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。在非正式程度高的社会中，社交网络在帮助成员应科普经验中的意外变化以及在成员之间以及网络外收集和分享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，社区成员并不总是有效地了解新的机会。这个职业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藏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播，以及开发一种创新方法，在传统方法过于昂贵的情况下收集经济网络数据。 该项目将通过对每个主题进行实验性实地研究来实现这些目标;收集一套新颖的网络经济学数据集，这些数据集也将提供给其他研究人员;并为网络经济学的研究生和本科生提供教育机会。本研究项目的成果将为如何最好地利用网络分析来传播经济信息提供信息，从而加速经济增长和提高美国人的生活水平，并为风险分担、社会学习和链接形成的研究做出贡献。首先，它提出了一个直接的实验测试，以衡量在何种程度上隐藏的收入限制风险分担。先前的工作依赖于观测数据和基于模型的测试。第二，它密切关注社区如何学习和汇总有关新农业技术的信息。 它将资助“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如何在网络中的个体之间传递和聚合。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。设计明确探讨了如何设计这些群体，以避免社会媒体的陷阱。第四，该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，但不能同时包含两者。 最后，教育部分旨在通过为本科生和研究生创造研究机会，尽可能广泛地提供新的面板数据集，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。

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.