Knowledge that Transforms

This study focuses on how college students form expectations about various major-specific outcomes. For this purpose, I collect a panel data set of Northwestern University undergraduates that contains their subjective expectations about major-specific outcomes. Although students tend to be overconfident about their future academic performance, they revise their expectations in expected ways. The updating process is found to be consistent with a Bayesian learning model. I show that learning plays a role in the decision to switch majors and that major switchers respond to information from their own major. I also present evidence that learning is general and not entirely major specific.

Gibbons and Katz’s asymmetric information model of the labor market predicts that wage losses following displacement should be larger for layoffs than for plant closings. This was borne out in their empirical work. In this article, we examine how the difference in wage losses across plant closing and layoff varies with race and gender. We find that the basic prediction by Gibbons and Katz holds only for white males. We augment their asymmetric information model with heterogeneous human capital and show that this augmented model can match the data.