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I am a graduate student in the Department of Economics at the Eller College of Management at the University of Arizona.  I am interested in microeconomic theory, and in particular mechanism design.

I expect to complete my Ph.D. in the spring of 2018 and will be available for interviews at the ASSA meetings in January.

I can be reached via e-mail at rosenthm@email.arizona.edu.  Below, I provide a description of my current projects.

 

Robust Incentives for Diversely Risk-Averse Agents  [Click here for a current draft]

ABSTRACT:

I study a general moral hazard problem in which the agent's risk preferences are unknown to the principal. The agent chooses not only the degree of effort to exert, but also potentially from several safe or risky actions for each level of effort. As some of these actions produce low output in expectation, the principal seeks a contract that is robust to her uncertainty about the agent's appetite for risk. I find that many contractual forms that are predicted by economic theory do not perform well in this environment. In particular, contracts that provide marginal incentives for the production of all levels of output do not provide the principal with a positive payoff guarantee. However, if the principal instead uses a binary contract with only two distinct transfer levels, the agent has no incentive to choose either very safe or very risky actions, regardless of his type. Consequently, there are environments in which binary contracts are uniquely optimal, even when the principal is subject to only local uncertainty about the agent's preferences.  

Dynamic Contracting with Optimistic Agents [Click here for a current draft]

ABSTRACT:

I study a contracting environment in which there are repeated interactions between a time-inconsistent agent who does not completely understand his own future behavior and a better-informed principal. Although the agent’s initial beliefs are incorrect, he learns to more accurately forecast his future behavior by inspecting his own choice history. However, the principal is able to manipulate the evolution of the agent’s beliefs by selectively pooling agent types, and this mechanic is the emphasis of the paper. I conclude that, in many circumstances, learning does little to protect the agent or to promote efficiency. Furthermore, if the agent’s beliefs initially reflect some degree of pessimism, his ability to learn can actually diminish his long-run average payoff. I show that while competition between principals protects the agent with a favorable up-front transfer, the critical inefficiencies demonstrated in the monopoly case still apply, with particularly inefficient contracts offered in early periods. Finally, I conclude with an analysis of restrictions to the allowable contract space that improve social welfare and facilitate learning by the agent.

Breaking the Binds of Conjugacy: Calibrating Belief Dynamics in the Economics of Climate Change,  with Ivan Rudik and Derek Lemoine.  [Draft available shortly]

ABSTRACT:

We use an economic model of climate change to demonstrate how to calibrate informational dynamics.  In particular, we calibrate a statistical model for updating beliefs about the climate's sensitivity to greenhouse gas emissions to the actual history of scientific progress.  We find that nonconjugate priors are critical for reflecting the observed dynamics of scientific knowledge.  In order to investigate the implications for policy, we extend recursive dynamic programming techniques to allow for nonconjugate learning about an uncertain parameter.  We find that today's policymaker must set emission policy without the expectation that new information will enable timely revisions to policy.