No Runs, Few Hits, and Many Errors: Street Stops, Bias, and Proactive Policing


Equilibrium models of racial discrimination in law enforcement encounters suggest that in the absence of racial discrimination, the proportion of searches yielding evidence of illegal activity (the hit rate) will be equal across races. Searches that disproportionately target one racial group, resulting in a relatively low hit rate, are inefficient and suggest bias. An unbiased officer who is seeking to maximize her hit rate would reduce the number of unproductive stops toward a group with the lower hit rate. An unbiased policing regime would generate no differences in hit rates between groups.

We use this framework to test for racial discrimination in pedestrian stops with data from the contentious “Stop, Question and Frisk” (SQF) program of the New York City Police Department (NYPD). SQF produced nearly five million citizen stops from 2004–2012. The stops are regulated by both Terry (federal) and DeBour (New York) case law on reasonable suspicion.

About the Author

Jeffrey Fagan is the Isidor and Seville Sulzbacher Professor of Law, Columbia Law School; Professor of Epidemiology, Mailman School of Public Health, Columbia University.