QUANTIFYING BIAS IN DRIVING‐UNDER‐THE‐INFLUENCE ENFORCEMENT

I. INTRODUCTION The Civil Rights Act of 1964 created a legal cause of action in Federal Courts in claims of discrimination on the basis of age, race, or gender under the Equal Protection Clause of the 14th Amendment to the U.S. Constitution. Although it is a widely held belief that racial bias exists in law enforcement, litigants using this cause of action have found it difficult to prove that an officer singled out a suspect because of race. Certainly, some progress has been made in understanding racial discrimination. Field studies conducted in labor, housing, and automobile markets have provided clear evidence of both intentional and unintentional racial bias (Bertrand and Mullainathan 2002; Yinger 1995; Zhao, Ondrich, and Yinger 2006). Laboratory experiments conducted by psychologists have provided evidence of subtle, axiomatic forms of racial bias (Greenwald, Oakes, Hoffman 2002). However, proving racial bias outside the laboratory has been considerably more challenging. Market-based studies are usually limited by omitted variable bias and the correlation in the United States between race and other demographic variables, such as income and education, adds to this problem (Kennedy et al. 1998). How to measure racial bias remains a research question of considerable currency. The issue of racial bias is particularly critical for the field of law enforcement, where police agencies are frequently accused of racial bias in carrying out their public safety duties. With the support of civil rights organizations, a number of plaintiffs have been successful in cases brought against state and local police authorities. However, claims of racial bias that lead to verdicts of guilt on the part of public agencies are in the minority. For example, in 2007 the State of California dismissed all 322 racial profiling cases it faced for lack of sufficient evidence. The rationale given for the blanket dismissals is that it is almost impossible to prove that an officer singled someone out because of race alone. There is general agreement that more detailed data are required to provide proof of the existence of racial bias. Fortunately, the quality of data available for use in investigating issues relating to racial bias in traffic stops is increasing. As of 2004, 29 states instituted data collection processes to record motorists' race or ethnicity during traffic stops. In addition, technological innovations in information gathering, storage, and retrieval allow officers to capture more details associated with traffic stop assessments. These data allow for more accurate quantitative tests of racial bias to be carried out than was possible in the past. The challenge in testing for racial bias in law enforcement datasets is that it is hard to identify the marginal suspect from assessment process outcome data. These challenges have been well documented and the difficulty in establishing the link between outcome data and marginal assessment decisions has become known as the "infra-marginality" problem (see the discussion in Ayers 2002). Quantitative attempts to analyze claims of racial bias in law enforcement have mainly focused on two statistics: the "search rate" and the "find rate." The search rate is calculated by dividing the number of individuals in a racial group who are assessed (or searched) for a crime by the total number of individuals within that racial group in the general population of interest. All else equal, significantly different search rates across racial groups are suggestive of police officers targeting some racial group more than others, and this concept is referred to as "statistical discrimination" (Arrow 1973). The use of this statistic has been criticized for two major reasons. First, the population size of a racial group at risk of an assessment is difficult to obtain. Second, racial disparities in assessment rates do not necessarily imply racial bias if race is correlated with other factors (see Blumstein 1982, 1993; Engel et al. …