.35% 53.98% -2.13% 3.79% -8.12% 5.99% 86.00% -10.29% 1.94% -9.97% -0.32% 88.57% -4.26% 2.35% -9.56% 5.30% 25.49% -4.48% 4.41% -7.50% 3.02% 8.65% -7.59% 2.21% -9.70% 2.11% 84.76% -7.46% 3.90% -8.01% 0.55% 91.86% 16.59% 23.25% 11.34% 5.25% 70.77% 5.42% 3.49% -8.42% 13.83% 96.68% 1.63% 12.37% 0.46% 1.17% 77.60% -10.26% 2.02% -9.89% -0.37% 66.67% 14.90% 22.91% 11.00% 3.90% 43.37% 3.51% 8.43% -3.48% 6.99% 66.90% -5.72% 4.34% -7.57% 1.84% 78.79% -3.17% 2.56% -9.35% 6.18% 97.14% -5.24% 4.00% -7.91% 2.67% 30.02% 1.13% 4.51% -7.40% 8.53% 92.78% -4.10% 3.20% -8.71% 4.60% 94.90% -4.48% 3.60% -8.31% 3.83% 77.39% -10.52% 1.39% -10.52% 0.00% 90.00% 5.07% 9.15% -2.76% 7.83% 81.27% -0.44% 11.80% -0.11% -0.33% 49.38% -7.70% 0.00% -11.91% 4.21% 100.00% -3.25% 7.40% -4.51% 1.26% 70.53% -9.63% 2.90% -9.01% -0.62% 66.67% -3.89% 7.58% -4.33% 0.44% 63.85% 15.79% 41.02% 29.11% -13.32% 2.47% -5.32% 4.57% -7.34% 2.02% 83.17% -8.59% 1.73% -10.18% 1.59% 86.19% 1.90% 9.89% -2.02% 3.92% 55.91% 18.63% 24.86% 12.95% 5.68% 18.77% -4.53% 2.22% -9.69% 5.16% 100.00% -1.68% 6.77% -5.14% 3.47% 4.92% -2.99% 4.45% -7.46% 4.47% 79.50% -5.48% 4.30% -7.61% 2.13% 89.94% -1.37% 7.77% -4.14% 2.77% 57.55% 26.69% 31.75% 19.84% 6.85% 17.66% -2.40% 2.69% -9.22% 6.82% 91.80% 10.11% 24.79% 12.88% -2.77% 26.90% 8.60% 25.08% 13.17% -4.57% 30.00% -1.93% 5.46% -6.45% 4.52% 57.93% 7.65% 6.39% -5.52% 13.17% 84.53% -5.94% 2.86% -9.05% 3.10% 90.79% -9.69% 2.31% -9.60% -0.10% 80.56% -3.79% 3.26% -8.65% 4.86% 95.93% 7.81% 22.67% 10.76% -2.95% 48.28% 1.91% 10.59% -1.32% 3.23% 47.29% -7.62% 2.93% -8.98% 1.36% 78.11% -0.52% 2.54% -9.37% 8.85% 98.13% -8.75% 3.33% -8.58% -0.17% 58.11% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table II.B.2: Statewide Average Comparisons for Hispanic Drivers (Sorted Alphabetically) 2015-2016 Department Name Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Hispanic Stops 11.76% 6.54% 5.53% 2.47% 10.03% 11.30% 7.26% 5.73% 7.57% 3.41% 9.18% 5.68% 21.36% 2.94% 19.78% 5.54% 3.32% 7.83% 14.23% 9.06% 12.84% 26.78% 11.76% 8.24% 18.76% 20.87% 3.26% 8.55% 28.06% 29.15% 13.95% 11.64% 8.17% 4.70% 16.49% 7.76% Difference Between Hispanic Difference Between Difference Town and State Residents Town and State Between Net Non-Resident Average Age 16+ Average Differences Hispanic Stops -1.24% 5.18% -6.73% 5.48% 81.62% -6.46% 2.47% -9.44% 2.97% 85.04% -7.47% 2.75% -9.16% 1.68% 72.73% -10.53% 2.20% -9.71% -0.82% 22.22% -2.97% 2.37% -9.54% 6.57% 96.55% -1.70% 3.46% -8.45% 6.75% 94.57% -5.74% 4.65% -7.26% 1.52% 82.24% -7.27% 5.53% -6.38% -0.89% 70.28% -5.43% 5.17% -6.74% 1.31% 66.07% -9.59% 2.61% -9.30% -0.29% 71.21% -3.82% 3.62% -8.29% 4.47% 84.01% -7.32% 2.80% -9.11% 1.78% 82.72% 8.36% 22.87% 10.96% -2.60% 40.80% -10.06% 1.91% -10.00% -0.06% 78.31% 6.78% 11.92% 0.01% 6.76% 64.86% -7.46% 2.20% -9.71% 2.25% 89.19% -9.68% 2.09% -9.82% 0.14% 77.78% -5.17% 6.92% -4.99% -0.18% 25.83% 1.23% 5.06% -6.85% 8.08% 93.09% -3.94% 5.21% -6.70% 2.76% 56.99% -0.16% 6.71% -5.20% 5.04% 73.55% 13.78% 27.54% 15.63% -1.85% 21.89% -1.24% 4.07% -7.84% 6.59% 92.67% -4.76% 2.99% -8.92% 4.17% 87.86% 5.76% 8.78% -3.13% 8.88% 86.79% 7.87% 15.96% 4.05% 3.82% 45.50% -9.74% 3.06% -8.85% -0.89% 81.25% -4.45% 3.19% -8.72% 4.27% 94.71% 15.06% 7.10% -4.81% 19.86% 72.49% 16.15% 28.88% 16.97% -0.82% 18.69% 0.95% 2.74% -9.17% 10.13% 94.40% -1.36% 7.33% -4.58% 3.22% 77.19% -4.83% 3.46% -8.45% 3.62% 82.84% -8.30% 4.28% -7.63% -0.68% 58.82% 3.39% 2.83% -9.08% 12.47% 93.55% -5.24% 2.68% -9.23% 3.99% 92.68% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table II.B.3: Statewide Average Comparisons for Minority Drivers (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City** Groton Long Point** Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Minority Difference Between Difference Minority Difference Between Residents Town and State Between Net Non-Resident Stops Town and State Average Age 16+ Average Differences Minority Stops 31.21% 0.61% 25.62% 0.39% 0.22% 59.87% 16.21% -14.39% 9.82% -15.41% 1.02% 87.76% 25.55% -5.05% 5.76% -19.47% 14.41% 91.44% 22.02% -8.58% 13.49% -11.74% 3.16% 78.25% 62.83% 32.23% 61.51% 36.28% -4.06% 58.83% 13.71% -16.89% 8.49% -16.74% -0.15% 75.00% 71.65% 41.05% 73.25% 48.02% -6.97% 11.46% 24.35% -6.25% 12.71% -12.52% 6.27% 54.65% 17.18% -13.42% 8.11% -17.12% 3.70% 82.28% 8.98% -21.62% 3.25% -21.98% 0.36% 89.66% 20.74% -9.86% 8.62% -16.61% 6.75% 21.85% 12.70% -17.90% 6.12% -19.11% 1.21% 9.35% 11.60% -19.00% 3.79% -21.44% 2.43% 87.11% 21.83% -8.77% 10.57% -14.66% 5.89% 84.07% 39.95% 9.35% 38.64% 13.41% -4.05% 70.55% 32.26% 1.66% 7.17% -18.06% 19.72% 95.11% 31.18% 0.58% 20.56% -4.67% 5.26% 81.10% 6.95% -23.65% 4.60% -20.63% -3.03% 73.68% 69.24% 38.64% 51.63% 26.40% 12.24% 45.38% 27.82% -2.78% 13.98% -11.25% 8.47% 70.93% 24.37% -6.23% 14.58% -10.65% 4.42% 81.00% 15.03% -15.57% 5.56% -19.67% 4.09% 96.26% 19.28% -11.32% 8.65% -16.58% 5.26% 26.18% 30.76% 0.16% 10.00% -15.23% 15.39% 92.77% 23.79% -6.81% 12.59% -12.64% 5.82% 88.02% 20.60% -10.00% 11.81% -13.42% 3.42% 69.86% 7.93% -22.67% 3.19% -22.04% -0.63% 79.69% 31.55% 0.95% 17.95% -7.28% 8.22% 81.15% 29.51% -1.09% 26.90% 1.67% -2.76% 59.04% 9.09% -21.51% 0.00% -25.2300% 3.72% 100.00% 26.02% -4.58% 20.39% -4.84% 0.26% 64.79% 8.24% -22.36% 5.67% -19.56% -2.80% 61.36% 41.62% 11.02% 30.92% 5.69% 5.34% 56.76% 71.01% 40.41% 80.76% 55.53% -15.12% 3.09% 25.92% -4.68% 13.40% -11.83% 7.15% 77.74% 8.91% -21.69% 4.26% -20.97% -0.71% 81.69% 41.97% 11.37% 27.95% 2.72% 8.65% 54.82% 46.86% 16.26% 34.86% 9.63% 6.63% 24.92% 13.56% -17.04% 5.58% -19.65% 2.61% 100.00% 36.57% 5.97% 23.49% -1.74% 7.71% 5.75% 26.03% -4.57% 11.62% -13.61% 9.03% 79.25% 15.96% -14.64% 7.56% -17.67% 3.03% 88.75% 21.85% -8.75% 15.18% -10.05% 1.30% 57.75% 59.40% 28.80% 45.00% 19.77% 9.03% 21.70% 21.24% -9.36% 7.15% -18.08% 8.72% 85.61% 64.34% 33.74% 62.82% 37.59% -3.85% 28.24% 40.78% 10.18% 43.57% 18.34% -8.16% 36.43% 17.95% -12.65% 9.69% -15.54% 2.89% 58.28% 37.61% 7.01% 14.51% -10.72% 17.72% 84.11% 16.20% -14.40% 5.76% -19.47% 5.07% 91.03% 7.44% -23.16% 5.02% -20.21% -2.95% 80.25% 24.45% -6.15% 10.51% -14.72% 8.56% 91.95% 43.26% 12.66% 40.80% 15.57% -2.91% 51.68% 39.24% 8.64% 29.09% 3.86% 4.78% 42.33% 10.34% -20.26% 5.15% -20.08% -0.18% 77.54% 34.71% 4.11% 10.75% -14.48% 18.60% 96.85% 7.53% -23.07% 5.32% -19.91% -3.16% 52.67% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table II.B.3: Statewide Average Comparisons for Minority Drivers (Sorted Alphabetically) 2015-2016 Department Name Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Minority Difference Between Difference Minority Difference Between Residents Town and State Between Net Non-Resident Stops Town and State Average Age 16+ Average Differences Minority Stops 20.89% -9.71% 10.00% -15.23% 5.52% 79.59% 12.97% -17.63% 2.47% -22.76% 5.13% 84.92% 12.56% -18.04% 4.63% -20.60% 2.57% 64.00% 6.58% -24.02% 3.37% -21.86% -2.16% 25.00% 16.16% -14.44% 4.37% -20.86% 6.42% 96.02% 19.23% -11.37% 7.29% -17.94% 6.57% 89.83% 19.83% -10.77% 17.20% -8.03% -2.74% 74.82% 13.05% -17.55% 9.77% -15.46% -2.09% 73.91% 15.27% -15.33% 10.83% -14.40% -0.93% 66.37% 11.25% -19.35% 7.65% -17.58% -1.77% 68.97% 29.32% -1.28% 14.60% -10.63% 9.35% 78.21% 12.15% -18.45% 6.17% -19.06% 0.61% 80.41% 43.49% 12.89% 43.86% 18.63% -5.74% 43.33% 7.52% -23.08% 4.35% -20.88% -2.20% 77.36% 53.40% 22.80% 27.20% 1.97% 20.83% 62.11% 11.90% -18.70% 4.91% -20.32% 1.62% 88.05% 8.12% -22.48% 2.09% -23.14% 0.66% 84.09% 14.78% -15.82% 11.02% -14.21% -1.60% 31.61% 37.35% 6.75% 11.91% -13.32% 20.07% 92.79% 26.34% -4.26% 14.05% -11.18% 6.92% 58.65% 24.27% -6.33% 11.14% -14.09% 7.76% 77.88% 56.98% 26.38% 48.10% 22.87% 3.51% 21.44% 25.05% -5.55% 9.85% -15.38% 9.83% 89.76% 16.96% -13.64% 5.82% -19.41% 5.77% 87.50% 39.92% 9.32% 21.79% -3.44% 12.76% 85.35% 49.40% 18.80% 37.60% 12.37% 6.44% 49.72% 6.72% -23.88% 7.26% -17.97% -5.91% 81.82% 20.12% -10.48% 8.28% -16.95% 6.47% 92.33% 48.43% 17.83% 12.47% -12.76% 30.59% 75.46% 37.76% 7.16% 34.55% 9.32% -2.16% 27.45% 27.64% -2.96% 8.09% -17.14% 14.18% 93.27% 58.94% 28.34% 43.92% 18.69% 9.65% 63.52% 24.56% -6.04% 12.73% -12.50% 6.46% 80.59% 9.53% -21.07% 6.12% -19.11% -1.96% 53.62% 25.53% -5.07% 5.43% -19.80% 14.73% 90.63% 29.91% -0.69% 12.82% -12.41% 11.71% 95.15% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table II.B.4/II.B.5 a: Ratio of Minority EDP to Minority Stops (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City Groton Long Point Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury* Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number of % Minority % Minority Stops Stops EDP 1,713 26.56% 25.07% 232 17.67% 13.28% 1,902 22.29% 12.89% 1,095 21.28% 16.54% 1,055 51.85% 42.68% 1,547 11.96% 13.12% 1,175 70.98% 61.82% 1,613 20.83% 14.21% 676 12.43% 10.32% 381 6.82% 6.89% 1,951 17.43% 14.48% 638 9.25% 8.39% 425 7.53% 5.04% 385 16.88% 15.68% 2,036 34.77% 31.97% 1,354 35.30% 15.92% 838 26.73% 21.13% 185 2.16% 5.82% 3,419 66.28% 40.04% 1,002 23.45% 16.55% 331 19.34% 19.16% 267 11.61% 7.50% 1,575 16.32% 12.63% 4,171 29.94% 17.52% 1,569 20.08% 18.84% 1,665 16.16% 15.97% 343 4.37% 6.32% 1,754 27.88% 24.64% 278 22.66% 18.40% 32 3.13% 18.40% 1,028 21.21% 18.40% 1,554 7.34% 8.31% 2,012 39.07% 29.50% 1,777 64.72% 50.07% 431 22.74% 15.84% 1,355 7.75% 6.47% 4,486 37.63% 26.68% 832 40.99% 31.44% 22 22.73% 11.37% 308 29.87% 21.86% 910 21.76% 17.96% 1,557 15.16% 11.55% 1,561 21.27% 16.91% 2,162 56.57% 38.88% 2,305 21.74% 13.79% 8,350 60.57% 46.32% 1,397 37.29% 33.74% 1,059 17.47% 11.29% Absolute Difference 1.49% 4.39% 9.40% 4.74% 9.17% -1.16% 9.16% 6.62% 2.11% -0.06% 2.95% 0.86% 2.49% 1.21% 2.80% 19.39% 5.60% -3.66% 26.23% 6.90% 0.18% 4.11% 3.69% 12.42% 1.24% 0.19% -1.95% 3.24% 4.26% -15.27% 2.81% -0.97% 9.57% 14.65% 6.90% 1.28% 10.95% 9.54% 11.36% 8.01% 3.80% 3.61% 4.35% 17.68% 7.95% 14.25% 3.55% 6.18% Ratio 1.06 1.33 1.73 1.29 1.21 0.91 1.15 1.47 1.20 0.99 1.20 1.10 1.49 1.08 1.09 2.22 1.26 0.37 1.66 1.42 1.01 1.55 1.29 1.71 1.07 1.01 0.69 1.13 1.23 0.17 1.15 0.88 1.32 1.29 1.44 1.20 1.41 1.30 2.00 1.37 1.21 1.31 1.26 1.45 1.58 1.31 1.11 1.55 Table II.B.4/II.B.5 a: Ratio of Minority EDP to Minority Stops (Sorted Alphabetically) 2015-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Winchester Windsor Windsor Locks Wolcott Woodbridge Number of % Minority % Minority Stops Stops EDP 1,343 30.45% 18.98% 1,908 14.57% 9.47% 482 5.60% 8.80% 1,125 23.02% 17.55% 1,512 35.32% 36.92% 1,385 32.78% 24.65% 847 7.79% 8.50% 1,485 30.37% 19.51% 206 4.37% 6.73% 1,122 18.18% 14.26% 600 10.50% 4.60% 38 13.16% 6.98% 310 6.13% 6.13% 694 16.86% 7.55% 3,076 18.86% 13.11% 1,041 14.89% 19.57% 1,255 11.00% 12.42% 177 12.43% 17.23% 1,740 10.17% 11.34% 1,296 25.54% 17.94% 1,610 9.44% 10.23% 2,232 39.78% 38.83% 665 7.07% 7.36% 385 43.64% 27.87% 363 8.26% 8.65% 214 8.88% 6.38% 145 13.10% 12.18% 710 35.49% 18.23% 867 18.34% 15.43% 2,608 21.47% 15.64% 969 48.81% 40.14% 1,262 19.33% 13.89% 601 17.97% 10.59% 3,344 35.02% 24.14% 1,487 44.18% 35.60% 84 5.95% 9.46% 2,397 18.77% 18.06% 791 43.24% 16.60% 483 36.65% 29.32% 1,556 23.71% 17.39% 238 7.56% 7.02% 1,457 49.69% 33.16% 763 22.28% 18.76% 168 22.62% 8.18% 586 23.55% 17.31% Absolute Difference 11.47% 5.10% -3.19% 5.48% -1.61% 8.13% -0.70% 10.86% -2.36% 3.93% 5.90% 6.17% -0.01% 9.31% 5.74% -4.68% -1.42% -4.80% -1.17% 7.60% -0.79% 0.95% -0.29% 15.77% -0.38% 2.50% 0.92% 17.26% 2.91% 5.83% 8.68% 5.44% 7.38% 10.87% 8.59% -3.50% 0.71% 26.63% 7.33% 6.32% 0.54% 16.53% 3.52% 14.44% 6.24% Ratio 1.60 1.54 0.64 1.31 0.96 1.33 0.92 1.56 0.65 1.28 2.28 1.88 1.00 2.23 1.44 0.76 0.89 0.72 0.90 1.42 0.92 1.02 0.96 1.57 0.96 1.39 1.08 1.95 1.19 1.37 1.22 1.39 1.70 1.45 1.24 0.63 1.04 2.60 1.25 1.36 1.08 1.50 1.19 2.77 1.36 Table II.B.4/II.B.5 b: Ratio of Black EDP to Black Stops (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City Groton Long Point Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury* Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number of Stops 1,713 232 1,902 1,095 1,055 1,547 1,175 1,613 676 381 1,951 638 425 385 2,036 1,354 838 185 3,419 1,002 331 267 1,575 4,171 1,569 1,665 343 1,754 278 32 1,028 1,554 2,012 1,777 431 1,355 4,486 832 22 308 910 1,557 1,561 2,162 2,305 8,350 1,397 1,059 % Black Absolute Stops % Black EDP Difference 11.85% 9.48% 2.37% 9.05% 3.47% 5.58% 8.25% 3.48% 4.78% 7.67% 2.94% 4.73% 43.03% 31.15% 11.89% 4.46% 4.07% 0.39% 37.70% 26.46% 11.24% 7.44% 3.93% 3.51% 3.55% 2.02% 1.53% 3.15% 1.50% 1.65% 7.48% 3.94% 3.54% 1.72% 1.19% 0.54% 2.82% 1.20% 1.62% 10.91% 5.63% 5.28% 7.86% 6.12% 1.74% 12.78% 3.57% 9.21% 12.77% 6.72% 6.05% 0.54% 1.54% -1.00% 37.06% 16.95% 20.10% 8.28% 4.19% 4.09% 12.08% 7.92% 4.16% 2.62% 0.88% 1.74% 6.79% 4.14% 2.65% 14.12% 5.27% 8.85% 6.76% 5.85% 0.91% 5.05% 4.34% 0.71% 1.46% 2.23% -0.77% 5.99% 5.62% 0.37% 10.43% 5.47% 4.96% 3.13% 5.47% -2.34% 12.06% 5.47% 6.59% 1.93% 1.92% 0.01% 27.68% 16.09% 11.60% 35.79% 21.57% 14.22% 12.99% 4.26% 8.73% 2.07% 1.39% 0.68% 21.13% 9.92% 11.21% 11.30% 7.75% 3.55% 9.09% 2.63% 6.46% 17.86% 9.71% 8.14% 9.56% 5.61% 3.95% 6.42% 3.04% 3.38% 8.71% 4.91% 3.80% 16.33% 9.97% 6.35% 6.59% 3.46% 3.13% 35.58% 22.60% 12.98% 13.82% 11.43% 2.38% 4.15% 2.29% 1.86% Ratio 1.25 2.61 2.38 2.61 1.38 1.10 1.42 1.89 1.76 2.10 1.90 1.45 2.35 1.94 1.28 3.58 1.90 0.35 2.19 1.98 1.53 2.99 1.64 2.68 1.15 1.16 0.65 1.07 1.91 0.57 2.21 1.01 1.72 1.66 3.05 1.49 2.13 1.46 3.46 1.84 1.71 2.11 1.77 1.64 1.90 1.57 1.21 1.81 Table II.B.4/II.B.5 b: Ratio of Black EDP to Black Stops (Sorted Alphabetically) 2015-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Winchester Windsor Windsor Locks Wolcott Woodbridge Number of Stops 1,343 1,908 482 1,125 1,512 1,385 847 1,485 206 1,122 600 38 310 694 3,076 1,041 1,255 177 1,740 1,296 1,610 2,232 665 385 363 214 145 710 867 2,608 969 1,262 601 3,344 1,487 84 2,397 791 483 1,556 238 1,457 763 168 586 % Black Absolute Stops % Black EDP Difference 10.57% 5.53% 5.05% 5.71% 1.98% 3.73% 2.70% 2.86% -0.16% 12.44% 6.29% 6.15% 16.40% 12.02% 4.38% 18.27% 7.52% 10.75% 2.36% 1.57% 0.79% 16.16% 6.26% 9.90% 1.46% 1.51% -0.06% 6.51% 4.26% 2.24% 5.00% 0.79% 4.21% 5.26% 2.67% 2.59% 2.90% 1.82% 1.08% 4.61% 1.13% 3.48% 3.87% 2.68% 1.19% 6.63% 5.80% 0.83% 4.86% 3.45% 1.41% 3.39% 5.25% -1.86% 4.89% 3.40% 1.49% 12.81% 5.76% 7.05% 4.22% 2.81% 1.41% 16.17% 11.73% 4.44% 2.86% 1.81% 1.05% 23.64% 12.10% 11.53% 3.03% 2.89% 0.14% 5.14% 1.58% 3.56% 4.83% 2.91% 1.92% 18.17% 5.87% 12.30% 10.61% 5.30% 5.31% 8.51% 3.78% 4.73% 24.25% 14.34% 9.91% 7.53% 3.90% 3.63% 8.65% 3.04% 5.62% 12.74% 7.64% 5.09% 22.26% 16.40% 5.86% 3.57% 2.07% 1.50% 8.34% 5.31% 3.03% 18.08% 4.91% 13.17% 5.18% 4.22% 0.95% 7.13% 4.66% 2.47% 3.36% 1.42% 1.94% 35.21% 20.06% 15.15% 11.40% 7.15% 4.25% 7.14% 2.53% 4.61% 13.31% 4.77% 8.54% Ratio 1.91 2.89 0.94 1.98 1.36 2.43 1.50 2.58 0.96 1.53 6.32 1.97 1.59 4.07 1.44 1.14 1.41 0.65 1.44 2.22 1.50 1.38 1.58 1.95 1.05 3.25 1.66 3.09 2.00 2.25 1.69 1.93 2.85 1.67 1.36 1.72 1.57 3.68 1.23 1.53 2.36 1.76 1.60 2.82 2.79 Table II.B.4/II.B.5 c: Ratio of Hispanic EDP to Hispanic Stops (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City Groton Long Point Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury* Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number % Hispanic % Hispanic Absolute of Stops Stops EDP Difference 1,713 13.72% 13.48% 0.23% 232 4.74% 4.89% -0.15% 1,902 11.30% 6.57% 4.74% 1,095 10.59% 8.53% 2.06% 1,055 7.20% 8.53% -1.32% 1,547 6.92% 5.65% 1.27% 1,175 32.00% 30.39% 1.61% 1,613 11.53% 8.08% 3.45% 676 7.54% 4.98% 2.56% 381 2.89% 3.57% -0.69% 1,951 7.69% 6.24% 1.44% 638 6.11% 5.17% 0.94% 425 3.53% 2.76% 0.77% 385 3.12% 6.77% -3.65% 2,036 25.64% 18.59% 7.05% 1,354 20.01% 7.99% 12.02% 838 13.60% 11.84% 1.76% 185 1.08% 2.62% -1.54% 3,419 27.49% 17.77% 9.72% 1,002 13.97% 9.11% 4.86% 331 5.74% 7.25% -1.51% 267 7.87% 3.49% 4.37% 1,575 7.43% 6.04% 1.39% 4,171 13.69% 8.24% 5.45% 1,569 8.03% 8.02% 0.01% 1,665 7.09% 6.09% 1.00% 343 2.04% 2.76% -0.72% 1,754 17.05% 12.44% 4.60% 278 7.55% 7.26% 0.30% 32 0.00% 7.26% -7.26% 1,028 6.91% 7.26% -0.35% 1,554 2.83% 4.05% -1.22% 2,012 9.74% 8.62% 1.13% 1,777 28.14% 24.41% 3.73% 431 6.03% 6.34% -0.31% 1,355 4.58% 2.84% 1.73% 4,486 13.37% 10.23% 3.15% 832 28.49% 21.13% 7.36% 22 13.64% 5.55% 8.09% 308 11.04% 7.76% 3.28% 910 9.23% 7.70% 1.53% 1,557 7.71% 6.07% 1.64% 1,561 11.34% 8.77% 2.57% 2,162 38.99% 26.03% 12.96% 2,305 11.97% 6.37% 5.60% 8,350 23.43% 18.60% 4.82% 1,397 21.90% 18.58% 3.32% 1,059 11.43% 6.23% 5.19% Ratio 1.02 0.97 1.72 1.24 0.84 1.23 1.05 1.43 1.51 0.81 1.23 1.18 1.28 0.46 1.38 2.50 1.15 0.41 1.55 1.53 0.79 2.25 1.23 1.66 1.00 1.16 0.74 1.37 1.04 0.00 0.95 0.70 1.13 1.15 0.95 1.61 1.31 1.35 2.46 1.42 1.20 1.27 1.29 1.50 1.88 1.26 1.18 1.83 Table II.B.4/II.B.5 c: Ratio of Hispanic EDP to Hispanic Stops (Sorted Alphabetically) 2015-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Winchester Windsor Windsor Locks Wolcott Woodbridge Number % Hispanic % Hispanic Absolute of Stops Stops EDP Difference 1,343 17.42% 8.90% 8.52% 1,908 6.71% 4.82% 1.89% 482 2.28% 4.02% -1.74% 1,125 8.71% 7.14% 1.57% 1,512 17.46% 19.88% -2.42% 1,385 12.35% 9.48% 2.87% 847 4.72% 4.41% 0.32% 1,485 10.91% 7.68% 3.23% 206 2.91% 3.84% -0.93% 1,122 9.98% 7.43% 2.55% 600 5.17% 3.45% 1.72% 38 7.89% 3.68% 4.22% 310 2.26% 3.44% -1.18% 694 10.66% 3.99% 6.67% 3,076 11.80% 6.68% 5.12% 1,041 5.86% 7.43% -1.57% 1,255 5.10% 6.72% -1.62% 177 7.34% 8.28% -0.93% 1,740 3.39% 4.41% -1.01% 1,296 8.41% 6.07% 2.34% 1,610 4.66% 5.10% -0.44% 2,232 20.70% 19.99% 0.71% 665 3.31% 3.34% -0.03% 385 17.92% 12.66% 5.26% 363 3.31% 4.01% -0.70% 214 3.74% 4.19% -0.45% 145 6.21% 7.16% -0.95% 710 14.79% 8.33% 6.46% 867 6.81% 6.01% 0.79% 2,608 11.58% 8.64% 2.94% 969 23.53% 22.66% 0.87% 1,262 9.51% 6.22% 3.29% 601 9.15% 5.62% 3.53% 3,344 17.02% 10.28% 6.73% 1,487 20.71% 15.19% 5.53% 84 2.38% 4.23% -1.85% 2,397 8.68% 8.37% 0.31% 791 23.51% 8.66% 14.85% 483 30.64% 23.08% 7.56% 1,556 12.28% 8.10% 4.18% 238 3.78% 4.56% -0.78% 1,457 10.71% 9.07% 1.64% 763 7.21% 7.28% -0.07% 168 13.69% 4.34% 9.35% 586 6.14% 5.54% 0.60% Ratio 1.96 1.39 0.57 1.22 0.88 1.30 1.07 1.42 0.76 1.34 1.50 2.15 0.66 2.67 1.77 0.79 0.76 0.89 0.77 1.39 0.91 1.04 0.99 1.42 0.83 0.89 0.87 1.78 1.13 1.34 1.04 1.53 1.63 1.66 1.36 0.56 1.04 2.71 1.33 1.52 0.83 1.18 0.99 3.16 1.11 Table II.B.6/II.B.7 a: Ratio of Minority Resident Population to Minority Resident Stops (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City* Groton Long Point* Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London Number of Residents 14,979 13,855 16,083 14,675 16,982 23,532 109,401 48,439 12,847 7,992 21,049 10,540 9,779 11,357 64,361 14,004 10,391 10,255 40,229 24,114 9,164 5,553 33,218 45,567 20,318 26,217 8,716 46,370 7,960 2,030 31,520 17,672 50,012 93,669 11,527 14,073 46,667 47,445 5,843 38,747 43,135 14,918 25,099 57,164 14,138 100,702 21,835 Minority Minority Residents Resident Stops Resident Stops Difference 25.62% 2,009 31.86% 6.23% 9.82% 261 6.90% -2.92% 5.76% 1,352 8.51% 2.74% 13.49% 1,024 13.38% -0.11% 61.51% 1,049 80.46% 18.95% 8.49% 1,894 8.03% -0.46% 73.25% 2,678 73.86% 0.61% 12.71% 2,273 24.68% 11.97% 8.11% 742 9.43% 1.32% 3.25% 268 4.48% 1.22% 8.62% 4,288 19.85% 11.22% 6.12% 2,288 12.28% 6.16% 3.79% 725 4.00% 0.21% 10.57% 431 12.53% 1.96% 38.64% 1,330 52.26% 13.62% 7.17% 710 6.90% -0.27% 20.56% 499 35.67% 15.12% 4.60% 280 3.57% -1.03% 51.63% 3,832 75.21% 23.58% 13.98% 1,531 18.55% 4.57% 14.58% 206 20.39% 5.81% 5.56% 166 2.41% -3.15% 8.65% 6,291 17.88% 9.23% 10.00% 1,779 11.02% 1.02% 12.59% 892 17.60% 5.01% 11.81% 1,856 14.76% 2.96% 3.19% 291 4.47% 1.28% 17.95% 1,672 21.11% 3.16% 26.90% 440 35.00% 8.10% 0.00% 31 0.00% 0.00% 20.39% 1,706 23.80% 3.41% 5.67% 2,311 5.88% 0.21% 30.92% 1,539 44.05% 13.14% 80.76% 4,282 72.40% -8.36% 13.40% 386 19.43% 6.03% 4.26% 1,586 4.22% -0.03% 27.95% 5,598 41.55% 13.60% 34.86% 1,430 50.56% 15.70% 5.58% 16 0.00% -5.58% 23.49% 1,509 36.91% 13.42% 11.62% 1,290 11.63% 0.01% 7.56% 1,430 5.80% -1.76% 15.18% 2,309 19.36% 4.18% 45.00% 4,709 66.51% 21.51% 7.15% 2,061 9.56% 2.41% 62.82% 11,123 79.28% 16.46% 43.57% 1,786 59.80% 16.23% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 1.24 0.70 1.48 0.99 1.31 0.95 1.01 1.94 1.16 1.38 2.30 2.01 1.05 1.19 1.35 0.96 1.74 0.78 1.46 1.33 1.40 0.43 2.07 1.10 1.40 1.25 1.40 1.18 1.30 0 1.17 1.04 1.42 0.90 1.45 0.99 1.49 1.45 0.00 1.57 1.00 0.77 1.28 1.48 1.34 1.26 1.37 Table II.B.6/II.B.7 a: Ratio of Minority Resident Population to Minority Resident Stops (Sorted Alphabetically) 2015-2016 Department Name New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Number of Residents 21,891 24,978 20,171 11,549 19,608 68,034 31,638 8,330 11,017 11,918 14,605 9,660 7,480 7,507 6,955 18,111 16,224 13,260 32,010 17,773 20,162 34,301 98,070 15,078 40,980 10,782 6,224 29,251 27,678 23,800 36,530 83,964 15,760 18,154 49,650 44,518 7,255 19,410 21,607 20,176 12,973 23,222 10,117 9,133 13,175 7,119 Minority Minority Residents Resident Stops Resident Stops Difference 9.69% 1,398 14.95% 5.26% 14.51% 1,494 20.28% 5.77% 5.76% 1,594 4.77% -0.99% 5.02% 334 4.79% -0.23% 10.51% 692 9.10% -1.41% 40.80% 1,720 50.93% 10.13% 29.09% 3,043 45.97% 16.88% 5.15% 948 7.70% 2.55% 10.75% 466 10.09% -0.66% 5.32% 835 7.43% 2.11% 10.00% 1,057 14.00% 4.00% 2.47% 650 5.85% 3.37% 4.63% 75 12.00% 7.37% 3.37% 955 5.65% 2.28% 4.37% 251 5.18% 0.81% 7.29% 2,348 6.64% -0.64% 17.20% 1,244 14.31% -2.89% 9.77% 1,285 9.81% 0.04% 10.83% 392 9.69% -1.14% 7.65% 1,660 8.13% 0.49% 14.60% 1,242 17.87% 3.27% 6.17% 2,215 5.15% -1.03% 43.86% 2,758 49.31% 5.45% 4.35% 894 5.37% 1.02% 27.20% 888 44.59% 17.40% 4.91% 318 5.97% 1.06% 2.09% 183 3.83% 1.74% 11.02% 4,124 16.00% 4.99% 11.91% 464 13.58% 1.67% 14.05% 1,700 26.29% 12.24% 11.14% 3,666 13.15% 2.01% 48.10% 2,177 65.96% 17.86% 9.85% 1,109 11.27% 1.42% 5.82% 524 6.87% 1.05% 21.79% 1,676 31.68% 9.90% 37.60% 3,402 44.74% 7.14% 7.26% 156 3.85% -3.42% 8.28% 1,719 5.35% -2.93% 12.47% 1,052 35.27% 22.80% 34.55% 1,165 57.85% 23.30% 8.09% 1,119 10.01% 1.92% 43.92% 1,878 62.94% 19.02% 12.73% 703 16.93% 4.20% 6.12% 356 8.99% 2.87% 5.43% 146 6.16% 0.74% 12.82% 197 11.68% -1.15% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 1.54 1.40 0.83 0.95 0.87 1.25 1.58 1.50 0.94 1.40 1.40 2.36 2.59 1.68 1.18 0.91 0.83 1.00 0.90 1.06 1.22 0.83 1.12 1.23 1.64 1.22 1.83 1.45 1.14 1.87 1.18 1.37 1.14 1.18 1.45 1.19 0.53 0.65 2.83 1.67 1.24 1.43 1.33 1.47 1.14 0.91 Table II.B.6/II.B.7 b: Ratio of Black Resident Population to Black Resident Stops (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City* Groton Long Point* Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London Number of Black Resident Residents Black Residents Resident Stops Stops Difference 14,979 9.74% 2,009 17.37% 7.63% 13,855 1.41% 261 1.53% 0.12% 16,083 0.65% 1,352 2.51% 1.86% 14,675 1.74% 1,024 2.64% 0.90% 16,982 54.76% 1,049 74.83% 20.07% 23,532 1.76% 1,894 2.96% 1.19% 109,401 31.82% 2,678 41.15% 9.33% 48,439 3.24% 2,273 10.07% 6.84% 12,847 1.05% 742 3.23% 2.18% 7,992 0.00% 268 0.75% 0.75% 21,049 1.27% 4,288 9.63% 8.36% 10,540 0.00% 2,288 2.58% 2.58% 9,779 0.79% 725 1.38% 0.59% 11,357 3.69% 431 9.51% 5.82% 64,361 6.42% 1,330 12.86% 6.43% 14,004 0.00% 710 1.83% 1.83% 10,391 6.03% 499 15.23% 9.20% 10,255 1.10% 280 1.43% 0.33% 40,229 22.52% 3,832 42.30% 19.79% 24,114 2.47% 1,531 4.31% 1.84% 9,164 5.96% 206 13.59% 7.63% 5,553 0.00% 166 0.60% 0.60% 33,218 2.63% 6,291 9.51% 6.87% 45,567 1.73% 1,779 3.88% 2.14% 20,318 2.20% 892 5.61% 3.40% 26,217 1.80% 1,856 3.29% 1.48% 8,716 0.92% 291 2.75% 1.83% 46,370 2.03% 1,672 4.37% 2.33% 7,960 7.70% 440 15.00% 7.30% 2,030 0.00% 31 0.00% 0.00% 31,520 6.07% 1,706 12.49% 6.42% 17,672 0.70% 2,311 1.04% 0.34% 50,012 18.28% 1,539 34.70% 16.42% 93,669 35.80% 4,282 41.97% 6.17% 11,527 3.10% 386 10.88% 7.78% 14,073 0.49% 1,586 0.95% 0.46% 46,667 10.15% 5,598 23.78% 13.62% 47,445 7.80% 1,430 12.66% 4.86% 5,843 0.00% 16 0.00% 0.00% 38,747 11.68% 1,509 23.79% 12.11% 43,135 2.23% 1,290 4.19% 1.95% 14,918 1.32% 1,430 2.17% 0.85% 25,099 4.11% 2,309 8.10% 3.99% 57,164 10.67% 4,709 18.35% 7.68% 14,138 1.06% 2,061 2.47% 1.41% 100,702 32.16% 11,123 49.39% 17.23% 21,835 15.18% 1,786 23.85% 8.67% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 1.78 1.08 3.85 1.52 1.37 1.68 1.29 3.11 3.08 N/A 7.56 N/A 1.75 2.58 2.00 N/A 2.52 1.30 1.88 1.74 2.28 N/A 3.61 2.24 2.54 1.82 3.00 2.15 1.95 N/A 2.06 1.48 1.90 1.17 3.51 1.93 2.34 1.62 N/A 2.04 1.87 1.64 1.97 1.72 2.33 1.54 1.57 Table II.B.6/II.B.7 b: Ratio of Black Resident Population to Black Resident Stops (Sorted Alphabetically) 2015-2016 Department Name New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Number of Black Resident Residents Black Residents Resident Stops Stops Difference 21,891 1.69% 1,398 3.72% 2.03% 24,978 2.99% 1,494 5.76% 2.76% 20,171 0.68% 1,594 0.50% -0.18% 11,549 1.33% 334 2.10% 0.76% 19,608 2.91% 692 4.62% 1.71% 68,034 13.13% 1,720 24.01% 10.88% 31,638 8.96% 3,043 26.06% 17.10% 8,330 0.00% 948 1.48% 1.48% 11,017 1.31% 466 2.58% 1.27% 11,918 0.96% 835 3.23% 2.27% 14,605 2.73% 1,057 5.20% 2.47% 9,660 0.00% 650 2.46% 2.46% 7,480 1.87% 75 6.67% 4.80% 7,507 1.17% 955 2.72% 1.55% 6,955 0.00% 251 1.59% 1.59% 18,111 0.77% 2,348 1.06% 0.30% 16,224 3.77% 1,244 7.64% 3.87% 13,260 2.25% 1,285 3.89% 1.64% 32,010 2.07% 392 3.32% 1.25% 17,773 1.46% 1,660 2.71% 1.25% 20,162 3.68% 1,242 6.76% 3.09% 34,301 1.34% 2,215 2.21% 0.88% 98,070 12.86% 2,758 20.78% 7.92% 15,078 0.82% 894 1.90% 1.09% 40,980 12.76% 888 26.80% 14.05% 10,782 1.40% 318 1.26% -0.14% 6,224 0.00% 183 1.64% 1.64% 29,251 2.12% 4,124 4.90% 2.78% 27,678 2.90% 464 6.47% 3.57% 23,800 4.70% 1,700 15.18% 10.48% 36,530 1.34% 3,666 3.27% 1.94% 83,964 17.37% 2,177 34.08% 16.71% 15,760 2.29% 1,109 4.51% 2.22% 18,154 1.24% 524 3.44% 2.20% 49,650 5.65% 1,676 9.43% 3.77% 44,518 17.70% 3,402 23.05% 5.34% 7,255 1.25% 156 0.64% -0.61% 19,410 1.22% 1,719 1.51% 0.30% 21,607 2.75% 1,052 10.74% 7.99% 20,176 4.08% 1,165 6.95% 2.87% 12,973 1.01% 1,119 1.70% 0.69% 23,222 32.20% 1,878 52.40% 20.20% 10,117 4.27% 703 8.25% 3.98% 9,133 1.04% 356 4.78% 3.74% 13,175 1.53% 146 2.05% 0.52% 7,119 1.94% 197 2.54% 0.60% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 2.21 1.92 0.74 1.57 1.59 1.83 2.91 N/A 1.97 3.35 1.90 N/A 3.56 2.32 N/A 1.39 2.03 1.73 1.60 1.85 1.84 1.66 1.62 2.33 2.10 0.90 N/A 2.31 2.23 3.23 2.45 1.96 1.97 2.77 1.67 1.30 0.51 1.24 3.91 1.70 1.68 1.63 1.93 4.59 1.34 1.31 Table II.B.6/II.B.7 c: Ratio of Hispanic Resident Population to Hispanic Resident Stops (Sorted Alphabetically) 2015-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City* Groton Long Point* Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London Number of Residents 14,979 13,855 16,083 14,675 16,982 23,532 109,401 48,439 12,847 7,992 21,049 10,540 9,779 11,357 64,361 14,004 10,391 10,255 40,229 24,114 9,164 5,553 33,218 45,567 20,318 26,217 8,716 46,370 7,960 2,030 31,520 17,672 50,012 93,669 11,527 14,073 46,667 47,445 5,843 38,747 43,135 14,918 25,099 57,164 14,138 100,702 21,835 Hispanic Hispanic Residents Resident Stops Resident Stops Difference 14.03% 2,009 13.39% -0.64% 2.76% 261 1.92% -0.84% 2.67% 1,352 3.25% 0.58% 6.65% 1,024 8.11% 1.45% 4.78% 1,049 4.48% -0.30% 3.45% 1,894 4.12% 0.67% 36.20% 2,678 30.77% -5.43% 7.65% 2,273 13.46% 5.81% 3.79% 742 4.72% 0.93% 1.94% 268 1.49% -0.45% 2.35% 4,288 7.98% 5.63% 4.41% 2,288 8.30% 3.89% 2.21% 725 2.21% 0.00% 3.90% 431 1.62% -2.28% 23.25% 1,330 38.42% 15.17% 3.49% 710 2.68% -0.82% 12.37% 499 19.84% 7.47% 2.02% 280 1.79% -0.23% 22.91% 3,832 31.42% 8.51% 8.43% 1,531 12.54% 4.11% 4.34% 206 6.80% 2.45% 2.56% 166 1.20% -1.35% 4.00% 6,291 6.82% 2.82% 4.51% 1,779 5.06% 0.54% 3.20% 892 2.80% -0.40% 3.60% 1,856 4.58% 0.98% 1.39% 291 0.69% -0.70% 9.15% 1,672 12.02% 2.87% 11.80% 440 18.41% 6.61% 0.00% 31 0.00% 0.00% 7.40% 1,706 7.44% 0.04% 2.90% 2,311 2.08% -0.83% 7.58% 1,539 8.06% 0.48% 41.02% 4,282 29.54% -11.47% 4.57% 386 4.40% -0.17% 1.73% 1,586 1.58% -0.15% 9.89% 5,598 14.40% 4.50% 24.86% 1,430 36.92% 12.06% 2.22% 16 0.00% -2.22% 6.77% 1,509 11.53% 4.76% 4.45% 1,290 4.42% -0.03% 4.30% 1,430 2.45% -1.86% 7.77% 2,309 10.35% 2.58% 31.75% 4,709 46.74% 14.99% 2.69% 2,061 2.72% 0.03% 24.79% 11,123 29.00% 4.22% 25.08% 1,786 34.88% 9.80% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 0.95 0.69 1.22 1.22 0.94 1.19 0.85 1.76 1.24 0.77 3.39 1.88 1.00 0.42 1.65 0.77 1.60 0.88 1.37 1.49 1.56 0.47 1.71 1.12 0.87 1.27 0.50 1.31 1.56 0 1.00 0.72 1.06 0.72 0.96 0.91 1.46 1.49 0.00 1.70 0.99 0.57 1.33 1.47 1.01 1.17 1.39 Table II.B.6/II.B.7 c: Ratio of Hispanic Resident Population to Hispanic Resident Stops (Sorted Alphabetically) 2015-2016 Department Name New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Number of Residents 21,891 24,978 20,171 11,549 19,608 68,034 31,638 8,330 11,017 11,918 14,605 9,660 7,480 7,507 6,955 18,111 16,224 13,260 32,010 17,773 20,162 34,301 98,070 15,078 40,980 10,782 6,224 29,251 27,678 23,800 36,530 83,964 15,760 18,154 49,650 44,518 7,255 19,410 21,607 20,176 12,973 23,222 10,117 9,133 13,175 7,119 Hispanic Hispanic Residents Resident Stops Resident Stops Difference 5.46% 1,398 9.30% 3.84% 6.39% 1,494 10.84% 4.46% 2.86% 1,594 2.13% -0.73% 2.31% 334 2.10% -0.22% 3.26% 692 1.73% -1.53% 22.67% 1,720 26.22% 3.55% 10.59% 3,043 15.97% 5.38% 2.93% 948 3.90% 0.97% 2.54% 466 2.15% -0.40% 3.33% 835 3.71% 0.38% 5.18% 1,057 7.10% 1.91% 2.47% 650 2.92% 0.45% 2.75% 75 4.00% 1.25% 2.20% 955 2.20% 0.00% 2.37% 251 2.79% 0.42% 3.46% 2,348 2.09% -1.38% 4.65% 1,244 3.70% -0.96% 5.53% 1,285 4.90% -0.63% 5.17% 392 4.85% -0.32% 2.61% 1,660 2.29% -0.32% 3.62% 1,242 4.11% 0.49% 2.80% 2,215 2.12% -0.68% 22.87% 2,758 25.31% 2.43% 1.91% 894 2.01% 0.10% 11.92% 888 15.32% 3.39% 2.20% 318 2.52% 0.32% 2.09% 183 2.19% 0.10% 6.92% 4,124 9.19% 2.27% 5.06% 464 4.96% -0.10% 5.21% 1,700 9.41% 4.20% 6.71% 3,666 8.32% 1.61% 27.54% 2,177 30.82% 3.29% 4.07% 1,109 3.79% -0.29% 2.99% 524 3.24% 0.26% 8.78% 1,676 13.42% 4.64% 15.96% 3,402 20.49% 4.53% 3.06% 156 1.92% -1.14% 3.19% 1,719 1.57% -1.62% 7.10% 1,052 22.91% 15.80% 28.88% 1,165 50.04% 21.16% 2.74% 1,119 4.20% 1.46% 7.33% 1,878 7.77% 0.44% 3.46% 703 4.98% 1.52% 4.28% 356 3.93% -0.35% 2.83% 146 2.74% -0.09% 2.68% 197 4.57% 1.89% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 1.70 1.70 0.74 0.91 0.53 1.16 1.51 1.33 0.84 1.11 1.37 1.18 1.45 1.00 1.18 0.60 0.79 0.89 0.94 0.88 1.14 0.76 1.11 1.05 1.28 1.14 1.05 1.33 0.98 1.80 1.24 1.12 0.93 1.09 1.53 1.28 0.63 0.49 3.22 1.73 1.53 1.06 1.44 0.92 0.97 1.70 Table II.B.8: Departments with Disparities Relative to Descriptive Benchmarks (Sorted by Total Score) 2015-2016 Department Name Wethersfield East Hartford Stratford Darien Trumbull New Britain Manchester New Haven Newington Windsor Orange Wolcott Norwich Hamden Bloomfield Fairfield Danbury Middletown Vernon Woodbridge West Hartford Derby Waterbury Bristol Cheshire Hartford Meriden Middlebury Norwalk Willimantic Wilton Berlin New London Bridgeport Cromwell Enfield Groton Town Ledyard Portland Redding New Milford Groton City* New Canaan Watertown Ansonia Avon Clinton East Windsor North Haven Plymouth Ridgefield South Windsor State Average M B H 30.59% 10.44% 19.86% 12.24% 11.62% 20.83% 12.98% 19.72% 13.83% 20.07% 12.35% 17.72% 18.60% 14.73% 13.17% 12.56% 12.47% 15.39% 11.71% 12.76% M 26.63% 26.23% 15.77% 19.39% 17.26% 17.68% 10.95% 14.25% 11.47% 16.53% 10.86% 14.44% 12.42% EDP B 13.17% 20.10% 11.53% 9.21% 12.30% 11.21% 12.98% 5.05% 15.15% 9.90% H 14.85% 12.02% 6.46% 12.96 21.51% 13.60% 16.46% 13.62% 17.23% 14.99% 19.02% 20.20% 16.88% 13.14% 18.95% 17.10% 16.42% 20.07% 13.62% 13.42% 12.24% 6.43% 12.11% 10.48% 15.12% 17.86% 11.97% 11.22% 9.20% 16.71% 6.84% 8.36% 8.52% 9.35% 10.75% 11.60% 11.89% 8.85% 8.14% 5.31% 8.54% 11.19% Resident Population M B H 22.80% 7.99% 15.80% 23.58% 19.79% 17.40% 14.05% 10.87% 6.05% 14.65% 14.22% 11.36% 6.46% 15.70% 12.06% 10.88% 21.16% 10.13% 16.23% 11.24% 5.28% 9.23% 5.85% 8.73% 6.17% 9.31% 5.82% 6.87% 7.38% 7.78% 7.37% 6.67% 5.19% 7.30% 5.60% 5.62% 7.63% 5.58% 6.16% 7.63% 6.15% 5.90% 5.12% 7.05% 5.81% 5.63% 8.09% 10.13% 23.30% 14.18% 14.41% 15.17% Total 8.5 6.0 6.0 4.5 4.5 4.0 4.0 4.0 4.0 4.0 3.5 3.5 3.0 3.0 3.0 2.5 2.5 2.5 2.5 2.5 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Table II.C.5.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2015-2016 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Capitol Police Central CT State University Canton Cheshire Clinton Coventry Cromwell VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.05 (0.181) 1,094 -0.023 (0.681) 252 1.101*** (0.317) 1,291 -0.039 (0.443) 670 -0.266 (0.185) 934 0.216 (0.333) 1,189 -0.865*** (0.232) 842 0.446** (0.196) 1,672 -1.401** (0.695) 403 -1.847* (1.015) 51 0.464 (0.288) 562 -0.925 (1.029) 174 -0.16 (0.22) 1,295 -0.204 (0.495) 498 -0.234 (0.735) 203 -0.892* (0.479) 322 Black 0.016 (0.188) 1,067 0.175 (0.9) 151 1.224*** (0.37) 1,232 0.237 (0.533) 597 -0.218 (0.187) 914 0.218 (0.355) 1,148 -0.870*** (0.236) 823 0.362* (0.209) 1,642 -1.400* (0.767) 272 -37.71 (.) 29 0.364 (0.294) 549 -1.125 (1.083) 139 -0.302 (0.236) 1,235 0.017 (0.799) 231 -1.816** (0.754) 158 -0.900* (0.507) 314 Hispanic 0.355* (0.185) 1,056 14.752*** (0.999) 138 0.518** (0.237) 1,327 0.392 (0.379) 697 -0.677* (0.378) 418 -0.04 (0.399) 1,007 -0.673** (0.263) 689 0.15 (0.174) 1,712 0.297 (0.523) 531 2.856** (1.237) 54 0.403 (0.299) 547 0.915 (0.89) 62 0.408* (0.242) 1,253 -0.17 (0.442) 608 -0.612 (0.728) 141 -1.789 (1.114) 89 Black or Hispanic 0.175 (0.146) 1,309 0.799 (0.748) 274 0.715*** (0.209) 1,474 0.36 (0.32) 818 -0.256 (0.181) 975 0.094 (0.271) 1,261 -0.780*** (0.224) 1,190 0.214 (0.143) 1,886 -0.201 (0.443) 567 0.684 (0.73) 66 0.402* (0.231) 677 0.249 (0.637) 200 0.086 (0.179) 1,418 -0.112 (0.391) 650 -0.865 (0.538) 250 -1.018** (0.471) 327 Table II.C.5.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2015-2016 Department Department of Motor Vehicle Danbury Darien Derby Eastern CT State University East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.424 (0.462) 318 -0.266 (0.315) 594 -0.105 (0.278) 710 -0.385 (0.311) 707 1.921 (1.505) 25 84.265 (.) 7 -0.191 (0.208) 820 -0.326 (0.279) 782 -0.832* (0.47) 273 (.) 26 -0.066 (0.132) 3,308 -0.209* (0.123) 2,521 0.218 (0.232) 1,375 -0.578** (0.258) 1,397 -0.207 (0.827) 128 -0.014 (0.2) 1,261 Black -0.162 (0.452) 308 -0.296 (0.333) 581 -0.023 (0.305) 688 -0.239 (0.326) 699 1.905 (1.51) 24 Hispanic -0.157 (0.572) 280 -0.049 (0.2) 865 0.295 (0.258) 753 -0.463 (0.303) 692 17.664*** (1.731) 9 Black or Hispanic 0.016 (0.386) 356 -0.083 (0.188) 948 0.18 (0.212) 870 -0.363 (0.24) 823 2.138 (1.35) 27 (.) (.) (.) -0.197 (0.21) 804 -0.275 (0.297) 770 -0.826* (0.492) 265 2.360* (1.342) 83 -0.06 (0.148) 3,205 -0.063 (0.134) 2,429 0.178 (0.301) 1,291 -0.313 (0.336) 1,330 -0.505 (0.991) 107 0.069 (0.257) 1,156 0.033 (0.237) 653 -0.058 (0.223) 881 0.381 (1.018) 146 0.779 (0.851) 107 0.272 (0.169) 3,161 -0.124 (0.139) 2,403 0.056 (0.292) 1,261 0.257 (0.301) 1,355 -0.11 (0.189) 1,104 -0.141 (0.189) 980 -0.593 (0.439) 286 (.) 76 0.062 (0.182) 1,295 (.) 0.073 (0.117) 3,467 -0.101 (0.105) 2,841 0.112 (0.224) 1,431 (0.23) 1,464 0.122 (0.838) 146 0.032 (0.16) 1,445 Table II.C.5.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2015-2016 Department Groton City Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Mashantucket Pequot Police Meriden Middletown Milford Mohegan Tribal Police Monroe Naugatuck New Britain VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.185 (0.519) 279 0.445 (0.383) 355 -0.161 (0.33) 1,339 -0.1 (0.218) 1,075 -0.314 (0.239) 772 -0.619 (0.871) 321 -0.552 (0.451) 770 0.137 (0.099) 3,230 0.138* (0.075) 5,766 1.009** (0.445) 332 0.987** (0.465) 285 -0.24 (0.303) 785 -0.144 (0.177) 1,535 -0.440* (0.25) 1,393 -0.094 (0.287) 1,283 0.157 (0.17) 1,299 Black -0.778 (0.628) 223 0.506 (0.417) 342 -0.047 (0.523) 1,005 -0.14 (0.221) 1,061 -0.304 (0.24) 762 -0.471 (1.021) 298 -0.486 (0.568) 642 0.209** (0.105) 3,096 Hispanic -0.459 (0.673) 252 -0.382 (0.434) 288 -0.12 (0.415) 1,256 0.51 (0.326) 738 -0.02 (0.264) 555 -0.022 (0.967) 191 0.387 (0.502) 829 0.105 (0.125) 2,710 Black or Hispanic -0.486 (0.476) 316 0.139 (0.319) 389 -0.089 (0.325) 1,438 -0.009 (0.207) 1,184 -0.156 (0.227) 1,037 -0.442 (0.718) 331 0.08 (0.381) 967 0.173* (0.089) 3,655 (.) (.) (.) 0.958** (0.447) 328 0.828* (0.464) 279 -0.355 (0.323) 756 0.209 (0.29) 443 -0.171 (0.485) 197 -0.062 (0.339) 749 0.305 (0.264) 501 0.383 (0.37) 322 -0.205 (0.246) 846 (.) (.) (.) -0.344 (0.277) 1,347 -0.253 (0.3) 1,261 0.096 (0.175) 1,265 0.527** (0.261) 1,353 -0.124 (0.223) 1,336 -0.114 (0.13) 1,775 0.133 (0.197) 1,489 -0.19 (0.192) 1,454 -0.036 (0.12) 2,146 Table II.C.5.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2015-2016 Department New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Putnam VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.043 (0.221) 1,713 0.063 (0.071) 6,073 -0.159 (0.221) 920 0.101 (0.388) 783 0.068 (0.187) 1,471 0.064 (0.281) 1,493 2.808*** (1.067) 135 -0.052 (0.225) 898 -0.134 (0.214) 811 -0.331** (0.146) 1,648 0.995** (0.437) 868 -0.267 (0.193) 1,211 -1.686 (1.412) 225 0.343 (0.335) 875 -0.748 (0.574) 488 -2.167** (0.927) 135 Black 0.124 (0.266) 1,642 0.069 (0.072) 5,961 -0.11 (0.235) 873 0.307 (0.436) 727 0.05 (0.203) 1,409 0.205 (0.338) 1,457 3.097*** (1.142) 115 0.155 (0.247) 873 -0.229 (0.218) 791 -0.237 (0.153) 1,592 1.184*** (0.442) 734 -0.395* (0.203) 1,167 -1.796 (1.475) 185 0.039 (0.355) 841 -0.696 (0.586) 473 -1.913** (0.911) 98 Hispanic 0.172 (0.229) 1,702 0.206*** (0.077) 4,678 0.292 (0.206) 974 0.161 (0.309) 844 -0.179 (0.158) 1,590 0.820*** (0.265) 1,482 0.349 (1.705) 20 0.131 (0.331) 825 -0.492** (0.221) 763 0.443*** (0.17) 1,510 -0.268 (0.378) 942 -0.076 (0.238) 1,095 -0.329 (0.961) 144 0.481 (0.313) 944 -0.439 (0.467) 405 -1.943* (1.082) 97 Black or Hispanic 0.154 (0.181) 1,846 0.122* (0.065) 8,076 0.098 (0.172) 1,155 0.19 (0.266) 920 -0.094 (0.135) 1,800 0.520** (0.217) 1,614 2.190* (1.209) 148 0.147 (0.207) 970 -0.352* (0.18) 1,013 0.072 (0.123) 1,861 0.177 (0.302) 1,009 -0.257 (0.166) 1,341 -0.95 (0.751) 220 0.282 (0.246) 1,022 -0.47 (0.37) 559 -1.717** (0.686) 215 Table II.C.5.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2015-2016 Department Redding Ridgefield Rocky Hill Southern CT State University Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.249 (0.664) 135 0.524* (0.3) 1,681 0.244 (0.269) 1,052 -0.407 (0.88) 92 -0.137 (0.356) 1,076 -0.844 (1.015) 63 0.636 (0.486) 1,002 -0.416* (0.226) 909 0.615* (0.35) 1,079 -0.02 (0.159) 1,501 0.632 (0.702) 261 -0.477 (0.359) 309 -0.612 (0.541) 196 0.471 (0.958) 89 15.743*** (1.529) 32 -0.328 (0.229) 698 Black 0.299 (0.837) 120 0.285 (0.367) 1,533 0.409 (0.3) 1,024 -0.442 (0.865) 88 -0.256 (0.406) 1,026 -1.245 (2.02) 32 0.354 (0.55) 980 -0.507* (0.262) 865 0.419 (0.354) 1,067 0.02 (0.169) 1,432 1.155 (1.156) 183 -0.531 (0.365) 300 -0.556 (0.817) 125 0.471 (0.958) 89 15.895*** (1.421) 27 -0.231 (0.244) 679 Hispanic 0.106 (0.587) 201 0.905*** (0.288) 1,740 0.149 (0.297) 1,002 -2.055 (1.576) 36 0.296 (0.386) 1,102 -0.444 (0.943) 73 0.899 (0.675) 804 0.680** (0.345) 807 0.644* (0.336) 1,135 0.164 (0.162) 1,520 2.320** (1.061) 113 -0.47 (0.453) 257 -0.852 (0.654) 183 -1.172 (1.214) 65 -2.014 (1.999) 31 -0.271 (0.276) 658 Black or Hispanic 0.307 (0.484) 232 0.685*** (0.234) 1,841 0.321 (0.221) 1,128 -0.737 (0.745) 103 0.111 (0.285) 1,156 -0.074 (0.778) 111 0.537 (0.424) 1,044 -0.027 (0.212) 959 0.584** (0.262) 1,235 0.08 (0.131) 1,876 1.457** (0.737) 294 -0.533* (0.303) 381 -0.771 (0.518) 236 -0.126 (0.737) 142 14.930*** (1.413) 66 -0.25 (0.199) 810 Table II.C.5.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2015-2016 Department University of Connecticut Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.231 (0.271) 604 0.046 (0.327) 817 0.543*** (0.188) 2,060 -0.061 (0.312) 469 -0.181 (0.22) 1,327 0.075 (0.662) 318 -0.233* (0.133) 2,272 -0.073 (0.158) 1,057 0.043 (0.205) 1,712 0.069 (0.24) 609 -1.117* (0.59) 222 0.012 (0.203) 1,495 0.221 (0.174) 1,069 0.093 (0.317) 587 0.352 (1.144) 83 0.155 (1.053) 54 Black -0.28 (0.36) 535 0.032 (0.345) 809 0.473** (0.196) 2,032 -0.021 (0.316) 463 -0.121 (0.251) 1,271 0.075 (0.662) 318 -0.329** (0.151) 2,113 -0.041 (0.161) 1,038 0.193 (0.232) 1,643 0.216 (0.246) 592 -1.037* (0.612) 174 0.079 (0.238) 1,426 0.198 (0.18) 1,015 0.183 (0.329) 572 0.352 (1.144) 83 0.548 (1.089) 51 Hispanic 0.118 (0.526) 307 -0.457 (0.365) 730 0.194 (0.161) 2,171 -0.245 (0.326) 457 -0.064 (0.243) 1,289 -0.272 (0.412) 412 -0.305** (0.142) 2,187 0.174 (0.164) 1,032 0.186 (0.227) 1,668 0.165 (0.211) 691 0.406 (0.319) 444 -0.046 (0.204) 1,528 -0.083 (0.274) 653 -0.154 (0.459) 493 -0.431 (1.154) 90 -0.925 (1.236) 56 Black or Hispanic -0.202 (0.303) 572 -0.19 (0.256) 890 0.296** (0.133) 2,394 -0.131 (0.261) 641 -0.031 (0.187) 1,438 -0.176 (0.364) 457 -0.313*** (0.114) 2,596 0.08 (0.131) 1,382 0.187 (0.17) 1,846 0.215 (0.184) 840 0.183 (0.303) 470 -0.01 (0.165) 1,671 0.123 (0.166) 1,165 0.057 (0.283) 626 -0.228 (0.808) 155 0.409 (0.953) 96 Table II.C.5.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2015-2016 Department Woodbridge Yale University CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.545 (0.417) 470 0.864 (0.655) 103 -0.004 (0.156) 2,775 -0.013 (0.123) 3,905 0.186 (0.29) 1,916 0.128 (0.092) 6,258 0.138 (0.141) 4,939 -0.037 (0.093) 5,496 -0.092 (0.117) 5,325 0.11 (0.107) 3,619 -0.15 (0.121) 2,960 0.097 (0.139) 2,452 0.092 (0.141) 4,200 0.023 (0.234) 2,791 Black 0.305 (0.432) 449 1.017 (0.679) 92 0.042 (0.166) 2,668 -0.012 (0.134) 3,744 -0.021 (0.357) 1,790 0.121 (0.115) 5,850 0.159 (0.174) 4,730 -0.022 (0.112) 5,220 -0.022 (0.134) 5,155 0.108 (0.116) 3,410 -0.107 (0.13) 2,798 0.138 (0.153) 2,341 0.233 (0.165) 4,057 0.074 (0.269) 2,751 Hispanic 1.289* (0.74) 324 -149.184 (.) 30 0.159 (0.207) 2,513 -0.07 (0.119) 3,972 0.701** (0.305) 1,797 0.098 (0.123) 5,755 -0.028 (0.157) 4,864 0.119 (0.13) 5,095 0.229* (0.135) 5,097 0.144 (0.11) 3,473 0.046 (0.143) 2,612 -0.043 (0.171) 2,219 0.224 (0.16) 4,029 0.157 (0.252) 2,817 Black or Hispanic 0.461 (0.394) 485 0.384 (0.605) 102 0.132 (0.141) 3,033 -0.043 (0.097) 4,529 0.362 (0.24) 2,014 0.115 (0.089) 6,317 0.027 (0.122) 5,081 0.019 (0.089) 5,655 0.079 (0.1) 5,586 0.119 (0.089) 4,357 -0.028 (0.108) 3,352 0.057 (0.124) 2,670 0.209* (0.121) 4,388 0.127 (0.186) 2,964 Table II.C.5.2: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2015-2016 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Capitol Police Central CT State University Canton Cheshire Clinton Coventry Cromwell VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.196 (0.221) 685 -0.204 (0.993) 108 1.769*** (0.469) 549 -0.130 (0.568) 434 -0.321 (0.226) 631 0.658 (0.561) 600 -0.702** (0.328) 318 0.690** (0.278) 835 -1.762* (0.945) 196 1.191 (1.551) 12 0.471 (0.396) 356 0.797 (0.834) 55 -0.233 (0.296) 765 -0.180 (0.546) 295 1.784* (0.963) 95 -0.739 (0.975) 77 Black 0.154 (0.23) 668 1.267 (1.289) 56 1.824*** (0.527) 525 0.202 (0.669) 344 -0.284 (0.227) 618 0.699 (0.622) 573 -0.715** (0.337) 308 0.631** (0.295) 819 -1.677* (0.971) 150 3.553** (1.768) 22 0.448 (0.416) 347 0.010 (1.033) 37 -0.237 (0.325) 667 0.222 (0.877) 160 -14.118*** (1.554) 48 -1.062 (1.084) 76 Hispanic 0.251 (0.235) 662 15.240*** (1.458) 33 0.558 (0.347) 542 0.519 (0.494) 505 -1.009** (0.459) 274 1.354** (0.683) 488 -0.779** (0.372) 243 0.365 (0.276) 839 0.032 (0.667) 298 1.371* (0.799) 31 0.595 (0.408) 362 2.273 (1.545) 20 0.145 (0.33) 707 0.678 (0.627) 281 0.160 (0.885) 55 -3.317** (1.511) 25 Black or Hispanic 0.174 (0.186) 817 1.752* (0.966) 119 1.000*** (0.292) 644 0.465 (0.413) 581 -0.343 (0.22) 660 1.008** (0.465) 655 -0.686** (0.309) 432 0.481** (0.217) 925 -0.487 (0.543) 320 (.) 0.524* (0.312) 439 0.585 (0.935) 56 -0.050 (0.242) 798 0.567 (0.501) 381 -0.378 (0.789) 100 -1.358 (0.827) 104 Table II.C.5.2: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2015-2016 Department Department of Motor Vehicle Danbury Darien Derby Eatern CT State University East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.170 (0.514) 244 -0.421 (0.457) 294 -0.112 (0.416) 396 -0.439 (0.444) 323 2.423 (.) 13 0.000 (.) 3 -0.405 (0.292) 467 -0.105 (0.447) 385 -0.609 (0.778) 122 0.000 (.) 22 -0.008 (0.157) 2,325 -0.401** (0.173) 1,500 0.379 (0.3) 642 -0.337 (0.335) 683 -1.124 (1.121) 68 -0.270 (0.284) 755 Black 0.110 (0.502) 230 -0.341 (0.509) 273 0.103 (0.514) 362 -0.315 (0.46) 319 Hispanic -0.041 (0.673) 195 -0.223 (0.296) 448 0.392 (0.445) 384 -0.465 (0.416) 302 Black or Hispanic 0.243 (0.429) 261 -0.230 (0.274) 488 0.155 (0.343) 461 -0.381 (0.337) 379 (.) (.) (.) (.) (.) (.) -0.381 (0.293) 458 -0.056 (0.493) 373 -0.813 (0.82) 117 17.927*** (1.705) 35 0.038 (0.176) 2,255 -0.292 (0.198) 1,412 0.241 (0.37) 592 -0.033 (0.468) 604 -0.971 (1.118) 66 -0.332 (0.388) 613 0.154 (0.322) 399 -0.033 (0.332) 484 15.320*** (1.169) 49 -0.166 (1.494) 52 0.230 (0.22) 2,195 -0.364 (0.23) 1,407 0.058 (0.371) 575 -0.107 (0.492) 603 0.000 (.) 27 -0.257 (0.281) 724 -0.166 (0.249) 641 -0.033 (0.284) 545 -0.347 (0.697) 137 (.) 0.123 (0.143) 2,406 -0.335** (0.159) 1,607 0.102 (0.29) 673 -0.053 (0.347) 685 0.069 (0.948) 80 -0.296 (0.24) 822 Table II.C.5.2: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2015-2016 Department Groton City Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Mashantucket Pequot Police Meriden Middletown Milford Mohegan Tribal Police Monroe Naugatuck New Britain VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.304 (0.646) 138 0.843* (0.51) 198 -0.105 (0.429) 938 0.155 (0.362) 308 -0.458 (0.289) 421 -1.652 (1.063) 215 -0.499 (0.442) 568 0.016 (0.143) 1,445 0.127 (0.111) 2,708 1.367* (0.731) 124 1.622** (0.811) 139 -0.001 (0.664) 211 -0.270 (0.352) 488 -0.305 (0.349) 741 -0.466 (0.44) 704 0.413* (0.226) 635 Black -0.533 (0.792) 82 0.840 (0.587) 188 -0.221 (0.74) 654 0.179 (0.37) 306 -0.431 (0.29) 419 -1.737 (1.324) 192 -0.723 (0.525) 483 0.026 (0.153) 1,358 Hispanic 0.135 (0.8) 146 0.083 (0.576) 185 -0.348 (0.459) 821 0.518 (0.556) 171 -0.120 (0.337) 307 0.243 (1.154) 104 1.225* (0.653) 607 0.094 (0.173) 1,244 Black or Hispanic -0.013 (0.616) 181 0.398 (0.428) 229 -0.303 (0.397) 957 0.211 (0.341) 330 -0.266 (0.282) 576 -1.102 (0.939) 234 0.365 (0.415) 715 0.072 (0.128) 1,619 (.) (.) (.) 1.367* (0.731) 124 1.356* (0.796) 128 -0.206 (0.642) 121 0.955* (0.504) 183 -0.277 (1.096) 73 -0.855 (0.626) 180 0.891** (0.419) 223 0.713 (0.612) 160 -0.561 (0.483) 228 (.) (.) (.) -0.085 (0.39) 723 -0.918* (0.5) 694 0.381 (0.234) 614 1.190*** (0.445) 690 -0.171 (0.293) 752 0.015 (0.185) 857 0.593** (0.296) 799 -0.353 (0.279) 808 0.132 (0.17) 1,037 Table II.C.5.2: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2015-2016 Department New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Putnam VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.050 (0.34) 748 0.030 (0.092) 3,269 -0.052 (0.295) 621 0.563 (0.597) 558 -0.108 (0.291) 660 -0.048 (0.402) 969 0.713 (1.462) 21 0.060 (0.32) 461 0.289 (0.356) 323 -0.540*** (0.19) 1,126 0.642 (0.514) 544 -0.021 (0.266) 717 -0.979 (0.968) 154 1.425** (0.619) 253 -0.845 (1.334) 75 -33.891*** (1.633) 50 Black 0.387 (0.445) 638 0.044 (0.093) 3,202 0.034 (0.33) 571 1.039 (0.725) 486 0.036 (0.333) 603 0.017 (0.509) 835 0.630 (1.795) 13 0.042 (0.351) 443 0.193 (0.368) 313 -0.434** (0.198) 1,084 0.934* (0.49) 428 -0.188 (0.281) 687 -1.204 (1) 123 0.869 (0.615) 196 -0.845 (1.334) 75 -32.913*** (1.778) 41 Hispanic -0.254 (0.359) 719 0.053 (0.102) 2,623 0.245 (0.259) 657 0.486 (0.388) 569 0.054 (0.29) 700 0.925** (0.38) 984 2.067 (1.513) 22 -0.047 (0.477) 338 -0.205 (0.405) 286 0.570*** (0.207) 1,046 -0.998* (0.526) 477 -0.278 (0.35) 635 -1.617 (1.706) 84 0.861 (0.571) 245 -0.468 (1.431) 46 -19.561*** (2.449) 28 Black or Hispanic -0.003 (0.294) 834 0.042 (0.084) 4,244 0.127 (0.219) 764 0.574* (0.341) 645 0.036 (0.232) 790 0.523* (0.31) 1,072 (.) -0.038 (0.3) 480 0.065 (0.291) 391 0.034 (0.153) 1,266 -0.277 (0.385) 555 -0.200 (0.23) 792 -1.174 (0.813) 155 0.830** (0.422) 343 -0.523 (0.916) 120 -2.224** (1.041) 89 Table II.C.5.2: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2015-2016 Department Redding Ridgefield Rocky Hill Southern CT State University Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -1.032 (0.947) 69 0.612* (0.348) 1,080 0.007 (0.313) 707 -2.094 (2.212) 44 0.085 (0.458) 796 -1.555 (1.16) 28 0.862 (0.657) 643 -0.549 (0.356) 444 0.363 (0.507) 805 -0.262 (0.229) 648 0.576 (0.886) 155 -0.674 (0.594) 131 -1.173* (0.63) 176 15.442*** (1.242) 35 35.686*** (2.048) 20 -0.226 (0.369) 304 Black -0.535 (1.031) 52 0.336 (0.433) 962 0.226 (0.359) 626 -2.052 (2.178) 42 0.037 (0.533) 760 2.056 (5.643) 19 0.447 (0.741) 628 -0.851* (0.467) 364 -0.054 (0.513) 779 -0.316 (0.248) 611 2.006 (2.81) 74 -0.636 (0.614) 123 -0.841 (0.836) 114 15.442*** (1.242) 35 36.444*** (2.374) 15 -0.174 (0.43) 290 Hispanic 0.795 (1.039) 63 1.202*** (0.354) 1,105 -0.075 (0.352) 686 -2640.196 (.) 12 0.837 (0.537) 732 -0.697 (1.24) 16 0.662 (0.861) 370 0.817 (0.603) 263 0.648 (0.431) 841 -0.204 (0.251) 602 2.229 (1.674) 96 0.565 (0.769) 105 -0.934 (0.631) 159 -45.820*** (7.36) 24 18.452*** (2.492) 20 -0.446 (0.443) 262 Black or Hispanic 0.366 (0.758) 105 0.852*** (0.283) 1,185 0.110 (0.268) 771 -2.266 (2.406) 49 0.574 (0.373) 882 0.025 (1.056) 50 0.535 (0.562) 707 -0.239 (0.353) 428 0.379 (0.336) 886 -0.306 (0.198) 769 (.) -0.127 (0.505) 169 -0.850* (0.513) 206 0.650 (1.105) 79 (.) -0.261 (0.321) 348 Table II.C.5.2: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2015-2016 Department University of Connecticut Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.099 (0.394) 294 0.812* (0.447) 505 0.700** (0.349) 778 0.099 (0.464) 209 -0.032 (0.311) 788 0.365 (1.114) 65 -0.315 (0.219) 862 0.456 (0.301) 439 0.070 (0.262) 940 -0.143 (0.337) 347 -1.478 (1.154) 61 -0.167 (0.271) 791 0.515** (0.218) 735 -0.088 (0.383) 386 1.041 (1.675) 37 0.000 (.) 13 Black 0.041 (0.53) 260 0.781* (0.453) 484 0.505 (0.377) 749 0.165 (0.477) 206 0.082 (0.366) 705 0.365 (1.114) 65 -0.303 (0.258) 804 0.736** (0.336) 423 0.220 (0.286) 921 -0.085 (0.359) 338 -1.499 (1.172) 58 -0.049 (0.332) 741 0.527** (0.222) 696 0.007 (0.399) 375 1.041 (1.675) 37 0.000 (.) 11 Hispanic -0.402 (0.686) 130 0.093 (0.489) 412 0.158 (0.307) 795 0.005 (0.504) 205 -0.010 (0.348) 709 -0.832 (0.851) 116 -0.419* (0.245) 809 0.210 (0.274) 439 0.066 (0.307) 883 -0.285 (0.346) 347 -0.080 (0.473) 199 -0.142 (0.284) 871 0.140 (0.318) 463 0.164 (0.624) 253 -16.263*** (1.531) 44 0.000 (.) 14 Black or Hispanic -0.146 (0.44) 280 0.454 (0.345) 562 0.269 (0.251) 855 0.094 (0.392) 284 0.101 (0.26) 863 -0.169 (0.604) 188 -0.359* (0.193) 943 0.405* (0.229) 553 0.177 (0.223) 1,036 -0.119 (0.281) 433 -0.314 (0.442) 215 -0.113 (0.228) 937 0.405** (0.206) 802 0.064 (0.345) 422 -0.565 (1.102) 100 0.000 (.) 27 Table II.C.5.2: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2015-2016 Department Woodbridge Yale University CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.272 (0.564) 194 1.044 (1.321) 47 -0.060 (0.179) 2,071 0.028 (0.173) 2,132 0.418 (0.349) 1,243 0.252** (0.104) 4,756 0.143 (0.176) 2,977 -0.081 (0.107) 4,100 0.020 (0.13) 3,779 -0.003 (0.137) 2,232 -0.175 (0.15) 2,176 0.083 (0.172) 1,656 0.311* (0.158) 2,962 0.468* (0.284) 1,411 Black 0.020 (0.57) 187 1.616 (1.581) 43 -0.038 (0.191) 1,978 0.061 (0.197) 2,032 0.186 (0.429) 1,151 0.204 (0.133) 4,405 0.213 (0.227) 2,874 -0.095 (0.131) 3,862 0.094 (0.15) 3,630 0.010 (0.151) 2,085 -0.144 (0.166) 2,051 0.086 (0.194) 1,568 0.455** (0.186) 2,842 0.623* (0.338) 1,343 Hispanic 1.584* (0.886) 85 -0.186 (1.054) 48 -0.033 (0.232) 1,835 0.154 (0.175) 2,105 0.919** (0.374) 1,201 0.104 (0.139) 4,311 -0.121 (0.202) 2,928 0.143 (0.151) 3,772 0.302* (0.156) 3,560 -0.003 (0.146) 2,055 0.146 (0.179) 1,929 -0.193 (0.211) 1,477 0.188 (0.199) 2,759 0.087 (0.352) 1,396 Black or Hispanic 0.226 (0.504) 207 (.) 0.016 (0.159) 2,213 0.107 (0.139) 2,391 0.561* (0.296) 1,403 0.153 (0.101) 4,766 -0.003 (0.158) 3,067 -0.020 (0.104) 4,175 0.163 (0.115) 3,948 -0.014 (0.117) 2,598 0.007 (0.136) 2,422 -0.048 (0.155) 1,780 0.309** (0.144) 3,061 0.368 (0.247) 1,483 Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 64,920 0.000 (.) 68,499 0.053 (0.051) 119,471 -1.257*** (0.065) 118,565 1.240*** (0.035) 91,547 0.000 (.) 58,758 0.000 (.) 101,309 -0.474*** (0.049) 72,403 9.326*** (0.085) 149,679 0.000 (.) 67,358 0.000 (.) 71,920 0.000 (.) 43,503 0.000 (.) 50,250 0.037 (0.068) 53,160 0.000 (.) 57,686 Black 0.000 (.) 64,920 0.000 (.) 68,499 0.122** (0.058) 119,471 -1.447*** (0.073) 118,565 1.350*** (0.035) 91,547 0.000 (.) 58,758 0.000 (.) 101,309 -0.480*** (0.052) 72,403 -0.741*** (0.104) 149,679 0.000 (.) 67,358 0.000 (.) 71,920 0.000 (.) 43,503 0.000 (.) 50,250 0.081 (0.072) 53,160 0.000 (.) 57,686 Hispanic 0.000 (.) 64,920 0.000 (.) 68,499 0.605*** (0.056) 119,471 50.029*** (0.057) 118,565 0.716*** (0.064) 91,547 0.000 (.) 58,758 0.000 (.) 101,309 -0.530*** (0.046) 72,403 0.123* (0.067) 149,679 0.000 (.) 67,358 0.000 (.) 71,920 0.000 (.) 43,503 0.000 (.) 50,250 1.027*** (0.111) 53,160 0.000 (.) 57,686 Black or Hispanic 0.000 (.) 64,920 0.000 (.) 68,499 0.437*** (0.043) 119,471 -1.122*** (0.049) 118,565 0.710*** (0.036) 91,547 0.000 (.) 58,758 0.000 (.) 101,309 1.585*** (0.033) 72,403 1.426*** (0.058) 149,679 0.000 (.) 67,358 0.000 (.) 71,920 0.000 (.) 43,503 0.000 (.) 50,250 1.459*** (0.064) 53,160 0.000 (.) 57,686 Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City Groton Long Point Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 141,448 -0.201*** (0.051) 134,555 -0.812*** (0.227) 351,048 0.000 (.) 66,001 0.000 (.) 45,353 0.000 (.) 84,798 7.990*** (0.169) 99,302 0.000 (.) 54,236 0.000 (.) 67,134 0.000 (.) 80,006 0.276*** (0.066) 150,181 0.000 (.) 38,899 0.000 (.) 61,789 0.033 (0.099) 351,048 -1.739*** (0.464) 350,825 Black 0.000 (.) 141,448 -0.134** (0.052) 134,555 -0.599** (0.238) 351,048 0.000 (.) 66,001 0.000 (.) 45,353 0.000 (.) 84,798 -1.780*** (0.198) 99,302 0.000 (.) 54,236 0.000 (.) 67,134 0.000 (.) 80,006 -0.029 (0.073) 150,181 0.000 (.) 38,899 0.000 (.) 61,789 0.140 (0.107) 351,048 -1.733*** (0.517) 350,825 Hispanic 0.000 (.) 141,448 -0.257*** (0.054) 134,555 -0.947*** (0.27) 351,048 0.000 (.) 66,001 0.000 (.) 45,353 0.000 (.) 84,798 -0.437*** (0.129) 99,302 0.000 (.) 54,236 0.000 (.) 67,134 0.000 (.) 80,006 -0.266*** (0.076) 150,181 0.000 (.) 38,899 0.000 (.) 61,789 0.233** (0.118) 351,048 -0.993** (0.402) 350,825 Black or Hispanic 0.000 (.) 141,448 -0.240*** (0.042) 134,555 21.921*** (0.17) 351,048 0.000 (.) 66,001 0.000 (.) 45,353 0.000 (.) 84,798 -1.148*** (0.111) 99,302 0.000 (.) 54,236 0.000 (.) 67,134 0.000 (.) 80,006 -14.893*** (0.041) 150,181 0.000 (.) 38,899 0.000 (.) 61,789 0.199** (0.086) 351,048 -1.410*** (0.324) 350,825 Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.174* (0.1) 351,048 0.000 (.) 38,453 0.000 (.) 48,770 0.000 (.) 81,273 0.466*** (0.076) 57,145 0.000 (.) 38,874 -0.236*** (0.038) 124,529 -0.458*** (0.061) 351,048 0.000 (.) 66,923 0.431*** (0.06) 103,475 -0.936*** (0.072) 138,890 0.000 (.) 45,310 -0.847*** (0.049) 104,913 0.000 (.) 73,771 0.000 (.) 56,164 Black 0.089 (0.108) 351,048 0.000 (.) 38,453 0.000 (.) 48,770 0.000 (.) 81,273 0.394*** (0.084) 57,145 0.000 (.) 38,874 -0.310*** (0.04) 124,529 -0.432*** (0.063) 351,048 0.000 (.) 66,923 0.495*** (0.061) 103,475 -1.051*** (0.076) 138,890 0.000 (.) 45,310 -0.814*** (0.051) 104,913 0.000 (.) 73,771 0.000 (.) 56,164 Hispanic -0.170 (0.122) 351,048 0.000 (.) 38,453 0.000 (.) 48,770 0.000 (.) 81,273 -0.387*** (0.107) 57,145 0.000 (.) 38,874 -0.611*** (0.044) 124,529 0.648*** (0.049) 351,048 0.000 (.) 66,923 5.101*** (0.079) 103,475 -0.743*** (0.088) 138,890 0.000 (.) 45,310 5.872*** (0.045) 104,913 0.000 (.) 73,771 0.000 (.) 56,164 Black or Hispanic -0.024 (0.088) 351,048 0.000 (.) 38,453 0.000 (.) 48,770 0.000 (.) 81,273 0.065 (0.07) 57,145 0.000 (.) 38,874 -0.622*** (0.035) 124,529 0.219*** (0.045) 351,048 0.000 (.) 66,923 0.258*** (0.054) 103,475 12.380*** (0.045) 138,890 0.000 (.) 45,310 3.074*** (0.035) 104,913 0.000 (.) 73,771 0.000 (.) 56,164 Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 57,662 0.000 (.) 71,643 0.805*** (0.073) 34,553 0.000 (.) 87,918 0.000 (.) 31,195 0.000 (.) 47,926 0.000 (.) 56,240 0.000 (.) 27,730 0.000 (.) 66,000 0.000 (.) 42,875 0.395*** (0.041) 79,410 0.000 (.) 84,173 0.000 (.) 63,829 1.777*** (0.09) 96,896 0.000 (.) 47,191 Black 0.000 (.) 57,662 0.000 (.) 71,643 2.452*** (0.084) 34,553 0.000 (.) 87,918 0.000 (.) 31,195 0.000 (.) 47,926 0.000 (.) 56,240 0.000 (.) 27,730 0.000 (.) 66,000 0.000 (.) 42,875 0.434*** (0.043) 79,410 0.000 (.) 84,173 0.000 (.) 63,829 7.204*** (0.093) 96,896 0.000 (.) 47,191 Hispanic 0.000 (.) 57,662 0.000 (.) 71,643 -0.664*** (0.11) 34,553 0.000 (.) 87,918 0.000 (.) 31,195 0.000 (.) 47,926 0.000 (.) 56,240 0.000 (.) 27,730 0.000 (.) 66,000 0.000 (.) 42,875 4.279*** (0.046) 79,410 0.000 (.) 84,173 0.000 (.) 63,829 4.012*** (0.092) 96,896 0.000 (.) 47,191 Black or Hispanic 0.000 (.) 57,662 0.000 (.) 71,643 -0.837*** (0.087) 34,553 0.000 (.) 87,918 0.000 (.) 31,195 0.000 (.) 47,926 0.000 (.) 56,240 0.000 (.) 27,730 0.000 (.) 66,000 0.000 (.) 42,875 0.153*** (0.036) 79,410 0.000 (.) 84,173 0.000 (.) 63,829 -0.479*** (0.069) 96,896 0.000 (.) 47,191 Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 37,901 0.000 (.) 75,013 0.000 (.) 77,451 0.332*** (0.055) 169,531 21.044*** (0.063) 351,048 0.902*** (0.137) 108,174 -0.080 (0.11) 119,046 0.000 (.) 87,897 -0.419*** (0.065) 79,086 0.000 (.) 59,420 0.000 (.) 27,311 0.037 (0.103) 128,834 0.000 (.) 70,335 0.000 (.) 21,633 0.000 (.) 46,353 Black 0.000 (.) 37,901 0.000 (.) 75,013 0.000 (.) 77,451 0.254*** (0.06) 169,531 -0.654*** (0.068) 351,048 10.340*** (0.146) 108,174 1.819*** (0.071) 119,046 0.000 (.) 87,897 -0.191*** (0.072) 79,086 0.000 (.) 59,420 0.000 (.) 27,311 0.002 (0.105) 128,834 0.000 (.) 70,335 0.000 (.) 21,633 0.000 (.) 46,353 Hispanic 0.000 (.) 37,901 0.000 (.) 75,013 0.000 (.) 77,451 1.038*** (0.065) 169,531 -0.783*** (0.071) 351,048 0.893*** (0.139) 108,174 1.766*** (0.089) 119,046 0.000 (.) 87,897 -0.393*** (0.07) 79,086 0.000 (.) 59,420 0.000 (.) 27,311 10.242*** (0.057) 128,834 0.000 (.) 70,335 0.000 (.) 21,633 0.000 (.) 46,353 Black or Hispanic 0.000 (.) 37,901 0.000 (.) 75,013 0.000 (.) 77,451 -4.428*** (0.044) 169,531 -0.789*** (0.051) 351,048 0.323*** (0.105) 108,174 -0.568*** (0.095) 119,046 0.000 (.) 87,897 19.116*** (0.046) 79,086 0.000 (.) 59,420 0.000 (.) 27,311 -0.646*** (0.102) 128,834 0.000 (.) 70,335 0.000 (.) 21,633 0.000 (.) 46,353 Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.324*** (0.098) 155,526 0.234 (.) 139,197 -0.156*** (0.037) 351,048 0.000 (.) 64,223 0.000 (.) 63,894 -0.274*** (0.088) 62,431 0.000 (.) 60,399 0.000 (.) 50,184 0.000 (.) 18,057 0.000 (.) 77,311 -0.113** (0.048) 68,568 0.000 (.) 83,328 0.000 (.) 51,852 0.000 (.) 78,844 0.000 (.) 38,160 Black 0.408*** (0.104) 155,526 0.268 (.) 139,197 -0.089** (0.039) 351,048 0.000 (.) 64,223 0.000 (.) 63,894 5.761*** (0.086) 62,431 0.000 (.) 60,399 0.000 (.) 50,184 0.000 (.) 18,057 0.000 (.) 77,311 -0.026 (0.05) 68,568 0.000 (.) 83,328 0.000 (.) 51,852 0.000 (.) 78,844 0.000 (.) 38,160 Hispanic 0.692*** (0.137) 155,526 0.826*** (0.054) 139,197 0.259*** (0.036) 351,048 0.000 (.) 64,223 0.000 (.) 63,894 -4.366*** (0.088) 62,431 0.000 (.) 60,399 0.000 (.) 50,184 0.000 (.) 18,057 0.000 (.) 77,311 0.815*** (0.046) 68,568 0.000 (.) 83,328 0.000 (.) 51,852 0.000 (.) 78,844 0.000 (.) 38,160 Black or Hispanic 0.618*** (0.09) 155,526 0.246 (.) 139,197 0.118*** (0.029) 351,048 0.000 (.) 64,223 0.000 (.) 63,894 0.112 (0.075) 62,431 0.000 (.) 60,399 0.000 (.) 50,184 0.000 (.) 18,057 0.000 (.) 77,311 0.548*** (0.04) 68,568 0.000 (.) 83,328 0.000 (.) 51,852 0.000 (.) 78,844 0.000 (.) 38,160 Table II.D.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Winsted Wolcott Woodbridge CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -35.100*** (0.154) 57,989 -0.024 (0.156) 351,048 0.000 (.) 65,082 -0.102*** (0.026) 198,127 -0.735 (0.947) 64,707 -0.534*** (0.055) 198,126 -0.407** (0.171) 198,121 -0.393*** (0.085) 76,540 -0.365 (0.229) 159,093 -0.901* (0.525) 128,854 1.411*** (0.449) 54,479 0.120 (0.148) 194,540 0.237*** (0.026) 198,126 0.235 (0.606) 103,524 15.579*** (0.035) 198,126 Black 0.374** (0.161) 57,989 0.038 (0.166) 351,048 0.000 (.) 65,082 -0.075*** (0.028) 198,127 -0.970 (0.967) 64,707 -0.454*** (0.063) 198,126 -0.743*** (0.181) 198,121 -0.437*** (0.102) 76,540 -0.251 (0.276) 159,093 -1.256** (0.525) 128,854 1.211*** (0.466) 54,479 0.116 (0.156) 194,540 0.288*** (0.028) 198,126 0.350 (0.728) 103,524 -0.377*** (0.043) 198,126 Hispanic -0.794*** (0.177) 57,989 0.596*** (0.142) 351,048 0.000 (.) 65,082 -0.103*** (0.031) 198,127 -1.383 (0.905) 64,707 -0.527*** (0.065) 198,126 -0.981*** (0.16) 198,121 0.044 (0.08) 76,540 -0.498* (0.276) 159,093 1.418 (1.029) 128,854 2.122*** (0.693) 54,479 0.099 (0.177) 194,540 0.155*** (0.031) 198,126 -0.245 (0.562) 103,524 24.948*** (0.036) 198,126 Black or Hispanic -1.001*** (0.131) 57,989 0.204* (0.123) 351,048 0.000 (.) 65,082 -0.104*** (0.023) 198,127 -1.814*** (0.668) 64,707 8.980*** (0.038) 198,126 -1.030*** (0.144) 198,121 11.045*** (0.027) 76,540 -0.362* (0.217) 159,093 -0.676 (0.508) 128,854 1.786*** (0.418) 54,479 0.196 (0.135) 194,540 0.284*** (0.023) 198,126 0.006 (0.469) 103,524 -0.240*** (0.032) 198,126 Table II.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 64,920 0.000 (.) 68,499 -14.970*** (0.051) 119,471 0.962 (.) 118,565 2.139 (1.356) 91,547 0.000 (.) 58,758 0.000 (.) 101,309 -0.067 (.) 72,403 -0.344 (.) 149,679 0.000 (.) 67,358 0.000 (.) 71,920 0.000 (.) 43,503 0.000 (.) 50,250 0.214 (.) 53,160 0.000 (.) 57,686 Black 0.000 (.) 64,920 0.000 (.) 68,499 -0.066 (0.11) 119,471 0.708*** (0.091) 118,565 2.224*** (0.662) 91,547 0.000 (.) 58,758 0.000 (.) 101,309 0.070 (.) 72,403 -0.769 (.) 149,679 0.000 (.) 67,358 0.000 (.) 71,920 0.000 (.) 43,503 0.000 (.) 50,250 1.327 (.) 53,160 0.000 (.) 57,686 Hispanic 0.000 (.) 64,920 0.000 (.) 68,499 -0.422 (.) 119,471 8.881 (.) 118,565 -1.253 (.) 91,547 0.000 (.) 58,758 0.000 (.) 101,309 0.097 (2.376) 72,403 0.376 (.) 149,679 0.000 (.) 67,358 0.000 (.) 71,920 0.000 (.) 43,503 0.000 (.) 50,250 -0.607 (.) 53,160 0.000 (.) 57,686 Black or Hispanic 0.000 (.) 64,920 0.000 (.) 68,499 -0.629*** (0.166) 119,471 3.283*** (0.172) 118,565 1.795 (.) 91,547 0.000 (.) 58,758 0.000 (.) 101,309 0.102 (.) 72,403 -0.119 (.) 149,679 0.000 (.) 67,358 0.000 (.) 71,920 0.000 (.) 43,503 0.000 (.) 50,250 0.200 (.) 53,160 0.000 (.) 57,686 Table II.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City Groton Long Point VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 141,448 0.833*** (0.244) 134,555 -0.609** (0.263) 351,048 0.000 (.) 66,001 0.000 (.) 45,353 0.000 (.) 84,798 -0.056 (0.89) 99,302 0.000 (.) 54,232 0.000 (.) 67,134 0.000 (.) 80,006 -0.792 (.) 150,181 0.000 (.) 38,899 0.000 (.) 61,789 0.053 (0.105) 351,048 -1.666*** (0.583) 350,825 Black 0.000 (.) 141,448 0.915*** (0.225) 134,555 -0.372 (0.323) 351,048 0.000 (.) 66,001 0.000 (.) 45,353 0.000 (.) 84,798 20.087*** (5.306) 99,302 0.000 (.) 54,232 0.000 (.) 67,134 0.000 (.) 80,006 -1.043 (.) 150,181 0.000 (.) 38,899 0.000 (.) 61,789 0.179 (0.116) 351,048 -1.513** (0.752) 350,825 Hispanic 0.000 (.) 141,448 39.123*** (0.079) 134,555 -0.661** (0.329) 351,048 0.000 (.) 66,001 0.000 (.) 45,353 0.000 (.) 84,798 1.434 (.) 99,302 0.000 (.) 54,232 0.000 (.) 67,134 0.000 (.) 80,006 -0.478 (.) 150,181 0.000 (.) 38,899 0.000 (.) 61,789 0.291** (0.129) 351,048 -0.922** (0.452) 350,825 Black or Hispanic 0.000 (.) 141,448 -0.504** (0.228) 134,555 -0.499** (0.219) 351,048 0.000 (.) 66,001 0.000 (.) 45,353 0.000 (.) 84,798 1.033 (.) 99,302 0.000 (.) 54,232 0.000 (.) 67,134 0.000 (.) 80,006 -0.761 (.) 150,181 0.000 (.) 38,899 0.000 (.) 61,789 0.249*** (0.093) 351,048 -1.308*** (0.388) 350,825 Table II.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Groton Town Guilford Hamden Hartford Ledyard Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.155 (0.102) 351,048 0.000 (.) 38,453 0.000 (.) 48,770 0.000 (.) 81,273 -0.640 (.) 57,145 0.000 (.) 38,874 -0.122 (.) 124,529 -0.395*** (0.064) 351,048 0.000 (.) 66,923 2.128*** (0.104) 103,475 0.482** (0.24) 138,890 0.000 (.) 45,310 -2.437 (.) 104,913 0.000 (.) 73,771 0.000 (.) 56,164 Black 0.042 (0.112) 351,048 0.000 (.) 38,453 0.000 (.) 48,770 0.000 (.) 81,273 -0.822 (4.159) 57,145 0.000 (.) 38,874 0.682 (.) 124,529 -0.357*** (0.066) 351,048 0.000 (.) 66,923 2.601*** (0.802) 103,475 0.520*** (0.201) 138,890 0.000 (.) 45,310 -2.546 (.) 104,913 0.000 (.) 73,771 0.000 (.) 56,164 Hispanic -0.193 (0.129) 351,048 0.000 (.) 38,453 0.000 (.) 48,770 0.000 (.) 81,273 1.630 (.) 57,145 0.000 (.) 38,874 -0.006 (0.05) 124,529 0.690*** (0.052) 351,048 0.000 (.) 66,923 2.161 (.) 103,475 0.508 (0.628) 138,890 0.000 (.) 45,310 -3.371 (.) 104,913 0.000 (.) 73,771 0.000 (.) 56,164 Black or Hispanic -0.053 (0.091) 351,048 0.000 (.) 38,453 0.000 (.) 48,770 0.000 (.) 81,273 1.150*** (0.217) 57,145 0.000 (.) 38,874 0.400 (.) 124,529 0.224*** (0.047) 351,048 0.000 (.) 66,923 1.842*** (0.094) 103,475 3.844 (.) 138,890 0.000 (.) 45,310 -2.434 (.) 104,913 0.000 (.) 73,771 0.000 (.) 56,164 Table II.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 57,662 0.000 (.) 71,643 -0.059 (.) 34,553 0.000 (.) 87,918 0.000 (.) 31,195 0.000 (.) 47,926 0.000 (.) 56,240 0.000 (.) 27,730 0.000 (.) 66,000 0.000 (.) 42,875 1.430*** (0.074) 79,410 0.000 (.) 84,173 0.000 (.) 63,829 -1.236*** (0.159) 96,896 0.000 (.) 47,191 Black 0.000 (.) 57,662 0.000 (.) 71,643 0.071 (.) 34,553 0.000 (.) 87,918 0.000 (.) 31,195 0.000 (.) 47,926 0.000 (.) 56,240 0.000 (.) 27,730 0.000 (.) 66,000 0.000 (.) 42,875 1.961*** (0.075) 79,410 0.000 (.) 84,173 0.000 (.) 63,829 -1.159*** (0.114) 96,896 0.000 (.) 47,191 Hispanic 0.000 (.) 57,662 0.000 (.) 71,643 0.230 (.) 34,553 0.000 (.) 87,918 0.000 (.) 31,195 0.000 (.) 47,926 0.000 (.) 56,240 0.000 (.) 27,730 0.000 (.) 66,000 0.000 (.) 42,875 2.146** (0.976) 79,410 0.000 (.) 84,173 0.000 (.) 63,829 -0.770 (3.776) 96,896 0.000 (.) 47,191 Black or Hispanic 0.000 (.) 57,662 0.000 (.) 71,643 0.546 (.) 34,553 0.000 (.) 87,918 0.000 (.) 31,195 0.000 (.) 47,926 0.000 (.) 56,240 0.000 (.) 27,730 0.000 (.) 66,000 0.000 (.) 42,875 2.576*** (0.134) 79,410 0.000 (.) 84,173 0.000 (.) 63,829 -0.924*** (0.136) 96,896 0.000 (.) 47,191 Table II.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 37,901 0.000 (.) 75,013 0.000 (.) 77,451 -0.408 (0.388) 169,531 -0.342*** (0.065) 351,048 -0.700 (.) 108,174 4.631 (.) 119,046 0.000 (.) 87,897 2.301*** (0.099) 79,086 0.000 (.) 59,420 0.000 (.) 27,311 -2.401*** (0.429) 128,834 0.000 (.) 70,335 0.000 (.) 21,633 0.000 (.) 46,353 Black 0.000 (.) 37,901 0.000 (.) 75,013 0.000 (.) 77,451 -0.552*** (0.193) 169,531 -0.272*** (0.07) 351,048 -0.588 (.) 108,174 0.481 (0.371) 119,046 0.000 (.) 87,897 -0.602*** (0.078) 79,086 0.000 (.) 59,420 0.000 (.) 27,311 -2.352*** (0.392) 128,834 0.000 (.) 70,335 0.000 (.) 21,633 0.000 (.) 46,353 Hispanic 0.000 (.) 37,901 0.000 (.) 75,013 0.000 (.) 77,451 -0.071 (.) 169,531 -0.491*** (0.073) 351,048 -0.363 (.) 108,174 0.234 (0.67) 119,046 0.000 (.) 87,897 0.180 (0.111) 79,086 0.000 (.) 59,420 0.000 (.) 27,311 -2.570*** (0.536) 128,834 0.000 (.) 70,335 0.000 (.) 21,633 0.000 (.) 46,353 Black or Hispanic 0.000 (.) 37,901 0.000 (.) 75,013 0.000 (.) 77,451 0.040 (.) 169,531 -0.456*** (0.053) 351,048 -0.260 (.) 108,174 -31.743*** (6.524) 119,046 0.000 (.) 87,897 -5.854*** (0.07) 79,086 0.000 (.) 59,420 0.000 (.) 27,311 -3.248*** (0.261) 128,834 0.000 (.) 70,335 0.000 (.) 21,633 0.000 (.) 46,353 Table II.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.126 (.) 155,526 -0.212*** (0.06) 139,197 0.038 (0.039) 351,048 0.000 (.) 64,223 0.000 (.) 63,894 -0.660*** (0.116) 62,431 0.000 (.) 60,399 0.000 (.) 50,184 0.000 (.) 18,057 0.000 (.) 77,311 -5.094*** (0.052) 68,568 0.000 (.) 83,328 0.000 (.) 51,852 0.000 (.) 78,844 0.000 (.) 38,160 Black 0.575 (.) 155,526 -0.345*** (0.062) 139,197 0.170*** (0.042) 351,048 0.000 (.) 64,223 0.000 (.) 63,894 -0.389*** (0.117) 62,431 0.000 (.) 60,399 0.000 (.) 50,184 0.000 (.) 18,057 0.000 (.) 77,311 -6.676*** (0.053) 68,568 0.000 (.) 83,328 0.000 (.) 51,852 0.000 (.) 78,844 0.000 (.) 38,160 Hispanic 0.521 (.) 155,526 -1.116 (.) 139,197 0.376*** (0.038) 351,048 0.000 (.) 64,223 0.000 (.) 63,894 -0.114 (0.11) 62,431 0.000 (.) 60,399 0.000 (.) 50,184 0.000 (.) 18,057 0.000 (.) 77,311 5.124*** (0.072) 68,568 0.000 (.) 83,328 0.000 (.) 51,852 0.000 (.) 78,844 0.000 (.) 38,160 Black or Hispanic 0.812 (.) 155,526 -0.395*** (0.047) 139,197 0.306*** (0.031) 351,048 0.000 (.) 64,223 0.000 (.) 63,894 5.006*** (1.508) 62,431 0.000 (.) 60,399 0.000 (.) 50,184 0.000 (.) 18,057 0.000 (.) 77,311 -3.612*** (0.039) 68,568 0.000 (.) 83,328 0.000 (.) 51,852 0.000 (.) 78,844 0.000 (.) 38,160 Table II.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status by Department, All Traffic Stops 2015-2016 Department Winsted Wolcott Woodbridge CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.458 (.) 57,989 31.919 (213.704) 351,048 0.000 (.) 65,082 -0.126*** (0.028) 198,126 -64.732*** (12.923) 64,706 -0.471*** (0.066) 198,126 -3.140 (.) 198,121 -0.345*** (0.123) 76,538 -0.855** (0.426) 159,093 -25.599 (.) 128,852 -338.291 (.) 54,478 0.412*** (0.11) 194,540 0.191*** (0.027) 198,126 -205.739 (.) 103,524 -0.583*** (0.044) 198,126 Black -0.405 (.) 57,989 0.168 (0.204) 351,048 0.000 (.) 65,082 -0.086*** (0.03) 198,126 -77.503 (112.589) 64,706 -0.382*** (0.079) 198,126 -3.540 (.) 198,121 -0.429** (0.169) 76,538 -75.544*** (5.654) 159,093 -569.664 (.) 128,852 -126.159 (.) 54,478 0.286*** (0.102) 194,540 0.245*** (0.03) 198,126 -146.476 (.) 103,524 -0.515*** (0.05) 198,126 Hispanic -1.175 (.) 57,989 5.552*** (0.166) 351,048 0.000 (.) 65,082 -0.053 (0.034) 198,126 -209.528 (.) 64,706 -0.467*** (0.075) 198,126 -5.146 (.) 198,121 -24.095 (.) 76,538 -0.240 (0.307) 159,093 -79.763 (60.219) 128,852 -398.415 (.) 54,478 -6.400*** (2.052) 194,540 0.110*** (0.033) 198,126 -62.896 (.) 103,524 -0.069 (0.045) 198,126 Black or Hispanic -0.968 (.) 57,989 0.202 (0.136) 351,048 0.000 (.) 65,082 -0.091*** (0.025) 198,126 -133.445*** (14.492) 64,706 -0.440*** (0.054) 198,126 -0.731* (0.419) 198,121 -0.130 (0.092) 76,538 -0.326 (0.255) 159,093 -20.285 (.) 128,852 0.986 (134.426) 54,478 0.295*** (0.078) 194,540 0.225*** (0.024) 198,126 150.421 (.) 103,524 -0.352*** (0.036) 198,126 Table II.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2015-2016 Ansonia White Non-White 14.3% 9.1% N/A 0.37 49 22 White Non-White Black 10.5% 0.169 19 Black White 53.3% N/A 15 White 9.1% N/A 22 White 56.1% N/A 57 White 65.4% N/A 26 White 37.5% N/A 48 White 56.7% N/A 97 White Non-White 56.3% 0.039 48 Non-White 12.2% 0.169 90 Non-White 66.7% 0.703 21 Non-White 75% 0.144 4 Non-White 41.7% 0.071 12 Non-White 50% 0.07 4 Non-White Black 58.7% 0.133 46 Black 12.8% 0.226 86 Black 66.7% 0.703 21 Black 75% 0.144 4 Black 41.7% 0.071 12 Black 33.3% 0.645 3 Black White Non-White 50% 60% N/A 0.41 32 15 White Non-White 17.1% 13.6% N/A 0.125 35 22 White Non-White 55.7% 46.7% N/A 2.543 115 259 Black 60% 0.41 15 Black 13.6% 0.125 22 Black 46.7% 2.56 255 Berlin Bloomfield Bridgeport Bristol Brookfield Cheshire Clinton Hispanic 11.1% 0.154 27 Hispanic 46.2% 0.074 13 Hispanic Hispanic 6.8% 0.108 44 Hispanic 56% 25 Hispanic 60% 0.053 5 Hispanic 38.5% 0.004 13 Hispanic 50% 0.135 8 Hispanic Danbury Darien Derby East Hartford Hispanic 53.3% 0.045 15 Hispanic 6.7% 0.957 15 Hispanic 40.8%** 5.294 125 Black or Hispanic 10.9% 0.251 46 Black or Hispanic 47.6% 0.158 21 Black or Hispanic 58.5% 0.127 53 Black or Hispanic 10.4% 0.035 125 Black or Hispanic 60% 0.154 45 Black or Hispanic 66.7% 0.005 9 Black or Hispanic 40% 0.043 25 Black or Hispanic 45.5% 0.506 11 Black or Hispanic 40% 1.006 15 Black or Hispanic 58.6% 0.455 29 Black or Hispanic 11.4% 0.467 35 Black or Hispanic 44.3%** 4.562 377 Table II.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2015-2016 East Haven Enfield Fairfield Farmington Glastonbury Greenwich Groton City Groton Town Hartford Ledyard White 45.5% N/A 44 White 45.5% N/A 121 White 62.9% N/A 70 White 64.9% N/A 57 White 64.3% N/A 42 White 30.6% N/A 36 White 50% N/A 20 White 70.7% N/A 41 White 35.7% N/A 14 White 22.7% N/A 22 White Non-White 50% 0.044 6 Non-White 44.4% 0.009 27 Non-White 56.3% 0.519 48 Non-White 61.9% 0.06 21 Non-White 50% 0.801 12 Non-White 27.8% 0.044 18 Non-White 18.2%* 3.028 11 Non-White 60% 0.703 20 Non-White 12.8%** 3.843 47 Non-White 9.1% 0.917 11 Non-White Black 50% 0.044 6 Black 36.4% 0.624 22 Black 56.5% 0.466 46 Black 61.1% 0.086 18 Black 50% 0.801 12 Black 29.4% 0.007 17 Black 18.2%* 3.028 11 Black 63.2% 0.344 19 Black 12.8%** 3.843 47 Black 2.694 10 Black Madison Manchester White Non-White 62.8% 57.1% N/A 0.892 113 161 Black 57.1% 0.909 156 Hispanic 52.9% 0.276 17 Hispanic 29.4% 1.561 17 Hispanic 57.5% 0.307 40 Hispanic 52.6% 0.911 19 Hispanic 53.3% 0.559 15 Hispanic 32% 0.014 25 Hispanic 45.5% 0.059 11 Hispanic 62.5% 0.214 8 Hispanic 18.6% 1.756 43 Hispanic Hispanic 50% 0.07 6 Hispanic 52.6% 1.635 57 Black or Hispanic 52.2% 0.273 23 Black or Hispanic 32.4% 1.967 37 Black or Hispanic 57.6% 0.434 85 Black or Hispanic 56.8% 0.632 37 Black or Hispanic 53.8% 0.731 26 Black or Hispanic 29.3% 0.015 41 Black or Hispanic 33.3% 1.172 21 Black or Hispanic 61.5% 0.61 26 Black or Hispanic 15.6%* 3.297 90 Black or Hispanic 7.7% 1.3 13 Black or Hispanic 50% 0.088 8 Black or Hispanic 55.3% 1.71 208 Table II.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2015-2016 Meriden Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Newington Newtown White 33.3% N/A 21 White 53.7% N/A 82 White 44.6% N/A 74 White 42.9% N/A 42 White 50% N/A 82 White 32.5% N/A 117 White 65.2% N/A 46 White 10.9% N/A 128 White 36.5% N/A 85 White 55.8% N/A 43 White 49.3% N/A 73 White 52.4% N/A 42 Non-White Black Hispanic 36.8% 36.8% 34.6% 0.054 0.054 0.009 19 19 26 Non-White Black Hispanic 52.5% 51.7% 70.8% 0.02 0.055 2.242 61 60 24 Non-White Black Hispanic 52.6% 52.6% 47.1% 0.651 0.651 0.034 38 38 17 Non-White Black Hispanic 8.3%** 8.3%** 50% 4.878 4.878 0.139 12 12 8 Non-White Black Hispanic 45.5% 45.5% 43.5% 0.143 0.143 0.306 22 22 23 Non-White Black Hispanic 38.3% 38.5% 35.3% 0.887 0.915 0.24 120 117 173 Non-White Black Hispanic 52.9% 53.3% 73.3% 0.793 0.68 0.338 17 15 15 Non-White Black Hispanic 6.8% 6.7%* 6%* 2.655 2.838 2.835 620 616 248 Non-White Black Hispanic 42.1% 40.4% 35.6% 0.456 0.21 0.012 57 52 59 Non-White Black Hispanic 37.5% 37.5% 85.7% 0.908 0.908 2.243 8 8 7 Non-White Black Hispanic 20.9%*** 23.1%*** 38.1% 9.182 7.281 1.355 43 39 42 Non-White Black Hispanic 42.9% 42.9% 66.7% 0.218 0.218 0.432 7 7 6 Black or Hispanic 37.2% 0.092 43 Black or Hispanic 56.6% 0.147 83 Black or Hispanic 50% 0.366 54 Black or Hispanic 21.1% 2.697 19 Black or Hispanic 43.2% 0.534 44 Black or Hispanic 37.1% 0.76 286 Black or Hispanic 63.3% 0.028 30 Black or Hispanic 6.5%* 3.324 846 Black or Hispanic 38% 0.045 108 Black or Hispanic 64.3% 0.311 14 Black or Hispanic 30.9%** 5.464 81 Black or Hispanic 53.8% 0.009 13 Table II.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2015-2016 North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Putnam Redding Ridgefield Rocky Hill White 37% N/A 27 White 26.8% N/A 41 White 47.1% N/A 136 White 45.9% N/A 61 White 64.3% N/A 14 White 4.1% N/A 49 White 46.9% N/A 96 White 27.3% N/A 55 White 1.8% N/A 167 White 0.1% N/A 707 White 50% N/A 26 White 33.9% N/A 59 Non-White 20% 0.967 10 Non-White 39.7% 1.868 68 Non-White 39.4% 1.396 104 Non-White 40% 0.121 10 Non-White 47.4% 0.93 19 Non-White Black 20% 0.967 10 Black 39.7% 1.868 68 Black 38.2% 1.849 102 Black 40% 0.121 10 Black 47.4% 0.93 19 Black 0.085 2 Non-White 33.3% 1.124 18 Non-White 0.043 1 Black 33.3% 1.124 18 Black 1.463 4 Non-White 1.463 4 Black 0.073 4 Non-White 1.8%** 5.256 57 Non-White 85.7%* 2.88 7 Non-White 46.2% 0.694 13 0.055 3 Black 2.4%*** 7.466 42 Black 83.3% 2.201 6 Black 46.2% 0.694 13 Hispanic 33.3% 0.016 3 Hispanic 35.3% 0.626 34 Hispanic 44.4% 0.093 45 Hispanic 37.5% 0.202 8 Hispanic 0.085 2 Hispanic Black or Hispanic 23.1% 0.78 13 Black or Hispanic 39.4% 1.994 99 Black or Hispanic 40.6% 1.197 143 Black or Hispanic 38.9% 0.277 18 Black or Hispanic 56% 0.255 25 Black or Hispanic Hispanic 45.2% 0.028 31 Hispanic 10% 1.36 10 Hispanic 0.127 3 Black or Hispanic 40.8% 0.481 49 Black or Hispanic 7.1% 2.539 14 Black or Hispanic 0.091 5 Hispanic 0.115 81 Hispanic 80% 1.524 5 Hispanic 25% 0.458 16 0.146 8 Black or Hispanic 0.8% 1.966 123 Black or Hispanic 81.8%* 3.246 11 Black or Hispanic 34.5% 0.003 29 Table II.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2015-2016 Seymour South Windsor Stamford Stratford Suffield Torrington Trumbull University of Connecticut Vernon Wallingford Waterbury Waterford White 4.3% N/A 23 White 76.6% N/A 47 White 12% N/A 83 White 43.3% N/A 60 White 66.7% N/A 30 White 10.5% N/A 76 White 40.5% N/A 84 White 63.6% N/A 55 White 64.1% N/A 223 White 52.9% N/A 376 White 40.9% N/A 44 White 53.8% N/A 93 Non-White Black 0.315 7 Non-White 77.5% 0.01 40 Non-White 7.1% 0.888 56 Non-White 41.9% 0.028 74 Non-White 66.7% 0.315 7 Black 76.9% 0.001 39 Black 6% 1.294 50 Black 42.9% 0.003 70 Black 66.7% 3 Non-White 17.6% 0.675 17 Non-White 32.5% 0.732 40 Non-White 60% 0.048 10 Non-White 49.3%** 4.958 71 Non-White 49.3% 0.534 134 Non-White 34.8% 0.432 69 Non-White 48.1% 0.265 27 3 Black 21.4% 1.31 14 Black 31.6% 0.882 38 Black 62.5% 0.004 8 Black 49.3%** 4.868 69 Black 50% 0.331 130 Black 35.8% 0.292 67 Black 50% 0.116 26 Hispanic Hispanic 59.1% 2.231 22 Hispanic 13.3% 0.055 83 Hispanic 34% 0.954 47 Hispanic 100%* 2.769 6 Hispanic 25% 1.985 12 Hispanic 35% 0.203 20 Hispanic 100%* 2.727 5 Hispanic 48.8%* 3.452 41 Hispanic 45.9% 2.199 159 Hispanic 22.7%* 3.352 44 Hispanic 51.7% 0.037 29 Black or Hispanic 8.3% 0.232 12 Black or Hispanic 71.7% 0.331 60 Black or Hispanic 10.7% 0.095 131 Black or Hispanic 38.6% 0.367 114 Black or Hispanic 87.5% 1.333 8 Black or Hispanic 20.8% 1.713 24 Black or Hispanic 32.8% 0.874 58 Black or Hispanic 76.9% 0.829 13 Black or Hispanic 49.5%** 6.454 109 Black or Hispanic 47.9% 1.637 288 Black or Hispanic 30.6% 1.493 111 Black or Hispanic 48% 0.433 50 Table II.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2015-2016 White Non-White Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Winsted CSP Headquarters CSP Troop A Black Hispanic Black or Hispanic 41.7% 0.216 12 White Non-White Black Hispanic Black or Hispanic 78.5% 57.3%*** 58.3%*** 60.3%*** 59.3%*** N/A 17.968 15.574 14.912 23.116 321 103 96 121 216 White Non-White Black Hispanic Black or Hispanic 17.6% 18.5% 18.8% 12.8% 16.5% N/A 0.013 0.023 0.392 0.032 51 65 64 39 103 White Non-White Black Hispanic Black or Hispanic #DIV/0! #DIV/0! N/A 136 1 1 White Non-White Black Hispanic Black or Hispanic 38.6% 35.6% 33.3% 50% 37.5% N/A 0.12 0.343 0.649 0.019 88 45 42 14 56 White Non-White Black Hispanic Black or Hispanic 43.8% 31.1% 31.1% 32.5% 32.5% N/A 1.895 1.895 2.056 2.508 73 45 45 77 120 White Non-White Black Hispanic Black or Hispanic 45.8% 36.4% 36.4% 48.5% 45.5% N/A 0.326 0.326 0.055 0.001 48 11 11 33 44 White Non-White Black Hispanic Black or Hispanic 74.2% 68.4% 66.7% 66.7% 64.5% N/A 0.195 0.316 0.282 0.683 31 19 18 15 31 White Non-White Black Hispanic Black or Hispanic 71.4% 58.3% 58.3% 100% 57.5% N/A 0.734 0.734 1.259 0.847 14 36 36 3 40 White Non-White Black Hispanic Black or Hispanic 69.7% #DIV/0! 50% N/A 5.79 5.79 0.886 33 3 3 6 White Non-White Black Hispanic Black or Hispanic 48.3% 16.7%* 16.7%* 35.7% 28% N/A 3.564 3.564 0.604 2.323 29 12 12 14 25 White Non-White Black Hispanic Black or Hispanic 42.7% 20.3%*** 19.7%*** 32% 26%*** N/A 12.225 12.441 2.583 10.078 192 79 76 75 146 Table II.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2015-2016 CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L White 50.5% N/A 103 White 33% N/A 291 White 51.7% N/A 178 White 40% N/A 215 White 51.2% N/A 82 White 35.9% N/A 131 White 39.8% N/A 113 White 40% N/A 45 White 44.7% N/A 141 White 44.6% N/A 184 Non-White Black Hispanic 38.9% 43.8% 33.3% 0.825 0.251 1.266 18 16 12 Non-White Black Hispanic 56.8%*** 57.8%*** 31.7% 16.199 16.831 0.04 88 83 60 Non-White Black Hispanic 41.9% 46.4% 38.7% 1.004 0.268 1.778 31 28 31 Non-White Black Hispanic 41.8% 42.6% 34.2% 0.068 0.136 0.454 67 61 38 Non-White Black Hispanic 32.4%* 33.3%* 40% 3.448 3.03 0.965 34 33 25 Non-White Black Hispanic 25.4%** 23.7%** 23.8%** 4.25 5.667 4.406 205 194 122 Non-White Black Hispanic 37.9% 38% 30.9% 0.102 0.091 1.798 140 137 94 Non-White Black Hispanic 29.4% 29.4% 44.4% 1.188 1.188 0.137 51 51 27 Non-White Black Hispanic 27.3%** 22.4%*** 27.6%* 5.005 7.558 2.89 55 49 29 Non-White Black Hispanic 53.6% 50% 20.8%** 0.794 0.272 4.914 28 26 24 Black or Hispanic 40% 0.886 25 Black or Hispanic 47.2%*** 8.19 142 Black or Hispanic 45.3% 0.67 53 Black or Hispanic 39.8% 0.001 93 Black or Hispanic 36.4%* 2.933 55 Black or Hispanic 24%** 6.466 308 Black or Hispanic 35.4% 0.623 223 Black or Hispanic 35.6% 0.229 73 Black or Hispanic 25%*** 8.136 76 Black or Hispanic 36.7% 0.969 49 Table II.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2015-2016 Ansonia White Non-White 9.3% 5% N/A 0.346 43 20 White Non-White Black 5.6% 0.237 18 Black White Non-White 3% N/A 0.248 8 33 White Non-White Black 3.2% 0.265 31 Black White 45% N/A 80 White 22% N/A 59 White 28% N/A 25 White Non-White 41.8% 0.21 134 Non-White 16.7% 0.172 12 Non-White 22.2% 0.184 18 Non-White Black 41.5% 0.242 130 Black 10% 0.766 10 Black 23.5% 0.105 17 Black White Non-White 13.6% N/A 1.65 22 11 White Non-White Black Bristol Bridgeport Darien East Hartford Enfield Fairfield Hispanic 8.3% 0.018 24 Hispanic 41.7% 0.16 12 Hispanic Hispanic 44.4% 0.006 9 Hispanic 34.2% 1.841 73 Hispanic 11.1% 0.57 9 Hispanic 14.3% 0.951 14 Hispanic Glastonbury Greenwich 1.505 10 Black Hispanic 20% 0.266 15 Hispanic Ledyard Manchester Meriden White Non-White 35.9% 35.7% N/A 39 42 White Non-White Black 35.7% 42 Black Hispanic 42.9% 0.212 14 Hispanic Black or Hispanic 7.1% 0.131 42 Black or Hispanic 35.3% 0.001 17 Black or Hispanic 4.3% 0.353 47 Black or Hispanic 37.5% 0.108 16 Black or Hispanic 38.6% 0.97 202 Black or Hispanic 10.5% 1.225 19 Black or Hispanic 19.4% 0.58 31 Black or Hispanic 27.3% 0.375 11 Black or Hispanic 12% 0.028 25 Black or Hispanic 9.1% 1.292 11 Black or Hispanic 35.2% 0.005 54 Black or Hispanic 31.8% 1.43 22 Table II.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2015-2016 Middletown Milford Naugatuck New Britain New Haven White 32.5% N/A 40 White 27.8% N/A 36 White 25.9% N/A 27 White 22.5% N/A 80 White 7.8% N/A 103 White Non-White 35% 0.038 20 Non-White 47.1% 1.914 17 Non-White Black 35% 0.038 20 Black 47.1% 1.914 17 Black 1.974 6 Non-White 26.8% 0.329 56 Non-White 4.8% 1.445 418 Non-White 1.974 6 Black 27.3% 0.402 55 Black 4.8% 1.402 415 Black Hispanic 45.5% 0.634 11 Hispanic 25% 0.035 12 Hispanic 25% 0.003 8 Hispanic 15.9% 1.156 82 Hispanic 5.1% 0.778 175 Hispanic White Non-White 12% 23.1% N/A 0.789 25 13 White Non-White Black 27.3% 1.283 11 Black Hispanic 30.8% 2.005 13 Hispanic New London Newington North Haven Norwalk Norwich Plainfield Plainville White 8.3% N/A 24 White 37.2% N/A 94 White 4.7% N/A 43 White 26.5% N/A 49 Black or Hispanic 36.7% 0.132 30 Black or Hispanic 37.9% 0.757 29 Black or Hispanic 14.3% 0.729 14 Black or Hispanic 20.7% 0.092 135 Black or Hispanic 4.9% 1.47 576 Black or Hispanic 18.2% 0.734 22 Black or Hispanic 29.2% 2.222 24 Black or Hispanic 20% Non-White 30%** 4.12 40 Non-White 25% 2.5 60 Non-White Black 30%** 4.12 40 Black 23.7%* 3.037 59 Black Hispanic 25% 2.4 24 Hispanic 34.3% 0.096 35 Hispanic 10 Black or Hispanic 29.5%** 4.292 61 Black or Hispanic 27.8% 1.871 90 Black or Hispanic 0.097 2 Non-White 12.5% 0.731 8 0.049 1 Black 12.5% 0.731 8 0.097 2 Hispanic 14.3% 0.49 7 0.146 3 Black or Hispanic 13.3% 1.115 15 Table II.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2015-2016 Plymouth Rocky Hill Stamford Stratford Trumbull University of Connecticut Vernon Wallingford Waterbury Waterford West Hartford West Haven White 14.7% N/A 34 White 20.7% N/A 29 White 16% N/A 25 White 25% N/A 36 White 23.1% N/A 39 White 59.1% N/A 44 White 49% N/A 145 White 47.5% N/A 99 White 20% N/A 20 White 44.1% N/A 34 White 58.3% N/A 120 White 9.4% N/A 32 Non-White Black Hispanic 0.51 3 Non-White 0.51 3 Black 1.008 6 Hispanic 28.6% 0.203 7 Hispanic 23.8% 0.442 21 Hispanic 21.4% 0.112 28 Hispanic 25% 0.019 12 Hispanic 100%* 3.233 5 Hispanic 41.7% 0.616 36 Hispanic 35.5% 1.374 31 Hispanic Black or Hispanic 1.498 9 Black or Hispanic 22.2% 0.513 0.513 0.01 2 2 9 Non-White Black Black or Hispanic 8.7% 4.5% 14% 0.584 1.615 0.053 23 22 43 Non-White Black Black or Hispanic 24.2% 26.7% 24.1% 0.005 0.024 0.009 33 30 58 Non-White Black Black or Hispanic 9.1% 9.1% 14.7% 1.861 1.861 0.821 22 22 34 Non-White Black Black or Hispanic 62.5% 57.1% 75% 0.033 0.009 1.018 8 7 12 Non-White Black Black or Hispanic 37.3% 38% 40% 2.084 1.799 1.736 51 50 85 Non-White Black Black or Hispanic 30.8% 28%* 32.1%* 2.332 3.079 3.453 26 25 56 Non-White Black Black or Hispanic 11.4% 12.1% 7% 0.752 0.603 5.28 2.68 35 33 24 57 Non-White Black Hispanic Black or Hispanic 44.4% 44.4% 53.3% 47.6% 0.355 0.064 9 9 15 21 Non-White Black Hispanic Black or Hispanic 32%** 34.8%** 33.3%*** 33.8%*** 5.772 4.318 7.788 10.113 25 23 42 65 Non-White Black Hispanic Black or Hispanic 12.5% 12.8% 3.6% 9.3% 0.188 0.217 0.808 48 47 28 75 Table II.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2015-2016 Westport Wethersfield Willimantic CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F CSP Troop G CSP Troop H CSP Troop I White 10% N/A 40 White 29.7% N/A 37 White 41.5% N/A 41 White 26.2% N/A 65 White 30% N/A 60 White 26.3% N/A 194 White 41.3% N/A 109 White 34.4% N/A 131 White 45.5% N/A 44 White 25% N/A 52 White 32.7% N/A 52 White 34.8% N/A 23 Non-White Black 28.6%* 22.2% 3.465 1.56 21 18 Non-White Black 23.5% 23.5% 0.223 0.223 17 17 Non-White Black 40% 40% 0.007 0.007 10 10 Non-White Black 5.9%* 6.3%* 3.232 2.943 17 16 Non-White Black 22.2% 28.6% 0.23 0.006 9 7 Non-White Black 49.3%*** 50%*** 12.034 12.133 67 62 Non-White Black 36.8% 38.9% 0.132 0.037 19 18 Non-White Black 36.1% 36.4% 0.039 0.047 36 33 Non-White Black 22.7%* 23.8%* 3.22 2.814 22 21 Non-White Black 13.6%* 12.1%** 3.126 4.093 103 99 Non-White Black 24.2% 25% 1.012 0.808 62 60 Non-White Black 10%* 10%* 3.681 3.681 20 20 Hispanic 16.7% 0.239 6 Hispanic 8.8%** 4.892 34 Hispanic 42.3% 0.005 26 Hispanic 24.1% 0.043 29 Hispanic 28.6% 0.006 7 Hispanic 20% 0.838 50 Hispanic 38.9% 0.037 18 Hispanic 31.8% 0.054 22 Hispanic 17.6%** 4.037 17 Hispanic 12.7%* 2.888 63 Hispanic 25.7% 0.486 35 Hispanic 35.7% 0.003 14 Black or Hispanic 20.8% 1.457 24 Black or Hispanic 13.7%* 3.376 51 Black or Hispanic 41.7% 36 Black or Hispanic 18.2% 0.943 44 Black or Hispanic 33.3% 0.052 12 Black or Hispanic 36.9%* 3.8 111 Black or Hispanic 40.6% 0.004 32 Black or Hispanic 35.3% 0.014 51 Black or Hispanic 20%** 5.615 35 Black or Hispanic 12.7%** 4.499 158 Black or Hispanic 26.4% 0.646 91 Black or Hispanic 21.9% 1.124 32 Table II.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2015-2016 CSP Troop K CSP Troop L White Non-White 34.4% 15.4%* N/A 3.246 64 26 White Non-White 29.2% 37.5% N/A 0.45 96 16 Black 15.4%* 3.246 26 Black 33.3% 0.108 15 Hispanic 23.5% 0.725 17 Hispanic 25% 0.09 12 Black or Hispanic 19%* 2.936 42 Black or Hispanic 29.6% 0.002 27 PART APPENDIX: TRAFFIC STOP ANALYSIS AND FINDINGS, 2013-16 Table III.A.5: Basis for Stop (Sorted bu % Speeding) 2013-2016 Department Name Portland Suffield New Milford Ridgefield Newtown Simsbury Easton Southington Guilford Redding Wolcott CSP Headquarters Weston Old Saybrook Enfield Seymour Groton Long Point Madison Thomaston CSP Troop E Windsor Locks Canton Bethel Granby Putnam CSP Troop G Groton City Waterford East Hampton Cheshire CSP Troop B CSP Troop H Plainfield Central CT State University Woodbridge CSP Troop I Coventry Monroe Norwich CSP Troop C CSP Troop K New Canaan Windsor CSP Troop A Stonington Brookfield Total 537 3,164 10,735 23,058 24,587 10,450 1,720 14,321 9,935 6,502 1,554 42,418 1,262 9,327 20,857 10,851 311 10,547 2,190 62,377 7,647 4,561 9,812 3,324 4,451 74,391 6,204 12,779 1,729 15,697 22,465 56,262 4,674 6,912 5,652 40,475 4,952 14,744 19,061 76,490 58,366 16,029 16,778 62,347 7,512 7,548 Speed Defective Display of Equipment Moving Related Cell Phone Registration Lights Plates Violation Violation 62.4% 61.7% 57.5% 50.9% 50.7% 49.8% 49.6% 48.2% 48.0% 47.0% 45.0% 44.9% 42.4% 41.7% 41.5% 38.2% 37.9% 37.6% 37.4% 37.4% 37.3% 37.1% 37.0% 36.9% 36.3% 35.7% 34.3% 33.7% 33.6% 33.6% 33.6% 33.6% 33.2% 33.0% 32.2% 32.2% 31.8% 31.7% 31.7% 31.3% 30.9% 30.9% 30.5% 29.0% 28.9% 28.7% 8.4% 3.8% 8.7% 15.9% 8.9% 8.9% 6.0% 12.1% 10.5% 6.0% 19.9% 9.1% 10.5% 9.3% 3.1% 4.1% 15.1% 6.6% 2.0% 3.8% 7.7% 8.3% 13.7% 13.4% 10.3% 8.0% 8.0% 6.2% 7.9% 17.7% 2.9% 6.0% 3.3% 8.7% 18.1% 4.9% 12.1% 14.4% 10.4% 5.3% 7.7% 13.3% 5.5% 7.9% 10.6% 25.3% 2.2% 1.0% 4.6% 7.3% 4.3% 2.2% 4.4% 6.7% 1.8% 13.7% 1.2% 4.0% 4.1% 7.8% 4.6% 5.6% 1.9% 10.7% 1.6% 10.1% 2.9% 2.6% 5.2% 3.6% 0.7% 15.5% 2.3% 3.1% 11.3% 7.2% 15.2% 7.2% 1.0% 6.5% 13.0% 8.8% 4.7% 8.4% 2.1% 9.6% 8.8% 9.1% 3.3% 16.0% 9.0% 3.4% 1.7% 10.3% 7.5% 6.8% 10.6% 8.8% 3.4% 7.9% 13.4% 7.9% 7.0% 1.3% 4.2% 14.3% 16.0% 12.8% 2.9% 7.9% 20.0% 3.3% 13.9% 11.2% 6.2% 13.0% 22.1% 2.1% 17.8% 17.1% 8.6% 9.8% 6.8% 2.0% 16.1% 13.9% 6.6% 3.1% 9.4% 11.4% 17.0% 4.6% 3.2% 15.3% 24.3% 3.1% 11.5% 12.3% 1.1% 0.2% 1.0% 0.2% 2.9% 2.1% 0.9% 1.1% 0.7% 0.5% 1.7% 2.3% 0.4% 0.4% 2.3% 1.5% 0.3% 1.3% 3.3% 0.9% 1.2% 0.5% 1.7% 2.4% 3.3% 1.3% 1.0% 5.2% 2.1% 3.5% 2.6% 1.5% 1.8% 7.0% 6.1% 1.0% 0.9% 2.6% 2.4% 1.6% 2.6% 4.1% 2.0% 2.1% 1.1% 1.0% 0.2% 0.0% 0.6% 0.0% 0.1% 0.1% 0.2% 0.2% 0.1% 0.0% 0.3% 0.1% 0.1% 0.3% 0.6% 0.3% 0.3% 0.4% 0.5% 0.1% 0.6% 0.3% 0.2% 0.5% 0.4% 0.1% 0.2% 0.7% 0.6% 0.1% 0.3% 0.1% 0.4% 0.1% 0.7% 0.1% 1.1% 0.2% 0.2% 0.2% 0.2% 0.1% 0.2% 0.1% 0.3% 0.3% 6.0% 11.9% 4.8% 2.1% 5.6% 7.2% 6.0% 3.5% 3.8% 4.9% 5.0% 7.2% 4.4% 5.6% 6.7% 4.7% 1.6% 8.4% 9.8% 9.8% 3.3% 12.9% 3.6% 12.6% 6.4% 15.0% 4.0% 11.6% 13.5% 8.2% 6.0% 14.4% 18.2% 3.9% 3.2% 13.1% 10.6% 10.2% 9.7% 5.5% 6.6% 5.1% 5.3% 11.6% 9.8% 8.6% Other 2.4% 0.9% 3.4% 3.6% 1.9% 2.5% 5.2% 1.1% 1.4% 6.2% 5.7% 2.5% 14.0% 2.4% 3.6% 1.9% 4.8% 5.4% 6.8% 3.6% 6.0% 6.3% 1.9% 2.0% 2.5% 3.6% 2.7% 4.0% 5.6% 1.2% 5.4% 6.0% 4.8% 4.0% 3.4% 3.3% 4.4% 2.2% 3.1% 4.5% 4.5% 2.1% 1.8% 5.6% 7.5% 1.9% Seatbelt Stop Sign 1.9% 0.3% 0.9% 1.9% 1.2% 1.3% 1.7% 5.6% 1.8% 3.1% 0.5% 14.2% 0.2% 0.8% 5.8% 2.4% 5.5% 2.0% 0.5% 2.0% 8.1% 1.8% 4.5% 3.9% 4.8% 3.3% 6.2% 1.1% 1.4% 4.0% 3.2% 2.7% 1.6% 4.7% 2.8% 3.8% 8.0% 2.8% 3.2% 3.7% 3.1% 2.2% 5.0% 5.3% 2.8% 1.9% 3.7% 4.6% 3.1% 4.6% 7.7% 7.2% 14.9% 5.5% 8.6% 8.5% 4.5% 0.7% 13.5% 8.9% 4.4% 19.0% 28.0% 8.2% 9.0% 1.9% 8.6% 9.2% 16.0% 3.1% 2.1% 0.4% 15.4% 1.0% 4.2% 6.5% 3.8% 0.7% 14.2% 2.9% 2.8% 2.4% 3.9% 10.0% 5.2% 2.9% 4.6% 5.3% 6.6% 1.4% 5.0% 8.1% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 0.6% 0.4% 0.6% 0.4% 0.6% 0.4% 0.9% 0.8% 0.3% 1.2% 1.7% 0.9% 0.7% 1.9% 1.2% 1.3% 0.6% 1.1% 1.3% 1.9% 1.2% 0.7% 0.4% 0.7% 0.2% 2.0% 1.8% 2.9% 2.7% 1.6% 2.3% 1.8% 1.5% 1.5% 2.7% 1.6% 2.0% 1.3% 1.3% 1.4% 1.4% 0.7% 0.8% 2.4% 1.7% 0.7% 0.0% 0.6% 0.2% 0.9% 0.7% 1.5% 4.0% 0.8% 0.5% 0.1% 0.1% 9.7% 1.1% 1.2% 0.6% 0.9% 0.0% 6.8% 0.2% 21.9% 0.6% 0.3% 0.4% 0.8% 0.1% 9.3% 0.1% 0.6% 0.1% 0.1% 14.7% 20.6% 0.0% 4.2% 2.5% 22.8% 7.6% 0.6% 0.7% 27.3% 23.3% 0.4% 0.3% 10.8% 3.3% 0.0% 9.1% 4.3% 6.5% 4.9% 4.5% 7.5% 1.9% 5.7% 9.1% 0.3% 3.3% 1.1% 3.9% 4.4% 8.2% 6.8% 0.0% 2.6% 6.6% 2.1% 7.6% 8.1% 7.7% 6.0% 10.4% 1.5% 5.0% 11.0% 7.6% 4.5% 2.0% 1.6% 3.2% 9.1% 4.5% 1.6% 2.7% 2.9% 12.2% 1.1% 1.3% 9.2% 13.0% 1.6% 7.8% 7.5% 0.2% 0.1% 0.2% 0.2% 0.2% 0.1% 0.8% 0.2% 0.1% 0.4% 0.2% 0.7% 0.1% 0.2% 0.3% 0.3% 1.0% 0.3% 0.4% 0.7% 0.2% 0.2% 0.3% 0.1% 0.1% 1.6% 0.9% 0.8% 0.4% 0.3% 0.6% 1.0% 0.3% 0.3% 0.7% 1.0% 0.4% 0.2% 0.5% 0.5% 0.8% 0.5% 0.2% 1.7% 0.5% 0.1% 0.2% 0.1% 0.4% 0.4% 0.2% 0.3% 0.2% 0.6% 0.1% 0.2% 3.8% 1.5% 0.3% 0.9% 1.1% 0.2% 0.0% 0.6% 0.7% 0.4% 0.7% 0.4% 1.2% 0.9% 0.2% 0.6% 0.2% 1.2% 0.4% 1.7% 0.8% 1.1% 0.3% 0.3% 0.5% 0.4% 0.4% 1.1% 0.5% 0.5% 0.9% 1.6% 1.3% 1.3% 0.3% 0.2% Table III.A.5: Basis for Stop (Sorted bu % Speeding) 2013-2016 Department Name Avon Greenwich Derby CSP Troop F CSP Troop L Watertown Wilton Department of Motor Vehicle Westport Groton Town East Windsor Bloomfield Clinton Ansonia Darien Rocky Hill Bristol Southern CT State University Meriden CSP Troop D East Hartford Naugatuck Fairfield Glastonbury North Haven North Branford Plymouth Orange Berlin Middlebury Danbury University of Connecticut Hartford Torrington Shelton Farmington Cromwell Wethersfield Plainville Vernon Milford South Windsor New Haven Winsted West Haven Manchester Total 3,032 21,143 9,545 72,523 36,248 4,756 14,686 6,552 18,526 16,582 2,999 14,019 7,686 14,567 9,355 11,192 15,977 2,627 7,964 48,663 23,652 15,788 21,144 14,705 7,750 3,431 6,618 12,025 17,684 502 17,401 7,476 18,646 20,578 1,937 14,942 5,843 13,159 11,742 11,503 10,313 10,285 43,076 1,996 15,848 20,973 Speed Defective Display of Equipment Moving Related Cell Phone Registration Lights Plates Violation Violation 28.6% 28.6% 28.5% 28.3% 28.2% 26.7% 26.6% 26.0% 25.8% 25.7% 25.2% 25.2% 24.5% 24.4% 24.2% 24.2% 24.1% 23.4% 23.3% 23.0% 22.5% 22.4% 22.4% 22.0% 21.8% 21.6% 20.6% 20.5% 20.0% 19.7% 19.5% 19.2% 19.1% 19.0% 18.4% 18.3% 17.9% 17.6% 17.6% 16.4% 15.9% 15.5% 15.2% 15.1% 14.7% 14.7% 1.8% 11.1% 14.4% 4.9% 4.2% 9.9% 9.8% 15.4% 21.6% 5.4% 12.4% 6.4% 6.1% 14.6% 10.0% 11.7% 11.8% 6.7% 11.0% 3.8% 13.4% 9.6% 14.5% 15.2% 14.1% 4.8% 12.7% 18.5% 19.2% 26.9% 37.6% 5.5% 20.5% 5.6% 6.7% 17.4% 12.2% 5.0% 8.9% 5.9% 12.1% 8.6% 6.8% 3.5% 5.9% 9.9% 6.3% 16.3% 10.1% 10.7% 19.8% 17.5% 12.4% 9.7% 4.9% 13.1% 7.0% 2.6% 3.1% 4.1% 7.1% 9.1% 11.7% 2.5% 5.8% 17.4% 13.7% 5.1% 9.2% 13.0% 11.8% 25.9% 3.9% 7.0% 6.6% 1.6% 12.8% 3.1% 4.1% 2.4% 6.4% 16.4% 13.2% 9.6% 9.3% 5.3% 5.4% 9.5% 5.7% 7.3% 14.1% 9.4% 18.1% 7.3% 4.3% 2.6% 6.8% 5.3% 16.9% 1.4% 10.0% 14.6% 11.1% 12.9% 20.2% 12.4% 11.7% 13.2% 8.9% 11.4% 5.9% 4.1% 2.9% 13.0% 7.1% 14.2% 9.3% 7.1% 14.5% 13.9% 9.1% 2.6% 4.6% 26.4% 3.0% 27.8% 9.0% 11.4% 17.3% 14.4% 18.9% 17.6% 9.7% 18.7% 7.7% 14.8% 17.9% 14.0% 0.8% 2.9% 2.1% 0.7% 3.8% 6.3% 2.1% 1.3% 3.4% 2.3% 3.2% 5.4% 4.3% 2.9% 8.0% 2.4% 3.0% 0.6% 1.7% 2.0% 2.8% 4.2% 2.6% 1.7% 2.0% 2.1% 10.3% 5.6% 2.9% 1.2% 1.0% 2.4% 4.7% 4.5% 6.7% 1.2% 1.4% 12.8% 6.0% 3.3% 6.4% 11.3% 5.0% 5.9% 5.7% 3.6% 0.1% 0.2% 0.1% 0.2% 0.9% 0.1% 0.3% 1.1% 0.2% 0.2% 0.3% 0.1% 0.5% 0.4% 0.1% 0.2% 0.2% 0.0% 0.5% 0.3% 0.1% 0.5% 0.3% 0.3% 0.2% 0.6% 0.3% 0.2% 0.1% 0.6% 0.3% 0.7% 0.4% 0.8% 0.3% 0.3% 0.2% 0.2% 0.3% 0.5% 0.3% 0.5% 0.2% 0.6% 1.3% 0.3% 11.3% 7.0% 5.2% 7.3% 6.5% 4.4% 10.8% 15.3% 6.3% 13.7% 9.1% 6.6% 12.7% 6.0% 5.6% 8.4% 5.7% 4.9% 6.5% 6.4% 3.2% 8.2% 6.6% 7.3% 5.4% 14.5% 7.1% 4.0% 6.5% 6.4% 3.8% 12.9% 5.6% 3.9% 13.1% 13.1% 8.7% 11.4% 9.3% 18.2% 8.6% 5.3% 3.4% 9.0% 5.5% 6.4% Other 15.4% 3.6% 5.5% 3.3% 6.1% 1.5% 2.4% 12.0% 2.0% 5.8% 3.9% 1.5% 3.4% 4.4% 2.3% 4.1% 2.6% 4.2% 6.8% 8.7% 1.7% 5.6% 6.1% 3.4% 3.4% 5.4% 6.1% 1.7% 2.8% 21.5% 3.0% 7.0% 4.5% 5.0% 17.1% 1.8% 2.5% 4.7% 6.0% 4.7% 16.0% 1.1% 10.1% 10.4% 5.1% 1.6% Seatbelt Stop Sign 0.5% 1.3% 0.9% 2.8% 3.1% 7.7% 0.6% 2.6% 1.6% 3.9% 6.7% 2.5% 8.6% 2.4% 8.6% 3.6% 8.1% 5.5% 4.4% 4.2% 11.7% 5.9% 10.5% 3.1% 7.6% 1.1% 3.0% 1.4% 5.8% 3.2% 0.6% 1.4% 3.1% 0.9% 1.7% 2.2% 2.1% 1.7% 2.0% 2.0% 3.8% 9.0% 3.9% 4.9% 1.7% 10.2% 8.3% 7.8% 10.5% 1.6% 2.7% 10.4% 6.0% 1.5% 9.7% 5.3% 7.8% 12.7% 6.9% 16.9% 4.9% 11.1% 9.2% 0.6% 14.6% 2.8% 7.2% 13.4% 4.4% 8.2% 4.9% 5.4% 10.4% 3.0% 4.7% 8.4% 3.7% 16.0% 10.7% 15.1% 5.1% 5.5% 6.2% 3.7% 7.3% 9.6% 7.7% 9.3% 8.6% 5.1% 15.3% 9.9% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 0.7% 1.4% 6.2% 1.0% 4.1% 1.9% 1.0% 0.9% 0.8% 0.5% 4.2% 1.3% 0.9% 1.0% 0.9% 1.9% 3.1% 1.8% 5.0% 4.1% 8.8% 0.4% 3.8% 4.3% 3.8% 5.4% 1.0% 2.2% 1.7% 0.6% 1.0% 0.5% 6.6% 1.0% 1.9% 2.0% 3.2% 7.0% 2.8% 2.1% 2.5% 1.9% 2.9% 4.6% 0.9% 3.2% 0.5% 3.5% 1.0% 34.1% 10.8% 1.3% 0.2% 5.2% 2.3% 0.2% 0.2% 2.2% 1.0% 0.0% 7.5% 0.5% 0.6% 0.4% 1.3% 20.1% 1.4% 0.8% 1.3% 1.0% 3.0% 0.4% 0.2% 3.4% 4.1% 0.0% 1.2% 0.8% 3.8% 0.3% 1.7% 1.2% 0.1% 1.4% 0.0% 1.2% 0.5% 0.7% 0.7% 1.6% 0.2% 0.6% 7.3% 6.6% 9.2% 1.2% 0.7% 6.5% 8.6% 4.2% 9.2% 8.1% 7.2% 19.3% 5.9% 9.4% 7.3% 8.8% 9.9% 36.7% 10.7% 1.3% 5.7% 9.9% 9.8% 5.5% 10.7% 4.8% 5.3% 16.9% 15.7% 6.4% 9.2% 3.2% 10.3% 12.8% 11.0% 8.8% 14.9% 6.1% 8.2% 12.1% 10.2% 8.2% 26.4% 16.4% 9.0% 13.5% 0.3% 1.3% 0.4% 0.6% 0.9% 0.3% 0.6% 1.1% 0.2% 0.1% 1.2% 0.1% 0.7% 0.3% 0.4% 0.3% 0.7% 1.1% 1.3% 1.0% 0.6% 0.3% 0.8% 0.3% 0.9% 0.6% 0.3% 0.6% 0.4% 0.2% 0.9% 0.1% 0.3% 0.4% 0.2% 0.4% 0.2% 0.3% 0.3% 0.1% 0.3% 0.2% 0.6% 0.7% 0.4% 0.6% 0.1% 1.3% 1.6% 0.6% 1.4% 0.4% 1.8% 2.5% 2.0% 1.2% 0.4% 1.3% 1.3% 0.7% 1.4% 0.5% 0.3% 0.1% 1.2% 0.8% 4.3% 0.8% 0.6% 0.5% 0.9% 0.3% 4.3% 1.2% 0.2% 0.8% 0.9% 0.8% 3.4% 0.4% 0.7% 0.1% 0.1% 4.1% 3.2% 0.9% 0.6% 0.6% 2.8% 0.2% 2.3% 2.3% Table III.A.5: Basis for Stop (Sorted bu % Speeding) 2013-2016 Department Name Stamford Willimantic Western CT State University Wallingford New London Newington Middletown Trumbull Branford Norwalk East Haven Waterbury Stratford Hamden West Hartford Bridgeport New Britain Eastern CT State University Yale State Capitol Police Total 25,049 9,646 137 28,202 7,143 16,964 8,576 8,190 16,351 17,413 8,261 7,358 8,057 14,061 25,939 13,438 20,595 499 2,511 728 Speed Defective Display of Equipment Moving Related Cell Phone Registration Lights Plates Violation Violation 14.4% 13.7% 11.7% 11.6% 11.6% 11.1% 11.0% 10.9% 10.6% 10.4% 10.2% 9.0% 9.0% 8.6% 8.6% 8.4% 7.1% 5.0% 1.2% 0.4% 16.3% 10.1% 12.4% 15.2% 12.5% 4.6% 4.2% 15.9% 17.0% 17.9% 11.6% 16.5% 8.5% 20.9% 23.4% 22.4% 10.3% 4.2% 6.3% 1.5% 0.8% 7.8% 0.0% 10.0% 2.5% 13.1% 7.9% 23.7% 25.6% 13.8% 11.0% 9.5% 17.4% 14.3% 15.6% 2.8% 7.5% 0.6% 6.3% 0.7% 12.3% 20.3% 1.5% 14.7% 9.7% 26.3% 19.8% 9.1% 4.4% 8.9% 12.0% 4.2% 11.2% 11.4% 6.5% 4.0% 10.8% 15.4% 7.3% 22.7% 3.7% 1.2% 0.0% 5.3% 1.3% 3.7% 7.7% 6.3% 0.8% 2.9% 4.9% 3.6% 4.7% 1.6% 3.4% 3.2% 4.0% 1.6% 2.3% 1.6% 0.2% 0.4% 0.0% 0.9% 0.6% 1.1% 0.7% 0.3% 0.1% 0.5% 0.6% 0.7% 0.2% 0.3% 0.4% 0.6% 0.5% 0.2% 0.4% 0.0% 4.9% 8.7% 8.0% 7.3% 7.6% 9.4% 8.6% 3.8% 5.1% 6.3% 5.7% 8.6% 9.9% 5.1% 14.7% 6.9% 6.8% 5.2% 6.6% 20.3% Other 5.8% 9.0% 33.6% 3.7% 7.0% 4.7% 4.0% 4.2% 4.1% 6.1% 5.8% 3.6% 5.3% 7.5% 3.0% 3.0% 5.8% 5.6% 16.7% 5.1% Seatbelt Stop Sign 3.1% 4.7% 7.3% 6.9% 7.2% 1.2% 8.2% 4.9% 2.1% 4.1% 1.3% 6.5% 4.1% 1.1% 3.1% 9.8% 3.2% 4.8% 1.3% 0.5% 12.9% 8.0% 9.5% 11.1% 16.4% 8.1% 13.1% 6.1% 5.5% 6.8% 22.6% 6.5% 9.3% 7.0% 3.6% 13.1% 24.5% 55.5% 1.7% 3.0% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 0.1% 3.8% 1.5% 3.0% 1.8% 2.8% 3.3% 3.5% 3.7% 2.3% 3.7% 9.2% 7.6% 4.4% 4.1% 2.0% 4.7% 0.6% 3.4% 0.4% 0.6% 1.9% 0.0% 0.1% 2.4% 0.1% 0.1% 1.1% 0.5% 6.8% 1.1% 4.8% 0.6% 3.4% 1.5% 5.3% 0.1% 0.2% 0.0% 0.5% 20.2% 8.8% 13.9% 8.8% 18.5% 9.7% 8.7% 8.4% 19.0% 9.4% 5.5% 13.9% 9.3% 13.5% 9.8% 14.5% 10.7% 0.2% 45.0% 42.3% 0.2% 0.6% 0.7% 0.1% 0.3% 0.4% 0.7% 0.4% 0.6% 2.0% 1.1% 0.8% 0.8% 0.5% 0.7% 1.7% 0.7% 0.2% 0.7% 0.5% 4.6% 0.9% 0.0% 1.3% 0.9% 3.7% 1.9% 1.3% 1.0% 1.9% 2.7% 2.5% 2.0% 0.5% 1.7% 2.4% 3.4% 0.6% 0.7% 0.3% Table III.A.6: Basis for Stop (Sorted by % Registration) 2013-2016 Department Name North Branford Branford Trumbull CSP Troop L Watertown Stratford CSP Troop D Farmington Greenwich CSP Troop A West Hartford CSP Troop G CSP Troop B Hamden West Haven Norwalk Redding East Hartford Cromwell Newington Groton Town Woodbridge Glastonbury Danbury Wilton North Haven Bristol East Hampton East Haven CSP Troop F Madison CSP Troop E Derby Wallingford Department of Motor Vehicle CSP Troop C Wethersfield Waterbury South Windsor Manchester Plainville Fairfield Rocky Hill New Canaan Stonington CSP Troop K CSP Troop I Total 3,431 16,351 8,190 36,248 4,756 8,057 48,663 14,942 21,143 62,347 25,939 74,391 22,465 14,061 15,848 17,413 6,502 23,652 5,843 16,964 16,582 5,652 14,705 17,401 14,686 7,750 15,977 1,729 8,261 72,523 10,547 62,377 9,545 28,202 6,552 76,490 13,159 7,358 10,285 20,973 11,742 21,144 11,192 16,029 7,512 58,366 40,475 Registration 25.9% 25.6% 23.7% 19.8% 17.5% 17.4% 17.4% 16.4% 16.3% 16.0% 15.6% 15.5% 15.2% 14.3% 14.1% 13.8% 13.7% 13.7% 13.2% 13.1% 13.1% 13.0% 13.0% 12.8% 12.4% 11.8% 11.7% 11.3% 11.0% 10.7% 10.7% 10.1% 10.1% 10.0% 9.7% 9.6% 9.6% 9.5% 9.5% 9.4% 9.3% 9.2% 9.1% 9.1% 9.0% 8.8% 8.8% Speed Related 21.6% 10.6% 10.9% 28.2% 26.7% 9.0% 23.0% 18.3% 28.6% 29.0% 8.6% 35.7% 33.6% 8.6% 14.7% 10.4% 47.0% 22.5% 17.9% 11.1% 25.7% 32.2% 22.0% 19.5% 26.6% 21.8% 24.1% 33.6% 10.2% 28.3% 37.6% 37.4% 28.5% 11.6% 26.0% 31.3% 17.6% 9.0% 15.5% 14.7% 17.6% 22.4% 24.2% 30.9% 28.9% 30.9% 32.2% Cell Phone 4.8% 17.0% 15.9% 4.2% 9.9% 8.5% 3.8% 17.4% 11.1% 7.9% 23.4% 8.0% 2.9% 20.9% 5.9% 17.9% 6.0% 13.4% 12.2% 4.6% 5.4% 18.1% 15.2% 37.6% 9.8% 14.1% 11.8% 7.9% 11.6% 4.9% 6.6% 3.8% 14.4% 15.2% 15.4% 5.3% 5.0% 16.5% 8.6% 9.9% 8.9% 14.5% 11.7% 13.3% 10.6% 7.7% 4.9% Defective Display of Equipment Moving Lights Plates Violation Violation 7.1% 4.4% 9.1% 6.8% 5.3% 11.2% 4.1% 11.4% 7.3% 3.1% 6.5% 2.1% 6.8% 11.4% 17.9% 8.9% 7.9% 2.9% 17.3% 26.3% 14.6% 6.6% 14.2% 4.6% 16.9% 9.3% 8.9% 8.6% 12.0% 2.6% 7.9% 3.3% 4.3% 14.7% 1.4% 4.6% 14.4% 4.2% 18.7% 14.0% 18.9% 7.1% 13.2% 15.3% 11.5% 3.2% 3.1% 2.1% 0.8% 6.3% 3.8% 6.3% 4.7% 2.0% 1.2% 2.9% 2.1% 3.4% 1.3% 2.6% 1.6% 5.7% 2.9% 0.5% 2.8% 1.4% 3.7% 2.3% 6.1% 1.7% 1.0% 2.1% 2.0% 3.0% 2.1% 4.9% 0.7% 1.3% 0.9% 2.1% 5.3% 1.3% 1.6% 12.8% 3.6% 11.3% 3.6% 6.0% 2.6% 2.4% 4.1% 1.1% 2.6% 1.0% 0.6% 0.1% 0.3% 0.9% 0.1% 0.2% 0.3% 0.3% 0.2% 0.1% 0.4% 0.1% 0.3% 0.3% 1.3% 0.5% 0.0% 0.1% 0.2% 1.1% 0.2% 0.7% 0.3% 0.3% 0.3% 0.2% 0.2% 0.6% 0.6% 0.2% 0.4% 0.1% 0.1% 0.9% 1.1% 0.2% 0.2% 0.7% 0.5% 0.3% 0.3% 0.3% 0.2% 0.1% 0.3% 0.2% 0.1% 14.5% 5.1% 3.8% 6.5% 4.4% 9.9% 6.4% 13.1% 7.0% 11.6% 14.7% 15.0% 6.0% 5.1% 5.5% 6.3% 4.9% 3.2% 8.7% 9.4% 13.7% 3.2% 7.3% 3.8% 10.8% 5.4% 5.7% 13.5% 5.7% 7.3% 8.4% 9.8% 5.2% 7.3% 15.3% 5.5% 11.4% 8.6% 5.3% 6.4% 9.3% 6.6% 8.4% 5.1% 9.8% 6.6% 13.1% Other 5.4% 4.1% 4.2% 6.1% 1.5% 5.3% 8.7% 1.8% 3.6% 5.6% 3.0% 3.6% 5.4% 7.5% 5.1% 6.1% 6.2% 1.7% 2.5% 4.7% 5.8% 3.4% 3.4% 3.0% 2.4% 3.4% 2.6% 5.6% 5.8% 3.3% 5.4% 3.6% 5.5% 3.7% 12.0% 4.5% 4.7% 3.6% 1.1% 1.6% 6.0% 6.1% 4.1% 2.1% 7.5% 4.5% 3.3% Seatbelt Stop Sign 1.1% 2.1% 4.9% 3.1% 7.7% 4.1% 4.2% 2.2% 1.3% 5.3% 3.1% 3.3% 3.2% 1.1% 1.7% 4.1% 3.1% 11.7% 2.1% 1.2% 3.9% 2.8% 3.1% 0.6% 0.6% 7.6% 8.1% 1.4% 1.3% 2.8% 2.0% 2.0% 0.9% 6.9% 2.6% 3.7% 1.7% 6.5% 9.0% 10.2% 2.0% 10.5% 3.6% 2.2% 2.8% 3.1% 3.8% 5.4% 5.5% 6.1% 2.7% 10.4% 9.3% 2.8% 5.5% 7.8% 1.4% 3.6% 0.4% 3.8% 7.0% 15.3% 6.8% 8.5% 7.2% 6.2% 8.1% 5.3% 2.8% 8.2% 3.7% 6.0% 4.9% 9.2% 4.2% 22.6% 1.6% 8.2% 1.9% 10.5% 11.1% 1.5% 2.9% 3.7% 6.5% 9.3% 9.9% 7.3% 4.4% 11.1% 5.3% 5.0% 4.6% 2.4% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 5.4% 3.7% 3.5% 4.1% 1.9% 7.6% 4.1% 2.0% 1.4% 2.4% 4.1% 2.0% 2.3% 4.4% 0.9% 2.3% 1.2% 8.8% 3.2% 2.8% 0.5% 2.7% 4.3% 1.0% 1.0% 3.8% 3.1% 2.7% 3.7% 1.0% 1.1% 1.9% 6.2% 3.0% 0.9% 1.4% 7.0% 9.2% 1.9% 3.2% 2.8% 3.8% 1.9% 0.7% 1.7% 1.4% 1.6% 0.4% 0.5% 1.1% 10.8% 1.3% 0.6% 20.1% 1.2% 3.5% 10.8% 1.5% 9.3% 14.7% 3.4% 0.2% 6.8% 0.1% 1.4% 0.1% 0.1% 0.2% 2.5% 1.0% 1.2% 0.2% 3.0% 0.6% 0.1% 1.1% 34.1% 6.8% 21.9% 1.0% 0.1% 5.2% 27.3% 1.4% 4.8% 0.7% 0.6% 0.0% 1.3% 0.5% 0.4% 3.3% 23.3% 22.8% 4.8% 19.0% 8.4% 0.7% 6.5% 9.3% 1.3% 8.8% 6.6% 1.6% 9.8% 1.5% 2.0% 13.5% 9.0% 9.4% 0.3% 5.7% 14.9% 9.7% 8.1% 4.5% 5.5% 9.2% 8.6% 10.7% 9.9% 7.6% 5.5% 1.2% 2.6% 2.1% 9.2% 8.8% 4.2% 1.1% 6.1% 13.9% 8.2% 13.5% 8.2% 9.8% 8.8% 9.2% 7.8% 1.3% 1.6% 0.6% 0.6% 0.4% 0.9% 0.3% 0.8% 1.0% 0.4% 1.3% 1.7% 0.7% 1.6% 0.6% 0.5% 0.4% 2.0% 0.4% 0.6% 0.2% 0.4% 0.1% 0.7% 0.3% 0.9% 0.6% 0.9% 0.7% 0.4% 1.1% 0.6% 0.3% 0.7% 0.4% 0.1% 1.1% 0.5% 0.3% 0.8% 0.2% 0.6% 0.3% 0.8% 0.3% 0.5% 0.5% 0.8% 1.0% 0.3% 1.0% 1.3% 1.4% 0.4% 2.0% 0.8% 0.1% 1.3% 1.3% 1.7% 0.6% 0.8% 0.5% 2.3% 1.9% 0.2% 4.3% 0.1% 3.7% 1.2% 0.5% 0.5% 0.9% 1.8% 0.9% 0.3% 0.4% 2.7% 0.6% 0.6% 0.4% 1.6% 1.3% 2.5% 0.5% 4.1% 2.5% 0.6% 2.3% 3.2% 0.6% 0.5% 1.6% 0.3% 0.9% 0.4% Table III.A.6: Basis for Stop (Sorted by % Registration) 2013-2016 Department Name Monroe Middletown Old Saybrook Willimantic New Britain Ridgefield Winsted Cheshire CSP Troop H Darien East Windsor Orange Southington Berlin Central CT State University Shelton Avon Yale Meriden New Haven Seymour Milford Vernon Bethel Naugatuck Westport Coventry New Milford Enfield Easton Newtown Weston Ansonia Hartford CSP Headquarters Plymouth Granby Brookfield Windsor Waterford University of Connecticut Clinton Windsor Locks Bridgeport Bloomfield Canton New London Total 14,744 8,576 9,327 9,646 20,595 23,058 1,996 15,697 56,262 9,355 2,999 12,025 14,321 17,684 6,912 1,937 3,032 2,511 7,964 43,076 10,851 10,313 11,503 9,812 15,788 18,526 4,952 10,735 20,857 1,720 24,587 1,262 14,567 18,646 42,418 6,618 3,324 7,548 16,778 12,779 7,476 7,686 7,647 13,438 14,019 4,561 7,143 Registration 8.4% 7.9% 7.8% 7.8% 7.5% 7.3% 7.3% 7.2% 7.2% 7.1% 7.0% 7.0% 6.7% 6.6% 6.5% 6.4% 6.3% 6.3% 5.8% 5.7% 5.6% 5.4% 5.3% 5.2% 5.1% 4.9% 4.7% 4.6% 4.6% 4.4% 4.3% 4.1% 4.1% 4.1% 4.0% 3.9% 3.6% 3.4% 3.3% 3.1% 3.1% 3.1% 2.9% 2.8% 2.6% 2.6% 2.5% Speed Related 31.7% 11.0% 41.7% 13.7% 7.1% 50.9% 15.1% 33.6% 33.6% 24.2% 25.2% 20.5% 48.2% 20.0% 33.0% 18.4% 28.6% 1.2% 23.3% 15.2% 38.2% 15.9% 16.4% 37.0% 22.4% 25.8% 31.8% 57.5% 41.5% 49.6% 50.7% 42.4% 24.4% 19.1% 44.9% 20.6% 36.9% 28.7% 30.5% 33.7% 19.2% 24.5% 37.3% 8.4% 25.2% 37.1% 11.6% Cell Phone 14.4% 4.2% 9.3% 10.1% 10.3% 15.9% 3.5% 17.7% 6.0% 10.0% 12.4% 18.5% 12.1% 19.2% 8.7% 6.7% 1.8% 6.3% 11.0% 6.8% 4.1% 12.1% 5.9% 13.7% 9.6% 21.6% 12.1% 8.7% 3.1% 6.0% 8.9% 10.5% 14.6% 20.5% 9.1% 12.7% 13.4% 25.3% 5.5% 6.2% 5.5% 6.1% 7.7% 22.4% 6.4% 8.3% 12.5% Defective Display of Equipment Moving Lights Plates Violation Violation 11.4% 19.8% 14.3% 20.3% 10.8% 6.8% 14.8% 9.8% 2.0% 11.7% 11.1% 13.9% 7.9% 9.1% 13.9% 9.0% 18.1% 7.3% 5.9% 7.7% 12.8% 9.7% 17.6% 6.2% 13.0% 10.0% 9.4% 7.5% 16.0% 3.4% 10.6% 4.2% 12.4% 3.0% 1.3% 14.5% 13.0% 12.3% 24.3% 17.1% 26.4% 20.2% 13.9% 4.0% 12.9% 11.2% 9.7% 2.6% 7.7% 0.4% 1.2% 4.0% 0.2% 5.9% 3.5% 1.5% 8.0% 3.2% 5.6% 1.1% 2.9% 7.0% 6.7% 0.8% 2.3% 1.7% 5.0% 1.5% 6.4% 3.3% 1.7% 4.2% 3.4% 0.9% 1.0% 2.3% 0.9% 2.9% 0.4% 2.9% 4.7% 2.3% 10.3% 2.4% 1.0% 2.0% 5.2% 2.4% 4.3% 1.2% 3.2% 5.4% 0.5% 1.3% 0.2% 0.7% 0.3% 0.4% 0.5% 0.0% 0.6% 0.1% 0.1% 0.1% 0.3% 0.2% 0.2% 0.1% 0.1% 0.3% 0.1% 0.4% 0.5% 0.2% 0.3% 0.3% 0.5% 0.2% 0.5% 0.2% 1.1% 0.6% 0.6% 0.2% 0.1% 0.1% 0.4% 0.4% 0.1% 0.3% 0.5% 0.3% 0.2% 0.7% 0.7% 0.5% 0.6% 0.6% 0.1% 0.3% 0.6% 10.2% 8.6% 5.6% 8.7% 6.8% 2.1% 9.0% 8.2% 14.4% 5.6% 9.1% 4.0% 3.5% 6.5% 3.9% 13.1% 11.3% 6.6% 6.5% 3.4% 4.7% 8.6% 18.2% 3.6% 8.2% 6.3% 10.6% 4.8% 6.7% 6.0% 5.6% 4.4% 6.0% 5.6% 7.2% 7.1% 12.6% 8.6% 5.3% 11.6% 12.9% 12.7% 3.3% 6.9% 6.6% 12.9% 7.6% Other 2.2% 4.0% 2.4% 9.0% 5.8% 3.6% 10.4% 1.2% 6.0% 2.3% 3.9% 1.7% 1.1% 2.8% 4.0% 17.1% 15.4% 16.7% 6.8% 10.1% 1.9% 16.0% 4.7% 1.9% 5.6% 2.0% 4.4% 3.4% 3.6% 5.2% 1.9% 14.0% 4.4% 4.5% 2.5% 6.1% 2.0% 1.9% 1.8% 4.0% 7.0% 3.4% 6.0% 3.0% 1.5% 6.3% 7.0% Seatbelt Stop Sign 2.8% 8.2% 0.8% 4.7% 3.2% 1.9% 4.9% 4.0% 2.7% 8.6% 6.7% 1.4% 5.6% 5.8% 4.7% 1.7% 0.5% 1.3% 4.4% 3.9% 2.4% 3.8% 2.0% 4.5% 5.9% 1.6% 8.0% 0.9% 5.8% 1.7% 1.2% 0.2% 2.4% 3.1% 14.2% 3.0% 3.9% 1.9% 5.0% 1.1% 1.4% 8.6% 8.1% 9.8% 2.5% 1.8% 7.2% 10.0% 13.1% 8.9% 8.0% 24.5% 4.6% 5.1% 6.5% 0.7% 4.9% 7.8% 3.0% 5.5% 4.7% 2.9% 5.1% 8.3% 1.7% 14.6% 8.6% 19.0% 7.7% 9.6% 16.0% 13.4% 9.7% 3.9% 3.1% 4.4% 14.9% 7.7% 13.5% 16.9% 10.7% 0.7% 10.4% 3.1% 8.1% 6.6% 1.0% 16.0% 6.9% 8.6% 13.1% 12.7% 9.2% 16.4% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 1.3% 3.3% 1.9% 3.8% 4.7% 0.4% 4.6% 1.6% 1.8% 0.9% 4.2% 2.2% 0.8% 1.7% 1.5% 1.9% 0.7% 3.4% 5.0% 2.9% 1.3% 2.5% 2.1% 0.4% 0.4% 0.8% 2.0% 0.6% 1.2% 0.9% 0.6% 0.7% 1.0% 6.6% 0.9% 1.0% 0.7% 0.7% 0.8% 2.9% 0.5% 0.9% 1.2% 2.0% 1.3% 0.7% 1.8% 0.6% 0.1% 1.2% 1.9% 0.1% 0.9% 1.6% 0.1% 20.6% 7.5% 0.2% 3.4% 0.8% 4.1% 4.2% 1.7% 0.5% 0.0% 1.3% 0.7% 0.9% 0.5% 1.2% 0.4% 0.8% 2.3% 7.6% 0.2% 0.6% 4.0% 0.7% 1.1% 0.0% 3.8% 9.7% 0.2% 0.8% 0.0% 0.3% 0.6% 0.8% 1.0% 0.6% 5.3% 2.2% 0.3% 2.4% 2.9% 8.7% 4.4% 8.8% 10.7% 4.9% 16.4% 4.5% 1.6% 7.3% 7.2% 16.9% 5.7% 15.7% 9.1% 11.0% 7.3% 45.0% 10.7% 26.4% 6.8% 10.2% 12.1% 7.7% 9.9% 9.2% 2.7% 6.5% 8.2% 1.9% 4.5% 3.9% 9.4% 10.3% 1.1% 5.3% 6.0% 7.5% 13.0% 11.0% 3.2% 5.9% 7.6% 14.5% 19.3% 8.1% 18.5% 0.2% 0.7% 0.2% 0.6% 0.7% 0.2% 0.7% 0.3% 1.0% 0.4% 1.2% 0.6% 0.2% 0.4% 0.3% 0.2% 0.3% 0.7% 1.3% 0.6% 0.3% 0.3% 0.1% 0.3% 0.3% 0.2% 0.4% 0.2% 0.3% 0.8% 0.2% 0.1% 0.3% 0.3% 0.7% 0.3% 0.1% 0.1% 0.2% 0.8% 0.1% 0.7% 0.2% 1.7% 0.1% 0.2% 0.3% 1.1% 1.9% 0.9% 0.9% 3.4% 0.4% 0.2% 1.7% 1.1% 1.4% 0.4% 1.2% 0.6% 0.2% 0.3% 0.7% 0.1% 0.7% 1.2% 2.8% 0.2% 0.6% 0.9% 1.2% 0.8% 2.0% 0.4% 0.4% 1.1% 0.2% 0.2% 0.3% 0.7% 3.4% 1.5% 4.3% 0.9% 0.2% 1.3% 1.2% 0.8% 1.3% 0.7% 2.4% 1.3% 0.4% 0.9% Table III.A.6: Basis for Stop (Sorted by % Registration) 2013-2016 Department Name Southern CT State University Torrington Groton City Portland Simsbury Norwich Groton Long Point Guilford Middlebury Thomaston Wolcott Plainfield Suffield Stamford State Capitol Police Putnam Eastern CT State University Western CT State University Total 2,627 20,578 6,204 537 10,450 19,061 311 9,935 502 2,190 1,554 4,674 3,164 25,049 728 4,451 499 137 Registration 2.5% 2.4% 2.3% 2.2% 2.2% 2.1% 1.9% 1.8% 1.6% 1.6% 1.2% 1.0% 1.0% 0.8% 0.7% 0.7% 0.6% 0.0% Speed Related 23.4% 19.0% 34.3% 62.4% 49.8% 31.7% 37.9% 48.0% 19.7% 37.4% 45.0% 33.2% 61.7% 14.4% 0.4% 36.3% 5.0% 11.7% Cell Phone 6.7% 5.6% 8.0% 8.4% 8.9% 10.4% 15.1% 10.5% 26.9% 2.0% 19.9% 3.3% 3.8% 16.3% 1.5% 10.3% 4.2% 12.4% Defective Display of Equipment Moving Lights Plates Violation Violation 11.4% 27.8% 17.8% 1.7% 8.8% 17.0% 2.9% 13.4% 2.6% 20.0% 7.0% 16.1% 10.3% 12.3% 22.7% 22.1% 15.4% 1.5% 0.6% 4.5% 1.0% 1.1% 2.1% 2.4% 0.3% 0.7% 1.2% 3.3% 1.7% 1.8% 0.2% 3.7% 1.6% 3.3% 1.6% 0.0% 0.0% 0.8% 0.2% 0.2% 0.1% 0.2% 0.3% 0.1% 0.6% 0.5% 0.3% 0.4% 0.0% 0.2% 0.0% 0.4% 0.2% 0.0% 4.9% 3.9% 4.0% 6.0% 7.2% 9.7% 1.6% 3.8% 6.4% 9.8% 5.0% 18.2% 11.9% 4.9% 20.3% 6.4% 5.2% 8.0% Other 4.2% 5.0% 2.7% 2.4% 2.5% 3.1% 4.8% 1.4% 21.5% 6.8% 5.7% 4.8% 0.9% 5.8% 5.1% 2.5% 5.6% 33.6% Seatbelt Stop Sign 5.5% 0.9% 6.2% 1.9% 1.3% 3.2% 5.5% 1.8% 3.2% 0.5% 0.5% 1.6% 0.3% 3.1% 0.5% 4.8% 4.8% 7.3% 0.6% 15.1% 15.4% 3.7% 7.2% 5.2% 28.0% 8.6% 8.4% 9.0% 4.5% 14.2% 4.6% 12.9% 3.0% 2.1% 55.5% 9.5% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 1.8% 1.0% 1.8% 0.6% 0.4% 1.3% 0.6% 0.3% 0.6% 1.3% 1.7% 1.5% 0.4% 0.1% 0.4% 0.2% 0.6% 1.5% 0.4% 0.3% 0.1% 0.0% 1.5% 0.7% 0.0% 0.5% 0.0% 0.2% 0.1% 0.0% 0.6% 0.6% 0.5% 0.1% 0.2% 0.0% 36.7% 12.8% 5.0% 9.1% 7.5% 12.2% 0.0% 9.1% 6.4% 6.6% 3.3% 3.2% 4.3% 20.2% 42.3% 10.4% 0.2% 13.9% 1.1% 0.4% 0.9% 0.2% 0.1% 0.5% 1.0% 0.1% 0.2% 0.4% 0.2% 0.3% 0.1% 0.2% 0.5% 0.1% 0.2% 0.7% 0.1% 0.4% 0.2% 0.2% 0.3% 0.5% 0.0% 0.1% 0.8% 0.7% 3.8% 0.3% 0.1% 4.6% 0.3% 0.2% 0.6% 0.0% Table III.A.7: Basis for Stop (Sorted by % Cell Phone) 2013-2016 Department Name Danbury Middlebury Brookfield West Hartford Bridgeport Westport Hamden Hartford Wolcott Berlin Orange Woodbridge Norwalk Cheshire Farmington Branford Waterbury Stamford Trumbull Ridgefield Department of Motor Vehicle Wallingford Glastonbury Groton Long Point Ansonia Fairfield Derby Monroe North Haven Bethel Granby East Hartford New Canaan Plymouth New London East Windsor Western CT State University Cromwell Southington Milford Coventry Bristol Rocky Hill East Haven Greenwich Meriden Stonington Total 17,401 502 7,548 25,939 13,438 18,526 14,061 18,646 1,554 17,684 12,025 5,652 17,413 15,697 14,942 16,351 7,358 25,049 8,190 23,058 6,552 28,202 14,705 311 14,567 21,144 9,545 14,744 7,750 9,812 3,324 23,652 16,029 6,618 7,143 2,999 137 5,843 14,321 10,313 4,952 15,977 11,192 8,261 21,143 7,964 7,512 Speed Cell Phone Related 37.6% 26.9% 25.3% 23.4% 22.4% 21.6% 20.9% 20.5% 19.9% 19.2% 18.5% 18.1% 17.9% 17.7% 17.4% 17.0% 16.5% 16.3% 15.9% 15.9% 15.4% 15.2% 15.2% 15.1% 14.6% 14.5% 14.4% 14.4% 14.1% 13.7% 13.4% 13.4% 13.3% 12.7% 12.5% 12.4% 12.4% 12.2% 12.1% 12.1% 12.1% 11.8% 11.7% 11.6% 11.1% 11.0% 10.6% 19.5% 19.7% 28.7% 8.6% 8.4% 25.8% 8.6% 19.1% 45.0% 20.0% 20.5% 32.2% 10.4% 33.6% 18.3% 10.6% 9.0% 14.4% 10.9% 50.9% 26.0% 11.6% 22.0% 37.9% 24.4% 22.4% 28.5% 31.7% 21.8% 37.0% 36.9% 22.5% 30.9% 20.6% 11.6% 25.2% 11.7% 17.9% 48.2% 15.9% 31.8% 24.1% 24.2% 10.2% 28.6% 23.3% 28.9% Registration 12.8% 1.6% 3.4% 15.6% 2.8% 4.9% 14.3% 4.1% 1.2% 6.6% 7.0% 13.0% 13.8% 7.2% 16.4% 25.6% 9.5% 0.8% 23.7% 7.3% 9.7% 10.0% 13.0% 1.9% 4.1% 9.2% 10.1% 8.4% 11.8% 5.2% 3.6% 13.7% 9.1% 3.9% 2.5% 7.0% 0.0% 13.2% 6.7% 5.4% 4.7% 11.7% 9.1% 11.0% 16.3% 5.8% 9.0% Defective Display of Equipment Moving Lights Plates Violation Violation 4.6% 2.6% 12.3% 6.5% 4.0% 10.0% 11.4% 3.0% 7.0% 9.1% 13.9% 6.6% 8.9% 9.8% 11.4% 4.4% 4.2% 12.3% 9.1% 6.8% 1.4% 14.7% 14.2% 2.9% 12.4% 7.1% 4.3% 11.4% 9.3% 6.2% 13.0% 2.9% 15.3% 14.5% 9.7% 11.1% 1.5% 17.3% 7.9% 9.7% 9.4% 8.9% 13.2% 12.0% 7.3% 5.9% 11.5% 1.0% 1.2% 1.0% 3.4% 3.2% 3.4% 1.6% 4.7% 1.7% 2.9% 5.6% 6.1% 2.9% 3.5% 1.2% 0.8% 3.6% 3.7% 6.3% 0.2% 1.3% 5.3% 1.7% 0.3% 2.9% 2.6% 2.1% 2.6% 2.0% 1.7% 2.4% 2.8% 4.1% 10.3% 1.3% 3.2% 0.0% 1.4% 1.1% 6.4% 0.9% 3.0% 2.4% 4.9% 2.9% 1.7% 1.1% 0.3% 0.6% 0.3% 0.4% 0.6% 0.2% 0.3% 0.4% 0.3% 0.1% 0.2% 0.7% 0.5% 0.1% 0.3% 0.1% 0.7% 0.2% 0.3% 0.0% 1.1% 0.9% 0.3% 0.3% 0.4% 0.3% 0.1% 0.2% 0.2% 0.2% 0.5% 0.1% 0.1% 0.3% 0.6% 0.3% 0.0% 0.2% 0.2% 0.3% 1.1% 0.2% 0.2% 0.6% 0.2% 0.5% 0.3% 3.8% 6.4% 8.6% 14.7% 6.9% 6.3% 5.1% 5.6% 5.0% 6.5% 4.0% 3.2% 6.3% 8.2% 13.1% 5.1% 8.6% 4.9% 3.8% 2.1% 15.3% 7.3% 7.3% 1.6% 6.0% 6.6% 5.2% 10.2% 5.4% 3.6% 12.6% 3.2% 5.1% 7.1% 7.6% 9.1% 8.0% 8.7% 3.5% 8.6% 10.6% 5.7% 8.4% 5.7% 7.0% 6.5% 9.8% Other 3.0% 21.5% 1.9% 3.0% 3.0% 2.0% 7.5% 4.5% 5.7% 2.8% 1.7% 3.4% 6.1% 1.2% 1.8% 4.1% 3.6% 5.8% 4.2% 3.6% 12.0% 3.7% 3.4% 4.8% 4.4% 6.1% 5.5% 2.2% 3.4% 1.9% 2.0% 1.7% 2.1% 6.1% 7.0% 3.9% 33.6% 2.5% 1.1% 16.0% 4.4% 2.6% 4.1% 5.8% 3.6% 6.8% 7.5% Seatbelt Stop Sign 0.6% 3.2% 1.9% 3.1% 9.8% 1.6% 1.1% 3.1% 0.5% 5.8% 1.4% 2.8% 4.1% 4.0% 2.2% 2.1% 6.5% 3.1% 4.9% 1.9% 2.6% 6.9% 3.1% 5.5% 2.4% 10.5% 0.9% 2.8% 7.6% 4.5% 3.9% 11.7% 2.2% 3.0% 7.2% 6.7% 7.3% 2.1% 5.6% 3.8% 8.0% 8.1% 3.6% 1.3% 1.3% 4.4% 2.8% 3.7% 8.4% 8.1% 3.6% 13.1% 9.7% 7.0% 10.7% 4.5% 4.7% 3.0% 2.8% 6.8% 6.5% 5.5% 5.5% 6.5% 12.9% 6.1% 4.6% 1.5% 11.1% 8.2% 28.0% 16.9% 4.4% 10.5% 10.0% 4.9% 16.0% 3.1% 7.2% 5.3% 10.4% 16.4% 7.8% 9.5% 6.2% 5.5% 7.7% 3.9% 9.2% 11.1% 22.6% 7.8% 14.6% 5.0% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 1.0% 0.6% 0.7% 4.1% 2.0% 0.8% 4.4% 6.6% 1.7% 1.7% 2.2% 2.7% 2.3% 1.6% 2.0% 3.7% 9.2% 0.1% 3.5% 0.4% 0.9% 3.0% 4.3% 0.6% 1.0% 3.8% 6.2% 1.3% 3.8% 0.4% 0.7% 8.8% 0.7% 1.0% 1.8% 4.2% 1.5% 3.2% 0.8% 2.5% 2.0% 3.1% 1.9% 3.7% 1.4% 5.0% 1.7% 1.2% 0.0% 0.0% 1.5% 5.3% 2.3% 3.4% 3.8% 0.1% 4.1% 3.4% 2.5% 6.8% 0.1% 1.2% 0.5% 4.8% 0.6% 1.1% 0.9% 5.2% 0.1% 1.0% 0.0% 0.0% 1.3% 1.0% 0.6% 3.0% 0.4% 0.8% 1.4% 0.4% 0.2% 2.4% 0.2% 0.0% 0.1% 0.8% 0.5% 7.6% 0.6% 0.5% 1.1% 3.5% 1.3% 3.3% 9.2% 6.4% 7.5% 9.8% 14.5% 9.2% 13.5% 10.3% 3.3% 15.7% 16.9% 4.5% 9.4% 4.5% 8.8% 19.0% 13.9% 20.2% 8.4% 4.9% 4.2% 8.8% 5.5% 0.0% 9.4% 9.8% 9.2% 2.9% 10.7% 7.7% 6.0% 5.7% 9.2% 5.3% 18.5% 7.2% 13.9% 14.9% 5.7% 10.2% 2.7% 9.9% 8.8% 5.5% 6.6% 10.7% 7.8% 0.9% 0.2% 0.1% 0.7% 1.7% 0.2% 0.5% 0.3% 0.2% 0.4% 0.6% 0.7% 2.0% 0.3% 0.4% 0.6% 0.8% 0.2% 0.4% 0.2% 1.1% 0.1% 0.3% 1.0% 0.3% 0.8% 0.4% 0.2% 0.9% 0.3% 0.1% 0.6% 0.5% 0.3% 0.3% 1.2% 0.7% 0.2% 0.2% 0.3% 0.4% 0.7% 0.3% 1.1% 1.3% 1.3% 0.5% 0.9% 0.8% 0.2% 1.7% 2.4% 2.0% 0.5% 3.4% 3.8% 0.2% 1.2% 0.5% 1.9% 1.7% 0.1% 1.0% 2.5% 4.6% 1.3% 0.4% 2.5% 1.3% 0.5% 0.0% 0.7% 0.6% 1.6% 1.1% 0.9% 1.2% 0.9% 4.3% 1.6% 4.3% 0.9% 0.4% 0.0% 0.1% 0.6% 0.6% 0.4% 0.3% 0.5% 2.7% 1.3% 1.2% 0.3% Table III.A.7: Basis for Stop (Sorted by % Cell Phone) 2013-2016 Department Name Guilford Weston Norwich Putnam New Britain Willimantic Darien Watertown Manchester Wilton Naugatuck Old Saybrook CSP Headquarters Newtown Plainville Simsbury Central CT State University New Milford South Windsor Stratford Portland Canton CSP Troop G Groton City CSP Troop A East Hampton CSP Troop K Windsor Locks New Haven Southern CT State University Shelton Madison Bloomfield Yale Waterford Clinton Easton CSP Troop H Redding West Haven Vernon Torrington Windsor University of Connecticut Groton Town CSP Troop C Wethersfield Total 9,935 1,262 19,061 4,451 20,595 9,646 9,355 4,756 20,973 14,686 15,788 9,327 42,418 24,587 11,742 10,450 6,912 10,735 10,285 8,057 537 4,561 74,391 6,204 62,347 1,729 58,366 7,647 43,076 2,627 1,937 10,547 14,019 2,511 12,779 7,686 1,720 56,262 6,502 15,848 11,503 20,578 16,778 7,476 16,582 76,490 13,159 Speed Cell Phone Related 10.5% 10.5% 10.4% 10.3% 10.3% 10.1% 10.0% 9.9% 9.9% 9.8% 9.6% 9.3% 9.1% 8.9% 8.9% 8.9% 8.7% 8.7% 8.6% 8.5% 8.4% 8.3% 8.0% 8.0% 7.9% 7.9% 7.7% 7.7% 6.8% 6.7% 6.7% 6.6% 6.4% 6.3% 6.2% 6.1% 6.0% 6.0% 6.0% 5.9% 5.9% 5.6% 5.5% 5.5% 5.4% 5.3% 5.0% 48.0% 42.4% 31.7% 36.3% 7.1% 13.7% 24.2% 26.7% 14.7% 26.6% 22.4% 41.7% 44.9% 50.7% 17.6% 49.8% 33.0% 57.5% 15.5% 9.0% 62.4% 37.1% 35.7% 34.3% 29.0% 33.6% 30.9% 37.3% 15.2% 23.4% 18.4% 37.6% 25.2% 1.2% 33.7% 24.5% 49.6% 33.6% 47.0% 14.7% 16.4% 19.0% 30.5% 19.2% 25.7% 31.3% 17.6% Registration 1.8% 4.1% 2.1% 0.7% 7.5% 7.8% 7.1% 17.5% 9.4% 12.4% 5.1% 7.8% 4.0% 4.3% 9.3% 2.2% 6.5% 4.6% 9.5% 17.4% 2.2% 2.6% 15.5% 2.3% 16.0% 11.3% 8.8% 2.9% 5.7% 2.5% 6.4% 10.7% 2.6% 6.3% 3.1% 3.1% 4.4% 7.2% 13.7% 14.1% 5.3% 2.4% 3.3% 3.1% 13.1% 9.6% 9.6% Defective Display of Equipment Moving Lights Plates Violation Violation 13.4% 4.2% 17.0% 22.1% 10.8% 20.3% 11.7% 5.3% 14.0% 16.9% 13.0% 14.3% 1.3% 10.6% 18.9% 8.8% 13.9% 7.5% 18.7% 11.2% 1.7% 11.2% 2.1% 17.8% 3.1% 8.6% 3.2% 13.9% 7.7% 11.4% 9.0% 7.9% 12.9% 7.3% 17.1% 20.2% 3.4% 2.0% 7.9% 17.9% 17.6% 27.8% 24.3% 26.4% 14.6% 4.6% 14.4% 0.7% 0.4% 2.4% 3.3% 4.0% 1.2% 8.0% 6.3% 3.6% 2.1% 4.2% 0.4% 2.3% 2.9% 6.0% 2.1% 7.0% 1.0% 11.3% 4.7% 1.1% 0.5% 1.3% 1.0% 2.1% 2.1% 2.6% 1.2% 5.0% 0.6% 6.7% 1.3% 5.4% 2.3% 5.2% 4.3% 0.9% 1.5% 0.5% 5.7% 3.3% 4.5% 2.0% 2.4% 2.3% 1.6% 12.8% 0.1% 0.1% 0.2% 0.4% 0.5% 0.4% 0.1% 0.1% 0.3% 0.3% 0.5% 0.3% 0.1% 0.1% 0.3% 0.1% 0.1% 0.6% 0.5% 0.2% 0.2% 0.3% 0.1% 0.2% 0.1% 0.6% 0.2% 0.6% 0.2% 0.0% 0.3% 0.4% 0.1% 0.4% 0.7% 0.5% 0.2% 0.1% 0.0% 1.3% 0.5% 0.8% 0.2% 0.7% 0.2% 0.2% 0.2% 3.8% 4.4% 9.7% 6.4% 6.8% 8.7% 5.6% 4.4% 6.4% 10.8% 8.2% 5.6% 7.2% 5.6% 9.3% 7.2% 3.9% 4.8% 5.3% 9.9% 6.0% 12.9% 15.0% 4.0% 11.6% 13.5% 6.6% 3.3% 3.4% 4.9% 13.1% 8.4% 6.6% 6.6% 11.6% 12.7% 6.0% 14.4% 4.9% 5.5% 18.2% 3.9% 5.3% 12.9% 13.7% 5.5% 11.4% Other 1.4% 14.0% 3.1% 2.5% 5.8% 9.0% 2.3% 1.5% 1.6% 2.4% 5.6% 2.4% 2.5% 1.9% 6.0% 2.5% 4.0% 3.4% 1.1% 5.3% 2.4% 6.3% 3.6% 2.7% 5.6% 5.6% 4.5% 6.0% 10.1% 4.2% 17.1% 5.4% 1.5% 16.7% 4.0% 3.4% 5.2% 6.0% 6.2% 5.1% 4.7% 5.0% 1.8% 7.0% 5.8% 4.5% 4.7% Seatbelt Stop Sign 1.8% 0.2% 3.2% 4.8% 3.2% 4.7% 8.6% 7.7% 10.2% 0.6% 5.9% 0.8% 14.2% 1.2% 2.0% 1.3% 4.7% 0.9% 9.0% 4.1% 1.9% 1.8% 3.3% 6.2% 5.3% 1.4% 3.1% 8.1% 3.9% 5.5% 1.7% 2.0% 2.5% 1.3% 1.1% 8.6% 1.7% 2.7% 3.1% 1.7% 2.0% 0.9% 5.0% 1.4% 3.9% 3.7% 1.7% 8.6% 13.5% 5.2% 2.1% 24.5% 8.0% 4.9% 10.4% 9.9% 6.0% 13.4% 8.9% 0.7% 7.7% 7.3% 7.2% 2.9% 3.1% 9.3% 9.3% 3.7% 9.2% 0.4% 15.4% 1.4% 4.2% 4.6% 8.6% 8.6% 0.6% 5.1% 8.2% 12.7% 1.7% 1.0% 6.9% 14.9% 0.7% 8.5% 15.3% 9.6% 15.1% 6.6% 16.0% 5.3% 2.9% 3.7% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 0.3% 0.7% 1.3% 0.2% 4.7% 3.8% 0.9% 1.9% 3.2% 1.0% 0.4% 1.9% 0.9% 0.6% 2.8% 0.4% 1.5% 0.6% 1.9% 7.6% 0.6% 0.7% 2.0% 1.8% 2.4% 2.7% 1.4% 1.2% 2.9% 1.8% 1.9% 1.1% 1.3% 3.4% 2.9% 0.9% 0.9% 1.8% 1.2% 0.9% 2.1% 1.0% 0.8% 0.5% 0.5% 1.4% 7.0% 0.5% 1.1% 0.7% 0.1% 0.1% 1.9% 7.5% 1.3% 0.6% 0.2% 0.8% 1.2% 9.7% 0.7% 0.0% 1.5% 4.2% 0.2% 0.7% 0.6% 0.0% 0.3% 9.3% 0.1% 10.8% 0.1% 23.3% 0.6% 0.7% 0.4% 1.7% 6.8% 2.2% 0.0% 0.6% 1.0% 4.0% 20.6% 0.1% 0.2% 1.2% 0.3% 0.3% 0.8% 0.2% 27.3% 1.4% 9.1% 3.9% 12.2% 10.4% 10.7% 8.8% 7.3% 6.5% 13.5% 8.6% 9.9% 4.4% 1.1% 4.5% 8.2% 7.5% 9.1% 6.5% 8.2% 9.3% 9.1% 8.1% 1.5% 5.0% 1.6% 7.6% 1.3% 7.6% 26.4% 36.7% 11.0% 2.6% 19.3% 45.0% 11.0% 5.9% 1.9% 1.6% 0.3% 9.0% 12.1% 12.8% 13.0% 3.2% 8.1% 1.1% 6.1% 0.1% 0.1% 0.5% 0.1% 0.7% 0.6% 0.4% 0.3% 0.6% 0.6% 0.3% 0.2% 0.7% 0.2% 0.3% 0.1% 0.3% 0.2% 0.2% 0.8% 0.2% 0.2% 1.6% 0.9% 1.7% 0.4% 0.8% 0.2% 0.6% 1.1% 0.2% 0.3% 0.1% 0.7% 0.8% 0.7% 0.8% 1.0% 0.4% 0.4% 0.1% 0.4% 0.2% 0.1% 0.1% 0.5% 0.3% 0.1% 0.3% 0.5% 0.2% 3.4% 0.9% 1.4% 0.4% 2.3% 1.8% 0.8% 0.9% 1.5% 0.2% 3.2% 0.3% 0.3% 0.4% 0.6% 2.0% 0.2% 0.4% 0.6% 0.2% 1.3% 0.4% 0.9% 0.7% 2.8% 0.1% 0.7% 0.6% 1.3% 0.7% 1.2% 1.3% 0.2% 1.1% 0.2% 2.3% 0.9% 0.4% 1.3% 0.8% 1.2% 0.5% 4.1% Table III.A.7: Basis for Stop (Sorted by % Cell Phone) 2013-2016 Department Name CSP Troop F CSP Troop I North Branford Newington Middletown CSP Troop L Eastern CT State University Seymour CSP Troop D CSP Troop E Suffield Winsted Plainfield Enfield CSP Troop B Thomaston Avon State Capitol Police Total 72,523 40,475 3,431 16,964 8,576 36,248 499 10,851 48,663 62,377 3,164 1,996 4,674 20,857 22,465 2,190 3,032 728 Speed Cell Phone Related 4.9% 4.9% 4.8% 4.6% 4.2% 4.2% 4.2% 4.1% 3.8% 3.8% 3.8% 3.5% 3.3% 3.1% 2.9% 2.0% 1.8% 1.5% 28.3% 32.2% 21.6% 11.1% 11.0% 28.2% 5.0% 38.2% 23.0% 37.4% 61.7% 15.1% 33.2% 41.5% 33.6% 37.4% 28.6% 0.4% Registration 10.7% 8.8% 25.9% 13.1% 7.9% 19.8% 0.6% 5.6% 17.4% 10.1% 1.0% 7.3% 1.0% 4.6% 15.2% 1.6% 6.3% 0.7% Defective Display of Equipment Moving Lights Plates Violation Violation 2.6% 3.1% 7.1% 26.3% 19.8% 6.8% 15.4% 12.8% 4.1% 3.3% 10.3% 14.8% 16.1% 16.0% 6.8% 20.0% 18.1% 22.7% 0.7% 1.0% 2.1% 3.7% 7.7% 3.8% 1.6% 1.5% 2.0% 0.9% 0.2% 5.9% 1.8% 2.3% 2.6% 3.3% 0.8% 1.6% 0.2% 0.1% 0.6% 1.1% 0.7% 0.9% 0.2% 0.3% 0.3% 0.1% 0.0% 0.6% 0.4% 0.6% 0.3% 0.5% 0.1% 0.0% 7.3% 13.1% 14.5% 9.4% 8.6% 6.5% 5.2% 4.7% 6.4% 9.8% 11.9% 9.0% 18.2% 6.7% 6.0% 9.8% 11.3% 20.3% Other 3.3% 3.3% 5.4% 4.7% 4.0% 6.1% 5.6% 1.9% 8.7% 3.6% 0.9% 10.4% 4.8% 3.6% 5.4% 6.8% 15.4% 5.1% Seatbelt Stop Sign 2.8% 3.8% 1.1% 1.2% 8.2% 3.1% 4.8% 2.4% 4.2% 2.0% 0.3% 4.9% 1.6% 5.8% 3.2% 0.5% 0.5% 0.5% 1.6% 2.4% 5.4% 8.1% 13.1% 2.7% 55.5% 19.0% 2.8% 1.9% 4.6% 5.1% 14.2% 4.4% 3.8% 9.0% 8.3% 3.0% Administrative Traffic Control Unlicensed Window Offense STC Violation Signal Operation Tint 1.0% 1.6% 5.4% 2.8% 3.3% 4.1% 0.6% 1.3% 4.1% 1.9% 0.4% 4.6% 1.5% 1.2% 2.3% 1.3% 0.7% 0.4% 34.1% 22.8% 0.4% 0.1% 0.1% 10.8% 0.2% 0.9% 20.1% 21.9% 0.6% 1.6% 0.0% 0.6% 14.7% 0.2% 0.5% 0.5% 1.2% 1.6% 4.8% 9.7% 8.7% 0.7% 0.2% 6.8% 1.3% 2.1% 4.3% 16.4% 3.2% 8.2% 2.0% 6.6% 7.3% 42.3% 0.6% 1.0% 0.6% 0.4% 0.7% 0.9% 0.2% 0.3% 1.0% 0.7% 0.1% 0.7% 0.3% 0.3% 0.6% 0.4% 0.3% 0.5% 0.6% 0.4% 0.3% 3.7% 1.9% 1.4% 0.6% 0.2% 0.8% 0.4% 0.1% 0.2% 0.3% 1.1% 0.8% 0.7% 0.1% 0.3% Table III.A.8: Basis for Stop (Sorted by % Equipment Violation) 2013-2016 Department Name Newington Torrington Wethersfield South Windsor University of Connecticut Middletown Plymouth Plainville Windsor West Haven Clinton Putnam State Capitol Police Thomaston Waterford Willimantic Vernon Wallingford Winsted Central CT State University Darien New Canaan Wilton Orange Stamford East Haven Norwich Manchester Enfield Bloomfield Groton City Avon Cromwell New Britain Plainfield Naugatuck Groton Town Stratford Eastern CT State University Total 16,964 20,578 13,159 10,285 7,476 8,576 6,618 11,742 16,778 15,848 7,686 4,451 728 2,190 12,779 9,646 11,503 28,202 1,996 6,912 9,355 16,029 14,686 12,025 25,049 8,261 19,061 20,973 20,857 14,019 6,204 3,032 5,843 20,595 4,674 15,788 16,582 8,057 499 Equipment Speed Moving Violations* Related Cell Phone Registration Violation 34.8% 33.5% 31.6% 30.9% 30.3% 30.1% 29.4% 28.4% 27.8% 27.2% 26.3% 26.1% 24.6% 24.5% 24.1% 22.8% 22.3% 22.1% 21.5% 21.2% 21.2% 21.1% 21.1% 20.9% 20.8% 20.3% 20.1% 20.1% 20.1% 19.7% 19.2% 19.1% 18.9% 18.7% 18.7% 18.5% 18.3% 18.2% 17.8% 11.1% 19.0% 17.6% 15.5% 19.2% 11.0% 20.6% 17.6% 30.5% 14.7% 24.5% 36.3% 0.4% 37.4% 33.7% 13.7% 16.4% 11.6% 15.1% 33.0% 24.2% 30.9% 26.6% 20.5% 14.4% 10.2% 31.7% 14.7% 41.5% 25.2% 34.3% 28.6% 17.9% 7.1% 33.2% 22.4% 25.7% 9.0% 5.0% 4.6% 5.6% 5.0% 8.6% 5.5% 4.2% 12.7% 8.9% 5.5% 5.9% 6.1% 10.3% 1.5% 2.0% 6.2% 10.1% 5.9% 15.2% 3.5% 8.7% 10.0% 13.3% 9.8% 18.5% 16.3% 11.6% 10.4% 9.9% 3.1% 6.4% 8.0% 1.8% 12.2% 10.3% 3.3% 9.6% 5.4% 8.5% 4.2% 13.1% 2.4% 9.6% 9.5% 3.1% 7.9% 3.9% 9.3% 3.3% 14.1% 3.1% 0.7% 0.7% 1.6% 3.1% 7.8% 5.3% 10.0% 7.3% 6.5% 7.1% 9.1% 12.4% 7.0% 0.8% 11.0% 2.1% 9.4% 4.6% 2.6% 2.3% 6.3% 13.2% 7.5% 1.0% 5.1% 13.1% 17.4% 0.6% 9.4% 3.9% 11.4% 5.3% 12.9% 8.6% 7.1% 9.3% 5.3% 5.5% 12.7% 6.4% 20.3% 9.8% 11.6% 8.7% 18.2% 7.3% 9.0% 3.9% 5.6% 5.1% 10.8% 4.0% 4.9% 5.7% 9.7% 6.4% 6.7% 6.6% 4.0% 11.3% 8.7% 6.8% 18.2% 8.2% 13.7% 9.9% 5.2% Other 4.7% 5.0% 4.7% 1.1% 7.0% 4.0% 6.1% 6.0% 1.8% 5.1% 3.4% 2.5% 5.1% 6.8% 4.0% 9.0% 4.7% 3.7% 10.4% 4.0% 2.3% 2.1% 2.4% 1.7% 5.8% 5.8% 3.1% 1.6% 3.6% 1.5% 2.7% 15.4% 2.5% 5.8% 4.8% 5.6% 5.8% 5.3% 5.6% Seatbelt Stop Sign 1.2% 0.9% 1.7% 9.0% 1.4% 8.2% 3.0% 2.0% 5.0% 1.7% 8.6% 4.8% 0.5% 0.5% 1.1% 4.7% 2.0% 6.9% 4.9% 4.7% 8.6% 2.2% 0.6% 1.4% 3.1% 1.3% 3.2% 10.2% 5.8% 2.5% 6.2% 0.5% 2.1% 3.2% 1.6% 5.9% 3.9% 4.1% 4.8% 8.1% 15.1% 3.7% 9.3% 16.0% 13.1% 10.4% 7.3% 6.6% 15.3% 6.9% 2.1% 3.0% 9.0% 1.0% 8.0% 9.6% 11.1% 5.1% 2.9% 4.9% 5.3% 6.0% 3.0% 12.9% 22.6% 5.2% 9.9% 4.4% 12.7% 15.4% 8.3% 6.2% 24.5% 14.2% 13.4% 5.3% 9.3% 55.5% Administrative Traffic Control Unlicensed Offense STC Violation Signal Operation 2.8% 1.0% 7.0% 1.9% 0.5% 3.3% 1.0% 2.8% 0.8% 0.9% 0.9% 0.2% 0.4% 1.3% 2.9% 3.8% 2.1% 3.0% 4.6% 1.5% 0.9% 0.7% 1.0% 2.2% 0.1% 3.7% 1.3% 3.2% 1.2% 1.3% 1.8% 0.7% 3.2% 4.7% 1.5% 0.4% 0.5% 7.6% 0.6% 0.1% 0.3% 1.4% 0.7% 0.8% 0.1% 0.2% 0.0% 0.3% 0.2% 1.0% 0.1% 0.5% 0.2% 0.6% 1.9% 1.2% 0.1% 1.6% 4.2% 7.5% 0.4% 0.2% 3.4% 0.6% 1.1% 0.7% 0.6% 0.6% 2.2% 0.1% 0.5% 0.1% 0.1% 0.0% 0.8% 0.2% 0.6% 0.2% 9.7% 12.8% 6.1% 8.2% 3.2% 8.7% 5.3% 8.2% 13.0% 9.0% 5.9% 10.4% 42.3% 6.6% 11.0% 8.8% 12.1% 8.8% 16.4% 9.1% 7.3% 9.2% 8.6% 16.9% 20.2% 5.5% 12.2% 13.5% 8.2% 19.3% 5.0% 7.3% 14.9% 10.7% 3.2% 9.9% 8.1% 9.3% 0.2% 0.4% 0.4% 0.3% 0.2% 0.1% 0.7% 0.3% 0.3% 0.2% 0.4% 0.7% 0.1% 0.5% 0.4% 0.8% 0.6% 0.1% 0.1% 0.7% 0.3% 0.4% 0.5% 0.6% 0.6% 0.2% 1.1% 0.5% 0.6% 0.3% 0.1% 0.9% 0.3% 0.2% 0.7% 0.3% 0.3% 0.1% 0.8% 0.2% Table III.A.8: Basis for Stop (Sorted by % Equipment Violation) 2013-2016 Department Name Trumbull Milford Granby Shelton Glastonbury Windsor Locks Ansonia Rocky Hill Old Saybrook New Haven Westport Monroe Cheshire East Windsor Seymour Norwalk Guilford Woodbridge Newtown Brookfield Hamden Stonington Farmington Wolcott CSP Troop L North Haven New London Bristol Canton Berlin Southern CT State University Watertown West Hartford Coventry East Hampton Greenwich Hartford Simsbury Waterbury Total 8,190 10,313 3,324 1,937 14,705 7,647 14,567 11,192 9,327 43,076 18,526 14,744 15,697 2,999 10,851 17,413 9,935 5,652 24,587 7,548 14,061 7,512 14,942 1,554 36,248 7,750 7,143 15,977 4,561 17,684 2,627 4,756 25,939 4,952 1,729 21,143 18,646 10,450 7,358 Equipment Speed Moving Violations* Related Cell Phone Registration Violation 17.1% 17.0% 16.8% 16.7% 16.7% 16.5% 16.4% 16.2% 15.8% 15.6% 15.6% 15.3% 15.1% 15.0% 14.8% 14.2% 14.2% 14.0% 13.8% 13.8% 13.7% 13.1% 12.9% 12.8% 12.8% 12.5% 12.5% 12.4% 12.3% 12.3% 12.1% 12.0% 11.9% 11.8% 11.7% 11.7% 11.4% 11.4% 11.1% 10.9% 15.9% 36.9% 18.4% 22.0% 37.3% 24.4% 24.2% 41.7% 15.2% 25.8% 31.7% 33.6% 25.2% 38.2% 10.4% 48.0% 32.2% 50.7% 28.7% 8.6% 28.9% 18.3% 45.0% 28.2% 21.8% 11.6% 24.1% 37.1% 20.0% 23.4% 26.7% 8.6% 31.8% 33.6% 28.6% 19.1% 49.8% 9.0% 15.9% 12.1% 13.4% 6.7% 15.2% 7.7% 14.6% 11.7% 9.3% 6.8% 21.6% 14.4% 17.7% 12.4% 4.1% 17.9% 10.5% 18.1% 8.9% 25.3% 20.9% 10.6% 17.4% 19.9% 4.2% 14.1% 12.5% 11.8% 8.3% 19.2% 6.7% 9.9% 23.4% 12.1% 7.9% 11.1% 20.5% 8.9% 16.5% 23.7% 5.4% 3.6% 6.4% 13.0% 2.9% 4.1% 9.1% 7.8% 5.7% 4.9% 8.4% 7.2% 7.0% 5.6% 13.8% 1.8% 13.0% 4.3% 3.4% 14.3% 9.0% 16.4% 1.2% 19.8% 11.8% 2.5% 11.7% 2.6% 6.6% 2.5% 17.5% 15.6% 4.7% 11.3% 16.3% 4.1% 2.2% 9.5% 3.8% 8.6% 12.6% 13.1% 7.3% 3.3% 6.0% 8.4% 5.6% 3.4% 6.3% 10.2% 8.2% 9.1% 4.7% 6.3% 3.8% 3.2% 5.6% 8.6% 5.1% 9.8% 13.1% 5.0% 6.5% 5.4% 7.6% 5.7% 12.9% 6.5% 4.9% 4.4% 14.7% 10.6% 13.5% 7.0% 5.6% 7.2% 8.6% Other 4.2% 16.0% 2.0% 17.1% 3.4% 6.0% 4.4% 4.1% 2.4% 10.1% 2.0% 2.2% 1.2% 3.9% 1.9% 6.1% 1.4% 3.4% 1.9% 1.9% 7.5% 7.5% 1.8% 5.7% 6.1% 3.4% 7.0% 2.6% 6.3% 2.8% 4.2% 1.5% 3.0% 4.4% 5.6% 3.6% 4.5% 2.5% 3.6% Seatbelt Stop Sign 4.9% 3.8% 3.9% 1.7% 3.1% 8.1% 2.4% 3.6% 0.8% 3.9% 1.6% 2.8% 4.0% 6.7% 2.4% 4.1% 1.8% 2.8% 1.2% 1.9% 1.1% 2.8% 2.2% 0.5% 3.1% 7.6% 7.2% 8.1% 1.8% 5.8% 5.5% 7.7% 3.1% 8.0% 1.4% 1.3% 3.1% 1.3% 6.5% 6.1% 7.7% 3.1% 5.1% 8.2% 8.6% 16.9% 11.1% 8.9% 8.6% 9.7% 10.0% 6.5% 7.8% 19.0% 6.8% 8.6% 2.8% 7.7% 8.1% 7.0% 5.0% 5.5% 4.5% 2.7% 4.9% 16.4% 9.2% 9.2% 4.7% 0.6% 10.4% 3.6% 3.9% 4.2% 7.8% 10.7% 7.2% 6.5% Administrative Traffic Control Unlicensed Offense STC Violation Signal Operation 3.5% 2.5% 0.7% 1.9% 4.3% 1.2% 1.0% 1.9% 1.9% 2.9% 0.8% 1.3% 1.6% 4.2% 1.3% 2.3% 0.3% 2.7% 0.6% 0.7% 4.4% 1.7% 2.0% 1.7% 4.1% 3.8% 1.8% 3.1% 0.7% 1.7% 1.8% 1.9% 4.1% 2.0% 2.7% 1.4% 6.6% 0.4% 9.2% 1.1% 0.5% 0.8% 1.7% 1.0% 0.6% 0.0% 0.5% 1.2% 0.7% 2.3% 0.6% 0.1% 0.2% 0.9% 6.8% 0.5% 2.5% 0.7% 0.0% 3.4% 3.3% 1.2% 0.1% 10.8% 3.0% 2.4% 0.6% 0.3% 4.1% 0.4% 1.3% 1.5% 7.6% 0.1% 3.5% 3.8% 1.5% 4.8% 8.4% 10.2% 6.0% 11.0% 5.5% 7.6% 9.4% 8.8% 4.4% 26.4% 9.2% 2.9% 4.5% 7.2% 6.8% 9.4% 9.1% 4.5% 4.5% 7.5% 13.5% 7.8% 8.8% 3.3% 0.7% 10.7% 18.5% 9.9% 8.1% 15.7% 36.7% 6.5% 9.8% 2.7% 7.6% 6.6% 10.3% 7.5% 13.9% 0.4% 0.3% 0.1% 0.2% 0.3% 0.2% 0.3% 0.3% 0.2% 0.6% 0.2% 0.2% 0.3% 1.2% 0.3% 2.0% 0.1% 0.7% 0.2% 0.1% 0.5% 0.5% 0.4% 0.2% 0.9% 0.9% 0.3% 0.7% 0.2% 0.4% 1.1% 0.3% 0.7% 0.4% 0.4% 1.3% 0.3% 0.1% 0.8% Table III.A.8: Basis for Stop (Sorted by % Equipment Violation) 2013-2016 Department Name Yale Fairfield Suffield CSP Troop B Madison Bridgeport North Branford East Hartford Southington New Milford Meriden Bethel Redding Derby Ridgefield CSP Troop D CSP Troop C CSP Troop K Danbury CSP Troop A Branford Department of Motor Vehicle Middlebury CSP Headquarters Weston CSP Troop E Easton CSP Troop H CSP Troop I CSP Troop F CSP Troop G Groton Long Point Portland Western CT State University Total 2,511 21,144 3,164 22,465 10,547 13,438 3,431 23,652 14,321 10,735 7,964 9,812 6,502 9,545 23,058 48,663 76,490 58,366 17,401 62,347 16,351 6,552 502 42,418 1,262 62,377 1,720 56,262 40,475 72,523 74,391 311 537 137 Equipment Speed Moving Violations* Related Cell Phone Registration Violation 10.8% 10.7% 10.5% 10.5% 10.2% 10.2% 10.2% 10.1% 9.7% 9.5% 9.3% 9.3% 8.6% 8.1% 7.4% 7.2% 6.9% 6.8% 6.7% 6.6% 6.3% 6.3% 5.2% 5.1% 5.0% 4.8% 4.7% 4.6% 4.6% 4.1% 4.0% 3.5% 3.2% 1.5% 1.2% 22.4% 61.7% 33.6% 37.6% 8.4% 21.6% 22.5% 48.2% 57.5% 23.3% 37.0% 47.0% 28.5% 50.9% 23.0% 31.3% 30.9% 19.5% 29.0% 10.6% 26.0% 19.7% 44.9% 42.4% 37.4% 49.6% 33.6% 32.2% 28.3% 35.7% 37.9% 62.4% 11.7% 6.3% 14.5% 3.8% 2.9% 6.6% 22.4% 4.8% 13.4% 12.1% 8.7% 11.0% 13.7% 6.0% 14.4% 15.9% 3.8% 5.3% 7.7% 37.6% 7.9% 17.0% 15.4% 26.9% 9.1% 10.5% 3.8% 6.0% 6.0% 4.9% 4.9% 8.0% 15.1% 8.4% 12.4% 6.3% 9.2% 1.0% 15.2% 10.7% 2.8% 25.9% 13.7% 6.7% 4.6% 5.8% 5.2% 13.7% 10.1% 7.3% 17.4% 9.6% 8.8% 12.8% 16.0% 25.6% 9.7% 1.6% 4.0% 4.1% 10.1% 4.4% 7.2% 8.8% 10.7% 15.5% 1.9% 2.2% 0.0% 6.6% 6.6% 11.9% 6.0% 8.4% 6.9% 14.5% 3.2% 3.5% 4.8% 6.5% 3.6% 4.9% 5.2% 2.1% 6.4% 5.5% 6.6% 3.8% 11.6% 5.1% 15.3% 6.4% 7.2% 4.4% 9.8% 6.0% 14.4% 13.1% 7.3% 15.0% 1.6% 6.0% 8.0% Other 16.7% 6.1% 0.9% 5.4% 5.4% 3.0% 5.4% 1.7% 1.1% 3.4% 6.8% 1.9% 6.2% 5.5% 3.6% 8.7% 4.5% 4.5% 3.0% 5.6% 4.1% 12.0% 21.5% 2.5% 14.0% 3.6% 5.2% 6.0% 3.3% 3.3% 3.6% 4.8% 2.4% 33.6% Seatbelt Stop Sign 1.3% 10.5% 0.3% 3.2% 2.0% 9.8% 1.1% 11.7% 5.6% 0.9% 4.4% 4.5% 3.1% 0.9% 1.9% 4.2% 3.7% 3.1% 0.6% 5.3% 2.1% 2.6% 3.2% 14.2% 0.2% 2.0% 1.7% 2.7% 3.8% 2.8% 3.3% 5.5% 1.9% 7.3% 1.7% 4.4% 4.6% 3.8% 8.2% 13.1% 5.4% 7.2% 5.5% 3.1% 14.6% 16.0% 8.5% 10.5% 4.6% 2.8% 2.9% 4.6% 3.7% 1.4% 5.5% 1.5% 8.4% 0.7% 13.5% 1.9% 14.9% 0.7% 2.4% 1.6% 0.4% 28.0% 3.7% 9.5% Administrative Traffic Control Unlicensed Offense STC Violation Signal Operation 3.4% 3.8% 0.4% 2.3% 1.1% 2.0% 5.4% 8.8% 0.8% 0.6% 5.0% 0.4% 1.2% 6.2% 0.4% 4.1% 1.4% 1.4% 1.0% 2.4% 3.7% 0.9% 0.6% 0.9% 0.7% 1.9% 0.9% 1.8% 1.6% 1.0% 2.0% 0.6% 0.6% 1.5% 0.0% 1.3% 0.6% 14.7% 6.8% 5.3% 0.4% 1.4% 0.8% 0.2% 1.3% 0.4% 0.1% 1.0% 0.9% 20.1% 27.3% 23.3% 1.2% 10.8% 0.5% 5.2% 0.0% 9.7% 1.1% 21.9% 4.0% 20.6% 22.8% 34.1% 9.3% 0.0% 0.0% 0.0% 45.0% 9.8% 4.3% 2.0% 2.6% 14.5% 4.8% 5.7% 5.7% 6.5% 10.7% 7.7% 0.3% 9.2% 4.9% 1.3% 1.1% 1.3% 9.2% 1.6% 19.0% 4.2% 6.4% 1.1% 3.9% 2.1% 1.9% 1.6% 1.6% 1.2% 1.5% 0.0% 9.1% 13.9% 0.7% 0.8% 0.1% 0.6% 0.3% 1.7% 0.6% 0.6% 0.2% 0.2% 1.3% 0.3% 0.4% 0.4% 0.2% 1.0% 0.5% 0.8% 0.9% 1.7% 0.6% 1.1% 0.2% 0.7% 0.1% 0.7% 0.8% 1.0% 1.0% 0.6% 1.6% 1.0% 0.2% 0.7% Table III.A.9: Outcome of Stop (Sorted by % Infraction Ticket) 2013-2016 Department Name CSP Headquarters CSP Troop F Danbury CSP Troop G CSP Troop H CSP Troop C CSP Troop I CSP Troop E CSP Troop K Department of Motor Vehicle CSP Troop A Meriden Hartford Derby Bridgeport CSP Troop D Norwalk Trumbull Branford New Haven Greenwich CSP Troop B East Hartford Southern CT State University CSP Troop L Western CT State University New London Manchester Darien Groton Long Point Woodbridge Groton City Farmington West Hartford Ridgefield Waterbury Wolcott North Haven Berlin New Milford Fairfield Orange Granby New Britain Hamden Bristol Watertown Glastonbury Westport East Windsor North Branford Wallingford Coventry Stamford Ansonia Newington Rocky Hill N Infraction 42,418 86.1% 72,523 78.3% 17,401 75.4% 74,391 75.1% 56,262 73.3% 76,490 72.4% 40,475 70.3% 62,377 68.9% 58,366 65.7% 6,552 65.1% 62,347 64.4% 7,964 64.1% 18,646 64.1% 9,545 63.6% 13,438 62.2% 48,663 59.5% 17,413 58.8% 8,190 58.6% 16,351 58.5% 43,076 54.2% 21,143 53.9% 22,465 50.7% 23,652 49.5% 2,627 47.2% 36,248 47.0% 137 45.3% 7,143 44.1% 20,963 44.1% 9,355 43.8% 311 43.7% 5,652 42.5% 6,204 39.3% 14,942 39.3% 25,939 39.3% 23,058 39.3% 7,358 39.1% 1,544 39.1% 7,750 38.4% 17,684 38.3% 10,735 36.9% 21,144 36.5% 12,025 36.2% 3,324 36.0% 20,595 35.0% 14,061 34.5% 15,977 33.9% 4,756 33.2% 14,705 32.9% 18,526 32.8% 2,999 32.7% 3,431 32.6% 28,202 31.8% 4,952 31.5% 25,049 31.4% 14,567 31.2% 16,964 28.4% 11,192 28.2% UAR Mis. Sum. 0.9% 2.7% 0.3% 2.9% 1.2% 2.7% 0.7% 6.1% 1.2% 5.6% 0.3% 3.3% 0.6% 5.0% 0.5% 5.4% 0.5% 4.2% 0.0% 5.1% 0.7% 5.3% 1.8% 10.7% 3.0% 13.3% 0.3% 10.7% 1.1% 5.7% 0.5% 7.8% 1.2% 6.3% 0.4% 9.1% 0.3% 6.1% 1.3% 7.3% 0.6% 3.5% 0.7% 6.2% 1.1% 12.2% 1.0% 7.3% 0.9% 7.1% 0.7% 4.4% 4.2% 5.1% 0.6% 6.7% 0.8% 4.2% 0.0% 1.6% 0.1% 9.4% 1.1% 4.7% 1.9% 6.7% 4.5% 4.9% 0.1% 2.3% 4.3% 19.9% 0.5% 6.2% 0.8% 7.8% 0.3% 4.8% 0.4% 5.2% 0.7% 6.1% 0.4% 7.0% 0.4% 7.7% 1.7% 8.5% 0.2% 5.2% 1.7% 7.0% 0.5% 6.1% 0.6% 6.9% 0.8% 3.4% 1.1% 7.7% 0.3% 8.1% 4.2% 6.2% 0.1% 9.7% 0.3% 2.9% 0.9% 3.6% 0.3% 5.6% 0.9% 3.8% Written Warning 2.6% 6.4% 0.3% 2.4% 5.3% 10.4% 6.4% 6.1% 9.6% 7.0% 6.5% 4.0% 4.7% 0.1% 4.8% 10.1% 0.8% 7.8% 0.1% 11.7% 14.7% 30.6% 12.4% 33.7% 10.1% 13.1% 7.5% 6.9% 13.7% 43.4% 11.0% 19.0% 2.8% 5.3% 44.1% 3.6% 32.0% 3.1% 34.5% 34.3% 1.2% 2.4% 26.3% 0.7% 4.0% 42.7% 47.9% 32.7% 32.7% 16.0% 24.0% 4.0% 22.2% 0.4% 0.3% 61.3% 11.7% Verbal No Warning Disposition 6.5% 1.1% 10.9% 1.3% 19.2% 1.1% 14.0% 1.8% 12.1% 2.5% 12.4% 1.2% 16.1% 1.5% 17.2% 1.8% 18.6% 1.4% 20.3% 2.4% 21.4% 1.7% 18.3% 1.0% 13.9% 0.9% 24.7% 0.6% 25.5% 0.8% 20.8% 1.2% 31.5% 1.3% 22.3% 1.9% 31.0% 4.1% 24.5% 1.0% 25.1% 2.2% 9.5% 2.3% 22.3% 2.6% 10.4% 0.4% 31.9% 3.1% 35.8% 0.7% 37.1% 2.0% 40.0% 1.7% 36.5% 1.0% 10.3% 1.0% 35.4% 1.6% 33.5% 2.4% 46.4% 2.8% 44.6% 1.4% 13.1% 1.1% 31.3% 1.8% 21.3% 1.0% 47.3% 2.6% 19.7% 2.4% 20.4% 2.7% 52.9% 2.6% 53.1% 1.0% 28.9% 0.7% 53.0% 1.2% 55.0% 1.1% 8.9% 5.9% 11.5% 0.8% 25.4% 1.6% 29.3% 1.2% 40.8% 1.7% 27.7% 7.3% 51.9% 1.8% 33.3% 3.3% 64.1% 0.8% 62.8% 1.2% 3.5% 0.9% 54.5% 0.7% Table III.A.9: Outcome of Stop (Sorted by % Infraction Ticket) 2013-2016 Department Name Bethel South Windsor Yale Norwich Cromwell Newtown Monroe Naugatuck Brookfield New Canaan Southington East Haven Groton Town Shelton Windsor Locks Stratford Madison Middletown Milford Stonington East Hampton Bloomfield Cheshire Winsted Enfield Canton Weston Plainville Wilton Windsor Easton Vernon Seymour Simsbury Willimantic University of Connecticut Old Saybrook Waterford Guilford Central CT State University Avon State Capitol Police Wethersfield Plymouth West Haven Clinton Thomaston Redding Portland Torrington Suffield Plainfield Eastern CT State University Putnam Middlebury N Infraction 9,812 27.7% 10,285 27.7% 2,511 27.7% 19,061 27.7% 5,843 27.0% 24,587 25.9% 14,744 25.7% 15,788 25.4% 7,548 25.2% 16,029 25.1% 14,321 24.7% 8,261 24.6% 16,582 24.5% 1,937 24.3% 7,647 24.2% 8,057 24.2% 10,547 23.8% 8,576 23.0% 10,313 23.0% 7,512 22.3% 1,729 22.3% 14,019 22.3% 15,697 22.2% 1,996 20.4% 20,857 20.3% 4,561 20.0% 1,262 19.9% 11,742 18.9% 14,686 18.7% 16,776 18.6% 1,720 18.0% 11,503 17.8% 10,851 17.2% 10,450 17.1% 9,646 16.7% 7,474 16.4% 9,327 16.0% 12,779 14.8% 9,935 14.3% 6,912 14.2% 3,032 14.2% 728 13.9% 13,159 13.8% 6,622 13.8% 15,848 12.9% 7,685 12.2% 2,190 11.7% 6,502 11.4% 537 10.8% 20,578 9.4% 3,164 8.5% 4,674 6.8% 499 5.0% 4,451 4.1% 502 3.4% UAR Mis. Sum. 0.4% 1.9% 0.4% 4.4% 2.8% 8.1% 0.9% 5.6% 0.5% 6.7% 0.2% 2.4% 0.3% 3.6% 0.4% 1.0% 0.6% 2.3% 0.1% 2.4% 0.1% 3.1% 1.5% 8.3% 2.5% 5.4% 0.6% 8.1% 0.6% 3.7% 1.8% 9.6% 0.9% 2.2% 1.4% 8.8% 1.6% 6.2% 1.3% 2.9% 0.3% 10.5% 1.5% 5.3% 0.7% 4.0% 0.9% 6.5% 0.5% 2.8% 2.3% 3.9% 0.1% 4.1% 0.8% 3.6% 0.2% 4.5% 0.1% 3.0% 0.1% 4.1% 1.7% 6.4% 0.5% 3.4% 0.2% 2.5% 1.2% 7.6% 0.5% 2.8% 0.6% 5.7% 1.0% 4.4% 0.2% 2.0% 0.1% 3.3% 1.0% 1.7% 0.3% 4.0% 1.5% 10.1% 0.9% 1.6% 0.7% 2.5% 1.1% 5.7% 0.5% 3.2% 0.1% 1.9% 0.0% 3.2% 0.4% 3.0% 0.0% 5.9% 1.7% 4.7% 0.2% 1.4% 1.9% 2.3% 0.2% 4.2% Written Warning 53.7% 2.8% 38.6% 56.6% 17.5% 39.1% 44.9% 24.2% 29.8% 2.1% 63.5% 1.9% 32.0% 4.9% 38.7% 0.7% 40.2% 16.1% 23.9% 1.3% 62.9% 56.6% 65.8% 27.2% 70.1% 13.2% 33.4% 1.6% 33.9% 5.8% 67.3% 45.8% 7.1% 29.8% 7.0% 24.4% 63.5% 33.3% 79.0% 8.4% 24.0% 3.2% 1.1% 8.1% 3.7% 69.1% 14.3% 39.6% 42.5% 26.6% 55.5% 4.2% 17.0% 42.1% 14.1% Verbal No Warning Disposition 15.6% 0.8% 63.1% 1.6% 22.3% 0.5% 8.8% 0.3% 44.9% 3.4% 32.2% 0.3% 24.3% 1.3% 48.6% 0.5% 40.7% 1.4% 69.2% 1.1% 8.4% 0.3% 61.2% 2.5% 35.1% 0.4% 60.1% 2.1% 31.9% 0.8% 61.2% 2.6% 31.8% 1.0% 48.7% 2.1% 43.6% 1.8% 69.1% 3.1% 3.7% 0.3% 12.8% 1.6% 6.9% 0.4% 41.8% 3.2% 6.0% 0.3% 58.3% 2.3% 40.6% 1.8% 73.5% 1.6% 41.2% 1.5% 71.9% 0.6% 8.5% 2.0% 26.8% 1.5% 71.5% 0.3% 49.7% 0.6% 65.3% 2.2% 55.6% 0.5% 13.4% 0.8% 44.8% 1.7% 4.1% 0.4% 72.8% 1.1% 50.9% 8.3% 78.0% 0.7% 71.2% 2.2% 71.6% 4.0% 78.6% 1.5% 11.4% 0.5% 68.8% 1.6% 44.9% 2.0% 43.6% 0.0% 58.2% 2.4% 29.8% 0.2% 81.9% 0.7% 76.0% 0.4% 49.4% 0.2% 76.1% 2.0% Table III.A.10: Outcome of Stop (Sorted by % Warning) 2013-2016 Department Name Eastern CT State University Putnam Middlebury Plainfield Portland Suffield Torrington Redding Thomaston Guilford West Haven Central CT State University State Capitol Police Clinton University of Connecticut Plymouth Simsbury Seymour Waterford Windsor Old Saybrook Enfield Easton Wilton Plainville Avon Weston Naugatuck Cheshire Vernon Willimantic Wethersfield Madison Southington Canton New Canaan Newtown Windsor Locks Brookfield Stonington Bloomfield Bethel Monroe Winsted Milford Groton Town East Hampton Rocky Hill South Windsor Norwich Shelton Newington Middletown Stamford East Haven Ansonia Cromwell N 499 4,451 502 4,674 537 3,164 20,578 6,502 2,190 9,935 15,848 6,912 728 7,685 7,474 6,622 10,450 10,851 12,779 16,776 9,327 20,857 1,720 14,686 11,742 3,032 1,262 15,788 15,697 11,503 9,646 13,159 10,547 14,321 4,561 16,029 24,587 7,647 7,548 7,512 14,019 9,812 14,744 1,996 10,313 16,582 1,729 11,192 10,285 19,061 1,937 16,964 8,576 25,049 8,261 14,567 5,843 Warnings Infraction 93.0% 5.0% 91.6% 4.1% 90.2% 3.4% 86.2% 6.8% 86.0% 10.8% 85.3% 8.5% 84.8% 9.4% 84.5% 11.4% 83.1% 11.7% 83.1% 14.3% 82.3% 12.9% 81.2% 14.2% 81.2% 13.9% 80.5% 12.2% 79.9% 16.4% 79.7% 13.8% 79.5% 17.1% 78.6% 17.2% 78.1% 14.8% 77.7% 18.6% 76.9% 16.0% 76.1% 20.3% 75.8% 18.0% 75.1% 18.7% 75.0% 18.9% 74.9% 14.2% 74.1% 19.9% 72.8% 25.4% 72.7% 22.2% 72.6% 17.8% 72.3% 16.7% 72.3% 13.8% 72.0% 23.8% 71.9% 24.7% 71.5% 20.0% 71.3% 25.1% 71.3% 25.9% 70.6% 24.2% 70.5% 25.2% 70.4% 22.3% 69.4% 22.3% 69.3% 27.7% 69.2% 25.7% 69.0% 20.4% 67.5% 23.0% 67.1% 24.5% 66.6% 22.3% 66.3% 28.2% 65.9% 27.7% 65.4% 27.7% 65.0% 24.3% 64.8% 28.4% 64.8% 23.0% 64.6% 31.4% 63.1% 24.6% 63.1% 31.2% 62.4% 27.0% No UAR Mis. Sum. Disposition 0.2% 1.4% 0.4% 1.9% 2.3% 0.2% 0.2% 4.2% 2.0% 1.7% 4.7% 0.7% 0.0% 3.2% 0.0% 0.0% 5.9% 0.2% 0.4% 3.0% 2.4% 0.1% 1.9% 2.0% 0.5% 3.2% 1.6% 0.2% 2.0% 0.4% 0.7% 2.5% 1.5% 0.1% 3.3% 1.1% 0.3% 4.0% 0.7% 1.1% 5.7% 0.5% 0.5% 2.8% 0.5% 0.9% 1.6% 4.0% 0.2% 2.5% 0.6% 0.5% 3.4% 0.3% 1.0% 4.4% 1.7% 0.1% 3.0% 0.6% 0.6% 5.7% 0.8% 0.5% 2.8% 0.3% 0.1% 4.1% 2.0% 0.2% 4.5% 1.5% 0.8% 3.6% 1.6% 1.0% 1.7% 8.3% 0.1% 4.1% 1.8% 0.4% 1.0% 0.5% 0.7% 4.0% 0.4% 1.7% 6.4% 1.5% 1.2% 7.6% 2.2% 1.5% 10.1% 2.2% 0.9% 2.2% 1.0% 0.1% 3.1% 0.3% 2.3% 3.9% 2.3% 0.1% 2.4% 1.1% 0.2% 2.4% 0.3% 0.6% 3.7% 0.8% 0.6% 2.3% 1.4% 1.3% 2.9% 3.1% 1.5% 5.3% 1.6% 0.4% 1.9% 0.8% 0.3% 3.6% 1.3% 0.9% 6.5% 3.2% 1.6% 6.2% 1.8% 2.5% 5.4% 0.4% 0.3% 10.5% 0.3% 0.9% 3.8% 0.7% 0.4% 4.4% 1.6% 0.9% 5.6% 0.3% 0.6% 8.1% 2.1% 0.3% 5.6% 0.9% 1.4% 8.8% 2.1% 0.3% 2.9% 0.8% 1.5% 8.3% 2.5% 0.9% 3.6% 1.2% 0.5% 6.7% 3.4% Table III.A.10: Outcome of Stop (Sorted by % Warning) 2013-2016 Department Name Westport Stratford Yale Watertown Hamden Glastonbury Ridgefield East Windsor Wallingford Coventry Orange Granby New Milford Berlin Fairfield New Britain Groton Long Point Wolcott Groton City North Branford Bristol North Haven Darien West Hartford Farmington Western CT State University Manchester Woodbridge New London Southern CT State University CSP Troop L CSP Troop B Greenwich New Haven Waterbury East Hartford Norwalk Branford CSP Troop D Bridgeport Trumbull CSP Troop K CSP Troop A Department of Motor Vehicle Derby CSP Troop E CSP Troop C CSP Troop I Meriden Danbury Hartford CSP Troop H CSP Troop F CSP Troop G CSP Headquarters N Warnings Infraction 18,526 61.9% 32.8% 8,057 61.9% 24.2% 2,511 60.9% 27.7% 4,756 59.4% 33.2% 14,061 59.1% 34.5% 14,705 58.1% 32.9% 23,058 57.2% 39.3% 2,999 56.9% 32.7% 28,202 55.9% 31.8% 4,952 55.5% 31.5% 12,025 55.4% 36.2% 3,324 55.1% 36.0% 10,735 54.7% 36.9% 17,684 54.2% 38.3% 21,144 54.1% 36.5% 20,595 53.7% 35.0% 311 53.7% 43.7% 1,544 53.3% 39.1% 6,204 52.5% 39.3% 3,431 51.7% 32.6% 15,977 51.6% 33.9% 7,750 50.4% 38.4% 9,355 50.2% 43.8% 25,939 49.8% 39.3% 14,942 49.3% 39.3% 137 48.9% 45.3% 20,963 47.0% 44.1% 5,652 46.4% 42.5% 7,143 44.6% 44.1% 2,627 44.1% 47.2% 36,248 42.0% 47.0% 22,465 40.1% 50.7% 21,143 39.8% 53.9% 43,076 36.2% 54.2% 7,358 34.9% 39.1% 23,652 34.7% 49.5% 17,413 32.3% 58.8% 16,351 31.0% 58.5% 48,663 30.9% 59.5% 13,438 30.3% 62.2% 8,190 30.0% 58.6% 58,366 28.2% 65.7% 62,347 27.9% 64.4% 6,552 27.3% 65.1% 9,545 24.9% 63.6% 62,377 23.4% 68.9% 76,490 22.8% 72.4% 40,475 22.5% 70.3% 7,964 22.3% 64.1% 17,401 19.5% 75.4% 18,646 18.7% 64.1% 56,262 17.4% 73.3% 72,523 17.4% 78.3% 74,391 16.4% 75.1% 42,418 9.1% 86.1% No UAR Mis. Sum. Disposition 0.8% 3.4% 1.2% 1.8% 9.6% 2.6% 2.8% 8.1% 0.5% 0.5% 6.1% 0.8% 0.2% 5.2% 1.1% 0.6% 6.9% 1.6% 0.1% 2.3% 1.1% 1.1% 7.7% 1.7% 4.2% 6.2% 1.8% 0.1% 9.7% 3.3% 0.4% 7.0% 1.0% 0.4% 7.7% 0.7% 0.4% 5.2% 2.7% 0.3% 4.8% 2.4% 0.7% 6.1% 2.6% 1.7% 8.5% 1.2% 0.0% 1.6% 1.0% 0.5% 6.2% 1.0% 1.1% 4.7% 2.4% 0.3% 8.1% 7.3% 1.7% 7.0% 5.9% 0.8% 7.8% 2.6% 0.8% 4.2% 1.0% 4.5% 4.9% 1.4% 1.9% 6.7% 2.8% 0.7% 4.4% 0.7% 0.6% 6.7% 1.7% 0.1% 9.4% 1.6% 4.2% 5.1% 2.0% 1.0% 7.3% 0.4% 0.9% 7.1% 3.1% 0.7% 6.2% 2.3% 0.6% 3.5% 2.2% 1.3% 7.3% 1.0% 4.3% 19.9% 1.8% 1.1% 12.2% 2.6% 1.2% 6.3% 1.3% 0.3% 6.1% 4.1% 0.5% 7.8% 1.2% 1.1% 5.7% 0.8% 0.4% 9.1% 1.9% 0.5% 4.2% 1.4% 0.7% 5.3% 1.7% 0.0% 5.1% 2.4% 0.3% 10.7% 0.6% 0.5% 5.4% 1.8% 0.3% 3.3% 1.2% 0.6% 5.0% 1.5% 1.8% 10.7% 1.0% 1.2% 2.7% 1.1% 3.0% 13.3% 0.9% 1.2% 5.6% 2.5% 0.3% 2.9% 1.3% 0.7% 6.1% 1.8% 0.9% 2.7% 1.1% Table III.A.11: Outcome of Stop (Sorted by % UAR) 2013-2016 Department Name West Hartford Waterbury Wallingford New London Hartford Yale Groton Town Canton Farmington Putnam Stratford Meriden Bristol Vernon Plainfield New Britain Milford Wethersfield East Haven Bloomfield Middletown New Haven Stonington Norwalk Danbury CSP Troop H Willimantic Bridgeport Clinton East Hartford East Windsor Groton City Waterford Southern CT State University Avon Norwich Madison Rocky Hill Ansonia CSP Headquarters Plymouth CSP Troop L Winsted Darien North Haven Plainville Westport Western CT State University Cheshire West Haven CSP Troop G CSP Troop B Fairfield CSP Troop A Windsor Locks CSP Troop I Greenwich N 25,939 7,358 28,202 7,143 18,646 2,511 16,582 4,561 14,942 4,451 8,057 7,964 15,977 11,503 4,674 20,595 10,313 13,159 8,261 14,019 8,576 43,076 7,512 17,413 17,401 56,262 9,646 13,438 7,685 23,652 2,999 6,204 12,779 2,627 3,032 19,061 10,547 11,192 14,567 42,418 6,622 36,248 1,996 9,355 7,750 11,742 18,526 137 15,697 15,848 74,391 22,465 21,144 62,347 7,647 40,475 21,143 UAR Mis. Sum. Infraction 4.5% 4.9% 39.3% 4.3% 19.9% 39.1% 4.2% 6.2% 31.8% 4.2% 5.1% 44.1% 3.0% 13.3% 64.1% 2.8% 8.1% 27.7% 2.5% 5.4% 24.5% 2.3% 3.9% 20.0% 1.9% 6.7% 39.3% 1.9% 2.3% 4.1% 1.8% 9.6% 24.2% 1.8% 10.7% 64.1% 1.7% 7.0% 33.9% 1.7% 6.4% 17.8% 1.7% 4.7% 6.8% 1.7% 8.5% 35.0% 1.6% 6.2% 23.0% 1.5% 10.1% 13.8% 1.5% 8.3% 24.6% 1.5% 5.3% 22.3% 1.4% 8.8% 23.0% 1.3% 7.3% 54.2% 1.3% 2.9% 22.3% 1.2% 6.3% 58.8% 1.2% 2.7% 75.4% 1.2% 5.6% 73.3% 1.2% 7.6% 16.7% 1.1% 5.7% 62.2% 1.1% 5.7% 12.2% 1.1% 12.2% 49.5% 1.1% 7.7% 32.7% 1.1% 4.7% 39.3% 1.0% 4.4% 14.8% 1.0% 7.3% 47.2% 1.0% 1.7% 14.2% 0.9% 5.6% 27.7% 0.9% 2.2% 23.8% 0.9% 3.8% 28.2% 0.9% 3.6% 31.2% 0.9% 2.7% 86.1% 0.9% 1.6% 13.8% 0.9% 7.1% 47.0% 0.9% 6.5% 20.4% 0.8% 4.2% 43.8% 0.8% 7.8% 38.4% 0.8% 3.6% 18.9% 0.8% 3.4% 32.8% 0.7% 4.4% 45.3% 0.7% 4.0% 22.2% 0.7% 2.5% 12.9% 0.7% 6.1% 75.1% 0.7% 6.2% 50.7% 0.7% 6.1% 36.5% 0.7% 5.3% 64.4% 0.6% 3.7% 24.2% 0.6% 5.0% 70.3% 0.6% 3.5% 53.9% Written Warning 5.3% 3.6% 4.0% 7.5% 4.7% 38.6% 32.0% 13.2% 2.8% 42.1% 0.7% 4.0% 42.7% 45.8% 4.2% 0.7% 23.9% 1.1% 1.9% 56.6% 16.1% 11.7% 1.3% 0.8% 0.3% 5.3% 7.0% 4.8% 69.1% 12.4% 16.0% 19.0% 33.3% 33.7% 24.0% 56.6% 40.2% 11.7% 0.3% 2.6% 8.1% 10.1% 27.2% 13.7% 3.1% 1.6% 32.7% 13.1% 65.8% 3.7% 2.4% 30.6% 1.2% 6.5% 38.7% 6.4% 14.7% Verbal No Warning Disposition 44.6% 1.4% 31.3% 1.8% 51.9% 1.8% 37.1% 2.0% 13.9% 0.9% 22.3% 0.5% 35.1% 0.4% 58.3% 2.3% 46.4% 2.8% 49.4% 0.2% 61.2% 2.6% 18.3% 1.0% 8.9% 5.9% 26.8% 1.5% 81.9% 0.7% 53.0% 1.2% 43.6% 1.8% 71.2% 2.2% 61.2% 2.5% 12.8% 1.6% 48.7% 2.1% 24.5% 1.0% 69.1% 3.1% 31.5% 1.3% 19.2% 1.1% 12.1% 2.5% 65.3% 2.2% 25.5% 0.8% 11.4% 0.5% 22.3% 2.6% 40.8% 1.7% 33.5% 2.4% 44.8% 1.7% 10.4% 0.4% 50.9% 8.3% 8.8% 0.3% 31.8% 1.0% 54.5% 0.7% 62.8% 1.2% 6.5% 1.1% 71.6% 4.0% 31.9% 3.1% 41.8% 3.2% 36.5% 1.0% 47.3% 2.6% 73.5% 1.6% 29.3% 1.2% 35.8% 0.7% 6.9% 0.4% 78.6% 1.5% 14.0% 1.8% 9.5% 2.3% 52.9% 2.6% 21.4% 1.7% 31.9% 0.8% 16.1% 1.5% 25.1% 2.2% Table III.A.11: Outcome of Stop (Sorted by % UAR) 2013-2016 Department Name Glastonbury Old Saybrook Shelton Manchester Brookfield Watertown Seymour CSP Troop E Wolcott CSP Troop D University of Connecticut CSP Troop K Cromwell Enfield Thomaston New Milford Granby Torrington South Windsor Naugatuck Bethel Orange Trumbull North Branford Stamford Newington Derby Berlin East Hampton Branford Monroe CSP Troop C State Capitol Police CSP Troop F Simsbury Wilton Eastern CT State University Middlebury Hamden Newtown Guilford Ridgefield New Canaan Windsor Central CT State University Woodbridge Redding Coventry Weston Southington Easton Department of Motor Vehicle Suffield Groton Long Point Portland N 14,705 9,327 1,937 20,963 7,548 4,756 10,851 62,377 1,544 48,663 7,474 58,366 5,843 20,857 2,190 10,735 3,324 20,578 10,285 15,788 9,812 12,025 8,190 3,431 25,049 16,964 9,545 17,684 1,729 16,351 14,744 76,490 728 72,523 10,450 14,686 499 502 14,061 24,587 9,935 23,058 16,029 16,776 6,912 5,652 6,502 4,952 1,262 14,321 1,720 6,552 3,164 311 537 UAR Mis. Sum. Infraction 0.6% 6.9% 32.9% 0.6% 5.7% 16.0% 0.6% 8.1% 24.3% 0.6% 6.7% 44.1% 0.6% 2.3% 25.2% 0.5% 6.1% 33.2% 0.5% 3.4% 17.2% 0.5% 5.4% 68.9% 0.5% 6.2% 39.1% 0.5% 7.8% 59.5% 0.5% 2.8% 16.4% 0.5% 4.2% 65.7% 0.5% 6.7% 27.0% 0.5% 2.8% 20.3% 0.5% 3.2% 11.7% 0.4% 5.2% 36.9% 0.4% 7.7% 36.0% 0.4% 3.0% 9.4% 0.4% 4.4% 27.7% 0.4% 1.0% 25.4% 0.4% 1.9% 27.7% 0.4% 7.0% 36.2% 0.4% 9.1% 58.6% 0.3% 8.1% 32.6% 0.3% 2.9% 31.4% 0.3% 5.6% 28.4% 0.3% 10.7% 63.6% 0.3% 4.8% 38.3% 0.3% 10.5% 22.3% 0.3% 6.1% 58.5% 0.3% 3.6% 25.7% 0.3% 3.3% 72.4% 0.3% 4.0% 13.9% 0.3% 2.9% 78.3% 0.2% 2.5% 17.1% 0.2% 4.5% 18.7% 0.2% 1.4% 5.0% 0.2% 4.2% 3.4% 0.2% 5.2% 34.5% 0.2% 2.4% 25.9% 0.2% 2.0% 14.3% 0.1% 2.3% 39.3% 0.1% 2.4% 25.1% 0.1% 3.0% 18.6% 0.1% 3.3% 14.2% 0.1% 9.4% 42.5% 0.1% 1.9% 11.4% 0.1% 9.7% 31.5% 0.1% 4.1% 19.9% 0.1% 3.1% 24.7% 0.1% 4.1% 18.0% 0.0% 5.1% 65.1% 0.0% 5.9% 8.5% 0.0% 1.6% 43.7% 0.0% 3.2% 10.8% Written Warning 32.7% 63.5% 4.9% 6.9% 29.8% 47.9% 7.1% 6.1% 32.0% 10.1% 24.4% 9.6% 17.5% 70.1% 14.3% 34.3% 26.3% 26.6% 2.8% 24.2% 53.7% 2.4% 7.8% 24.0% 0.4% 61.3% 0.1% 34.5% 62.9% 0.1% 44.9% 10.4% 3.2% 6.4% 29.8% 33.9% 17.0% 14.1% 4.0% 39.1% 79.0% 44.1% 2.1% 5.8% 8.4% 11.0% 39.6% 22.2% 33.4% 63.5% 67.3% 7.0% 55.5% 43.4% 42.5% Verbal No Warning Disposition 25.4% 1.6% 13.4% 0.8% 60.1% 2.1% 40.0% 1.7% 40.7% 1.4% 11.5% 0.8% 71.5% 0.3% 17.2% 1.8% 21.3% 1.0% 20.8% 1.2% 55.6% 0.5% 18.6% 1.4% 44.9% 3.4% 6.0% 0.3% 68.8% 1.6% 20.4% 2.7% 28.9% 0.7% 58.2% 2.4% 63.1% 1.6% 48.6% 0.5% 15.6% 0.8% 53.1% 1.0% 22.3% 1.9% 27.7% 7.3% 64.1% 0.8% 3.5% 0.9% 24.7% 0.6% 19.7% 2.4% 3.7% 0.3% 31.0% 4.1% 24.3% 1.3% 12.4% 1.2% 78.0% 0.7% 10.9% 1.3% 49.7% 0.6% 41.2% 1.5% 76.0% 0.4% 76.1% 2.0% 55.0% 1.1% 32.2% 0.3% 4.1% 0.4% 13.1% 1.1% 69.2% 1.1% 71.9% 0.6% 72.8% 1.1% 35.4% 1.6% 44.9% 2.0% 33.3% 3.3% 40.6% 1.8% 8.4% 0.3% 8.5% 2.0% 20.3% 2.4% 29.8% 0.2% 10.3% 1.0% 43.6% 0.0% Table III.A.12: Number of Searches(Sorted by % Search) 2013-2016 Department Name Waterbury Bridgeport Stratford Derby Yale Milford Middletown Vernon West Hartford Danbury Norwich Wallingford Norwalk New London New Haven Glastonbury Wolcott Wilton Wethersfield Meriden Plainville Clinton North Haven East Hartford New Britain Willimantic Naugatuck Trumbull East Hampton South Windsor Newington West Haven University of Connecticut East Haven Waterford Plymouth Berlin Westport Old Saybrook Windsor Locks Enfield Shelton Stamford Darien Manchester Suffield Watertown Ansonia Farmington Winsted Bloomfield Thomaston Groton City Rocky Hill Branford CSP Troop A CSP Troop L Stops 7,358 13,438 8,057 9,545 2,511 10,313 8,576 11,503 25,939 17,401 19,061 28,202 17,413 7,143 43,076 14,705 1,544 14,686 13,159 7,964 11,742 7,685 7,750 23,652 20,595 9,646 15,788 8,190 1,729 10,285 16,964 15,848 7,474 8,261 12,779 6,622 17,684 18,526 9,327 7,647 20,857 1,937 25,049 9,355 20,963 3,164 4,756 14,567 14,942 1,996 14,019 2,190 6,204 11,192 16,351 62,347 36,248 Searches N % 1,468 20.0% 1,305 9.7% 737 9.1% 804 8.4% 211 8.4% 858 8.3% 703 8.2% 891 7.7% 1,930 7.4% 1,253 7.2% 1,236 6.5% 1,785 6.3% 1,093 6.3% 448 6.3% 2,670 6.2% 908 6.2% 95 6.2% 903 6.1% 794 6.0% 454 5.7% 666 5.7% 416 5.4% 374 4.8% 1,138 4.8% 989 4.8% 442 4.6% 716 4.5% 351 4.3% 73 4.2% 426 4.1% 693 4.1% 612 3.9% 284 3.8% 304 3.7% 465 3.6% 224 3.4% 563 3.2% 581 3.1% 289 3.1% 235 3.1% 628 3.0% 58 3.0% 747 3.0% 271 2.9% 602 2.9% 89 2.8% 133 2.8% 406 2.8% 406 2.7% 52 2.6% 351 2.5% 53 2.4% 145 2.3% 260 2.3% 375 2.3% 1,422 2.3% 826 2.3% Table III.A.12: Number of Searches(Sorted by % Search) 2013-2016 Searches Department Name Fairfield Bristol Plainfield CSP Troop H Seymour Canton Portland East Windsor CSP Troop C State Capitol Police New Milford Groton Town Granby CSP Troop G CSP Troop E Orange CSP Troop D Southern CT State University Greenwich Torrington Windsor CSP Troop B CSP Troop K Cheshire Monroe Coventry North Branford Hamden Hartford Woodbridge CSP Troop I Cromwell Newtown Brookfield New Canaan Redding Madison Avon Bethel CSP Troop F Middlebury CSP Headquarters Guilford Westren CT State University Simsbury Easton Weston Putnam Ridgefield Stonington Southington Central CT State University Department of Motor Vehicle Eastern CT State University Groton Long Point Stops 21,144 15,977 4,674 56,262 10,851 4,561 537 2,999 76,490 728 10,735 16,582 3,324 74,391 62,377 12,025 48,663 2,627 21,143 20,578 16,776 22,465 58,366 15,697 14,744 4,952 3,431 14,061 18,646 5,652 40,475 5,843 24,587 7,548 16,029 6,502 10,547 3,032 9,812 72,523 502 42,418 9,935 137 10,450 1,720 1,262 4,451 23,058 7,512 14,321 6,912 6,552 499 311 N 477 359 105 1,256 242 96 11 60 1,430 13 191 292 58 1,283 1,061 202 814 43 345 315 255 338 861 227 212 71 48 194 255 77 523 71 291 88 176 70 103 28 80 589 4 318 74 1 76 11 8 28 121 37 64 16 15 1 0 % 2.3% 2.2% 2.2% 2.2% 2.2% 2.1% 2.0% 2.0% 1.9% 1.8% 1.8% 1.8% 1.7% 1.7% 1.7% 1.7% 1.7% 1.6% 1.6% 1.5% 1.5% 1.5% 1.5% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.3% 1.2% 1.2% 1.2% 1.1% 1.1% 1.0% 0.9% 0.8% 0.8% 0.8% 0.7% 0.7% 0.7% 0.7% 0.6% 0.6% 0.6% 0.5% 0.5% 0.4% 0.2% 0.2% 0.2% 0.0% Table III.B.1: Statewide Average Comparisons for Black Drivers (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City** Groton Long Point** Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Black Stops 16.11% 8.15% 9.19% 5.81% 53.35% 4.79% 37.42% 9.12% 3.91% 4.01% 8.88% 3.14% 3.49% 12.90% 7.93% 11.29% 14.77% 3.64% 37.60% 8.10% 13.64% 3.95% 9.35% 14.03% 8.58% 8.07% 4.72% 7.69% 14.99% 2.89% 12.62% 2.16% 34.05% 38.53% 2.77% 23.76% 15.22% 3.59% 20.20% 12.63% 5.91% 10.02% 17.72% 6.15% 41.73% 17.36% 4.23% 14.12% 5.59% 4.23% 12.21% 21.48% 19.74% 3.04% 18.41% 2.78% 8.00% 5.03% Difference Between Town and State Average 2.02% -5.94% -4.90% -8.28% 39.26% -9.30% 23.33% -4.97% -10.18% -10.08% -5.21% -10.95% -10.60% -1.19% -6.16% -2.80% 0.68% -10.45% 23.51% -5.99% -0.45% -10.14% -4.74% -0.06% -5.51% -6.02% -9.37% -6.40% 0.90% -11.20% -1.47% -11.93% 19.96% 24.44% -11.32% 9.67% 1.13% -10.50% 6.11% -1.46% -8.18% -4.07% 3.63% -7.94% 27.64% 3.27% -9.86% 0.03% -8.50% -9.86% -1.88% 7.39% 5.65% -11.05% 4.32% -11.31% -6.09% -9.06% Black Difference NonResidents Difference Between Between Net Resident Age 16+ Town and State Average Differences Black Stops 9.74% 0.62% 1.40% 57.09% 1.41% -7.71% 1.76% 91.09% 0.65% -8.47% 3.57% 94.10% 1.74% -7.38% -0.90% 83.16% 54.76% 45.64% -6.38% 53.76% 1.76% -7.36% -1.94% 78.19% 31.82% 22.70% 0.63% 17.06% 3.24% -5.88% 0.91% 52.30% 1.05% -8.07% -2.11% 77.97% 0.00% -9.12% -0.96% 94.54% 1.27% -7.85% 2.64% 64.35% 0.00% -9.12% -1.83% 65.98% 0.79% -8.33% -2.26% 84.97% 3.69% -5.43% 4.24% 59.42% 6.42% -2.70% -3.46% 66.16% 0.00% -9.12% 6.32% 96.88% 6.03% -3.09% 3.77% 83.90% 1.10% -8.02% -2.43% 68.25% 22.52% 13.40% 10.11% 46.29% 2.47% -6.65% 0.66% 78.92% 5.96% -3.16% 2.71% 79.95% 0.00% -9.12% -1.02% 97.06% 2.63% -6.49% 1.75% 44.34% 1.73% -7.39% 7.32% 94.00% 2.20% -6.92% 1.40% 90.17% 1.80% -7.32% 1.30% 81.89% 0.92% -8.20% -1.16% 85.99% 2.03% -7.09% 0.69% 83.03% 7.70% -1.42% 2.32% 58.49% 0.00% -9.12% -2.08% 100.00% 6.07% -3.05% 1.58% 64.34% 0.70% -8.42% -3.51% 73.49% 18.28% 9.16% 10.80% 54.55% 35.80% 26.68% -2.23% 34.68% 0.49% -8.63% -2.69% 85.27% 10.15% 1.03% 8.64% 53.92% 7.80% -1.32% 2.45% 32.92% 0.00% -9.12% -1.38% 94.44% 11.68% 2.56% 3.55% 32.22% 2.23% -6.89% 5.43% 85.73% 1.32% -7.80% -0.38% 86.58% 4.11% -5.01% 0.94% 58.22% 10.67% 1.55% 2.08% 30.33% 1.06% -8.06% 0.12% 88.24% 32.16% 23.04% 4.60% 29.06% 15.18% 6.06% -2.79% 38.39% 1.69% -7.43% -2.43% 59.03% 2.99% -6.13% 6.16% 87.44% 0.68% -8.44% -0.06% 93.96% 1.33% -7.79% -2.08% 82.07% 2.91% -6.21% 4.32% 91.23% 13.13% 4.01% 3.39% 46.00% 8.96% -0.16% 5.81% 36.36% 0.00% -9.12% -1.93% 79.23% 1.31% -7.81% 12.13% 98.06% 0.96% -8.16% -3.15% 56.92% 2.73% -6.39% 0.29% 77.21% 0.00% -9.12% 0.06% 89.19% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table III.B.1: Statewide Average Comparisons for Black Drivers (Sorted Alphabetically) 2013-2016 Department Name Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Black Stops 5.03% 2.52% 3.84% 4.36% 10.13% 6.36% 6.87% 5.24% 15.97% 3.41% 17.12% 3.25% 30.90% 4.46% 2.65% 4.95% 19.19% 14.92% 8.81% 29.03% 11.24% 7.61% 14.85% 26.05% 4.75% 9.98% 18.57% 7.10% 8.63% 43.50% 14.11% 3.91% 7.84% 19.99% Difference Between Town and State Average -9.06% -11.57% -10.25% -9.73% -3.96% -7.73% -7.22% -8.85% 1.88% -10.68% 3.03% -10.84% 16.81% -9.63% -11.44% -9.14% 5.10% 0.83% -5.28% 14.94% -2.85% -6.48% 0.76% 11.96% -9.34% -4.11% 4.48% -6.99% -5.46% 29.41% 0.02% -10.18% -6.25% 5.90% Black Difference NonResidents Difference Between Between Net Resident Age 16+ Town and State Average Differences Black Stops 1.87% -7.25% -1.81% 66.67% 1.17% -7.95% -3.63% 54.46% 0.00% -9.12% -1.13% 94.40% 0.77% -8.35% -1.38% 93.13% 3.77% -5.35% 1.40% 76.46% 2.25% -6.87% -0.86% 79.13% 2.07% -7.05% -0.17% 73.68% 1.46% -7.66% -1.19% 75.55% 3.68% -5.44% 7.33% 84.42% 1.34% -7.78% -2.89% 77.91% 12.86% 3.74% -0.71% 30.60% 0.82% -8.30% -2.54% 72.54% 12.76% 3.64% 13.18% 61.69% 1.40% -7.72% -1.92% 90.78% 0.00% -9.12% -2.32% 91.38% 2.12% -7.00% -2.13% 39.06% 2.90% -6.22% 11.33% 92.81% 4.70% -4.42% 5.25% 60.66% 1.34% -7.78% 2.51% 86.76% 17.37% 8.25% 6.69% 15.92% 2.29% -6.83% 3.98% 89.83% 1.24% -7.88% 1.40% 87.85% 5.65% -3.47% 4.22% 87.61% 17.70% 8.58% 3.38% 52.89% 1.25% -7.87% -1.47% 85.00% 1.22% -7.90% 3.79% 95.46% 2.75% -6.37% 10.85% 89.20% 4.08% -5.04% -1.95% 55.18% 1.01% -8.11% 2.65% 95.90% 32.20% 23.08% 6.33% 57.57% 4.27% -4.85% 4.87% 81.65% 1.04% -8.08% -2.10% 56.41% 1.53% -7.59% 1.33% 86.78% 1.94% -7.18% 13.08% 96.81% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table III.B.2: Statewide Average Comparisons for Black Drivers (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City** Groton Long Point** Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Hispanic Stops 12.68% 5.74% 13.20% 11.99% 7.50% 7.01% 28.80% 13.17% 9.06% 2.50% 6.92% 7.83% 4.91% 4.83% 26.39% 16.70% 12.95% 2.72% 26.67% 14.79% 6.94% 8.78% 7.28% 14.08% 8.30% 8.13% 2.65% 18.52% 13.27% 3.54% 8.74% 3.49% 8.75% 27.60% 4.09% 15.01% 32.33% 5.98% 9.34% 10.07% 6.67% 11.15% 41.75% 9.93% 21.63% 21.06% 8.94% 21.04% 5.84% 3.96% 9.26% 21.02% 14.17% 5.26% 12.53% 3.68% 11.70% 5.63% 3.54% 1.33% Difference Between Town and State Average 0.23% -6.71% 0.75% -0.46% -4.95% -5.44% 16.35% 0.72% -3.39% -9.95% -5.53% -4.62% -7.54% -7.62% 13.94% 4.25% 0.50% -9.73% 14.22% 2.34% -5.51% -3.67% -5.17% 1.63% -4.15% -4.32% -9.80% 6.07% 0.82% -8.91% -3.71% -8.96% -3.70% 15.15% -8.36% 2.56% 19.88% -6.47% -3.11% -2.38% -5.78% -1.30% 29.30% -2.52% 9.18% 8.61% -3.51% 8.59% -6.61% -8.49% -3.19% 8.57% 1.72% -7.19% 0.08% -8.77% -0.75% -6.82% -8.91% -11.12% Hispanic Difference Residents Difference Between Between Net Non-Resident Age 16+ Town and State Average Differences Hispanic Stops 14.03% 2.12% -1.89% 61.72% 2.76% -9.15% 2.44% 87.93% 2.67% -9.24% 9.98% 94.09% 6.65% -5.26% 4.79% 76.53% 4.78% -7.13% 2.18% 80.61% 3.45% -8.46% 3.02% 79.84% 36.20% 24.29% -7.94% 14.75% 7.65% -4.26% 4.98% 50.29% 3.79% -8.12% 4.73% 84.21% 1.94% -9.97% 0.02% 85.96% 2.35% -9.56% 4.03% 63.17% 4.41% -7.50% 2.88% 34.72% 2.21% -9.70% 2.16% 82.30% 3.90% -8.01% 0.39% 70.21% 23.25% 11.34% 2.60% 60.67% 3.49% -8.42% 12.67% 94.88% 12.37% 0.46% 0.04% 75.16% 2.02% -9.89% 0.16% 63.83% 22.91% 11.00% 3.22% 43.94% 8.43% -3.48% 5.82% 67.18% 4.34% -7.57% 2.05% 76.92% 2.56% -9.35% 5.68% 94.70% 4.00% -7.91% 2.75% 48.19% 4.51% -7.40% 9.03% 91.61% 3.20% -8.71% 4.55% 91.85% 3.60% -8.31% 3.99% 74.67% 1.39% -10.52% 0.72% 90.91% 9.15% -2.76% 8.83% 79.62% 11.80% -0.11% 0.93% 55.04% 0.00% -11.91% 3.00% 100.00% 7.40% -4.51% 0.80% 65.79% 2.90% -9.01% 0.05% 66.28% 7.58% -4.33% 0.63% 65.61% 41.02% 29.11% -13.96% 26.20% 1.73% -10.18% 1.82% 87.70% 9.89% -2.02% 4.58% 53.77% 24.86% 12.95% 6.93% 18.56% 2.22% -9.69% 3.21% 93.33% 6.77% -5.14% 2.03% 42.57% 4.45% -7.46% 5.09% 79.21% 4.30% -7.61% 1.83% 85.98% 7.77% -4.14% 2.84% 53.61% 31.75% 19.84% 9.45% 18.20% 2.69% -9.22% 6.70% 91.95% 24.79% 12.88% -3.70% 27.78% 25.08% 13.17% -4.56% 29.32% 5.46% -6.45% 2.94% 60.94% 6.39% -5.52% 14.11% 85.51% 2.86% -9.05% 2.43% 86.48% 2.31% -9.60% 1.11% 84.56% 3.26% -8.65% 5.46% 93.31% 22.67% 10.76% -2.19% 41.76% 10.59% -1.32% 3.03% 40.41% 2.93% -8.98% 1.80% 78.41% 2.54% -9.37% 9.45% 97.35% 3.33% -8.58% -0.19% 65.12% 5.18% -6.73% 5.98% 79.04% 2.47% -9.44% 2.62% 92.23% 2.75% -9.16% 0.24% 78.95% 2.20% -9.71% -1.41% 50.85% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table III.B.2: Statewide Average Comparisons for Black Drivers (Sorted Alphabetically) 2013-2016 Department Name Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Hispanic Stops 8.98% 10.48% 7.78% 6.00% 7.59% 3.13% 9.98% 5.60% 20.23% 2.92% 18.47% 4.65% 4.11% 7.65% 15.25% 8.88% 12.47% 28.50% 11.65% 6.96% 17.84% 19.55% 4.99% 8.58% 28.90% 26.56% 13.26% 10.23% 7.56% 4.21% 10.82% 8.26% Difference Between Town and State Average -3.47% -1.97% -4.67% -6.45% -4.86% -9.32% -2.47% -6.85% 7.78% -9.53% 6.02% -7.80% -8.34% -4.80% 2.80% -3.57% 0.02% 16.05% -0.80% -5.49% 5.39% 7.10% -7.46% -3.87% 16.45% 14.11% 0.81% -2.22% -4.89% -8.24% -1.63% -4.19% Hispanic Difference Residents Difference Between Between Net Non-Resident Age 16+ Town and State Average Differences Hispanic Stops 2.37% -9.54% 6.07% 96.23% 3.46% -8.45% 6.48% 93.21% 4.65% -7.26% 2.59% 81.06% 5.53% -6.38% -0.07% 72.35% 5.17% -6.74% 1.88% 61.90% 2.61% -9.30% -0.02% 71.87% 3.62% -8.29% 5.82% 84.31% 2.80% -9.11% 2.26% 76.93% 22.87% 10.96% -3.19% 29.21% 1.91% -10.00% 0.47% 80.82% 11.92% 0.01% 6.01% 66.06% 2.20% -9.71% 1.91% 92.52% 2.09% -9.82% 1.48% 92.22% 6.92% -4.99% 0.20% 28.51% 5.06% -6.85% 9.65% 92.79% 5.21% -6.70% 3.13% 55.87% 6.71% -5.20% 5.22% 71.65% 27.54% 15.63% 0.42% 16.31% 4.07% -7.84% 7.04% 89.66% 2.99% -8.92% 3.43% 88.82% 8.78% -3.13% 8.51% 85.78% 15.96% 4.05% 3.05% 48.50% 3.06% -8.85% 1.39% 90.48% 3.19% -8.72% 4.85% 95.28% 7.10% -4.81% 21.26% 86.64% 28.88% 16.97% -2.86% 19.56% 2.74% -9.17% 9.98% 94.61% 7.33% -4.58% 2.36% 72.73% 3.46% -8.45% 3.56% 80.62% 4.28% -7.63% -0.61% 52.38% 2.83% -9.08% 7.44% 84.43% 2.68% -9.23% 5.04% 94.86% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table III.B.3: Statewide Average Comparisons for Minority Drivers (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City** Groton Long Point** Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Minority Difference Minority Difference Between Residents Difference Between Between Net Non-Resident Stops Town and State Average Age 16+ Town and State Average Differences Minority Stops 29.75% 0.67% 25.62% 0.39% 0.27% 59.15% 16.62% -12.46% 9.82% -15.41% 2.96% 84.13% 24.44% -4.64% 5.76% -19.47% 14.83% 92.78% 19.86% -9.22% 13.49% -11.74% 2.52% 77.12% 62.81% 33.73% 61.51% 36.28% -2.55% 57.86% 12.24% -16.84% 8.49% -16.74% -0.09% 78.07% 68.29% 39.21% 73.25% 48.02% -8.81% 16.57% 23.55% -5.53% 12.71% -12.52% 7.00% 51.61% 14.89% -14.19% 8.11% -17.12% 2.93% 81.23% 8.00% -21.08% 3.25% -21.98% 0.90% 89.32% 17.37% -11.71% 8.62% -16.61% 4.89% 60.64% 12.62% -16.46% 6.12% -19.11% 2.65% 44.43% 10.28% -18.80% 3.79% -21.44% 2.63% 85.27% 19.58% -9.50% 10.57% -14.66% 5.16% 61.80% 36.31% 7.23% 38.64% 13.41% -6.17% 62.87% 30.55% 1.47% 7.17% -18.06% 19.53% 94.40% 28.76% -0.32% 20.56% -4.67% 4.35% 80.18% 7.11% -21.97% 4.60% -20.63% -1.34% 65.04% 65.91% 36.83% 51.63% 26.40% 10.44% 45.48% 24.32% -4.76% 13.98% -11.25% 6.49% 70.58% 22.21% -6.87% 14.58% -10.65% 3.78% 79.28% 14.07% -15.01% 5.56% -19.67% 4.66% 93.80% 18.38% -10.70% 8.65% -16.58% 5.88% 45.88% 30.01% 0.93% 10.00% -15.23% 16.16% 92.06% 21.11% -7.97% 12.59% -12.64% 4.67% 87.67% 20.36% -8.72% 11.81% -13.42% 4.71% 70.51% 8.18% -20.90% 3.19% -22.04% 1.14% 86.03% 29.59% 0.51% 17.95% -7.28% 7.79% 78.74% 31.74% 2.66% 26.90% 1.67% 0.99% 58.41% 7.07% -22.01% 0.00% -25.2300% 3.22% 100.00% 23.98% -5.10% 20.39% -4.84% -0.26% 63.83% 8.00% -21.08% 5.67% -19.56% -1.52% 61.38% 43.89% 14.81% 30.92% 5.69% 9.13% 56.72% 67.23% 38.15% 80.76% 55.53% -17.37% 31.63% 8.16% -20.92% 4.26% -20.97% 0.06% 82.23% 41.97% 12.89% 27.95% 2.72% 10.17% 53.55% 48.61% 19.53% 34.86% 9.63% 9.90% 23.56% 10.56% -18.52% 5.58% -19.65% 1.13% 88.68% 31.02% 1.94% 23.49% -1.74% 3.68% 35.79% 25.05% -4.03% 11.62% -13.61% 9.57% 80.33% 13.87% -15.21% 7.56% -17.67% 2.46% 84.94% 22.43% -6.65% 15.18% -10.05% 3.40% 55.72% 60.80% 31.72% 45.00% 19.77% 11.95% 22.19% 19.02% -10.06% 7.15% -18.08% 8.01% 86.29% 64.99% 35.91% 62.82% 37.59% -1.68% 29.53% 40.03% 10.95% 43.57% 18.34% -7.39% 34.84% 14.83% -14.25% 9.69% -15.54% 1.29% 59.48% 38.23% 9.15% 14.51% -10.72% 19.87% 84.38% 13.29% -15.79% 5.76% -19.47% 3.68% 87.02% 9.12% -19.96% 5.02% -20.21% 0.25% 82.43% 23.05% -6.03% 10.51% -14.72% 8.68% 90.54% 43.92% 14.84% 40.80% 15.57% -0.73% 44.81% 38.29% 9.21% 29.09% 3.86% 5.35% 38.84% 10.20% -18.88% 5.15% -20.08% 1.20% 75.60% 33.73% 4.65% 10.75% -14.48% 19.13% 96.13% 6.97% -22.11% 5.32% -19.91% -2.19% 60.43% 21.23% -7.85% 10.00% -15.23% 7.38% 77.54% 11.36% -17.72% 2.47% -22.76% 5.03% 90.43% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table III.B.3: Statewide Average Comparisons for Minority Drivers (Sorted Alphabetically) 2013-2016 Department Name Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Minority Difference Minority Difference Between Residents Difference Between Between Net Non-Resident Stops Town and State Average Age 16+ Town and State Average Differences Minority Stops 9.31% -19.77% 4.63% -20.60% 0.84% 72.00% 4.67% -24.41% 3.37% -21.86% -2.55% 55.77% 14.44% -14.64% 4.37% -20.86% 6.22% 93.93% 17.57% -11.51% 7.29% -17.94% 6.43% 88.72% 20.93% -8.15% 17.20% -8.03% -0.11% 74.52% 13.58% -15.50% 9.77% -15.46% -0.03% 75.64% 15.80% -13.28% 10.83% -14.40% 1.12% 66.01% 10.52% -18.56% 7.65% -17.58% -0.98% 69.15% 29.54% 0.46% 14.60% -10.63% 11.09% 79.99% 9.76% -19.32% 6.17% -19.06% -0.26% 75.82% 41.03% 11.95% 43.86% 18.63% -6.67% 30.51% 7.92% -21.16% 4.35% -20.88% -0.28% 75.63% 50.90% 21.82% 27.20% 1.97% 19.85% 63.30% 10.21% -18.87% 4.91% -20.32% 1.45% 90.09% 7.58% -21.50% 2.09% -23.14% 1.64% 92.17% 13.84% -15.24% 11.02% -14.21% -1.03% 33.02% 36.80% 7.72% 11.91% -13.32% 21.04% 91.44% 25.40% -3.68% 14.05% -11.18% 7.50% 59.17% 22.75% -6.33% 11.14% -14.09% 7.76% 76.42% 58.24% 29.16% 48.10% 22.87% 6.29% 16.38% 25.42% -3.66% 9.85% -15.38% 11.72% 88.45% 15.37% -13.71% 5.82% -19.41% 5.70% 86.32% 37.36% 8.28% 21.79% -3.44% 11.73% 84.71% 46.88% 17.80% 37.60% 12.37% 5.43% 50.90% 10.54% -18.54% 7.26% -17.97% -0.58% 84.96% 20.47% -8.61% 8.28% -16.95% 8.34% 93.07% 49.11% 20.03% 12.47% -12.76% 32.79% 87.17% 34.74% 5.66% 34.55% 9.32% -3.66% 28.08% 25.33% -3.75% 8.09% -17.14% 13.39% 93.44% 56.22% 27.14% 43.92% 18.69% 8.45% 60.93% 24.06% -5.02% 12.73% -12.50% 7.48% 79.84% 8.62% -20.46% 6.12% -19.11% -1.35% 54.07% 19.30% -9.78% 5.43% -19.80% 10.02% 84.90% 31.00% 1.92% 12.82% -12.41% 14.32% 94.63% * The demographics for the host town were used as a proxy benchmark and should be viewed with caution. **Census populations within the political sub-division are used as the basis for the benchmark. Table III.B.4/III.B.5 a: Ratio of Minority EDP to Minority Stops (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City Groton Long Point Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number of % Minority % Minority Stops Stops EDP 5,489 26.47% 25.07% 787 16.01% 13.28% 6,396 21.58% 12.89% 3,406 20.35% 16.54% 4,921 52.23% 42.68% 5,295 11.77% 13.12% 4,876 66.63% 61.82% 5,268 19.87% 14.21% 2,306 12.62% 10.32% 1,463 4.37% 6.89% 6,273 15.57% 14.48% 1,907 9.54% 8.39% 1,284 7.40% 5.04% 1,386 16.31% 15.68% 5,472 33.11% 31.97% 3,631 31.20% 15.92% 2,690 26.21% 21.13% 640 3.75% 5.82% 10,239 64.38% 40.04% 2,250 20.36% 16.55% 1,065 18.59% 19.16% 611 13.58% 7.50% 4,071 15.48% 12.63% 9,276 29.98% 17.52% 4,332 17.57% 18.84% 5,255 16.31% 15.97% 1,197 6.60% 6.32% 6,728 27.97% 24.64% 1,521 23.93% 18.40% 80 3.75% 18.40% 3,839 19.80% 18.40% 3,431 6.79% 8.31% 5,482 39.69% 29.50% 7,798 61.61% 50.07% 3,382 7.51% 6.47% 6,903 37.03% 26.68% 2,789 43.89% 31.44% 208 11.54% 11.37% 2,205 26.89% 21.86% 2,937 19.75% 17.96% 4,965 13.35% 11.55% 4,908 20.13% 16.91% 6,468 58.15% 38.88% 6,348 19.03% 13.79% 15,368 60.36% 46.32% 2,350 34.81% 33.74% 4,182 14.54% 11.29% Absolute Difference 1.40% 2.73% 8.68% 3.81% 9.55% -1.35% 4.82% 5.66% 2.30% -2.51% 1.10% 1.15% 2.36% 0.63% 1.14% 15.29% 5.08% -2.07% 24.34% 3.80% -0.57% 6.08% 2.85% 12.46% -1.27% 0.34% 0.28% 3.33% 5.53% -14.65% 1.40% -1.51% 10.20% 11.54% 1.04% 10.35% 12.44% 0.17% 5.03% 1.79% 1.80% 3.22% 19.26% 5.24% 14.04% 1.07% 3.25% Ratio 1.06 1.21 1.67 1.23 1.22 0.90 1.08 1.40 1.22 0.64 1.08 1.14 1.47 1.04 1.04 1.96 1.24 0.64 1.61 1.23 0.97 1.81 1.23 1.71 0.93 1.02 1.04 1.14 1.30 0.20 1.08 0.82 1.35 1.23 1.16 1.39 1.40 1.01 1.23 1.10 1.16 1.19 1.50 1.38 1.30 1.03 1.29 Table III.B.4/III.B.5 a: Ratio of Minority EDP to Minority Stops (Sorted Alphabetically) 2013-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Winchester Windsor Windsor Locks Wolcott Woodbridge Number of % Minority % Minority Stops Stops EDP 4,354 31.92% 18.98% 9,738 11.39% 9.47% 1,255 6.85% 8.80% 2,828 20.90% 17.55% 5,845 36.29% 36.92% 5,786 35.10% 24.65% 2,432 8.02% 8.50% 4,234 29.97% 19.51% 823 6.32% 6.73% 3,561 18.28% 14.26% 1,889 8.79% 4.60% 115 7.83% 6.98% 1,094 4.30% 6.13% 2,587 15.04% 7.55% 8,968 17.34% 13.11% 3,549 18.32% 19.57% 3,241 10.61% 12.42% 559 11.99% 17.23% 4,331 9.35% 11.34% 3,456 25.87% 17.94% 5,259 7.40% 10.23% 10,464 38.07% 38.83% 1,966 6.56% 7.36% 1,573 45.90% 27.87% 984 7.22% 8.65% 639 7.51% 6.38% 4,506 12.67% 12.18% 2,866 35.31% 18.23% 2,754 18.16% 15.43% 7,825 20.60% 15.64% 2,462 51.02% 40.14% 3,329 20.43% 13.89% 2,041 15.04% 10.59% 8,882 34.97% 24.14% 3,289 44.18% 35.60% 448 12.28% 9.46% 6,873 19.74% 18.06% 3,622 44.73% 16.60% 1,889 34.25% 29.32% 4,087 22.12% 17.39% 677 7.39% 7.02% 5,462 47.71% 33.16% 2,353 22.52% 18.76% 662 16.62% 8.18% 2,175 27.86% 17.31% Absolute Difference 12.94% 1.92% -1.94% 3.35% -0.64% 10.45% -0.48% 10.46% -0.41% 4.03% 4.19% 0.84% -1.84% 7.49% 4.23% -1.25% -1.80% -5.25% -1.99% 7.93% -2.83% -0.76% -0.80% 18.03% -1.43% 1.13% 0.49% 17.08% 2.72% 4.96% 10.88% 6.53% 4.45% 10.83% 8.58% 2.82% 1.68% 28.12% 4.93% 4.73% 0.36% 14.55% 3.76% 8.44% 10.56% Ratio 1.68 1.20 0.78 1.19 0.98 1.42 0.94 1.54 0.94 1.28 1.91 1.12 0.70 1.99 1.32 0.94 0.85 0.70 0.82 1.44 0.72 0.98 0.89 1.65 0.83 1.18 1.04 1.94 1.18 1.32 1.27 1.47 1.42 1.45 1.24 1.30 1.09 2.69 1.17 1.27 1.05 1.44 1.20 2.03 1.61 Table III.B.4/III.B.5 b: Ratio of Black EDP to Black Stops (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City Groton Long Point Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number of Stops 5,489 787 6,396 3,406 4,921 5,295 4,876 5,268 2,306 1,463 6,273 1,907 1,284 1,386 5,472 3,631 2,690 640 10,239 2,250 1,065 611 4,071 9,276 4,332 5,255 1,197 6,728 1,521 80 3,839 3,431 5,482 7,798 3,382 6,903 2,789 208 2,205 2,937 4,965 4,908 6,468 6,348 15,368 2,350 4,182 % Black Absolute Stops % Black EDP Difference 13.14% 9.48% 3.65% 7.62% 3.47% 4.15% 7.97% 3.48% 4.50% 6.34% 2.94% 3.41% 43.04% 31.15% 11.89% 4.36% 4.07% 0.29% 35.62% 26.46% 9.17% 7.16% 3.93% 3.23% 3.17% 2.02% 1.15% 1.78% 1.50% 0.28% 7.59% 3.94% 3.64% 1.94% 1.19% 0.75% 2.49% 1.20% 1.29% 10.32% 5.63% 4.69% 7.31% 6.12% 1.19% 11.54% 3.57% 7.97% 13.23% 6.72% 6.52% 1.25% 1.54% -0.29% 36.62% 16.95% 19.67% 7.07% 4.19% 2.87% 10.99% 7.92% 3.06% 3.76% 0.88% 2.89% 7.34% 4.14% 3.20% 14.27% 5.27% 9.00% 6.23% 5.85% 0.38% 5.80% 4.34% 1.46% 3.34% 2.23% 1.11% 6.17% 5.62% 0.55% 9.34% 5.47% 3.87% 3.75% 5.47% -1.72% 10.26% 5.47% 4.79% 1.54% 1.92% -0.37% 28.73% 16.09% 12.64% 35.25% 21.57% 13.69% 2.13% 1.39% 0.74% 20.56% 9.92% 10.64% 12.73% 7.75% 4.98% 2.40% 2.63% -0.22% 16.64% 9.71% 6.93% 9.40% 5.61% 3.79% 5.46% 3.04% 2.42% 8.54% 4.91% 3.62% 15.97% 9.97% 6.00% 5.32% 3.46% 1.86% 37.47% 22.60% 14.88% 13.11% 11.43% 1.67% 3.30% 2.29% 1.01% Ratio 1.39 2.19 2.29 2.16 1.38 1.07 1.35 1.82 1.57 1.19 1.92 1.63 2.08 1.83 1.19 3.23 1.97 0.81 2.16 1.69 1.39 4.29 1.77 2.71 1.07 1.34 1.50 1.10 1.71 0.69 1.88 0.81 1.79 1.63 1.53 2.07 1.64 0.92 1.71 1.68 1.80 1.74 1.60 1.54 1.66 1.15 1.44 Table III.B.4/III.B.5 b: Ratio of Black EDP to Black Stops (Sorted Alphabetically) 2013-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Winchester Windsor Windsor Locks Wolcott Woodbridge Number of Stops 4,354 9,738 1,255 2,828 5,845 5,786 2,432 4,234 823 3,561 1,889 115 1,094 2,587 8,968 3,549 3,241 559 4,331 3,456 5,259 10,464 1,966 1,573 984 639 4,506 2,866 2,754 7,825 2,462 3,329 2,041 8,882 3,289 448 6,873 3,622 1,889 4,087 677 5,462 2,353 662 2,175 % Black Absolute Stops % Black EDP Difference 11.14% 5.53% 5.61% 4.32% 1.98% 2.34% 2.63% 2.86% -0.23% 11.21% 6.29% 4.92% 17.25% 12.02% 5.23% 18.53% 7.52% 11.01% 2.38% 1.57% 0.81% 15.40% 6.26% 9.14% 2.07% 1.51% 0.55% 6.82% 4.26% 2.56% 3.81% 0.79% 3.02% 5.22% 2.67% 2.55% 2.01% 1.82% 0.19% 4.02% 1.13% 2.89% 3.52% 2.68% 0.85% 8.23% 5.80% 2.43% 4.26% 3.45% 0.81% 4.47% 5.25% -0.78% 4.53% 3.40% 1.13% 13.40% 5.76% 7.64% 2.49% 2.81% -0.32% 14.37% 11.73% 2.64% 2.59% 1.81% 0.78% 25.05% 12.10% 12.94% 3.76% 2.89% 0.87% 2.97% 1.58% 1.39% 4.57% 2.91% 1.66% 16.78% 5.87% 10.91% 10.20% 5.30% 4.90% 7.78% 3.78% 4.00% 24.70% 14.34% 10.36% 8.80% 3.90% 4.90% 7.01% 3.04% 3.97% 13.74% 7.64% 6.09% 23.56% 16.40% 7.16% 6.70% 2.07% 4.62% 8.98% 5.31% 3.67% 16.68% 4.91% 11.77% 5.08% 4.22% 0.86% 6.61% 4.66% 1.95% 3.10% 1.42% 1.68% 35.54% 20.06% 15.48% 12.75% 7.15% 5.60% 6.65% 2.53% 4.11% 17.66% 4.77% 12.88% Ratio 2.02 2.18 0.92 1.78 1.43 2.46 1.51 2.46 1.36 1.60 4.82 1.95 1.10 3.55 1.32 1.42 1.23 0.85 1.33 2.33 0.89 1.23 1.43 2.07 1.30 1.88 1.57 2.86 1.92 2.06 1.72 2.26 2.31 1.80 1.44 3.23 1.69 3.40 1.20 1.42 2.18 1.77 1.78 2.63 3.70 Table III.B.4/III.B.5 c: Ratio of Hispanic EDP to Hispanic Stops (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City Groton Long Point Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number % Hispanic % Hispanic Absolute of Stops Stops EDP Difference 5,489 12.52% 13.48% -0.97% 787 6.10% 4.89% 1.21% 6,396 11.66% 6.57% 5.10% 3,406 11.60% 8.53% 3.07% 4,921 6.87% 8.53% -1.66% 5,295 7.08% 5.65% 1.44% 4,876 29.16% 30.39% -1.23% 5,268 11.58% 8.08% 3.50% 2,306 7.42% 4.98% 2.43% 1,463 1.91% 3.57% -1.66% 6,273 6.38% 6.24% 0.13% 1,907 6.24% 5.17% 1.07% 1,284 4.13% 2.76% 1.37% 1,386 3.68% 6.77% -3.09% 5,472 23.87% 18.59% 5.28% 3,631 17.21% 7.99% 9.22% 2,690 12.30% 11.84% 0.47% 640 1.56% 2.62% -1.06% 10,239 26.14% 17.77% 8.36% 2,250 12.27% 9.11% 3.16% 1,065 6.67% 7.25% -0.58% 611 8.84% 3.49% 5.34% 4,071 6.34% 6.04% 0.30% 9,276 13.91% 8.24% 5.66% 4,332 7.16% 8.02% -0.86% 5,255 6.66% 6.09% 0.57% 1,197 2.76% 2.76% -0.01% 6,728 18.52% 12.44% 6.08% 1,521 10.32% 7.26% 3.06% 80 0.00% 7.26% -7.26% 3,839 7.66% 7.26% 0.40% 3,431 3.00% 4.05% -1.04% 5,482 9.54% 8.62% 0.92% 7,798 25.38% 24.41% 0.97% 3,382 4.05% 2.84% 1.21% 6,903 13.27% 10.23% 3.04% 2,789 30.19% 21.13% 9.06% 208 7.21% 5.55% 1.66% 2,205 9.02% 7.76% 1.26% 2,937 7.73% 7.70% 0.03% 4,965 6.73% 6.07% 0.66% 4,908 10.55% 8.77% 1.79% 6,468 40.83% 26.03% 14.80% 6,348 10.87% 6.37% 4.50% 15,368 21.49% 18.60% 2.89% 2,350 20.30% 18.58% 1.71% 4,182 9.49% 6.23% 3.26% Ratio 0.93 1.25 1.78 1.36 0.81 1.25 0.96 1.43 1.49 0.54 1.02 1.21 1.50 0.54 1.28 2.15 1.04 0.60 1.47 1.35 0.92 2.53 1.05 1.69 0.89 1.09 1.00 1.49 1.42 0.00 1.06 0.74 1.11 1.04 1.42 1.30 1.43 1.30 1.16 1.00 1.11 1.20 1.57 1.71 1.16 1.09 1.52 Table III.B.4/III.B.5 c: Ratio of Hispanic EDP to Hispanic Stops (Sorted Alphabetically) 2013-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Winchester Windsor Windsor Locks Wolcott Woodbridge Number % Hispanic % Hispanic Absolute of Stops Stops EDP Difference 4,354 17.71% 8.90% 8.81% 9,738 5.25% 4.82% 0.42% 1,255 3.67% 4.02% -0.36% 2,828 8.24% 7.14% 1.10% 5,845 17.60% 19.88% -2.27% 5,786 12.63% 9.48% 3.15% 2,432 4.32% 4.41% -0.09% 4,234 11.76% 7.68% 4.08% 823 4.25% 3.84% 0.41% 3,561 10.33% 7.43% 2.90% 1,889 4.45% 3.45% 1.00% 115 2.61% 3.68% -1.07% 1,094 1.37% 3.44% -2.07% 2,587 9.39% 3.99% 5.40% 8,968 10.87% 6.68% 4.19% 3,549 6.90% 7.43% -0.53% 3,241 5.40% 6.72% -1.32% 559 5.90% 8.28% -2.37% 4,331 3.23% 4.41% -1.17% 3,456 9.20% 6.07% 3.13% 5,259 4.37% 5.10% -0.73% 10,464 19.56% 19.99% -0.42% 1,966 2.80% 3.34% -0.55% 1,573 19.33% 12.66% 6.66% 984 2.34% 4.01% -1.67% 639 3.91% 4.19% -0.28% 4,506 7.06% 7.16% -0.10% 2,866 16.36% 8.33% 8.04% 2,754 6.94% 6.01% 0.92% 7,825 11.44% 8.64% 2.80% 2,462 25.71% 22.66% 3.05% 3,329 9.76% 6.22% 3.55% 2,041 7.30% 5.62% 1.68% 8,882 16.70% 10.28% 6.42% 3,289 19.40% 15.19% 4.21% 448 4.46% 4.23% 0.23% 6,873 8.76% 8.37% 0.39% 3,622 26.53% 8.66% 17.87% 1,889 28.32% 23.08% 5.24% 4,087 12.04% 8.10% 3.94% 677 3.69% 4.56% -0.87% 5,462 9.43% 9.07% 0.36% 2,353 6.93% 7.28% -0.35% 662 9.37% 4.34% 5.03% 2,175 7.54% 5.54% 2.00% Ratio 1.99 1.09 0.91 1.15 0.89 1.33 0.98 1.53 1.11 1.39 1.29 0.71 0.40 2.36 1.63 0.93 0.80 0.71 0.73 1.52 0.86 0.98 0.84 1.53 0.58 0.93 0.99 1.97 1.15 1.32 1.13 1.57 1.30 1.62 1.28 1.05 1.05 3.06 1.23 1.49 0.81 1.04 0.95 2.16 1.36 Table III.B.6/III.B.7a: Ratio of Minority Resident Population to Minority Resident Stops (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City* Groton Long Point* Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number of Residents 14,979 13,855 16,083 14,675 16,982 23,532 109,401 48,439 12,847 7,992 21,049 10,540 9,779 11,357 64,361 14,004 10,391 10,255 40,229 24,114 9,164 5,553 33,218 45,567 20,318 26,217 8,716 46,370 7,960 2,030 31,520 17,672 50,012 93,669 14,073 46,667 47,445 5,843 38,747 43,135 14,918 25,099 57,164 14,138 100,702 21,835 21,891 Minority Minority Residents Resident Stops Resident Stops Difference 25.62% 5,611 31.55% 5.92% 9.82% 838 9.55% -0.27% 5.76% 4,313 7.23% 1.47% 13.49% 3,441 12.96% -0.53% 61.51% 4,595 80.76% 19.25% 8.49% 6,307 6.96% -1.53% 73.25% 10,382 73.74% 0.49% 12.71% 7,595 23.98% 11.27% 8.11% 2,510 8.41% 0.30% 3.25% 1,039 3.75% 0.50% 8.62% 7,773 13.80% 5.18% 6.12% 4,746 11.36% 5.24% 3.79% 1,945 3.86% 0.06% 10.57% 2,932 14.90% 4.34% 38.64% 4,831 48.56% 9.92% 7.17% 2,122 7.54% 0.37% 20.56% 1,560 34.87% 14.32% 4.60% 918 4.68% 0.08% 51.63% 11,572 73.44% 21.82% 13.98% 3,536 16.71% 2.73% 14.58% 829 16.65% 2.07% 5.56% 430 3.49% -2.08% 8.65% 13,065 15.88% 7.23% 10.00% 4,972 10.14% 0.14% 12.59% 2,252 17.27% 4.68% 11.81% 6,212 14.21% 2.41% 3.19% 1,213 3.13% -0.06% 17.95% 7,310 18.19% 0.24% 26.90% 2,243 36.51% 9.61% 0.00% 78 0.00% 0.00% 20.39% 6,363 22.60% 2.21% 5.67% 5,384 5.70% 0.03% 30.92% 6,036 44.25% 13.33% 80.76% 10,488 81.72% 0.97% 4.26% 4,370 3.50% -0.76% 27.95% 9,787 41.76% 13.81% 34.86% 5,538 53.43% 18.57% 5.58% 116 5.17% -0.41% 23.49% 4,825 35.40% 11.91% 11.62% 4,654 10.92% -0.71% 7.56% 4,829 6.38% -1.18% 15.18% 7,927 19.78% 4.60% 45.00% 14,520 67.10% 22.10% 7.15% 5,537 7.55% 0.40% 62.82% 24,705 79.85% 17.04% 43.57% 3,301 56.44% 12.87% 9.69% 5,428 11.88% 2.19% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 1.23 0.97 1.26 0.96 1.31 0.82 1.01 1.89 1.04 1.15 1.60 1.86 1.02 1.41 1.26 1.05 1.70 1.02 1.42 1.20 1.14 0.63 1.84 1.01 1.37 1.20 0.98 1.01 1.36 0 1.11 1.01 1.43 1.01 0.82 1.49 1.53 0.93 1.51 0.94 0.84 1.30 1.49 1.06 1.27 1.30 1.23 Table III.B.6/III.B.7a: Ratio of Minority Resident Population to Minority Resident Stops (Sorted Alphabetically) 2013-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Number of Residents 24,978 20,171 11,549 19,608 68,034 31,638 8,330 11,017 11,918 14,605 9,660 7,480 7,507 6,955 18,111 16,224 13,260 32,010 17,773 20,162 34,301 98,070 15,078 40,980 10,782 6,224 29,251 27,678 23,800 36,530 83,964 15,760 18,154 49,650 44,518 7,255 19,410 21,607 20,176 12,973 23,222 10,117 9,133 13,175 7,119 Minority Minority Residents Resident Stops Resident Stops Difference 14.51% 4,758 21.29% 6.78% 5.76% 9,417 4.50% -1.25% 5.02% 1,070 5.14% 0.12% 10.51% 1,769 9.55% -0.96% 40.80% 8,232 51.28% 10.48% 29.09% 9,766 45.71% 16.62% 5.15% 2,884 8.04% 2.89% 10.75% 1,406 11.17% 0.42% 5.32% 2,246 5.74% 0.42% 10.00% 3,788 14.78% 4.78% 2.47% 1,432 5.03% 2.55% 4.63% 184 7.61% 2.98% 3.37% 1,764 5.22% 1.85% 4.37% 1,141 5.00% 0.62% 7.29% 7,464 6.12% -1.17% 17.20% 3,930 15.19% -2.01% 9.77% 3,891 9.23% -0.54% 10.83% 1,045 9.95% -0.88% 7.65% 4,662 7.27% -0.37% 14.60% 3,596 16.91% 2.31% 6.17% 6,949 4.86% -1.31% 43.86% 15,702 45.48% 1.63% 4.35% 2,549 5.69% 1.34% 27.20% 3,423 43.97% 16.77% 4.91% 609 5.25% 0.34% 2.09% 680 1.91% -0.18% 11.02% 12,479 15.28% 4.26% 11.91% 1,695 15.22% 3.31% 14.05% 4,685 25.46% 11.41% 11.14% 11,732 12.90% 1.76% 48.10% 5,330 67.22% 19.13% 9.85% 3,008 12.47% 2.62% 5.82% 1,655 6.04% 0.22% 21.79% 5,015 29.55% 7.76% 37.60% 8,559 42.62% 5.03% 7.26% 516 3.88% -3.39% 8.28% 5,322 4.94% -3.34% 12.47% 2,950 28.10% 15.63% 34.55% 4,674 51.56% 17.01% 8.09% 2,969 8.22% 0.12% 43.92% 5,971 61.71% 17.79% 12.73% 2,226 16.67% 3.94% 6.12% 941 8.40% 2.27% 5.43% 649 6.93% 1.51% 12.82% 766 12.27% -0.55% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 1.47 0.78 1.02 0.91 1.26 1.57 1.56 1.04 1.08 1.48 2.03 1.64 1.55 1.14 0.84 0.88 0.94 0.92 0.95 1.16 0.79 1.04 1.31 1.62 1.07 0.92 1.39 1.28 1.81 1.16 1.40 1.27 1.04 1.36 1.13 0.53 0.60 2.25 1.49 1.02 1.41 1.31 1.37 1.28 0.96 Table III.B.6/III.B.7b: Ratio of Black Resident Population to Black Resident Stops (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City* Groton Long Point* Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number of Black Resident Residents Black Residents Resident Stops Stops Difference 14,979 9.74% 5,611 17.95% 8.21% 13,855 1.41% 838 2.63% 1.21% 16,083 0.65% 4,313 2.23% 1.57% 14,675 1.74% 3,441 2.79% 1.05% 16,982 54.76% 4,595 75.26% 20.49% 23,532 1.76% 6,307 2.71% 0.95% 109,401 31.82% 10,382 40.18% 8.36% 48,439 3.24% 7,595 9.15% 5.91% 12,847 1.05% 2,510 2.59% 1.54% 7,992 0.00% 1,039 0.96% 0.96% 21,049 1.27% 7,773 6.39% 5.12% 10,540 0.00% 4,746 1.73% 1.73% 9,779 0.79% 1,945 1.34% 0.55% 11,357 3.69% 2,932 10.44% 6.75% 64,361 6.42% 4,831 9.67% 3.24% 14,004 0.00% 2,122 1.56% 1.56% 10,391 6.03% 1,560 14.55% 8.52% 10,255 1.10% 918 2.18% 1.08% 40,229 22.52% 11,572 41.27% 18.76% 24,114 2.47% 3,536 3.99% 1.52% 9,164 5.96% 829 9.89% 3.93% 5,553 0.00% 430 0.47% 0.47% 33,218 2.63% 13,065 8.31% 5.68% 45,567 1.73% 4,972 3.58% 1.85% 20,318 2.20% 2,252 5.60% 3.39% 26,217 1.80% 6,212 3.46% 1.66% 8,716 0.92% 1,213 1.81% 0.90% 46,370 2.03% 7,310 3.78% 1.74% 7,960 7.70% 2,243 17.21% 9.51% 2,030 0.00% 78 0.00% 0.00% 31,520 6.07% 6,363 11.72% 5.65% 17,672 0.70% 5,384 1.06% 0.36% 50,012 18.28% 6,036 36.05% 17.77% 93,669 35.80% 10,488 44.75% 8.95% 14,073 0.49% 4,370 0.98% 0.49% 46,667 10.15% 9,787 23.45% 13.30% 47,445 7.80% 5,538 14.68% 6.88% 5,843 0.00% 116 0.86% 0.86% 38,747 11.68% 4,825 24.33% 12.66% 43,135 2.23% 4,654 4.00% 1.76% 14,918 1.32% 4,829 2.42% 1.10% 25,099 4.11% 7,927 8.34% 4.23% 57,164 10.67% 14,520 17.51% 6.84% 14,138 1.06% 5,537 2.09% 1.03% 100,702 32.16% 24,705 51.62% 19.46% 21,835 15.18% 3,301 23.14% 7.97% 21,891 1.69% 5,428 3.43% 1.74% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 1.84 1.86 3.41 1.61 1.37 1.54 1.26 2.83 2.46 #DIV/0! 5.02 #DIV/0! 1.70 2.83 1.50 #DIV/0! 2.41 1.98 1.83 1.61 1.66 #DIV/0! 3.16 2.06 2.54 1.92 1.98 1.86 2.23 #DIV/0! 1.93 1.51 1.97 1.25 2.01 2.31 1.88 #DIV/0! 2.08 1.79 1.83 2.03 1.64 1.97 1.60 1.52 2.03 Table III.B.6/III.B.7b: Ratio of Black Resident Population to Black Resident Stops (Sorted Alphabetically) 2013-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Number of Black Resident Residents Black Residents Resident Stops Stops Difference 24,978 2.99% 4,758 6.33% 3.33% 20,171 0.68% 9,417 0.88% 0.20% 11,549 1.33% 1,070 2.43% 1.10% 19,608 2.91% 1,769 4.69% 1.78% 68,034 13.13% 8,232 24.54% 11.41% 31,638 8.96% 9,766 24.51% 15.55% 8,330 0.00% 2,884 2.05% 2.05% 11,017 1.31% 1,406 3.06% 1.75% 11,918 0.96% 2,246 2.49% 1.53% 14,605 2.73% 3,788 5.65% 2.92% 9,660 0.00% 1,432 2.51% 2.51% 7,480 1.87% 184 4.89% 3.02% 7,507 1.17% 1,764 2.89% 1.72% 6,955 0.00% 1,141 1.23% 1.23% 18,111 0.77% 7,464 0.92% 0.16% 16,224 3.77% 3,930 6.79% 3.03% 13,260 2.25% 3,891 3.70% 1.45% 32,010 2.07% 1,045 3.35% 1.28% 17,773 1.46% 4,662 2.87% 1.41% 20,162 3.68% 3,596 7.12% 3.44% 34,301 1.34% 6,949 1.55% 0.22% 98,070 12.86% 15,702 18.95% 6.10% 15,078 0.82% 2,549 2.63% 1.81% 40,980 12.76% 3,423 27.87% 15.12% 10,782 1.40% 609 2.13% 0.73% 6,224 0.00% 680 0.74% 0.74% 29,251 2.12% 12,479 4.98% 2.86% 27,678 2.90% 1,695 6.67% 3.77% 23,800 4.70% 4,685 14.41% 9.71% 36,530 1.34% 11,732 2.80% 1.47% 83,964 17.37% 5,330 33.70% 16.32% 15,760 2.29% 3,008 4.85% 2.56% 18,154 1.24% 1,655 2.66% 1.42% 49,650 5.65% 5,015 9.51% 3.86% 44,518 17.70% 8,559 22.72% 5.02% 7,255 1.25% 516 1.74% 0.49% 19,410 1.22% 5,322 1.58% 0.36% 21,607 2.75% 2,950 8.95% 6.20% 20,176 4.08% 4,674 6.57% 2.48% 12,973 1.01% 2,969 1.75% 0.74% 23,222 32.20% 5,971 51.85% 19.65% 10,117 4.27% 2,226 8.89% 4.62% 9,133 1.04% 941 3.61% 2.57% 13,175 1.53% 649 2.47% 0.93% 7,119 1.94% 766 4.70% 2.76% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 2.11 1.29 1.82 1.61 1.87 2.74 #DIV/0! 2.34 2.58 2.07 #DIV/0! 2.61 2.47 #DIV/0! 1.20 1.80 1.65 1.62 1.96 1.94 1.16 1.47 3.22 2.19 1.52 #DIV/0! 2.35 2.30 3.07 2.10 1.94 2.12 2.15 1.68 1.28 1.39 1.30 3.26 1.61 1.73 1.61 2.08 3.47 1.61 2.42 Table III.B.6/III.B.7c: Ratio of Hispanic Resident Population to Hispanic Resident Stops (Sorted Alphabetically) 2013-2016 Department Name Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Groton City* Groton Long Point* Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Number of Residents 14,979 13,855 16,083 14,675 16,982 23,532 109,401 48,439 12,847 7,992 21,049 10,540 9,779 11,357 64,361 14,004 10,391 10,255 40,229 24,114 9,164 5,553 33,218 45,567 20,318 26,217 8,716 46,370 7,960 2,030 31,520 17,672 50,012 93,669 14,073 46,667 47,445 5,843 38,747 43,135 14,918 25,099 57,164 14,138 100,702 21,835 21,891 Hispanic Hispanic Residents Resident Stops Resident Stops Difference 14.03% 5,611 12.60% -1.43% 2.76% 838 2.51% -0.25% 2.67% 4,313 3.20% 0.53% 6.65% 3,441 8.02% 1.37% 4.78% 4,595 4.44% -0.34% 3.45% 6,307 3.66% 0.22% 36.20% 10,382 31.78% -4.42% 7.65% 7,595 13.77% 6.12% 3.79% 2,510 4.30% 0.51% 1.94% 1,039 1.54% -0.40% 2.35% 7,773 5.15% 2.80% 4.41% 4,746 8.28% 3.87% 2.21% 1,945 2.21% 0.00% 3.90% 2,932 2.86% -1.04% 23.25% 4,831 37.38% 14.13% 3.49% 2,122 3.77% 0.28% 12.37% 1,560 19.68% 7.31% 2.02% 918 1.85% -0.17% 22.91% 11,572 30.56% 7.65% 8.43% 3,536 11.34% 2.91% 4.34% 829 5.79% 1.45% 2.56% 430 1.86% -0.70% 4.00% 13,065 6.02% 2.03% 4.51% 4,972 5.03% 0.51% 3.20% 2,252 4.48% 1.28% 3.60% 6,212 4.88% 1.28% 1.39% 1,213 0.66% -0.73% 9.15% 7,310 10.92% 1.77% 11.80% 2,243 16.50% 4.70% 0.00% 78 0.00% 0.00% 7.40% 6,363 7.80% 0.40% 2.90% 5,384 2.17% -0.73% 7.58% 6,036 7.01% -0.57% 41.02% 10,488 36.21% -4.80% 1.73% 4,370 1.21% -0.51% 9.89% 9,787 14.87% 4.97% 24.86% 5,538 37.87% 13.01% 2.22% 116 1.72% -0.50% 6.77% 4,825 9.53% 2.77% 4.45% 4,654 4.64% 0.19% 4.30% 4,829 2.86% -1.45% 7.77% 7,927 10.31% 2.54% 31.75% 14,520 48.44% 16.68% 2.69% 5,537 2.31% -0.38% 24.79% 24,705 27.23% 2.45% 25.08% 3,301 32.20% 7.12% 5.46% 5,428 6.91% 1.45% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 0.90 0.91 1.20 1.21 0.93 1.06 0.88 1.80 1.14 0.79 2.19 1.88 1.00 0.73 1.61 1.08 1.59 0.92 1.33 1.34 1.33 0.73 1.51 1.11 1.40 1.35 0.48 1.19 1.40 0.00 1.05 0.75 0.92 0.88 0.70 1.50 1.52 0.77 1.41 1.04 0.66 1.33 1.53 0.86 1.10 1.28 1.27 Table III.B.6/III.B.7c: Ratio of Hispanic Resident Population to Hispanic Resident Stops (Sorted Alphabetically) 2013-2016 Department Name Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Number of Residents 24,978 20,171 11,549 19,608 68,034 31,638 8,330 11,017 11,918 14,605 9,660 7,480 7,507 6,955 18,111 16,224 13,260 32,010 17,773 20,162 34,301 98,070 15,078 40,980 10,782 6,224 29,251 27,678 23,800 36,530 83,964 15,760 18,154 49,650 44,518 7,255 19,410 21,607 20,176 12,973 23,222 10,117 9,133 13,175 7,119 Hispanic Hispanic Residents Resident Stops Resident Stops Difference 6.39% 4,758 10.87% 4.48% 2.86% 9,417 2.06% -0.80% 2.31% 1,070 1.96% -0.35% 3.26% 1,769 2.71% -0.55% 22.67% 8,232 25.90% 3.23% 10.59% 9,766 16.48% 5.88% 2.93% 2,884 3.68% 0.75% 2.54% 1,406 2.84% 0.30% 3.33% 2,246 2.67% -0.66% 5.18% 3,788 7.60% 2.42% 2.47% 1,432 2.03% -0.45% 2.75% 184 2.17% -0.58% 2.20% 1,764 1.64% -0.55% 2.37% 1,141 1.93% -0.44% 3.46% 7,464 2.20% -1.26% 4.65% 3,930 4.20% -0.46% 5.53% 3,891 4.63% -0.90% 5.17% 1,045 5.36% 0.19% 2.61% 4,662 1.97% -0.64% 3.62% 3,596 4.48% 0.86% 2.80% 6,949 2.66% -0.14% 22.87% 15,702 22.84% -0.03% 1.91% 2,549 1.65% -0.26% 11.92% 3,423 14.75% 2.83% 2.20% 609 1.81% -0.39% 2.09% 680 1.03% -1.06% 6.92% 12,479 9.02% 2.11% 5.06% 1,695 5.31% 0.25% 5.21% 4,685 9.63% 4.41% 6.71% 11,732 8.50% 1.79% 27.54% 5,330 32.93% 5.39% 4.07% 3,008 5.12% 1.05% 2.99% 1,655 2.24% -0.75% 8.78% 5,015 13.12% 4.34% 15.96% 8,559 18.65% 2.68% 3.06% 516 1.16% -1.90% 3.19% 5,322 1.41% -1.78% 7.10% 2,950 17.22% 10.12% 28.88% 4,674 44.09% 15.21% 2.74% 2,969 3.54% 0.80% 7.33% 5,971 7.84% 0.50% 3.46% 2,226 5.03% 1.57% 4.28% 941 4.25% -0.03% 2.83% 649 4.01% 1.18% 2.68% 766 3.13% 0.45% *Census populations within the political sub-division are used as the basis for the benchmark. Ratio 1.70 0.72 0.85 0.83 1.14 1.56 1.25 1.12 0.80 1.47 0.82 0.79 0.75 0.81 0.63 0.90 0.84 1.04 0.76 1.24 0.95 1.00 0.86 1.24 0.82 0.49 1.30 1.05 1.85 1.27 1.20 1.26 0.75 1.49 1.17 0.38 0.44 2.42 1.53 1.29 1.07 1.45 0.99 1.42 1.17 Table III.B.8: Departments with Disparities Relative to Descriptive Benchmarks (Sorted by Total Score) 2013-2016 Department Name Wethersfield Stratford East Hartford New Britain Hamden Manchester Trumbull Norwich Darien New Haven Newington Waterbury Windsor Woodbridge Meriden Orange Bloomfield Fairfield West Hartford Derby Middletown Bristol Hartford Norwalk Willimantic Wolcott Berlin South Windsor Vernon New London Danbury Easton Enfield Redding Waterford Wilton Cheshire Ansonia Clinton Cromwell Groton City* Groton Town Windsor Locks State Average M B H 32.79% 10.85% 21.26% 19.85% 13.18% 10.44% 10.11% 11.95% 10.80% 10.17% 21.04% 11.33% 19.53% 12.67% 19.87% 14.11% 14.32% 13.08% 19.13% 12.13% 16.16% 11.73% M 28.12% 18.03% 24.34% 19.26% 10.20% 10.35% 17.08% 10.45% 15.29% 14.04% 12.94% 10.88% 14.55% 10.56% 12.44% 10.46% 12.46% 10.83% 11.54% 10.02% 14.83% 11.09% EDP B 11.77% 12.94% 19.67% H 17.87% 14.80% 12.64% 10.64% 10.91% 11.01% 7.97% 14.88% 5.61% 10.36% 15.48% 12.88% Resident Population M B H 15.63% 6.20% 10.12% 16.77% 15.12% 21.82% 18.76% 22.10% 16.68% 13.33% 17.77% 13.81% 13.30% 8.04% 16.62% 15.55% 17.04% 19.46% 19.13% 17.79% 16.32% 19.65% 18.57% 6.88% 19.25% 20.49% 14.32% 11.91% 11.27% 8.52% 12.66% 5.91% 10.48% 17.01% 11.41% 11.41% 12.87% 9.71% 9.22% 8.81% 9.14% 11.89% 9.00% 6.09% 6.52% 13.01% 6.12% 13.69% 8.44% 15.21% 5.03% 5.10% 7.64% 14.13% 6.08% 5.34% 7.49% 5.40% 7.23% 5.68% 11.72% 13.39% 5.12% 8.21% 5.24% 6.75% 9.51% 5.65% 5.60% Total 8.5 6 6 5 5 5 4.5 4 4 4 4 4 4 4 3.5 3.5 3 2.5 2.5 2 2 2 2 2 2 2 1.5 1.5 1.5 1 1 1 1 1 1 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Table III.C.7.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2013-16 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Capitol Police Central CT State University Canton Cheshire Clinton Coventry Cromwell VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.086 (0.098) 4,168 -0.100 (0.367) 777 0.239 (0.158) 4,453 0.146 (0.241) 2,010 0.040 (0.082) 4,389 0.120 (0.182) 3,651 -0.372*** (0.106) 3,251 0.136 (0.118) 4,938 -0.681** (0.299) 1,866 0.035 (0.51) 168 -0.023 (0.161) 2,084 -0.361 (0.522) 931 0.108 (0.136) 3,936 -0.408 (0.272) 1,968 -0.648 (0.445) 904 -0.535** (0.231) 1,304 Black 0.058 (0.102) 4,091 0.140 (0.47) 662 0.200 (0.168) 4,355 -0.034 (0.279) 1,896 0.049 (0.082) 4,280 0.198 (0.192) 3,630 -0.371*** (0.107) 3,167 0.129 (0.125) 4,860 -0.622* (0.378) 1,756 0.025 (0.59) 159 -0.044 (0.164) 2,040 -0.597 (0.559) 713 0.063 (0.145) 3,863 -0.257 (0.346) 1,913 -1.292** (0.545) 754 -0.546** (0.256) 1,271 Hispanic 0.319*** (0.108) 3,962 1.789** (0.744) 584 0.232* (0.135) 4,551 0.364* (0.221) 2,123 -0.152 (0.152) 2,050 0.110 (0.175) 3,695 -0.259** (0.116) 2,684 -0.048 (0.101) 5,125 -0.324 (0.231) 1,970 1.173** (0.462) 178 0.439** (0.181) 2,043 0.927 (0.729) 589 0.205 (0.164) 3,790 0.161 (0.246) 2,021 -0.160 (0.346) 904 0.414 (0.435) 999 Black or Hispanic 0.186** (0.081) 4,813 0.574 (0.381) 758 0.203* (0.111) 5,017 0.214 (0.181) 2,262 0.027 (0.08) 4,595 0.138 (0.134) 3,920 -0.331*** (0.099) 4,572 0.022 (0.084) 5,655 -0.402** (0.205) 2,033 0.595 (0.373) 219 0.175 (0.132) 2,461 0.401 (0.449) 1,097 0.126 (0.115) 4,166 0.044 (0.206) 2,111 -0.470 (0.298) 1,125 -0.231 (0.226) 1,334 Table III.C.7.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2013-16 Department Department of Motor Vehicle Danbury Darien Derby Eastern CT State University East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.209 (0.254) 1,097 0.008 (0.19) 1,818 -0.217 (0.158) 2,001 -0.138 (0.17) 2,270 -0.276 (0.684) 126 0.758 (0.849) 237 -0.188* (0.113) 2,640 -0.192 (0.185) 1,934 -0.001 (0.264) 971 -1.390** (0.606) 259 -0.063 (0.091) 7,467 -0.082 (0.08) 6,223 0.185 (0.146) 3,583 -0.007 (0.13) 4,682 0.207 (0.448) 961 0.031 (0.117) 3,270 Black -0.111 (0.261) 1,069 -0.064 (0.209) 1,751 -0.254 (0.172) 1,938 -0.017 (0.181) 2,238 -0.304 (0.678) 125 0.578 (0.981) 180 -0.188* (0.114) 2,578 -0.117 (0.2) 1,875 -0.016 (0.269) 963 -1.719** (0.733) 217 -0.109 (0.101) 7,298 -0.023 (0.085) 6,058 0.173 (0.181) 3,402 0.057 (0.163) 4,466 0.186 (0.499) 884 -0.079 (0.139) 3,051 Hispanic -0.181 (0.308) 1,010 0.132 (0.123) 2,415 0.132 (0.155) 2,058 -0.212 (0.162) 2,235 0.472 (0.751) 169 38.003*** (6.171) 170 -0.184 (0.123) 2,150 -0.204 (0.151) 2,107 0.486 (0.484) 816 0.202 (0.464) 311 0.101 (0.109) 7,163 0.103 (0.09) 5,927 0.214 (0.176) 3,394 0.075 (0.155) 4,548 0.080 (0.555) 710 0.043 (0.107) 3,555 Black or Hispanic -0.076 (0.219) 1,220 0.093 (0.115) 2,634 -0.035 (0.123) 2,346 -0.144 (0.129) 2,589 0.046 (0.524) 231 0.998 (0.958) 249 -0.178* (0.102) 3,510 -0.192 (0.128) 2,294 0.087 (0.239) 1,033 -0.437 (0.391) 368 -0.025 (0.078) 7,803 0.030 (0.067) 7,064 0.196 (0.134) 3,708 0.058 (0.116) 4,890 0.186 (0.389) 1,076 -0.014 (0.092) 3,959 Table III.C.7.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2013-16 Department Groton City Groton Long Point Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck New Britain New Canaan VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.273 (0.203) 1,425 35.185*** (3.266) 8 0.433*** (0.132) 3,192 -0.204 (0.229) 3,312 -0.132 (0.104) 3,281 -0.232* (0.127) 2,739 -0.061 (0.297) 2,241 0.131* (0.076) 5,563 -0.037 (0.179) 1,368 0.000 (.) 7 -0.078 (0.142) 1,999 -0.091 (0.148) 2,533 -0.143 (0.155) 4,173 0.167 (0.146) 3,805 -0.008 (0.087) 4,205 0.034 (0.144) 4,779 Black -0.372 (0.233) 1,339 35.185*** (3.266) 8 0.473*** (0.143) 3,112 -0.367 (0.335) 3,214 -0.147 (0.105) 3,238 -0.211 (0.129) 2,695 0.231 (0.384) 2,160 0.194** (0.082) 5,322 -0.005 (0.182) 1,351 0.000 (.) 7 -0.061 (0.147) 1,963 -0.051 (0.164) 2,449 -0.043 (0.172) 4,106 0.109 (0.155) 3,714 -0.045 (0.09) 4,097 0.142 (0.176) 4,619 Hispanic -0.281 (0.24) 1,333 37.362 (92874526.889) 14 0.129 (0.162) 2,999 -0.021 (0.253) 3,356 -0.082 (0.157) 2,186 -0.034 (0.139) 2,216 0.899** (0.35) 2,129 0.045 (0.096) 4,796 -0.008 (0.138) 1,774 Black or Hispanic -0.310* (0.184) 1,539 -0.025 (0.188) 1,755 -0.119 (0.179) 2,392 0.378** (0.172) 4,147 0.041 (0.131) 3,924 0.053 (0.068) 5,988 0.216 (0.154) 4,809 -0.040 (0.125) 2,182 -0.084 (0.127) 2,716 0.168 (0.127) 4,423 0.034 (0.107) 4,301 0.023 (0.063) 7,176 0.182 (0.12) 5,121 0.322*** (0.114) 3,412 -0.127 (0.206) 3,450 -0.119 (0.099) 3,611 -0.147 (0.118) 3,716 0.563** (0.258) 2,318 0.131* (0.069) 6,250 -0.013 (0.123) 2,077 Table III.C.7.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2013-16 Department New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Putnam Redding VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.103* (0.053) 13,042 -0.114 (0.178) 1,431 0.230 (0.223) 2,718 0.030 (0.101) 4,396 0.025 (0.128) 8,158 0.298 (0.468) 610 0.166 (0.151) 2,247 -0.049 (0.103) 3,244 -0.319*** (0.123) 2,779 0.169 (0.21) 2,798 -0.166 (0.11) 3,411 -0.298 (0.486) 822 0.191 (0.151) 3,566 -0.082 (0.327) 1,535 0.268 (0.512) 1,008 0.333 (0.348) 1,349 Black -0.099* (0.053) 12,805 -0.064 (0.187) 1,383 0.129 (0.247) 2,621 -0.029 (0.111) 4,229 0.049 (0.15) 7,994 0.413 (0.545) 534 0.222 (0.159) 2,207 -0.105 (0.105) 3,175 -0.215* (0.129) 2,664 0.340 (0.261) 2,649 -0.228* (0.117) 3,286 -0.309 (0.49) 820 0.144 (0.16) 3,514 0.021 (0.339) 1,522 0.530 (0.531) 965 0.126 (0.414) 1,188 Hispanic 0.099 (0.062) 9,033 0.284* (0.165) 1,516 0.581*** (0.209) 2,826 -0.035 (0.09) 4,682 -0.003 (0.138) 8,066 -0.214 (0.499) 647 0.006 (0.184) 2,103 -0.353*** (0.111) 3,022 0.292** (0.145) 2,505 0.027 (0.221) 2,835 -0.121 (0.138) 3,101 0.129 (0.454) 823 0.035 (0.136) 3,634 -0.028 (0.326) 1,557 -0.605 (0.574) 502 0.468 (0.3) 1,445 Black or Hispanic -0.036 (0.049) 16,493 0.099 (0.139) 1,800 0.412** (0.166) 2,940 -0.031 (0.076) 5,366 0.030 (0.105) 8,511 0.103 (0.366) 786 0.099 (0.127) 2,440 -0.229*** (0.088) 4,049 0.027 (0.105) 3,103 0.122 (0.174) 2,914 -0.173* (0.097) 3,780 -0.157 (0.356) 939 0.070 (0.11) 3,927 0.037 (0.242) 1,646 0.139 (0.432) 1,192 0.385 (0.246) 1,509 Table III.C.7.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2013-16 Department Ridgefield Rocky Hill Southern CT State University Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull University of Connecticut VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.222 (0.183) 5,045 0.057 (0.131) 3,205 0.116 (0.238) 613 -0.008 (0.201) 2,984 0.581 (0.583) 400 0.182 (0.254) 2,657 -0.219 (0.14) 2,532 0.173 (0.267) 3,618 -0.059 (0.112) 3,172 -0.008 (0.272) 1,925 -0.243 (0.158) 1,222 0.125 (0.388) 617 -0.529 (0.534) 531 -0.209 (0.176) 3,948 -0.033 (0.124) 2,544 -0.268 (0.196) 1,281 Black -0.027 (0.238) 4,872 0.093 (0.152) 3,080 0.113 (0.241) 604 0.026 (0.217) 2,951 0.606 (0.683) 345 -0.140 (0.281) 2,569 -0.232 (0.154) 2,434 0.299 (0.298) 3,491 -0.048 (0.122) 3,010 0.293 (0.317) 1,776 -0.268* (0.161) 1,197 0.624 (0.526) 528 -0.437 (0.678) 361 -0.147 (0.189) 3,911 0.043 (0.132) 2,464 -0.542* (0.277) 1,118 Hispanic 0.181 (0.156) 5,219 -0.037 (0.154) 3,054 0.267 (0.42) 298 0.105 (0.214) 2,930 -0.080 (0.496) 411 0.450 (0.398) 2,363 0.558*** (0.206) 2,300 0.104 (0.201) 3,719 0.244** (0.112) 3,217 -0.409 (0.432) 1,472 -0.345* (0.182) 989 -0.859* (0.518) 500 -0.166 (0.526) 613 -0.208 (0.153) 4,040 -0.189 (0.132) 2,427 -0.270 (0.328) 1,109 Black or Hispanic 0.117 (0.133) 5,429 0.040 (0.115) 3,387 0.115 (0.233) 661 0.067 (0.158) 3,135 0.137 (0.398) 444 0.054 (0.227) 2,681 0.068 (0.13) 2,700 0.203 (0.173) 3,841 0.097 (0.092) 3,869 -0.006 (0.265) 1,937 -0.287** (0.143) 1,529 -0.077 (0.356) 670 -0.219 (0.407) 730 -0.183 (0.127) 4,246 -0.065 (0.104) 2,999 -0.450** (0.22) 1,226 Table III.C.7.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2013-16 Department Vernon Western CT State University Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.041 (0.162) 2,879 1.253 (1.242) 36 0.146 (0.111) 6,350 0.105 (0.218) 1,091 0.134 (0.126) 3,516 0.070 (0.335) 1,260 0.009 (0.082) 6,485 -0.059 (0.098) 3,169 -0.763 (1.076) 141 0.084 (0.106) 5,246 0.054 (0.111) 2,864 -0.071 (0.228) 1,260 0.179 (0.143) 3,653 -0.031 (0.09) 3,931 -0.054 (0.162) 2,088 1.276* (0.686) 463 Black -0.026 (0.167) 2,854 0.000 (.) 17 0.144 (0.121) 6,248 0.126 (0.22) 1,079 0.209 (0.141) 3,412 0.252 (0.363) 1,193 -0.025 (0.09) 6,119 -0.060 (0.1) 3,110 -0.688 (1.087) 140 0.088 (0.117) 5,104 0.097 (0.114) 2,809 -0.069 (0.239) 1,232 0.069 (0.162) 3,525 -0.072 (0.091) 3,797 -0.063 (0.171) 2,037 1.085 (0.709) 459 Hispanic -0.335* (0.189) 2,745 -0.717 (1.464) 39 0.049 (0.096) 6,627 -0.127 (0.215) 1,034 0.216 (0.135) 3,473 -0.407 (0.287) 1,268 0.016 (0.085) 6,349 0.025 (0.105) 2,968 -1.308 (0.928) 77 0.113 (0.126) 5,010 0.189* (0.098) 3,259 0.421*** (0.14) 1,813 -0.025 (0.134) 3,742 0.141 (0.14) 2,493 -0.053 (0.227) 1,882 0.938 (0.91) 333 Black or Hispanic -0.154 (0.13) 3,129 -0.130 (1.123) 56 0.079 (0.079) 7,219 0.006 (0.179) 1,475 0.234** (0.104) 3,859 -0.129 (0.237) 1,381 -0.013 (0.068) 7,496 -0.024 (0.083) 3,981 -0.888 (0.753) 166 0.095 (0.09) 5,610 0.150* (0.084) 3,983 0.324** (0.131) 1,954 0.019 (0.11) 4,049 -0.036 (0.085) 4,260 -0.050 (0.146) 2,200 0.831 (0.581) 555 Table III.C.7.1: Logistic Regression of Minority Status on Daylight by Department, All Traffic Stops 2013-16 Department Wolcott Woodbridge Yale University CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.541 (0.431) 539 0.270 (0.187) 1,364 0.258 (0.182) 825 0.170** (0.08) 11,067 0.080 (0.072) 13,422 0.285* (0.163) 5,902 0.315*** (0.056) 22,611 0.067 (0.085) 14,899 0.089 (0.055) 17,957 0.004 (0.067) 18,118 0.161*** (0.058) 12,888 0.168** (0.071) 10,338 0.005 (0.082) 7,710 0.111 (0.079) 13,566 -0.127 (0.129) 8,693 Black 0.733 (0.452) 535 0.325 (0.199) 1,319 0.238 (0.189) 777 0.150* (0.086) 10,660 0.079 (0.078) 13,049 0.191 (0.181) 5,824 0.233*** (0.07) 21,455 0.073 (0.104) 14,557 0.092 (0.064) 17,266 0.006 (0.076) 17,625 0.108* (0.061) 12,298 0.156** (0.076) 9,872 -0.016 (0.089) 7,432 0.114 (0.089) 13,239 -0.045 (0.145) 8,574 Hispanic 0.204 (0.413) 507 0.456 (0.278) 1,091 -0.032 (0.294) 527 0.153 (0.102) 10,155 0.047 (0.066) 13,875 0.397** (0.164) 5,857 0.250*** (0.074) 21,218 -0.101 (0.093) 14,690 -0.041 (0.073) 16,812 0.146* (0.079) 17,467 0.177*** (0.062) 12,121 0.140 (0.085) 9,194 0.095 (0.097) 7,162 0.300*** (0.085) 13,397 0.208 (0.129) 8,811 Black or Hispanic 0.394 (0.326) 589 0.375** (0.177) 1,465 0.183 (0.178) 877 0.161** (0.071) 11,998 0.063 (0.055) 15,654 0.285** (0.126) 6,135 0.240*** (0.053) 22,931 -0.012 (0.072) 15,358 0.029 (0.051) 18,601 0.063 (0.058) 19,016 0.137*** (0.049) 15,287 0.144** (0.063) 11,596 0.029 (0.071) 8,482 0.207*** (0.065) 14,409 0.102 (0.1) 9,261 Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops 2013-16 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Capitol Police Central CT State University Canton Cheshire Clinton Coventry VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.047 (0.103) 4,091 -0.219 (0.429) 678 0.171 (0.165) 4,328 0.095 (0.291) 1,517 0.027 (0.088) 4,114 0.165 (0.195) 3,366 -0.019 (0.168) 2,423 0.107 (0.129) 4,640 -0.937*** (0.329) 1,618 -0.253 (0.8) 56 0.011 (0.166) 2,024 -0.410 (0.599) 868 0.102 (0.153) 3,516 -0.524* (0.309) 1,789 -0.490 (0.506) 746 Black 0.021 (0.106) 4,016 0.093 (0.585) 506 0.138 (0.178) 4,233 -0.221 (0.328) 1,404 0.043 (0.089) 4,012 0.254 (0.204) 3,347 -0.015 (0.169) 2,361 0.134 (0.136) 4,545 -0.940** (0.429) 1,467 -0.874 (1.154) 38 -0.018 (0.171) 1,981 -0.447 (0.707) 566 0.074 (0.162) 3,459 -0.449 (0.428) 1,656 -1.498** (0.694) 614 Hispanic 0.321*** (0.112) 3,866 2.659** (1.155) 451 0.186 (0.14) 4,405 0.102 (0.284) 1,661 -0.136 (0.166) 1,903 0.093 (0.188) 3,512 0.071 (0.184) 1,994 0.004 (0.109) 4,848 -0.486* (0.264) 1,691 2.260* (1.183) 72 0.315 (0.195) 1,993 0.840 (0.674) 455 0.205 (0.185) 3,407 0.016 (0.286) 1,820 -0.037 (0.366) 747 Black or Hispanic 0.163* (0.084) 4,719 0.731 (0.466) 617 0.149 (0.116) 4,865 -0.010 (0.215) 1,772 0.026 (0.087) 4,309 0.166 (0.144) 3,725 0.032 (0.155) 3,422 0.054 (0.09) 5,352 -0.593*** (0.229) 1,754 1.242** (0.63) 93 0.115 (0.141) 2,402 0.446 (0.477) 938 0.135 (0.128) 3,774 -0.116 (0.237) 1,893 -0.359 (0.31) 992 Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops 2013-16 Department Cromwell Department of Motor Vehicle Danbury Darien Derby Eastern CT State University East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.742** (0.298) 938 -0.156 (0.344) 789 -0.206 (0.243) 1,295 -0.307* (0.171) 1,798 -0.040 (0.196) 2,037 -0.100 (0.98) 81 0.480 (1.265) 172 -0.186 (0.127) 2,215 -0.179 (0.209) 1,724 0.090 (0.337) 684 -6.350*** (2.304) 80 -0.090 (0.103) 6,457 -0.074 (0.086) 5,878 0.182 (0.152) 3,366 0.032 (0.137) 4,475 Black -0.799** (0.329) 918 -0.033 (0.352) 772 -0.384 (0.269) 1,254 -0.325* (0.186) 1,717 0.112 (0.207) 2,007 -0.100 (0.98) 81 -6.876* (3.624) 98 -0.183 (0.128) 2,155 -0.062 (0.225) 1,668 0.098 (0.349) 678 0.000 (.) 24 -0.136 (0.114) 6,270 -0.012 (0.092) 5,719 0.189 (0.188) 3,192 0.123 (0.172) 4,268 Hispanic 0.365 (0.532) 596 0.172 (0.409) 729 0.052 (0.171) 1,746 0.039 (0.166) 1,852 -0.141 (0.189) 1,980 0.066 (0.768) 118 0.000 (.) 38 -0.199 (0.138) 1,781 -0.172 (0.169) 1,852 0.189 (0.541) 528 0.313 (0.861) 146 0.038 (0.125) 6,261 0.187* (0.099) 5,629 0.246 (0.192) 3,185 0.111 (0.164) 4,328 Black or Hispanic -0.360 (0.274) 978 0.138 (0.288) 872 -0.042 (0.158) 1,908 -0.110 (0.133) 2,112 -0.040 (0.149) 2,329 0.039 (0.662) 155 -1.520 (0.945) 173 -0.171 (0.114) 2,956 -0.146 (0.142) 2,045 0.120 (0.308) 751 -0.795 (0.769) 171 -0.053 (0.089) 6,835 0.057 (0.073) 6,706 0.220 (0.143) 3,483 0.099 (0.123) 4,682 Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops 2013-16 Department Granby Greenwich Groton City Groton Long Point Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middletown Milford Monroe Naugatuck VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.227 (0.465) 820 0.174 (0.136) 2,836 -0.270 (0.215) 1,300 0.499 (3.613) 5 0.229 (0.152) 2,671 -0.159 (0.251) 2,760 -0.006 (0.121) 3,021 -0.216 (0.191) 1,902 0.016 (0.31) 1,912 0.157* (0.084) 5,133 0.237 (0.331) 586 0.115 (0.169) 1,627 -0.154 (0.21) 1,488 -0.214 (0.165) 4,064 0.223 (0.152) 3,628 Black 0.250 (0.503) 748 0.068 (0.164) 2,620 -0.350 (0.249) 1,216 0.499 (3.613) 5 0.272* (0.164) 2,568 -0.321 (0.382) 2,444 -0.016 (0.122) 2,979 -0.199 (0.196) 1,873 0.235 (0.393) 1,847 0.173* (0.09) 4,909 0.252 (0.337) 580 0.151 (0.175) 1,601 -0.086 (0.239) 1,327 -0.142 (0.184) 3,970 0.162 (0.16) 3,547 Hispanic 0.105 (0.64) 564 0.169 (0.128) 3,066 -0.255 (0.263) 1,184 0.499 (3.613) 5 0.067 (0.182) 2,505 -0.002 (0.279) 2,725 -0.098 (0.178) 1,992 0.221 (0.215) 1,598 1.028*** (0.392) 1,900 0.076 (0.105) 4,381 -0.015 (0.253) 741 -0.009 (0.224) 1,399 -0.229 (0.23) 1,354 0.260 (0.183) 4,008 0.136 (0.141) 3,719 Black or Hispanic 0.232 (0.419) 977 0.108 (0.109) 3,447 -0.283 (0.198) 1,392 0.203 (0.129) 2,852 -0.077 (0.228) 3,012 -0.016 (0.114) 3,325 0.000 (0.173) 2,529 0.583** (0.272) 2,222 0.138* (0.075) 5,766 0.087 (0.222) 872 0.116 (0.149) 1,780 -0.144 (0.177) 1,535 0.061 (0.135) 4,302 0.108 (0.113) 4,079 Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops 2013-16 Department New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.023 (0.096) 3,821 0.043 (0.148) 4,680 0.156** (0.065) 10,357 0.071 (0.21) 1,110 0.131 (0.271) 2,041 0.044 (0.105) 4,230 0.011 (0.138) 7,859 0.361 (0.466) 538 0.186 (0.172) 2,077 0.030 (0.122) 2,663 -0.286** (0.145) 2,119 0.203 (0.264) 2,307 -0.194 (0.12) 3,244 -0.524 (0.601) 453 0.171 (0.159) 3,394 Black -0.055 (0.099) 3,718 0.144 (0.179) 4,522 0.164** (0.066) 10,198 0.178 (0.224) 1,067 0.244 (0.288) 1,924 -0.024 (0.115) 4,072 0.026 (0.162) 7,669 0.532 (0.542) 474 0.191 (0.18) 2,040 -0.033 (0.125) 2,607 -0.146 (0.152) 2,031 0.341 (0.337) 1,942 -0.259** (0.129) 3,124 -0.444 (0.607) 451 0.142 (0.171) 3,343 Hispanic 0.069 (0.076) 5,457 0.186 (0.165) 4,698 0.196*** (0.073) 7,169 0.458** (0.207) 1,163 0.759*** (0.24) 2,224 -0.003 (0.095) 4,513 0.008 (0.148) 7,743 -0.188 (0.524) 558 0.056 (0.205) 1,921 -0.207 (0.129) 2,467 0.393** (0.172) 1,936 -0.218 (0.27) 2,254 -0.237 (0.15) 2,930 0.191 (0.647) 472 0.076 (0.142) 3,433 Black or Hispanic 0.029 (0.07) 6,532 0.177 (0.127) 5,003 0.172*** (0.058) 13,097 0.310* (0.172) 1,395 0.570*** (0.194) 2,394 -0.013 (0.08) 5,178 0.028 (0.113) 8,194 0.128 (0.381) 722 0.099 (0.143) 2,265 -0.136 (0.103) 3,314 0.114 (0.124) 2,400 -0.009 (0.214) 2,385 -0.236** (0.106) 3,600 -0.234 (0.482) 668 0.093 (0.115) 3,744 Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops 2013-16 Department Plymouth Putnam Redding Ridgefield Rocky Hill Southern CT State University Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.150 (0.337) 1,461 0.138 (0.507) 924 0.296 (0.34) 1,313 0.132 (0.186) 4,939 0.072 (0.148) 3,048 0.132 (0.292) 517 -0.076 (0.206) 2,763 0.299 (0.841) 198 0.039 (0.257) 2,393 -0.216 (0.152) 2,476 0.040 (0.285) 3,328 0.023 (0.17) 2,208 0.051 (0.305) 1,707 -0.166 (0.198) 843 0.199 (0.453) 466 Black -0.021 (0.354) 1,449 0.199 (0.545) 885 0.109 (0.412) 1,138 -0.098 (0.245) 4,740 0.138 (0.171) 2,878 0.113 (0.297) 509 -0.002 (0.225) 2,694 0.679 (1.111) 181 -0.196 (0.293) 2,315 -0.213 (0.167) 2,380 0.194 (0.328) 3,205 -0.021 (0.189) 2,100 0.419 (0.368) 1,534 -0.188 (0.203) 826 0.703 (0.716) 355 Hispanic 0.077 (0.329) 1,366 -0.919 (0.771) 348 0.464 (0.318) 1,394 0.141 (0.165) 5,095 -0.067 (0.176) 2,804 0.145 (0.574) 238 -0.010 (0.218) 2,732 -0.499 (0.886) 138 0.129 (0.394) 2,009 0.467** (0.223) 2,254 0.009 (0.207) 3,122 0.441** (0.176) 2,260 -0.437 (0.505) 1,050 -0.110 (0.231) 662 -0.121 (0.591) 287 Black or Hispanic 0.045 (0.252) 1,566 -0.088 (0.471) 1,142 0.382 (0.256) 1,456 0.067 (0.139) 5,303 0.036 (0.129) 3,221 0.106 (0.282) 554 -0.006 (0.163) 2,906 0.123 (0.61) 247 -0.087 (0.237) 2,454 0.065 (0.14) 2,638 0.129 (0.183) 3,495 0.209 (0.14) 2,663 0.067 (0.307) 1,743 -0.118 (0.177) 1,064 0.396 (0.42) 519 Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops 2013-16 Department Thomaston Torrington Trumbull University of Connecticut Vernon Western CT State University Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -1.351 (0.838) 318 -0.119 (0.21) 3,571 0.054 (0.139) 2,316 -0.288 (0.215) 1,138 -0.002 (0.17) 2,603 -0.130 (1.123) 56 0.202 (0.123) 5,362 2.408** (0.94) 264 0.209 (0.135) 3,450 0.709* (0.423) 919 0.128 (0.087) 6,133 -0.099 (0.134) 2,558 -1.979 (2.285) 49 0.015 (0.117) 4,471 0.048 (0.12) 2,677 Black -1.149 (0.843) 158 -0.073 (0.228) 3,538 0.077 (0.148) 2,247 -0.564* (0.331) 1,004 0.003 (0.176) 2,579 Hispanic 0.027 (0.738) 347 0.081 (0.181) 3,680 -0.125 (0.154) 2,054 -0.192 (0.376) 942 -0.344* (0.2) 2,504 Black or Hispanic -0.389 (0.524) 459 -0.013 (0.152) 3,871 -0.000 (0.118) 2,727 -0.427* (0.254) 1,088 -0.144 (0.137) 2,834 0.181 (0.135) 5,243 2.336** (0.94) 262 0.324** (0.151) 3,348 0.878* (0.466) 872 0.102 (0.096) 5,798 -0.089 (0.137) 2,504 -1.979 (2.285) 49 0.022 (0.129) 4,343 0.084 (0.123) 2,629 -0.005 (0.108) 5,607 2.430*** (0.801) 264 0.221 (0.147) 3,375 -0.504 (0.378) 932 0.136 (0.089) 6,012 0.173 (0.137) 2,434 -0.533 (1.132) 50 0.090 (0.146) 4,261 0.186* (0.107) 3,030 0.061 (0.089) 6,196 2.342*** (0.658) 380 0.299*** (0.113) 3,786 0.059 (0.306) 1,050 0.111 (0.073) 7,116 0.041 (0.112) 3,233 -0.356 (0.956) 117 0.047 (0.101) 4,782 0.147 (0.092) 3,723 Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops 2013-16 Department Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Yale University CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.071 (0.264) 1,039 0.138 (0.153) 3,480 -0.001 (0.099) 3,596 -0.088 (0.174) 1,939 1.038 (0.766) 378 0.780 (0.507) 422 0.287 (0.201) 1,302 0.500 (0.332) 399 0.118 (0.088) 10,368 0.097 (0.075) 12,874 0.206 (0.174) 5,614 0.220*** (0.059) 22,278 0.053 (0.09) 14,619 0.128** (0.059) 17,393 0.026 (0.07) 17,815 Black 0.012 (0.271) 1,019 0.052 (0.174) 3,326 -0.033 (0.101) 3,483 -0.087 (0.186) 1,889 0.907 (0.769) 351 1.003* (0.535) 420 0.301 (0.212) 1,260 0.509 (0.344) 375 0.098 (0.095) 9,981 0.114 (0.081) 12,464 0.113 (0.192) 5,543 0.140* (0.073) 21,116 0.053 (0.109) 14,285 0.148** (0.068) 16,720 0.028 (0.08) 17,298 Hispanic 0.357** (0.164) 1,502 -0.014 (0.144) 3,553 0.269* (0.157) 2,277 -0.060 (0.24) 1,687 1.212 (1.025) 190 0.637 (0.509) 419 0.406 (0.295) 1,033 0.281 (0.527) 237 0.086 (0.115) 9,491 0.091 (0.07) 13,146 0.319* (0.17) 5,510 0.219*** (0.078) 20,664 -0.211** (0.1) 14,058 -0.018 (0.078) 16,285 0.190** (0.082) 17,051 Black or Hispanic 0.273* (0.153) 1,628 0.017 (0.119) 3,843 0.014 (0.095) 3,897 -0.061 (0.158) 2,039 0.685 (0.637) 410 0.731* (0.381) 486 0.344* (0.188) 1,395 0.482 (0.315) 425 0.093 (0.08) 11,226 0.102* (0.058) 14,989 0.205 (0.133) 5,884 0.180*** (0.056) 22,645 -0.075 (0.077) 15,061 0.063 (0.054) 18,013 0.095 (0.06) 18,653 Table III.C.7.2: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Traffic Stops 2013-16 Department CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.121** (0.061) 12,610 0.211*** (0.075) 9,798 0.080 (0.088) 7,360 0.126 (0.084) 13,088 -0.105 (0.135) 8,089 Black 0.077 (0.064) 12,032 0.188** (0.08) 9,364 0.079 (0.095) 7,043 0.132 (0.094) 12,746 -0.006 (0.151) 7,870 Hispanic 0.102 (0.066) 11,865 0.232** (0.091) 8,706 0.169 (0.104) 6,785 0.217** (0.092) 12,789 0.268** (0.135) 8,188 Black or Hispanic 0.081 (0.051) 14,966 0.197*** (0.067) 11,000 0.118 (0.076) 8,088 0.166** (0.07) 13,915 0.147 (0.105) 8,765 Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2013-2016 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Capitol Police Central CT State University Canton Cheshire Clinton Coventry VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.146 (0.124) 2,435 -0.333 (0.477) 447 0.533** (0.229) 1,893 0.170 (0.286) 1,492 0.106 (0.1) 2,778 0.485 (0.345) 1,574 -0.226 (0.15) 1,305 0.482*** (0.182) 2,230 -0.671* (0.373) 1,020 0.154 (0.716) 84 0.081 (0.249) 957 -0.159 (0.75) 481 -0.170 (0.2) 2,004 -0.561 (0.39) 986 -0.299 (0.536) 381 Black 0.108 (0.129) 2,387 -0.030 (0.552) 366 0.545** (0.246) 1,839 0.016 (0.336) 1,409 0.111 (0.101) 2,705 0.573 (0.367) 1,567 -0.226 (0.152) 1,262 0.488** (0.197) 2,190 -0.668 (0.51) 962 0.178 (0.77) 81 0.130 (0.257) 935 -0.165 (0.696) 137 -0.209 (0.218) 1,941 0.052 (0.539) 710 -0.819 (0.696) 263 Hispanic 0.226* (0.136) 2,313 1.134 (0.909) 310 0.417** (0.189) 1,918 0.449 (0.274) 1,540 -0.148 (0.187) 1,421 0.940** (0.371) 1,576 -0.193 (0.167) 994 -0.203 (0.163) 2,325 -0.187 (0.294) 1,127 0.997 (0.696) 88 0.393 (0.272) 945 0.856 (0.929) 277 0.116 (0.245) 1,915 0.614* (0.35) 1,039 0.344 (0.466) 304 Black or Hispanic 0.168 (0.102) 2,780 0.352 (0.449) 453 0.420*** (0.159) 2,113 0.276 (0.223) 1,654 0.083 (0.098) 2,909 0.746*** (0.266) 1,703 -0.213 (0.143) 1,800 0.069 (0.131) 2,547 -0.355 (0.258) 1,166 0.553 (0.527) 109 0.253 (0.201) 1,150 0.563 (0.574) 435 -0.061 (0.169) 2,081 0.508* (0.293) 1,085 -0.036 (0.373) 414 Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2013-2016 Department Cromwell Department of Motor Vehicle Danbury Darien Derby Eastern CT State University East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.047 (0.376) 623 0.029 (0.308) 639 0.541* (0.293) 791 0.102 (0.244) 952 0.006 (0.249) 1,174 0.820 (1.765) 82 -0.224 (1.12) 87 -0.190 (0.155) 1,442 -0.274 (0.291) 936 0.553 (0.536) 384 -1.185 (0.803) 148 0.003 (0.121) 4,523 -0.320*** (0.123) 3,111 0.370* (0.213) 1,531 0.003 (0.195) 2,154 Black -0.036 (0.419) 596 0.229 (0.317) 601 0.489 (0.327) 753 0.112 (0.27) 924 0.211 (0.268) 1,153 0.820 (1.765) 82 -18.567*** (2.525) 42 -0.182 (0.156) 1,404 -0.196 (0.349) 864 0.513 (0.544) 357 -2.325 (1.484) 85 -0.032 (0.137) 4,413 -0.321** (0.136) 3,004 0.330 (0.268) 1,432 -0.063 (0.273) 2,019 Hispanic -0.340 (0.626) 463 -0.299 (0.388) 557 -0.040 (0.193) 1,000 0.572** (0.252) 979 -0.184 (0.232) 1,140 0.568 (0.712) 108 821.832 (.) 48 -0.087 (0.172) 1,188 -0.299 (0.234) 1,044 1.377 (0.925) 288 0.038 (0.693) 188 0.076 (0.158) 4,230 -0.126 (0.158) 2,934 0.034 (0.251) 1,436 0.004 (0.255) 2,045 Black or Hispanic -0.109 (0.339) 655 0.067 (0.265) 703 0.062 (0.177) 1,098 0.337* (0.194) 1,082 -0.050 (0.184) 1,338 0.647 (0.812) 148 0.807 (1.291) 134 -0.142 (0.139) 1,906 -0.284 (0.201) 1,119 0.745 (0.469) 466 -0.289 (0.587) 232 0.019 (0.108) 4,655 -0.249** (0.109) 3,359 0.190 (0.194) 1,566 -0.017 (0.19) 2,184 Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2013-2016 Department Granby Greenwich Groton City Groton Long Point Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middletown Milford Monroe Naugatuck VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.327 (0.573) 423 0.119 (0.169) 1,783 -0.111 (0.259) 886 67.500 (.) 10 0.593*** (0.188) 1,651 -0.136 (0.273) 2,340 0.185 (0.167) 1,203 -0.030 (0.169) 1,282 0.011 (0.353) 1,222 0.146 (0.11) 2,647 0.155 (0.254) 716 0.290 (0.217) 938 -0.241 (0.26) 1,053 -0.101 (0.222) 2,053 0.080 (0.22) 1,935 Black -0.156 (0.612) 388 0.017 (0.212) 1,662 -0.175 (0.311) 828 Hispanic 0.468 (0.746) 203 -0.035 (0.162) 1,794 -0.364 (0.311) 814 Black or Hispanic 0.149 (0.483) 411 -0.044 (0.138) 1,996 -0.250 (0.236) 952 0.653*** (0.212) 1,570 -0.431 (0.412) 2,149 0.171 (0.168) 1,189 -0.008 (0.171) 1,262 0.119 (0.471) 1,108 0.159 (0.121) 2,506 0.276 (0.266) 699 0.241 (0.224) 919 -0.295 (0.315) 1,005 0.089 (0.255) 2,012 -0.033 (0.236) 1,902 0.390 (0.239) 1,511 -0.222 (0.302) 2,204 0.053 (0.256) 864 -0.033 (0.187) 949 1.326** (0.515) 1,306 -0.108 (0.14) 2,288 0.047 (0.201) 869 -0.091 (0.29) 794 -0.113 (0.314) 982 0.657** (0.268) 2,076 0.072 (0.181) 1,994 0.574*** (0.17) 1,701 -0.259 (0.249) 2,420 0.144 (0.155) 1,308 -0.001 (0.161) 1,734 0.701** (0.336) 1,398 0.061 (0.1) 2,915 0.098 (0.176) 1,014 0.122 (0.188) 1,003 -0.184 (0.228) 1,101 0.393** (0.192) 2,208 0.006 (0.154) 2,156 Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2013-2016 Department New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.061 (0.118) 2,256 0.236 (0.225) 2,409 -0.089 (0.071) 6,567 -0.051 (0.238) 882 0.219 (0.259) 1,891 0.019 (0.166) 1,918 0.204 (0.165) 5,625 -0.275 (0.726) 139 0.375 (0.238) 1,103 0.431** (0.188) 1,038 -0.514*** (0.158) 1,783 0.147 (0.271) 1,573 0.226 (0.161) 1,732 -0.143 (0.557) 518 0.077 (0.276) 1,433 Black 0.009 (0.122) 2,188 0.494* (0.295) 2,327 -0.081 (0.072) 6,431 0.014 (0.259) 827 0.203 (0.299) 1,758 0.003 (0.185) 1,822 0.173 (0.201) 5,505 -0.576 (1.199) 61 0.360 (0.257) 1,078 0.393** (0.192) 1,010 -0.368** (0.166) 1,705 0.110 (0.326) 1,414 0.034 (0.172) 1,664 -0.216 (0.563) 516 -0.127 (0.32) 1,405 Hispanic 0.034 (0.095) 3,009 0.308 (0.227) 2,415 0.066 (0.084) 4,816 0.162 (0.211) 928 0.623*** (0.237) 1,990 -0.048 (0.157) 2,001 -0.088 (0.183) 5,545 1.967** (0.862) 152 -0.079 (0.291) 991 -0.037 (0.2) 978 0.405** (0.18) 1,630 0.137 (0.341) 1,548 -0.299 (0.222) 1,532 -0.111 (0.556) 469 -0.144 (0.257) 1,498 Black or Hispanic 0.030 (0.088) 3,598 0.379** (0.187) 2,524 -0.030 (0.065) 8,170 0.028 (0.181) 1,086 0.456** (0.191) 2,100 -0.046 (0.128) 2,230 0.036 (0.138) 5,841 0.141 (0.604) 224 0.121 (0.203) 1,162 0.183 (0.158) 1,271 -0.012 (0.132) 1,984 0.053 (0.244) 1,651 -0.074 (0.145) 1,863 -0.167 (0.403) 626 -0.194 (0.212) 1,583 Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2013-2016 Department Plymouth Putnam Redding Ridgefield Rocky Hill Southern CT State University Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.606 (0.554) 611 0.384 (0.68) 324 0.091 (0.459) 740 0.411* (0.225) 3,165 0.048 (0.183) 1,867 -0.053 (0.321) 368 0.241 (0.251) 2,212 0.561 (0.835) 111 0.420 (0.32) 1,798 -0.093 (0.217) 1,133 0.267 (0.386) 2,394 -0.148 (0.157) 1,611 -0.019 (0.325) 995 -0.021 (0.279) 432 -0.139 (0.415) 549 Black 0.790 (0.597) 539 0.501 (0.69) 259 0.040 (0.51) 629 0.059 (0.29) 2,968 -0.063 (0.217) 1,772 -0.085 (0.325) 363 0.270 (0.268) 2,190 0.426 (1.172) 73 0.165 (0.365) 1,667 -0.081 (0.258) 1,078 0.453 (0.428) 2,261 -0.202 (0.177) 1,522 0.285 (0.374) 953 0.029 (0.288) 418 0.402 (0.528) 478 Hispanic -0.737 (0.687) 507 -0.312 (1.607) 97 0.156 (0.452) 737 0.282 (0.193) 3,246 0.071 (0.225) 1,755 0.148 (0.677) 155 0.512* (0.275) 2,122 0.404 (0.781) 109 0.475 (0.48) 1,535 0.467 (0.315) 991 0.152 (0.265) 2,499 0.080 (0.166) 1,587 -0.453 (0.627) 552 -0.169 (0.301) 380 -0.957* (0.505) 426 Black or Hispanic 0.073 (0.479) 693 0.279 (0.618) 398 0.088 (0.349) 841 0.210 (0.164) 3,391 -0.003 (0.165) 1,955 -0.021 (0.308) 399 0.396** (0.198) 2,311 0.514 (0.562) 192 0.253 (0.288) 1,777 0.143 (0.208) 1,159 0.269 (0.232) 2,564 -0.065 (0.132) 1,864 0.031 (0.348) 1,069 -0.049 (0.239) 536 -0.200 (0.355) 596 Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2013-2016 Department Thomaston Torrington Trumbull University of Connecticut Vernon Western CT State University Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.045 (0.8) 216 -0.286 (0.28) 2,029 -0.216 (0.221) 728 -0.099 (0.27) 664 0.423* (0.221) 1,669 0.000 (.) 5 0.262 (0.188) 2,361 0.690* (0.352) 352 0.062 (0.167) 2,078 -0.549 (0.686) 379 -0.066 (0.144) 2,212 0.001 (0.153) 1,585 -0.332 (1.757) 36 0.069 (0.151) 2,755 0.277 (0.194) 1,133 Black 0.902 (1.163) 148 -0.287 (0.314) 1,954 -0.244 (0.254) 672 -0.247 (0.393) 579 0.426* (0.225) 1,656 0.000 (.) 7 0.316 (0.213) 2,310 0.749** (0.36) 349 0.127 (0.194) 2,012 -0.029 (0.789) 285 -0.006 (0.168) 2,090 0.060 (0.159) 1,550 0.676 (1.576) 29 0.026 (0.17) 2,678 0.243 (0.203) 1,108 Hispanic -0.492 (0.804) 229 -0.204 (0.258) 1,996 0.109 (0.278) 693 -0.191 (0.438) 505 -0.229 (0.255) 1,558 Black or Hispanic -0.019 (0.594) 361 -0.249 (0.206) 2,147 -0.082 (0.193) 821 -0.321 (0.314) 634 0.131 (0.176) 1,793 0.227 (0.181) 2,396 0.523 (0.352) 341 0.256 (0.195) 2,040 -0.153 (0.506) 510 -0.152 (0.156) 2,123 0.049 (0.16) 1,495 0.000 (.) 9 0.183 (0.188) 2,623 0.072 (0.178) 1,178 0.255* (0.145) 2,561 0.673** (0.283) 488 0.217 (0.144) 2,243 -0.114 (0.443) 592 -0.083 (0.123) 2,442 0.028 (0.128) 1,907 -0.948 (1.633) 51 0.124 (0.132) 2,916 0.149 (0.147) 1,391 Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2013-2016 Department Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Yale University CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.218 (0.381) 466 0.180 (0.199) 2,055 0.021 (0.116) 2,262 0.261 (0.224) 1,099 2.860* (1.594) 287 1.160 (0.809) 205 -0.189 (0.269) 618 0.082 (0.243) 488 0.141 (0.103) 6,336 -0.015 (0.113) 5,872 0.399* (0.219) 2,959 0.346*** (0.073) 10,919 0.313** (0.124) 6,151 0.078 (0.07) 10,526 0.109 (0.09) 8,645 Black 0.157 (0.386) 441 0.113 (0.244) 1,976 -0.037 (0.118) 2,170 0.215 (0.238) 1,069 2.605* (1.441) 278 1.174 (0.824) 189 -0.037 (0.286) 597 0.030 (0.255) 457 0.120 (0.112) 6,055 -0.039 (0.127) 5,674 0.129 (0.252) 2,910 0.214** (0.094) 10,208 0.388** (0.16) 5,950 0.039 (0.083) 10,032 0.121 (0.104) 8,348 Hispanic 0.254 (0.224) 711 -0.058 (0.192) 2,105 0.369** (0.179) 1,496 0.516 (0.336) 981 -0.933 (1.002) 203 1.682** (0.832) 208 0.332 (0.388) 516 -0.539 (0.463) 278 0.143 (0.143) 5,687 -0.084 (0.108) 5,911 0.643** (0.252) 2,851 0.186* (0.1) 10,048 -0.094 (0.14) 5,932 -0.025 (0.096) 9,710 0.224** (0.109) 8,247 Black or Hispanic 0.251 (0.208) 769 0.004 (0.159) 2,244 0.041 (0.111) 2,441 0.329 (0.204) 1,158 0.691 (0.803) 329 1.319** (0.638) 287 0.140 (0.25) 663 -0.067 (0.237) 511 0.142 (0.095) 6,741 -0.074 (0.088) 6,579 0.357* (0.186) 3,131 0.207*** (0.072) 10,954 0.119 (0.109) 6,292 0.005 (0.067) 10,783 0.149* (0.079) 9,052 Table III.C.7.3: Logistic Regression of Minority Status on Daylight by Department, All Moving Violations 2013-2016 Department CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.327*** (0.079) 6,745 0.245** (0.099) 5,513 0.091 (0.113) 4,024 0.221** (0.105) 7,273 0.133 (0.189) 3,415 Black 0.252*** (0.085) 6,374 0.227** (0.107) 5,243 0.065 (0.125) 3,840 0.222* (0.119) 7,063 0.229 (0.216) 3,352 Hispanic 0.137 (0.089) 6,170 0.206* (0.12) 4,906 0.116 (0.139) 3,665 0.245** (0.125) 6,974 0.170 (0.214) 3,444 Black or Hispanic 0.187*** (0.068) 7,747 0.217** (0.087) 6,111 0.087 (0.101) 4,345 0.221** (0.091) 7,540 0.194 (0.157) 3,621 Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations 2013-2016 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Capitol Police Central CT State University Canton Cheshire Clinton Coventry VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.130 (0.13) 2,385 -0.264 (0.576) 392 0.345 (0.238) 1,803 0.208 (0.348) 1,116 0.131 (0.108) 2,628 0.595 (0.382) 1,382 -0.319 (0.24) 826 0.380* (0.205) 2,089 -0.996** (0.418) 860 -175.269 (.) 14 0.076 (0.266) 928 -0.079 (0.809) 450 -0.286 (0.237) 1,721 -0.419 (0.427) 842 -0.358 (0.659) 316 Black 0.104 (0.135) 2,339 0.325 (0.69) 294 0.369 (0.256) 1,726 0.025 (0.397) 1,032 0.146 (0.109) 2,561 0.763* (0.41) 1,357 -0.336 (0.244) 799 0.389* (0.219) 2,021 -1.271** (0.608) 723 2.934 (2.405) 30 0.110 (0.282) 899 -1.321 (1.434) 113 -0.301 (0.254) 1,679 0.770 (0.641) 502 -1.884** (0.884) 193 Hispanic 0.253* (0.143) 2,247 2.032** (1.003) 218 0.256 (0.195) 1,823 0.299 (0.356) 1,105 -0.230 (0.205) 1,330 1.003** (0.415) 1,314 -0.052 (0.268) 586 -0.092 (0.179) 2,165 -0.309 (0.342) 919 1.974 (1.309) 46 0.196 (0.311) 871 0.765 (0.78) 213 0.178 (0.294) 1,618 0.320 (0.396) 929 -0.022 (0.546) 261 Black or Hispanic 0.176 (0.108) 2,724 0.776 (0.551) 377 0.277* (0.165) 2,025 0.188 (0.265) 1,267 0.102 (0.106) 2,754 0.853*** (0.296) 1,546 -0.233 (0.223) 1,136 0.093 (0.143) 2,404 -0.597** (0.293) 981 0.107 (0.227) 1,120 0.627 (0.641) 366 -0.092 (0.194) 1,869 0.461 (0.344) 964 -0.393 (0.465) 363 Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations 2013-2016 Department Cromwell DMV Danbury Darien Derby Eastern CT State University East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.263 (0.545) 334 -0.205 (0.43) 420 0.340 (0.406) 506 -0.079 (0.281) 830 0.076 (0.291) 1,062 64.126 (.) 25 1.256 (2.017) 40 -0.242 (0.169) 1,277 -0.259 (0.335) 775 1.071 (0.78) 219 -50.294*** (11.281) 55 -0.027 (0.141) 3,855 -0.294** (0.132) 2,943 0.264 (0.218) 1,445 0.078 (0.204) 2,027 Black 0.070 (0.659) 302 0.030 (0.45) 364 0.152 (0.462) 451 0.018 (0.308) 783 0.304 (0.313) 1,037 64.126 (.) 25 406.690 (.) 15 -0.234 (0.17) 1,241 -0.158 (0.412) 700 1.088 (0.818) 201 0.000 (.) 14 -0.053 (0.159) 3,699 -0.311** (0.148) 2,792 0.193 (0.275) 1,345 0.083 (0.291) 1,848 Hispanic -1.150 (0.918) 195 0.249 (0.442) 354 -0.408 (0.272) 671 0.448* (0.271) 863 -0.140 (0.279) 1,000 0.851 (1.105) 73 10.148*** (1.592) 30 -0.072 (0.19) 1,046 -0.064 (0.273) 897 0.832 (2.476) 109 -0.890 (1.734) 48 0.032 (0.182) 3,590 0.061 (0.172) 2,781 0.186 (0.293) 1,295 0.057 (0.275) 1,856 Black or Hispanic -0.248 (0.48) 417 0.219 (0.35) 478 -0.267 (0.246) 746 0.232 (0.214) 948 0.035 (0.217) 1,207 1.186 (0.967) 85 -0.172 (0.151) 1,705 -0.077 (0.236) 977 0.740 (0.714) 268 -1.678 (1.289) 74 0.006 (0.125) 4,085 -0.181 (0.118) 3,206 0.197 (0.207) 1,474 0.065 (0.203) 2,057 Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations 2013-2016 Department Granby Greenwich Groton City Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middletown Milford Monroe Naugatuck New Britain VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.282 (0.559) 342 0.415** (0.205) 1,536 -0.187 (0.28) 801 0.349 (0.223) 1,277 -0.117 (0.308) 1,719 0.190 (0.196) 1,093 -0.197 (0.289) 766 0.114 (0.397) 1,088 0.262** (0.124) 2,444 0.056 (0.437) 340 0.708** (0.29) 710 -0.457 (0.372) 532 -0.108 (0.246) 1,989 0.123 (0.23) 1,841 0.015 (0.134) 2,006 Black -0.064 (0.623) 309 0.309 (0.263) 1,347 -0.265 (0.337) 744 0.413* (0.251) 1,218 -0.498 (0.484) 1,130 0.161 (0.199) 1,079 -0.194 (0.297) 754 0.214 (0.538) 993 0.228* (0.136) 2,306 0.122 (0.454) 336 0.657** (0.307) 682 -0.711 (0.517) 386 0.059 (0.284) 1,927 -0.012 (0.246) 1,811 -0.025 (0.14) 1,938 Hispanic 0.335 (0.763) 167 0.257 (0.208) 1,531 -0.517 (0.348) 725 0.518* (0.293) 1,149 -0.185 (0.353) 1,594 0.092 (0.323) 742 0.029 (0.306) 575 1.423** (0.573) 1,082 -0.041 (0.155) 2,044 0.099 (0.347) 415 -0.211 (0.344) 627 -0.066 (0.533) 395 0.588** (0.286) 1,910 0.135 (0.195) 1,899 0.015 (0.107) 2,708 Black or Hispanic 0.189 (0.487) 357 0.194 (0.17) 1,715 -0.367 (0.257) 856 0.495** (0.201) 1,391 -0.266 (0.292) 1,984 0.144 (0.18) 1,190 -0.070 (0.257) 1,014 0.711* (0.375) 1,304 0.127 (0.111) 2,708 0.060 (0.3) 484 0.310 (0.233) 805 -0.270 (0.352) 488 0.358* (0.206) 2,134 0.052 (0.163) 2,063 0.000 (0.097) 3,234 Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations 2013-2016 Department New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.207 (0.239) 2,358 0.123 (0.087) 5,248 0.247 (0.295) 675 -0.011 (0.315) 1,412 0.076 (0.173) 1,792 0.162 (0.178) 5,375 0.127 (1.017) 106 0.396 (0.281) 1,017 0.567** (0.228) 834 -0.512*** (0.187) 1,393 0.099 (0.335) 1,135 0.209 (0.179) 1,652 -0.293 (0.788) 252 -0.008 (0.3) 1,350 0.359 (0.604) 538 Black 0.438 (0.322) 2,278 0.135 (0.088) 5,159 0.399 (0.326) 619 0.105 (0.339) 1,290 0.084 (0.192) 1,685 0.103 (0.216) 5,154 2.025 (1.545) 48 0.291 (0.303) 995 0.550** (0.236) 795 -0.323 (0.198) 1,329 0.013 (0.398) 911 0.036 (0.194) 1,588 -0.103 (0.833) 226 -0.143 (0.347) 1,239 0.528 (0.607) 461 Hispanic 0.400 (0.251) 2,351 0.125 (0.099) 3,859 0.336 (0.282) 690 0.831*** (0.268) 1,559 -0.045 (0.164) 1,872 -0.139 (0.191) 5,261 2.850** (1.305) 95 -0.007 (0.339) 852 0.200 (0.242) 752 0.589*** (0.222) 1,224 -0.041 (0.354) 1,132 -0.469* (0.24) 1,460 -0.030 (0.866) 238 -0.137 (0.279) 1,382 -1.040 (1.069) 356 Black or Hispanic 0.425** (0.204) 2,458 0.128* (0.077) 6,566 0.312 (0.234) 818 0.585*** (0.218) 1,688 0.003 (0.135) 2,091 -0.014 (0.146) 5,631 0.781 (0.879) 190 0.079 (0.24) 1,077 0.335* (0.189) 1,009 0.100 (0.159) 1,570 -0.029 (0.285) 1,273 -0.152 (0.158) 1,782 -0.141 (0.607) 403 -0.186 (0.227) 1,488 0.142 (0.504) 613 Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations 2013-2016 Department Putnam Redding Ridgefield Rocky Hill Southern CT State University Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.484 (0.845) 229 0.058 (0.459) 669 0.295 (0.23) 3,049 0.040 (0.208) 1,739 -0.225 (0.436) 317 0.198 (0.269) 2,034 2.652 (1.682) 74 0.246 (0.336) 1,602 -0.057 (0.24) 1,102 -0.058 (0.417) 2,136 -0.183 (0.225) 1,162 0.060 (0.392) 829 -0.038 (0.393) 269 -0.165 (0.478) 430 -0.325 (1.237) 70 Black -0.691 (0.877) 168 0.144 (0.543) 553 -0.074 (0.304) 2,822 0.020 (0.244) 1,633 -0.277 (0.443) 313 0.271 (0.292) 2,012 193.840 (.) 44 -0.011 (0.387) 1,483 -0.041 (0.277) 1,027 0.167 (0.457) 1,989 -0.300 (0.257) 1,084 0.411 (0.464) 700 0.036 (0.411) 258 0.424 (0.727) 332 16.841*** (1.33) 23 Hispanic -77.194 (.) 28 0.248 (0.477) 712 0.266 (0.211) 3,162 0.154 (0.26) 1,587 0.257 (1.084) 118 0.425 (0.293) 1,972 1.000 (2.153) 44 0.159 (0.475) 1,206 0.463 (0.336) 927 -0.071 (0.274) 2,005 0.258 (0.242) 1,153 -0.451 (0.669) 283 0.870* (0.52) 213 -0.081 (0.591) 254 -0.327 (1.647) 90 Black or Hispanic -0.850 (0.77) 220 0.154 (0.371) 813 0.158 (0.174) 3,305 0.071 (0.185) 1,831 -0.209 (0.415) 344 0.343 (0.212) 2,149 1.318 (1.248) 92 0.051 (0.298) 1,585 0.191 (0.224) 1,133 0.037 (0.24) 2,347 0.019 (0.193) 1,337 0.194 (0.408) 877 0.286 (0.331) 330 0.298 (0.439) 469 0.015 (0.902) 163 Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations 2013-2016 Department Torrington Trumbull University of Connecticut Vernon Western CT State University Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.271 (0.354) 1,606 0.188 (0.262) 639 -0.093 (0.306) 579 0.533** (0.24) 1,514 0.000 (.) 7 0.422* (0.223) 1,808 0.000 (.) 22 0.058 (0.183) 2,028 0.120 (1.125) 155 0.067 (0.157) 2,027 0.093 (0.217) 1,324 0.000 (.) 8 -0.027 (0.163) 2,339 0.330 (0.218) 1,025 0.685 (0.444) 320 Black -0.341 (0.415) 1,534 0.199 (0.292) 594 -0.401 (0.482) 492 0.513** (0.244) 1,502 Hispanic 0.148 (0.307) 1,688 0.241 (0.364) 462 0.018 (0.52) 390 -0.235 (0.265) 1,349 Black or Hispanic -0.103 (0.256) 1,891 0.219 (0.236) 735 -0.279 (0.38) 545 0.192 (0.187) 1,634 0.521** (0.263) 1,697 0.000 (.) 22 0.161 (0.21) 1,964 0.541 (1.314) 133 0.193 (0.18) 1,909 0.208 (0.223) 1,293 0.000 (.) 13 -0.071 (0.186) 2,242 0.299 (0.229) 1,003 0.543 (0.448) 291 0.272 (0.22) 1,810 145.216*** (6.634) 39 0.217 (0.21) 1,964 -0.401 (0.694) 216 -0.077 (0.171) 1,965 0.080 (0.215) 1,242 0.329* (0.177) 2,051 52.410 (18177166.684) 59 0.234 (0.157) 2,188 -0.256 (0.632) 297 0.074 (0.136) 2,305 0.119 (0.177) 1,589 0.143 (0.21) 2,211 0.087 (0.203) 1,067 0.217 (0.285) 552 0.063 (0.146) 2,496 0.189 (0.164) 1,270 0.282 (0.262) 600 Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations 2013-2016 Department Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Yale CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E CSP Troop F CSP Troop G VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.151 (0.21) 1,951 -0.057 (0.133) 2,037 0.241 (0.236) 1,070 21.181*** (3.76) 159 1.442 (1.26) 99 0.005 (0.297) 590 0.071 (0.505) 233 0.143 (0.114) 5,890 -0.033 (0.116) 5,487 0.396* (0.239) 2,748 0.234*** (0.079) 10,617 0.394*** (0.13) 5,977 0.118 (0.074) 10,155 0.118 (0.096) 8,239 0.264*** (0.083) 6,577 Black 0.104 (0.258) 1,828 -0.119 (0.135) 1,957 0.187 (0.252) 1,040 20.405*** (2.319) 152 1.442 (1.26) 99 0.107 (0.317) 571 0.012 (0.546) 217 0.093 (0.123) 5,624 -0.014 (0.13) 5,174 0.127 (0.267) 2,653 0.079 (0.099) 9,903 0.460*** (0.168) 5,783 0.085 (0.088) 9,673 0.126 (0.112) 7,881 0.199** (0.09) 6,212 Hispanic -0.023 (0.208) 2,018 0.657*** (0.21) 1,344 0.449 (0.363) 896 -2.366* (1.346) 115 2.572*** (0.986) 147 0.391 (0.391) 492 -0.150 (0.934) 104 0.007 (0.16) 5,210 -0.016 (0.115) 5,477 0.383 (0.255) 2,509 0.138 (0.108) 9,494 -0.112 (0.15) 5,531 -0.002 (0.102) 9,334 0.255** (0.116) 7,730 0.052 (0.095) 5,982 Black or Hispanic 0.029 (0.171) 2,149 0.021 (0.126) 2,193 0.284 (0.217) 1,125 0.949 (1.113) 203 1.937** (0.788) 195 0.207 (0.264) 632 0.066 (0.471) 249 0.061 (0.105) 6,235 -0.021 (0.094) 6,192 0.225 (0.196) 2,967 0.121 (0.077) 10,675 0.146 (0.116) 6,121 0.043 (0.071) 10,446 0.165* (0.085) 8,751 0.116 (0.072) 7,554 Table III.C.7.4: Logistic Regression of Minority Status on Daylight with Officer Fixed-Effects by Department, All Moving Violations 2013-2016 Department CSP Troop H CSP Troop I CSP Troop K CSP Troop L VOD Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.237** (0.105) 5,169 0.188 (0.122) 3,856 0.260** (0.111) 6,961 0.015 (0.209) 2,885 Black 0.191* (0.114) 4,922 0.178 (0.133) 3,641 0.249** (0.127) 6,630 0.063 (0.236) 2,788 Hispanic 0.218* (0.128) 4,581 0.189 (0.151) 3,414 0.290** (0.138) 6,080 0.347 (0.236) 2,837 Black or Hispanic 0.196** (0.094) 5,747 0.194* (0.108) 4,121 0.248** (0.098) 7,255 0.200 (0.172) 3,185 Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-2016 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 305,077 0.000 (.) 193,044 13.452*** (0.023) 271,655 -1.192*** (0.038) 258,671 1.331*** (0.019) 411,921 0.000 (.) 123,329 0.000 (.) 169,499 -1.303*** (0.037) 236,459 -0.521*** (0.049) 474,222 0.000 (.) 112,756 0.000 (.) 517,105 0.000 (.) 16,867 0.000 (.) 98,557 0.122*** (0.041) 134,719 Black 0.000 (.) 305,077 0.000 (.) 193,044 -0.263*** (0.032) 271,655 -1.348*** (0.044) 258,671 1.425*** (0.019) 411,921 0.000 (.) 123,329 0.000 (.) 169,499 -1.413*** (0.039) 236,459 4.287*** (0.059) 474,222 0.000 (.) 112,756 0.000 (.) 517,105 0.000 (.) 16,867 0.000 (.) 98,557 0.163*** (0.043) 134,719 Hispanic 0.000 (.) 305,077 0.000 (.) 193,044 0.507*** (0.032) 271,655 3.063*** (0.031) 258,671 1.603*** (0.032) 411,921 0.000 (.) 123,329 0.000 (.) 169,499 -0.963*** (0.036) 236,459 -1.042*** (0.042) 474,222 0.000 (.) 112,756 0.000 (.) 517,105 0.000 (.) 16,867 0.000 (.) 98,557 14.777*** (0.061) 134,719 Black or Hispanic 0.000 (.) 305,077 0.000 (.) 193,044 0.134*** (0.024) 271,655 157.379*** (0.026) 258,671 0.786*** (0.019) 411,921 0.000 (.) 123,329 0.000 (.) 169,499 -1.540*** (0.032) 236,459 -1.981*** (0.035) 474,222 0.000 (.) 112,756 0.000 (.) 517,105 0.000 (.) 16,867 0.000 (.) 98,557 -0.172*** (0.037) 134,719 Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-2016 Department Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 189,786 0.000 (.) 187,249 -0.270*** (0.029) 410,998 -0.671*** (0.118) Black 0.000 (.) 189,786 0.000 (.) 187,249 -0.224*** (0.03) 410,998 -0.600*** (0.13) Hispanic 0.000 (.) 189,786 0.000 (.) 187,249 -0.376*** (0.032) 410,998 -0.888*** (0.149) Black or Hispanic 0.000 (.) 189,786 0.000 (.) 187,249 -0.366*** (0.024) 410,998 29.994*** (0.099) 0.000 (.) 253,231 0.000 (.) 134,888 0.000 (.) 2,999 5.834*** (0.108) 314,421 0.000 (.) 145,747 0.000 (.) 227,258 0.000 (.) 204,310 -18.852*** (0.025) 445,837 0.000 (.) 131,742 0.000 (.) 48,499 0.000 (.) 253,231 0.000 (.) 134,888 0.000 (.) 2,999 11.669*** (0.124) 314,421 0.000 (.) 145,747 0.000 (.) 227,258 0.000 (.) 204,310 -0.216*** (0.042) 445,837 0.000 (.) 131,742 0.000 (.) 48,499 0.000 (.) 253,231 0.000 (.) 134,888 0.000 (.) 2,999 -0.319*** (0.087) 314,421 0.000 (.) 145,747 0.000 (.) 227,258 0.000 (.) 204,310 5.004*** (0.03) 445,837 0.000 (.) 131,742 0.000 (.) 48,499 0.000 (.) 253,231 0.000 (.) 134,888 0.000 (.) 2,999 -0.933*** (0.074) 314,421 0.000 (.) 145,747 0.000 (.) 227,258 0.000 (.) 204,310 -0.120*** (0.032) 445,837 0.000 (.) 131,742 0.000 (.) 48,499 Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-2016 Department Groton City Groton Long Point Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.239*** (0.04) Black 0.208*** (0.044) Hispanic 0.464*** (0.049) Black or Hispanic 0.354*** (0.036) -1.744*** (0.308) -1.733*** (0.339) -1.149*** (0.308) 57.238*** (0.231) -0.173*** (0.041) -0.129*** (0.044) 10.112*** (0.03) -0.264*** (0.036) 0.000 (.) 142,414 0.000 (.) 225,598 0.000 (.) 508,527 0.000 (.) 186,196 0.678*** (0.019) 397,469 -0.316*** (0.03) 0.000 (.) 142,414 0.000 (.) 225,598 0.000 (.) 508,527 0.000 (.) 186,196 0.647*** (0.02) 397,469 -0.289*** (0.031) 0.000 (.) 142,414 0.000 (.) 225,598 0.000 (.) 508,527 0.000 (.) 186,196 0.204*** (0.023) 397,469 0.686*** (0.025) 0.000 (.) 142,414 0.000 (.) 225,598 0.000 (.) 508,527 0.000 (.) 186,196 0.550*** (0.017) 397,469 0.337*** (0.023) 0.000 (.) 203,097 0.372*** (0.028) 391,994 0.230*** (0.035) 421,729 0.000 (.) 105,305 -0.522*** (0.026) 317,853 0.000 (.) 203,097 0.451*** (0.029) 391,994 0.193*** (0.037) 421,729 0.000 (.) 105,305 -0.480*** (0.027) 317,853 0.000 (.) 203,097 -0.236*** (0.039) 391,994 0.190*** (0.041) 421,729 0.000 (.) 105,305 10.999*** (0.025) 317,853 0.000 (.) 203,097 0.208*** (0.025) 391,994 0.221*** (0.03) 421,729 0.000 (.) 105,305 0.998*** (0.019) 317,853 Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-2016 Department New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 227,173 0.000 (.) 214,249 0.000 (.) 214,641 0.000 (.) 386,734 0.000 (.) 97,154 0.000 (.) 267,943 0.000 (.) 109,910 0.000 (.) 286,179 0.000 (.) 126,348 0.000 (.) 90,349 0.000 (.) 19,061 0.000 (.) 149,447 0.348*** (0.028) 254,968 0.000 (.) 4,674 Black 0.000 (.) 227,173 0.000 (.) 214,249 0.000 (.) 214,641 0.000 (.) 386,734 0.000 (.) 97,154 0.000 (.) 267,943 0.000 (.) 109,910 0.000 (.) 286,179 0.000 (.) 126,348 0.000 (.) 90,349 0.000 (.) 19,061 0.000 (.) 149,447 0.358*** (0.03) 254,968 0.000 (.) 4,674 Hispanic 0.000 (.) 227,173 0.000 (.) 214,249 0.000 (.) 214,641 0.000 (.) 386,734 0.000 (.) 97,154 0.000 (.) 267,943 0.000 (.) 109,910 0.000 (.) 286,179 0.000 (.) 126,348 0.000 (.) 90,349 0.000 (.) 19,061 0.000 (.) 149,447 0.098*** (0.033) 254,968 0.000 (.) 4,674 Black or Hispanic 0.000 (.) 227,173 0.000 (.) 214,249 0.000 (.) 214,641 0.000 (.) 386,734 0.000 (.) 97,154 0.000 (.) 267,943 0.000 (.) 109,910 0.000 (.) 286,179 0.000 (.) 126,348 0.000 (.) 90,349 0.000 (.) 19,061 0.000 (.) 149,447 0.291*** (0.024) 254,968 0.000 (.) 4,674 Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-2016 Department Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 212,067 0.000 (.) 194,126 0.000 (.) 196,127 0.000 (.) 76,667 0.000 (.) 169,657 0.000 (.) 146,465 0.195*** (0.028) 387,545 15.907*** (0.036) Black 0.000 (.) 212,067 0.000 (.) 194,126 0.000 (.) 196,127 0.000 (.) 76,667 0.000 (.) 169,657 0.000 (.) 146,465 0.048 (0.031) 387,545 -0.571*** (0.039) Hispanic 0.000 (.) 212,067 0.000 (.) 194,126 0.000 (.) 196,127 0.000 (.) 76,667 0.000 (.) 169,657 0.000 (.) 146,465 0.704*** (0.035) 387,545 -0.705*** (0.041) Black or Hispanic 0.000 (.) 212,067 0.000 (.) 194,126 0.000 (.) 196,127 0.000 (.) 76,667 0.000 (.) 169,657 0.000 (.) 146,465 2.939*** (0.025) 387,545 -0.709*** (0.029) 0.497*** (0.082) 349,842 -0.601*** (0.04) 305,297 0.000 (.) 222,195 -1.094*** (0.043) 310,948 0.000 (.) 92,104 0.000 (.) 49,874 4.877*** (0.089) 349,842 -0.675*** (0.046) 305,297 0.000 (.) 222,195 -1.001*** (0.047) 310,948 0.000 (.) 92,104 0.000 (.) 49,874 1.038*** (0.086) 349,842 -1.092*** (0.059) 305,297 0.000 (.) 222,195 -0.527*** (0.039) 310,948 0.000 (.) 92,104 0.000 (.) 49,874 0.263*** (0.065) 349,842 -0.905*** (0.038) 305,297 0.000 (.) 222,195 -0.799*** (0.031) 310,948 0.000 (.) 92,104 0.000 (.) 49,874 Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-2016 Department Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.842*** (0.024) 346,525 0.000 (.) 208,960 0.000 (.) 151,812 0.000 (.) 115,740 0.430*** (0.027) 330,636 -0.087*** (0.025) 369,142 -0.263*** (0.021) Black 0.949*** (0.024) 346,525 0.000 (.) 208,960 0.000 (.) 151,812 0.000 (.) 115,740 0.581*** (0.028) 330,636 0.519 (.) 369,142 -0.250*** (0.022) Hispanic 0.640*** (0.029) 346,525 0.000 (.) 208,960 0.000 (.) 151,812 0.000 (.) 115,740 0.583*** (0.031) 330,636 -4.014*** (0.033) 369,142 0.243*** (0.02) Black or Hispanic 1.071*** (0.022) 346,525 0.000 (.) 208,960 0.000 (.) 151,812 0.000 (.) 115,740 0.702*** (0.023) 330,636 -1.033*** (0.022) 369,142 0.022 (0.016) 0.000 (.) 183,586 0.000 (.) 172,019 -1.235*** (0.052) 300,904 0.000 (.) 53,024 0.000 (.) 227,075 0.000 (.) 153,234 -0.811*** (0.022) 257,212 0.000 (.) 183,586 0.000 (.) 172,019 0.740*** (0.053) 300,904 0.000 (.) 53,024 0.000 (.) 227,075 0.000 (.) 153,234 -0.870*** (0.024) 257,212 0.000 (.) 183,586 0.000 (.) 172,019 2.727*** (0.057) 300,904 0.000 (.) 53,024 0.000 (.) 227,075 0.000 (.) 153,234 -0.567*** (0.026) 257,212 0.000 (.) 183,586 0.000 (.) 172,019 0.258*** (0.041) 300,904 0.000 (.) 53,024 0.000 (.) 227,075 0.000 (.) 153,234 -0.864*** (0.019) 257,212 Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-2016 Department Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -0.199*** (0.026) 220,004 0.000 (.) 254,743 0.000 (.) 180,899 0.000 (.) 214,071 0.419*** (0.031) 151,341 0.368*** (0.096) 163,929 -0.225*** (0.085) Black -0.172*** (0.027) 220,004 0.000 (.) 254,743 0.000 (.) 180,899 0.000 (.) 214,071 0.425*** (0.033) 151,341 3.922*** (0.101) 163,929 -0.083 (0.088) Hispanic 1.175*** (0.027) 220,004 0.000 (.) 254,743 0.000 (.) 180,899 0.000 (.) 214,071 -0.301*** (0.051) 151,341 2.272*** (0.111) 163,929 0.087 (0.083) Black or Hispanic 0.633*** (0.022) 220,004 0.000 (.) 254,743 0.000 (.) 180,899 0.000 (.) 214,071 0.098*** (0.028) 151,341 0.521*** (0.082) 163,929 -0.075 (0.066) 0.000 (.) 150,739 -0.002 (0.014) 652,945 -0.306*** (0.103) 233,446 -0.306*** (0.039) 652,945 -0.618*** (0.108) 652,867 0.470 (0.514) 253,896 -0.064 (0.137) 597,864 0.000 (.) 150,739 -0.011 (0.015) 652,945 -0.391*** (0.107) 233,446 -0.274*** (0.044) 652,945 -0.976*** (0.113) 652,867 0.307 (0.589) 253,896 0.083 (0.163) 597,864 0.000 (.) 150,739 -0.075*** (0.017) 652,945 -0.252** (0.102) 233,446 -0.389*** (0.048) 652,945 -0.983*** (0.12) 652,867 -0.183 (0.466) 253,896 -0.404** (0.167) 597,864 0.000 (.) 150,739 -0.048*** (0.012) 652,945 -0.346*** (0.084) 233,446 8.228*** (0.023) 652,945 -1.153*** (0.093) 652,867 0.128 (0.399) 253,896 -0.159 (0.125) 597,864 Table III.D.1.1: Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-2016 Department CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.030 (0.06) 652,950 1584.850 (.) 180,269 0.260*** (0.084) 639,239 0.291*** (0.015) 652,945 0.260 (0.198) 378,779 688.076*** (0.02) 652,942 Black 0.073 (0.057) 652,950 0.584* (0.328) 180,269 0.239*** (0.088) 639,239 0.349*** (0.016) 652,945 0.197 (0.228) 378,779 -0.290*** (0.027) 652,942 Hispanic 0.169** (0.073) 652,950 0.943** (0.439) 180,269 0.166 (0.104) 639,239 0.187*** (0.019) 652,945 0.725*** (0.233) 378,779 0.091*** (0.026) 652,942 Black or Hispanic 0.124** (0.05) 652,950 193.480 (.) 180,269 0.261*** (0.075) 639,239 0.338*** (0.014) 652,945 0.436** (0.178) 378,779 -0.117*** (0.02) 652,942 Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department Ansonia Avon Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry Cromwell Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 305,077 0.000 (.) 193,044 0.714 (.) 271,655 2.739 (.) 258,671 0.801 (2.059) 411,921 0.000 (.) 123,329 0.000 (.) 169,499 -2.146 (.) 236,459 -0.860 (.) 474,222 0.000 (.) 112,504 0.000 (.) 515,977 0.000 (.) 16,867 0.000 (.) 98,556 1.203 (.) 134,719 Black 0.000 (.) 305,077 0.000 (.) 193,044 0.342 (.) 271,655 1.900*** (0.488) 258,671 0.819 (2.762) 411,921 0.000 (.) 123,329 0.000 (.) 169,499 -1.220 (.) 236,459 -0.873 (.) 474,222 0.000 (.) 112,504 0.000 (.) 515,977 0.000 (.) 16,867 0.000 (.) 98,556 0.101 (.) 134,719 Hispanic 0.000 (.) 305,077 0.000 (.) 193,044 -0.055 (.) 271,655 6.453*** (0.047) 258,671 -0.527*** (0.066) 411,921 0.000 (.) 123,329 0.000 (.) 169,499 4.415 (.) 236,459 -0.470 (.) 474,222 0.000 (.) 112,504 0.000 (.) 515,977 0.000 (.) 16,867 0.000 (.) 98,556 -0.812 (.) 134,719 Black or Hispanic 0.000 (.) 305,077 0.000 (.) 193,044 0.698 (.) 271,655 5.017*** (0.328) 258,671 0.648 (.) 411,921 0.000 (.) 123,329 0.000 (.) 169,499 1.424 (.) 236,459 -0.965 (.) 474,222 0.000 (.) 112,504 0.000 (.) 515,977 0.000 (.) 16,867 0.000 (.) 98,556 -0.183 (.) 134,719 Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington Glastonbury Granby Greenwich Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 189,786 0.000 (.) 187,249 1.063*** (0.164) 410,586 -0.474*** (0.138) Black 0.000 (.) 189,786 0.000 (.) 187,249 1.289*** (0.217) 410,586 10.831 (.) Hispanic 0.000 (.) 189,786 0.000 (.) 187,249 -1.236*** (0.178) 410,586 -99.707 (.) Black or Hispanic 0.000 (.) 189,786 0.000 (.) 187,249 -0.267** (0.111) 410,586 -0.301** (0.14) 0.000 (.) 253,231 0.000 (.) 134,888 0.000 (.) 2,999 -0.549 (.) 314,421 0.000 (.) 145,743 0.000 (.) 227,258 0.000 (.) 204,310 1.186 (.) 445,837 0.000 (.) 131,742 0.000 (.) 48,499 0.000 (.) 253,231 0.000 (.) 134,888 0.000 (.) 2,999 -0.629 (.) 314,421 0.000 (.) 145,743 0.000 (.) 227,258 0.000 (.) 204,310 -0.308 (.) 445,837 0.000 (.) 131,742 0.000 (.) 48,499 0.000 (.) 253,231 0.000 (.) 134,888 0.000 (.) 2,999 0.587 (.) 314,421 0.000 (.) 145,743 0.000 (.) 227,258 0.000 (.) 204,310 -4.468 (.) 445,837 0.000 (.) 131,742 0.000 (.) 48,499 0.000 (.) 253,231 0.000 (.) 134,888 0.000 (.) 2,999 0.205 (.) 314,421 0.000 (.) 145,743 0.000 (.) 227,258 0.000 (.) 204,310 4.655*** (0.324) 445,837 0.000 (.) 131,742 0.000 (.) 48,499 Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department Groton City Groton Long Point Groton Town Guilford Hamden Hartford Madison Manchester Meriden Middlebury Middletown Milford Monroe Naugatuck Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.246*** (0.041) Black 0.232*** (0.045) Hispanic 0.483*** (0.051) Black or Hispanic 0.383*** (0.037) -1.662*** (0.32) -1.613*** (0.361) 5.224 (.) -1.450*** (0.242) -0.178*** (0.041) -0.152*** (0.045) -0.416*** (0.05) -0.289*** (0.036) 0.000 (.) 142,414 0.000 (.) 225,598 0.000 (.) 508,527 0.000 (.) 186,196 0.455 (.) 397,469 -0.228*** (0.031) 0.000 (.) 142,414 0.000 (.) 225,598 0.000 (.) 508,527 0.000 (.) 186,196 0.390** (0.196) 397,469 -0.194*** (0.032) 0.000 (.) 142,414 0.000 (.) 225,598 0.000 (.) 508,527 0.000 (.) 186,196 -0.093*** (0.023) 397,469 0.726*** (0.026) 0.000 (.) 142,414 0.000 (.) 225,598 0.000 (.) 508,527 0.000 (.) 186,196 0.344 (.) 397,469 0.335*** (0.024) 0.000 (.) 203,038 0.912*** (0.169) 391,994 0.462*** (0.042) 421,729 0.000 (.) 105,305 4.876 (.) 317,853 0.000 (.) 203,038 1.512 (.) 391,994 0.447** (0.18) 421,729 0.000 (.) 105,305 6.275*** (0.269) 317,853 0.000 (.) 203,038 3.327 (.) 391,994 0.090 (0.403) 421,729 0.000 (.) 105,305 3.499*** (1.066) 317,853 0.000 (.) 203,038 2.128*** (0.039) 391,994 4.405 (.) 421,729 0.000 (.) 105,305 4.930*** (0.048) 317,853 Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department New Britain New Canaan New Haven New London New Milford Newington Newtown North Branford North Haven Norwalk Norwich Old Saybrook Orange Plainfield Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 227,173 0.000 (.) 214,249 0.000 (.) 214,641 0.000 (.) 386,734 0.000 (.) 97,154 0.000 (.) 267,943 0.000 (.) 109,910 0.000 (.) 286,179 0.000 (.) 126,348 0.000 (.) 90,349 0.000 (.) 19,061 0.000 (.) 149,447 0.384*** (0.043) 254,968 0.000 (.) 4,674 Black 0.000 (.) 227,173 0.000 (.) 214,249 0.000 (.) 214,641 0.000 (.) 386,734 0.000 (.) 97,154 0.000 (.) 267,943 0.000 (.) 109,910 0.000 (.) 286,179 0.000 (.) 126,348 0.000 (.) 90,349 0.000 (.) 19,061 0.000 (.) 149,447 -9.962 (.) 254,968 0.000 (.) 4,674 Hispanic 0.000 (.) 227,173 0.000 (.) 214,249 0.000 (.) 214,641 0.000 (.) 386,734 0.000 (.) 97,154 0.000 (.) 267,943 0.000 (.) 109,910 0.000 (.) 286,179 0.000 (.) 126,348 0.000 (.) 90,349 0.000 (.) 19,061 0.000 (.) 149,447 -15.430*** (0.04) 254,968 0.000 (.) 4,674 Black or Hispanic 0.000 (.) 227,173 0.000 (.) 214,249 0.000 (.) 214,641 0.000 (.) 386,734 0.000 (.) 97,154 0.000 (.) 267,943 0.000 (.) 109,910 0.000 (.) 286,179 0.000 (.) 126,348 0.000 (.) 90,349 0.000 (.) 19,061 0.000 (.) 149,447 0.692** (0.318) 254,968 0.000 (.) 4,674 Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department Plainville Plymouth Portland Putnam Redding Ridgefield Rocky Hill Seymour Shelton Simsbury South Windsor Southington Stamford Stonington Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.000 (.) 212,067 0.000 (.) 194,122 0.000 (.) 196,127 0.000 (.) 76,667 0.000 (.) 169,653 0.000 (.) 146,465 0.230 (.) 387,545 -0.264*** (0.037) Black 0.000 (.) 212,067 0.000 (.) 194,122 0.000 (.) 196,127 0.000 (.) 76,667 0.000 (.) 169,653 0.000 (.) 146,465 0.230 (.) 387,545 -0.211*** (0.04) Hispanic 0.000 (.) 212,067 0.000 (.) 194,122 0.000 (.) 196,127 0.000 (.) 76,667 0.000 (.) 169,653 0.000 (.) 146,465 -0.033 (0.034) 387,545 -0.406*** (0.041) Black or Hispanic 0.000 (.) 212,067 0.000 (.) 194,122 0.000 (.) 196,127 0.000 (.) 76,667 0.000 (.) 169,653 0.000 (.) 146,465 0.196 (.) 387,545 -0.379*** (0.03) 1.020 (.) 349,842 2.114 (.) 305,297 0.000 (.) 222,195 -0.381 (0.245) 310,948 0.000 (.) 92,104 0.000 (.) 49,873 1.088 (.) 349,842 166.848 (.) 305,297 0.000 (.) 222,195 38.851 (.) 310,948 0.000 (.) 92,104 0.000 (.) 49,873 0.448 (.) 349,842 -0.788*** (0.071) 305,297 0.000 (.) 222,195 -0.349*** (0.076) 310,948 0.000 (.) 92,104 0.000 (.) 49,873 1.356 (.) 349,842 -0.538*** (0.047) 305,297 0.000 (.) 222,195 -0.625*** (0.047) 310,948 0.000 (.) 92,104 0.000 (.) 49,873 Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department Stratford Suffield Thomaston Torrington Trumbull Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 0.721 (6.154) 346,525 0.000 (.) 208,960 0.000 (.) 151,812 0.000 (.) 115,740 -0.506 (.) 330,636 0.962 (.) 369,142 -0.047** (0.022) Black 0.885 (.) 346,525 0.000 (.) 208,960 0.000 (.) 151,812 0.000 (.) 115,740 0.100 (.) 330,636 1.268 (.) 369,142 0.032 (0.024) Hispanic 0.224 (.) 346,525 0.000 (.) 208,960 0.000 (.) 151,812 0.000 (.) 115,740 -0.216 (.) 330,636 1.997 (.) 369,142 0.393*** (0.021) Black or Hispanic 0.723 (2.931) 346,525 0.000 (.) 208,960 0.000 (.) 151,812 0.000 (.) 115,740 0.222 (0.306) 330,636 1.930 (.) 369,142 0.245*** (0.017) 0.000 (.) 183,586 0.000 (.) 172,019 -0.097 (0.114) 300,903 0.000 (.) 53,024 0.000 (.) 227,063 0.000 (.) 153,234 -0.267*** (0.043) 257,212 0.000 (.) 183,586 0.000 (.) 172,019 0.154 (.) 300,903 0.000 (.) 53,024 0.000 (.) 227,063 0.000 (.) 153,234 -0.101*** (0.034) 257,212 0.000 (.) 183,586 0.000 (.) 172,019 0.614*** (0.172) 300,903 0.000 (.) 53,024 0.000 (.) 227,063 0.000 (.) 153,234 -0.789 (.) 257,212 0.000 (.) 183,586 0.000 (.) 172,019 0.403 (.) 300,903 0.000 (.) 53,024 0.000 (.) 227,063 0.000 (.) 153,234 -0.323 (.) 257,212 Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department Wethersfield Willimantic Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian -3.166 (.) 220,004 0.000 (.) 254,743 0.000 (.) 180,899 0.000 (.) 214,071 0.695 (.) 151,077 -0.443 (.) 163,929 -0.175* (0.091) Black -5.037 (.) 220,004 0.000 (.) 254,743 0.000 (.) 180,899 0.000 (.) 214,071 0.728 (.) 151,077 -0.477 (.) 163,929 -0.026 (0.096) Hispanic 8.747*** (0.074) 220,004 0.000 (.) 254,743 0.000 (.) 180,899 0.000 (.) 214,071 -0.400 (.) 151,077 -1.717 (.) 163,929 114.332 (.) Black or Hispanic -3.319 (.) 220,004 0.000 (.) 254,743 0.000 (.) 180,899 0.000 (.) 214,071 0.471 (.) 151,077 -1.238 (.) 163,929 -0.063 (0.068) 0.000 (.) 150,739 -0.034** (0.015) 652,938 0.141 (0.217) 233,441 -0.224*** (0.043) 652,938 -2.049 (1.56) 652,865 -243.793 (.) 253,889 -1.911 (.) 597,847 0.000 (.) 150,739 -0.029* (0.017) 652,938 -0.753 (1.139) 233,441 62.647 (.) 652,938 -2.211 (47.965) 652,865 -106.946 (.) 253,889 -328.650 (.) 597,847 0.000 (.) 150,739 -0.030 (0.019) 652,938 -4.167 (.) 233,441 -0.309*** (0.061) 652,938 -3.214*** (1.172) 652,865 -23.345 (.) 253,889 -83.730*** (0.594) 597,847 0.000 (.) 150,739 -0.036*** (0.013) 652,938 -0.346 (0.377) 233,441 -0.266*** (0.039) 652,938 -0.272 (0.373) 652,865 -23.561 (140.609) 253,889 0.041 (0.144) 597,847 Table III.D.1.2: Doubly-Robust Inverse Propensity Score Weighted Logistic Regression of Minority Status on Department, All Traffic Stops 2013-16 Department CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L Estimate Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Coefficient SE ESS Non-Caucasian 1.279 (.) 652,938 -14.097 (.) 180,263 0.330*** (0.045) 639,230 0.254*** (0.016) 652,938 -10.618 (.) 378,776 -0.508*** (0.027) 652,938 Black 0.022 (0.05) 652,938 -1.364 (.) 180,263 -0.541*** (0.091) 639,230 0.309*** (0.017) 652,938 -8.868 (.) 378,776 -0.206*** (0.034) 652,938 Hispanic 0.124** (0.058) 652,938 -23.772*** (4.233) 180,263 -99.950*** (1.166) 639,230 0.131*** (0.019) 652,938 -15.485 (.) 378,776 0.035 (0.027) 652,938 Black or Hispanic 0.079** (0.04) 652,938 0.057 (2.631) 180,263 0.352*** (0.043) 639,230 0.279*** (0.014) 652,938 -6.482 (.) 378,776 -0.252*** (0.021) 652,938 Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 Ansonia Berlin Bethel Bloomfield Branford Bridgeport Bristol Brookfield Canton Cheshire Clinton Coventry White 15.7% N/A 115 White 62.7% N/A 67 White 67.3% N/A 52 White 58.7% N/A 46 White 30.3% N/A 142 White 0.8% N/A 1588 White 51% N/A 153 White 30.6% N/A 147 White 59% N/A 39 White 51.6% N/A 159 White 53.6% N/A 349 White 34.6% N/A 78 Non-White 10.9% 0.614 46 Non-White 54.5% 0.460 22 Non-White 40% 2.680 10 Non-White 46.2% 2.372 208 Non-White 17.6% 1.179 17 Non-White 1.8%*** 7.207 1766 Non-White 54.3% 0.161 46 Non-White 42.9% 1.263 21 Non-White 50% 0.172 6 Non-White 34.9%* 3.777 43 Non-White 64.7% 0.808 17 Non-White 37.5% 0.027 8 Black Hispanic Black or Hispanic 11.9% 15.9% 13.1% 0.346 0.002 0.255 42 44 84 Black Hispanic Black or Hispanic 52.4% 53.3% 52% 0.709 0.754 1.343 21 30 50 Black Hispanic Black or Hispanic 40% 40%* 40%** 2.680 3.656 5.184 10 15 25 Black Hispanic Black or Hispanic 46.6% 59.3% 48.1% 2.201 0.002 1.738 206 27 231 Black Hispanic Black or Hispanic 18.8% 33.3% 26.5% 0.927 0.070 0.191 16 18 34 Black Hispanic Black or Hispanic 1.9%*** 1.1% 1.6%** 8.436 0.860 5.243 1652 1291 2889 Black Hispanic Black or Hispanic 55.6% 54.8% 54.3% 0.292 0.288 0.293 45 73 116 Black Hispanic Black or Hispanic 50% 31.6% 38.2% 2.472 0.007 0.737 16 19 34 Black Hispanic Black or Hispanic 60% 33.3% 50% 0.002 0.748 0.219 5 3 8 Black Hispanic Black or Hispanic 34.9%* 22.9%*** 29.9%*** 3.777 9.511 9.893 43 35 77 Black Hispanic Black or Hispanic 64.3% 43.3% 48.8% 0.621 1.164 0.346 14 30 43 Black Hispanic Black or Hispanic 37.5% 28.6% 33.3% 0.027 0.104 0.009 8 7 15 Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 Cromwell Danbury Darien Derby East Hampton East Hartford East Haven East Windsor Easton Enfield Fairfield Farmington White 66.7% N/A 39 White 4.1% N/A 386 White 54.8% N/A 93 White 4.4% N/A 340 White 26% N/A 146 White 50.9% N/A 267 White 43.2% N/A 74 White 42.9% N/A 28 White 2.6% N/A 39 White 46.3% N/A 246 White 59.7% N/A 201 White 59.4% N/A 165 Non-White Black Hispanic 76.9% 83.3% 0.481 1.228 3.644 13 12 2 Non-White Black Hispanic 17%*** 18.6%*** 5.9% 13.268 15.316 0.748 47 43 153 Non-White Black Hispanic 65.9% 65.9% 56.1% 1.419 1.419 0.018 41 41 41 Non-White Black Hispanic 7% 7% 11.1%* 0.561 0.561 3.045 43 43 36 Non-White Black Hispanic 45.5% 55.6%* 50% 1.941 3.687 1.681 11 9 6 Non-White Black Hispanic 45.8% 45.9% 41%** 1.906 1.798 5.475 546 540 283 Non-White Black Hispanic 40% 40% 54.5% 0.038 0.038 0.873 10 10 22 Non-White Black Hispanic 12.5% 12.5% 75% 2.485 2.485 1.452 8 8 4 Non-White Black Hispanic Black or Hispanic 71.4% 0.107 14 Black or Hispanic 8.7%** 5.057 195 Black or Hispanic 61.7% 0.844 81 Black or Hispanic 9.1%* 2.751 77 Black or Hispanic 57.1%** 6.038 14 Black or Hispanic 43.9%** 3.998 813 Black or Hispanic 50% 0.412 32 Black or Hispanic 33.3% 0.317 12 Black or Hispanic 0.079 3 Non-White 49% 0.114 49 Non-White 50%* 2.842 118 Non-White 55% 0.256 40 0.079 3 Black or Hispanic 40.5% 0.773 74 Black or Hispanic 54.3% 1.178 197 Black or Hispanic 50% 1.800 72 0.053 2 Black 45.5% 0.012 44 Black 50.4% 2.521 113 Black 52.9% 0.483 34 0.026 1 Hispanic 36.4% 1.170 33 Hispanic 59.1% 0.009 88 Hispanic 50% 1.163 40 Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 Glastonbury Granby Greenwich Groton City Groton Town Hamden Hartford Madison Manchester Meriden Middlebury Middletown White 58.7% N/A 254 White 76.7% N/A 43 White 26.3% N/A 80 White 41% N/A 61 White 62.3% N/A 159 White 20% N/A 40 White 1.2% N/A 1636 White 53.4% N/A 58 White 54.2% N/A 212 White 31% N/A 142 White Non-White 51.6% 1.012 62 Non-White 50% 1.932 6 Non-White 22.4% 0.235 49 Non-White 40.8% 0.000 49 Non-White 56.3% 0.691 64 Non-White 18.2% 0.062 99 Non-White 1.8% 2.034 2167 Non-White 50% 0.009 2 Non-White 53.6% 0.022 267 Non-White 35.9% 0.698 117 Non-White Black 50.8% 1.194 59 Black 50% 1.932 6 Black 22.2% 0.251 45 Black 41.7% 0.005 48 Black 56.5% 0.631 62 Black 18.2% 0.062 99 Black 1.8% 2.334 2109 Black 50% 0.009 2 Black 53.3% 0.046 261 Black 36.2% 0.783 116 Black Hispanic 43.9%** 4.138 57 Hispanic 83.3% 0.131 6 Hispanic 26.4% 0.000 72 Hispanic 28.1% 1.496 32 Hispanic 42.4%** 4.442 33 Hispanic 16.7% 0.090 18 Hispanic 1.9% 2.588 1385 Hispanic 27.3% 2.535 11 Hispanic 50.4% 0.434 115 Hispanic 33.8% 0.256 139 Hispanic Black or Hispanic 48.2%* 3.434 112 Black or Hispanic 66.7% 0.502 12 Black or Hispanic 23.7% 0.166 114 Black or Hispanic 36.4% 0.307 77 Black or Hispanic 51.1%* 2.939 90 Black or Hispanic 18.3% 0.059 115 Black or Hispanic 1.9%* 2.848 3421 Black or Hispanic 30.8% 2.185 13 Black or Hispanic 52.2% 0.235 370 Black or Hispanic 35.5% 0.810 251 Black or Hispanic N/A 116 5 White Non-White 46.6% 47.4% N/A 0.039 410 213 5 Black 47.4% 0.037 211 6 Hispanic 47.9% 0.046 73 11 Black or Hispanic 47.2% 0.022 282 Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 Milford Monroe Naugatuck New Britain New Canaan New Haven New London New Milford Newington Newtown White 43.4% N/A 403 White 50% N/A 108 White 26.8% N/A 585 White 37.3% N/A 249 White 62.4% N/A 109 White 17.3% N/A 329 White 35.1% N/A 205 White 51.9% N/A 131 White 34.3% N/A 207 White 21.8% N/A 316 White Non-White Black Hispanic 34.4%** 34.9%** 33.3%* 4.419 3.855 3.578 192 189 108 Non-White Black Hispanic 20%** 16.7%*** 50% 6.129 6.920 20 18 18 Non-White Black Hispanic 28.8% 30.2% 28.2% 0.187 0.507 0.085 111 106 110 Non-White Black Hispanic 39.1% 38.7% 37.1% 0.166 0.098 0.004 266 261 442 Non-White Black Hispanic 61.3% 63% 65.5% 0.012 0.003 0.096 31 27 29 Non-White Black Hispanic 12.5%** 12.5%** 12.9%* 5.318 5.500 3.227 1522 1515 521 Non-White Black Hispanic 35% 34.5% 32.5% 0.000 0.012 0.231 120 113 120 Non-White Black Hispanic 41.7% 40.9% 68.8% 0.851 0.912 1.627 24 22 16 Non-White Black Hispanic 21.6%** 22%** 31.4% 5.784 5.122 0.336 116 109 156 Non-White Black Hispanic 20% 22.9% 29.6% 0.071 0.019 0.868 40 35 27 Non-White Black Hispanic North Branford North Haven White Non-White 39.3% 19.4%** N/A 4.692 107 36 Black 19.4%** 4.692 36 Hispanic 26.1% 1.409 23 Black or Hispanic 33.8%** 6.599 293 Black or Hispanic 31.4%* 3.677 35 Black or Hispanic 29.1% 0.401 206 Black or Hispanic 37.7% 0.010 687 Black or Hispanic 63.6% 0.024 55 Black or Hispanic 12.6%** 5.580 1998 Black or Hispanic 33.5% 0.128 224 Black or Hispanic 57.1% 0.304 35 Black or Hispanic 27.8% 2.333 263 Black or Hispanic 26.2% 0.565 61 Black or Hispanic 42.9%* 3.203 7 Black or Hispanic 22.4%** 4.799 58 Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Putnam Redding Ridgefield Rocky Hill Southern CT State University White 24% N/A 192 White 44.1% N/A 424 White 52.5% N/A 223 White 47.1% N/A 51 White 10.4% N/A 96 White 42.1% N/A 466 White 21.4% N/A 229 White 8.5% N/A 189 White 0.3% N/A 772 White 43.5% N/A 46 White 38.8% N/A 160 White 8.1% N/A 62 Non-White 26.8% 0.511 355 Non-White 38.8% 1.897 273 Non-White 37.5% 1.339 16 Non-White 46.8% 0.001 47 Non-White 28.6% 2.089 7 Non-White 31%* 3.648 84 Non-White 21.1% 0.001 19 Non-White Black 26.6% 0.465 353 Black 38.7% 1.943 266 Black 37.5% 1.339 16 Black 46.8% 0.001 47 Black 40%** 3.973 5 Black 30.5%** 3.880 82 Black 21.1% 0.001 19 Black Hispanic 31.6% 2.632 171 Hispanic 32.7%** 5.910 147 Hispanic 33.3%* 2.811 21 Hispanic 66.7% 1.784 15 Hispanic 0.463 4 Hispanic 49.1% 1.893 116 Hispanic 13.8% 0.912 29 Hispanic Black or Hispanic 28.4% 1.415 517 Black or Hispanic 36.8%** 4.618 400 Black or Hispanic 35.1%* 3.813 37 Black or Hispanic 51.7% 0.234 60 Black or Hispanic 22.2% 1.133 9 Black or Hispanic 40.9% 0.071 193 Black or Hispanic 14.9% 1.020 47 Black or Hispanic 0.369 4 Non-White 1.7%* 3.141 59 Non-White 70% 2.314 10 Non-White 47.5% 1.017 40 Non-White 16.3% 1.693 43 0.277 3 Black 2.3%** 4.608 44 Black 66.7% 1.624 9 Black 45.9% 0.648 37 Black 16.7% 1.815 42 0.553 6 Hispanic 1.1% 1.657 91 Hispanic 71.4% 1.905 7 Hispanic 22.5%* 3.690 40 Hispanic 20% 1.398 10 0.829 9 Black or Hispanic 1.5%** 3.911 135 Black or Hispanic 68.8%* 3.033 16 Black or Hispanic 33.8% 0.553 77 Black or Hispanic 17.3% 2.243 52 Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 Seymour White Non-White 1.8% 8.3%** N/A 4.075 274 24 White Non-White Black 9.5%** 4.991 21 Black Hispanic 4.2% 0.613 24 Hispanic White 60.4% N/A 53 White 68.5% N/A 165 White 64.9% N/A 37 White 16.7% N/A 174 White 64.5% N/A 31 White 32% N/A 175 White 65.1% N/A 43 White 7.6% N/A 144 White 27.3% N/A 227 White 45.5% N/A 154 Non-White 25% 1.910 4 Non-White 67.6% 0.022 105 Non-White 66.7% 0.007 6 Non-White 10.8% 1.699 93 Non-White 50% 0.171 2 Non-White 34.7% 0.297 202 Non-White 80% 0.447 5 Non-White Black 25% 1.910 4 Black 67.6% 0.020 102 Black 66.7% 0.007 6 Black 11% 1.428 82 Black Hispanic 100% 2.509 4 Hispanic 61.5% 0.690 39 Hispanic 71.4% 0.113 7 Hispanic 18.9% 0.252 127 Hispanic Black or Hispanic 7%** 4.010 43 Black or Hispanic 33.3% 0.419 6 Black or Hispanic 62.5% 0.013 8 Black or Hispanic 67.4% 0.041 138 Black or Hispanic 69.2% 0.082 13 Black or Hispanic 16% 0.029 206 Black or Hispanic 1.720 1 Black 35.1% 0.384 194 Black 80% 0.447 5 Black 0.165 2 Non-White 20.7% 1.054 58 Non-White 39.7% 0.687 78 0.165 2 Black 21.8% 0.691 55 Black 40% 0.610 75 4.700 3 Hispanic 28.6% 0.363 105 Hispanic 100%* 3.488 7 Hispanic 25%* 2.921 8 Hispanic 21.4% 0.632 42 Hispanic 42.9% 0.078 35 4.700 3 Black or Hispanic 32.9% 0.038 292 Black or Hispanic 90.9%* 2.795 11 Black or Hispanic 22.2% 2.317 9 Black or Hispanic 22.2% 0.871 90 Black or Hispanic 40.9% 0.539 110 Shelton Simsbury South Windsor Southington Stamford Stonington Stratford Suffield Thomaston Torrington Trumbull Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 University of Connecticut Vernon Wallingford Waterbury Waterford Watertown West Hartford West Haven Weston Westport Wethersfield Willimantic White 65.8% N/A 117 White 61.9% N/A 512 White 51.8% N/A 1036 White 58.6% N/A 152 White 46.9% N/A 311 White 48.1% N/A 27 White 70.5% N/A 1161 White 23.9% N/A 117 White 0.6% N/A 160 White 42.2% N/A 325 White 33.7% N/A 332 White 41.1% N/A 163 Non-White Black Hispanic 65.4% 60% 83.3% 0.002 0.253 1.522 26 20 12 Non-White Black Hispanic 47.9%*** 48.9%*** 50%** 9.151 7.704 4.865 146 141 98 Non-White Black Hispanic 49.1% 49.4% 39.8%*** 0.656 0.487 16.564 279 265 399 Non-White Black Hispanic 37%*** 37.4%*** 30.7%*** 16.135 15.501 22.626 200 198 137 Non-White Black Hispanic 50% 53.9% 42.7% 0.243 1.199 0.475 82 76 82 Non-White Black Hispanic 35.3% 37.5% 40% 0.703 0.462 0.195 17 16 10 Non-White Black Hispanic 51.4%*** 51.8%*** 55%*** 35.298 32.158 30.667 259 245 371 Non-White Black Hispanic 16.4% 16.5% 15.9% 2.010 1.915 1.672 110 109 69 Non-White Black Hispanic Black or Hispanic 68.8% 0.097 32 Black or Hispanic 49.6%*** 10.147 238 Black or Hispanic 43.8%*** 10.477 658 Black or Hispanic 34.5%*** 24.603 330 Black or Hispanic 46.4% 0.014 151 Black or Hispanic 38.5% 0.506 26 Black or Hispanic 53.8%*** 49.128 608 Black or Hispanic 16.3% 2.643 178 Black or Hispanic 0.006 1 Hispanic 40% 0.110 70 Hispanic 29.8% 1.058 262 Hispanic 30.4%* 3.499 125 0.006 1 Black or Hispanic 34.3%* 3.452 230 Black or Hispanic 30.3% 0.998 435 Black or Hispanic 32.4%* 2.759 173 Non-White 32.9%** 4.024 173 Non-White 30.3% 0.650 185 Non-White 35.8% 0.461 53 Black 32.1%** 4.603 162 Black 31.1% 0.364 180 Black 36.7% 0.299 49 Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 Wilton Windsor Windsor Locks Winsted Wolcott Woodbridge Yale University CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D White 65.8% N/A 73 White 50% N/A 34 White 12.4% N/A 169 White 13.6% N/A 301 White 3.2% N/A 62 White 40% N/A 20 White 40.9% N/A 22 White 40.4% N/A 141 White 40.6% N/A 623 White 44% N/A 248 White 44.8% N/A 880 White 49.5% N/A 543 Non-White 72.2% 0.463 36 Non-White 52.1% 0.045 94 Non-White 22.6% 2.243 31 Non-White 13.6% 0.000 22 Non-White Black 70.6% 0.246 34 Black 53.3% 0.106 92 Black 20.7% 1.435 29 Black 13.6% 0.000 22 Black Hispanic 59.3% 0.361 27 Hispanic 52.9% 0.039 17 Hispanic 31.6%** 5.094 19 Hispanic 30.8%* 2.985 13 Hispanic Black or Hispanic 63.8% 0.055 58 Black or Hispanic 54.3% 0.189 105 Black or Hispanic 25%** 4.584 48 Black or Hispanic 21.4% 1.275 28 Black or Hispanic 0.067 0.067 0.033 2 2 1 Non-White Black Hispanic 46.7% 46.7% 0.156 0.156 15 15 Non-White Black Hispanic 23.5% 24% 50% 2.266 2.114 0.382 51 50 24 Non-White Black Hispanic 30.4% 31.1% 27.4%* 2.197 1.814 3.145 79 74 62 Non-White Black Hispanic 31.3%*** 30.3%*** 34%* 8.507 10.465 3.738 367 357 300 Non-White Black Hispanic 42.4% 45.2% 38.2% 0.028 0.016 0.398 33 31 34 Non-White Black Hispanic 42.3% 43.6% 27.2%*** 0.490 0.105 19.885 253 234 191 Non-White Black Hispanic 43.3% 45.7% 42.2% 1.190 0.420 1.655 90 81 90 0.100 3 Black or Hispanic 54.2% 0.878 24 Black or Hispanic 32.4% 0.540 74 Black or Hispanic 28.8%** 4.070 132 Black or Hispanic 31.7%*** 10.818 640 Black or Hispanic 39% 0.480 59 Black or Hispanic 36.6%*** 7.758 418 Black or Hispanic 45.2% 0.908 157 Table III.E.5.1: Chi-Square Test of Hit-Rate by Departments, All Consent and Other Searches 2013-2016 CSP Troop E CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L White 35.9% N/A 638 White 51.7% N/A 302 White 37.2% N/A 422 White 36.4% N/A 360 White 42.8% N/A 173 White 39.8% N/A 472 White 43.5% N/A 543 Non-White Black Hispanic 36% 35.9% 28.2% 0.001 0.000 2.340 214 206 103 Non-White Black Hispanic 29.1%*** 28.9%*** 35.3%** 17.373 17.214 5.948 117 114 68 Non-White Black Hispanic 28.7%*** 28.1%*** 25.6%*** 7.491 8.491 10.158 498 477 270 Non-White Black Hispanic 33.2% 32.9% 30%* 0.917 1.088 2.941 464 456 290 Non-White Black Hispanic 28.9%*** 29.2%** 38.8% 6.701 6.272 0.413 149 144 103 Non-White Black Hispanic 32.7% 32.2%* 33.1% 2.534 2.761 1.998 156 146 133 Non-White Black Hispanic 36% 34.9% 35.3% 1.671 2.142 2.349 86 83 102 Black or Hispanic 32.9% 0.802 292 Black or Hispanic 31.8%*** 17.730 176 Black or Hispanic 27.3%*** 12.156 721 Black or Hispanic 31.7% 2.404 726 Black or Hispanic 32.4%** 4.682 238 Black or Hispanic 32.2%** 4.262 270 Black or Hispanic 34.8%** 4.118 178 Table III.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2013-2016 Ansonia Bloomfield White 11.8% N/A 102 White 37.5% N/A 16 White Non-White Black Hispanic 5.1% 5.6% 13.2% 1.389 1.12537273 0.050370673 39 36 38 Non-White Black Hispanic 20.8% 21.7% 27.3% 1.338 1.157502823 0.150673126 24 23 11 Non-White Black Hispanic White Non-White Black White 22.1%* N/A 104 White 6.7% N/A 60 White 28.4% N/A 67 White 60% N/A 35 White 44.3%** N/A 70 White 32.4%*** N/A 111 White 31% N/A 29 White 16% N/A 25 Non-White Black Bethel Hispanic Bloomfield Branford Bridgeport Bristol Canton Cheshire Clinton Coventry Danbury Hispanic 18.2% 2.770 2.499038462 0.090472028 10 9 11 Non-White Black Hispanic 3.1% 3.3% 3.7% 1.419 1.230193515 0.746666667 161 153 108 Non-White Black Hispanic 14.3% 15.4% 43.8% 1.194 0.946580193 2.308768657 14 13 32 Non-White Black Hispanic 40% 50%* 0.716 0.148369565 2.775 5 4 2 Non-White Black Hispanic 19%** 19%** 7.7%*** 4.347 4.347261905 6.197335569 21 21 13 Non-White Black Hispanic 33.3% 0.00400521 12 Non-White Black Hispanic 100% 100% 50% 2.069 2.068965517 0.568965517 1 1 4 Non-White Black Hispanic 33.3% 33.3% 18.2% 1.216 1.216084656 0.026181818 9 9 11 Black or Hispanic 9.6% 0.208097492 73 Black or Hispanic 21.4% 0.000681649 14 Black or Hispanic 33.3% 3.824774056 12 Black or Hispanic 32.1% 0.130049261 28 Black or Hispanic 10% 1.529622378 20 Black or Hispanic 3.2% 1.562714392 250 Black or Hispanic 34.1% 0.410770519 44 Black or Hispanic 33.3% 1.478904992 6 Black or Hispanic 15.2% 8.372941749 33 Black or Hispanic 33.3% 0.00400521 12 Black or Hispanic 60% 1.56677116 5 Black or Hispanic 26.3% 0.70605848 19 Table III.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2013-2016 Darien Derby East Hartford East Haven Enfield Fairfield Farmington Glastonbury Greenwich Groton City Hamden Hartford White 45.3% N/A 53 White 12.3% N/A 57 White 43.1% N/A 181 White 18.2% N/A 33 White 25.5% N/A 110 White 28.8% N/A 73 White 31.3% N/A 32 White 38.8% N/A 147 White 15.7% N/A 51 White 22% N/A 41 White 20.7% N/A 29 White 10% N/A 20 Non-White Black 50% 50% 0.130 0.129841487 20 20 Non-White Black Hispanic 43.5% 0.02113134 23 Hispanic Black or Hispanic 47.6% 0.051425945 42 Black or Hispanic 1.907 14 Non-White 38.3% 1.098 298 Non-White 50% 1.193 2 Non-White 31.8% 0.381 22 Non-White 26.9% 0.051 52 Non-White 25% 0.065 4 Non-White 36.7% 0.047 30 Non-White 10.3% 0.445 29 Non-White 32% 0.820 25 Non-White 11.4% 1.448 70 Non-White 26.1% 2.303 69 1.640067912 12 Hispanic 33.7% 3.235785981 169 Hispanic 33.3% 0.403045231 3 Hispanic 33.3% 0.492251678 18 Hispanic 26.5% 0.06063449 34 Hispanic 15.4% 1.189903846 13 Hispanic 27.3% 1.535538965 33 Hispanic 9.3% 0.853788292 43 Hispanic 29.2% 0.424735824 24 Hispanic 3.487072946 26 Black or Hispanic 36.5% 2.352369669 457 Black or Hispanic 40% 1.243636364 5 Black or Hispanic 29.7% 0.25961696 37 Black or Hispanic 26.8% 0.072347659 82 Black or Hispanic 17.6% 1.05393728 17 Black or Hispanic 30.5% 1.242806816 59 Black or Hispanic 7.5% 1.99763904 67 Black or Hispanic 30.4% 0.802249988 46 Black or Hispanic 10.3% 2.023504613 78 Black or Hispanic 22.6% 1.641840348 106 1.907346491 14 Black 38.4%* 1.043586199 292 Black 50% 1.193181818 2 Black 30% 0.181038324 20 Black 28% 0.008577833 50 Black 25% 0.065454545 4 Black 35.7% 0.093283582 28 Black 7.7% 0.973832196 26 Black 33.3% 1.015463096 24 Black 11.4% 1.448464627 70 Black 26.5% 2.387266436 68 1.975528365 8 Hispanic 17.9% 0.64614245 39 Table III.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2013-2016 White Non-White Black White Non-White Black White 35.8% N/A 81 White 9.6%*** N/A 73 White 39.5%* N/A 258 White 25.6% N/A 246 White 30% N/A 30 White 16% N/A 188 White 25.7% N/A 152 White 43.2% N/A 44 White 15%*** N/A 254 White 28.6% N/A 28 Non-White 32.9% 0.151 85 Non-White 33.9%*** 11.851 59 Non-White 29.2%* 3.431 106 Non-White 21.2% 0.806 113 Non-White Madison Hispanic 14.3% 0.255041662 7 Hispanic Madison Manchester Meriden Middletown Milford Monroe Naugatuck New Britain New Canaan New Haven New London 1.632 4 Non-White 22.4% 1.145 49 Non-White 22.2% 0.426 117 Non-White 12.5% 2.692 8 Non-White 8.4%*** 9.923 989 Non-White 33.3% 0.117 18 Black Hispanic 32.9% 34.1% 0.150603561 0.036617972 85 44 Black Hispanic 34.5%* 20%*** 12.24065226 3.239026893 58 80 Black Hispanic 29.5% 34.8%* 3.221792108 0.37124388 105 46 Black Hispanic 21.6% 17.8% 0.660059176 1.887944054 111 73 Black Hispanic 16.7% 1.632 0.443076923 4 6 Black Hispanic 22.9% 18.6% 1.290071366 0.178431824 48 43 Black Hispanic 22.4% 18.8% 0.376891098 2.480078184 116 218 Black Hispanic 12.5% 62.5% 2.692329545 1.016504329 8 8 Black Hispanic 8.4%* 9.9%*** 9.783707121 3.652441229 985 355 Black Hispanic 29.4% 10.5% 0.00363607 2.200528348 17 19 Black or Hispanic 9.1% 1.291866029 11 Black or Hispanic 12.5% 0.418599919 8 Black or Hispanic 32.3% 0.274292928 127 Black or Hispanic 26.3% 8.145234913 137 Black or Hispanic 30.2% 3.566174751 149 Black or Hispanic 19.8% 1.999342492 182 Black or Hispanic 10% 1.6 10 Black or Hispanic 20.9% 1.009849566 86 Black or Hispanic 20% 1.955166032 330 Black or Hispanic 33.3% 0.449630231 15 Black or Hispanic 8.8% 8.986455611 1312 Black or Hispanic 20.6% 0.533571727 34 Table III.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2013-2016 New Milford Newington North Haven Norwalk Norwich Old Saybrook Orange Plainfield Plainville Plymouth Rocky Hill Seymour White 30.2% N/A 43 White 11.4% N/A 70 White 25.9%** N/A 81 White 14.2% N/A 141 White 32.9% N/A 292 White 35.8% N/A 53 White 22.7% N/A 22 White 9.9% N/A 81 White 25.5% N/A 204 White 14.4% N/A 90 White 25% N/A 84 White Non-White 18.2% 0.634 11 Non-White 20% 1.400 35 Non-White 4%** 5.584 25 Non-White 18.4% 1.119 228 Non-White 26% 2.369 169 Non-White Black 20% 0.418637291 10 Black 21.9% 1.910615764 32 Black 4% 5.583992304 25 Black 18.5% 1.15745954 227 Black 26.1%* 2.314268697 165 Black 2.151 4 Non-White 25% 0.026 16 Non-White 16.7% 0.278 6 Non-White 18.4% 0.868 38 Non-White 15.4% 0.008 13 Non-White 28.6% 0.081 14 Non-White 2.150943396 4 Black 25% 0.026471613 16 Black 25% 0.920894701 4 Black 18.9%* 0.731518147 37 Black 15.4% 0.008069801 13 Black 28.6% 0.080547945 14 Black Hispanic 0.429107277 1 Hispanic 17% 0.780747956 53 Hispanic 22.2%* 0.107027027 18 Hispanic 18.9% 1.081673422 127 Hispanic 23.3%** 3.301019236 103 Hispanic 2.150943396 4 Hispanic Hispanic 0.436107103 4 Hispanic 39.6% 3.813733882 48 Hispanic 15.8% 0.022661212 19 Hispanic 11.1% 1.637314254 18 Hispanic Black or Hispanic 20% 0.418637291 10 Black or Hispanic 18.8% 1.604122565 85 Black or Hispanic 11.9% 3.261838677 42 Black or Hispanic 19% 1.582570335 348 Black or Hispanic 24.8% 4.324966033 258 Black or Hispanic 4.165318958 8 Black or Hispanic 31.8% 0.458333333 22 Black or Hispanic 12.5% 0.055129029 8 Black or Hispanic 29.8% 0.554283583 84 Black or Hispanic 12.9% 0.045356112 31 Black or Hispanic 18.8% 0.506866417 32 Black or Hispanic 9.1% 2.0625 11 Table III.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2013-2016 Simsbury South Windsor Stamford Stratford Torrington Trumbull University of Connecticut Vernon Wallingford Waterbury Waterford West Hartford White 48.3% N/A 29 White 16.3% N/A 43 White 15.2% N/A 46 White 16.8% N/A 107 White 20.2% N/A 114 White 26.8% N/A 71 White 56% N/A 75 White 47.5%* N/A 320 White 42.6% N/A 244 White 35.5%*** N/A 62 White 35.4%* N/A 130 White 49%*** N/A 510 Non-White 25% 0.768 4 Non-White 21.1% 0.206 19 Non-White 8.1% 0.978 37 Non-White 23.7% 1.603 114 Non-White 15.2% 0.419 33 Non-White 18.2% 0.908 33 Non-White 60% 0.081 15 Non-White 37.5% 3.353 112 Non-White 33.3% 1.578 54 Non-White 9.8%*** 14.169 82 Non-White 51.5%** 2.888 33 Non-White 32.1%** 8.254 84 Black 25% 0.768103448 4 Black 22.2% 0.303208833 18 Black 5.6% 1.929425952 36 Black 24.1% 1.736481565 108 Black 15.2% 0.418906558 33 Black 18.8% 0.77008216 32 Black 50% 0.150576923 12 Black 38.9% 2.416118894 108 Black 32% 1.93850537 50 Black 10%*** 13.61324117 80 Black 54.8% 3.9772815 31 Black 33.8%*** 6.473569803 80 Hispanic 100% 1.034482759 1 Hispanic 20% 0.079680526 10 Hispanic 22.6% 0.674964937 31 Hispanic 18.7% 0.10349242 75 Hispanic 27.3% 0.55369373 22 Hispanic 33.3% 0.307112676 18 Hispanic 85.7% 2.32907563 7 Hispanic 38.5%* 2.06438903 78 Hispanic 36.5% 0.986172746 85 Hispanic 10.1%*** 12.1644061 69 Hispanic 35.3% 9.65623E-05 34 Hispanic 31.5%*** 15.45038733 165 Black or Hispanic 40% 0.117241379 5 Black or Hispanic 22.2% 0.38738767 27 Black or Hispanic 13.6% 0.055335968 66 Black or Hispanic 22.2% 1.213469365 180 Black or Hispanic 21.2% 0.020989545 52 Black or Hispanic 24% 0.117328891 50 Black or Hispanic 63.2% 0.317730994 19 Black or Hispanic 38.9% 3.497379481 185 Black or Hispanic 34.3% 2.486282307 134 Black or Hispanic 9.6% 20.38933478 146 Black or Hispanic 41.7% 0.692222718 60 Black or Hispanic 32.4% 18.45330639 241 Table III.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2013-2016 West Haven Westport Wethersfield Willimantic White 16.4% N/A 73 White 22.2% N/A 176 White 19.7% N/A 203 White 37.7% N/A 146 White Non-White 9.9% 1.463 81 Non-White 22.1% 0.000 86 Non-White 15.3% 0.776 85 Non-White 30.2% 0.798 43 Non-White Black 10%** 1.392427644 80 Black 20.5% 0.086343084 78 Black 15.9% 0.572120058 82 Black 31.7%* 0.492023054 41 Black Hispanic 3.9%* 4.696558773 51 Hispanic 18.4% 0.258779824 38 Hispanic 14% 1.862586877 136 Hispanic 27.2%* 3.175125548 114 Hispanic White 40% N/A 10 White 29.7% N/A 74 White 30.1% N/A 259 White 30.1% N/A 156 White 35.6% N/A 556 White 39.7% N/A 305 White 27% N/A 371 Non-White 20% 1.496 25 Non-White 17.5% 2.047 40 Non-White 28.7% 0.092 143 Non-White 31.8% 0.026 22 Non-White 40.5% 1.336 173 Non-White 35% 0.460 60 Non-White 24.8% 0.206 109 Black 20.8% 1.332296296 24 Black 17.1% 1.972054367 35 Black 28.4%** 0.133983512 141 Black 35% 0.197829776 20 Black 42.2%*** 2.250033275 154 Black 37% 0.133568701 54 Black 24.5% 0.249607535 106 Hispanic Black or Hispanic 7.6% 3.77716647 131 Black or Hispanic 19.8% 0.227251404 116 Black or Hispanic 14.8% 1.758133401 216 Black or Hispanic 28.6% 2.806904811 154 Black or Hispanic 25% 0.273504274 8 Black or Hispanic Hispanic 17.1%* 1.972054367 35 Hispanic 19.2% 5.029397115 120 Hispanic 36.4% 0.351101325 22 Hispanic 20.4% 12.26302011 147 Hispanic 44.3% 0.444681379 61 Hispanic 24.2% 0.207560538 62 0.294 0.399327731 Black or Hispanic 17.4% 2.999375992 69 Black or Hispanic 23.8% 2.583025209 256 Black or Hispanic 34.2% 0.238379123 38 Black or Hispanic 32.1% 1.058889663 296 Black or Hispanic 41.7% 0.137969985 103 Black or Hispanic 24.1% 0.484493487 158 Windsor Locks Yale University CSP Headquarters CSP Troop A CSP Troop B CSP Troop C CSP Troop D CSP Troop E Table III.E.5.2: Chi-Square Test of Hit-Rate by Departments, All Consent Searches 2013-2016 CSP Troop F CSP Troop G CSP Troop H CSP Troop I CSP Troop K CSP Troop L White 45.6%*** N/A 158 White 18.8% N/A 149 White 26.9% N/A 167 White 40.4%*** N/A 94 White 29.3% N/A 215 White 26.6% N/A 263 Non-White 23.8%*** 8.935 63 Non-White 16.3% 0.397 257 Non-White 20.6% 1.996 194 Non-White 12.5%*** 15.665 72 Non-White 21.5% 1.767 79 Non-White 19.6% 1.023 46 Black 23%*** 9.441045439 61 Black 15.8% 0.596026843 247 Black 20.9%** 1.773736782 191 Black 13.2% 14.16387529 68 Black 22.7% 1.225622001 75 Black 18.2% 1.414814496 44 Hispanic 20.5%*** 8.142603273 39 Hispanic 15.5% 0.556086273 142 Hispanic 15.6%** 5.779244435 141 Hispanic 36.7%** 0.217587646 60 Hispanic 22.5% 1.22256633 71 Hispanic 24.6% 0.105004841 61 Black or Hispanic 22.1% 14.05127071 95 Black or Hispanic 15.6% 0.790729675 372 Black or Hispanic 19.1% 3.944246003 324 Black or Hispanic 24.6% 6.26090937 126 Black or Hispanic 21.6% 2.597297724 139 Black or Hispanic 21.6% 0.993170471 102