Andrew Little, UC Berkeley

"A Behavioral Theory of Discrimination in Policing"

"A Behavioral Theory of Discrimination in Policing" (with Ryan Hubert)

Racial disparities in policing are well documented. In addition to officer animus towards some groups (``taste-based discrimination''), these could be driven by officers' beliefs that some groups commit crimes at a higher rate (``statistical discrimination''). But where do these beliefs come from, and what if they are incorrect? We analyze of formal model where officers form these beliefs using crime statistics, but make a common inferential mistake when doing so: they do not fully adjust for the fact that they will detect more crime in communities that they police more heavily. This creates a feedback loop where officers (incorrectly) believe there is relatively more crime in communities that are policed more heavily, which leads to continued over-policing. This inferential mistake amplifies whatever disparities would otherwise exist due to taste-based or statistical discrimination. We first analyze this dynamic in a model with a single, representative officer, and then extend to the case of multiple officers. Since crime data are generated by the decisions of all, discrimination driven by false beliefs becomes contagious. As a result, inferential mistakes can exacerbate discrimination even among officers with no animus who sincerely believe disparities are driven by real group differences.