A Discrimination Report Card

TL;DR

This paper introduces an Empirical Bayes grading method to assess racial biases in U.S. employers, providing a reliable, interpretable ranking system with quantified uncertainty, based on a large correspondence experiment.

Contribution

It presents a novel Empirical Bayes approach for grading racial bias in employers, balancing informativeness and error rates, with a practical rubric for interpretation.

Findings

A four-grade ranking reduces misranking probability to 5%.

The method explains nearly half of the variation in racial contact gaps.

Grades include uncertainty measures for better interpretation.

Abstract

We develop an Empirical Bayes grading scheme that balances the informativeness of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the racial biases of 97 U.S. employers. A four-grade ranking limits the chances that a randomly selected pair of firms is mis-ranked to 5% while explaining nearly half of the variation in firms' racial contact gaps. The grades are presented alongside measures of uncertainty about each firm's contact gap in an accessible rubric that is easily adapted to other settings where ranks and levels are of simultaneous interest.