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Everyday Econometricians: Selection Neglect and Overoptimism When Learning from Others

Kai Barron, Steffen Huck and Philippe Jehiel ()
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Steffen Huck: WZB - Wissenschaftszentrum Berlin für Sozialforschung

PSE-Ecole d'économie de Paris (Postprint) from HAL

Abstract: This study explores selection neglect in an experimental investment game where individuals can learn from others' outcomes. Experiment 1 examines aggregate-level equilibrium behavior. We find strong evidence of selection neglect and corroborate several comparative static predictions of Jehiel's (2018) model, showing that the severity of the bias is aggravated by the sophistication of other individuals and moderated when information is more correlated across individuals. Experiment 2 focuses on individual decision-making, isolating the influence of beliefs from possible confounding factors. This allows us to classify individuals according to their degree of naïvety and explore the limits of, and potential remedies for, selection neglect.

Date: 2024-08
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Published in American Economic Journal: Microeconomics, 2024, 16 (3), pp.162-198. ⟨10.1257/mic.20200030⟩

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Journal Article: Everyday Econometricians: Selection Neglect and Overoptimism When Learning from Others (2024) Downloads
Working Paper: Everyday Econometricians: Selection Neglect and Overoptimism When Learning from Others (2024)
Working Paper: Everyday econometricians: Selection neglect and overoptimism when learning from others (2023) Downloads
Working Paper: Everyday econometricians: Selection neglect and overoptimism when learning from others (2023) Downloads
Working Paper: Everyday econometricians: Selection neglect and overoptimism when learning from others (2022) Downloads
Working Paper: Everyday econometricians: Selection neglect and overoptimism when learning from others (2022) Downloads
Working Paper: Everyday econometricians: Selection neglect and overoptimism when learning from others (2019) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:hal:pseptp:halshs-04811339

DOI: 10.1257/mic.20200030

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