雇主学习、统计歧视与大学声望

P. Bordón, Breno Braga
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引用次数: 23

摘要

摘要本文研究了雇主是否将大学声望作为员工不可观察生产力的一个信号。我们的测试基于雇主学习统计歧视模型,该模型表明,如果雇主使用大学声誉来预测工人不可观察的素质,那么随着工人获得劳动力市场经验,大学声誉对收入的重要性应该变得不那么重要。在这个框架中,我们使用回归不连续设计来估计那些勉强被智利两所最负盛名的大学之一录取的大学毕业生在劳动力市场第一年的工资溢价为13%,而那些几乎被这两所学校拒绝的大学毕业生。然而,我们发现,对于拥有6年或以上劳动力市场经验的工人,这一溢价降至4%。这一结果表明,对于雇主来说,大学声誉变得不那么重要了,因为员工在整个职业生涯中都在展示自己的素质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employer Learning, Statistical Discrimination and University Prestige
Abstract This paper investigates whether employers use university prestige as a signal of workers’ unobservable productivity. Our test is based on employer learning-statistical discrimination models, which suggest that if employers use university reputation to predict a worker’s unobservable quality, then college prestige should become less important for earnings as a worker gains labor market experience. In this framework, we use a regression discontinuity design to estimate a 13% wage premium for college graduates in their first year of the labor market who were barely accepted by one of the two most prestigious universities in Chile compared with those barely rejected by these two schools. However, we find that this premium decreases to 4% for workers with 6 or more years of labor market experience. This result suggests that college prestige becomes less important for employers as workers reveal their quality throughout their careers.
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