Talk Title: A simple, statistically robust test of discrimination

Imagine auditing a bank for potential discrimination. If White loan recipients were found to repay their loans less often than racial minorities, it would suggest a double standard, with a relatively lenient bar applied to White applicants. Such an “outcome test” is one of the most popular empirical strategies for detecting discrimination. Outcome tests are simple to apply and their logic seemingly simple to understand. But almost immediately after their introduction 30 years ago, outcome tests were found to suffer from deep theoretical limitations. Researchers have since worked to reconcile the intuitive appeal of outcome tests with their formal limitations, though with limited success. In this talk, I'll explain how a small tweak to standard outcome tests yields surprisingly strong statistical guarantees while preserving the logistical and rhetorical simplicity of the traditional approach. Applying our “robust outcome test” to 2.8 million police stops across California, we find evidence of pervasive discrimination — a pattern that would have been missed by the standard outcome test.