Assistant Professor of Politics and Public Affairs
Title: The Bias Is Built In: How Administrative Records Mask Racially Biased Policing
Abstract: Researchers often lack the necessary data to credibly estimate racial bias in policing. In particular, police administrative records lack information on civilians police observe but do not investigate. In this paper, we show that if police racially discriminate when choosing whom to investigate, analyses using administrative records to estimate racial discrimination in police behavior are statistically biased, rendering many quantities of interest unidentified---even among investigated individuals---absent strong and untestable assumptions. Using principal stratification in a causal mediation framework, we derive the exact form of the statistical bias that results from traditional estimation approaches. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show traditional estimation techniques can severely underestimate levels of racially biased policing or mask discrimination entirely. We conclude by outlining a general and feasible design for future studies that is robust to this inferential snare.