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

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.