I study the problem of a government with low credibility, who decides to make a reform to remove ex-post time inconsistent incentives due to lack of commitment. The government has to take a policy action, but has the ability to commit to limiting its discretionary power. 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 extremely sensitive to assumptions about the publics beliefs, particularly after unexpected events. To overcome this limitation, I study the optimal robust policy that implements public trust for all beliefs that are consistent with common knowledge of rationality. I focus on robustness to all extensive-form rationalizable beliefs and provide a characterization. I show that the robust policy exhibits both partial and permanent reputation building along its path, as well as endogenous transitory reputation losses. In addition, I demonstrate that almost surely the policy maker eventually convinces the public she does not face a time consistency problem and she is able to do this with an exponential arrival rate. This implies that as we consider more patient policy makers, the payoff of robust policies converge to the complete information benchmark. I finally explore how further restrictions on beliefs alter optimal policy and accelerate reputation building
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.
We study an endowment economy in which agents face income risk, as if uncertain returns on a portfolio, and agents can only make transfers in states when they are actively participating in the market. Besides income risk, agents also have limited and stochastic market access, with a probability distribution governed by an underlying social network. While network connections may serve to dissipate shocks, they may also provide obstacles to the sharing of risk, as when participation frictions are generated through the network.
We identify and quantify the value of key players in terms of whether they are likely to be able to smooth the resulting market participation risk and how valuable that smoothing would be when they are there. We define financial centrality in economic terms, given the model, as the ex ante marginal social value of injecting an infinitesimal amount of liquidity to the agent.
We show that the most financially central agents are not only those who trade often -- as in standard network models -- but are more likely to trade when there are few traders, when income risk is high, when income shocks are positively correlated, when attitudes toward risk are more sensitive in the aggregate, when there are distressed institutions, and when there are tail risks. We extend our framework to allow for endogenous market participation. Observational evidence from village risk sharing network data is consistent with our model.
It is common for researchers studying infinite horizon dynamic games in a lab experiment to pay participants for a randomly chosen round or all rounds. We argue that these payment schemes typically implement different outcomes than the target game for a large class of solution concepts (e.g., subgame perfect equilibria, Markov equilibria, renegotiation-proof equilibria, rationalizability). For instance, a compensation scheme that pays subjects for a randomly chosen round induces a time-dependent discounting function. Future periods are discounted more heavily than the discount rate in a way that can change the theoretical predictions both quantitatively and qualitatively. We rigorously characterize the mechanics of the problems induced by these payment methods, developing measures to describe the extent and shape of the distortions. We also establish that a simple payment scheme, paying participants for the last (randomly occurring) round, robustly implements the predicted outcomes for any infinite horizon dynamic game with time separable utility, exponential discounting and a payoff invariant solution concept.
We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors’ previous guesses before guessing again. Agents are drawn from a mixture of learning types— Bayesian, who face incomplete information about others’ types, and DeGroot, who average their neighbors’ previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model; (2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reducedform analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively