Collaborative Research: Empirical Analysis of Social Networks with Unreported Links

协作研究：具有未报告链接的社交网络的实证分析

• 批准号: 1919454
• 负责人: Arthur Lewbel
• 金额: $ 18.21万
• 依托单位: Boston College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919454&HistoricalAwards=false
• 关键词: Collaborative; Research; Empirical; Analysis; Social

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.

在许多社会经济背景下，一个人的行为取决于他自己的特点，以及他人的结果和特点。 这种依赖称为链接;具有链接的个体是邻居，邻居的集合称为网络。一个社交网络是由相互联系的个体组成的。 这通常发生在应用经济研究中，因为研究数据中的联系往往没有得到很好的衡量。 这个项目将估计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。 本项目提出的方法适用于广泛的社交网络。 它还提供了一个一般方法来比较各种类型的社会效果给定的群体特征。 该项目提供了一种有效的政策分析方法，解决了由于数据问题或网络连接中的测量错误而带来的挑战。该项目的结果提供了一种方法来衡量政策的效果时，没有信息的网络结构。该项目的结果将对社交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，并在此过程中导致改善美国公民的福祉。该项目确定和估计社会网络模型时，网络链接被错误分类或未观察到。它首先推导和表征的条件下，一些错误分类的链接不干扰社会影响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社会效应的一致估计。 这种方法不需要重复观察单个网络成员。 该项目将应用此估计的数据从田纳西州的学生/教师的成就比（星星）项目。在不观察每个教室中的潜在网络的情况下，本研究识别和估计了同伴和情境对学生数学成绩的影响。结果表明，在较大的班级中，同伴效应往往更大，并且增加同伴效应会显着提高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。