When is Discrimination Unfair?

Using a vignette-based survey experiment on Amazon’s Mechanical Turk, we measure how people’s assessments of the fairness of race-based hiring decisions vary with the motivation and circumstances surrounding the discriminatory act and the races of the parties involved. Regardless of their political leaning, our subjects react in very similar ways to the employer’s motivations for the action, such as the quality of information on which statistical discrimination is based. Compared to conservatives, moderates and liberals are much less accepting of discriminatory actions, and consider the discriminatee’s race when making their fairness assessments. We describe four pre-registered models of fairness – (simple) utilitarianism, race-blind rules (RBRs), racial in-group bias, and belief-based utilitarianism (BBU) – and show that the latter two are inconsistent with major aggregate patterns in our data. Instead, we argue that a two-group framework, in which one group (mostly self-described conservatives) values employers’ decision rights and the remaining respondents value utilitarian concerns, explains our main findings well. In this model, both groups also value applying a consistent set of fairness rules in a race-blind manner