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Gender Bias in the Enforcement of Traffic Laws: Evidence based on a new empirical test
Abstract
In the United States, a majority of the drivers who receive a traffic ticket are male, and male drivers are more likely to receive a ticket after being stopped by the police. This paper develops and conducts an empirical test for the existence of police gender bias (taste-based discrimination) in traffic ticketing. The test is based on a model's prediction of how the gender composition of ticketed drivers should vary across groups of police officers who use unbiased, but potentially different ticketing standards. The test is useful for determining whether the gender disparity in traffic tickets results from gender bias or a higher tendency of male drivers to break traffic laws. In addition, the test offers an improvement over the "differences-in-differences" test for discrimination which has been applied in other contexts. When applied to data on traffic tickets issued by male and female police officers in Boston, the new test rejects the null hypothesis of unbiased ticketing.
... An example of post-stop outcome analysis consists of checking whether the search for drugs among stopped vehicles is biased against the driver's race. In this respect, starting from the influential paper proposed in (Knowles et al., 2001), several extensions and critiques have been presented (Antonovics & Knight, 2009;Anwar & Fang, 2006;Gardner, 2009;Rowe, 2009;Sanga, 2009). We refer to the surveys (Tillyer et al., 2010;Engel, 2008) for extensive references. ...
Discrimination data analysis has been investigated for the last fifty years in a large body of social, legal, and economic studies. Recently, discrimination discov-ery and prevention has become a blooming research topic in the knowledge discov-ery community. This chapter provides a multi-disciplinary annotated bibliography of the literature on discrimination data analysis, with the intended objective to pro-vide a common basis to researchers from a multi-disciplinary perspective. We cover legal, sociological, economic and computer science references.
... Blalock et al. (2007) examine traffic ticketing data from Bloomington and Highland Park in Illinois, Wichita, Boston, and the entire state of Tennessee, and find that women are more likely to receive citations in three of the five locations, while men are more likely to receive citations in the other two locations. Rowe (2009) extends Anwar and Fang's rank test to examine gender bias in ticketing. Makowsky and Stratmann (2009), using the Massachusetts traffic data, examine whether local police officers pursue certain objectives other than effective policing, such as raising local government revenues from out-of-towners. ...
We focus on a particular kind of discretionary behavior on the part of traffic officers when issuing speeding tickets–what we term speed discounting. It is anecdotally said that officers often give motorists a break by reporting a lower speed on their citation than the actual speed that they observe the vehicle doing. Verifying the level of police discretion in the speed discounting behavior and ascertaining the presence of racial bias among police officers are the main objectives of this paper. Using a unique dataset that contains the race of the officer and of the motorist and cited vehicle speed, we apply the rank order test and the difference-in-differences method to detect racial prejudice in the speed discounting behavior.
The growing use of data mining practices by both government and commercial entities leads to both great promises and challenges. They hold the promise of facilitating an information environment which is fair, accurate and efficient. At the same time, they might lead to practices which are both invasive and discriminatory, yet in ways the law has yet to grasp. This point is demonstrated by showing how the common measures for mitigating privacy concerns, such as a priori limiting measures (particularly access controls, anonymity and purpose specification) are mechanisms that are increasingly failing solutions against privacy and discrimination issues in this novel context.
Instead, a focus on (a posteriori) accountability and transparency may be more useful. This requires improved detection of discrimination and privacy violations as well as designing and implementing techniques that are discrimination-free and privacy-preserving. This requires further (technological) research.
But even with further technological research, there may be new situations and new mechanisms through which privacy violations or discrimination may take place. Novel predictive models can prove to be no more than sophisticated tools to mask the “classic” forms of discrimination, by hiding discrimination behind new proxies. Also, discrimination might be transferred to new forms of population segments, dispersed throughout society and only connected by some attributes they have in common. Such groups will lack political force to defend their interests. They might not even know what is happening.
With regard to privacy, the adequacy of the envisaged European legal framework is discussed in the light of data mining and profiling. The European Union is currently revising the data protection legislation. The question whether these new proposals will adequately address the issues raised in this book is dealt with.
In the United States, police officers often decide to give drivers they stop for traffic violations a warning, which imposes no fine, instead of a ticket. Officers are also legally permitted to stop drivers for the purpose of detecting other crimes. This paper addresses two questions about the role of discretion and ulterior motives in traffic stops. First, under what conditions may it be efficient to let many stopped drivers go with only a warning? Using a model of law enforcement based on Shavell (1991), who does not consider warnings, I show that the ulterior motive of detecting other crimes is a simple way to rationalize the existence of warnings in an efficient enforcement scheme. Second, I test the model against data on traffic tickets and warnings in Massachusetts to determine whether police discriminate against out-of-town drivers because of the ulterior motive of ticket revenue. I find support for the notion that discrimination against out-of-town drivers is motivated by revenue. In the model, the revenue motive is an aspect of efficient enforcement for a local government.
We review the economics literature that deals with identifying bias, or taste for discrimination, using statistical evidence. A unified model is developed that encompasses several different strategies studied in the literature. We also discuss certain more theoretical questions concerning the proper objective of discrimination law.
This dissertation examines issues related to the efficiency and effectiveness of government policies to provide public goods. In the first essay, I develop an empirical test for whether police officers discriminate on driver gender when enforcing traffic laws. The test is designed to only detect discrimination that is unrelated to providing safe conditions on the roads. The empirical method developed in the essay may be applicable in a number of other contexts where evaluators (such as police officers, judges, or mortgage lenders) may potentially discriminate when making decisions regarding subjects who belong to different demographic groups.
The second essay makes a theoretical argument showing that because police officers can detect many different crimes by making a traffic stop, the widespread practice of giving stopped traffic law violators a warning instead of a fine can be efficient. Warnings would at first seem to be inefficient because they lower the expected penalty from breaking the law, and thereby reduce deterrence for a given amount of public resources devoted to detecting and stopping violations. My argument therefore points out an efficiency rationale for providing individual government agents discretion in deciding which detected law breakers to penalize.
In the third and final essay, my co-author Daniel Eisenberg and I use the Vietnam draft lottery to test the commonly held presumption that smoking as a young person strongly predicts smoking in later adulthood. This presumption, well documented by many observational studies, underlies many anti-smoking policies in the United States. Yet some of the persistence of smoking over time might be attributable to individual factors, such as tolerance for health risks, which are difficult to account for in observational data. Using variation in smoking induced by the draft lottery, we do not find a strong relationship between smoking in early and late adulthood, suggesting that anti-smoking policies directed at young people may not be effective in achieving the policy goal of reducing adult smoking rates.
Law enforcement officers are allowed to exercise a significant amount of street-level discretion in a variety of ways. In this paper, we focus on a particular prominent kind of discretionary behavior by traffic officers when issuing speeding tickets, speed discounting. Officers partially forgive motorists by writing a lower speed level than the speed that officers observe. Verifying the level of speed discounting by different groups of officers and motorists and ascertaining the presence of racial disparities in this lenient policing are the main objectives of this paper. We find that minority officers, particularly African-Americans, are harsher on all motorists but even harsher on minority motorists regarding speed discounting. The minority-on-minority disparity appears to be stronger in situations involving Hispanic officers, infrequently ticketing officers, male motorists, those driving old vehicles, and minority neighborhoods.
We propose a simple model of trooper behavior to design empirical tests for whether troopers of different races are monolithic in their search behavior, and whether they exhibit relative racial prejudice in motor vehicle searches. Our test of relative racial prejudice provides a partial solution to the well-known infra-marginality and omitted-variables problems associated with outcome tests. When applied to a unique dataset from Florida, our tests soundly reject the hypothesis that troopers of different races are monolithic in their search behavior, but the tests fail to reject the hypothesis that troopers of different races do not exhibit relative racial prejudice.
Police checking for illegal drugs are much more likely to search the vehicles of African-American motorists than those of white motorists. This paper develops a model of police and motorist behavior that suggests an empirical test for distinguishing whether this disparity is due to racial prejudice or to the police's objective to maximize arrests. When applied to vehicle search data from Maryland, our test results are consistent with the hypothesis of no racial prejudice against African-American motorists. However, if police have utility only for searches yielding large drug finds, then our analysis would suggest bias against white drivers. The model's prediction regarding nonrace characteristics is also largely supported by the data.
In the United States, the police frequently give drivers stopped for minor trac violations a warning, which imposes no fine, instead of a ticket. Also, the police are legally permitted to stop drivers for the purpose of detecting other crimes. This paper addresses two questions about the role of discretion and ulterior motives in trac stops. First, under what conditions may it be ecient to let many stopped drivers go with only a warning? Using a model of law enforcement based on Shavell (1991), who does not consider warnings, I show that the ulterior motive of detecting other crimes is a simple way to rationalize the existence of warnings in an ecient enforcement scheme. Second, I test the model against data on trac
This article assesses the strengths and weaknesses of using “outcome tests” to assess racial disparities in police practices. An outcome test, for example, might assess whether the probability of finding contraband was higher for whites who are searched than for minorities who are searched.
In the United States, police officers often decide to give drivers they stop for traffic violations a warning, which imposes no fine, instead of a ticket. Officers are also legally permitted to stop drivers for the purpose of detecting other crimes. This paper addresses two questions about the role of discretion and ulterior motives in traffic stops. First, under what conditions may it be efficient to let many stopped drivers go with only a warning? Using a model of law enforcement based on Shavell (1991), who does not consider warnings, I show that the ulterior motive of detecting other crimes is a simple way to rationalize the existence of warnings in an efficient enforcement scheme. Second, I test the model against data on traffic tickets and warnings in Massachusetts to determine whether police discriminate against out-of-town drivers because of the ulterior motive of ticket revenue. I find support for the notion that discrimination against out-of-town drivers is motivated by revenue. In the model, the revenue motive is an aspect of efficient enforcement for a local government.
Knowles, Persico, and Todd (2001) present a model of police and motorist behavior in the context of vehicle searches and test it using data from Maryland. Their work marked a resurgence in interest on how to interpret purported evidence of statistical and racial discrimination. The main implication of the their model is that in the absence of racial discrimination, the proportion of searches yielding drugs (or “hit rate”) will be equated across races. A relatively low hit rate for any group suggests that police may improve their overall hit rate by shifting resources away from that group and is thus evidence toward discrimination. Using data on vehicle searches by the Maryland State Police (MSP), they find no bias against blacks relative to whites but significant bias against white females and particularly Hispanics.In this paper, I reconsider the Knowles et al. analysis. An important feature of the data used by Knowles et al. is that they are limited to searches occurring on Interstate 95, which was also the focus of the racial profiling lawsuit filed against the MSP in 1993. However, while the suit focused on I-95 searches, the settlement required the MSP to record all vehicle searches, of which I-95 searches constitute about one-third. When considering all MSP searches, I find evidence toward racial discrimination against blacks and especially Hispanics, and that these disparities have increased in recent years.
Speeding tickets are determined not only by the speed of the offender, but also by incentives faced by police officers and their vote-maximizing principals. We hypothesize that police officers issue fines more frequently when drivers have a higher opportunity cost of contesting a ticket, and when drivers are not residents of the local municipality. We also predict that local officers are more likely to issue a ticket to out-of-town drivers when fiscal conditions are tight and legal limits prevent increases in property taxes. Using data from traffic stops in Massachusetts, we find support for our hypotheses. (JEL H76, R41)
The magnitude of the interaction effect in nonlinear models does not equal the marginal effect of the interaction term, can be of opposite sign, and its statistical significance is not calculated by standard software. We present the correct way to estimate the magnitude and standard errors of the interaction effect in nonlinear models.
This second edition of Gary S. Becker's The Economics of Discrimination has been expanded to include three further discussions of the problem and an entirely new introduction which considers the contributions made by others in recent years and some of the more important problems remaining. Mr. Becker's work confronts the economic effects of discrimination in the market place because of race, religion, sex, color, social class, personality, or other non-pecuniary considerations. He demonstrates that discrimination in the market place by any group reduces their own real incomes as well as those of the minority. The original edition of The Economics of Discrimination was warmly received by economists, sociologists, and psychologists alike for focusing the discerning eye of economic analysis upon a vital social problemâdiscrimination in the market place. "This is an unusual book; not only is it filled with ingenious theorizing but the implications of the theory are boldly confronted with facts. . . . The intimate relation of the theory and observation has resulted in a book of great vitality on a subject whose interest and importance are obvious."âM.W. Reder, American Economic Review "The author's solution to the problem of measuring the motive behind actual discrimination is something of a tour de force. . . . Sociologists in the field of race relations will wish to read this book."âKarl Schuessler, American Sociological Review
[Excerpt] We test for the existence of gender bias in power relationships. Specifically, we examine whether police officers are less likely to issue traffic tickets to men or to women during traffic stops. Whereas the conventional wisdom, which we document with surveys, is that women are less likely to receive tickets, our analysis shows otherwise. Examination of a pooled sample of traffic stops from five locations reveals no gender bias, but does show significant regional variation in the likelihood of citations. Analysis by location shows that women are more likely to receive citations in three of the five locations. Men are more likely to receive citations in the other two locations. To our knowledge, this study is the first to test for gender bias in traffic stops, and clearly refutes the conventional wisdom that police are more lenient towards women.
This paper studies whether the gender composition of recruiting committees matters. We make use of the unique evidence provided
by Spanish public examinations, where the allocation of candidates to evaluating committees is random. We analyse how the
chances of success of 150,000 female and male candidates for positions in the four main Corps of the Spanish Judiciary from
1987 to 2007 were affected by the gender composition of their evaluation committee. We find that a female (male) candidate
is significantly less likely to be hired whenever she (he) is randomly assigned to a committee where the share of female (male)
evaluators is relatively greater. Evidence from multiple choice tests suggests that this is due to the fact that female majority
committees overestimate the quality of male candidates.
We estimate auto accident externalities (more specifically insurance externalities) using panel data on state-average insurance premiums and loss costs. Externalities appear to be substantial in traffic-dense states: in California, for example, we find that the increase in traffic density from a typical additional driver increases total statewide insurance costs of other drivers by $1,725–$3,239 per year, depending on the model. High–traffic density states have large economically and statistically significant externalities in all specifications we check. In contrast, the accident externality per driver in low-traffic states appears quite small. On balance, accident externalities are so large that a correcting Pigouvian tax could raise $66 billion annually in California alone, more than all existing California state taxes during our study period, and over $220 billion per year nationally.
We present a methodology for measuring the risks posed by drinking drivers that relies solely on readily available data on fatal crashes. The key to our identification strategy is a hidden richness inherent in two-car crashes. Drivers with alcohol in their blood are seven times more likely to cause a fatal crash; legally drunk drivers pose a risk 13 times greater than sober drivers. The externality per mile driven by a drunk driver is at least 30 cents. At current enforcement rates the punishment per arrest for drunk driving that internalizes this externality would be equivalent to a fine of $8,000.
This paper estimates the effect of judicial characteristics (political affiliation, race, and sex) on federal criminal sentencing using variation in judicial characteristics at the district level. The results suggest that judges’ race and sex have little influence on prison sentences in general but do affect racial and sex disparities. For serious offenses, increasing the proportion of female judges in a district decreases the sex disparity. I interpret this as evidence of a paternalistic bias among male judges that favors female offenders. The racial composition of the bench has mixed effects that are open to different interpretations. Finally, there is little evidence that the political composition of the district affects sentencing disparities.
This paper provides new evidence on racial profiling using information on the race of both motorists and officers. Extending the model of Knowles, Persico, and Todd (2001), we develop a new test for distinguishing between preference-based and statistical discrimination. Our test is based on the notion that if search decisions are driven purely by statistical discrimination, then they should be independent of officer race. Our results, by contrast, demonstrate that officers are more likely to search if officer race and driver race differ. We then investigate and rule out two alternative explanations for our findings. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
The NBA provides an intriguing place to assess discrimination: referees and players are involved in repeated interactions
in a high-pressure setting, with referees making split-second decisions that might allow implicit racial biases to become
evident. We find that more personal fouls are awarded against players when they are officiated by an opposite-race officiating
crew than when they are officiated by an own-race refereeing crew. These biases are sufficiently large so that they affect
the outcome of an appreciable number of games. Our results do not distinguish whether the bias stems from the actions of white
or black referees.
Knowles, Persico, and Todd (2001) develop a model of police search and offender behavior. Their model implies that if police are unprejudiced the rate of guilt should not vary across groups. Using data from Interstate 95 in Maryland, they find equal guilt rates for African-Americans and whites and conclude that the data is not consistent with racial prejudice against African-Americans. This paper generalizes the model of Knowles, Persico, and Todd by accounting for the fact that potential offenders are frequently not observed by the police, and by including two different levels of offense severity. We show that the data is consistent with prejudice against African-American males, no prejudice, and reverse discrimination, depending on the type of equilibrium that exists. Additional analyses, based on stratification by type of vehicle and time of day, do not shed any light on the nature of the equilibrium.
The key problem in testing for racial profiling in traffic stops is estimating the risk set, or "benchmark," against which to compare the race distribution of stopped drivers. To date, the two most common approaches have been to employ Census-based residential population data or to conduct traffic surveys in which observers tally the race distribution of drivers at a certain location. It is widely recognized that residential population data may provide poor estimates of the population at risk of a traffic stop; at the same time, traffic surveys have limitations and may be too costly to carry out on the ongoing basis required by recent legislation and litigation. In this paper, we propose a test for racial profiling that does not require explicit, external estimates of the risk set. Rather, our approach makes use of what we refer to as the "veil of darkness" hypothesis, which asserts that at night, police cannot determine the race of a motorist until they actually make a stop. The implication is that the race distribution of drivers stopped at night should equal the race distribution of drivers at risk of being stopped at night. If we further assume that racial differences in traffic patterns, driving behavior, and exposure to law enforcement do not vary between day and night, we can test for racial profiling by comparing the race distribution of stops made during daylight to the race distribution of stops made at night. We propose a means of weakening this assumption by restricting the sample to stops made during the evening hours and controlling for clock time while estimating day/night contrasts in the race distribution of stopped drivers. We provide conditions under which our estimates are robust to a substantial non-reporting problem present in our data and in many ...
How Dangerous are Drinking Drivers?” 39 rMakowsky, Michael and Stratmann, Thomas. “Political Economy at Any Speed: What Determines Traffic Citations?”
- Levitt
- Steven
- Porter
Levitt, Steven and Porter, Jack. “How Dangerous are Drinking Drivers?”, Journal of Political Economy, 2001. 39 rMakowsky, Michael and Stratmann, Thomas. “Political Economy at Any Speed: What Determines Traffic Citations?”, American Economic Review, March 2009
Testing for Racial Profiling in Traffic Stops from Behind a Veil of Darkness” Edlin, Aaron and Karaca-Mandic, Pinar. “The Accident Externality from Driving
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Grogger, Jeffrey and Ridgeway, Greg. “Testing for Racial Profiling in Traffic Stops from Behind a Veil of Darkness”, Journal of the American Statistical Association, Sept. 2006. Edlin, Aaron and Karaca-Mandic, Pinar. “The Accident Externality from Driving”, Journal of Political Economy, 2006
Boston police to get tough on tickets
- Bill Dedman
Dedman, Bill. "Boston police to get tough on tickets", The Boston Globe, Jan. 17 2004.
Massachusetts Racial and Gender Profiling Final Report: Executive Summary
- Erica Pierce
Erica Pierce. "Massachusetts Racial
and Gender Profiling Final Report: Executive Summary", Northeastern University Institute
on Race and Justice, May 2004.