Identifi cation of social interactions through partially overlapping peer groups

In this paper, we demonstrate that, in a context where peer groups do not overlap fully, it is possible to identify all the relevant parameters of the standard linear-in-means model of social interactions. We apply this novel identifi cation structure to study peer effects in the choice of college major. Results show that one is more likely to choose a major when many of her peers make the same choice. We also show that peers can divert students from majors in which they have a relative ability advantage, with adverse consequences on academic performance, entry wages, and job satisfaction.