Competitive nonlinear pricing under adverse selection

Andrea Attar (), Thomas Mariotti and François Salanié
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Andrea Attar: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique
Thomas Mariotti: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique

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Abstract: This article surveys recent attempts at characterizing competitive allocations under adverse selection when each informed agent can privately trade with several uninformed parties: that is, trade is nonexclusive. We rst show that requiring market outcomes to be robust to entry selects a unique candidate allocation, which involves cross-subsidies. We then study how to implement this allocation as the equilibrium outcome of a game in which the uninformed parties, acting as principals, compete by making oers to the informed agents. We show that equilibria typically fail to exist in competitive-screening games, in which these oers are simultaneous. We nally explore alternative extensive forms, and show that the candidate allocation can be implemented through a discriminatory ascending auction. These results yield sharp predictions for competitive nonexclusive markets.

Keywords: Adverse Selection; Entry-Proofness; Discriminatory Pricing; Nonexclusive; Markets; Ascending Auctions (search for similar items in EconPapers)
Date: 2022-04-04
New Economics Papers: this item is included in nep-com, nep-cta, nep-gth, nep-mic and nep-reg
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Working Paper: Competitive Nonlinear Pricing under Adverse Selection (2022)
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