Sequential Sensitivity Analysis for Multiple Assumptions: A Framework for Understanding Racial Disparity in Police Use of Force

TL;DR

This paper introduces a sequential sensitivity analysis framework to jointly assess how assumptions about police stops and encounter biases influence the interpretation of racial disparities in police use of force.

Contribution

It develops a novel framework that varies assumptions about stops and encounter biases simultaneously, revealing their interaction effects on racial disparity conclusions.

Findings

Substantial racial disparity in force under plausible stop discrimination levels.

Disparity conclusions are fragile to modest biases in encounters.

Joint analysis uncovers interaction effects missed by separate sensitivity analyses.

Abstract

Inferring racial discrimination in police use of force -- the average causal effect of civilian race on use of force -- requires two assumptions about policing prior to potential use of force: that officers do not discriminate in whom they would stop (no discrimination in stops) and that, conditional on patrol context, the probability that an encounter is with a minority rather than a white civilian does not vary across encounters (no bias in encounters). As Knox et al. (2020) show, violations of the first can mask racial disparity in force. Whether it reflects discrimination in force also depends on the second. Existing sensitivity analyses address one assumption at a time. We develop a framework that varies both sequentially and apply it to NYPD Stop, Question, and Frisk data (2003--2013). Under plausible levels of discrimination in stops, we find substantial racial disparity in force. However, the conclusion that this disparity reflects discrimination is fragile to modest departures from no bias in encounters that census-based calibration suggests are demographically feasible. By jointly addressing both confounding channels, the framework reveals how they interact in ways that separate analyses cannot, contributing to understanding what generates racial disparities and how they might be addressed.