Georgy LUKYANOV will defend his thesis on "Information Frictions in Macroeconomics" on Monday 11th December 2017, Room MF323 (Manufacture des Tabacs) at 10 am.
Supervisor: Christian HELLWIG, TSE researcher
Memberships are:
- Christian HELLWIG, TSE researcher
- Thomas MARIOTTI, TSE researcher
- Edouard CHALLE, Ecole Polytechnique
- Guillermo ORDONEZ, University of Pennsylvania
Abstract:
The first paper (co-authored with T. Su) develops a model in which a sender strategically communicates with a group of receivers whose payoffs depend on the sender's information. We show that, in the presence of coordination frictions among receivers, conflict of interests between the sender and the receivers arises endogenously. In general, equilibrium does not feature full information revelation, even for a benevolent sender whose objective is to maximize receivers' aggregate welfare. We demonstrate that exogenous biases in the sender's preferences can improve public information provision and raise welfare. The paper concludes with discussing two applications: the credit rating agencies and leadership in organizations.
The second paper builds a finite-horizon model to study the role of physical collateral in a model of strategic defaults, when the borrower can develop reputation for being honest. Asset ownership increases attractiveness of the reputational channel: the borrower who would prefer to remain in autarky in the absence of the asset applies for collateralized debt. Pledging the asset as collateral facilitates reputation building, which is especially successful at the times of asset price drops, because these are the times when default is most tempting. The model sheds some light on the co-movement of defaults and the household's financial and non-financial income.
The third note (co-authored with E. Mengus) explores conditions that admit recursive representation for a class of dynamic mechanism design problems. We derive a tight sufficient condition, called the common state property (CSP), which ensures that the implementation problem gets restarted each time agent's type visits the common state. This allows to characterize the principal's problem recursively: agent's past reports matter only up to the date of the most recent visit of the common state. This extends the approach by Fernandes and Phelan (2000) allowing to encompass a richer set of environments; the approach remains computationally manageable as long as the process passes through the common state sufficiently often. The CSP condition imposes no restrictions on agent's preferences and only concerns the properties of the evolution of agent's private information.
