We compare how well agents aggregate information in two repeated social learning environments. In the first setting agents have access to a public data set. In the second they have access to the same data, and also to the past actions of others. Despite the fact that actions contain no...

Experimental studies rarely consider the shape and nature of the education production function, which is useful for deriving optimal levels of input substitution in increasingly resource constrained environments. Because of the rapid expansion of EdTech as a substitute for traditional learning...

We study the design of optimal monetary policy under uncertainty in a dynamic stochastic general equilibrium models. We use a Markov jump-linear-quadratic (MJLQ) approach to study policy design, approximating the uncertainty by different discrete modes in a Markov chain, and by taking...

Popularity is self reinforcing. The attention garnered by popular options propels further interest in them. Yet rather than blindly follow the crowd, most pay attention to how well these items match their tastes. We model this role of social learning in guiding selective attention and market...

This paper develops a theoretical foundation for the social cost of carbon (SCC). The model highlights the source of debate over whether countries should use the global or domestic SCC for regulatory impact analysis. I identify conditions under which a country's decision to internalize the...

We introduce the possibility of direct punishment by specialized enforcers into a model of community enforcement. Specialized enforcers need to be given incentives to carry out costly punishments. Our main result shows that, when the specialized enforcement technology is sufficiently effective,...

We study the optimal provision of insurance against unobservable idiosyncratic shocks in a setting in which a benevolent government cannot commit. A continuum of agents and the government play an infinitely repeated game. Actions of the government are constrained only by the threat of reverting...