I am an Assistant Professor in the Department of Economics and Finance at Baruch College, CUNY. My interests are in microeconomic theory, with a focus on mechanism design and dynamic games. |  Contact Information: Zicklin Business School, Baruch College City University of New York 55 Lexington Avenue New York, NY 10010 dhruva.bhaskar@baruch.cuny.edu |
Research
Working Papers
Regulation Design in Insurance Markets (with Andrew McClellan and Evan D. Sadler).
Regulators often impose rules that constrain the behavior of market participants. We propose a new framework in which to study optimal regulation, representing the policy choice as a delegation problem overlaying a mechanism design problem: a regulator chooses a set of permitted mechanisms to influence how a mechanism designer and other players interact. We adopt this approach to study regulation in insurance markets. A regulator restricts the menus of contracts a firm is allowed to offer, the firm offers a permitted menu to each agent, and agents choose contracts from their menus. Under a risk-ordering condition, the regulator can implement her first-best allocation using a collection of two-option menus. Several extensions explore the robustness of this result, illustrating the broad applicability of our framework.
This paper studies the use of endogenous monitoring of agent behaviour in self-enforcing long-term relationships with adverse selection and moral hazard. While the use of monitoring to prevent moral hazard is well understood, its role for screening in the presence of adverse selection has been relatively unexplored. For the purposes of screening, monitoring is used in the opposite way to its more traditionally understood role. Moreover this gives rise to a tension between the two roles, leading to a distortion of the former. At the beginning of the relationship, sufficiently low intensity monitoring is used to incentivise low types of agents to shirk, despite the chance of being caught and fired. After high types have been identified, monitoring is used both to incentivise effort from high types (the traditional role), and to provide off-path (dis)incentives for low types looking to build a reputation early in the relationship. This results in a key trade-off: providing cost-efficient incentives to high types versus screening low types faster. The optimal contract involves inefficiently high monitoring after high types are identified to facilitate faster screening. Monitoring stochastically declines over the course of the relationship.
In several common allocation problems, transfers are unavailable, but incentives are partially aligned because the allocation to one player entails positive externalities to the other. We study the extent to which a designer can exploit this alignment when allocating a finite resource between n players. We identify a natural mechanism that partitions the type space into a collection of intervals and allocates the resource among players in the highest reported interval. While interim allocations are identical for all reports in the same interval, the exact allocation depends on the lowest reported interval. This novel feature is a crucial source of incentives. We show that our mechanism is optimal in special cases, and it is approximately efficient when n is large.
Dynamic Inspection and the Value of Information.
This paper studies a dynamic inspection game between a principal and agent with hidden type. The principal employs an agent with private information about his type, and strictly prefers his outside option to employing a low type. Every period that the agent is employed, the principal can perfectly monitor the agent's actions at a cost. The agent can either exert effort or shirk. The high type's preferences are aligned with the principal so he strictly prefers to exert effort. The low type finds it costly to exert effort, but may find it worthwhile to do so in order to deceive the principal. I show that the game generically has a unique equilibrium, which is a war of attrition between the principal and the low type. The main result finds that monitoring has value for the principal if and only if he is sufficiently pessimistic enough about the agent's type. Monitoring decreases as the agent's reputation grows. Eventually, either the low type is caught and fired, or the principal trusts the agent enough that he stops monitoring forever.