Research

Publications:

Settlements under Unequal Access to Justice (with Madina Kurmangaliyeva), Journal of Economic Behavior and Organization 193, pp. 237– 268.

Optimal Information Disclosure in Contests with Stochastic Prize Valuations (with Mariya Teteryatnikova), Economic Theory 

Working Papers:

Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents  (PDF) – R&R in Economic Theory

We develop a model where two players with asymmetric preferences engage in a contest game. The key novelty is the introduction of multi-dimensional rewards. We characterize the optimal prize allocation that maximizes aggregate effort. When heterogeneity in preferences is strong and the designer cannot assign identity-dependent prizes, the loser must get a positive reward, which is in stark contrast to the existing literature. Such allocation eliminates the advantage of the stronger competitor and incentivizes the opponent to exert more effort (the equilibrium effect). Using data from twelve professional tennis competitions where prizes include money and the ATP ranking points, we test our theoretical predictions empirically. The identification strategy relies on exogenous variation in prizes over both dimensions and random matching between the players. We document the equilibrium effect in the data and recover the underlying preference profiles that define its direction.

Optimal Information Disclosure in Contests with Communication (PDF)

We study optimal information disclosure in static contests where players do not know their own values of winning but can learn them, publicly or privately, from the designer. The designer chooses a disclosure policy that maximizes the total expected effort, and commits to it before observing the realized value profile. A distinct feature of our model is that conditional on receiving private information from the designer, contestants can communicate with each other. We show that the contestants share their private information if and only if the values of winning are positively correlated. Since communication can result in an asymmetric contest associated with lower expected effort, the designer prefers concealment to any other disclosure policy available. This result is in a stark contrast with the no communication benchmark where private disclosure is best when the values of winning are sufficiently positively correlated.

Optimal Information Disclosure in Competing Contests with Capacity Constrained Players (PDF)

This paper studies optimal information disclosure in competing contests with identical prizes where players face a capacity constraint on the total effort contribution. Each player is ex ante uninformed about the difficulty of the task to be performed in one of the contests. The task can be either difficult (associated with a high effort cost) or easy (associated with a low effort cost). Before the game starts, the designer of a contest with the unknown task type can commit to (1) fully disclose the task type or to (2) keep it private in order to maximize the aggregate effort exerted in her competition. When the capacity constraint is slack, the game is equivalent to the case with non-competing (independent) contests. Otherwise, the contests become linked, and there is a substitution effect that forces the players to reallocate their effort to a competition they perceive as the easiest. If the difficult task is sufficiently costly, then full disclosure mitigates the substitution effect. When the cost of performing a difficult task declines, concealment becomes more attractive.

Work in Progress:

Support Link Formation in Contests (with Mariya Teteryatnikova, James Tremewan, and Alexander Usvitskiy)

Strategic Attacks in Networks (with Mariya Teteryatnikova)

Uniform Pricing in Geographically Dispersed Chains (with Ekaterina Kazakova and Alexander Tarasov)

Dormant Projects:

When Justice is Hurried and When is it Buried? Wealth Inequality and the Duration of Cases in Court (with Madina Kurmangaliyeva)

Adverse Selection in Job Promotion Contests with Multi-dimensional Skills (with Anna Ponomarenko)