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английский
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(495) 772-95-90
27160
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Адрес: АУК "Покровский бульвар", Покровский б-р, д.11, каб. S1009
Время работы: 10:00-19:00
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Резюме (PDF, 92 Кб)
ORCID: 0000-0002-9893-9192
ResearcherID: P-8253-2017
Scopus AuthorID: 57200224536
Google Scholar
Руководитель
Пекарский С. Э.
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Анцыгина Анастасия Леонидовна

  • Начала работать в НИУ ВШЭ в 2017 году.
  • Научно-педагогический стаж: 8 лет.

Образование, учёные степени

  • 2017
    PhD: Европейский университет-институт во Флоренции
  • 2012

    Магистратура: Уральский федеральный университет им. первого Президента России Б.Н. Ельцина, специальность «Экономика»

Достижения и поощрения

Административные обязанности

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

Выпускные квалификационные работы студентов

Полный список ВКР

Учебные курсы (2020/2021 уч. год)

Учебные курсы (2019/2020 уч. год)

Учебные курсы (2018/2019 уч. год)

Учебные курсы (2017/2018 уч. год)

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

Конференции

  • 2020

    St. Petersburg Economic Seminar (Санкт-Петербург). Доклад: Settlements under Unequal Access to Justice: Why Do Policemen in Russia Settle as Often as CEOs? (with Madina Kurmangaliyeva)

  • 2019

    Bern Workshop on Contest Theory (Bern). Доклад: Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents

  • ASSET (Athens). Доклад: Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents

  • NES Brown Bag Seminar (Москва). Доклад: Settlements under Unequal Access to Justice: Why Do Policemen in Russia Settle as Often as CEOs? (with Madina Kurmangaliyeva)

  • Workshop on Information Acquisition, Diffusion and Disclosure in Markets (Вена). Доклад: The Optimal Information Revelation in Contests with Stochastic Abilities
  • 2018

    The Micro and Macro Foundations of Conflict (Bath). Доклад: Settlements under Unequal Access to Justice: Why Do Policemen in Russia Settle as Often as CEOs? (with Madina Kurmangaliyeva)

  • 2017

    RES PhD Meeting (London). Доклад: Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents: Theory and an Empirical Application

  • SAET (Faro). Доклад: Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents: Theory and an Empirical Application

  • Contests: Theory and Evidence (Norwich). Доклад: Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents: Theory and an Empirical Application

  • iCare 5 Conference (Пермь). Доклад: Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents: Theory and an Empirical Application

  • 2016

    SAEe Meeting (Bilbao). Доклад: Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents: Theory and Empirical Application

  • Econometric Society European Winter Meeting (Edinburg). Доклад: Optimal Allocation of Multi-Dimensional Prizes in Contests with Heterogeneous Agents: Theory and an Empirical Application

Неопубликованные статьи

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) – submitted

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. The Optimal Information Revelation in Contests with Stochastic Abilities (with Mariya Teteryatnikova)

We develop a model where two ex-ante identical players with stochastic abilities engage in a contest game. Before the competition starts, the designer, who aims to maximize the aggregate effort, gets a precise signal S and learns the ability profile. Prior to that, she commits to either (1) publicly disclose all information, or (2) disclose player- specific signals only, or (3) not disclose anything, or (4) use any combination of these three policies. Such rules are referred as simple disclosure regimes. We characterize the designer’s choice and find that public disclosure is never optimal. Such a policy results in a heterogeneous contest with a strictly positive probability, which reduces the expected aggregate effort. If the difference between various ability realizations is sufficiently small and high skills are relatively unlikely, the designer must report a profile where both contestants have high skills publicly and disclose other signals privately. Otherwise, she has to make all profiles with identical skills common knowledge and send an empty message in asymmetric states. Doing so, the designer keeps the competition even and at the same time provides some information to contestants. Alternatively, the designer can choose a persuasion disclosure rule and redefine the contestants’ types. We find that the optimal persuasion policy is unique, symmetric and anonymous. Moreover, it tends to outperform the best disclosure rule in the class of simple policies.

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.

Текущие проекты

1. The Optimal Information Revelation in Competing Contests with Capacity Constrained Players

2. The Optimal Feedback Policy in Dynamic Contests with Linked Players (with Mariya Teteryatnikova)


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