Social Networks with Unobserved Links
Social Networks with Unobserved Links
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DOI：
10.1086/722090
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发表时间：
2022-08
影响因子：
8.2
通讯作者：
Arthur Lewbel;Xi Qu;Xun Tang
中科院分区：
文献类型：
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作者：
Arthur Lewbel;Xi Qu;Xun Tang
我们在不观察任何网络链接的情况下，对线性社交网络模型进行点识别和估计。所需数据由许多小型个人网络组成，例如教室或村庄，每个人只被观察一次。我们将我们的估计器应用于田纳西州的项目STAR(学生-教师成就率)的数据。在没有观察到每个教室中的潜在网络的情况下，我们识别和估计了同伴和背景对学生数学表现的影响。我们发现，在大班中，同伴效应往往更大，而且增加同伴效应会显著提高学生在某些班级的平均考试成绩。
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.