Cabau, Noémie (2020) Game-theoretic analysis of network formation and drug pricing. PhD thesis, Concordia University.
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Abstract
This thesis consists of three chapters. Chapter 1 offers a model of endogenous network formation where agents form connections strategically to maximize information diffusion in their group. The model builds on the idea that groups of individuals who are better informed take better collective decisions. The equilibrium and efficient networks have simple architectures: e.g., loops and flowers that may or may not include all agents. If frictions happen during information transmission, optimal architectures centralize the connections around a single agent. Chapters 2 and 3 aim at rationalizing the use of secret rebates during the negotiations on drug prices. Secret rebates enable to hide the price a country pays for a pharmaceutical product from other countries. Two models of the interactions between public payers (countries) and a monopolist pharmaceutical firm are presented to rationalize the use of secret rebates. We reach the following conclusions. Manufacturers benefit from secret rebates because they avoid price interdependencies across markets; and reference countries gain from hiding the details of their deals when other countries would otherwise base their offers to the supplier on the prices they observe other countries pay. However, the use of secret rebates has mitigated social effects. In particular, they benefit the countries which negotiate first and hurt those which negotiate last.
Divisions: | Concordia University > Faculty of Arts and Science > Economics |
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Item Type: | Thesis (PhD) |
Authors: | Cabau, Noémie |
Institution: | Concordia University |
Degree Name: | Ph. D. |
Program: | Economics |
Date: | 22 July 2020 |
Thesis Supervisor(s): | Gordon, Sidartha and Li, Ming |
Keywords: | Game theory, game of network formation, collective action, negotiations, pharmaceuticals, secret rebates. |
ID Code: | 987442 |
Deposited By: | NOEMIE CABAU |
Deposited On: | 25 Nov 2020 16:21 |
Last Modified: | 25 Nov 2020 16:21 |
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