Panel Data for the Study of Network Economics and Risk Sharing

个人如何科普逆境或找到工作，除其他外，取决于他们在社区内和跨地理区域的关系的强度。因此，许多学科的研究，如经济学、社会学、计算机科学和统计学，都严重依赖于社交网络数据。 收集关于人口的详细网络数据的成本非常高，因此，研究分析往往只包括一小部分人口。该研究项目将开发创新方法，以低成本收集网络数据，并使用该方法创建两个关于弱势群体之间社会经济关系的大型数据集。研究人员将这些网络数据与经济和劳动力市场结果以及身心健康状况的信息进行匹配。 此外，研究人员将建立统计工具，以促进这些数据集的使用，并使这些数据集和工具包免费提供给其他研究人员。 该研究项目的结果将改善对社会学习、风险分担等几个主题的研究，从而改善政策制定。 这些成果还将提高政府和企业政策的有效性。 该项目将开发创建网络数据的新方法，使用这些方法建立两个关于弱势群体的大型数据集，并将这些数据集与关于若干经济和社会成果的数据合并，并向研究人员提供这些数据集。 为了研究大量的弱势群体样本，以及他们在当地社区和大的地理区域之间的网络关系，人们需要一种工具来使数据收集具有可扩展性。该项目将使用聚合关系数据（ARD）使其可行。通过询问个人与他们有联系的特定特征的人数（对于各种特征集），PI可以估计网络形成模型，该模型可以描绘社区与社区关系如何在不同地区之间变化。与详细、完整的网络数据相比，ARD的收集成本非常低。配备了ARD和适当的统计和计量经济学技术，研究人员可以抽样更多的受访者，并创建更多的网络数据。这一研究成果将使理论家、计量经济学家、统计学家和社会学家受益。这项研究的结果将使研究人员能够研究诸如扩散、社会学习、网络复用、风险分担和社会隔离等主题。它将使研究人员和政策制定者能够访问一个粒度数据集，可以更好地为一般政策提供信息，特别是关于弱势群体的政策。 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。

How individuals cope with adversity or find jobs, among other things, depends on the strength of their relationships within communities and across geographic areas. Research in many disciplines such as economics, sociology, computer science, and statistics therefore rely heavily on social network data. Collecting detailed network data on populations is very costly and, consequently, research often includes only a small part of populations in their analysis. This research project will develop innovative methods to inexpensively collect network data, use the method to create two large datasets on the socio-economic relationships among vulnerable populations. The researchers will match these network data with information on economic and labor market outcomes as well as physical and mental health status. In addition, the researchers will build statistical tools to facilitate the use of these data sets and make these datasets and the toolkit freely available to other researchers. The results of this research project will improve research on several topics such as social learning, risk sharing, and therefore improve policy making. The results will also increase the effectiveness of government and business policies. This project will develop new methods to create network data, use the methods to build two large data sets on vulnerable people, and merge these data sets with data on several economic and social outcomes and make the data sets available to researchers. To study a large sample of vulnerable populations, the network relationships among them both within the local community and across large geographic regions, one needs a tool to make the data collection scalable. This project will use Aggregate Relational Data (ARD) to make this feasible. By asking individuals the number of people with a particular trait they are linked to (for various sets of traits), the PIs can estimate a network formation model that gives a picture of how community-to-community relationships vary across regions. ARD is very cheap to collect, in contrast with detailed, complete network data. Equipped with ARD and appropriate statistical and econometric techniques, researchers can sample numerous more respondents and create more network data. This research product will benefit theorists, econometricians, statisticians, and sociologists. The results of this research will allow researchers to study topics such as diffusion, social learning, multiplexing in networks, risk sharing, and social isolation, among others. It will allow researchers and policymakers to access a granular dataset that can better inform policies generally and particularly on vulnerable populations. The results are likely to improve the quality and targeting of policies.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.