Papers

**Credible Reforms: A Robust Implementation Approach (Job Market Paper)**

We study the problem of a government with low credibility, who decides to make a reform to remove ex-post time inconsistent incentives. If the public believed the reform solved this time inconsistency problem, the policy maker could achieve complete discretion. However, if the public does not believe the reform to be successful, some discretion must be sacrificed in order to induce public trust. With repeated interactions, the policy maker can build reputation about her reformed incentives. However, equilibrium reputation dynamics are very sensitive to assumptions about the public beliefs, particularly after unexpected events. To overcome this limitation, we study the optimal robust policy, that implements public trust for all rationalizable beliefs.

When focusing on robustness to all extensive-form rationalizable beliefs, the robust policy exhibits both partial and permanent reputation building along its path, as well as endogenous transitory reputation losses. We then show that, almost surely the policy maker eventually convinces the public she does not face a time, and this happens with an exponential arrival rate. This implies that as policy makers become more patient, the payoff of robust policies converge to the complete information benchmark. We finally explore how further restrictions on beliefs alter optimal policy and accelerate reputation building.

**Testing Models of Social Learning on Networks: Evidence from a Framed Field Experiment**, with Arun Chandrasekhar and Juan Pablo Xandri

Theory has focused on two leading models of social learning on networks: Bayesian and DeGroot rules of thumb learning. These models can yield greatly divergent behavior; individuals employing rules ofthumb often double-count information and may not exhibit convergent behavior in the long run. By conducting a unique lab experiment in rural Karnataka, India, set up to exactly differentiate between these two models, we test which model best describes social learning processes on networks. We study experiments in which seven individuals are placed into a network, each with full knowledge of its structure. The participants attempt to learn the underlying (binary) state of the world. Individuals receive independent, identically distributed signals about the state in the first period only; thereafter, individuals make guesses about the underlying state of the world and these guesses are transmitted to their neighbors at the beginning of the following round. We consider various environments including incomplete information Bayesian models and provide evidence that individuals are best described by DeGroot models wherein they either take simple majority of opinions in their neighborhood

**A Note on Payments in Experiments of Infinitely Repeated Games with Discounting** with Arun Chandrasekhar

It is common for researchers studying repeated and dynamic games in a lab experiment to pay participants for all rounds or a randomly chosen round. We argue that these payment schemes typically implement different set of subgame perfect equilibrium (SPE) outcomes than the target game. Specifically, paying a participant for all rounds or for a randomly chosen round makes the game such that early rounds matter more to the agent, by lowering discounted future payments. In addition, we characterize the mechanics of the problems induced by these payment methods. We are able to measure the extent and shape of the distortions. We also establish that a simple payment scheme, paying participants for the last (randomly occurring) round, implements the game. The result holds for any dynamic game with time separable utility and discounting. A partial converse holds: any payment scheme implementing the SPE should generically be history and time independent and only depend on the contemporaneous decision

**Regulation and the Optimal Design of Financial Markets** with Robert Townsend (pdf coming soon)

We study a static version of a Diamond-Dybvig economy, where ex-ante identical households face ex-post idiosyncratic and aggregate risk. We introduce minimum scale restrictions on the set of available technologies, creating a need for coordinating investment. We focus on the case where all feasible allocations have some measure of uninsurable systemic risk.

We solve for the optimal mechanism design problem of providing idiosyncratic and aggregate insurance to households with private information. We find the unique efficient investment allocation that implements the optimal insurance contract, which consists of an unbalanced investment portfolio, to get a larger number of projects. We also provide a market based implementation of this allocation, where commercial banks (broker-dealers) sell insurance contracts to households, and finance firms’ investments. We allow free entry in both the commercial banks and firms sectors. This decentralized market arrangement implements the optimal allocation as its unique equilibrium, provided the following trading restrictions: (a) Households cannot engage in informal risk sharing (b) Firms get financing from at most one commercial bank and (c) Households cannot invest directly in firms, either by buying equity or bonds. However, regulation on commercial bank investments is not desirable, since it does not allow them to benefit from cross-subsidization strategies. This simple model gives some stark yet intuitive policy recommendations for regulation of financial markets.

**Network Financial Centrality and the Price of Personalized Debt** with Arun Chandrasekhar and Robert Townsend

We study efficient insurance arrangements where there is complete information but transfers are constrained by a stochastic process directed by an underlying social network. We analyze both the full commitment environment as well as the limited commitment environment. Under each regime we study the price of personalized debt - a bond requiring a given individual to pay