Complementary Bias: A Model of Two-Sided Statistical Discrimination

We introduce a model of two-sided statistical discrimination in which worker and firm beliefs are
complementary. Firms try to infer whether workers have made investments required for them to
be productive, and simultaneously, workers try to deduce whether firms have made investments
necessary for them to thrive. When multiple equilibria exist, group differences can be generated
and sustained by either side of the interaction – workers or firms. Strategic complementarity
complicates both empirical analysis designed to detect discrimination and policy meant to
alleviate it. Affirmative action is much less effective than in traditional statistical discrimination
models. More generally, we demonstrate the futility of one-sided policies to correct gender and
racial disparities. We analyze a two-sided version of “investment insurance” – a policy in which
the government (after observing a noisy version of the employer’s signal) offers to hire any
worker who it believes to be qualified and whom the employer does not offer a job – and show
that in our model it (weakly) dominates any alternative. The paper concludes by proposing a way
to identify statistical discrimination when beliefs are complements.