Torts and Discrimination

Abstract

Current tort law contains incentives to target individuals and communities based on race and gender. Surprisingly, the basis for such targeting is the seemingly neutral use of three different race- and gender-based statistical tables (for wages, life expectancy, and worklife expectancy) which, when used in tort damage calculations, result in a great disparity between damages awarded to whites versus blacks and men versus women. Thus, tort law’s remedial damage scheme perpetuates existing racial and gender inequalities by compensating individuals (especially children) based on their race and gender. Even worse, tort law creates ex-ante incentives for potential tortfeasors to engage in future discriminatory targeting of women and minorities. We provide the first full account of courts' existing discriminatory practices. We then address the deficiencies in the non-blended tables that courts use (tables that use race and gender as discriminating factors) and the reasons behind their continued use. We show how the various theories of tort law (corrective justice, distributive justice, and economic efficiency) have contributed to a misunderstanding of the proper damages calculation and illustrate how the very same theories can be used to engender a change in the current praxis. We then challenge the conventional wisdom that the use of race- and gender-based tables is justified on efficiency grounds, noting fatal flaws inherent in the tables, in how they are used in courts to calculate damages for individuals, and in the incentives they create. We reveal that similar discriminatory practices ironically exist in federal law, such as the Americans with Disabilities Act, the National Childhood Vaccine Injury Act, and even Title VII – whose goal is to combat gender and race discrimination. Finally, we propose a feasible, low-cost, logical solution that pushes toward a more efficient and less discriminatory tort law remedial system: courts should immediately terminate their use of non-blended tables.