Testing for Racial Profiling in Traffic Stops from Behind a Veil of Darkness

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 other studies of racial profiling. We propose an approach to assess the sensitivity of our results to departures from our maintained assumptions. Finally, we apply our method to data from Oakland, California. In this example, the data yield little evidence of racial profiling in traffic stops.