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Preprints, Working Papers, ...

Statistical discrimination without knowing statistics: blame social interactions?

Abstract : We consider a model where decision makers repeatedly receive candidates and assign to them a binary decision that we can interpret as hire/not hire. The decision makers base their decision on the characteristics of the candidate but they are also sensitive to the social influence exerted by the observed past choices of their peers. We characterize the long run frequency of decisions in the model, and show in particular that for candidates belonging to a group with "unfavorable" characteristics, the dynamics increase the rejection rate compared to a scenario with independent decisions, suggesting that influence between decision makers can generate effects very similar to those that result from statistical discrimination. In our model, we then relate the long run outcomes, existence and magnitude of reinforcement to the properties of the characteristics distribution.
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Preprints, Working Papers, ...
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Contributor : Emily Tanimura Connect in order to contact the contributor
Submitted on : Monday, January 4, 2021 - 7:41:04 PM
Last modification on : Friday, April 29, 2022 - 10:12:43 AM
Long-term archiving on: : Monday, April 5, 2021 - 9:45:10 PM


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  • HAL Id : hal-03096126, version 1



Emily Tanimura. Statistical discrimination without knowing statistics: blame social interactions?. 2021. ⟨hal-03096126⟩



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