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Separating risk from heterogeneity in education: a semiparametric approach

机译:从教育异质性中分离风险:半参数方法

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Returns to education are variable both within and between educational group. If uncertain pay-offs are a concern to individuals when selecting an education, wage variance is relevant. The variation is a combination of unobserved heterogeneity and pure uncertainty or risk. The first element is known to the individual, but unknown to the researcher; the second is unknown to both. As a result, the variance of wages observed in the data will overestimate the real magnitude of educational uncertainty and the effect that risk has on educational decisions. We apply a semiparametric estimation technique to tackle the selectivity issues. This method does not rely on distributional assumptions of the errors in the schooling choice and wage equations. Our results suggest that risk is decreasing in schooling. Private information accounts for a share varying between 0% and 13% of total wage variance observed depending on the educational level. Finally, we conclude that the estimation results are very sensitive to the functional relation that is imposed on the error structure.
机译:教育收益在教育集团内部和之间是可变的。如果选择教育时个人担心不确定的收益,那么工资差异就很重要。差异是未观察到的异质性和纯粹的不确定性或风险的组合。第一个要素是个人已知的,而研究人员则未知。两者都不为人知。结果,数据中观察到的工资差异会高估教育不确定性的真实程度以及风险对教育决策的影响。我们应用半参数估计技术来解决选择性问题。这种方法不依赖于就学选择和工资方程中误差的分布假设。我们的结果表明,上学的风险正在降低。根据教育程度的不同,私人信息占观察到的总工资差异的0%到13%之间。最后,我们得出结论,估计结果对施加于错误结构的函数关系非常敏感。

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