When Is Discrimination Unfair? (NBER Working Paper 30236)

Abstract

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.

The authors' own abstract, copied from nber.org.

How to cite it

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Kuhn, P., & Osaki, T. (2022). When Is Discrimination Unfair? (NBER Working Paper 30236). NBER Working Paper 30236. https://www.nber.org/papers/w30236

Kuhn, Peter, et al. "When Is Discrimination Unfair? (NBER Working Paper 30236)." NBER Working Paper 30236, 2022, https://www.nber.org/papers/w30236.

@article{kuhn2022when,
  title = {When Is Discrimination Unfair? (NBER Working Paper 30236)},
  author = {Peter Kuhn and Trevor Osaki},
  journal = {NBER Working Paper 30236},
  year = {2022},
  url = {https://www.nber.org/papers/w30236},
}

What it is

Peter Kuhn and Trevor Osaki survey people on which kinds of hiring and pay discrimination they consider unfair, and why.

Topics