Distinguishing Reverse Discrimination from Overcorrection: Statistical Methods for Clarifying this Neglected Distinction and Why It Matters

Abstract

This paper makes a distinction between what is often called reverse discrimination and a similar but non-identical entity which should be called overcorrection. We argue that the term reverse discrimination should be used only to describe cases where well-qualified non-minority applicants are unjustifiably denied desirable positions in organizations run by and/or staffed by minority employees; similarly, we argue that the term overcorrection should be used for circumstances where well-qualified non-minority applicants are unjustifiably denied desirable positions in organizations run by and/or staffed by non-minority employees who are actively instituting policies to expand the hiring and promotion of minorities. By this view, reverse discrimination and overcorrection constitute two components in a complete set of four conditions: discrimination, correction (or affirmative action), overcorrection, and reverse discrimination. We draw examples from Ricci v. DeStefano, and a simulation based on recent disparate impact litigation to show how tests of statistical significance can distinguish between reverse discrimination and overcorrection. The paper closes with a discussion of why the distinction is important - not because it would alter the final outcome of disparate impact cases, but because framing litigation as reverse discrimination arguably encourages plaintiffs to decline fair out-of-court settlements and pursue appeals that lack merit.