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 Fayetteville
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 Duke University

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 North Carolina
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 N.C. State University


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Volume 9, Number 2

Fall/Winter 2011
 

Driving While Non-White: Exploring Traffic Stops and Post-Stop Activities in North Carolina, 2005-2009

by

Cameron D. Lippard

and

Amy Dellinger Page

Appalachian State University

Introduction

    Social scientists have found that local, state, and highway law enforcement agencies stop Blacks and Hispanics at higher rates than Whites across the United States, a phenomenon known as "driving while Black" or "driving while Brown" (i.e., Bejarano 2001; Cox et al. 2001; Engel and Calnon 2004; Langan et al. 2001; Harris 1999; Gaines 2006; Rojek, Rosenfeld, and Decker 2004; Schafer et al. 2006; Smith and Petrocelli 2001). Research has also demonstrated that racial disparities become increasingly higher for Blacks and Hispanics during post-traffic stops activities (i.e., searches, citations, and arrests) (see Engel and Johnson 2006 for an exhaustive list of studies since the 1990s), with the exception of a small number of studies (see Novak 2004; Smith and Petrocelli 2001).

    Similar trends have been documented in North Carolina. A number of studies have shown that in many cases Blacks are overrepresented in pre- and post-traffic stop activities (see Miller 2008; Smith et al. 2003; Tomaskovic-Devey et al. 2006; Warren et al. 2006; Zingraff et al. 2000). Warren et al. (2006) also suggested these racial disparities become more pronounced when examining local police versus state highway patrol practices. However, Warren and Tomaskovic-Devey (2009) found that recent media accounts and new legislation against racial profiling in North Carolina have somewhat decreased the racial disparities Blacks faced particularly with North Carolina Highway Patrol searches. 

    The problem, however, is that research in North Carolina and across America has primarily focused on Black and White comparisons, excluding a growing Hispanic population. As suggested by Engel and Calnon (2004), like young Black males, young Hispanic males have faced an increased rate of citations searches, arrests, and use of force when controlling for extralegal characteristics. The Bureau of Justice Statistics (2005) also found that when examining police contacts in 2002, Hispanics were more likely to be ticketed, arrested, and experience force compared to Whites. 

    Including Hispanics in the analysis becomes more salient when we consider the relative "hypergrowth" of this population in non-traditional settlement areas in the American South. Many southern states including Georgia, North Carolina, South Carolina, and Tennessee saw an increase of at least 300% in the Hispanic population from 1990 to 2000 (i.e., Lippard and Gallagher 2011; Massey 2008; Smith and Furuseth 2006). From 2000 to 2010, North Carolina saw an 111% increase in the Latino population, which made Latinos about 8% of the state's population (Mackun and Wilson 2011). As a result, anti-immigrant sentiment has grown (see Marrow 2011; McClain 2006) and public outcry has led to many state legislators proposing laws targeting undocumented immigrants. Law enforcement agencies have also partnered with U.S. Immigration and Customs Enforcement (ICE) to curtail undocumented immigration. As of 2011, 92 of the 100 counties in North Carolina were participating as secure communities (biometric detection of undocumented immigrants) and eight specific law enforcement agencies were in 287g partnerships with ICE (ICE 2011). Across the U.S., these partnerships have led to police using the "Hispanic identity" to perform traffic and pedestrian stops to locate suspected undocumented immigrants (Aguirre 2004; Romero 2006). 

    This study explores traffic stop data from 2005 to 2009 collected by the North Carolina Department of Justice. While we acknowledge the complexities of measuring racial and ethnic disparities in policing, we argue, as have others (see Harris 1999; Rojek et al. 2004), that there are simple logical and analytical tools that highlight disproportional treatment of racial and ethnic minorities. Therefore, our intention is not to prove or disprove the existence of racial profiling in North Carolina law enforcement but to demonstrate that members of certain minority groups may face disproportionate treatment by the police within select towns, cities, and counties. We also consider whether the population size, type of law enforcement agency, the agency's relationship with ICE, and the initial purpose reported for the traffic stop correlate with the rates of stops and other policing activities. 

Literature Review

The Enigma of Racial Profiling in Policing

    Identifying racial profiling in police actions has been a difficult task for researchers. As conceptually defined by Warren and Tomaskovic-Devey (2009:344), racial profiling is "the practice of targeting or stopping an individual based primarily on race or ethnicity, rather than on individualized suspicion or probable cause." Researchers have relied on finding the existence of racial profiling by examining police-initiated actions, including traffic stops and post-stop activities (i.e., searches, citations, arrests). They also examined whether the police were acting on their personal prejudice toward certain groups to make their stops and arrests in "absence of evidence that such treatment is warranted or deserved" (Rojek et al. 2004:129). Consequently, studies have been required to control for extralegal factors including the demographics of the officer and the suspect, the context of the interaction (i.e., time of day, place, size of minority population), and the potential criminal behavior of the suspect. However, most researchers have admitted that even when controlling for these variables, it is difficult to pinpoint individual police officer bias as the major culprit of racial disparities because of two resounding issues (see Alpert et al. 2007; Engel and Calnon 2004; Engel and Johnson 2006; Rojek et al. 2006; Tomaskovi-Devey, Mason, and Zingraff 2004; Warren et al. 2006). 

    The first issue concerns whether racial profiling originates from the police officer's personal bias or from the organizational policies that encourage profiling via policing initiatives. These initiatives have focused on certain criminal activities (i.e., drug trafficking, speeding, DWI) often requiring police to target communities as potential suspects (see Aguirre 2004; Engel and Calnon 2004; Engel and Johnson 2006; Romero 2006; Warren et al. 2000). Harris (2002) noted that this shows up in police training that teaches officers to look for the clues of suspicious persons. For example, police training encourages officers to notice drug dealers' types of car or even demographic characteristics (i.e., race/ethnicity, age, and sex) in order to locate potential suspects of drug trafficking (Engel and Johnson 2006; Harris 2002; Ridgeway 2006). Police are also deployed to high-crime communities suspected of harboring the highest concentration of potential law-breakers, which increases the numbers of particular groups being stopped or arrested (see Warren et al. 2006; Warren and Tomaskovic-Devey 2009). 

    One example of recent policing initiatives that encouraged racial profiling was the War on Drugs. Engel and Calnon (2004) and Walker, Spohn, and Delone (2007) found that since the 1980s, the War on Drugs campaign has instructed police and special taskforces to target Blacks and Hispanics as "usual suspects" of drug trafficking, leading to these groups being searched more often than Whites. However, Engel and Calnon (2004:58-59) and Engel and Johnson (2006:607) provide comprehensive lists of studies on search and seizure disparities that demonstrate that despite high numbers of  minorities being searched, police were less likely to find minorities carrying drugs or contraband compared to Whites. 

    Harris (2002) also noted that the U.S. Supreme Court upheld the use of traffic stops as a pretext to determine whether a motorist uses or possesses drugs in the Whren v. U.S. case in 1996. While the Fourth Amendment of the U.S. Constitution protects citizens from illegal search and seizure practices, courts still grant police discretionary power to investigate potential criminal activities. As Novak (2004) and Ridgeway (2006) found, police officers often cited the use of traffic stops as the primary tool to investigate other criminal offenses and target certain groups who have been most likely to be found with drugs or contraband. 

    Research has also suggested that traffic stops may be a tool used by police to address public concerns of finding, arresting, and deporting undocumented immigrants. In 2008, a national survey of Hispanics in the U.S. found that 9% of Hispanics have been stopped by police for various traffic offenses and asked about their immigration status (Lopez and Minuskin 2008). In addition, Romero (2006:448) found that law enforcement agencies were targeting "Mexicanness" (i.e., brown skin color, bilingual speaking abilities, or living and shopping in Latino neighborhoods) to determine where to conduct immigration raids. Aguirre (2004) found that several court cases noted law enforcement agencies using the "Hispanic identity" to profile Mexican Americans as potential drug smugglers and undocumented aliens to curtail the international drug trade in many western U.S. states. 

    However, this recent police behavior was not necessarily due to officers' personal bias but law enforcement policies targeting undocumented immigration. For example, recent state legislation in Arizona (SB 1070) and North Carolina (SJR 1349) has suggested using local and state law enforcement to enforce immigration laws and condoned officers' use discretion when determining reasonable suspicion of individuals who may be undocumented (Anrig and Wang 2006; Lippard 2011; Luebke 2011). Racial profiling of Hispanics as "illegal" immigrants has also been structurally supported by various Immigration and Customs Enforcements partnerships set up with local and county law enforcement agencies throughout the United States, particularly in many southern states. This partnership has focused on training local law enforcement to know who to target for questioning and most of this training focuses on "Mexicans" as key suspects (ICE 2011). This was despite the fact that 44% of undocumented immigration comes from Europe, Asia, Africa, and other Latin American countries (see Passel 2006). Racial profiling cannot solely reside with individual police officer prejudice (see Warren et al. 2006) but may be due to state legislation and even police training. Organizational pressures that condone targeting certain groups can lead to racial disparities in police decisions and actions, suggesting a more systemic problem of structural racism. 

The Problems of Measurement 

    The concern of locating who or what is responsible for racial profiling lends itself to another significant issue in discovering racial profiling in police actions; developing an accurate measurement. Lundman (2010:78) noted that most research on racial profiling utilizes data from three sources: (1) citizen self-reports of traffic stop experiences, (2) direct observations of traffic stop activities through surveillance or ride-alongs, and (3) public traffic stop data self-reported by police officers through agency-required documentation or reported through surveys collected by researchers. All except the third source of data were indirect measurements for assessing the decision-making processes of police and thus, do not directly measure racial profiling that may be defined as an individual action. Also, data collected from citizen self-report data has been problematic because many minorities underreport being profiled due to social desirability effects (see Lundman and Kaufman 2003; Reitzel, Rice, and Piquero 2004; Tomaskovic-Devey et al. 2006). Direct observation data also has drawbacks because of the extraordinary cost and time required, having a willing law enforcement agency to participate, and hoping that police will act the same as they would without researchers present (Lundman 2010). 

    Police-collected data may also be flawed because police officers do not record all stops they make or leave out important contextual information (see Gaines 2006; Kowalski and Lundman 2007; Parker et al. 2004). Lundman (2010) noted that this data could be seriously flawed because officers may rely on their own judgments of what the driver's race or ethnicity might be if it is not included on the driver's licenses. More important, Lundman (2010) and Miller (2007) contend that this recent crackdown has led some police to leave out important information or not record all the stops. For example, Warren and Tomaskovic-Devey (2009) found that racial disparities in searches for North Carolina State Highway Patrol data had changed due to media coverage and a legislative crackdown on racial profiling in North Carolina. However, Lundman (2010) and Miller (2007) have suggested that any drop in racial disparities could have been due to police leaving out important data in their reports in resistance to governance and being called  racist. Zingraff et al. (2000) also found that crackdowns on racial profiling within the North Carolina State Highway Patrol decreased the number of warnings given and increased citations for all groups. 

    Finally, using this type of data requires a "baseline" or "benchmark" score that accounts for the potential number of drivers or law violators that could be subject to police stops to determine disproportional treatment. In one of the first attempts to measure racial profiling, Harris (1999) used U.S. Census data on the driving age population of Blacks and Whites in geographic areas in Ohio within certain police jurisdictions to calculate a baseline measurement and compared  the rate of stops reported by various law enforcement agencies in those areas. Harris (1999) also computed a second, more conservative baseline measurement of drivers by deducting 21% of Black households at that time in the U.S. who did not own a vehicle from the available driving age population. Kowalski and Lundman (2007) note other baseline measurements used have been the number of citizens in the area with driver's licenses and the number of citizens in contact with the police. 

    There are a number of issues with any baseline measurement of the probable law-breaking population (Gaines 2006; Kowalski and Lundman 2007; Rojek et al. 2004). Parker et al. (2004) noted that these measurements frequently ignore the spatial context in which drivers go to and from various locations crossing a number of town, city, and county law enforcement jurisdictions, making it difficult to suggest that the driving age population in one geographic location is the sole target of any policing action. Also, using the number of licensed drivers becomes a problem when all group members do not regularly drive or as with undocumented immigrants, do not possess driver's licenses but drive anyway because of work obligations or simply getting around in the American driving culture. 

Racial Disproportionality in Police Activities: Exploratory Possibilities

    Despite the issues in measuring racial profiling, ten years of research has provided pervasive evidence that Blacks and Hispanics face at least higher proportions of stops, searches, and arrests than Whites (i.e., Bejarano 2001; Cox, et al. 2001; Engel and Calnon 2004; Langan et al. 2001; Harris 1999; Gaines 2006; Rojek et al. 2004; Schafer et al. 2006; Smith and Petrocelli 2001). In fact, as Lundman (2010) attests, only a few studies have ever found evidence contrary to Blacks and Hispanics being stopped, searched, and arrested in higher numbers compared to Whites, regardless of the various conceptual and operational constructions of the measurements (see Novak 2004; Smith and Petrocelli 2001). Rojek et al. (2004) contended that finding disproportionate treatment does not clearly suggest individual or structural discrimination towards certain groups but is the first step in exposing a potential problem.

    More important, research on racial disparities in traffic stops matches a growing trend of research reporting the overrepresentation of racial and ethnic minorities throughout the American criminal justice system. As Walker et al. (2007) reported, racial and ethnic minorities were overrepresented in the initial interactions with the police, the harshness of judicial sentencing, and the rates of imprisonment compared to Whites, even though Whites were arrested more often than any other racial or ethnic group in America. While researchers may not be able to pinpoint individual and structural decisions that lead to the disparaging treatment, the data available has provided compelling evidence of disproportional treatment. 

    Harris (1999), Rojek et al. (2004), and Tomaskovic-Devey et al. (2003) also suggested that the initial measurement of disparities do not have to be complex. In examining racial disparities, Harris (1999) created a likelihood ratio comparing minority traffic stops to White traffic stops in the same geographical areas of Ohio. Rojek et al. (2004) also identified this computation as a "disproportionality score" and Tomaskovic-Devey et al. (2004) called the equation an "odds ratio." Tomaskovic-Devey et al. (2004:7) best explain how this score or ratio has been  constructed: 

An odds ratio compares that risk of some event (e.g., being stopped) of one group or another. An odds ratio of 1.00 means two groups have the same chance of an event given their incidence in the at-risk (i.e., driver) population. An odds ratio higher than 1 means minorities are stopped at higher rates than majority drivers. 
Harris (1999:286) provided a hypothetical statement to plug in this ratio calculation to determine possible differences in treatment among Blacks and non-Blacks: "If you're Black, you're _____ times as likely to be ticketed by this police department than if you are not Black." 

    These researchers agree that this type of measurement has problems. These ratios rely on traffic stop data collected by the police, which may be inaccurate due to police error or misgivings. However, this traffic data would then be a conservative estimate of what was really occurring, which should have reduced the likelihood of finding any racial disparities. Another problem with this ratio was that it often uses the driving age population for the baseline comparison measurement as a proxy for those who could potentially be stopped by the police. This was problematic because the driving age population is conservative and would be larger than the actual population that drives regularly and excludes those that come in from other areas or counties. However, despite being methodologically conservative and sometimes mislabeling their findings as racial profiling, studies that have used this ratio report the same disproportionate rates of Blacks being stopped and facing more post-stop activities as studies using multivariate analysis controlling for extralegal factors. 

    Rojek et al. (2004) also gives researchers some simple logic that can be applied to understand, without statistical scrutiny, whether it is just disproportionate events or actual racial disparity caused by possible prejudicial actions. Rojek et al. (2004:129) stated,

In principle, it should be possible to determine whether members of a particular race and ethnic group are subject to disparate treatment by the police. For example, if members of Group X are stopped by the police at a rate greater than based on their presence in the population of drivers, but are not cited or arrested at a correspondingly high rate, one might presume that disparity in treatment exists. 
Thus, if researchers have access to the post-stop activities data including searches, citations, and arrests, then they could look to see if the likelihood ratios go up or down after the initial stop. Alpert et al. (2007) also suggested that instead of using the driving population as a baseline measure, the actual records of post-stop activities (i.e., rates of searches, citations, or arrests) provide the most valid baseline available to locate violators and determine unequal treatment in post-stop activities by police. Therefore, if a group had high stop rates but low search, citation, or arrest rates, then there is a disparity because police were harassing with little just cause. For example, Engel and Calnon (2004) found that Blacks and Hispanics are more likely to be searched but are less likely to be found carrying any drugs or contraband compared to Whites. This was racial profiling with no substantial proof. However, as Rojek et al. (2004) suggested, if the stops were at the same levels of post-stop activities, then police were appropriately targeting individuals who were committing crimes more than other groups. 

    Certainly, researchers have also added a level of complexity to these simple analyses. Most often they tease out extralegal factors from the traffic data available. This has included the age, sex, and the initial reason for the stop reported by the police officer. Researchers have also pulled population sizes, as well as the type of law enforcement agency, and whether the agencies participated in police initiatives to combat drinking and driving, speeding, or not wearing a seat belt. Nonetheless, as Tomaskovic-Devey et al. (2004), the likelihood or odd ratios used gives researchers the first step of understanding racial disparities in police actions and deciding whether to further investigate the matter with multivariate analyses. 

Racial Disparities in North Carolina Policing

    Researchers have noted the disparaging trends of police traffic stops and post-stop activities that negatively affect Blacks in North Carolina (Miller 2008; Smith et al. 2003; Tomaskovic-Devey et al. 2006; Warren et al. 2006; Zingraff et al. 2000). In fact, Zingraff et al.'s (2000) studies on the North Carolina State Highway Patrol were the first comprehensive studies in the U.S. to examine the possibilities of racial profiling.  However, none of the North Carolina studies focused on populations outside of the Black-White dichotomy. 

    Of course, the lack of attention to other minority groups in North Carolina racial disparity research was not due to bad sampling but because of recent increases in the Hispanic population in the state. In fact, it has only been within the last 10 to 15 years that the Hispanic population in North Carolina has grown significantly large enough to consider and be recorded in police data. Like many nontraditional immigrant destinations, North Carolina has seen a dramatic increase in its Hispanic population, growing from around 30,000 in 1990 to around 170,000 in 2006 (Lippard and Gallagher 2011). For example, both Charlotte and Raleigh's Hispanic population was little over 8% of the total population in 2006, but in 1990 it was less than 2% (Lippard and Gallagher 2011). The same is true in small towns like Siler City, which in 1990 had 77 Hispanics in the area. By 2000, there were over 2,700 Hispanics, representing close to 40% of the total population (Lippard and Gallagher 2011). As of 2010, North Carolina's Hispanic population rose to 8.4% of the total population (Mackun and Wilson 2011). It should also be noted that much of this migration of Latinos to North Carolina and across the American South have been mostly immigrants. Based on the American Community Survey from 2005 to 2009, 68% of all foreign-born individuals in North Carolina were from Latin America (ACS 2010). More important, over 52% of all Hispanics in North Carolina are foreign-born immigrants and less than 43% are native-born citizens (ACS 2010). However, as Mucchetti (2005:10) notes, "Unfortunately, this hypergrowth has too often been accompanied by a hyperactivity in discriminatory practices such as racial profiling."

    Marrow (2011) and McClain (2006) both have indicated growing anti-immigrant sentiment among North Carolina White and Black residents (see also McClain et al. 2009). Since 2001, the state legislature has also attempted to pass laws to address undocumented immigration within the state (Luebke 2011). Recently, a new state Senate resolution (SJR 1349) has been issued to pursue a state law similar to Arizona's SB 1070 that would target undocumented immigrants who, as Romero (2004) suggested, look "Mexican-enough" to interrogate. In 2004, North Carolina also became the headquarters of one of the more active anti-immigrant lobbying groups led by William Gheen called American for Legal Immigration (ALI-PAC) (see http://www.alipac.us/). 

    North Carolina also has a large number of law enforcement agencies who have developed working partnerships with the U.S. Immigration and Customs Enforcement (ICE) to become registered as "secure communities." Secure communities is a program that partners local law enforcement with ICE to use biometrics (i.e., finger printing) to locate documented and undocumented immigrants with criminal histories. As of 2011, these partnerships exist in 92 of the 100 North Carolina counties. Also, within the large metropolitan areas of Charlotte, Raleigh, and Greensboro, County Sheriff and police departments have also signed on as 287g partners who receive federal training to locate and detain undocumented immigrants in the area, usually targeting Hispanic communities.

    Other research has noted that Hispanics face disproportionate rates of traffic stops than Whites across the United States. Based on 1999 nationally-representative data, Engel and Calnon (2004) found that like young Black males, young Hispanic males face an increased rate of citations, searches, arrests, and use of force when controlling for extralegal and legal characteristics. The Bureau of Justice Statistics (2005) also found that in 2002, Blacks and Hispanics were more likely to be ticketed, arrested, and experience physical force compared to Whites. This report also suggested that in some states and regions, Hispanics face higher rates of disproportionality than Blacks. 

    Smith and Petrocelli (2001) found that in Richmond, Virginia, all minorities were disproportionately stopped more than Whites but not necessarily searched, cited, and arrested as much as Whites. Rojek et al. (2004) and Schafer et al. (2006) found disproportionate rates of stops and even higher rates of unequal searches and arrests for Blacks and Hispanics across several rural, suburban, and urban areas in the Midwest. Finally, Alpert et al. (2007) found that while there were no patterns of discrimination in stops, post-stop activities showed a disparate treatment of Blacks and Hispanics in the Miami-Dade County area. 

    The purpose of this exploratory study is to determine whether racial disparities for non-Whites exist with a particular focus on Hispanics due to their significant population increase in North Carolina. While we are aware of the difficulties of measuring racial disparities using police-generated data, we are in no way suggesting that this is a direct measurement of racial profiling or a prediction or explanation of this phenomenon or why these disparities may exist. Rather, we provide a new chapter to the discussion by showing possible trends of disproportionate treatment within towns and cities, and across North Carolina from 2005 to 2009. We also believe that the recent social and political climate may be pushing law enforcement agencies to target Hispanics, regardless of their native- or foreign-born status, to find undocumented immigrants. This has been an increasing concern expressed due to recent state legislation like Arizona's SB 1070 and Georgia's HB 87 that encouraged racial profiling. Taking Harris' (1999) lead, much of this analysis relies on constructing likelihood ratios or "disproportionality scores" to begin the exploration. We also provide bivariate correlations to determine whether any extralegal factors may explain any disparities. 

Methods

Data Sources

    In 1999, the North Carolina General Assembly enacted G.S. 114.10.01, which required the state Attorney General to set up a Division of Criminal Statistics to collect traffic stop data (see http://www.ncga.state.nc.us/Sessions/2009/Bills/
Senate/PDF/S464v6.pdf).  This statute also required that law enforcement agencies across the state report any and all information about the number of traffic stops in a year including the driver's and officer's demographic information, initial reason for the stop, the action taken after the stop (i.e., warning, citation, searches, on-site arrests), and information about the arresting officer (see the statute for the full list of requirements). 

    This study used the traffic stop data collected and published by the North Carolina Department of Justice from March 2005 to March 2009 for 32 law enforcement agencies across the state. We also sampled reports that provided traffic stop data for police actions for the North Carolina State Highway Patrol. However, the state patrol reports covered a somewhat different period because data was only available from March 2006 to December 2009. This data represented the necessary comparative data on stops, searches, citations, and arrests for various racial and ethnic groups in question for this research. 

    However, not all data requested in the legislation is accessible to the public. Based on the statute, a portion of this data is available to the public via the NC Department of Justice website and it is presented in reports (see http://www.ncdoj.gov/
Crime/View-Traffic-Stop-Statistics.aspx). 
We used the following reports for each agency we sampled because they included information on the driver's race, ethnicity, and sex: "Initial Purpose of Traffic Stops by Driver's Sex, Race, and Ethnicity," "Drivers and Passengers Searched by Sex, Race, and Ethnicity," and "Enforcement Action Taken by Driver's Sex, Race, and Ethnicity." The "Initial Purpose of Traffic Stops" report provided counts of the total stops during the period examined and the initial purpose of the traffic stop, which included potential violations of: driving while impaired, safe movement, seat belt use, stop light/sign infractions, vehicle equipment and vehicle regulatory violations.  We used this information to consider pre-stop rates. The "Drivers and Passengers Searched" reports included searches of drivers and passengers. The "Enforcement Action Taken" reports included actions taken including receiving a verbal or written warning, citation, or on-site arrest. It also included whether the officer took no action, which was very rare and only representing less than 2% of all actions taken in most law enforcement reports sampled. These last two reports provided us with post-stop activity rates. 

    Each of these reports categorized the reported police actions based on the driver's sex, race, and ethnicity. The racial categories included were White, Black, Native American, Asian, and "Other." The reports also included ethnicity by reporting the numbers of Hispanics and non-Hispanics for each incident recorded. Based on the report computations, we were not able to distinguish "Hispanics" from the racial categories; therefore, Hispanics can be of any race. While we cannot separate Hispanics from other racial categories, we viewed this as providing a conservative estimate of the treatment of Hispanics and other groups compared to Whites. For example, counting Hispanics within the White racial category increases the numbers of Whites in various policing actions; thus, making it more difficult to identify disproportionate treatment less likely when comparing to Hispanics as an ethnic group. It should be noted that the reported driver's race or ethnicity was based on the police officer's categorization only and it did not include whether the drivers were foreign-born or native-born, immigrant or non-immigrant, etc. Thus, it was impossible to determine whether Hispanics included in this sample were native- or foreign-born. However, based on U.S. Census data, about 53% of all Hispanics in North Carolina are foreign-born (ACS 2010), which made the possibility of police officers pulling over immigrants more likely, especially with more emphasis placed or locating undocumented immigrants. 

    To determine any disproportional treatment, we also had to access data for our baseline measurements of the population likely to be stopped in the geographic locations within the police jurisdictions we selected. This data came from population counts collected by the U.S. Census Bureau via the American Community Survey's population estimates from 2005-2009 (see http://www.census.gov/acs/). Although these are estimates, they represented the most current information on population change and growth that matches the time span of traffic data we used. For each agency sampled, we pulled the total population and population percentage of each racial and ethnic group. We also calculated the driving-age population counts for each geographical area served by the sampled agencies. We focused on the age range of 15 to 75 years for each racial and ethnic group because this captures the optimum years of driving in the U.S. 

    Like Harris (1999), we also wanted to provide a more conservative estimate of who is likely to be stopped since we did not have the time or funds to do direct observations in each law enforcement agency jurisdiction. For this baseline, we extracted 2005-2009 population estimates of people who were 16 and older who drove to work within the law enforcement agencies' geographic area for each racial and ethnic group. On average, this figure reduced the original driving-age population reported by the U.S. Census by up to 50% across all groups identified.  Table 1 provides descriptive statistics of our samples' total population, each group's percentage of the driving population, and each group's percentage of workers who drive. 

 Click Here to View Table 1, a .pdf file. Remember to close window to resume reading the article

Sample

    There are over 500 law enforcement agencies in North Carolina. Based on the general statute, all state agencies and Sheriff's departments are required to report traffic stop data. However, only about 300 agencies were required to report the data based on how the statute defines "law enforcement officer," which excludes police departments that serve a population less than 10,000 people or exist in a municipality that have less than 5 or more officers for every 1,000 in the population (see www.ncdoj.gov/AgenciesRequiredList.aspx). 

    To extract a random sample, we also excluded state park, college, university, and hospital law enforcement agencies because they do not regularly enforce traffic laws and serve relatively small jurisdictions. With these exclusions, the potential sample of agencies dropped to 230. We also discovered that almost every law enforcement agency randomly selected was: (1) missing traffic stop reports completely or (2) was missing years of data in our selected time frame from 2005 to 2009. Due to the inconsistency of reports, we changed our sampling method to randomly select 40 counties of 100 in North Carolina and pulled law enforcement agencies that had completed traffic stop reports each year from 2005 to 2009. This limited the number of agencies that could be sampled to 123. 

    For each county in the sample, we pulled the selected reports for the county's Sheriff's department and one municipal police department. When selecting these agencies, we again noticed that while all the Sheriff's departments provided data several reports for the municipal police departments were missing; therefore, some county selections only included the Sheriff's reports. The final sample contained a total of 32 law enforcement agencies; 18 Sheriff's departments and 14 police departments across the state (see Table 1 for the list of selected agencies). Finally, we also sampled the reports for the North Carolina State Highway Patrol which were only available from March 2006 to December 2009. 

Data Analysis

    The bulk of the data analysis focused on computing a "disproportionality score" for each racial and ethnic group. As described by Harris (1999), Rojek et al. (2004), and Tomaskovic-Devey et al. (2004), this score indicated whether a particular racial or ethnic group was over- or under-represented in the various police actions reported compared to Whites. We first constructed a disproportionality score to determine unequal treatment in the number of stops for each minority group. This equation required calculating the percent of total stops of each racial and ethnic group divided by our baseline measurements of potential drivers (% of the driving population and % of workers 16 or older who drive). We calculated the same for Whites to use as the comparison group. Below is an example of the disproportionality score equation with "X" representing the minority group in question and Whites as the comparison group: 

(% of stops who were X / % of driving population who were X)


(% of stops who were White / % of driving population who were White)

Any disproportionality score that was greater than 1.0 indicated that Blacks or Hispanics were stopped at a higher rate than would be expected in the driving population compared to Whites. 

    We also analyzed the post-stop data using the disproportionality score. Based on Albert et al.'s (2007) recommendations of measuring post-stop data, we created a score that did not use the percent of the driving population as the baseline measurement of possible offenders. Rather, we used the percent of all stops for the given group compared to create the computation. Here is an example of this post-stop equation:

(% of total searches who were X / % of total stops who were X)


(% of total searches who were White / % of total stops who were White)

If we found a score higher than 1.0, then Blacks or Hispanics faced an overrepresentation of post-stop activities compared to Whites. We also used Rojek et al.'s (2004) logic that if stop ratios for Blacks and Hispanics are higher than Whites but post-stop ratios dropped more than 0.5 points, there may be racial or ethnic bias in the stops. 

    Each pre-stop and post-stop disproportionality score was computed for Whites, Blacks, Hispanics, and non-Hispanics. We did not calculate these scores for Native Americans and Asians because these populations were less than 1% in most selected areas and less than 5% of stops. We also did not compute a disproportionality score for the "Other" category reported because we could not match it with population estimates. However, we combined the police action counts for Blacks, Native Americans, Asians, and Others to create a "Non-White" category and compared it White population and police action totals.

     Our second part of this analysis was limited. As we suggested in the introduction of this study, we only wanted to establish the possibilities that non-Whites face disproportionate actions compared to Whites. We also recognize that our sample was relatively small, making any inferential statistics limited. In addition, our data is limited in the sense that we do not have a set of "extralegal" factors to consider other than the total population, the percent of any group's driving population in a given area, and whether the area patrolled by the law enforcement agency was a rural or urban space based on U.S. Census classifications. We did, however, have what the officers recorded as their initial reasons for traffic stops recorded and whether the agency in question participated in an ICE initiative. We retrieved law enforcement ICE partnerships from the ICE website, which provided a list of all counties and agencies in North Carolina who are registered as secure communities or 287g partners (see http://www.ice.gov/doclib/
secure-communities/pdf/sc-activated.pdf). We identified whether the police department or sheriff has an U.S. interstate highway included in its jurisdiction to control for the possibilities that drivers being stopped are coming from other areas across the state.

    With these variables in mind, we examined bivariate correlations between the stop and post-stop ratios for Whites, Blacks, non-Whites, Hispanics, and non-Hispanics compared to extralegal factors described above. We also examined the correlations between the percent of stops to post-stop activities to determine if the rate of stops increased or decreased the percent of post-stop activities. These correlation matrices helped identify what Rojek et al. (2004) suggested in which stop rates should increase post-stop activities if offenders were actually breaking the law in some way. If not, then there may be an issue of racial profiling. 

Results

Disproportionality Scores

    Four tables present disproportionality scores for each racial and ethnic minority group analyzed. For each table, we provide and bold-type any disproportionality scores that were above 1.0 for each law enforcement agency, noting both baseline stop rates in the first two columns of data. We also provide an average of all scores for the sample to represent overall findings at the bottom of each table. In addition, we bold and italicize scores throughout each table that were "extreme" scores (above 2.0) for any particular agency for racial and ethnic groups. Finally, we apply Rojek et al.'s (2004) logic to see if stops rates match post-stop activities and note any differences (.50 or more) between scores; particularly, citations and arrests scores to stop scores. From here, we present each racial and ethnic group's disproportionality scores starting with Blacks.

Black Disproportionality Scores

    Table 2 includes all stop and post-stop activity disproportionality scores for Blacks across the sample.

  Click here to see Table 2, a .pdf file. Remember to close window to resume reading the article.

When we examine the disparity between Black and White stops, 28 out of 34 agencies (82%) have stop scores that were higher than 1.0, suggesting that Blacks face disproportionate amounts of stops compared to Whites across the sample. This is also clear based on the average of all the stop scores across the sample in which Blacks are 1.69 times more likely to be stopped than Whites. Blacks also face higher rates of post-stop activities for over 60% of the sample, particularly searches and arrests. 

    There are also some "extreme" disproportionality scores that reached over 2.0 in this sample when comparing Blacks to Whites. For example, Blacks in Mecklenburg County interacting with the County Sheriff's Department are 2.05 and 2.44 times more likely to be stopped compared to Whites. Also, the Union County Sheriff's Department had scores of 2.94 and 3.35, respectively, for Black stops compared to Whites. One of the highest disproportionality scores for stops are found in Randolph County in which Blacks are 6.13 or 7.68 times more likely to be stopped compared to Whites by the local Sheriff's Department. 

     Using Rojek et al.'s (2004) logic, we find that post-stop disproportionality scores are lower than the traffic stop disporportionality scores for 17 of the 34 agencies sampled (50%). For example, the Pinehurst Police Department (PD), located in Moore County, has disproportionality stop scores at 6.38 and 6.35, respectively, but Black search (1.20), citation (1.01), and arrest (1.06) rates are much lower (though higher than Whites in the area). Other examples include Mecklenburg County Sheriff and the Charlotte PD that have high stop scores for Blacks but low post-stop activities. Thus, Blacks are stopped more compared to Whites in these areas but are not facing post-stop activities as much as their stop rates implied. 

Non-White Disproportionality Scores

     Table 3 includes our constructed racial category of "non-White,", which includes Blacks, Native Americans, Asians, and the NC Department of Justice's identification of a racial "Other." 

 Click Here to view Table 3, a .pdf file. Remember to close window to resume reading the article.

In this examination, 20 of the 34 (59%) law enforcement agencies sampled have disproportionality stop and post-stop scores higher than 1.0, indicating a disparity of non-White stops compared to Whites. Also, the average scores for all agencies are above 1.0. For example, non-Whites in Moore County are 1.74 or 1.94 times more likely than Whites to get stopped by the County Sheriff in the area. 

    There are a few extreme scores as well for non-Whites. For Pender County, non-Whites are 13.4 and 11 times more likely to be stopped than Whites when confronting the Surf City PD. Interestingly, this area has a very small non-White population and much of these stops may be people on vacation at beaches in the area. Raleigh PD has the same high rates in which non-Whites are 15 and 8 times more likely to be stopped compared to Whites in the area. This may be due to the number of interstate highways that run through the area, which we address in the bivariate analysis. 

    Eight of 34 (23%) agencies that have higher stop scores than post-stop activity scores when comparing non-Whites to Whites. For example, Raleigh PD has high stop scores (1.23 and 1.45) and search rates of 1.32 for non-Whites. However, their citation (.95) and arrest (.90) rates do not match the disparities in stop rates. This is also true with Surf City PD and Union County Sheriff in which non-Whites are stopped disproportionately more than Whites but are not searched, ticketed, or arrested at nearly the same rates. 

Hispanic Disproportionality Scores

    Table 4 shows disproportionality stops and post-stop scores comparing Hispanics to Whites. 

 Click here to see Table 4, a .pdf file. Remember to close the window to resume reading the article.

24 of the 34 (71%) law enforcement agencies has stop scores higher than 1.0. For example, in Guilford County, both the Guilford Sheriff and High Point PD have scores above 1.0 in stops and post-stops for Hispanics compared to Whites in the same area. This is true for the whole sample in which a majority of the post-stop activity scores are also over 1.0 and suggested that over the sample, Hispanics are 1.65 and 1.99 times more likely to be stopped than Whites.

    53 percent of the sample have stop and post-stop disproportionality scores at 2.0 or higher. For example, Hispanics in Nash County are 4.19 and 7.05 times more likely to be stopped than Whites based on the Nash County Sheriff reports. Also, Hispanics in Randolph County are 5.26 and 9.16 times more likely to be stopped than Whites based on the Randolph County Sheriff reports. Both Nash and Randolph County Sheriff Departments also have disproportionately searched, ticketed, and arrested Hispanics compared to Whites.

    55 percent of the sample have large differences in the rates of stops versus post-stop activities for Hispanics. For example, Hispanics in Henderson County are 2.63 and 3.30 times more likely to be stopped compared to Whites but are less likely to be searched (1.04), ticketed (1.12), and arrested (1.64) even though they face higher disparities compared to Whites. The same is true for Wake County with the Sheriff's Department and the Raleigh PD where stops rates are almost 2 times higher than the rates of getting a ticket or being arrested. 

Hispanic-to-non-Hispanic Disproportionality Scores

     Finally, Table 5 includes a better comparison of Hispanics to non-Hispanics since the NC Department of Justice data does not separate Hispanics from the racial categories reported in the traffic data we collected. 

  Click here to see Table 5, a .pdf file. Remember to close the window to resume reading the article.

However, the reports we use in this study did separate Hispanics from non-Hispanics, which can provide a comparison of what Hispanics face in comparison to non-Hispanics. Like all other groups examined in this report, a large majority (79%) of the sample had disproportionate stop rates higher than 1.0 for Hispanics compared to non-Hispanics. On average, Hispanics are 1.60 times more likely to be stopped than non-Hispanics across the sample. For example, when examining the Gaston County Sherriff data, Hispanics in Gaston County are 1.82 and 2.06 times more likely to be stopped compared to non-Hispanics. This is also true for post-stop activities in which Hispanics are overrepresented in searches, citations, and arrests compared to non-Hispanics. 

    Again, a majority (55%) of the law enforcement agencies have disproportionality stop and post-stop activity scores at or greater than 2.0. In Sampson County, Hispanics are 2.63 and 3.48 times more likely to be stopped compared to non-Hispanics based on Randolph Sheriff data. Similar results are evident in data from the Henderson Sheriff, Johnston Sheriff, Smithfield PD, Nash Sheriff, Stokes Sheriff, and Wake Sheriff.

    The stop and post-stop activities also suggest issues with higher stop rates than post-stop action rates. 47 percent of the law enforcement agencies sampled have disproportionality stop scores higher than the action scores. In Nash County, Hispanics are 3.49 and 4.10 times more likely to be stopped compared to non-Hispanics but the citation (.90) and arrest (1.07) scores were much lower. This is also true for Randolph County where the Sheriff's disproportionality stop scores suggests that Hispanics are four times more likely to be stopped but their citation (.78) and arrest (.52) are minimal compared to non-Hispanics. 

Bivariate Correlations

    We also provide biavariate analaysis to explore whether any independent variables identified would explain the increase or decrease of disproportionality of the traffic stop scores for each racial and ethnic minority group. The independent variables included were: total population, percent of total driving population, percent of minority driving population, type of law enforcement agency (sheriff versus police department), whether the law enforcement agencies were involved in ICE partnerships, whether there were interstate highways in the agencies' jurisdiction, and the initial reason reported by the police officer for the stop (i.e., dwi, speeding, running a stop sign, unsafe movement). We also examine correlations between the disproportionality scores for stops, searches, citations, and arrests. 

    First, we examined the relationships between the independent variables and the disproportionality traffic stop scores for each group examined. We find no significant relationships between variables for Black and Hispanic, non-Hispanic disproportionality scores. However, there is a moderate inverse relationship between the percent of Hispanic driving population and Hispanic disproportionality score (r(33) = -.387, p < .05). This was also true for the non-White category created (r(33) = -.547, p <.05). Both of these findings suggest that a decrease in the minority driving population actually increases the disproportionality stop scores for Hispanics and non-Whites. 

    Second, we examined whether there were significant differences between the stop and post-stop activity scores, which were implied in the previous tables. When examining each racial and ethnic group identified, only Hispanics versus Whites and Hispanics versus non-Hispanics disproportionality scores have significant relationships. There are moderate inverse relationships for Hispanic scores in which an increase in stop scores equaled a decrease in search (r(33) = -.369, p < .05), citation (r(33) = -.324, p < .05), and arrest scores (r(33) = -.374, p <.374). The Hispanic versus non-Hispanic stop and post-stop scores also have a moderate inverse relationship in which an increase in the stop scores equaled decreases in search (r(33) = -.111, p < .05), citation (r(33) = -.350, p <.05), and arrest scores (r(33) = -.475, p < .05). While there are no significant relationships found with Black and non-White disproportionality scores, they are still inverse relationships, which suggests that all of these groups' stop scores were much higher than any post-stop actions taken. Thus, disproportionate treatment in traffic stops by law enforcement agencies in this sample may be a reality for Blacks, Hispanics, and other non-Whites.

Discussion and Conclusions

    This study contributes to the ongoing examination of how Blacks, as well as other non-Whites face disproportionate rates of traffic stops and post-stop activities compared to Whites in North Carolina. The data show that Blacks, Hispanics, and non-Whites, in general, have higher stop rates than Whites for over 50% of the sample. In fact, some law enforcement agencies have extreme disproportionality scores in which Blacks and Hispanics are two to thirteen times more likely than Whites and non-Hispanics to be stopped. While the non-White disproportionality scores are certainly influenced by the high Black stop rates, these scores were also high compared to Whites. Moreover, the "Other" category collapsed into the non-White group represented a little over 5% of all stops but we do not know the specific groups included and more investigation into who are the others in this data is needed. We also find that the disproportionality rates for every racial and ethnic minority examined were higher for searches, citations, and arrests compared to Whites and non-Hispanics. 

    More important, however, is that there are noticeable discrepancies between the relatively high disproportionality rates of stops and the low rates of post-stop activities. As Rojek et al. (2004) suggested, this discrepancy implies possible racial profiling based on "perceived" criminal actions that lead to no real evidence of criminal activity. This is further supported by in the bivariate analysis in which higher stop rates results in lower search, citation, and arrest rates for all minority groups across the sample.

    We find no correlations between the disproportionality scores and our selected independent variables, with one exception. There is an inverse relationship between the scores and the percent of minority driving population in a given area for Hispanic and non-White scores. Thus, the smaller the minority driving population in a given area, the more likely Hispanics and non-Whites will have higher disparities in stops and post-stop activities. 

    It should also be noted that controlling for agency type and whether the agency was partnered with an ICE program had no significant effects but were positively correlated. We believe that these results may change now that over 90% of North Carolina counties have partnered with ICE since 2009. We also think these results would change if we could discern foreign-born and native-born Hispanics within the traffic stop data. However, the point here is that current policing policies target anyone who looks suspicious of being undocumented; thus, any Hispanic might be a target. 

    Finally, we find no significant relationships between the rates of stops for any particular group in this study compared to the police officer's reported reason for stopping the individuals, deflating the notion that Hispanics and non-Whites may be stopped because they commit more traffic offenses. However, we are cautious with using these results because a larger sample could affect the outcomes. We also think it may be necessary to determine whether the various law enforcement agencies are also pursuing other initiatives such as a drug trafficking crackdown.

    We recognize that this study does not find significant evidence of racial profiling by North Carolina law enforcement. However, we do find a persistent trend of racial and ethnic disparity in traffic data that has been well-documented by other researchers using more sophisticated analysis techniques. There continues to be a problem in North Carolina that Blacks, Hispanics, and in general, non-Whites, face disproportionate treatment by law enforcement agencies. 

    We also want to note that Hispanics are a new and growing population in North Carolina that needs to be included in racial profiling and disparity research. Our research suggests that this group may be a new target for law enforcement agencies. Of course, we cannot provide conclusive evidence because the data used is imperfect. For instance, as suggested in the literature review, officers report what they believe to be the racial or ethnic classification of the drivers they pull over. Also, when law enforcement agencies report traffic stop data to the state, they do not separate out Hispanics from other racial groups. More important, traffic stop reports do not specify whether Hispanic drivers, or any drivers for that matter, are immigrants, native- or foreign-born, or whether they are documented or undocumented. These kinds of additions to the reports could decrease the disproportionality scores we found or better specify what Hispanic immigrants and citizens are facing in North Carolina. Despite not knowing this particular information, we provide a new finding to this discussion in North Carolina that Hispanics, regardless of nativity, are being pulled over more than Whites.

    While we cannot say whether police officer bias, legislated policies, or institutional racism is at fault, this research demonstrates that efforts to eradicate rates of racial or ethnic disparity have not been fully successful (see Miller 2008; Smith et al. 2003; Tomaskovic-Devey et al. 2006; Warren et al. 2006; Warren and Tomaskovic-Devey 2009; Zingraff et al. 2000). It is highly likely that individual and institutional biases are operating. Given that certain levels of prejudice are subconscious and that laws still protect the use of traffic stops as pretexts to other suspected crimes, it would be nearly impossible for individual bias not to enter into individual officer's discretion about whether to stop certain individuals for potential traffic violations. This is likely compounded by organizational policies that may inadvertently support individual-level bias. Holdaway (1983) discussed police culture as an informal structure of norms and values that operate within the more rigid hierarchy of the police organization.  Police culture promotes a working "normative order."  Herbert (1998) defined this normative order as a set of rules and practices that are centered on core values. This subculture emerges, in part, from the occupational socialization of police recruits and the social isolation that often results from being a police officer.  Police officers command a certain level of status and respect in American society. 

    The socialization of police recruits not only focuses on the skills necessary for police work (e.g., firearms training, investigative techniques, etc.), but also on the values, beliefs and attitudes acceptable within the police subculture (Callan 1989; Schein 1992).  Van Maanen (1974:85) observed:

When a policeman dons his uniform, he enters a distinct subculture governed by norms and values designed to manage the strains created by his unique role in the community. 
The formal process of socialization begins at the training academy where recruits learn the occupational and organizational norms for the environment in which they now work.  This formal process centers on regulating authority and providing an ordered flow of communication and decision-making.  Informal socialization occurs through contact with senior officers and peer members in the field.  Other officers teach new recruits the "ropes" of policing as well as coping mechanisms: for example, group cohesion and solidarity (Goldsmith 1990).  Recruits are expected to display their loyalty to colleagues before reaping the same benefits of mutual protection from the group (Paoline 2001).  This informal network is more spontaneous and less regulated than the formal organizational hierarchy, and can therefore serve as an outlet for employees to socialize and express themselves openly (Young 1998). 

    The criminal justice system institutionalizes discretion when deciding which behavior(s) will be criminalized and the level of punishment to attach to each crime (Schulhofer 1988).   It is nearly impossible to separate the institutional policies and practices from individual level bias, especially when one can inadvertently support the other.  The fact that these effects are so difficult to tease apart, however, underscores the need for evaluating policies that may result in disproportional stops, searches, citations and arrests. It also calls for more research focusing on specific police behaviors through more sophisticated quantitative and qualitative research techniques pinpointing the reasons behind disproportionate treatment of racial and ethnic minorities during traffic stops. 

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