In this article, the authors illustrate the reasoning and importance behind veil of darkness methodological decisions while providing a straightforward road map for future research; they propose a new strategy to account for seasonal driving patterns and improve the VOD as a natural experiment; they also describe their use of multivariable regression analyses to demonstrate their preferred approach in action using Michigan State Police traffic stop data from 2021.
The veil of darkness (VOD) is a practical and rigorous methodology for examining racial disparities in police traffic stop behavior. Past research, however, has been littered with methodological inconsistencies inhibiting cross-study comparison and decisions regarding policy. Accordingly, the authors clarify four aspects of its implementation: 1) coding daylight, their treatment condition; 2) constructing an intertwilight period; 3) accounting for seasonal differences in driving or patrol patterns; and 4) modeling VOD multivariable regression equations. They discuss the theoretical and practical implications of methodological decisions as they pertain to the method's functionality as a natural experiment. Furthermore, the authors propose a novel weighting procedure to account for seasonal driving population differences. They examined more than 50,000 traffic stops conducted by Michigan State Police during 2021 to demonstrate their suggested framework for future analyses. (Published Abstract Provided)