1. Всероссийская олимпиада школьников "Высшая проба" (профиль "Экономика"): председатель методической комиссии / жюри (2018/2019 учебный год), член методической комиссии (2019/2020 и 2020/2021 учебный год). 

2. Участие в комиссиях по защите бакалаврских и магистерских выпускных квалификационных работ.

3. Отбор заявок, поданных на XX Апрельскую международную научную конференцию по проблемам развития экономики и общества (Москва, Россия).

4. Участие в отборе статей для HSE WP BRP Series.

5. Участие в отборе статей для Economic Journal, Southern Economic Journal, International Economic Review, International Journal of Game Theory, Journal of the European Economic Association.

Microeconomics-2 (Бакалавриат; где читается: Факультет экономических наук; направление "38.03.01. Экономика", направление "38.03.01. Экономика"; 2-й курс, 1, 2 модуль)Анг

1. Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents – submitted

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 the aggregate effort. When heterogeneity in preferences is strong and the designer cannot assign player-specific prizes, the loser must get a positive reward. This 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. Using data from four professional tennis competitions where prizes include money and the ATP ranking points, we propose a structural estimator and recover contestants’ skill- preference profiles. The identification strategy relies on the ATP betting market efficiency, exogenous variation in monetary and non-monetary prizes, and the random matching between players. We show that both reward items shape contestants’ incentives to exert effort and document a strong positive correlation between preferences over the two prize dimensions. Our counterfactual experiments reveal that the increase in first-round monetary losing rewards can indeed improve the total effort.

2. Settlements under Unequal Access to Justice: Why Do Policemen in Russia Settle as Often as CEOs? (with Madina Kurmangaliyeva) – R&R in Journal of Economic Behavior and Organization 

Settlements are hailed as a cost-efficient way to resolve civil cases, but the affluent settling with the disadvantaged can also be a symptom of unequal access to justice. We develop a model where the defendant (he) decides to either settle with the victim (she) or enter the contest to win in court. In the contest, both must exert costly effort subject to resource constraints. Relaxing the defendant’s constraint has two effects. First, it allows him to make better offers and, hence, settle more often (the volume effect). Second, additional resources improve the defendant’s bargaining position forcing the victim accept lower compensation (the price effect). Exploiting the blurred line between civil and criminal litigation and the random nature of traffic collisions, we apply the model to data on criminal traffic offenses in Russia. To test for the price effect, we focus on policemen whose connections or inside knowledge affect their resource constraint in the contest stage but not in the settlement stage. Our results indicate that policemen indeed settle more often as defendants and less often as victims than a comparable resource group. The price effect can be especially worrying in the context of intentional crimes when the defendant can choose the victim.

3. Optimal Information Disclosure in Contests with Stochastic Prize Valuations (with Mariya Teteryatnikova) – submitted

We study optimal information design in static contests where contestants do not know their values of winning. The designer aims at maximizing the total expected effort. Before the contest begins, she commits to the information technology that includes (1) a signal distribution conditional on each values profile (state) and (2) the type of signal disclosure to contestants – public, private or none at all. Upon observing the signal, contestants simultaneously choose effort that maximizes their expected payoff in an all-pay auction game. Using a mixture of analytical and numerical methods, we find that the optimal information technology involves private and positively correlated signals that never reveal the true state precisely if the contestants’ values of winning are different. In settings where public disclosure is a prerequisite, the optimal signal distribution generates symmetric beliefs about the values profile, so that a complete information concealment is optimal, while public and precise disclosure of each state is not. 

4. Information Disclosure in Contests with Communication 

We study information disclosure in static contests where players do not know their own values of winning. The designer chooses a disclosure policy that maximizes the total expected effort and commits to it before learning the realized value profile. The available disclosure regimes include (1) public disclosure, (2) private disclosure, when each contestant learns only his own type, and (3) concealment. A distinct feature of our model is that before the actual competition starts, contestants are allowed to communicate with each other by sending informative (truthful) or uninformative (empty) messages independently and simultaneously. Our results show that under private disclosure, the contestants reveal their types with a positive probability. The designer’s choice of the disclosure regime depends on the communication technology. If this technology is assortative (the types get revealed more often when the contestants’ values of winning are the same), then concealment always delivers the highest total effort. With a disassortative technology, the designer must choose private disclosure if and only if the correlation between the contestants’ values of winning is sufficiently high. These results are in a stark contrast with the no communication benchmark.

5. Optimal Information Disclosure in Competing Contests with Capacity Constrained Players 

This paper studies optimal information disclosure in competing contests with identical players. Each player faces a capacity constraint on the total effort contribution and 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 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 between contests is equivalent to the non-competitive case. Otherwise, the contests become linked, and there is a substitution effect which forces the players to reallocated their effort to a competition they perceive as easiest. If the difficult task is sufficiently costly, then full disclosure generates substantial competitive gains from revealing the easy task and, hence, mitigates the substitution effect. When the cost of performing a difficult task declines, full disclosure results in significant competitive losses from revealing the difficult task to the contestants, and concealment turns to be more attractive.

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

https://sites.google.com/site/aantsygi/home-1