Peter Kuhn is a visiting Professor of Economics at Princeton University, in the Industrial Relations Section. Peter Kuhn is a Labor and Personnel Economist with recent research interests in discrimination, turnover costs, China's labor markets, and the role of the internet as a labor market matchmaker.
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 do not distinguish between taste-based and statistical discrimination, but they react in very similar ways to other aspects of the act, 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 simple models of fairness –utilitarianism, race-blind rules (RBRs), racial in-group bias, and belief-based utilitarianism (BBU)-- and show that the latter two are inconsistent with major patterns in our data. Instead, we argue that a two-group model in which conservatives care only about race-blind rules (RBRs), while moderates and liberals care about both RBRs and utilitarian ethics can account for the main patterns we see.