The purpose of this paper is to draw a better understanding of the potential impact of daylight in officer decision making. In order to this, the authors test the veil of darkness hypothesis, which theorizes that racial bias in traffic stops can be tested by controlling for the impact of daylight, while operating under the assumption that driver patterns remain constant across race.
Publicly available traffic-stop records from the Louisville Metro Police Department for January 2010–2019. The analysis includes both propensity score matching to examine the impact of daylight in similarly situated stops and coefficients testing to analyze how VOD may vary in citation-specific models.
The results show that using PSM following the VOD hypothesis does show evidence of racial bias, with Black drivers more likely to be stopped. Moreover, the effects of daylight significantly varied across citation-specific models.
The data are self-reported from the officer and do not contain information on the vehicle make or model.
This paper shows that utilizing PSM and coefficients testing provides for a better analysis following the VOD hypothesis and does a better job of understanding the impact of daylight and the officer decision-making on traffic stops.
Based on the quality of the data, the findings show that the use of VOD allows for the performance of more rigorous analyses of traffic stop data – giving police departments a better way to examine if racial profiling is evident.
This is the first study (to the researchers' knowledge) that applies the statistical analyses of PSM to the confines of the veil of darkness hypothesis.
