A Bayesian Approach to Selection Bias Applied to Racial Profiling

University Of Arizona, Tucson AZ

Investigators

Abstract

This project proposes to investigate new statistical methods to control for selection bias when faced with significant data limitations. In general, using non-random samples of data can lead to quite biased estimates of model parameters. This, in turn, can lead to incorrect conclusions based on systematically biased estimates. This project controls for selection bias in regression analysis, with an application to the racial profiling context. Specifically, the project proposes a statistical model that will estimate a selection model without the need for individual-level data for the non-selected individuals. The first half of the project quantifies the potential loss in precision from using aggregate-level data rather than individual-level data when controlling for selection bias. The second half of the project applies the new model to the actual selection problem of racial profiling, modeling the selection for search of cars driving down the highway (rather than just the subset of stopped cars). This model will quantify the costs and benefits of racial profiling, including the rate of searching innocent motorists and the drugs seized from such stops. While these empirical results will not definitively answer whether racial profiling as a policy meets the high demands of the constitution, it will provide a beginning to answer this question with unbiased empirical results. Although the project focuses on selection bias in the racial profiling context, the method used has wide applicably to many empirical questions, and provides a significant step in controlling for selection bias when individual-level data is unavailable.