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，以及其他人的结果和特征。这种依赖称为链接；有链接的个人是邻居，邻居的集合称为网络。社交网络由相互关联的个人组成。这通常发生在应用经济研究中，因为在研究数据中往往没有很好地衡量联系。这个项目将评估当链接被错误分类或未在数据中报告时，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。它还提供了一种一般方法，用于比较给定群体特征的各种类型的社会影响。该项目提供了一种有效的策略分析方法，可解决因网络链路中的数据问题或测量错误而带来的挑战。该项目的结果提供了一种在没有关于网络结构的信息的情况下衡量政策效果的方法。该项目的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决策的效率，并在此过程中改善美国公民的福祉。该项目在网络链接被错误分类或未被观察到时识别和评估社交网络模型。它首先推导和刻画了某些对链接的错误分类不会干扰社会影响的标准工具变量估计量的一致性或渐近性质的条件。然后，在没有观察到网络联系的模型中，它构建了一个一致的社会影响估计器。这种方法不需要对单个网络成员进行重复观察。该项目将把这个估计器应用于田纳西州学生/教师成就率(STAR)项目的数据。在没有观察到每个教室中潜在的网络的情况下，这项研究识别和评估了同伴和背景对学生数学表现的影响。结果表明，在大班中，同伴效应往往更大，而且同伴效应的增加显著提高了学生的平均考试成绩。这项研究的结果将帮助企业和政策制定者在决策中考虑社会影响，从而提高美国公民的生活水平。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.