Testing for Racial Discrimination in Bail Setting Using Nonparametric Estimation of a Parametric Model

Black defendants are assigned systematically greater bail levels than whites accused of similar offenses and, partly as a result, have systematically lower probabilities of pre-trial release. We construct a simple model of optimal bail setting that allows us to measure how much of the bail difference is due to judicial bias against blacks, holding constant defendant heterogeneity that judges observe, regardless of whether we also observe it. We show how to use nonparametric methods to consistently estimate the model’s key parameter by using the judge’s first-order condition to form an auxiliary projection relationship involving defendants’ conditional choice probabilities. While the behavioral model requires parametric assumptions, they have a substantial payoff: under these assumptions, we need not make any assumptions at all on the conditional distribution of heterogeneity observed by judges but not researchers. We implement the model using 2000 and 2002 data for five counties, from the State Courts Processing Statistics. While our point estimates are somewhat imprecise, they suggest that judges set bail as if they value blacks’ lost freedom from a typical pre-trial jail stay by thousands of dollars less than they value whites’ lost freedom. ∗We thank David Abrams, David Card, John Donohue, Joe Hotz, Jon Klick, Pat Kline, Justin McCrary, Peter Reuter, Jesse Rothstein, Seth Sanders, Eric Talley, participants at the 2010 Institute for Research on Poverty Summer ResearchWorkshop; law-school workshop participants at the University of Pennsylvania, the University of California at Berkeley, Florida State University, and George Mason University; and economicsdepartment workshop participants at the University of California at Berkeley, the University of South Florida, and Syracuse University. We also thank Jeff Racine for his generous help in working with the R np package. Finally, we are grateful for financial support through NSF award number SES0718955.