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Title: Social Learning in the Labor Market: An Analysis of Siblings
Resulting in 1 citation.
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Rao, Neel |
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Social Learning in the Labor Market: An Analysis of Siblings Working Paper, Department of Economics, Harvard University, October 2011 Cohort(s): NLSY79 Publisher: Department of Economics, Harvard University Keyword(s): Armed Forces Qualifications Test (AFQT); Siblings; Wage Determination; Wage Models Permission to reprint the abstract has not been received from the publisher. This paper examines whether a worker's wage is based in part on information about the performance of her personal contacts. Embedding a sibling model into an employer learning framework, I develop a theory of labor markets with symmetric but imperfect information among employers in which workers are organized into disjoint social groups and workers in the same reference group have correlated abilities. I study wage determination under two alternative belief formation processes: individual learning, under which employers observe only a worker's own schooling and performance, and social learning, under which employers also observe those of her personal contacts. Using data on the AFQT scores of siblings in the NLSY79, I test for a form of statistical nepotism in which a sibling's performance is priced into a worker's wage. If learning is social, then an older sibling's test score should typically have a larger adjusted impact on a younger sibling's log wage than vice versa. By contrast, if learning is individual, then no such asymmetry should be present. The empirical findings provide strong support for the central prediction of the social learning model. Furthermore, I perform several exercises to identify social learning as the leading explanation for the main results, largely ruling out other potential factors, such as asymmetric skill formation, human capital transfers, and role model effects. |
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Bibliography Citation
Rao, Neel. "Social Learning in the Labor Market: An Analysis of Siblings." Working Paper, Department of Economics, Harvard University, October 2011. |