Essays on Social and Economic Networks

Abstract

Social and economic outcomes are shaped in fundamental ways by individuals' networks and interactions. This dissertation studies the transmission of ideas and information in social networks: how do people process and respond to this information, and what are the consequences for economies and societies? The first chapter considers social networks between inventors in the context of innovation, and asks what types of networks are likely to form. Firms face a choice between more secrecy, which protects existing intellectual property, and more openness, which allows learning from others. These decisions determine an endogenous learning network. The main result is that is at equilibrium, the learning network is at a critical threshold close to the emergence of a high-innovation cluster. There are therefore large benefits to policies that encourage more interaction, but designing such policies is subtle. The second chapter, co-authored with Benjamin Golub and Nir Hak, models people learning from each other about a changing unknown state, like a price or a macroeconomic variable. People combine more recent and older information to estimate the state, and we ask when these estimates are accurate. Social learning can perform quite well, but only if people have neighbors with diverse types of information. In the absence of diversity, learning is suboptimal on any network. The third chapter, co-authored with Kevin He, uses a model of social learning from behavioral economics to ask what types of network structures are conducive to learning. People learn from each other about an unknown state, and fail to understand that others' beliefs are correlated because of their common neighbors. When people are naive in this way, learning is actually less accurate on more connected networks. In partially segregated networks, divergent early signals can lead to persistent disagreement between groups.