The Fifth and Fourteenth Amendments prohibit law enforcement officers from engaging in discriminatory behavior on the basis of individuals’ race, ethnicity, or national origin. The Fifth Amendment protects against discrimination by federal law enforcement officers, and the Equal Protection Clause of the Fourteenth Amendment protects against discrimination by state and local law enforcement officers. Two federal statutes also prohibit discrimination by law enforcement agencies that receive federal financial assistance. Title VI of the Civil Rights Act of 1964prohibits discrimination on the basis of race, color, or national origin by all recipients of federal financial assistance. The Omnibus Crime Control and Safe Streets Act of 1968 prohibits discrimination on the basis of race, color, national origin, sex, or religion by law enforcement agencies that receive federal funds pursuant to that statute. In addition, a 1994 statute grants the Attorney General the authority to seek injunctive relief when a state or local law enforcement agency engages in a pattern or practice of conduct that violates the Constitution or federal law, regardless of whether the agency is a recipient of financial assistance. The Fourth Amendment guarantees the rights of people to be secure from unreasonable searches and seizures. The temporary detention of individuals during the stop of an automobile by police constitutes a seizure of persons within the meaning of the Fourth Amendment. The Supreme Court recently held that regardless of an officer’s actual motivation, a stop of an automobile is reasonable and permitted by the Fourth Amendment when the officer has probable cause to believe that a traffic violation occurred. The Court noted, however, that the Constitution prohibits selective enforcement of the law based on considerations such as race, but the constitutional basis for objecting to intentionally discriminatory application of laws is the equal protection provisions of the Constitution, not the Fourth Amendment. Some have expressed concern that the escalation of this country’s war on drugs has placed minorities at increased risk of discriminatory treatment by law enforcement. The allegation is that law enforcement officers stop minority motorists for minor traffic violations when, in reality, the stop is a pretext to search for drugs or other contraband in the vehicle. In 1986, the Drug Enforcement Administration (DEA) established Operation Pipeline, a highway drug interdiction program that trains federal, state, and local law enforcement personnel on indicators that officers should look for that would suggest possible drug trafficking activity among motorists. In a 1999 report, the American Civil Liberties Union (ACLU) stated that Operation Pipeline fostered the use of a racially biased drug courier profile, in part by using training materials that implicitly encouraged the targeting of minority motorists. DEA’s position is that it did not and does not teach or advocate using race as a factor in traffic stops. Further, according to DEA officials, a 1997 review of Operation Pipeline by the Justice Department’s Civil Rights Division, which is responsible for the enforcement of statutory provisions against discrimination, concluded that Operation Pipeline did not instruct trainees to use race as a factor in traffic stops. Representatives of organizations representing law enforcement officers have stated that racial profiling is unacceptable. The National Association of Police Organizations, representing more than 220,000 officers nationwide, has expressed opposition to pulling over an automobile, searching personal property, or detaining an individual solely on the basis of the individual’s race, ethnicity, gender, or age. The International Association of Chiefs of Police, one of the largest organizations representing police executives, stated that stopping and searching an individual simply because of race, gender, or economic level is unlawful and unconstitutional and should not be tolerated in any police organization. Neither group supports federally mandated collection of data on motorist stops. Lawsuits alleging racial profiling have been filed in a number of states, including Oklahoma, New Jersey, Maryland, Illinois, Florida, Pennsylvania, and Colorado. For example, in Colorado, a class action suit filed on behalf of 400 individuals asked the court to halt racially based stops by a Sheriff’s Department highway drug interdiction unit. Traffic infractions were cited as the reason for stopping the motorists, but tickets were not issued. The court ruled that investigatory stops based solely on motorists’ match with specified drug courier indicators violated the Fourth Amendment’s prohibitions against unreasonable seizures. A settlement was reached that awarded damages to the plaintiffs and disbanded the drug unit. In another case, a class action lawsuit filed by ACLU against the Maryland State Police resulted in a settlement that included a requirement that the state maintain computer records of motorist searches. These records are intended to enable the state to monitor for any patterns of discrimination.In yet another case, a Superior Court in New Jersey ruled that the New Jersey State Police engaged in discriminatory enforcement of the traffic laws. The Justice Department’s Civil Rights Division has recently completed investigations in New Jersey and Montgomery County, MD, which included reviewing complaints of discriminatory treatment of motorists. In the New Jersey case, Justice filed suit in U.S. District Court alleging that a pattern or practice of discriminatory law enforcement had occurred. The parties filed a joint application for entry of a consent decree, which the judge approved in December 1999. Under the consent decree, state troopers in New Jersey will be required to collect data on motorist stops and searches, including the race, ethnicity, and gender of motor vehicle drivers. In the Maryland case, the Justice Department and Montgomery County signed a Memorandum of Understanding in January 2000 that resolved the issues raised in Justice’s investigation. The agreement included the requirement that the Montgomery County Police Department document all traffic stops, including information on the race, ethnicity, and gender of drivers. Lack of empirical information on the existence and prevalence of racial profiling has led to calls for local law enforcement to collect data on which motorists are stopped, and why. To support local data collection efforts, the Bureau of Justice Assistance plans to release a Resource Guide in spring of 2000. The guide is expected to focus on how data can be collected to monitor for bias in traffic stops, with specific “lessons learned” and implementation guidance from communities that have begun the data collection process. Our objectives were to provide information on (1) analyses that have been conducted on racial profiling of motorists by law enforcement; and (2) federal, state, and local data currently available, or expected to be available soon, on motorist stops. To obtain information on analyses that have been conducted on racial profiling of motorists, we did a search of on-line databases and reviewed all of the quantitative analyses that we identified that attempted to address whether law enforcement officers stop motorists on the basis of race. We also contacted the authors of the analyses and obtained references to any other analysis or research sources they considered to be pertinent. Our criterion for selecting analyses to be included in this report was that they provide quantitative information on motorist stops, although these analyses might have also measured searches, arrests, and/or other activities. We used social science research principles to assess the methodological adequacy of the available analyses and to discuss factors that should be considered in collecting stronger empirical data. Our review is not intended to constitute a statement regarding the legal standard for proving discrimination in this context. To obtain information on the federal government’s efforts to collect data on racial profiling of motorists, we reviewed published and electronic literature and discussed data sources with officials at the Justice Department’s Bureau of Justice Statistics (BJS), officials in the office of the Attorney General, academic experts, the American Civil Liberties Union (ACLU), and several police associations. To obtain information on states’ efforts to collect data on racial profiling of motorists, we conducted Internet searches and reviewed the literature. We also held discussions with academic experts, state officials, ACLU officials, and representatives of the National Conference of State Legislatures. To obtain information on local efforts to collect data on racial profiling of motorists, we reviewed the literature and held discussions with academic experts, interest groups, local police officials, and knowledgeable federal officials. On the basis of these discussions, we judgmentally selected several communities that had voluntarily decided to require their police departments to collect motorist stop data. In September 1999, we visited four police departments in California—in San Diego, San Jose, Alameda, and Piedmont. We selected these police departments because they appeared to be furthest along in their plans for collecting data, could provide examples of different data collection methods, and varied greatly in size. We performed this work from August through February 2000 in accordance with generally accepted government auditing standards. We found no comprehensive, nationwide source of information on motorist stops to support an analysis of whether race has been a key factor in law enforcement agencies’ traffic stop practices. We identified five quantitative analyses on racial profiling that included data on motorist stops. The quantity and quality of information that these analyses provided varied, and the findings are inconclusive for determining whether racial profiling occurred. Although inconclusive, the cumulative results of the analyses indicate that in relation to the populations to which they were compared, African Americans in particular, and minorities in general, may have been more likely to be stopped on the roadways studied. A key limitation of the available analyses is that they did not fully examine whether the rates and/or severity of traffic violations committed by different groups may have put them at different levels of risk for being stopped. Such data would help determine whether minority motorists are stopped at the same level that they commit traffic law violations that are likely to prompt stops. Most analyses either compared the proportion of minorities among stopped motorists to their proportion in a different population (e.g., the U.S. population, the driving age population of a state) or did not use a benchmark comparison at all. There appears to be little comparative research on traffic violations committed by different racial groups, including possible differences in the type or seriousness of traffic violations. Therefore, there are no firm data indicating either that the types and seriousness of driving violations committed by whites and minorities are comparable, nor that they are not. Although we have no reason to expect that such differences exist, collecting research data on this issue— though difficult to do—could help eliminate this as a possible explanation for racial disparities in the stopping of motorists. The studies with the best research design collected data on the population of travelers on sections of interstate highways and on the portion of those travelers who violated at least one traffic law. The studies compared the racial composition of these groups against that of motorists who were stopped. However, the studies made no distinction between the seriousness of different traffic violations. Although violating any traffic law makes a driver eligible to be stopped, it is not clear that all violations are equally likely to prompt a stop. interview, respondents were asked whether they had committed a series of specific unsafe actions while driving. Demographic data, including race and ethnicity, were obtained on each respondent. Although answers to the unsafe or aggressive driving behavior questions were analyzed by some demographic characteristics, no analyses by race or ethnicity of driver were conducted. National Survey of Speeding and Other Unsafe Driving Actions, U.S. Department of Transportation, National Highway Traffic Safety Administration, September 15, 1998. study is notable in that it attempted to determine the percentage and characteristics of drivers who put themselves at risk for being stopped. However, we are uncertain whether traveling over the speed limit by at least 6 miles per hour on a major highway is the violation for which most police stops occurred. In a similar analysis of motorists traveling along a segment of Interstate 95 in northeastern Maryland, Lamberth found the following: (1) 17 percent of the cars had an African American driver; (2) 18 percent of cars exceeding the speed limit by at least 1 mile per hour or violating another traffic law had an African American driver; (3) 29 percent of the motorists stopped by the Maryland State Police were African American. This study also found that 92 percent of all motorists were violating the speeding law, 2 percent were violating another traffic law, and 7 percent were not violating any traffic law. However, we are uncertain whether Lamberth’s criteria for traffic violations were the basis for which most police stops were made. Another analysis examined motorist stops in Florida. Using data that were first presented in 1992 in two Florida newspaper articles, Harris reported that more than 70 percent of almost 1,100 motorists stopped over a 3-year period in the late 1980s along a segment of Interstate 95 in Volusia County, FL, were African American or Hispanic. In comparison, African Americans made up 12 percent of Florida’s driving age population and 15 percent of Florida drivers convicted of traffic offenses in 1991. Harris also reported that African Americans and Hispanics made up 12 percent and 9 percent, respectively, of the U.S. population. The findings reported by Harris were based on videotapes of almost 1,100 motorist stops made by Volusia County Sheriff deputies. However, videotapes of stops were not made for much of the 3-year period, and sometimes deputies taped over previous stops. Because no information was provided on other motorist stops made by the deputies over the 3-year period, we do not know whether the videotaped stops were representative of all stops made during that period. In addition, no information was provided on drivers who put themselves at risk for being stopped. The Philadelphia ACLU reported that motorists stopped by Philadelphia police in selected districts during 2 weeks in 1997 were more likely to be minority group members than would be expected from their representation in census data. Limitations of this analysis included the use of census data as a basis for comparison and an absence of information on drivers who put themselves at risk for being stopped. In addition, there were substantial amounts of missing data. The race of the driver was not recorded for about half of the approximately 1,500 police stops made during the 2 weeks. The New Jersey Attorney General’s Office reported that African Americans and Hispanics, respectively, represented 27 percent and 7 percent of the motorists stopped by New Jersey State Police on the New Jersey Turnpike. Interpreting these results is difficult because no benchmark was provided for comparison purposes. Because of the limited number of analyses and their methodological limitations, we believe the available data do not enable firm conclusions to be made from a social science perspective about racial profiling. For example, we question the validity of comparing the racial composition of a group of stopped motorists on a given roadway in a given location with the racial composition of a population that may be vastly different. It would be more valid to compare the racial characteristics of stopped motorists with those of the traveling population who violated similar traffic laws but were not stopped. This is what Lamberth did, although we are not certain that the traffic violations committed by the motorists observed in his studies were the same as those that prompted police stops. Nonetheless, Lamberth’s analyses went furthest by attempting to determine the racial composition of motorists at risk of being stopped by police as a function of traveling on the same roadways and violating traffic laws. We believe that the state of knowledge about racial profiling would be greater if Lamberth’s well-designed research were augmented with additional studies looking at the racial characteristics of persons who commit the types of violations that may result in stops. Other significant limitations of the available analyses were that the results of some analyses may have been skewed by missing data and may not have been representative of roadways and locations other than those reviewed. These limitations notwithstanding, we believe that in order to account for the disproportion in the reported levels at which minorities and whites are stopped on the roadways, (1) police officers would have to be substantially more likely to record the race of a driver during motorist stops if the driver was a minority than if the driver was white, and (2) the rate and/or severity of traffic violations committed by minorities would have to be substantially greater than those committed by whites. We have no reason to expect that either of these circumstances is the case. Appendix II contains a discussion of some of the methodological considerations and information needs involved in getting stronger original data from empirical research on the racial profiling of motorists. These include the need for high-quality data from multiple sources, such as from law enforcement records, surveys of motorists and police, and empirical research studies. By high quality, we mean data that are complete, accurate, and consistent and that provide specific information on the characteristics of the stop and the individuals involved in the stop in comparison to those who are not stopped. The accumulation of these data would form a better foundation for assessing whether, and to what extent, racial profiling exists on the roadways. Although the federal government has a limited role in making motorist stops, several federal activities currently planned or under way represent the first efforts to collect national level information. The Police Public Contact Survey conducted by BJS will include information on the characteristics of individuals reporting they were subject to traffic stops and other information about the stop. BJS is also conducting surveys of state and local law enforcement agencies to determine what motorist stop data they maintain. In addition, to help determine whether federal law enforcement agencies engage in racial profiling, three federal departments are under a presidential directive to collect information on the race, ethnicity, and gender of individuals whom they stop or search. A national household survey now under way asks respondents to discuss their contacts with police during motorist stops. As part of BJS’ 1999 Police Public Contact Survey, BJS is conducting interviews with 90,000 people aged 16 or older to ask them up to 36 questions pertaining to the most recent occasion (if any) during the prior 12 months that their motor vehicles were stopped by police officers. For example, the interview questions ask for information on the race of the motorist and police officer, the reason for the stop, whether a search was conducted, and whether the officer asked what the person was doing in that area. (See app. III for the survey questions to be asked.) BJS completed the survey in December 1999, and expects the results to be available in September 2000. BJS is conducting two surveys in an effort to determine whether law enforcement agencies collect stop data that can be used to address the question of racial profiling. One survey targets state police agencies; the other survey targets both state and local law enforcement agencies. In April 1999, BJS administered a survey of all state police agencies in the nation. The Survey of State Police Agencies asked, in general, whether the agency required its officers to report demographic information on the driver or other occupants of every vehicle stopped for a routine traffic violation. If the agency reported that it did collect such information, then more detailed questions were to be answered, such as whether individual records were kept detailing the driver’s race and immigration status and whether a search was conducted. BJS issued the results of the state police survey in February 2000. BJS found that 3 of the nation’s 49 state law enforcement agencies whose primary duties included highway patrol reported that they required officers to collect racial/ethnic data for all traffic stops. Of the three states, Nebraska and New Mexico reported storing the racial/ethnic data electronically, and New Jersey reported that it did not store the data electronically. BJS administers the Law Enforcement Management and Administrative Statistics (LEMAS) survey to a sample of state and local law enforcement agencies every 3 to 4 years. The survey collects information on the budget, salaries, and administrative practices of the agencies. The 1999 survey included a single question asking if the agencies collected data on traffic stops. The survey was sent to a sample of about 3,000 police/sheriff departments and was to include all agencies with 100 or more employees. The 1999 survey results are expected to be available during the summer of 2000. According to a BJS official, the 2000 LEMAS survey will contain more questions about what records are kept on motorist stops and whether they contain information on race. Pursuant to a presidential directive, three federal departments are to collect data on contacts between their law enforcement officers and the public. The directive did not instruct the departments to focus solely on motorist stops, but data on motorist stops are to be included. In June 1999, the President issued a memorandum on fairness in law enforcement that addressed the issue of racial profiling. The memorandum directed the Departments of Justice, the Interior, and the Treasury to design and implement a system for collecting and reporting statistics on the race, ethnicity, and gender of individuals who are stopped or searched by law enforcement. The three departments were tasked with developing data collection plans within 120 days and implementing field tests within 60 days of finalizing the plans. After 1 year of field testing, the departments are to report on complaints received that allege bias in law enforcement activities, the process for investigating and resolving complaints, and their outcome. The memorandum also required a report to the President within 120 days of the directive concerning each department’s training programs, policies, and practices regarding the use of race, ethnicity, and gender in law enforcement activities, as well as recommendations for improvement. The departments submitted data collection plans and proposed locations for the field tests to the White House in October 1999. (See app. IV for the list of data elements to be collected and all federal data collection test sites.) Federal law enforcement offices and proposed locations likely to be involved in motorist stops included the following: INS inspectors at the land border crossing at Del Rio, TX; INS border patrol agents from San Diego, CA; Yuma, AZ; and El Paso, TX; National Park Service officers at eight national parks; and National Park Service officers on three federally maintained memorial highways. According to Department of Justice plans, officials will also pursue a variety of techniques at some sites to try to determine if the characteristics of those stopped differed from populations encountered at the field site in general. Most traffic stops are made by state and local law enforcement officers. Consequently, state and local agencies are in the best position to collect law enforcement data on the characteristics of stopped motorists. Several states have introduced legislation that would require their state and/or local police departments to collect data on motorists’ traffic stops. However, few bills have passed. As of October 15, 1999, at least 15 states had taken some action to address concerns about racial profiling of motorists. Two of the 15 states—North Carolina and Connecticut—enacted legislation requiring the collection and compilation of data on motorist traffic stops. Similar legislation requiring the collection of specific stop data was introduced in 11 states. The legislation was pending in 7 of those 11 states and was either not carried over to the next legislative session or vetoed in 4. The two remaining states, New Jersey and Virginia, issued resolutions. New Jersey’s resolution calls for the investigation of racial profiling, and Virginia’s resolutions call for data on traffic stops to be compiled and analyzed. See table 1 for a list of the states that had proposed or enacted traffic stop bills or resolutions and their status as of October 15, 1999. All 13 states with data collection legislation proposed to collect data on driver’s race or ethnicity, the alleged traffic violation that resulted in a motorist stop, and whether an arrest was made. Most of these states also proposed to collect data on age, on whether a search was conducted, and on whether an oral warning or citation was issued. The number of data elements that each state proposed to collect ranged from 6 to 16. For a list of data elements that each of the 13 states proposed to collect, see appendix V. North Carolina passed legislation in April 1999 that called for the collection of statistics on a variety of law enforcement actions. Part of the legislation detailed what information on routine traffic stops by state law enforcement officers should be collected, maintained, and analyzed. All of the state’s approximately 40 state law enforcement agencies are to collect the data, although about 90 to 95 percent of all traffic stops are made by the North Carolina State Highway Patrol. Connecticut’s legislation passed in June 1999 and requires collection of certain traffic stop data on stops made by state as well as local police departments. In addition, Connecticut’s legislation bans the practice of racial profiling and calls for the collection of data on complaints that were generated as a result of law enforcement officer actions at traffic stops. North Carolina and Connecticut were both in the process of developing specifications for data collection. They planned to begin data collection January 1, 2000. We visited four California police departments—San Diego, San Jose, Alameda, and Piedmont—to learn about local efforts to collect traffic stop data. These departments had either begun or planned to begin to voluntarily collect traffic stop data. Some officials told us that their departments were interested in collecting traffic stop data because they wanted to address community concerns about racial profiling. San Jose began collecting data in June 1999, Alameda and Piedmont began collecting data in October 1999, and San Diego began collecting data January 2000. The departments generally planned to collect similar data; however, their data collection methods and plans for analyzing the data differed. All four police departments planned to collect data on five data elements: race or ethnicity, age, and gender of the driver; the reason for the traffic stop; and whether the stop resulted in a warning or citation or an arrest. In addition, Alameda, Piedmont, and San Diego planned to collect data on searches conducted during traffic stops. San Diego planned to collect six additional pieces of information. Table 2 summarizes the data that the four police departments will collect. In San Jose, officers use their police radios to report traffic stop information to the dispatcher, who then enters the data into a computer system. Officers can also use mobile computers located in their patrol cars to report traffic stop information, and this can be transmitted directly to the computer system. In San Diego, officers initially are collecting vehicle stop data using manually completed forms, and plan later to use a wireless system to transmit information to the department’s database. The Alameda police department also planned to use its computer-assisted dispatch system to collect data, but only on stops where citations are not issued, such as stops resulting in warnings or arrests. For stops in which the motorist receives a citation, traffic stop data are to be abstracted from patrol officers’ ticket books and from motor officers’ hand-held computer printouts and input into a citations database. Police officials in Piedmont, a police department consisting of 21 officers, decided that manually recording traffic stop information on paper forms would work best for its small department. Three of the four departments indicated that they expect to analyze their traffic stop data. A preliminary report, issued in December 1999 and providing analysis results on data collected between July and September 1999 in San Jose, indicated some racial disparity in traffic stops.According to the San Jose Police Department, the differences were due to socioeconomic factors rather than ethnicity. The report noted that more police were assigned to areas of San Jose that generated more police calls, and those neighborhoods tended to have more minorities. Because more police were available in these areas to make traffic stops, more stops were made there than in districts with a lower police presence. Within each police district, the stops reportedly reflected the demographics of the district. In the report, the San Jose Police Chief emphasized that more data were needed, along with the cooperation of the community to analyze what the data mean. Alameda officials told us they had no current plans to analyze their data, but the data will be available should there be a public request. None of the four departments planned to independently validate the accuracy of the data provided by the police officers. They said they rely on the integrity of the officers and supervisory oversight to ensure that the data are correct. Officials from two of the departments reported that the amount of data to be collected was limited so as not to be burdensome for officers. However, a lack of information may limit the types of analyses possible. For example, the data collection efforts do not require data on the specific violation for which a motorist was stopped, so questions about whether minorities were stopped more often for less serious violations cannot be answered. None of four localities planned to collect this information. Officials noted, however, that trade-offs needed to be considered: police officers would be more likely to record motorist data if the data collection requirements imposed on them were not overly detailed or burdensome. For a more detailed discussion on each of the four police departments’ traffic stop data collection plans, see appendix VI. The five quantitative examinations of racial profiling that we identified did not produce conclusive findings concerning whether and to what extent racial profiling exists. Although methodologically limited, their cumulative results indicate that in relation to the populations to which they were compared, African Americans in particular, and minorities in general, may have been more likely than whites to be stopped on the roadways studied. Because of methodological weaknesses in the existing analyses, we cannot determine whether the rate at which African Americans or other minorities are stopped is disproportionate to the rate at which they commit violations that put them at risk of being stopped. Although definitive studies may not be possible, we believe that more and better research data on the racial characteristics of persons who commit the types of violations that may result in stops could be collected. To date, little empirical information exists at the federal, state, or local levels to provide a clear picture of the existence and/or prevalence of racial profiling. Data collection efforts that are currently planned or under way should provide more data in the next few years to help shed light on the issue. These efforts are steps in the right direction. However, it remains to be seen whether these efforts will produce the type and quality of information needed for answering questions about racial profiling. We requested comments on a draft of this report from the Justice Department. Based on a January 18 meeting with a Deputy Associate Attorney General and other Justice officials, and technical comments provided by Justice, we made changes to the text as appropriate. In addition, Justice’s Acting Assistant Attorney General for Civil Rights provided us with written comments, which are printed in full in appendix VII. Justice agreed with us that there is a paucity of available data for assessing whether and to what extent racial profiling of motorists may exist. Justice also agreed that current data collection efforts by law enforcement agencies, as well as additional research studies, could generate information that may help answer questions about racial profiling. Justice felt, however, that our report set too high a standard for proving that law enforcement officers discriminate against minority motorists. We believe that Justice’s letter mischaracterized the conclusion of our report. Justice states that it disagrees with the “draft report’s conclusion that the only ‘conclusive empirical data indicating’ the presence of racial profiling would be data that proved the use of race to a scientific certainty.” Our conclusion, however, was that the “available research is currently limited to five quantitative analyses that contain methodological limitations; they have not provided conclusive empirical data from a social science standpoint to determine the extent to which racial profiling may occur” (page 1). We also noted that to account for the disproportion in the reported levels at which minorities and whites are stopped on roadways, (1) police officers would have to be substantially more likely to record the race of a driver during motorist stops if the driver was a minority than if the driver was white, and (2) the rate and/or severity of traffic violations committed by minorities would have to be substantially greater than those committed by whites. We do not believe that our approach to reviewing the research studies was so rigorous that we required “scientific certainty” in the data to draw conclusions about the occurrence of racial profiling. And we make clear in the report that our review was not intended to comment on the legal standard for proving discrimination in this context (see our Scope and Methodology section). With respect to Justice’s suggestion that we required research studies to provide scientific certainty of racial profiling, we would note that the concept of scientific certainty is generally not applicable to social science research. This is because social science research data are generally imperfect because they are collected in the “real world” rather than under controlled laboratory conditions. A fundamental, universally accepted, social science research principle that we did incorporate into our assessment of study results was whether the studies ruled out plausible alternative explanations for findings. We found that the available research on the racial profiling of motorists did not sufficiently rule out factors other than race—that is, other factors that may place motorists at risk of being stopped— that may have accounted for differences in stops. We observed that the two studies by Professor Lamberth were well-designed and went further than others in attempting to determine whether race was related to traffic violations that increased the risk of being stopped. But Lamberth established a criterion in each study that cast the net so wide that virtually the entire population of motorists was eligible to be stopped (i.e., traveling at least 1 and 6 miles above the speed limit, respectively, on two major interstate highways), and his studies provided little information about why motorists actually were stopped. Although law enforcement officers can use their discretion in deciding whom to stop, more information is needed on the actual reasons why they stop motorists before a firm conclusion can be made that the reason was race. As we indicate in the report, current data collection efforts by local, state, and federal law enforcement agencies may provide information on the reasons for stops that may help answer this question. With respect to what kind of data would be needed to “prove” the use of race in motorist stops, this issue was outside the scope of our work. We recognize that the evidentiary standards that a court may apply in ruling on an allegation of race-based selective enforcement of the law may be different from the social science principles that we used to review these studies. It was not our intention to express or imply anything about legal standards to prove discrimination. Justice also criticized our work for failing “to recognize or comment on the extensive scholarly debate on the subjects of the degree of statistical certainty, and the extent to which potential variables must be examined in order to demonstrate discrimination from a social science perspective.” We did not comment on the matter of statistical certainty because it was not the basis for our determination that the available research on racial profiling is inconclusive. The problems that we identified with the research studies dealt primarily with the design of the studies; that is, using inappropriate or questionable benchmarks to isolate race from other factors. More and better data are needed on what traffic violations trigger stops and whether race is related to them. Justice agrees that it is important to use an appropriate benchmark against which to compare the racial composition of stopped motorists. Justice disagrees, however, about the importance of examining whether certain driving behaviors or characteristics of vehicles may affect the likelihood of being stopped. In this context, Justice suggests that we make the unwarranted assumption in our report that severe traffic violations account for such a large proportion of traffic stops that they have a significant effect on the data. We did not intend, nor do we believe, that the report makes any assumptions about the reasons for which motorists are stopped. We simply believe that if the objective is to determine whether minority motorists are disproportionately more likely to be stopped than whites, then it is important to know what portion of the driving population on that roadway or in that jurisdiction commits the traffic offenses for which motorists are actually stopped—as opposed to being eligible to be stopped. This is the type of benchmark information that would isolate, to the extent possible, race from other variables that could influence traffic stops. As arranged with your office, unless you publicly announce the contents of this letter earlier, we plan no further distribution until 15 days after the date of this report. At that time, we will send a copy to other appropriate congressional parties, the Honorable Janet Reno, the Attorney General, and to others upon request. If you or your staff have any questions concerning this report, please contact me or Evi L. Rezmovic, Assistant Director, on 202-512-8777. Other key contributors to this report are listed in appendix VIII. As part of our work, we reviewed all available quantitative analyses that we could identify pertaining to the use of race as a factor in motorist stops. This appendix provides a summary of the design, results, and limitations for each of the five analyses. Lamberth, J.L (1994, unpublished). Revised Statistical Analysis of the Incidence of Police Stops and Arrests of Black Drivers/ Travelers on the New Jersey Turnpike Between Exits or Interchanges 1 and 3 From 1988 Through 1991. This analysis, done as part of a research study for a court case, provided a comparison of the races of vehicle occupants who were involved in traffic stops and arrests, drivers who violated traffic laws, and motorists in general who traveled along a segment of the southern end of the New Jersey Turnpike. The study involved three types of data collection: (1) direct observation of motorists from fixed observation points along the side of the road; (2) a moving survey in which an observer drove on the roadway and noted the races of drivers and whether they were speeding; and (3) obtaining law enforcement records from the New Jersey State Police (NJSP). In the first data collection effort, observers were stationed beside the road. Using binoculars, they noted the number of cars that passed the observation point, the race of the driver and/or any other occupant, and the vehicle’s state of registration. One observer was assigned to each lane of traffic, and a data recorder was present to record their observations. Observations were made in 18 randomly selected 3-hour blocks of time at 4 locations between 8 a.m. and 8 p.m. over a 2-week period in June 1993. The author noted that “most if not all” of the 26 pending cases in Gloucester County Superior court arose between these hours. Observers were reported to have been between 14 and 45 feet from the roadway. According to the observations, 42,706 cars were counted as traveling on the turnpike, and the race(s) of the occupants were recorded for nearly 100 percent. An African American driver and/or other occupant were in 14 percent of the cars. Seventy-six percent of the cars were registered out of state. In the second data collection effort, a moving survey was conducted to identify the racial distribution of all drivers on the road who violated the speed limit. In this phase, one observer drove at a constant 60 miles per hour (5 miles per hour above the speed limit at the time), and he recorded onto a tape recorder the race of each driver who passed him and whom he passed. The observer noted all cars that passed him as violators and all cars that he passed as nonviolators. In the moving survey, 1,768 cars were counted. More than 98 percent were speeding and classified as “violators.” Fifteen percent of the cars observed speeding had an African American driver or other occupant. A third data collection effort involved gathering data from NJSP. The data included the race of drivers who were stopped or arrested on randomly selected days between April 1988 and May 1991 along the section of the Turnpike covered by the traffic surveys and an additional section of the roadway. These data included 1,128 arrest reports from turnpike stops; 2,974 stops from patrol activity logs from 35 randomly selected days; and police radio logs from 25 of the selected days. (The 1988 radio logs had been destroyed.) Of the 2,974 stops, 870 were from the section covered by the traffic surveys. Data were not provided on the number of arrests from this section. Of 1,128 NJSP reports, the race of the driver/occupants was noted in 1,059 of them. According to these 1,059 reports, 73 percent of those arrested were African American. The patrol logs and radio logs noted 2,974 events as “stops.” Of the 2,974 stops, all but 78 noted the state of the registration of the car. Twenty-three percent of the stops were of New Jersey cars. Lamberth noted that race was “rarely if ever” noted on the patrol activity logs and that in the radio logs, race appears about one-third of the time for the records that had not been destroyed. (Out of 2,974 stops, race was not noted in 2,041, or 69 percent of the stops. Of the 870 stops that were in the sections covered by the traffic surveys, race was not recorded in 649, or 75 percent of them.) According to the available race data on all stops, 35 percent of drivers stopped were African American; 29 percent of all race- identified stops involved out-of-state African Americans; and 6 percent of the same stops involved in-state African Americans. Of the 221 race- identified stops from the section covered by the traffic surveys, 44 percent of the drivers were African American. In a separate analysis, Lamberth examined the race of individuals who were ticketed by three different units of the Moorestown, New Jersey State Police barracks. He compared the proportion of tickets issued to African Americans by the (1) Radar Unit, which used a remote van and left no discretion in the hands of patrol officers; (2) Tactical Patrol Unit, which concentrated on traffic problems at specific locations on the roadway and exercised more discretion on whom to stop than the Radar Unit; and (3) Patrol Unit, which was responsible for general law enforcement and exercised the most discretion among the three units. Lamberth found that African Americans received 18 percent of the tickets issued by the Radar Unit, about 24 percent of the tickets issued by the Tactical Patrol Unit, and about 34 percent of the tickets issued by the Patrol Unit. These results suggested that increasing levels of trooper discretion translated into increasing percentages of African American stops. Although the data suggest that African Americans may have been disproportionately represented among motorists stopped and arrested, because of several limitations in the study’s methodology, this study does not provide clear evidence of racial profiling of African American drivers. First, the percentage of drivers violating traffic laws was measured by determining the percentage of drivers who were driving at least 6 miles per hour over the posted speed limit. The study did not attempt to distinguish motorists who were driving 6 miles per hour over the speed limit from those who were speeding more excessively. On the basis of the criterion used to indicate speeding violation, the report concluded that 98 percent of the cars were violating at least one traffic law. We are uncertain whether this is an adequate indication of the type or seriousness of traffic violations that put motorists at risk for being stopped by police. We also do not know the reasons for which motorists were stopped. Second, the traffic surveys and the data on police stops and arrests were not from comparable time periods. The police data were from about 2 to 5 years prior to when the traffic surveys were conducted—the traffic surveys were done in June 1993, and the police data were from randomly selected days from April 1988 to May 1991. Third, the observed differences in the percentage of African Americans ticketed by Radar, Tactical Patrol, and general Patrol units may or may not have been due to discriminatory practices on the part of law enforcement officers. For the Tactical and general Patrol units, we do not know the reasons why tickets were issued, nor do we know if different groups may have been at different levels of risk for being stopped because they differed in their rates and/or severity of committing traffic violations. Fourth, among stopped vehicles, the occupants’ race was not recorded for three-fourths of cases along the portion of the highway where the traffic surveys were conducted; race was not recorded for two-thirds of cases along a larger portion of the highway. Therefore, the race of most motorists stopped is unknown. Statisticians performed calculations to determine the implications of the missing data for drawing conclusions about racial disparities in stops. The calculations revealed that if the probability of having race recorded if one was African American and stopped was up to three times greater than if one was white and stopped, then African Americans were stopped at higher rates than whites. Because we do not know what factors affected officers’ decisions to record race, the true extent to which officers tended to record race for African Americans versus whites is unknown. This analysis, done as part of a research study for a court case, provided a comparison between the racial distribution of motorists stopped by the Maryland State Police (MSP) on I-95 in northeastern Maryland, motorists whose cars were searched by MSP, all motorists on the roadway, and motorists on the roadway who violated traffic laws. The study involved two types of data collection: (1) a moving survey in which a team of researchers drove on the roadway and noted the race of drivers and whether they were speeding, and (2) obtaining law enforcement records from the Maryland State Police. In the first data collection effort, a moving survey was conducted to determine the races of highway motorists and the races of highway motorists who violated traffic laws. A team of observers drove separately at the posted speed limit (either 55 or 65 miles per hour) and recorded the race of each driver who passed him or her and whom he or she passed. The observer noted all cars who passed him or her as violators and all cars that he or she passed as nonviolators (unless they were observed violating some other traffic law.) Twenty-one observation sessions were conducted on randomly selected days between 8 a.m. and 8 p.m. during the period June to July 1996. In the moving survey, over 5,700 cars were counted. The author reported that driver’s race was identified for 97 percent of cars. Seventeen percent of cars had African American drivers, and 76 percent had white drivers. Ninety-three percent of cars were observed violating traffic laws. Eighteen percent of the violators were African American, and 75 percent were white. In the second data collection effort, data on motorists traveling a segment of I-95 were obtained from MSP. These data included information on (1) motorist stops made between May and September 1997 in Baltimore, Cecil, and Harford counties; (2) searches conducted between January 1995 and September 1997; (3) searches by MSP on roadways outside this corridor; and (4) drug arrests resulting from these searches. The MSP data indicated that along the I-95 segment studied, 11,823 stops were made by MSP between May and September 1997. Of the 11,823 vehicles stopped, it was reported that 29 percent had an African American driver, 2 percent had a Hispanic driver, 64 percent had a white driver, and 5 percent had a driver of another race/ethnicity. With respect to searches, 956 motorists were searched between January 1995 and September 1997. It was reported that 71 percent were African American, 6 percent were Hispanic, 21 percent were white, and 2 percent had a driver of another race/ethnicity. The proportion of searched cars in which contraband was found was the same for whites and African Americans and the same for I- 95 as compared to the rest of Maryland. In comparison, there were 1,549 motorist searches outside the I-95 segment. Of these searches, 32 percent were African American, 4 percent were another minority, and 64 percent were white. Although the data suggest that African Americans may have been disproportionately represented among motorists stopped and/or searched, because of several limitations in the study’s methodology, this study does not provide clear evidence of racial profiling of African American drivers. First, we are uncertain whether the study adequately measured the type or seriousness of traffic violations that put motorists at risk for being stopped by police. For example, motorists who greatly exceed the speed limit, commit certain types of violations, or commit several violations simultaneously may be more likely to be stopped than others. The measure used to determine whether a car was speeding was whether it was traveling at any speed over the posted limit. As with the New Jersey study by the same researcher, this study did not attempt to distinguish between motorists who drove 1 mile over the speed limit and those who sped more excessively. Furthermore, this study recorded whether traffic violations other than speeding were committed but treated them as equal in seriousness and equally likely to prompt a stop. This may or may not have been a valid assumption. In addition, we do not know the reasons for which motorists were stopped. Second, the data on police stops and police searches were not from comparable time periods. The data for stops were from May through September of 1997, and the data on searches were from January 1995 through September 1997. Lamberth noted in a correspondence to us that the stop data were not provided in time for his initial report. These problems do not necessarily indicate a systematic bias, however. Harris, David A.; Driving While Black and All Other Traffic Offenses: The Supreme Court and Pretextual Traffic Stops. Published in The Journal of Criminal Law and Criminology 87 (2): 1997. The analysis provides quantitative data from Florida and Maryland. The Florida data first appeared in two Florida newspaper articles in 1992. The Maryland data were obtained by the author from lawyers involved in a Maryland lawsuit. The journal article compares the racial characteristics of drivers involved in videotaped stops on a segment of I-95 in Volusia County, FL, over 3 years in the late 1980s (obtained from the County Sheriff’s Department by the Orlando Sentinel) with population and observational data. It was reported that videotapes of stops were not made for much of the 3-year period and sometimes deputies taped over previous stops. More than 70 percent of the persons stopped among nearly 1,100 videotaped stops on I- 95 were African American or Hispanic. African Americans, however, made up 12 percent of the driving age population in Florida, 15 percent of the traffic offenders in Florida in 1991, and 12 percent of the U.S. population. (Hispanics were 9 percent of the U.S. population.) Moreover, according to the Orlando Sentinel’s observations of 1,120 vehicles on I-95, about 5 percent of the drivers were dark-skinned. The article also noted that of the nearly 1,100 stops, 243 were made for swerving, 128 for exceeding the speed limit by more than 10 mph, 71 for burned-out tag lights, 46 for improper license tags, 45 for failure to signal, and a smattering of other offenses. Roughly half of the cars stopped were searched, 80 percent of the cars searched belonged to African American or Hispanic drivers, and African American and Hispanic drivers were detained for twice as long as whites. Only 9 of the 1,100 drivers stopped received tickets. In Maryland, the only data provided in the article are the percentages of African Americans and Hispanics among 732 motorists stopped and searched by 12 Maryland State Police officers with drug-sniffing dogs between January 1995 and June 1996. The article stated that 75 percent of the persons searched were African American; and 5 percent were Hispanic. Of the 12 officers involved, 2 stopped only African Americans. Over 95 percent of the drivers stopped by one officer were African American and 80 percent of the drivers stopped by six officers were African American. Because of several methodological limitations, this analysis does not provide clear evidence of racial profiling of African American or Hispanic drivers. For the Florida data, the validity of the comparisons made is questionable. For example, the data from the videotaped stops combined African Americans and Hispanics, but the comparison data for the driving age population of Florida included African Americans only. More importantly, no information was provided on the percentage of African Americans and Hispanics among traffic offenders. It is also not clear how accurately information on “dark-skinned” drivers was captured. In addition, there was an unknown amount of missing data because videotapes of stops were not made for much of the period. Therefore, we do not know whether the videotaped stops were representative of all stops. For the Maryland data, no comparative data are provided on the percentage of African Americans and Hispanics among motorists generally, among stopped motorists, or among motorists who violated traffic laws. The data for drivers in Maryland included only motorists who were stopped and consented to being searched. Plaintiffs’ Fourth Monitoring Report: Pedestrian and Car Stop Audit, Philadelphia Office of the American Civil Liberties Union, July 1998. This was an analysis of the racial characteristics of motorists and pedestrians stopped by the Philadelphia Police Department in selected districts and persons stopped by the department’s Narcotics Strike Force. All police incident reports recording interactions between police and civilians that involved stops and investigations of pedestrians or automobiles in the 8th, 9th, 18th, and 25th Police Districts for the week of October 6, 1997, were obtained. Hardcopy and computerized records were reviewed and coded according to whether tickets or arrests resulted from the stops and, if not, whether the record indicated any legal explanation for the stop. Previously unreported data were also provided on pedestrian and automobile stops in the 9th, 14th, and 18th Police Districts for the week of March 7, 1997. All reports filed by the Narcotics Strike Force for incidents in the 4th, 12th, 17th, 25th, and 35th Police Districts that involved a pedestrian or a vehicle stop during August 1997 were obtained. Records were coded in the same way as described above. Demographic data for all Philadelphia residents from a 1995 census were provided as a benchmark for the city as a whole, and demographic data by census tract from the 1990 U.S. census were provided as benchmarks for the district-specific analyses. (The report mentions that Philadelphia Police Districts approximately encompass specific census tracts.) For the week of March 7, there were police records of 516 motorist stops in the 3 districts. Overall, the race of the driver was recorded for only 51 percent of these stops, with race being recorded for between 40 and 58 percent of the stops in the three districts. For the week of October 6, there were police records of 1,083 motorist stops in the 4 districts. Overall, race of the driver was recorded for only 48 percent of these stops, with race being recorded for between 44 and 46 percent of the stops in three of the districts. (No separate data were provided for the 25th District, and no explanation was given for this omission.) In both weeks in each district, for stops with race of driver recorded, the driver was more likely to be a member of a minority group than would be expected on the basis of racial characteristics of the district as indicated by 1990 census tract data. Additionally, for stops with race recorded, the report indicated that minorities were more likely than whites to be involved in stops that were judged as not having a legally sufficient explanation than in stops judged to have a legally sufficient explanation for the March data, but not for the October data. There were records of 214 stops by the Narcotics Strike Force in August 1997. (Task Force data were not presented separately for motorists and pedestrian stops.) However, the race of the individual stopped was recorded for only 68 percent of the stops. For stops with race recorded, the report indicated that minorities were more likely to be involved in stops judged not to have a legally sufficient explanation—43 percent African American, 39 percent Hispanic, and 18 percent white—than in stops judged to have a legally sufficient explanation—33 percent African American, 47 percent Hispanic, and 20 percent white. Because of several methodological limitations, this analysis does not provide clear evidence of discriminatory targeting of minority drivers. First, data on the racial characteristics of most motorists covered in the study were not available. The absence of these data is a severe limitation because the race of most drivers stopped is unknown. Second, 1990 census tract data were used as benchmarks for the racial characteristics of the residents of the selected police districts. However, as the study notes, these census tract data were several years old at the time the study was conducted, and it is unknown how well these 1990 census data portrayed the 1997 population of these parts of Philadelphia. More importantly, no information was provided on the race of drivers who put themselves at risk for being stopped. Interim Report of the State Police Review Team Regarding Allegations of Racial Profiling, New Jersey Attorney General’s Office, April, 20, 1999. The report provides the racial characteristics of drivers stopped, searched, and arrested by the New Jersey State Police (NJSP) along the New Jersey Turnpike. Data were obtained from NJSP on the numbers of stops and searches made by troopers assigned to the Moorestown and Cranbury police barracks—two of three barracks assigned to the turnpike. Motorist stop data were from April 1997 through November 1998 (except February 1998). Data on motorist searches resulting from stops were from the same two barracks. Only data on searches for which motorists gave their consent for the search were available. Motorist search data were from selected months in 1994, all months in 1996 except February, and every month from April 1997 to February 1999. Data were obtained on motorist arrests made by troopers assigned to the Cranbury, Moorestown, and Newark barracks. Data on these arrests were from January 1996 through December 1998. Over 87,000 motorists were stopped by NJSP. Twenty-seven percent of motorists stopped were African American, 7 percent were Hispanic, 7 percent were another minority, and 59 percent were white. Little difference was reported between the two NJSP barracks in the racial characteristics of motorists stopped. Only 627, or less than 1 percent, of these stops involved a search, but the racial characteristics of the motorists searched were not reported separately. Racial characteristics were available for 1,193 motorists who gave consent for searches. Fifty-three percent of motorists searched were African American, 24 percent were Hispanic, 1 percent were another minority, and 21 percent were white. Little difference was reported between the two NJSP barracks in the racial characteristics of motorists searched. Approximately 2,900 motorists were identified in the state’s Computerized Criminal History Database as being arrested by troopers assigned to all three barracks. Sixty-two percent of motorists arrested were African American, 6 percent were of another minority, and 32 percent were white. Little difference between the three NJSP barracks in the racial characteristics of motorists arrested was reported. Because of several methodological limitations, this analysis does not provide clear evidence of racial profiling of minority drivers. First, direct comparisons between the racial characteristics of drivers stopped, drivers searched, and drivers arrested are problematic because comparable data for stops, searches, and arrests were not reported. Although there is some overlap, data for stops, searches, and arrests were reported for different time periods. Second, search data were provided for consent searches only. Data on instances when motorists denied troopers’ search requests were not available. Without data on denied search requests, it is not possible to know the racial characteristics of all motorists from which nonwarrant and nonprobable cause searches were requested. Overall, as the report acknowledges, it is difficult to interpret the significance of the study’s results because of the absence of any benchmark data, such as data from a survey to determine the racial or ethnic characteristics of turnpike motorists or the racial characteristics of motorists who put themselves at risk for being stopped. Determining whether and to what extent racial profiling may occur on the nation’s roadways is a complicated task that would require collecting more and better data than are currently available. Additional studies using comparison groups that are similar to the stopped motorist group in terms of their risk of being stopped for a traffic violation would contribute to our understanding of this issue. Federal, state, and local data collection efforts currently under way should augment the available information provided that the data are complete, accurate, consistent, and specific. To the extent that such data are gathered by a number of jurisdictions, a more complete picture of which motorists are stopped and why may emerge. Surveys of motorists and police officers and reviews of police protocols and training guides can also contribute to the state of knowledge about racial profiling. In our judgment, such a multifaceted examination of the issues is the means for developing a full and meaningful answer to questions about racial profiling. We have noted that some of the existing analyses may have made comparisons that were not valid. These analyses generally compared the racial characteristics of motorists who were stopped with the racial characteristics of a larger population. The larger population may have been a state’s driving age population or the U.S. population as a whole, among others. The limitation of such analyses is that they do not address whether different groups may have been at different levels of risk for being stopped because they differed in their rates and/or severity of committing traffic violations. Although discretion may play a part in an officer’s decision to pull over a driver, the justification for initiating a stop is a violation or infraction committed by drivers. The available research on racial profiling, however, has given very little attention to potential differences across groups in the relative risk of being stopped. Lamberth’s studies have been important steps in the direction of estimating the relative risks of being stopped, but they did not provide conclusive results. In both studies, Lamberth found that more than 9 out of 10 motorists violated a traffic law and were thus legally eligible for being stopped by the police. However, it is not clear that the driving violations that made motorists legally eligible for being stopped were the same violations that would prompt actual stops by law enforcement officers. For example, one of Lamberth’s studies considered only speeding, although this type of infraction is not the only reason that motorists are stopped. The extent to which motorists exceed the speed limit and/or the number of violations they commit simultaneously may also affect their likelihood of being stopped. Lamberth’s other study considered speeding plus other traffic law violations. However, this study also did not differentiate between the type or seriousness of different violations. For example, motorists who greatly exceeded the speed limit, committed certain types of violations, or committed several violations simultaneously may have been more likely to be stopped than others. None of the analyses that we identified examined whether there may be racial disparities in motorist stops that are related to the type or seriousness of the traffic violation committed. We recognize that it is difficult to determine which traffic violations specifically prompt a law enforcement officer to stop one motorist rather than another. Different jurisdictions and officers may use different criteria, and candid information on the criteria may be difficult to obtain. Nonetheless, to understand the extent to which motorist stops may have a discriminatory basis, data are needed on traffic violations— including the type and seriousness of those violations—that produce stops and the relative rates at which different groups of drivers in a particular jurisdiction commit those violations. Although we have no reason to expect that there are racial differences in committing traffic violations, such data would enable the most appropriate comparisons to be made in order to answer a key question; that is, how do the racial characteristics of motorists who are stopped for a particular traffic violation compare with the racial characteristics of all drivers who commit the same violation but are not stopped? Both observational studies and driver surveys may be useful in developing such comparative information. Federal, state, and local efforts to collect data on motorist stops should increase the amount of information on law enforcement practices on the roadways. However, the usefulness of such data for addressing research questions about racial profiling will depend on the extent to which the data are complete, accurate, consistent, and sufficiently specific to provide meaningful information. Although we recognize that no empirical data are likely to be perfect, it would be difficult to draw conclusions about racial profiling if (1) stop data were selectively recorded, (2) race or other stop information is inaccurately recorded, (3) different jurisdictions capture different information, and/or (4) the information recorded is too broad to understand what happened. For example, recording “vehicle code violation” as the reason for the stop—when such a code can represent anything from failing to signal a lane change within a designated distance to a serious speeding offense—could make it difficult to discern whether and how the traffic violations for which motorists are stopped differ between racial groups. In addition, confidence in the quality of data would be enhanced if provisions were made to validate the accuracy and completeness of data that are collected. Also, it would be constructive to have a mechanism in place for agencies to communicate and coordinate with one another to ensure that they are collecting comparable information, and at a sufficient level of specificity, to be useful for answering questions about racial profiling in a meaningful way. It could also be instructive to examine whether there was a correlation between the race of the law enforcement officer and that of the stopped motorist. In addition, information is needed on the extent to which officers exercise discretion in the process of stopping, citing, and searching drivers. Toward this end, a review of established police protocols and training guides could be useful. In addition, a survey of officers could provide information on what observations and judgments they factor into their decisions to make stops. Although survey data of this sort would be subject to response biases, including the possibility that respondents would offer socially acceptable responses, well-designed surveys of police officers could be a useful supplement to official data. Further, in addition to querying drivers about the frequency with which they were stopped, cited, and searched, driver surveys could also ask about how many miles the drivers typically drove and how often they committed infractions that were likely to prompt stops. Data from police records and surveys could then be compared with them. We estimate that it will take from 5 to 10 minutes to complete this interview with 10 minutes being the average time. If you have any comments regarding these estimates or any other aspect of this survey, send them to the Associate Director for Management Services, Room 2027, Bureau of the Census, Washington, DC 20233 or to the Office of Information and Regulatory Affairs, Office of Management and Budget, Washington, DC 20503. NOTICE – Your report to the Census Bureau is confidential by law (U.S. code 42, Sections 3789g and 3735). All identifiable information will be used only by persons engaged in and for the purposes of the survey, and may not be disclosed or released to others for any purpose. (5-14-99) Line no. FIELD REPRESENTATIVE – Complete a PPCS-1 for all persons 16+ in all interviewed households. Complete a PPCS-1 through Item D for each NCVS Type Z person or NCVS proxy interview. DO NOT complete any PPCS-1 forms if the household is a Type A. Personal (Self) Telephone (Self) Noninterview – FILL ITEM D FIELD REPRESENTATIVE – Read introduction INTRO 1– Now I have some additional questions about any contacts you may have had with the police at any time during the last 12 months, that is, any time since 1, 1998. Exclude contacts with private security guards, police officers you see on a social basis, police officers related to you, or any contacts that occurred outside the United States. Include contacts which occurred as a result of being in a vehicle that was stopped by the police. However, please exclude those contacts which occurred because your employment or volunteer work brought you into regular contact with the police. 1a. Did you have any contact with a police officer during the last 12 months, that is, any time since 1, 1998? 1b. Were any of these contacts with a police officer in person, that is face-to-face? CONTACT SCREEN QUESTIONS – Continued 1c. How would you best describe the reason or reasons for these in-person contacts with the police during the last 12 months, that is, any time since ______________1, 1998? As I read some reasons, tell me if any of the contacts occurred once, more than once, or not at all. box 2 to the FLAP on page 11. Mark (X) all that apply. A motor vehicle stop: (1) You were in a motor vehicle stopped by the police. . . . . . . . . . . . . . . . . . . . . . . . . . You contacted a police officer: (2) To report a crime . . . . . . . . . . . . . . . . . . . . . . (3) To report a crime you had witnessed . . . . . (4) To ask for assistance or information . . . . . (5) To let the police know about a problem in the neighborhood . . . . . . . . . . . . . . . . . . . . . (6) To tell the police about a traffic accident you had witnessed . . . . . . . . . . . . . . . . . . . . (7) For some other reason – Please specify . . . A police officer contacted you because: (8) You were involved in a traffic accident . . . (9) You were a witness to a traffic accident. . (10) You were the victim of a crime which someone else reported to the police . . . . . (11) The police thought you might have been a witness to a crime. . . . . . . . . . . . . . . . . . . . . (12) The police asked you questions about a crime they thought you were involved in . (13) The police had a warrant for your arrest . . (14) The police wanted to advise you about crime prevention information . . . . . . . . . . . (15) Some other reason we haven’t mentioned – Please specify . . . . . . . . . . . . . Was the motor vehicle stopped only once? (Is box 1 marked in Item 1c(1)?) Was the motor vehicle stopped more than once? (Is box 2 marked in Item 1c(1)?) Yes – Ask Item 1d No – SKIP to Item 37 1d. You said that you were in a motor vehicle that was stopped by the police on more than one occasion in the last 12 months. How many different times were you stopped? (Record actual number.) FIELD REPRESENTATIVE – Read introduction INTRO 2– You reported that you were in a motor vehicle that was stopped by the police on more than one occasion. For the following questions, please tell me about the most recent occasion. 2. How many people age 16 or over, INCLUDING YOURSELF, were in the vehicle? 3. Were you the driver? Yes No – SKIP to Item 37 4. How many police officers were present during (this/the most recent) incident? One – SKIP to Item 6 More than one (Record actual number.) FORM PPCS-1 (5-14-99) MOTOR VEHICLE STOPS – Continued 5. Were the police officers White, Black, or some other race? All White All Black All of some other race Mostly White Mostly Black Mostly some other race Equally mixed Don’t know race of any/some 6. Was the police officer White, Black, or some other race? White Black Some other race Don’t know 7. Were you arrested? Yes – SKIP to Item 9 No Don’t know 8. Did the police officer(s) threaten to arrest you? Yes No Don’t know 9. Did the police officer(s) search the vehicle? Yes – Ask Item 10 No Don’t know 10. At any time during (this/the most recent) incident did the police officer(s) ask permission to search the vehicle? Yes – Ask Item 11 No Don’t know 11. Did you give the police officer(s) permission to search the vehicle? Yes No Don’t know 12. Did the police officer(s) find any of the following items in the vehicle? (Read answer categories.) Mark (X) all that apply. Other evidence of a crime – Please specify 13. Do you think the police officer(s) had a legitimate reason to search the vehicle? Yes No Don’t know 14. At any time during (this/the most recent) incident, did the police officer(s) search you, frisk you, or pat you down? Yes – Ask Item 15 No Don’t know 15. At any time during (this/the most recent) incident, did the police officer(s) ask permission to search you, frisk you, or pat you down? Yes – Ask Item 16 No Don’t know 16. At any time during (this/the most recent) incident, did you give the police officer(s) permission to search you, frisk you, or pat you down? Yes No Don’t know 17. Did the police officer(s) find any of the following items on or near you? (Read answer categories.) Mark (X) all that apply. Other evidence of a crime – Please specify 18. Do you think the police officer(s) had a legitimate reason to search you, frisk you, or pat you down? FORM PPCS-1 (5-14-99) Page 3 19. Did the police officer(s) give a reason for stopping the vehicle? Yes – Ask Item 20 No Don’t know 20. What was the reason or reasons? Anything else? Mark (X) all that apply. A vehicle defect, such as a burned out tail light or an expired license plate Roadside check for drunk drivers To check the respondent’s license plate, driver’s license, or vehicle registration The police officer suspected the respondent of something Some other reason – Please specify 21. Would you say that the police officer(s) had a legitimate reason for stopping you? Given a warning? Given a traffic ticket? (Read answer categories.) Tested for drunk driving? Mark (X) all that apply. Charged with driving while under the influence of drugs or alcohol? Questioned about what you were doing in the area? 23. Not including anything just mentioned, were you charged with any of the following? (Read answer categories.) Mark (X) all that apply. Something else – Please specify 24. At any time during (this/the most recent) incident were you handcuffed? USE OF FORCE IN TRAFFIC STOPS 25a. During (this/the most recent) incident, did the police officer(s) for any reason use or threaten to use physical force against you, such as grabbing you or threatening to hit you? Yes – SKIP to Item 26 No Don’t know 25b. Aside from being handcuffed, did the police officer(s) for any reason use or threaten to use physical force against you, such as grabbing you or threatening to hit you? FORM PPCS-1 (5-14-99) Actually push or grab you in a way that did not cause pain? Actually push or grab you in a way that did cause pain? (Read answer categories) Actually kick you or hit you with the police officer’s hand or something held in the police officer’s hand? Mark (X) all that apply. Actually unleash a police dog that bit you? Actually spray you with a chemical or pepper spray? Actually point a gun at you but did not shoot? Actually fire a gun at you? Actually use some other form of physical force? – Please specify Threaten to push or grab you? Threaten to kick you or hit you with the police officer’s hand or something held in the police officer’s hand? Threaten you with a police dog? Threaten to spray you with a chemical or pepper spray? Threaten to fire a gun at you? Threaten to use some other form of physical force? – Please specify 27. Do you feel that any of the physical force used or threatened against you was excessive? Yes – Ask Item 28 No Don’t know 28. FIELD REPRESENTATIVE – Mark without asking when ONLY ONE box is marked in Item 26. Specifically, what type of physical force do you feel was excessive? (Read items marked in Item 26.) Actually pushing or grabbing the respondent in a way that did not cause pain? Mark (X) all that apply. Actually pushing or grabbing the respondent in a way that did cause pain? Actually kicking the respondent or hitting the respondent with the police officer’s hand or something held in the police officer’s hand? Actually unleashing a police dog that bit the respondent? Actually spraying the respondent with a chemical or pepper spray? Actually pointing a gun at the respondent but did not shoot? Actually firing a gun at the respondent? Actually using some other form of physical force? – Please specify Threatening to push or grab the respondent? Threatening to kick the respondent or hit the respondent with the police officer’s hand or something held in the police officer’s hand? Threatening the respondent with a police dog? Threatening to spray the respondent with a chemical or pepper spray? Threatening to fire a gun at the respondent? FORM PPCS-1 (5-14-99) USE OF FORCE IN TRAFFIC STOPS – Continued 29a. Were you injured as a result of (this/the most recent) incident? Yes No – SKIP to Item 30 29b. Did your injuries include any of the following? Broken bones or teeth knocked out (Read answer categories.) Mark (X) all that apply. Bruises, black eyes, cuts, scratches, or swelling Any other injury – Please specify 29c. What type of care did you receive for your (injury/injuries)? No care received Respondent treated self Emergency services only Hospitalization Other – Please specify 30. Do you think any of your actions during (this/the most recent) incident may have provoked the police officer(s) to use or threaten to use physical force? Yes No Don’t know 31. At any time during (this/the most recent) incident did you: (Read answer categories.) Argue with or disobey the police officer(s)? Curse at, insult, or call the police officer(s) a name? Mark (X) all that apply. Say something threatening to the police officer(s)? Resist being handcuffed or arrested? Resist being searched or having the vehicle searched? Try to escape by hiding, running away, or being in a high-speed chase? Grab, hit, or fight with the police officer(s)? Use a weapon to threaten the police officer(s)? Use a weapon to assault the police officer(s)? Do anything else that might have caused the police officer(s) to use or threaten to use physical force against you? – Please specify 32. Were you drinking at the time of (this/the most recent) incident? Yes No Don’t know 33. Were you using drugs at the time of (this/the most recent) incident? Yes No Don’t know 34. Looking back at (this/the most recent) incident, do you feel the police behaved properly or improperly? Properly – SKIP to Check Item B1 Improperly Don’t know – SKIP to Check Item B1 35. Did you take any formal action, such as filing a complaint or lawsuit? Yes – Ask Item 36 No Don’t know 36. With whom did you file a complaint or lawsuit? Law enforcement agency employing the police officer(s) (Read answer categories.) Mark (X) all that apply. FORM PPCS-1 (5-14-99) Was respondent the driver during the traffic stop? (Is box 1 marked in Item 3?) Was physical force used or threatened? (Is box 1 marked in Item 25a OR 25b?) Other than a motor vehicle stop, did the respondent have any other in-person contacts with the police? (Are there any entries marked in categories (2) through (15) on the FLAP on page 11?) USE OF FORCE IN OTHER FACE-TO-FACE CONTACTS 37. Earlier you reported you had a face-to-face contact with the police for the following reason(s), (Read items marked on the Flap on page 11.) Did (this/any of these) contact(s) result in the police handcuffing you or using or threatening to use physical force against you, such as by grabbing you or threatening to hit you during the last 12 months, that is, any time since ______________1, 1998? Yes – Ask Item 38 No Don’t know 38. On how many different occasions did the police handcuff you or use or threaten to use physical force against you? FIELD REPRESENTATIVE – Read Introduction INTRO 3–You reported that, on more than one occasion, you had contact with the police in which the police handcuffed you or used or threatened to use physical force against you. For the following questions, please tell me about the most recent occasion. 39. FIELD REPRESENTATIVE – Mark without asking when ONLY ONE box is marked on the FLAP on page 11. Which of these contacts that you reported earlier resulted in a police officer using or threatening to use physical force? Respondent contacted a police officer: To report a crime respondent had witnessed To ask for assistance or information To let the police know about a problem in the neighborhood To tell the police about a traffic accident respondent had witnessed For some other reason – Please specify A police officer contacted you because: Respondent was involved in a traffic accident Respondent was a witness to a traffic accident Respondent was the victim of a crime which someone else reported to the police The police thought the respondent might have been a witness to a crime The police asked the respondent questions about a crime they thought you were involved in The police had a warrant for the respondent’s arrest The police wanted to advise the respondent about crime prevention information For some other reason – Please specify 40. How many police officers were present during (this/the most recent) incident? Record actual number. FORM PPCS-1 (5-14-99) President Clinton directed the Attorney General, Secretary of the Treasury, and Secretary of the Interior in a June 9, 1999, memorandum to design and implement a system to collect and report statistics relating to race, ethnicity, and gender for law enforcement activities in their departments. Within 120 days of the directive, in consultation with the Attorney General, the departments were to develop proposals for collecting the data; and within 60 days of finalizing the proposals, the departments were to implement a 1-year field test. This appendix presents the field locations and data elements that the Attorney General’s October 1999 proposal indicated would be collected during the field test. Five agencies in three federal departments are to be involved in collecting data on individuals who are stopped or searched by law enforcement. The agencies include the Department of Justice’s Drug Enforcement Administration and the Immigration and Naturalization Service; the Department of the Interior’s National Park Service; and the Department of the Treasury’s U.S. Customs Service and uniformed division of the Secret Service. Between six and nine of the following Drug Enforcement Administration Operation Jetway sites are to be included in the field test: Detroit Metropolitan Airport; Newark International Airport; Chicago-O’Hare International Airport; George Bush Intercontinental Airport (Houston); Miami International Airport; Charleston, SC, bus station; Cleveland, OH, train station; Albuquerque, NM, train station; and Sacramento, CA, bus station. The following Immigration and Naturalization sites are to be included in the field test: John F. Kennedy International Airport (New York City); George Bush Intercontinental Airport (Houston); Seattle/Tacoma Airport; El Cajon, CA, Station; Yuma, AZ, Station; El Paso, TX, Station; and Del Rio, TX, land-border crossing. The National Park Service was the only agency identified by the Department of the Interior with regular public contact. The following Park Service sites are to be included in the field test. Lake Mead National Recreation Area (Nevada and Arizona); Yosemite National Park (California); Grand Canyon National Park (Arizona); Glen Canyon National Recreation Area (Arizona and Utah); National Expansion Memorial Park (Missouri); Indiana Dunes National Lake Shore (Indiana); Natchez Trace Parkway (Mississippi and Tennessee); Blue Ridge Parkway (Virginia and North Carolina); Valley Forge National Historical Park (Pennsylvania); Delaware Water Gap National Recreation Area (Pennsylvania and New Baltimore Washington Parkway (Washington, D.C., and Maryland). The Department of the Treasury identified the U.S. Customs Service and the uniformed division of the Secret Service as the agencies with regular public contact. The following sites are to be included in the field test: Chicago O’Hare International Airport; JFK International Airport (New York City); Newark International Airport; Miami International Airport; and Los Angeles International Airport. The Secret Service uniformed division will collect data in Washington D.C.. Agencies are to collect data describing demographic characteristics, such as gender, race, ethnicity, national origin, and date of birth based on agent’s observation, or from official documents such as drivers’ license when available. All participating agencies are to collect a core set of data elements, but they may collect additional data as they deem appropriate. Following is a core set of data elements contained in the data collection proposal: date of encounter, start time of contact, motorist’s gender, motorist’s race and ethnicity, motorist’s national origin, location of contact, motorist’s suspected criminal activity, reason for contact, external sources of information on person contacted, law enforcement action taken, and end time of contact. Proposed data elements Race or ethnicity Age Gender Reason for stop/violation Search conducted Who, what searched Legal basis of search Oral warning or citation Issued Arrest made Contraband; type, amount Property seized Resistance to arrest Officer use of force Resulting injuries Location, time of stop Investigation led to stop Officer demographics Passenger demographics Auto description, license number Number of Individuals stopped for routine traffic violations Total number of data elements to be collected 8 Including nature of offense for which arrest was made, whether felony or misdemeanor, and whether occupants checked for prior criminal record, outstanding warrants, or other criminal charges. The San Diego Police Department initiated its program to collect vehicle stop data as a result of concerns about police racial profiling that were expressed by community groups, such as the Urban League and the National Association for the Advancement of Colored People. Beginning January 1, 2000, San Diego’s police force, with 1,300 patrol and 60 motor officers, is to begin using forms to manually collect stop data. Later, plans are to use laptop or hand-held computers to collect information that would be sent to a department database via a new wireless system. Initial officer concerns about the data collection effort were addressed through departmental assurances that data would be collected in the aggregate, keeping officers’ and motorists’ names anonymous. In addition, the new data collection system is to track when a stop was initiated for a special assignment, such as when targeting African American gang members. For each stop, officers are to capture the following information: motorist’s race/ethnicity; motorist’s age; motorist’s gender; reason for the stop; whether a search was conducted and whom/what was searched; legal basis for the search; whether a consent form was obtained; whether an oral warning or citation was issued; whether an arrest was made; whether property was seized; whether contraband was found; and whether the officer was on special assignment. San Diego police officials said that they plan to enlist the assistance of a statistical expert in analyzing the data. They hope to obtain an initial analysis after the first 6 months of data collection. The department is also working with community-based organizations to address questions they have about the project and how data will be interpreted. San Diego has no plans to validate data submitted by officers. However, officials noted that actions by officers could always be reviewed and scrutinized by their supervisors. The San Jose Police Department also began its program to collect traffic stop data in response to community concerns about racial profiling by police. According to police officials, the data collection will allow them to learn more about the types of stops being made and to demonstrate the department’s commitment to working with all members of the community. In addition, if analysis of the data reveals a pattern suggesting that race was a factor in motorist stops, then additional training and supervision will be considered to ensure fair treatment for all. San Jose began collecting motorist stop data on June 1, 1999, and plans to continue the effort until May 31, 2000. For each stop, officers are to capture the following information: motorist’s race/ethnicity; motorist’s age; motorist’s gender; reason for the stop; and what action was taken during the stop, for example whether a citation was issued or whether an arrest was made. Identities of the officer and motorist involved in each stop will be kept anonymous and not included in any reports. San Jose officers call in traffic stop information by police radio to a radio dispatcher or by keying the information into a mobile computer terminal located in patrol cars. Dispatchers enter the radioed information into the computer-aided dispatch (CAD) system, and information entered into the mobile terminal is automatically entered into the CAD system. Officers use single digit alpha codes to identify traffic stop data elements. San Jose’s code system has been in place since the 1970s; however, what is new is the addition of three new data elements to the existing code system. In addition to gender and traffic stop disposition, San Jose now collects reason for stop, race, and age information. The hardware and software cost to implement the data collection system was less than $10,000. According to a police official, costs were minimal because the department was able to make modifications to its existing automated system, thereby avoiding the need to design a new, potentially costly, one. The department’s Crime Analysis Unit is to compile the statistics and prepare two formal reports; one summarizing results for the first 6 months of data collection, and the other summarizing results for the full year. An initial review of the data from July 1, 1999, to September 30, 1999, was released by the San Jose Police Department in December, 1999. Aggregate figures indicate that Hispanic citizens in particular were stopped at a rate above their representation in the population. A spokesman for the department stated that the results do not support this conclusion when the figures are disaggregated by police district, although population figures by police district are not available. The official explained that more officers are assigned to areas with higher calls for service, and thus more stops are made in these areas, which tend to have higher minority populations. More analysis will be forthcoming. If results suggest that race may be a factor in motorist stops, the department may decide to collect data beyond 1 year. San Jose does not plan to check the validity of the data being submitted by officers, except to see if officers have entered the correct number of codes. However, a police official told us that supervisors have access to data submitted by officers, and they can “stop-in” on an officer call at any time. According to Alameda Police Department officials, most of Alameda County’s police departments began to voluntarily collect motorist stop data in anticipation of state and federal legislation requiring the collection of such data. The Alameda Police began collecting motorist stop data on October 1, 1999. Alameda police officials told us that stop data are recorded on written or automated citations, if issued. For all noncitation stops, such as warnings or arrests, officers use the CAD system to call in each of the required data elements. For each stop, officers are to capture the following information: motorist’s race/ethnicity, motorist’s age, motorist’s gender, reason for the stop, who/what was searched, whether an oral warning was given, and whether an arrest was made. Alameda police officials said that information patrol officers write on citations will be keyed into an automated citations database. In addition, motorcycle officers have hand-held computers that they use to input and store traffic stop information. These data will be printed out and keyed into the automated citations database as well. A separate database is to contain the CAD-collected data for noncitation stops. Although officers’ and motorists’ information will be captured in the data system, the department has no plans to generate any reports from the data collected. According to Alameda police officials, the police department does not plan to analyze, validate, or publish its data. They said that the data would be made available to the public if requested. The Piedmont Police Department, located in Alameda County, began voluntary collection of motorist stop data in anticipation of pending state and federal legislation. Piedmont began collecting motorist stop data on October 1, 1999. According to a Piedmont police official, Piedmont is a small department with 21 officers who record motorist stop data manually. For each traffic stop, the officer is to fill out an index card that contains data fields for recording the motorist’s race, sex, and age. At the bottom of the card, the officer is to record the reason for stop, whether the vehicle was searched, whether an oral warning or citation was issued, and whether an arrest was made. No officer or motorist names will be included on the cards. A department official indicated that she expects a volume of no more than 400 cards per month. Information from the cards is to be input into an Excel spreadsheet for analysis, and results are to be tallied on a monthly basis. The department reportedly has no planned effort to validate the information that officers record on the cards. Piedmont police officials said that the watch commander can monitor the activity of officers by listening to interactions between the officers and motorists over the dispatch system. The watch commander can then compare the information overheard on the dispatch system with that recorded on the index cards submitted by the officers. Laurie E. Ekstrand (202-512-8777) Evi. L. Rezmovic (202-512-8777) In addition to those named above, David P. Alexander, Carla D. Brown, Ann H. Finley, Monica Kelly, Anne K. Rhodes-Kline, Jan B. Montgomery, and Douglas M. Sloane made key contributions to this report. Ordering Copies of GAO Reports The first copy of each GAO report and testimony is free. Additional copies are $2 each. Orders should be sent to the following address, accompanied by a check or money order made out to the Superintendent of Documents, when necessary. VISA and MasterCard credit cards are accepted, also. Orders for 100 or more copies to be mailed to a single address are discounted 25 percent. U.S. General Accounting Office P.O. Box 37050 Washington, DC 20013 Room 1100 700 4th St. NW (corner of 4th and G Sts. NW) U.S. General Accounting Office Washington, DC Orders may also be placed by calling (202) 512-6000 or by using fax number (202) 512-6061, or TDD (202) 512-2537. Each day, GAO issues a list of newly available reports and testimony. 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Summary: Pursuant to a congressional request, GAO provided information on the racial profiling of motorists, focusing on the: (1) findings and methodologies of analyses that have been conducted on racial profiling of motorists; and (2) federal, state, and local data available, or expected to be available, on motorist stops. GAO noted that: (1) GAO found no comprehensive, nationwide source of information that could be used to determine whether race has been a key factor in motorist stops; (2) the available research is limited to five quantitative analyses that contain methodological limitations; (3) they have not provided conclusive empirical data from a social science standpoint to determine the extent to which racial profiling may occur; (4) however, the cumulative results of the analyses indicate that in relation to the populations to which they were compared, African American motorists in particular, and minority motorists in general, were proportionately more likely than whites to be stopped on the roadways studied; (5) data on the relative proportion of minorities stopped on a roadway, however, is only part of the information needed from a social science perspective to assess the degree to which racial profiling may occur; (6) a key limitation of the available analyses is that they did not fully examine whether different groups may have been at different levels of risk for being stopped because they differed in their rates or severity of committing traffic violations; (7) although GAO has no reason to expect that this occurred, such data would help determine whether minority motorists are stopped at the same level that they commit traffic law violations that are likely to prompt stops; (8) several analyses compared the racial composition of stopped motorists against that of a different population, but the validity of these comparison groups was questionable; (9) federal, state, and local agencies are in various stages of gathering data on motorist stops, and these efforts should augment the empirical data available from racial profiling studies; (10) the federal government, which has a limited role in making motorist stops, is undertaking several efforts to collect data; (11) in accordance with a presidential directive, three federal departments are preparing to collect data on the race, ethnicity, and gender of individuals whom they stop or search; (12) state and local agencies are in the best position to provide law enforcement data on motorist stops because most motorist stops are made by state and local law enforcement officers; (13) a number of state legislatures are considering bills to require state or local police to collect race and other data on motorist stops; (14) several local jurisdictions are also making efforts to collect motorist stop data; and (15) whether the efforts that are underway will produce the type and quality of information needed to answer the questions about racial profiling remains to be seen.