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Luck vs Effort: Learning About Income from Different Friends and Neighbors - Gustavo Caballero

Date & Time:
March 25, 2015 | 3:00 pm - 4:00 pm
Location:
SS423
Speaker:
Gustavo Caballero

This paper presents a model of boundedly-Bayesian learning regarding the roles of effort and luck in determining incomes when individuals live in segregated societies. Each individual uses their observation of others’ effort and income, in addition to their own experience, as a sample of trials and errors to inform beliefs. However, by living in societies segregated by income and effort levels, individuals procure biased samples. Given human’s tendency to attribute representativeness to their own observation of uncertain phenomena, agents assume their samples as random. Therefore, segregated societies are expected to maintain significant disagreements about the roles of effort and luck in determining incomes. I simulate societies with varying degrees of segregation finding evidence that suggest a monotonic relationship between the variance in beliefs and the level of segregation. Moreover, high levels of segregation can lead to individuals making inefficient choices, and even converge towards an “incorrect” belief.

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