CAREER: Networks for Risk Sharing, Information Diffusion, and On-the-Job Support

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。在高度非正式性的社会中，社会网络在帮助成员应对意外的经历变化和收集和分享成员之间以及网络外的信息方面发挥着核心作用。然而，这些过程并不完美——相互保险的程度是有限的，社区成员并不总是能有效地了解新的机会。这个CAREER研究项目旨在调查这些缺陷的来源。具体而言，本研究项目将研究隐性收入对风险分担的作用，网络和网络中个人的特征如何影响生产信息的传递，以及在传统方法过于昂贵而无法使用的情况下，开发一种收集经济网络数据的创新方法。本项目将通过对每个主题进行实验实地研究来实现这些目标；收集一个新的网络经济学数据集，也将提供给其他研究人员；并为网络经济学的毕业生和本科生创造教育机会。这个研究项目的结果将提供如何最好地利用网络分析来传播经济信息，从而加快经济增长和提高美国人的生活水平的信息。本研究对风险分担、社会学习和联系形成的研究有贡献。首先，本文提出了一个直接的实验测试来衡量隐性收入对风险分担的限制程度。先前的工作依赖于观测数据和基于模型的测试。其次，它密切关注社区如何学习和汇总有关新农业技术的信息。它将补贴“种子”农民参加农民博览会，然后他们将把信息带回他们的社区。该项目旨在了解新信息是如何在网络中的个体之间传递和聚合的。第三，该项目询问，使用社交媒体在地理上孤立的员工之间建立联系是否可以提高工作绩效和士气。该设计明确探讨了如何设计这些群体以避免社交媒体的陷阱。第四，项目建议开始收集新的网络经济学面板数据集，以促进实证网络研究。现有的数据集通常包含详细的网络信息或消费和收入的详细信息，但不是两者都包含。最后，教育部分旨在通过为本科生和研究生创造研究机会，使新的面板数据集尽可能广泛地可用，以及组织针对研究生和早期研究人员的实证网络经济学研讨会，来改善对实证网络研究的获取。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).In societies with high levels of informality, social networks play a central role in both helping members cope with unexpected changes in experiences and collecting and sharing information among their members as well as those outside the networks. However, these processes are not perfect – there are limits to the extent of mutual insurance, and community members do not always learn effectively about new opportunities. This CAREER research project seeks to investigate the sources of these imperfections. Specifically, this research project will study the role of hidden income on risk sharing, how networks and the characteristics of individuals in the networks affect the transmission of productive information, as well as develop an innovative method to collect economic network data when conventional methods are too expensive to use. This project will achieve these objectives through the use of experimental field research on each of these topics; collection of a novel network economics data set that will also be made available to other researchers; and generating educational opportunities for graduates and undergraduates in network economics. The results of this research project will provide information on how best to use network analyses to spread economic information, hence speed up economic growth and improve the living standards of Americans.The proposed research makes contributions to the study of risk sharing, social learning, and link formation. First, it proposes a direct experimental test to measure the extent to which hidden income limits risk sharing. Prior work has relied on observational data and model-based tests. Second, it takes a close look at how communities learn and aggregate information about new agricultural technologies. It will subsidize attendance at farmer expos for “seed” farmers, who will then bring information back to their community. This project seeks to understand how new information is passed and aggregated across individuals in a network. Third, the project asks whether forming links between geographically-isolated workers using social media can improve job performance and morale. The design explicitly explores how to design these groups to avoid the pitfalls of social media. Fourth, the project proposes to begin collecting a new network economics panel dataset to facilitate empirical networks research. Exiting data sets typically contain detailed network information or detailed information on consumption and income, but not both. Finally, the educational component aims to improve access to empirical networks research through creating research opportunities for undergraduates and graduate students, making the new panel dataset as widely available as possible, and organizing a workshop on empirical network economics targeted at graduate students and early stage researchers.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.