Employers often decide job assignments or wages
after observing productivity signals from workers. Discrimination
can occur because employers have stereotypes (priors) against a
group of workers, or because they use signals differently depending
on the worker's group. This paper introduces an estimable Bayesian
framework that allows us to recover both the priors and the updating
behavior of evaluators who observe noisy signals from candidates.
Using data from a quasi-experiment in South Africa I test for the
precise form of racial discrimination. I find evidence of discrimination
without overtly negative priors. Discrimination occurs because white
evaluators use signals to update their priors about white candidates
but not when evaluating black candidates. Blacks, on the other hand,
use signals to update their priors about all candidates. The paper
uses the estimated structural parameters to simulate how evaluators
would choose among equally performing candidates as a tool to show
the relative importance of stereotypes and updating behavior on
discrimination.
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