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

by Jeffrey Grogger, Greg Ridgeway

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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 use 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 provide poor estimates of the population at risk of a traffic stop; at the same time, traffic surveys have limitations and are more costly to carry out than the alternative that we propose herein. In this article 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 call the “veil of darkness” hypothesis, which asserts that police are less likely to know the race of a motorist before making a stop after dark than they are during daylight. If we assume that racial differences in traffic patterns, driving behavior, and exposure to law enforcement do not vary between daylight and darkness, then we can test for racial profiling by comparing the race distribution of stops made during daylight to the race distribution of stops made after dark. 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 daylight/darkness contrasts in the race distribution of stopped drivers. We provide conditions under which our estimates are robust to a substantial nonreporting 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 and find that in this example the data yield little evidence of racial profiling in traffic stops.

Originally published in: Journal of the American Statistical Association, September 2006, Vol. 101, No. 475, pp. 878-887.

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