Essays on Macroeconomics and Information Economics
- Author(s): Zambrano Riveros, Jorge Andres
- Advisor(s): Atkeson, Andrew G
- et al.
My research interests are in the intersection of macroeconomics and information economics. I am particularly interested in the optimal design of incentives for individuals who interact over time in uncertain environments with imperfect information, and its consequences for aggregates. The dissertation is composed by three chapters that address three particular environments within this area.
The first chapter studies the optimal contract in a principal-agent model where a risk-neutral principal delegates to a risk-neutral agent the decision of whether to pursue a risky project or a safe one. The return from the risky project is unknown and the agent can acquire costly unobservable information about it before taking the decision. The optimal contract suggests that the principal should only reward the agent for outcomes that are significantly better than the safe return. It is also optimal to distort the project choice in favor of the risky one as a mechanism to induce the direct revelation of the uncertain state. In a managerial context, the findings explain why options and profit sharing compensation induce better decision making from CEOs, as well as why excessive risk taking might be optimal.
The second chapter explores the role of effort and human capital as mechanisms to alleviate the idiosyncratic risk faced by individuals in the presence of incomplete markets. I construct a DSGE model where effort and human capital determine the probability of being employed the next period. I show how in the stationary equilibrium individuals diversify between these mechanisms. As a result, I obtain a wealth distribution that better approximates the real one. The results shed light on the potential implication of combining policies of unemployment insurance and subsidies to education to improve the wealth distribution.
The third chapter studies dynamic stochastic models where current actions are constrained by a current state and determine the distribution over future states. The purpose of this paper is to provide a general learning process that allows agents to take the optimal decision when these endogenous transitions are unknown. Our paper generalizes previous results by not imposing parametric restrictions on the unknown transition functions.