How do police use race in traffic stops and searches? Tests based on observability of race

Ritter, Joseph A.

doi:10.1016/j.jebo.2017.02.005

Cited by 16 publications

(10 citation statements)

References 20 publications

(24 reference statements)

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“…First, VOD cannot account for the potential impact of vehicle profiling on disproportionality. Ritter (2017) has suggested that police officers may be more likely to stop drivers of vehicles that are more commonly associated with minority individuals at night. However, this hypothesis has not been validated empirically and we were not able to assess this with our data.…”

Section: Discussionmentioning

confidence: 99%

Veil of Darkness and Investigating Disproportionate Impact in Policing: When Researchers Disagree

Stacey

Bonner

2020

Police Quarterly

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Disproportionate impact in policing has long been a concern for researchers and practitioners alike, with much of the focus on traffic stops. While there are many methods used to determine disproportionality in traffic stops, the veil of darkness (VOD) approach has increasingly become one of the most popular frameworks. Although there is consensus on certain aspects of the method, researchers utilizing VOD disagree on the appropriate sampling strategies. This research examines the original VOD method and three different sample restrictions proposed within the VOD literature to demonstrate the effect each has on the conclusions drawn. The results indicate that there is variation in the estimates of disproportionality depending on the sampling strategy used. As such, researchers using the VOD method must be cautious in their sampling decisions in mid-size jurisdictions due to the impact these such choices have on the conclusions drawn about disproportionate impact.

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Section: Discussionmentioning

confidence: 99%

Veil of Darkness and Investigating Disproportionate Impact in Policing: When Researchers Disagree

Stacey

Bonner

2020

Police Quarterly

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“…Another issue is that there may be discrimination in the determination of fault for an accident as discussed in West (2018). 10 As of writing and to the authors knowledge, applications of the test include Grogger and Ridgeway (2006) in Oakland, CA; Ridgeway (2009) Cincinnati, OH; Ritter and Bael (2009) and Ritter (2017) in Minneapolis, MN;Wheeler (2010, 2012) as well as Horace and Rohlin (2016) in Syracuse, NY; Renauer, Henning, and Covelli (2009) in Portland, OR; Taniguchi et al (2016aTaniguchi et al ( , 2016bTaniguchi et al ( , 2016cTaniguchi et al ( , 2016d in Durham Greensboro, Raleigh, and Fayetteville, North Carolina; Masher 2016 Ross, Fazzalaro, Barone, and Kalinowski (2015, 2016, 2017a, 2017b, 2018 in Connecticut and Rhode Island, Racial & Identity Profiling Advisory Board (2020) in California, and Sanchagrin et al (2019) in Oregon. from the statistics, computer science, and economics literatures to real world problems.…”

Section: E N D N O T E Smentioning

confidence: 99%

“…1010As of writing and to the authors knowledge, applications of the test include Grogger and Ridgeway (2006) in Oakland, CA; Ridgeway (2009) Cincinnati, OH; Ritter and Bael (2009) and Ritter (2017) in Minneapolis, MN; Worden, McLean, and Wheeler (2010, 2012) as well as Horace and Rohlin (2016) in Syracuse, NY; Renauer, Henning, and Covelli (2009) in Portland, OR; Taniguchi et al. (2016a, 2016b, 2016c, 2016d) in Durham Greensboro, Raleigh, and Fayetteville, North Carolina; Masher (2016) in New Orleans, LA; Chanin et al.…”

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confidence: 99%

Testing for disparities in traffic stops: Best practices from the Connecticut model

Ross

Kalinowski

Barone

2020

Criminology & Public Policy

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Connecticut's novel approach to collecting and analyzing traffic stop data for evidence of disparate treatment is widely considered to be a model of best practice. Here, we provide an overview of Connecticut's framework, detail solutions to the canonical empirical challenges of analyzing traffic stop, and describe a data-driven approach to early intervention. Unlike most jurisdictions that simply produce an annual traffic stop report, Connecticut has developed an ongoing system for identifying and mitigating disparity. Connecticut's framework for identifying significant disparities on an annual basis relies on the so-called "preponderance of evidence" approach. Drawing from the cutting-edge of the empirical social science literature, this approach applies several, as opposed to a single, rigorous empirical test of disparity. For departments identified as having a disparity, Connecticut has developed a process for intervening on an annual basis. In that process, policing administrators engage with researchers to conduct an empirical exploration into possible contributing factors and enforcement policies. In Connecticut, this approach has transformed what had once been a war of anecdotes into a constructive data-driven conversation about policy. Variants of the Connecticut Model have recently been adopted by the State of Rhode Island, Oregon, and This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…There is an extensive literature that examines disparities in police treatment based on race. African American and Latino individuals are more often stopped by police in traffic stops and more likely to be searched when stopped (for example, Anwar & Fang, 2006;Gelman, Fagan, & Kiss, 2007;Gross & Barnes, 2002;Knowles, Persico, & Todd, 2001;Pierson et al, 2017;Ritter, 2017). They are also more likely to be stopped by police in stop-and-frisk interactions (Coviello & Persico, 2015;Ridgeway, 2007) and to be arrested (Bailey, 2014).…”

Section: Disparities In Police Stops and Their Effectsmentioning

confidence: 99%

Police Stops and Searches of Indigenous People in Minneapolis: The Roles of Race, Place, and Gender

Gorsuch¹,

Rho²

2019

iipj

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We examine disparities in police stops, searches, and arrests of Native Americans in Minneapolis, a major metropolitan area with a substantial Native American population. During the study period, 1.42% of women in Minneapolis reported their race as American Indian or Alaska Native, but the Minneapolis police reported that 6.43% of police stops of women (including vehicle stops and non-vehicle stops) involved Native Americans. Native American men comprise 1.51% of the male population and 3.29% of police stops of men. After they were stopped, 28% of Native American women were searched and 20% were arrested, over twice as often as women of any other race. The disproportionate stops of Native American women are concentrated in areas with high Native American residents.

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How do police use race in traffic stops and searches? Tests based on observability of race

Cited by 16 publications

References 20 publications

Veil of Darkness and Investigating Disproportionate Impact in Policing: When Researchers Disagree

Veil of Darkness and Investigating Disproportionate Impact in Policing: When Researchers Disagree

Testing for disparities in traffic stops: Best practices from the Connecticut model

Police Stops and Searches of Indigenous People in Minneapolis: The Roles of Race, Place, and Gender

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