Explicit and implicit belief-based gender discrimination: A hiring experiment

Understanding discrimination is key for designing policy interventions that promote equality in society. Economists have studied the topic intensively, typically taxonomizing discrimination as either taste-based or (accurate) statistical discrimination. To enrich this taxonomy, we design a hiring experiment that rules out both of these sources of discrimination along the gender dimension. Yet, we still detect substantial discrimination against women. We provide evidence of two forms of discrimination, explicit and implicit belief-based discrimination. Both rely on statistically inaccurate beliefs but differ in how clearly they reveal the decision-maker's gender bias. Our analysis highlights the central role played by contextual features of the choice environment in determining whether and how discrimination will manifest. We conclude by discussing how policy makers may design effective regulation to address specific forms of discrimination.