Research

  • Bargaining and centrality in networked markets (Job Market Paper)

  • Abstract. This paper studies bargaining in networked markets. Our contribution is twofold. First, we characterize market equilibria in our model, and find that players' equilibrium payoffs coincide with their degree of centrality in the network, as measured by Bonacich's centrality measure. This characterization allows us to map in a simple way network structures into market equilibrium outcomes, so that payoffs dispersion in networked markets is driven by players' network positions. Second, we show that the market equilibrium for our model converges to the so called eigenvector centrality measure. We show that the economic condition to reach convergence is that players' discount factor goes to one. In particular, we show how the discount factor, the matching technology and the network structure interact in a very particular way in order to see the eigenvector centrality as the limiting case of our market equilibrium. As an application, we analyze the special case of seller-buyer networks, showing how our framework may be useful to analyze price dispersion as a function of sellers and buyers' network positions. From a technical point of view, our results are based on the theory of linear complementarity problems.

    • Publications

  • A representative consumer theorem for discrete choice models in networked markets, Economics Letters, Volume 117, Issue 3, pages 862-865, (December 2012).

  • Congestion pricing and learning in traffic network games, Journal of Public Economic Theory, Volume 13, Issue 3, pages 351-367, (June 2011).

  • A payoff-based learning procedure and its application to traffic games, joint with Roberto Cominetti and Sylvain Sorin, Games and Economic Behavior, Volume 70, Issue 1, Pages 71-83 (September 2010).

    • Working Papers

  • Price competition, free entry, and welfare in congested markets, R&R at Games and Economic Behavior.

  • Price competition in networked markets of complement and substitutes, (New version coming soon!).

  • Maximum likelihood estimation of discrete choice models under threshold effects, (New version coming soon!).