Social Networks with Unobserved Links
提出一种无需观测网络链接即可识别和估计线性社会网络模型的方法，并应用于田纳西州STAR项目数据，发现班级规模影响同伴效应大小，增强同伴效应可提升部分班级平均成绩。
We point-identify and estimate linear social network models without observing any network links. The required data consist of many small networks of individuals, such as classrooms or villages, with individuals who are each observed only once. We apply our estimator to data from Tennessee’s Project STAR (Student-Teacher Achievement Ratio). Without observing the latent network in each classroom, we identify and estimate peer and contextual effects on students’ performance in mathematics. We find that peer effects tend to be larger in bigger classes and that increasing peer effects would significantly improve students’ average test scores in some classes.