Extending the Veil of Darkness Approach: An Examination of Racial Disproportionality in Traffic Stops in Durham, NC

• Published date: 01 December 2017
• Date: 01 December 2017
• DOI: http://doi.org/10.1177/1098611117721665
• Subject Matter: Articles

Article

Extending the Veil of

Darkness Approach: An

Examination of Racial

Disproportionality in

Traffic Stops in

Durham, NC

Travis A. Taniguchi

1

, Joshua A. Hendrix

1

,

Alison Levin-Rector

1

, Brian P. Aagaard

1

,

Kevin J. Strom

1

, and Stephanie A. Zimmer

1

Abstract

Developed in 2006, the veil of darkness approach is one of the most widely accepted

methods for assessing the impact of driver race on traffic stops. Building on the

original methodology, we innovate in three important ways to enhance the veil of

darkness approach: (a) invoke generalized linear mixed models to account for the

lack of independence among observations in traffic stop data sets, (b) decompose the

relationship between daylight and driver race to consider the role of driver sex, and

(c) assess variability in racial disproportionality across law enforcement units. Nearly

20,000 traffic stops are analyzed for the Durham (NC) Police Department. Results

indicate that more than 10% of the variability in the rate of Black drivers stopped is

accounted for by officer-level factors, racial disproportionality was only for male

drivers, and evidence of disproportionality was found among some units, but no

evidence was found among others.

Keywords

veil of darkness, traffic stops, racial disproportionality

1

RTI International, Research Triangle Park, NC, USA

Corresponding Author:

Travis A. Taniguchi, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC 27709,

USA.

Email: taniguchi@rti.org

Police Quarterly

2017, Vol. 20(4) 420–448

!The Author(s) 2017

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DOI: 10.1177/1098611117721665

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Racial prof‌iling by law enforcement occurs when authorities target individuals

because of their race or ethnicity rather than their behavior. It is a social issue

that is counter to the constitutional guarantee of equal treatment under the law,

and when it is perceived to exist, police–community relations may suf‌fer. Traf‌f‌ic

stops are not only the most common reason for contact with the police (Bureau

of Justice Statistics, 2016), for many people, traf‌f‌ic stops are likely to be the only

type of interaction they ever have with law enforcement. Thus, the nature of

police contact, its circumstances, and outcomes can have dramatic and possibly

unrivaled inf‌luence on how people view the police (see Smith, Graham, &

Adams, 1991; Worrall, 1999). It is well established that perceptions of unfair

or discriminatory practices by the police have serious implications for commu-

nity cooperation and civil disobedience (Cox & Fitzgerald, 1996; Tyler & Fagan,

2008), support for the police (Tyler & Wakslak, 2004), race relations, crime

reporting, and public safety (see Brown & Benedict, 2002 for a review).

Therefore, racial prof‌iling by police is a critical social issue and an important

research area in the social sciences.

Assessing how much racial prof‌iling by police occurs in the United States is a

complex issue, primarily because it is one of the most dif‌f‌icult social phenomena

to study scientif‌ically. It is an extremely challenging task to prove def‌initively

that the nature or outcome of a traf‌f‌ic stop would have been dif‌ferent if the

driver was of a dif‌ferent race, or that the of‌f‌icer has an explicit or implicit bias

toward people of color that inspires discriminatory police practices. Although a

Gallup survey in 2003 revealed that 59% of Americans consider racial prof‌iling

to be widespread among police stops (Ludwig, 2003; see also Weitzer & Tuch,

2002), this study cannot speak to whether perceptions of racially biased police

practices ref‌lect reality.

While an extensive body of literature exists examining the factors that inf‌lu-

ence police conduct once a community encounter occurs (Riksheim & Chermak,

1993), there is less knowledge of the factors that inf‌luence a police decision to

make a traf‌f‌ic stop in the f‌irst place. Traditionally, the impact of race on traf‌f‌ic

stops has been assessed by assuming that driving patterns can be approximated

using census population estimates (Baumgartner & Epp, 2012a, 2012b) or traf‌f‌ic

collision data (McDevitt & Iwama, 2016) or by conducting traf‌f‌ic surveys to

quantify the race distribution of motorists (Smith et al., 2004). All of these

approaches have serious limitations. Census populations are a poor proxy for

populations at risk for traf‌f‌ic stops, and traf‌f‌ic surveys are not only expensive,

they tend to have limited generalizability because of small sample sizes and

geographic coverage. Use of traf‌f‌ic collision data is a better denominator in

some respects because it more accurately ref‌lects the driving population

(Alpert, Smith, & Dunham, 2004). But this benchmark construction approach

assumes that accidents, and reporting of accidents to the police, do not vary

systematically. There have in fact been numerous attempts to establish an appro-

priate, unbiased benchmark with which to compare police traf‌f‌ic stop data, but

Taniguchi et al. 421