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On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach

Junpei Komiyama, Shunya Noda

arXiv 2 Oct 2020 · Theoretical Economics · 9 citations (OpenAlex)

arXiv:2010.01079 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We analyze statistical discrimination in hiring markets using a multi-armed bandit model. Myopic firms face workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, the underestimation tends to persist. Even a marginal imbalance in the population ratio frequently results in perpetual underestimation. We propose two policy solutions: a novel subsidy rule (the hybrid mechanism) and the Rooney Rule. Our results indicate that temporary affirmative actions effectively alleviate discrimination stemming from insufficient data.

Citation extraction

66
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Foster, D. and Vohra, R (1992) An economic argument for affirmative action1.00054100%
2Coate, S. and Loury, G. C (1993) Will affirmative-action policies eliminate negative stereotypes?1.00054100%
3Kannan, S., Morgenstern, J. H., Roth, A., Waggoner, B., and Wu, Z. S (2018) A smoothed analysis of the greedy algorithm for the linear contextual bandit problem0.8435460%
4Arrow, K (1973) The theory of discrimination0.84333100%
5Moro, A. and Norman, P (2004) A general equilibrium model of statistical discrimination0.84333100%
6Bastani, H., Bayati, M., and Khosravi, K (2021) Mostly exploration-free algorithms for contextual bandits0.73732100%
7Li, D., Raymond, L., and Bergman, P (2020) Hiring as exploration0.73732100%
8Abbasi-Yadkori, Y., Pál, D., and Szepesvári, C (2011) Improved algorithms for linear stochastic bandits0.6444250%
9Bardhi, A., Guo, Y., and Strulovici, B (2020) Early-career discrimination: Spiraling or self-correcting?0.64422100%
10Che, Y.-K., Kim, K., and Zhong, W (2019) Statistical discrimination in ratings-guided markets0.64422100%

Showing the top 10 of 66 scored citations.