Racial differences in speeding patterns: Exploring the differential offending hypothesis

Journal of Criminal Justice 40 (2012) 285–295

Contents lists available at SciVerse ScienceDirect

Journal of Criminal Justice

Racial differences in speeding patterns: Exploring the differential offending hypothesis Rob Tillyer a,⁎, Robin S. Engel b a b

University of Texas-San Antonio, Department of Criminal Justice, 501 W. Cesar E. Chavez Blvd., San Antonio, TX, 78207, USA University of Cincinnati, School of Criminal Justice, 600 Dyer Hall, Clifton Ave., PO Box 210389, Cincinnati, OH, USA

a r t i c l e

i n f o

Available online 16 June 2012

a b s t r a c t Purpose: Disproportionate minority contact during traffic stops has been a consistent source of commentary and study in recent years. While various theoretical perspectives have been employed to explain these empirical findings, the differential offending hypotheses has been largely ignored as a viable alternative explanation. Building on existing empirical evidence regarding criminal offending patterns and driving patterns, we examined the veracity of this explanation using data from an observational study of urban driving behavior. Methods: Data were collected using an observational methodology in an urban environment. These data were then used to estimate various regression models and test the differential offending hypothesis. Results: Analytic models indicated that Black drivers speed more frequently and engage in more severe speeding compared to White drivers, net of controls. Conclusions: The findings suggest that citizen risk for specific police behavior is partially attributable to differential behavior prior to the encounter. These results mirror the findings of previous research in other geographic locations using different methodologies; thus, contributing to the conclusion that understanding officer decision-making and behavior requires consideration of other factors beyond a citizen's race. © 2012 Elsevier Ltd. All rights reserved.

Introduction Racial and ethnic disparities in the criminal justice system have generated concern for several decades. Policy makers now discuss “DMC” (Disproportionate Minority Contacts) and seek additional research that explains this troubling pattern of disparate representation in the criminal justice system. Much of this recent attention stems from concerns over “racial profiling” by police, which was first brought to our collective attention in the mid-1990s (Harris, 1999). Since that time, civil rights activists have filed civil lawsuits alleging unequal treatment and sought remedies in criminal cases against defendants found with contraband (Harris, 2002; 2006). Likewise, legislative bodies have passed laws prohibiting profiling and requiring the collection of data during police-citizen stops, while court orders have required changes in data collection (Novak, 2004; M. Smith & Alpert, 2002). While academics have analyzed data and reported findings regarding the role of drivers’ race/ethnicity during traffic and pedestrian stops (see Withrow, 2006 for a review), police administrators have changed training, policies, and procedures in an effort to eradicate bias (Tillyer, Engel, & Wooldredge, 2008). Although these actions were likely motivated by different interests, these various stakeholders are united in their efforts to determine whether minority

⁎ Corresponding author. Tel.: + 1 210 458 2979. E-mail address: [email protected] (R. Tillyer). 0047-2352/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.jcrimjus.2012.04.001

citizens receive differential police attention and/or coercive outcomes compared to White citizens. Past research has consistently reported racial/ethnic disparities in both traffic stops (Withrow, 2006) and in other police-citizen encounters (Kochel, Wilson, & Mastrofski, 2011; National Research Council, 2004; Rosenfeld, Rojek, & Decker, 2012; Tillyer, Klahm, & Engel, 2012); however, why these patterns of treatment exist is still a subject of some debate. Recent theoretical explanations have suggested causal processes that produce such disparities (e.g., see Novak & Chamlin, 2012; Petrocelli, Piquero, & M. Smith, 2003; M. Smith & Alpert, 2002; Tomaskovic-Devey, Mason, & Zingraff, 2004). Drawing conclusions regarding the reasons for these disparities, however, remains largely speculative, and the single explanation that is often implied (even if unsubstantiated) is overt officer racism or racial discrimination (Engel, Calnon, & Bernard, 2002). Unfortunately, the majority of studies examining the treatment of minority citizens by police fail to consider alternative hypotheses. To date, one obvious explanation for differential processing of minority citizens has not been adequately considered. The differential offending hypothesis suggests that differential outcomes for specific groups are related to their behavior, rather than the discriminatory practices of the police (Chauhan, Reppucci, Burnette, & Reiner, 2010; D'Alessio & Stolzensburg, 2003; Piquero, 2008). Considerable evidence has been generated from the criminological literature suggesting some legitimacy for this explanation (Farrington, Loeber, Stouthamer-Loeber, VanKammen, & Schmidt, 1996; Felson, Deane, &

286

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

Armstrong, 2008; Hindelang, 1978; McNulty & Bellair, 2003); however, the policing literature has largely remained silent on the veracity of this alternative hypothesis. We aim to fill this gap in prior literature by exploring the empirical validity of the differential offending hypothesis as a potential explanation for the pattern of racial disparities reported in traffic stops. Building on previous empirical evidence regarding the driving behavior of Black motorists (Cherkauskas, 2011; Engel & Calnon, 2004; Engel et al., 2005; Lange, Johnson, & Voas, 2005; W. Smith et al., 2003), we analyze data of all observed drivers, regardless of race/ethnicity, in Cleveland, Ohio. Because these observation-based data were not collected by a law enforcement agency or generated from a self-report methodology, they offer an alternative, objective, and unobtrusive measure of driving behavior. Prior to describing our data collection protocols and results, we briefly summarize the research examining driver race/ethnicity and officer behavior. We supplement this material with an overview of evidence generated in other disciplines that focus on the competing hypotheses of differential offending versus differential processing (Piquero, 2008). We conclude by offering suggestions for future exploration of this issue based on our results, and raise important policy issues to be considered. Existing experiences & explanations Police research examining officer decision-making within traffic stops demonstrates fairly consistent evidence that racial/ethnic disparities exist. Based on a summary of the literature, roughly eighty percent of published studies reported racial/ethnic disparities in traffic stops (Withrow, 2006). Importantly, analyses of traffic stopping rates have been criticized due to the inability to develop appropriate and accurate benchmarks as baseline measures of drivers’ risk of being stopped (Tillyer, Engel, & Cherkauskas, 2010). This criticism underscores the need for an assessment of citizen behavior using alternative data to assist in determining whether the disparate patterns of traffic stops are justified. Considerable evidence has also amassed indicating differential treatment in post-stop outcomes (e.g., citations) for minority citizens when compared to White citizens (Engel et al., 2007; W. Smith et al., 2003; see Tillyer & Engel, in press for alternative findings). A recent meta-analysis examining a variety of policing behavior across 40 studies, including non-traffic stop police-citizen encounters, reported that Blacks have an elevated likelihood of arrest even after other legal and extralegal factors were considered (Kochel et al., 2011). Researchers also report that searches initiated during traffic stops demonstrate a pattern of racial disparity (Engel & Johnson, 2006; Pickerill, Mosher, & Pratt, 2009; Withrow, 2004) with more recent evidence indicating that young, Black males are at elevated risk for discretionary searches compared to other citizen groups (Rosenfeld et al., 2012; Tillyer et al., 2012). To date, several theoretical models and hypotheses have been offered to explain these empirical patterns and answer the call for better theoretical frameworks (Engel et al., 2002). For example, the race-outof-place hypothesis suggests that minorities receive differential treatment in areas where they stand in contrast to the demographic makeup of the community (Meehan & Ponder, 2002; Novak, 2004; Novak & Chamlin, 2012). The racial threat hypothesis has also been applied to officer treatment of minority citizens, suggesting that when the dominant majority feels threatened by an increase in the minority population, agents of social control (i.e., the police) are mobilized to reinforce the existing social order (Blalock, 1967; Novak & Chamlin, 2012; Petrocelli et al., 2003). Additional sociological explanations for differential treatment of minorities have also been offered, including the application of social disorganization, urban disadvantage, and collective efficacy principles to citizen treatment (Parker et al., 2004). Other explanations of officer behavior focus on factors directly associated with the police. For example, police deployment has been

identified as a potential form of structural discrimination that might account for differential treatment (Engel, M. Smith, & Cullen, in press; Tomaskovic-Devey et al., 2004). Saturation of police patrols in crimeprone areas is a common police deployment strategy, based on the now empirically-demonstrated premise that calls for service and criminal activity are not evenly distributed across geographic areas and that focusing on “hot spots” of criminal activity can reduce crime (e.g., see Braga et al., 1999; Sherman, Gartin, & Buerger, 1989; Weisburd & Green, 1995). Some research has suggested that policing styles in high-crime areas tend to be more proactive and aggressive compared to policing styles in other lower-crime areas (D. Smith, 1986; D. Smith, Visher, & Davidson, 1984; for review, see National Research Council, 2004). Racial/ethnic segregation in many urban areas has resulted in minorities disproportionately residing in high-crime, lowincome areas (Logan & Messner, 1987; Massey & Denton, 1993; Shihadeh & Flynn, 1996). Therefore, individuals in these communities have an elevated risk of criminal apprehension based strictly on their residence. This type of deployment may result in differential enforcement patterns across racial/ethnic groups that do not reflect individual officers’ intentions (Warren et al., 2006). Others suggest that the key explanatory mechanism is officer suspicion (Alpert, MacDonald, & Dunham, 2005). From this perspective, officers with heightened levels of suspicion are more likely to invoke their power over citizens and suspicion is often generated within specific situational conditions. Related, the social conditioning model (M. Smith & Alpert, 2007), borrowing from Skolnick's (1966; 1994) conception of the symbolic assailant, suggests officers are influenced by unconscious biases formed through their own experiences, experiences of their peers, or from the media. Minority citizens may be disproportionately viewed in a negative light, thus influencing their differential treatment. Collectively, these explanations associate the “cause” of differential treatment to societal factors, organizational philosophy, or officer-based preferences. Finally, it is possible that some officers are simply prejudiced against minority citizens thereby allowing those biases to influence their behavior (Tomaskovic-Devey et al., 2004; Warren et al., 2006). Thus, disparate patterns of minority treatment are a product of these “bad apples” (Sherman, 1974) and their decision-making during traffic stops. Importantly, when researchers attribute findings of racial/ethnic disparities to individual officer bias, they make important assumptions about officers’ motivation. They often assume that officers make conscious or unconscious enforcement decisions based on non-legal factors (i.e., citizen race/ethnicity) leading to differential outcomes; yet, officers’ motivation remains unmeasured. Research on citizen treatment is primarily limited to post-hoc assessments of officer decisionmaking and often results in unsubstantiated conclusions regarding the underlying reasons for these choices. Thus, the conclusion often reached is that differential outcomes indicate unwarranted or unjustified differential treatment of minorities by the police. While empirical evidence is beginning to appear regarding the accuracy of these theoretical explanations, we contend that another viable explanation for differential treatment of minorities needs to be reconsidered. Differential outcomes experienced by minorities may also be the result of differential law-violating behavior. This does not invalidate the previously developed explanations, but if supported, presents a viable alternative hypothesis that challenges the assumption that all citizens exhibit similar levels of pro-social behavior and should be treated equally. Differential offending hypothesis The differential offending hypothesis considers the possibility that certain racial/ethnic groups violate the law at differential rates. As summarized by Piquero, “the differential involvement hypothesis holds that minorities are overrepresented at every stage of the criminal and juvenile justice system because they commit more

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

crimes, for more extended periods of their lives, and more of the types of crime, such as violence, that lead to processing within the criminal justice system” (2008: 64). When applied directly to policing, this hypothesis suggests that differential offending by minorities draws increased scrutiny and leads to a higher likelihood of police intervention; this increased police attention is irrespective of individual police bias. Central to this hypothesis is the notion that different racial/ethnic groups have differential levels of risk for police attention. Evidence supporting this hypothesis is found in a variety of sources including research on criminal behavior and driving behavior. Criminal behavior Evidence regarding differential criminal behavior across racial/ ethnic groups was initially offered by Hindelang (1978) who compared official data to self-report data and reported that Blacks were overrepresented in rates of arrest for rape, aggravated assault, and simple assault compared to Whites. Subsequent research confirmed differential offending rates based on analyses of official data (D'Alessio & Stolzensburg, 2003), self-report data, (McNulty & Bellair, 2003), and longitudinal data (Farrington et al., 1996). In recent years, the rates of juvenile violence and delinquency across racial/ ethnic groups have also been examined with similar conclusions (e.g., Hawkins et al., 2000; Haynie & Payne, 2006; McNulty & Bellair, 2003; Vaughn et al., 2008). Importantly, while rates of delinquent or criminal behavior are higher for minority youth, these effects appear to be partially attenuated by social context. For example, Felson et al. (2008), using the Add Health data, reported that differential rates of involvement in delinquency were reduced but not eliminated once neighborhood factors (e.g., urban area, poor and uneducated parents, and single parent families) were considered (see also Chauhan et al., 2010). Vaughn et al. (2008) also indicated the importance of concentrated disadvantage in understanding the rates of violence by minority youth. Likewise, using the Pittsburgh Youth Study data, researchers reported that Black youth had substantial higher delinquency risk factors, including conduct disorder, delinquent peers, and neighborhood problems, which led to higher rates of arrest (Fite, Wynn, & Pardini, 2009). While some research suggests that social factors are an important element to consider, other studies have offered evidence that refutes the differential involvement hypothesis. For example, Piquero and Brame (2008) concluded that there is no difference in rates of serious delinquency among adolescence after comparing official data to selfreport data. Further, Chauhan et al. (2010) indicated that the level of neighborhood disadvantage explained the re-arrest rate for minority girls. Others have suggested that minorities, Blacks in particular, may use drugs more frequently in public places leading to a higher chance of detection (Beckett, Nyrop, Pfingst, & Bowen, 2005; Ramchand, Pacula, & Iguchi, 2006). Clearly, there is no consensus regarding the explanation for differential rates of minority involvement in criminal behavior. Yet, it is generally recognized that both differential involvement and differential treatment may interact to produce disparate rates of minority involvement with the criminal justice system (Piquero, 2008). For our purposes, it is important to simply note that a considerable body of evidence that examines criminal behavior has amassed that demonstrates support for the differential involvement hypothesis. Driving behavior Recent studies have also generated considerable evidence indicating that minority motorists engage in more risky driving behaviors compared to White drivers. For example, recent data from the National Youth Risk Behavior Survey (YRBS) indicated that Hispanics were more likely to ride with a driver who had been drinking and

287

were more likely to drink and drive compared to Whites (Everett et al., 2001). Data from the National Highway Traffic Safety Administration (NHTSA, 2008) also reported an elevated rate of drinking and driving by minorities (also see Baker, Braver, Chen, Pantula, & Massie, 1998; Braver, 2003; Harper et al., 2000; Royal, 2000), and Braver (2003) reported higher rates of Blood Alcohol Content (BAC) among Hispanic males. One explanation for these elevated rates is an attitudinal difference between White adolescents and Black and Hispanic adolescents, with the latter group reporting those behaviors as less dangerous (Ginsburg et al., 2008). Note, however, that minorities were not found to drink and drive more frequently in some studies (Abdel-Aty, & Abdelwahab, 2000; also see Caetano & Clark, 2000 and Voas et al., 1998 for conflicting results based on self-report data), and in some cases, similar rates of drinking and driving were discovered (Voas, Tippetts, & Fisher, 2000). Failure to use seatbelts within an automobile is another indicator of risky behavior. Several studies consistently report that minorities use seatbelts less often compared to Whites (Braver, 2003; Everett et al., 2001; Nelson, Bolen, & Kresnow, 1998; NHTSA, 2006). Baker et al. (1998) also reported minority juveniles were more likely to be killed in an automobile accident, possibly due to lower rates of seatbelt usage (also see Matteucci, Holbrook, Hoyt, & Molgaard, 1995; Nachiondo, Robinson, & Killen, 1996; Niemcryk, Kaufmann, Brawley, & Yount, 1997). Moreover, Black citizens were less likely to use seatbelts when travelling with children, as reported in the National Survey of the Use of Booster Seats (NSUBS) (NHTSB, 2009a; see also, Harper et al., 2000; Lerner, Dietrich, Billittier, Moscati, Connery, & Stiller, 2001; Wells, Williams, & Farmer, 2002). 1 Studies have also indicated that minorities are more likely to be seriously injured and killed in automobile accidents compared to White citizens (Chang, Lapham, & Barton, 1996; Davies & Griffin, 1996; Lee, Orsay, Lumpkin, Ramakrishman, & Callahan, 1996; Popkin & Council, 1993; Ross, Howard, Ganikos, & Taylor, 1991). Recent data from the NHTSA (2006) demonstrated that Hispanics and Blacks died more frequently in fatal crashes compared to Whites, while other data indicated that Black and Hispanic men had elevated likelihoods of dying as a result of a traffic related crash (Braver, 2003; see also Bell, Amoroso, Yore, Smith, & Jones, 2000). Finally, cell phone usage while driving has received recent attention. Preliminary analyses based on self-report data in Maryland indicated that Black drivers reported higher rates of cell phone usage while driving compared to Whites, and those using cell phones also were more likely to drive while drowsy, drive faster and more aggressively, and run a stop sign or red light (Beck, Yan, & Wang, 2007). Apart from drinking and driving, seat belt usage, and cell phone usage, speeding is also a risky driving behavior that is highly correlated with both citations and crashes (Gerard, 2011). Data from the NHTSA (2006) indicated that Black drivers were more likely to speed or engage in other moving violations compared to other motorists. In contrast, Tomaskovic-Devey et al., (2006) used a reverse record methodology to contact those who received speeding tickets and inquire about their frequency of speeding. Results from this self-report study indicated no racial differences in speeding behavior within thirty-five mile per hour zones and slightly lower average rates of speed for Black drivers within sixty-five mile per hour zones. More recently, Lundman and Kowalski (2009) examined two months of official traffic stop data for police agencies in Massachusetts. Importantly, data were drawn from only highway locations (speed limits of fifty-five and sixty-five miles per hour) and from traffic stops resulting in an official warning or a citation. Speeding was operationalized at fifteen miles per hour over the posted speed limit. Logistic regression results indicated that in the fifty-five miles per hour zones, younger drivers and male drivers were more likely to speed, but no race/ethnicity effect was discovered. However, in the sixty-five miles per hour zones, younger drivers, male drivers, and

288

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

Black drivers were more likely to speed compared to older drivers, female drivers, and White drivers, respectively. These results were confirmed in additional analyses that removed nighttime stops, included a proxy for social class, and considered commuting patterns. Moreover, analyses using OLS regression, with speeding as a continuous dependent measure, also did not change the results. Lundman and Kowalski (2009) acknowledged several weaknesses in their study, including only analyzing drivers that received either a warning or a ticket, the use of officers’ perceptions of the driver's race/ethnicity, gender, and age, and the use of official police data which may have been susceptible to tampering as the officers knew their behavior was being analyzed. Unfortunately, the possibility of sample bias – minorities were found more likely to speed because officers were more likely to stop minorities for speeding offenses – cannot be discounted. Further, if police are enforcing the laws in a racially biased manner as some scholars have argued (e.g., Harris, 1997), then analyses of official police stops would represent a biased sample of driving behavior. In summary, the available studies examining racial/ethnic differences in driving behavior provide growing support for the differential hypothesis. It is generally recognized, however, that studies based on self-reports and official police data have questionable validity. For example, even studies using a self-report methodology acknowledge that minorities may underreport police-citizen encounters in selfreport surveys (Tomaskovic-Devey et al., 2006). Likewise, relying on official police data to determine differences in criminal offending patterns assumes that the police have acted in a race-neutral manner when enforcing the law; yet this is the very assumption that is questioned by those concerned with police bias. As a result, using police data to demonstrate racial differences in driving behavior has limited utility.

Independent observations of driving behavior Despite these more recent publications using self-report and official police data to examine racial differences in driving behavior, early investigations in the mid-1990s recognized the need to independently assess driving behavior as an important risk factor for police apprehension. This need was initially addressed using an observation methodology that emerged out of legal cases in New Jersey and Maryland (i.e., State of New Jersey v. Soto, 1996; Wilkins v. Maryland State Police, 1994) under the direction of John Lamberth (1994; 1996). His “carousel” approach involved trained observers, driving at or near the speed limit on specific segments of particular highways, collecting driver characteristics of passing vehicles. Lamberth (1994; 1996) reported there were no racial differences in speeding, despite the overwhelming number of drivers who exceeded the speed limit (over 90% in both states). Critics argued this type of observation methodology was problematic because speeding was measured as a dichotomy rather than a metric, and the speeding threshold was limited to five miles per hour over the posted speed limit (Cherkauskas, 2011; Ekstrand, 2000; Engel & Calnon, 2004). These limitations inhibit the ability to fully capture the real risk of police intervention for speeding; thus, the conclusions derived from these early studies were questionable. Building upon Lamberth's original methodology, three separate studies in New Jersey, North Carolina, and Pennsylvania further independently assessed racial/ethnic differences in traffic violations (Cherkauskas, 2011; Engel & Calnon, 2004; Engel et al., 2005; Lange et al., 2001, 2005; W. Smith et al., 2003). These studies used stronger methodologies, which included better data collection, improved attempts for inter-rater reliability, and more sophisticated analytical techniques. Although these three studies varied in the specific methods and analyses used, they each reported support for the differential offending hypothesis.

Specifically, Lange et al. (2001, 2005) examined the issue of disproportionate traffic stops along the New Jersey Turnpike by considering racial/ethnic differences in speeding. Data for these analyses were drawn from a mounted camera on the side of the interstate that recorded the speed of the vehicle and took a picture of the driver. These pictures were then presented to three independent raters to determine the race/ethnicity of the driver. Speeding was defined as a vehicle traveling at least fifteen miles per hour above the posted speed limit. Analyses indicated that there were no statistical differences in speeding behavior among racial/ethnic groups within fiftyfive miles per hour zones, but younger drivers and male drivers were more likely to speed. Within the sixty-five mile per hour zones, however, Black drivers and drivers classified as ‘other’ joined younger and male drivers as significantly more likely to speed compared to their counterparts. W. Smith et al. (2003) examined speeding behavior of motorists in North Carolina using a method that involved human observers measuring vehicle speed and race/ethnicity of the driver across fourteen locations over a one-week period. Most observations occurred on interstates and the locations extended approximately ten to fifteen miles. Speed of passing vehicles was calculated using a stopwatch to measure the time elapsed between when a vehicle's front bumper and rear bumper passed the observers. Speeding was defined based on the average rate of speed at which vehicles were ticketed using official data from the same locations. Researchers acknowledged a possible underestimation of vehicle speed, but added a statistical correction in the multivariate models. Results indicated that Blacks were more likely to speed compared to Whites using several different measurements: above the first decile over the speed limit, above the median speed at which a citation was issued, and fifteen miles per hour above the posted speed limit. Finally, a large multiyear traffic study in the state of Pennsylvania also explored racial/ethnic differences in drivers’ offending behavior by examining speeding (Cherkauskas, 2011; Engel & Calnon, 2004; Engel et al., 2005). Observational data on speeding behavior, as well as driver and vehicle characteristics, were captured within twentyseven of Pennsylvania's sixty-seven counties. Locations were selected based on: traffic volume; likelihood of increased minority roadway usage; likelihood that travel patterns would not match residential populations; amount of Pennsylvania State Police (PSP) stop activity; and geography (designed for statewide analyses). Research teams of two independent observers utilized police RADAR equipment to detect drivers’ speeding behavior from stationary locations. Regarding race comparisons, this study reported that Non-Whites, and specifically Blacks, were significantly more likely to exceed the posted speed limit compared to White drivers. Furthermore, the race effects, particularly for Blacks, were stronger at more serious amounts over the limit (e.g., Blacks were 1.4 times more likely than non-Blacks to exceed speed limit by 10 or more m.p.h., but 2.0 times more likely to exceed limit by 25 m.p.h.). In summary, a body of evidence has accumulated that lends some credibility to the differential offending hypothesis. Despite different methodologies (i.e., automated versus human observations) and across various locations, three studies offer evidence that minority drivers exceed the speed limit at higher rates than White drivers. Importantly, however, these studies suffer from some methodological limitations that need to be resolved. For example, the New Jersey study (Lange et al., 2001, 2005) used black and white photographs to identify the race/ethnicity, gender, and age of the driver raising validity concerns. Further, the study had a high rate of unusable photographs (Kociewiewski, 2002), it studied only one roadway (New Jersey Turnpike), and a degree of disagreement existed between the three independent raters (W. Smith et al., 2003). The North Carolina study (W. Smith et al., 2003) collected information over a single week period, examined primarily interstate highways, and did not use equipment to collect the speed of passing motorists.

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

289

Our study addresses some of the limitations noted in previous work on speeding behavior using an observational approach and aims to answer two specific research questions related to risk of police intervention. First, do Black drivers violate the posted speed limit with greater frequency compared to White drivers? Second, within the pool of observed speeders, do Black drivers speed at higher rates of speed compared to White drivers? To increase objectivity and avoid the limitations of official data and self-report data, independent observers positioned at strategic locations were used to collect data on passing vehicles. Roadway observational data provides an objective measure of speeding, while also offering an unbiased assessment of drivers’ characteristics. Additionally, driving behavior is measured in lower speed zones to complement previous work that examined higher speed zones. Finally, previous observational research has considered only major highways or interstates with high-posted speed limits; our study examines driving behavior within an urban environment.

months of observation time, and a target of 2.5% of the possible time sampled. Observations were not scheduled for the winter months due to the challenges of collecting data in poor weather conditions. 4 A schedule was created for each location that involved observations across all days of the week (including weekends) and both morning and evening rush hour periods. Initially, observations were conducted across fifteen observation sites between May and November, 2006. A series of rules were developed that eliminated some observations from analyses. Criteria for inclusion in the analyses included: each location had to achieve a minimum number of observation hours, all observations had to be conducted using a RADAR or LASER unit, 5 only observations of Black and White drivers were included, observations had to be conducted in weather conditions determined to be at least fair or better, and only observations in 25 or 35 mile per hour zones were analyzed. All observations that met these criteria were considered for analyses. Additional cases were eliminated due to missing data on variables of interest including speed, driver gender, driver age, and vehicle characteristics. Missing data resulted from the implementation of a specific protocol developed for collection of driver, vehicle, and location characteristics and to ensure inter-rater reliability. All observation sessions involved two observers collecting information. Once a vehicle was selected for observation, the first observer called out all relevant information. This information was recorded and then independently confirmed by the second coder. Instances of disagreement or an inability to identify the characteristic of interest were coded as missing (4.2% for driver race). After applying the sampling criteria and removing all missing data, 25,346 observations remained for analysis.

Data collection

Measures

Driving pattern data were collected in 2006 as part of a larger examination of all police traffic stopping practices in Cleveland, Ohio. At the time of the study, the population of Cleveland was estimated at slightly less than 500,000 with Black citizens representing 51% of the population, White citizens 42% of the population, and Hispanic citizens roughly 7% of the population (U.S. Census, 2000). Through a federal grant, the Cleveland Division of Police (CDP) received funding to study officer decision-making during traffic stops to aid CDP administrators in determining if racial and/or ethnic disparities in traffic stops and post-stop outcomes existed, and if evident, the possible sources of these disparities. An academic research team was contracted to execute the grant, and as part of the data collection process, independent observations of specific roadway segments were conducted in an effort to systematically observe and record traffic flow patterns, driving behavior, and pre-specified information on passing vehicles. In consultation with the CDP, several protocols were adopted to guide the selection of observation locations and data collection. Observation locations selected were required to: 1) reflect major thoroughfares and ensure a sufficiently large population of traffic and a cross-section of racial/ethnic groups; 2) possess a high degree of driving infractions, in particular, speeding; 3) allow observers an unobstructed view of the roadway; 4) not possess any unnatural obstructions, such as construction or road closures; and 5) ensure safety of the observation teams. The first two criteria were adopted to ensure a large sample of the driving population was recorded, while the remaining criteria were necessary for valid and accurate data collection and safety concerns. Across the city, a total of fifteen locations were originally selected based on these criteria. 2 Using Wilson‘s recommendations (2000), between two and three percent of all possible observation time was targeted for observation, which translated to roughly sixty hours per location (58.5 hours) across the time period of the study. The number of hours was a product of the following considerations: twelve hours per day of observation time (no nighttime observations 3), seven days per week, seven

Observers were selected from a pool of qualified graduate and undergraduate students and received eight hours of training on the specific methodology for this study, including data collection protocols, radar/laser equipment usage, and confidentiality issues. All training was conducted by the research team in conjunction with law enforcement personnel. Two instruments, a location sheet and an observation form, were created, pilot tested, and used for data collection. The location sheet collected information on the actual location of the observations, including date of the observation, day of the week, time of the day, type of violation(s) observed, location of the observation, type of area, speed limit on the road, type of road, number of lanes, lane being observed, weather conditions, and vehicle sampling technique. The observation form was used to record driving behavior, vehicle characteristics, and driver characteristics of passing vehicles. These variables were then grouped into speeding related variables, driver characteristics, vehicle characteristics, and location characteristics. Four dependent variables were generated from measuring the speed of passing vehicles in the 25 or 35 mile per hour zones. Specifically, dichotomous variables were created to reflect any violation of the posted speed limit, vehicles travelling at least five miles per hour above the posted limit, and vehicles travelling at least ten miles per hour about the posted limit. A fourth dependent variable was a continuous measure of speed over the limit. Due to the skew of this variable, it was transformed using the square root function. As reported in Table 1, descriptive statistics indicate a wide range of vehicle speeds with an average speed slightly below the legal limit (-0.53 miles per hour). Overall, 45.9% of all observed vehicles exceeded the speed limit by at least one mile per hour, 20.7% of vehicles were travelling at least five miles per hour over the legal limit, and 4.5% exceeded the speed limit by at least ten miles per hour. Driver characteristics included driver's race, gender, and age. Observations involving White and Black drivers were dichotomized for analysis, with 59.2% of observations involving White drivers. Driver gender was also dichotomized, with 60.3% of all cases

Finally, the Pennsylvania study (Cherkauskas, 2011; Engel & Calnon, 2004; Engel et al., 2005) only examined driving behavior on major thoroughfares in non-urban areas, and relied on observations of drivers’ race from stationary locations. Although this study established inter-rater reliability, the validity of the racial identifications remained unknown. Despite these flaws, collectively there is support for the differential hypothesis. It is unknown, however, if these findings apply to driving patterns in urban areas, where minority groups consistently voice concerns regarding aggressive use of traffic stops, and have alleged police bias. The present study

290

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

Table 1 Descriptive statistics (N = 25,346) Variables Dependent Variables Speed Relative to Legal Limit Speeding Violation Speeding 5 Miles Per Hour or more Speeding 10 Miles Per Hour or more Driver Characteristics White Black Male Under 25 Years of Age 26 to 65 Years of Age 65 Years of Age and Older Vehicle Characteristics In State Car Condition - Good Condition - Fair Condition - Poor Modifications Passengers Location Characteristics Every Vehicle Sampled 2nd Vehicle Sampled 3rd Vehicle Sampled Driving Lane Observed Passing Lane Observed Middle Lane Observed Driving & Passing Lane Observed Spring Summer Fall Weekday Rush Hour Residential Area Commercial Area Mixed Use Area

Min.

Max.

Mean

S.D.

-33 0 0 0

30 1 1 1

-0.52 0.46 0.21 0.05

6.37 0.50 0.41 0.21

0 0 0 0 0 0

1 1 1 1 1 1

0.59 0.41 0.60 0.14 0.75 0.11

0.49 0.49 0.49 0.35 0.43 0.31

0 0 0 0 0 0 0

1 1 1 1 1 1 1

0.98 0.64 0.79 0.18 0.04 0.06 0.29

0.15 0.48 0.41 0.38 0.20 0.23 0.45

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0.37 0.40 0.23 0.68 0.12 0.06 0.15 0.24 0.42 0.34 0.72 0.27 0.15 0.45 0.40

0.48 0.49 0.42 0.47 0.32 0.24 0.35 0.42 0.49 0.47 0.45 0.44 0.36 0.50 0.49

involving male drivers. Driver age was recorded as 25 years of age or younger (14.2%), 26 to 65 years of age (75.2%), and over the age of 65 (10.6%); in the multivariate analyses to follow, 26 to 65 years is the reference category. Vehicle characteristics included if the vehicle was registered in Ohio, the type of vehicle, its condition, presence of modifications, and the number of passengers in the vehicle. The overwhelming majority of vehicles were registered in-state (97.8%). Vehicle type was collapsed into a simple dichotomy indicating if the vehicle was a car (i.e., sedan or sport coupe) (63.9%) or other type of vehicle. Vehicle condition was measured as “good” if there were no visible blemishes or cosmetic defects to the exterior of the vehicle, “fair” if there were visible blemishes (dents) or the vehicle was older, and “poor” if there were visible cosmetic defects to the exterior of the vehicle, such as broken head or taillight(s), mirror(s), muffler, window(s), or severe body damage. For the multivariate analyses, only the poor condition variable (4%) is included in the models. Vehicle modifications (5.5%) captured whether the vehicle had customizable, after-market features which would draw attention to the vehicle while on the roadway, including tinted windows, high performance exhaust systems, or spinners (aftermarket rims). Finally, the presence of a passenger was measured as a dichotomous variable, with 28.5% of observed vehicles possessing at least one occupant in addition to the driver. Location variables recorded the specific method of vehicle selection (i.e., sampling), the lane observed, day and time of the observation (i.e., season, weekday, and rush hour), and contextual information. Selection of the vehicle was dictated by the flow of traffic at the location. Whenever possible, every passing vehicle was recorded, but during busy hours this sampling technique was modified to record information on every other vehicle or every third vehicle. Public transportation vehicles such as buses, city, state or federal

cars and trucks, emergency response vehicles, and taxicabs were not included in the observations. The majority of observations captured either every passing vehicle (37.2%) or every other passing vehicle (40.0%), with the remaining observations conducted using every third passing vehicle (22.9%). The majority of observations focused on the driving lane (67.6%). As reported in Table 1, the day and time of the observation were used to create dichotomous variables representing the season (i.e., spring, summer, or fall), weekdays, and rush hour (morning and afternoon). Finally, the context of the observation was recorded and dichotomized into residential, commercial, and mixed land-use areas. Residential area was identified by the preponderance of single or multi-family dwellings and is the reference category in multivariate analyses that follow. Commercial areas primarily consisted of service-oriented establishments such as restaurants, gas stations, or any other non-residential or industrial building; all other areas were coded as mixed-use.

Results To test the differential offending hypothesis as it relates to speeding behavior, a series of multivariate regression models were initially estimated. Logistic regression models were estimated for the models examining the dichotomous distinction between speeding and nonspeeding. Logistic regression models are appropriate to use when assessing the simultaneous impact of independent variables on a dichotomous dependent variable (Menard, 1995). These models are reported in Table 2 with each reflecting the dependent variables of speeding one mile per hour above the posted speed limit, five miles per hour above the speed limit, and ten miles per hour above the posted speed limit, respectively. These models were computed with identical variables to assess the independent variables’ impact across different speeding thresholds. The model examining our second research question is a traditional ordinary-least squares regression model with a modified dependent variable to correct for skewness. Results of this model are reported in Table 3. The “any speeding” model was defined as exceeding the speed limit by at least one per hour (nearly half of all observations reported this type of speeding). While Black drivers were statistically significantly more likely than White drivers to speed, the results are not substantively strong (odds ratio = 1.09). Driver age was also a significant predictor of speeding as drivers identified as under the age of 25 were 1.1 times more likely to speed compared to middle-aged drivers, and older drivers 1.4 times less likely to speed compared to middle-aged drivers. Vehicle characteristics were related to speeding behavior; specifically, sedans/sports cars were more likely to speed compared to all other types of vehicles, vehicles in poor condition were less likely to speed, and vehicles with passengers were also less likely to exceed the posted speed limit. Finally, a series of location characteristics were also associated with an increased likelihood of speeding. Locations that sampled every second vehicle were more likely to document speeding vehicles compared to those that recorded the speed of all passing vehicles. 6 Other relevant factors included driving lane (i.e., right-hand lane) observations witnessing more speeding vehicles and seasonal effects. Likewise, speeding appeared to occur more frequently on weekends compared to weekdays, while rush-hour time periods exhibited more instances of speeding compared to off-peak hours. Finally, commercial and mixed-use areas were associated with elevated rates of speeding compared to residential areas. The second logistic regression model examined speeding behavior measured as at least five miles per hour above the posted speed limit. The vast majority of the findings reported in the initial model are repeated here, although there are slight increases in the strength of most predictor variables. While Black motorists were significantly more likely than Whites to be observed exceeding the speed limit

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

291

Table 2 Logistic speeding models (N = 25,346) Any Speeding Variables Driver Characteristics Black Male Under 25 Years of Age Over 65 Years of Age Vehicle Characteristics In State Type Condition - Poor Modifications Passengers Location Characteristics 2nd Vehicle Sampled 3rd Vehicle Sampled Driving Lane Observed Spring Fall Weekday Rush Hour Commercial Area Mixed Use Area Intercept Model Chi-Square Nagelkerke R Squared

5 Miles over the Limit

10 Miles over the Limit

Coeff.

Odds Ratio

Coeff.

Odds Ratio

Coeff.

Odds Ratio

0.09** 0.02 0.13** -0.32***

1.09 – 1.14 1.38

0.13*** -0.03 0.15** -0.26***

1.14 – 1.17 1.30

0.24*** 0.05 0.28** -0.22

1.28 – 1.32 –

-0.07 0.08** -0.42*** 0.00 -0.16***

– 1.08 1.52 – 1.17

-0.03 0.09* -0.45*** 0.03 -0.24***

– 1.09 1.57 – 1.27

0.00 0.18** -0.75*** 0.26* -0.36***

– 1.19 2.12 1.30 1.43

0.10** -0.06 0.32*** 0.33*** -0.14*** -0.36*** 0.17*** 1.53*** 1.13*** -1.53 2,042.91*** 10.3

1.10 – 1.37 1.39 1.14 1.42 1.18 4.61 3.08

-0.22*** -0.25*** 0.44*** 0.13** -0.26*** -0.46*** 0.24*** 1.88*** 1.64*** -2.80 1,513.20*** 9.1

1.25 1.28 1.55 1.14 1.29 1.59 1.27 6.56 5.17

-0.34*** -0.37*** 0.46*** 0.03 -0.40*** -0.71*** 0.36*** 2.01*** 2.00*** -4.77 519.26*** 6.6

1.41 1.43 1.59 – 1.46 2.02 1.44 7.47 7.40

Note: * = p ≤ .05; ** = p ≤ .01; *** p ≤ .001. Note: All odds ratios have been inverted for ease of interpretation. Reference categories: Drivers aged 25-65, vehicles in fair or good condition, every vehicle sampled, observations involving passing or middle lanes, summer, and residential areas.

by at least five miles per hour, again this effect was not substantively strong (odds ratio = 1.14). The final logistic model examined speeding defined as at least ten miles per hour over the posted speed limit. Once again, Black drivers were significantly more likely than Whites to be observed speeding, yet this differences was of greater substantive significance than in

Table 3 OLS speeding model (N = 11,641) Variables

Min.

Max.

Mean

S.D.

Average Speeding Violation

1

30

4.82

3.37

Coeff.

Std. Error

1.76***

0.06

0.04** -0.01 0.06** -0.05*

0.01 0.02 0.02 0.03

-0.00 0.05** -0.10* 0.04 -0.09***

0.05 0.01 0.04 0.03 0.02

-0.11*** -0.11*** 0.16*** -0.01 -0.11*** -0.17*** 0.08*** 0.38*** 0.46*** 5.4

0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03

Intercept Driver Characteristics Black Male Under 25 Years of Age Over 65 Years of Age Vehicle Characteristics In State Type Condition - Poor Modifications Passengers Location Characteristics 2nd Vehicle Sampled 3rd Vehicle Sampled Driving Lane Observed Spring Fall Weekday Rush Hour Commercial Area Mixed Use Area Adjusted R Squared

Note: * = p ≤ .05; ** = p ≤ .01; *** p ≤ .001. Reference categories: Drivers aged 25-65, vehicles in fair or good condition, every vehicle sampled, observations involving passing or middle lanes, summer, and residential areas.

previous models (odds ratio = 1.3). In comparison to the other models, vehicles with modifications exhibited a positive relationship with speeding, while older drivers and spring observations were no longer statistically related to speeding. Again, commercial and mixed-use areas were much more likely to be associated with speeding compared to residential areas. Across all three models, Black drivers were consistently more likely to be observed speeding compared to White drivers and the substantive effects of these coefficients increased as the speeding threshold was raised. This pattern suggests that Black drivers are more likely to speed, and speed at greater levels of severity, compared to White drivers. To further examine this possibility, the 11,641 observations (46%) in which drivers exceeded the speed limit by at least one mile per hour were examined separately using an OLS regression model (see Table 3). When the observations of vehicles travelling under or at the posted speed limit were eliminated, the average amount over the limit was 4.8 miles per hour (s.d. = 3.4) with a maximum value of 30. Results from this model support many of the findings reported in the logistic regression models. Again, Black drivers were more likely to speed at higher rates compared to White drivers, net of other factors. Younger drivers speed at higher rates while older drivers generally drive slower compared to middle-aged drivers. The other variables operated in similar fashion to the previous analyses with sports cars, observations of the driving lane, rush hour observations, and observations conducted in non-residential areas associated with higher rates of speed. Vehicles in poor condition or with passengers, observations conducted using a less frequent sampling strategy, fall observations, and weekday observations were linked with lower rates of speed. Discussion The recent focus on police decision-making during traffic stops has largely examined the relationship between citizen race/ethnicity and police behavior, documenting that minorities receive unequal treatment across a range of outcomes (Engel & Johnson, 2006;

292

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

Kochel et al., 2011; Rosenfeld et al., 2012; Tillyer et al., 2012). Despite application of theoretical explanations to explain these racial disparities (e.g., Novak & Chamlin, 2012; M. Smith & Alpert, 2007; Tomaskovic-Devey et al., 2004), few have tested competing hypotheses, including the differential offending hypothesis. Considerable evidence exists in the criminological literature supporting the legitimacy of this explanation as a factor in understanding differential treatment of minorities within the broader criminal justice system (D'Alessio & Stolzensburg, 2003; McNulty & Bellair, 2003). Further, research on driving behavior has also documented differences across racial/ethnic groups, which has specific relevance for policing as police-citizen encounters most frequently occur within the context of traffic stops (Eith & Durose, 2011). We explored the legitimacy of the differential offending hypothesis by using observational data to assess the frequency and severity of speeding in Black and White driving populations. Our analytic models examining different speeding thresholds consistently offered support for the differential offending hypothesis, demonstrating that Black drivers exceeded the posted speed limit more frequently and more severely compared to White drivers. These results support previous findings of racial differences in speeding that were gathered using different methods and measures (Cherkauskas, 2011; Engel & Calnon, 2004; Engel et al., 2005; Lange et al., 2001, 2005; Lundman & Kowalski, 2009; W. Smith et al., 2003). In short, the findings suggest that, irrespective of police behavior, driving behavior matters, and that Black drivers engage in driving behaviors that increase their risk of being stopped by police. 7 Collectively, when our findings are considered in combination with previous observation studies, the importance of considering the differential offending hypothesis becomes more compelling. Across a variety of data collection methods (stationary cameras, rolling observation, and stationary observations) and locations (highway traffic in New Jersey, North Carolina, and Pennsylvania; urban traffic in Cleveland, Ohio) findings suggest that Black motorists are more likely to speed, and the severity of their speeding is greater, compared to White drivers. This support of differential offending, however, does not eliminate alternative possibilities to explain racial/ethnic differences in traffic stops. In fact, it is likely that differential offending rates, in combination with differential processing patterns – including police deployment, individual and collective police bias – place minorities at an even greater risk for traffic stops. Perhaps this is why Piquero (2008: 59, 60) has recently argued that making a distinction between differential offending and differential processing of minority contacts across the criminal justice system “no longer seems a helpful way to frame the discussion” and further that analysts “may thus be wise to abandon this empirical quest.” While Piquero (2008) is accurate that these distinctions are often difficult (and at times impossible) to make empirically, we disagree that this approach should be abandoned – at least for traffic stops – because of the important differences in policy outcomes that might be based on such distinctions. When considering previous commentary on the role of citizen race and its influence on officer behavior, these findings suggest further consideration be given to the differential offending hypothesis. If differential behavior is partially responsible for heightened risk of police attention, then conclusions regarding officer intent and bias need to be tempered. This is consistent with DeLisi's argument (2011) that subjective and objective realities do not always match, and empirically based assessments of behavior are needed to avoid inaccurate conclusions. This is not to suggest that officer bias may not be influential on behavior, only that consideration of alternative explanations may complement previous work and more accurately model actual processes. Our study's data collection methodology also improves upon previous work. Specifically, we collected observational data in an urban environment according to a robust, scientific method that avoided

the reliance on official or self-report data. Concerns of official data reflecting more serious violations (Elliott & Ageton, 1980) or being influenced by officer bias (Lundman & Kowalski, 2009) are not relevant when using observational data. While self-report data have significant strengths (Krohn, Thornberry, Gibson, & Baldwin, 2010), they do suffer from potential biases, as they require objective and accurate recall of behavior, which may be challenging when measuring speeding behavior. Despite the strengths of observational studies, data of this type have been criticized as potentially unreliable and subject to error due to the reliance on third-party observations of driver characteristics. To partially address this concern, our methodology required two observers to independently agree on all driver characteristics prior to recording that information; any situations in which the raters disagreed were coded as missing data. While it is possible that measurement error exists in accurately identifying driver race/ethnicity, extreme care was taken when recording this information (see the aforementioned training and data collection protocols), and the most challenging group to correctly identify, Hispanics, were removed from analytical consideration to minimize this threat. Correctly identifying driver age also presents a challenge for this type of data collection. To minimize measurement error, we adopted broad age categories (i.e., under 25 years of age, 26-65, etc.) and emphasized the importance of accuracy when training observers. It is possible, however, that some measurement error remained despite these precautions. This is a limitation of our observational research and underscores the importance of viewing our findings within the larger body of work using different data collection methods. To provide the best possible opportunity for observers to accurately record driver characteristics, information was collected in specific conditions. For example, no observations were conducted in the winter or when the weather conditions created poor visibility. It is possible that driving patterns vary during winter months or during times with poor weather conditions that reduce visibility, and therefore, our findings may not be generalizable to these time periods. Importantly, for this limitation to bias the findings, driving patterns would need to vary by race during these periods. For example, while all drivers may speed less frequently during winter conditions, Black drivers would need to drive differently from White drivers to influence the results. In addition, only locations that allowed clear visibility of on-coming traffic were selected. Our data include observations conducted across ten urban locations; therefore it is also possible that these unique locations do not reflect driving patterns in other parts of the City of Cleveland. These locations, however, were carefully selected through a process that involved input from law enforcement regarding driving behaviors at those locations, site visits to visually assess the appropriateness of the locations, and considerations of general traffic patterns across the city. Analyses of data collected at these ten individual sites did not demonstrate any significant variability in the general trends revealed at the aggregate level. Therefore, we are confident that our results reflect driving patterns across the City of Cleveland; generalizability to other urban areas, however, is unknown and remains a limitation of our study. Conclusion To date, policy recommendations based on racial profiling research have focused solely on changing police behavior. Over the last two decades, police agencies across the country have committed tremendous time and resources into reducing individual officer bias through a variety of methods including cultural training for officers, changes in policies, procedures, and discipline, stop and citation tracking systems, and early warning systems to name a few. While it is often presumed these efforts reduce officer bias, claims of effectiveness have rarely been evaluated empirically. Most concerning is that despite these interventions, along with nearly two decade of

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

academic scholarship, and political focus on reducing police bias, the racial and ethnic disparities initially reported in traffic stops and poststop outcomes persist (e.g., Rosenfeld et al., 2012; Tillyer & Engel, in press). The growing evidence in support of the differential offending hypothesis, however, suggests that it is time to consider the possibility that focusing attention solely on police behavior has obscured important issues about citizens’ behavior. Reported racial differences in speeding are extremely important because aggressive driving behaviors not only increase the risk for encounters with law enforcement, but also for involvement in serious and fatal accidents (NHTSA, 2009b). Statistics from the National Highway Transportation Safety Administration demonstrate that more people die in traffic accidents every year than from any other non-disease or illness related cause, and are the leading cause of death for every age from three to thirty-four years old. In addition, the 5,811,000 reported traffic accidents nationwide in 2008 had an estimated economic cost of $230.6 billion dollars (NHTSA, 2011). Despite the tremendous focus on reforming police behavior, little effort has been placed on changing citizens’ behaviors to enhance public safety and reduce the risk of initial police intervention. Unfortunately, the collective response to evidence demonstrating racial differences in aggressive driving behavior has not been met with a serious public health debate about how to reduce these risky behaviors among segments of the population. Likewise, criminological research that demonstrates racial differences in criminal offending (another type of risky behavior) overwhelming results in calls for reform of the criminal justice system. This is not to suggest that reform efforts are not needed in the criminal justice system; rather our point is to simply acknowledge that more attention should also be given to reducing behaviors that initially put minorities at increased risk for intervention by criminal justice officials. These ideas are particularly important to consider for research in police bias, and specifically traffic stop studies. Many studies have demonstrated consistent differences in the proportion of traffic stops for minorities compared to Whites, across multiple years, even where police reform efforts have been significant. Moving forward, it seems apparent that something more than changes in police recruitment, training, supervision, and management is needed to further reduce racial/ethnic disparities in traffic stops. In addition to the important police reform efforts currently being conducted across the country, serious policy discussions that include public health approaches to reducing racial disparities in aggressive driving patterns should be included. More broadly, while our results reflect the empirical reality of driving behavior and a potential alternative hypothesis for understanding police-citizen encounters, these findings should not be interpreted as indicating that race is the sole or even primary explanation for driving behavior. Indeed, the limited overall strength of the statistical models suggests that other unmeasured factors may be stronger predictors of speeding behavior than those that we have measured. Further, it is quite likely that race operates as a proxy for other, unmeasured constructs that affect human behavior. Cultural experiences, socialization processes, and genetic composition are a few of the many potential factors that work independently and collectively to influence behavior. For example, Unnever and Gabbidon (2011) recently posited that a variety of factors including perceptions of fairness in the criminal justice system, perceptions of racial discrimination and the effects of negative stereotypes, and racial socialization processes influence behavior, particularly for African Americans. As a large body of work in criminology demonstrates, understanding human behavior is much more complex than simply categorizing individuals by their race. In effect, our theoretical understanding and application of these underlying processes to policecitizen encounters is not well developed. It is conceivable that not only would consideration of such factors improve the strength of our empirical models, but more importantly, it would spur a more

293

holistic, theoretical discussion of differential behavior and how that influences police-citizen encounters. Notes 1. Conversely, data from the National Occupant Protection Use Survey (NOPUS) indicated equivalent rates of use (Glassbrenner, 2003; Glassbrenner, Carra, & Nichols, 2004) and data from a single state, North Carolina, suggested that Blacks use seatbelts more frequently than Whites (W. Smith et al., 2003). 2. These criteria were aimed at generating a sample that reflected the driving population of the city and improved on previous observation methodologies that relied on single observations points (Lange et al., 2001; 2005) or single stretches of highway (W. Smith et al., 2003). While selection of more locations would improve the representativeness of the sample, there is no reason to believe that the driving patterns at these locations are unique or different from the broader driving patterns across the city. 3. Similar to previous research (Lundman & Kowalski, 2009), our observations are limited to the daytime due to the methodological challenges associated with accurately collecting race information at night. Even if there are racial differences in roadway usage by time of day, there is no evidence to suggest that Black drivers are more likely than White drivers to speed at night. 4. Ideally, observations would have been conducted throughout the year; however, there is no evidence to suggest that traffic patterns are fundamentally different during the non-observation periods in the City of Cleveland, with the exception of poor weather conditions potentially suppressing the rate of speeding for all motorists. 5. Nearly 90% of all observations were conducted with a RADAR gun; the remaining observations were conducted with a LASER unit. It is possible that speeding rates are influenced by the ownership of RADAR detectors, and that ownership may vary across racial groups. 6. This result is somewhat counter-intuitive as it was expected the likelihood of speeding would decrease with a less frequent sampling strategy (as demonstrated in the subsequent models). One potential explanation for this result is that due to the low speeding threshold for this model (i.e., one mile per hour above the posted limit) a significant number of drivers (45.9%) exceed the posted speed limit. 7. Speeding violations are only one of many potential reasons for an officer to initiate a police stop. The data used in this study were drawn from a larger study of police-citizen encounters, which included information on traffic stops (see Engel et al., 2006 for a complete description). Speeding was reported as the reason for the stop in roughly a quarter of all traffic stops initiated and was the second most frequent reason for an officer to initiate a traffic stop (moving misdemeanors was the most frequent reason). Officers may also initiate contact for a variety of other legal reasons; however, speeding represents a significant safety concern in lower speed areas and is a strong influencer of risk particularly when it is excessive. We do not suggest that speeding is the only factor that increases risk of police action, but it is one important element affecting citizen risk of police behavior thereby offering a partial test of the differential offending hypothesis.

References Abdel-Aty, M. A., & Abdelwahab, H. T. (2000). Exploring the relationship between alcohol and the driver characteristics in motor vehicle accidents. Accident Analysis and Prevention, 32, 473–482. Alpert, G. P., MacDonald, J. M., & Dunham, R. R. (2005). Police suspicion and discretionary decision making during citizen stops. Criminology, 43, 407–434. Baker, S. P., Braver, E. R., Chen, L., Pantula, J. F., & Massie, D. (1998). Motor vehicle occupant deaths among Hispanic and Black children and teenagers. Archives of Pediatrics & Adolescent Medicine, 152, 1209–1212. Beck, K. H., Yan, F., & Wang, M. Q. (2007). Cell phone users, reported crash risk, unsafe driving behaviors and dispositions: A survey of motorists in Maryland. Journal of Safety Research, 38, 683–688. Beckett, K., Nyrop, K., Pfingst, L., & Bowen, M. (2005). Drug use, drug possession arrests, and the question of race: Lessons from Seattle. Social Problems, 52, 419–444. Bell, N. S., Amoroso, P. J., Yore, M. M., Smith, G. S., & Jones, B. H. (2000). Self-reported risk-taking behaviors and hospitalization for motor vehicle injury among active duty army personnel. American Journal of Preventative Medicine, 18, 85–95. Blalock, H. (1967). Toward a Theory of Minority-Group Relations. New York, NY: Wiley. Braga, A. A., Weisburd, D. L., Waring, E. J., Mazerolle, L. G., Spelman, W., & Gajewski, F. (1999). Problem-oriented policing in violent crime places: A randomized controlled experiment. Criminology, 37, 541–580. Braver, E. R. (2003). Race, Hispanic origin, and socioeconomic status in relation to motor vehicle occupant death rates and risk factors among adults. Accident Analysis and Prevention, 35, 295–309. Caetano, R., & Clark, C. L. (2000). Hispanics, Blacks and Whites driving under the influence of alcohol: Results from the 1995 National Alcohol Survey. Accident Analysis and Prevention, 32, 57–64. Chang, I., Lapham, S. C., & Barton, K. J. (1996). Drinking environment and sociodemographic factors among DWI offenders. Journal of Studies on Alcohol, 57, 659–669. Chauhan, P., Reppucci, N. D., Burnette, M., & Reiner, S. (2010). Race, neighborhood disadvantage, and antisocial behavior among female juvenile offenders. Journal of Community Psychology, 38, 532–540.

294

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295

Cherkauskas, J. M. (2011). The Role of Driving Behavior in the Debate over Racially Biased Policing. (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. (Accession Order No. [AAT 3471702]) D'Alessio, S. J., & Stolzensburg, L. (2003). Race and the probability of arrest. Social Forces, 81, 1381–1397. Davies, B. T., & Griffin, L. I. (1996). Over-representation of Hispanic drivers in alcohol related crashes in Texas. Proceedings of the 40th Annual Association for the Advancement of Automotive Medicine, Scientific Conference Poster (pp. 527–528). Des Plaines, Ill.: Association for the Advancement of Automotive Medicine. DeLisi, M. (2011). Where is the evidence for racial profiling? Journal of Criminal Justice, 39, 461–462. Eith, C., & Durose, M. R. (2011). Contact between Police and the Public, 2008. Washington, DC: U.S. Department of Justice. Ekstrand, L. E. (2000). Racial Profiling: Limited Data Available on Motorist Stops. Washington, D.C.: United States General Accounting Office. Elliott, D. S., & Ageton, S. S. (1980). Reconciling race and class differences in self-reported and official measures. American Sociological Review, 44, 95–110. Engel, R. S., & Calnon, J. M. (2004). Examining the influence of drivers’ characteristics during traffic stops with police: Results from a national survey. Justice Quarterly, 21, 49–90. Engel, R. S., Calnon, J. M., & Bernard, T. J. (2002). Theory and racial profiling: Shortcomings and future directions in research. Justice Quarterly, 19, 249–273. Engel, R. S., Calnon, J. M., Tillyer, R., Johnson, R. R., Liu, L., & Wang, X. (2005). Project on Police-Citizen Contacts, Year 2 Final Report. Cincinnati, OH.: University of Cincinnati Policing Institute. Engel, R. S., Frank, J., Tillyer, R., & Klahm, C. F. (2006). Cleveland Division of Police Traffic Stop Data Study: Final Report. Unpublished Manuscript. Available at: http://www. uc.edu/ccjr/reports.html Engel, R. S., & Johnson, R. (2006). Toward a better understanding of racial and ethnic disparities in search and seizure rates. Journal of Criminal Justice, 34, 605–617. Engel, R. S., Smith, M. R., & Cullen, F. T. (in press). Race, place, and drug enforcement: Revisiting Seattle with a new methodological approach. Criminology and Public Policy. Engel, R. S., Tillyer, R., Stoddard, C., & Johnson, R. (2007). Project on Police-Citizen Contacts: Year 3 & 4 Final Report, January 2004 – December 2005. Cincinnati, OH.: University of Cincinnati Policing Institute. Everett, S. A., Shults, R. A., Barrios, L. C., Sacks, J. J., Lowry, R., & Oeltmann, J. (2001). Trends and subgroup differences in transportation-related injury risk and safety behaviors among high school students, 1991-1997. Journal of Adolescent Health, 28, 228–234. Farrington, D. P., Loeber, R., Stouthamer-Loeber, M., VanKammen, W. B., & Schmidt, L. (1996). Self-reported delinquency and a combined delinquency seriousness scale based on boys, mothers, and teachers: Concurrent and predictive validity for African-Americans and Caucasians. Criminology, 34, 493–517. Felson, R. B., Deane, G., & Armstrong, D. P. (2008). Do theories of crime or violence explain race differences in delinquency? Social Science Research, 37, 624–641. Fite, P. J., Wynn, P., & Pardini, D. A. (2009). Explaining discrepancies in arrest rates between Black and White male juveniles. Journal of Consulting and Clinical Psychology, 77, 916–927. Gerard, D. (2011). Saving Lives with Traffic Safety Intervention. Cincinnati Police Department, Internal Document. Ginsburg, K. R., Winston, F. K., Senserrick, T. M., Garcia-Espana, F., Kinsman, S., Quistberg, D. A., et al. (2008). National young-driver survey: Teen perspective and experience with factors that affect driver safety. Pediatrics, 121, e1391–e1403. Glassbrenner, D. (2003). Safety Belt Use in 2002 – Demographic Characteristics. Washington, DC: National Highway Traffic Safety Administration. Glassbrenner, D., Carra, J. S., & Nichols, J. (2004). Recent estimates of safety belt use. Journal of Safety Research, 35, 237–244. Harper, J. S., Marine, W. M., Garrett, C. J., Lezotte, D., & Lowenstein, S. R. (2000). Motor vehicle crash fatalities: A comparison of Hispanic and non-Hispanic motorists in Colorado. Annals of Emergency Medicine, 36, 589–596. Harris, D. A. (1997). “Driving while Black” and all other traffic offenses: The Supreme Court and pretextual traffic stops. The Journal of Criminal Law and Criminology, 87, 544–582. Harris, D. A. (1999). The stories, the statistics, and the law: Why “driving while Black” matters. Minnesota Law Review, 84, 265–326. Harris, D. A. (2002). Profiles in Injustice: Why Racial Profiling cannot Work. New York: The New Press. Harris, D. A. (2006). US experiences with racial and ethnic profiling: history, current issues, and the future. Critical Criminology, 14, 213–239. Hawkins, D. F., Laub, J. H., Lauritsen, J. L., & Cothern, L. (2000). Race, Ethnicity, and Serious and Violent Juvenile Offending. Washington, DC: US Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention. Haynie, D. L., & Payne, D. C. (2006). Race, friendship networks, and violent delinquency. Criminology, 44, 775–805. Hindelang, M. J. (1978). Race and involvement in common law personal crimes. American Sociological Review, 43, 93–109. Kochel, T. R., Wilson, D. B., & Mastrofski, S. D. (2011). Effect of suspect race on officers’ arrest decisions. Criminology, 49(2), 473–512. Kociewiewski, D. (2002). “Study suggests racial gap in speeding in New Jersey” March 21. New York Times. Krohn, M. D., Thornberry, T. P., Gibson, C. L., & Baldwin, J. M. (2010). The development and impact of self-report measures of crime and delinquency. Journal of Quantitative Criminology, 26, 509–525. Lamberth, J. (1994). 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 the years 1988 through 1991. Unpublished report, Temple University.

Lamberth, J. (1996). A Report to the ACLU. New York: American Civil Liberties Union. Lange, J. E., Blackman, K. O., & Johnson, M. B. (2001). Speed Violation Survey of the New Jersey Turnpike: Final Report. Trenton, NJ: Office of the Attorney General. Lange, J. E., Johnson, M. B., & Voas, R. B. (2005). Testing the racial profiling hypothesis for seemingly disparate traffic stops on the New Jersey Turnpike. Justice Quarterly, 22, 193–223. Lee, P., Orsay, E., Lumpkin, J., Ramakrishman, V., & Callahan, E. (1996). Analysis of Hispanic motor vehicle trauma victims in Illinois. Academic Emergency Medicine, 3, 221–227. Lerner, E. B., Dietrich, V. K., Billittier, A. J., Moscati, R. M., Connery, C. M., & Stiller, G. (2001). The influence of demographic factors on seatbelt use by adults injured in motor vehicle crashes. Accident Analysis and Prevention, 33, 659–662. Logan, J. R., & Messner, S. F. (1987). Racial residential segregation and suburban violent crime. Social Science Quarterly, 68, 510–527. Lundman, R. J., & Kowalski, B. R. (2009). Speeding while Black? Assessing the generalizability of Lange et al.'s (2001, 2005) New Jersey turnpike speeding survey findings. Justice Quarterly, 26, 504–527. Massey, D. S., & Denton, N. A. (1993). American Apartheid: Segregation and the Making of the Underclass. Cambridge, MA: Harvard University Press. Matteucci, R. M., Holbrook, T. L., Hoyt, D. B., & Molgaard, C. (1995). Trauma among Hispanic children: a population-based study in a regionalized system of trauma care. American Journal of Public Health, 85, 1005–1008. McNulty, T. L., & Bellair, P. E. (2003). Explaining racial and ethnic differences in serious adolescent violent behavior. Criminology, 41, 709–748. Meehan, A. J., & Ponder, M. C. (2002). How roadway composition matters in analyzing police data on racial profiling. Police Quarterly, 5, 306–333. Menard, S. (1995). Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage Publications. Nachiondo, J. M., Robinson, T. N., & Killen, J. D. (1996). Do ethnicity and level of acculturation predict seat-belt use in adolescents? Pediatric Research, 39, 7. National Highway Traffic Safety Administration (NHTSA). (2006). Race and Ethnicity in Fatal Motor Vehicle Crashes, 1999-2004. www.nhtsa.dot.gov National Highway Traffic Safety Administration (NHTSA). (2008). National Survey of Drinking and Driving Attitudes and Behaviors: 2008. www.nhtsa.dot.gov National Highway Traffic Safety Administration (NHTSA). (2009a). Seat Belt Use in 2008-Race and Ethnicity Results among Occupants Traveling with Children. www.nhtsa.dot.gov National Highway Traffic Safety Administration (NHTSA). (2009b). Traffic Safety Facts, 2008: Speeding. Report no. DOT HS-811-166. Washington, DC: US Department of Transportation. National Highway Traffic Safety Administration (NHTSA). (2011). www.nhtsa.dot.gov National Research Council. (2004). Fairness & Effectiveness in Policing: The Evidence. Washington, DC: National Academies Press. Nelson, D. E., Bolen, J., & Kresnow, M. (1998). Trends in safety belt use by demographics and by type of state safety belt law, 1987 through 1993. American Journal of Public Health, 88, 245–249. Niemcryk, S. J., Kaufmann, C. R., Brawley, M., & Yount, S. I. (1997). Motor vehicle crashes, restraint use, and severity of injury in children in Nevada. American Journal of Preventive Medicine, 13, 109–114. Novak, K. J. (2004). Disparity and racial profiling in traffic enforcement. Police Quarterly, 7, 65–96. Novak, K. J., & Chamlin, M. B. (2012). Racial threat, suspicion, and police behavior: The impact of race and place in traffic enforcement. Crime and Delinquency, 58, 275–300. Parker, K. F., MacDonald, J., Alpert, G. P., Smith, M. R., & Piquero, A. (2004). A contextual study of racial profiling: Assessing the theoretical rationale for the study of racial profiling at the local level. American Behavioral Scientist, 47, 943–962. Petrocelli, M., Piquero, A., & Smith, M. R. (2003). Conflict theory and racial profiling: An empirical analysis of police traffic stop data. Journal of Criminal Justice, 31, 1–11. Pickerill, J. M., Mosher, C., & Pratt, T. C. (2009). Search and seizure, racial profiling and traffic stops: A disparate impact framework. Law and Policy, 31, 1–30. Piquero, A. R. (2008). Disproportionate minority contact. The Future of Children, 18, 59–79. Piquero, A. R., & Brame, R. W. (2008). Assessing the race-crime and ethnicity-crime relationship in a sample of serious adolescent delinquents. Crime & Delinquency, 54, 390–422. Popkin, C. L., & Council, F. M. (1993). A comparison of alcohol-related driving behavior of white and nonwhite North Carolina drivers. Accident Analysis and Prevention, 25, 355–364. Ramchand, R., Pacula, R. L., & Iguchi, M. (2006). Racial differences in marijuana-users’ risk of arrest in the United States. Drug and Alcohol Dependence, 84, 264–272. Rosenfeld, R., Rojek, J., & Decker, S. (2012). Age matters: Differences in police searches of young and older male drivers. Journal of Research in Crime and Delinquency, 49, 31–55. Ross, H. L., Howard, J. M., Ganikos, M. L., & Taylor, E. D. (1991). Drunk driving among American Blacks and Hispanics. Accident Analysis and Prevention, 23, 1–11. Royal, D. (2000). Racial and Ethnic Group Comparisons, National Surveys of Drinking and Driving Attitudes and Behavior – 1993, 1995, and 1997. Washington, DC: National Highway Traffic Safety Administration. Sherman, L. (1974). Police Corruption: A Sociological Perspective. Garden City, NY: Anchor. Sherman, L. W., Gartin, P. R., & Buerger, M. E. (1989). Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27, 27–55. Shihadeh, E. S., & Flynn, N. (1996). Segregation and crime: The effect of black social isolation on the rates of black urban violence. Social Forces, 74, 1325–1352. Skolnick, J. H. (1966/1994). Justice without Trial: Law Enforcement in Democratic Society. New York: Wiley.

R. Tillyer, R.S. Engel / Journal of Criminal Justice 40 (2012) 285–295 Smith, D. A. (1986). The neighborhood context of police behavior. In A. J. Reiss, Jr. & M. Tonry (Eds.), Communities and Crime, Volume 8 of Crime and Justice: A Review of Research. Chicago: University of Chicago Press. Smith, M. R., & Alpert, G. P. (2002). Searching for direction: Courts, social science, and the adjudication of racial profiling claims. Justice Quarterly, 19, 673–703. Smith, M. R., & Alpert, G. P. (2007). Explaining police bias: A theory of social conditioning and illusory correlation. Criminal Justice and Behavior, 34, 1262–1283. Smith, W. R., Tomaskovic-Devey, D., Zingraff, M. T., Mason, H. M., Warren, P. Y., Wright, C. P., McMurray, H., & Fenlon, C. R. (2003). The North Carolina Highway Traffic Study. Washington, DC: Final report delivered to the National Institute of Justice. U.S. Department of Justice. Smith, D. A., Visher, C. A., & Davidson, L. A. (1984). Equity and discretionary justice: The influence of race on police arrest decisions. The Journal of Criminal Law and Criminology, 75, 234–249. Tillyer, R. & Engel, R. S. (in press). The impact of drivers’ race, gender, and age during traffic stops: Assessing interaction terms and the social conditioning model. Crime & Delinquency. Available at: http://cad.sagepub.com/content/early/2010/ 11/26/0011128710389583 Tillyer, R., Engel, R. S., & Cherkauskas, J. C. (2010). Best practices in vehicle stop data collection and analysis. Policing, 33, 69–92. Tillyer, R., Engel, R. S., & Wooldredge, J. (2008). The intersection of racial profiling and the law. Journal of Criminal Justice, 36, 138–153. Tillyer, R., Klahm, C. F., & Engel, R. S. (2012). The discretion to search: A multi-level examination of driver demographics and officer characteristics. Journal of Contemporary Criminal Justice, 28, 184–205. Tomaskovic-Devey, D., Mason, M., & Zingraff, M. (2004). Looking for the driving while Black phenomena: Conceptualizing racial bias processes and their associated distributions. Police Quarterly, 7, 3–29. Tomaskovic-Devey, D., Wright, C. P., Czaja, R., & Miller, K. (2006). Self-reports of speeding stops by race: Results from the North Carolina Reverse Record Check Survey. Journal of Quantitative Criminology, 22, 279–297. U.S. Census. (2000). www.census.gov Unnever, J. D., & Gabbidon, S. L. (2011). A Theory of African American Offending: Race, Racism, and Crime. New York: Routledge. Vaughn, M. G., Wallace, J. M., Davis, L. E., Fernandes, G. T., & Howard, M. O. (2008). Variations in mental health problems, substance use, and delinquency between African American and Caucasian juvenile offenders. International Journal of Offender Therapy and Comparative Criminology, 52, 311–329. Voas, R. B., Tippetts, A. S., & Fisher, D. A. (2000). Ethnicity and Alcohol-Related Fatalities: 1990 to 1994. Washington, DC: National Highway Traffic Safety Administration.

295

Voas, R. B., Wells, J. K., Lestina, D. C., Williams, A. F., & Green, M. A. (1998). Drinking and driving in the United States: The 1996 national roadside survey. Accident Analysis and Prevention, 30, 267–275. Warren, P., Tomaskovic-Devey, D., Smith, W., Zingraff, M., & Mason, M. (2006). Driving while Black: Bias processes and racial disparity in police stops. Criminology, 44, 709–738. Weisburd, D., & Green, L. (1995). Policing drug hot spots: The Jersey City drug market analysis experiment. Justice Quarterly, 12, 711–736. Wells, J. K., Williams, A. F., & Farmer, C. F. (2002). Seat belt use among African Americans, Hispanics and Whites. Accident Analysis and Prevention, 34, 523–529. Wilson, J. (2000). Analysis of motorists stops on I-40 by race/ethnic origin (Rep. prepared for the U.S. Attorney General Office, District of Arizona). Unpublished manuscript. Withrow, B. L. (2004). Race-based policing: A descriptive analysis of the Wichita stop study. Police Practice and Research, 5, 223–240. Withrow, B. L. (2006). Racial Profiling: From Rhetoric to Reason. Upper Saddle River, NJ: Prentice Hall.

Cases Cited: State of New Jersey v. Soto, 734 A. 2d 350 (1996). Wilkins v. Maryland State Patrol, Civil Action No. CCB-93-468 (D.Md. 1994).

Rob Tillyer, Ph.D., is an assistant professor of criminal justice at the University of Texas, San Antonio. His research interests include decision-making within the criminal justice system, crime prevention, and victimology. His recent journal articles have appeared in Justice Quarterly, Criminal Justice and Behavior, and Crime and Delinquency.

Robin S. Engel, Ph.D., is an associate professor of criminal justice at the University of Cincinnati and Director of the University of Cincinnati Policing Institute. Her research includes empirical assessments of police behavior, police/minority relations, police supervision and management, criminal justice policies, criminal gangs, and violence reduction strategies. Her previous research has been published in Criminology, Justice Quarterly, Journal of Research in Crime and Delinquency, Journal of Criminal Justice, and Criminology and Public Policy. Her most recent work is focused on crime prevention, homicide reduction, and developing police–academic partnerships.