Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures
回顾并融合了教育回报与贝叶斯模型平均两派文献，用贝叶斯方法处理模型不确定性，基于美国NLSY数据估计大学教育回报，并检验若干“典型事实”是否仍成立。
Abstract. In this paper, we review and unite the literatures on returns to schooling and Bayesian model averaging. We observe that most studies seeking to estimate the returns to education have done so using particular (and often different across researchers) model specifications. Given this, we review Bayesian methods which formally account for uncertainty in the specification of the model itself, and apply these techniques to estimate the economic return to a college education. The approach described in this paper enables us to determine those model specifications which are most favored by the given data, and also enables us to use the predictions obtained from all of the competing regression models to estimate the returns to schooling. The reported precision of such estimates also account for the uncertainty inherent in the model specification. Using U.S. data from the National Longitudinal Survey of Youth (NLSY), we also revisit several ‘stylized facts’ in the returns to education literature and examine if they continue to hold after formally accounting for model uncertainty.