Identification of Social Interactions through Partially Overlapping Peer Groups
证明当同伴群体不完全重叠时，可以识别标准线性均值模型中的所有参数，并应用于大学专业选择中的同伴效应研究，发现同伴影响会让学生偏离自身优势专业，损害学业表现和职业结果。
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 identification 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.