Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, where do differences in these patterns come from and how large are their effects? Using network analysis tools, we explore how different social network structures affect technology diffusion and thereby a country’s rate of growth. The correlation between high-diffusion networks and income is strongly positive. But when we use a model to isolate the effect of a change in social networks on growth, the effect can be positive, negative, or zero. The reason is that networks diffuse both ideas and disease. Low-diffusion networks have evolved in countries where disease is prevalent because limited connectivity protects residents from epidemics. But a low-diffusion network in a low-disease environment compromises the diffusion of good ideas. In general, social networks have evolved to fit their economic and epidemiological environment. Trying to change networks in one country to mimic those in a higher-income country may well be counterproductive.

The use of order-flow information by financial firms has come to the forefront of the regulatory debate. Should a dealer who acquires information by taking client orders be allowed to use or share that information? We explore how information sharing affects dealers, clients, and issuer revenues in US Treasury auctions, in a model calibrated to auction results data, which we use to quantify counterfactuals. Sharing information reduces uncertainty about future value. With less uncertainty, risk-averse bidders bid more. For investors, the welfare effects of information sharing depend on how information is shared and how it affects asymmetry. The model shows that investors can benefit when dealers share information with each other, not when they share more with clients.