SIGNALING IN HIGHER EDUCATION: THE EFFECT OF ACCESS TO ELITE COLLEGES ON CHOICE OF MAJOR

I. INTRODUCTION It is a well-established fact that more educated individuals earn higher wages. There are two primary theories for the source of this education wage premium. The first is the human capital accumulation, or full information, model in which education directly enhances productivity and thereby increases an individual's wages. The second is the signaling model of the labor market first proposed by Spence (1973), wherein the monetary returns to schooling are partially explained by information asymmetries between individuals and employers. These two models lead to strikingly similar labor market predictions, which has made testing between them difficult. I provide a new test of the signaling model against the full information model using individuals' choices of college major. This test is novel in that it focuses on the quality of an individual's education as a potential signal, rather than the quantity of education. Furthermore, while other tests have focused on high school students or other specific groups (e.g., General Education Diploma takers in Tyler et al. 2000 and MBA students in Hussey 2012), this test sheds light onto the decision-making process of college-going students. In a full information model, productivity is costlessly observed by both the individual and the employer. Each individual chooses her optimal level of education to improve productivity and wages, given her ability and the marginal cost of schooling. In the signaling framework, information is asymmetric such that employers cannot perfectly observe individual ability or true productivity. Employers use education levels to infer expected productivity while each individual chooses her level of schooling to signal a higher innate ability and potential productivity to employers. The equilibrium result in both models is that higher ability individuals obtain more education and accordingly earn higher wages. While the equilibria in both models appear similar, the extent to which individuals sort on ability depends on the amount of information asymmetry in the labor market. Under the signaling model, the individual's social and private returns to education do not necessarily coincide. The private return is a combined effect of the social return from increased productivity and the signaling effect of being identified (perhaps falsely) as a high ability individual. This disparity between private and social returns could lead to an inefficient allocation of schooling in the competitive labor market. The implication that investment in schooling might lead to a deadweight loss for society has prompted many attempts to distinguish between these two theories (Bedard 2001; Hussey 2012; Lang and Kropp 1986; Riley 1979; Wolpin 1977) and to measure what portion of the returns to schooling is attributable to signaling effects (Altonji and Pierret 1997, 2001; Clark and Martorell 2014; Fang 2006; Tyler et al. 2000). However, the empirical evidence differentiating the two models has been fairly limited and testing between the full information and signaling models of education has proven difficult. I propose a new model, applying the signaling framework of Spence (1973) to the postsecondary environment. In this model, major field of study can be used by individuals to signal unobserved ability and productivity to employers. I show that this leads to a prediction that geographic areas with high access to elite colleges result in fewer science, technology, engineering, and mathematics (STEM) majors among lower ability students at nonelite colleges. This is distinct from the prediction of a full information model where the level of access to elite schools should only affect those high ability individuals who are eligible to attend an elite school. Using data from the National Center for Education Statistics' Baccalaureate & Beyond survey and the geographic variation in access to elite schools across the United States, I find evidence that is consistent with the signaling model prediction. …