Collaborative Research: Empirical Analysis of Social Networks with Unreported Links

在许多社会经济背景下，一个人的行为取决于他自己的特征，以及他人的结果和特征。这种依赖称为联系；具有链接的个体称为邻居，邻居的集合称为网络。社会网络由相互联系的个人组成。这通常发生在应用经济学研究中，因为研究数据往往不能很好地衡量联系。这个项目将评估当链接被错误分类或没有在数据中报告时，社交网络对个人结果的影响。本课题提出的方法适用于广泛的社交网络。它还提供了一种比较给定群体特征的各种社会效应的一般方法。该项目为政策分析提供了一种有效的方法，解决了由于网络链接中的数据问题或测量误差而带来的挑战。该项目的结果提供了一种在没有网络结构信息的情况下衡量政策效果的方法。本项目的研究结果将对社会网络和教育等政策的实证研究产生重大影响。这将提高商业和政策制定的效率，并在此过程中改善美国公民的福祉。当网络链接被错误分类或未被观察到时，该项目识别和估计社会网络模型。它首先推导并描述了一些条件，在这些条件下，一些错误的链接分类不会干扰社会效应的标准工具变量估计的一致性或渐近性质。然后，它在一个没有观察到网络链接的模型中构建了一个一致的社会效应估计。这种方法不需要重复观察个别网络成员。该项目将把这个估算器应用于田纳西州学生/教师成就比（STAR）项目的数据。在不观察每个教室的潜在网络的情况下，本研究确定并估计了同伴和情境对学生数学成绩的影响。研究结果表明，在班级规模较大的情况下，同伴效应往往更大，而同伴效应的增加显著提高了学生的平均考试成绩。这项研究的结果将有助于企业和政策制定者在决策过程中考虑社会影响，从而提高美国公民的生活水平。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In many social-economic contexts, an individual's behavior depend on his own characteristics, as well as the outcome and characteristics of others. Such dependence called a link; individuals with links are neighbors and a collection of neighbors is referred to as a network. A social network consists of linked individuals. This commonly occurs in applied economic research since links are often not well measured in the research data. This project will estimate the effects of social networks on individual outcomes when the links are either misclassified or not reported in data. The method proposed in this project is adaptable to a wide range of social networks. It also provide a general method for comparing various types of social effects given group characteristics. The project offers an efficient approach for policy analyses that resolves challenges due to data problems or measurement errors in network links. The results of this project provides a way to measure the effects of policies when there is no information on network structure. The results of this project will have a significant impact on empirical research on social networks and policies such as education. This will improve efficiency in business and policy decision making and in the process lead to improved well-being of U.S. citizens.This project identifies and estimates social network models when network links are either misclassified or unobserved. It first derives and characterizes conditions under which some misclassification of links does not interfere with the consistency or asymptotic properties of standard instrumental variable estimators of social effects. It then constructs a consistent estimator of social effects in a model where network links are not observed. This method does not require repeated observations of individual network members. The project will apply this estimator to data from Tennessee's Student/Teacher Achievement Ratio (STAR) Project. Without observing the latent network in each classroom, the research identifies and estimate peer and contextual effects on students' performance in mathematics. The results suggests that peer effects tend to be larger in bigger classes, and that increasing peer effects significantly improve students' average test scores. The results of this research will help businesses and policy makers account for social effects in decision making hence improve the living standards of U.S. citizens.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.