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  5. Signaling and Information Design
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Signaling and Information Design

Paper Session

Monday, Jan. 5, 2026 10:15 AM - 12:15 PM (EST)

Philadelphia Marriott Downtown, Room 310
Hosted By: Econometric Society
  • Chair: Ian Ball, Massachusetts Institute of Technology

Signaling Design

Matteo Camboni
,
University of Wisconsin-Madison
Mingzi Niu
,
Hebrew University of Jerusalem
Mallesh M. Pai
,
Rice University
Rakesh Vohra
,
University of Pennsylvania

Abstract

We revisit the classic job-market signaling model of Spence (1973), introducing profit-seeking schools as intermediaries that design the mapping from candidates’ efforts to job-market signals. Each school commits to an attendance fee and a monitoring policy. We show that, in equilibrium, a monopolist school captures the entire social surplus by committing to low information signals and charging fees that extract students’ surplus from being hired. In contrast, competition shifts surplus to students, with schools vying to attract high-ability students, enabling them to distinguish themselves from their lower-ability peers. However, this increased signal informativeness leads to more wasteful effort in equilibrium, contrasting with the usual argument that competition enhances social efficiency. This result may be reversed if schools face binding fee caps or students are credit-constrained.

Consumer Profiling via Information Design

Itay Perah Fainmesser
,
Johns Hopkins University
Andrea Galeotti
,
London Business School
Ruslan Momot
,
University of Michigan

Abstract

A platform uses purchase histories to profile consumers, form market segments, and disclose them to a seller. The seller tailors prices to these segments, generating new data that supports further profiling. We characterize the platform’s ability to learn consumer valuations over time. We show that the platform cannot accurately learn consumers' valuations above the seller's optimal uniform price, but can below it. There is a complementarity between information design and other tools available to the platform to influence pricing—such as listing fees or proportional sales fees—for learning the valuations of high-value consumers.

Reputational Underpricing

Stepan Aleksenko
,
University of California-Los Angeles
Jacob Kohlhepp
,
University of California-Los Angeles

Abstract

Consumer reviews reflect both product quality and price, with more favorable reviews for a lower-priced product. We study whether this review behavior induces a firm to manage its reputation by underpricing its product below consumers' willingness to pay. We introduce a model with a privately informed firm repeatedly selling its product to rational consumers who learn product quality from past value-based reviews and the current price. We characterize the necessary and sufficient condition for underpricing, which depends on the relative amount of vertical versus horizontal quality differentiation. This condition implies that underpricing need not occur even if the firm is perfectly patient. Reputation management via underpricing, when it occurs, unambiguously benefits consumers.

Algorithmic Attention and Content Creation on Social Media Platforms

Yi Chen
,
Cornell University
Fei Li
,
University of North Carolina-Chapel Hill
Marcel Preuss
,
Cornell University

Abstract

This paper develops a theoretical framework to examine how a social media platform allocates attention through recommendation algorithms and how this in turn shapes content creation and consumption. Creators and viewers, as the two sides of the algorithm, fall into different categories based on interest. Creators are also heterogeneous in ability. We show that a platform, to maximize advertisement revenue, optimally filters out low-ability creators, restricts the reach of medium-ability creators to relevant audiences only, and propagates viral content for high-ability ones at the expense of relevance. The attention a creator receives grows disproportionally in his ability and the popularity of his category. We show the source of the inefficiencies of the algorithm by contrasting it with a welfare-maximizing benchmark. We additionally study the effect of monetary transfers in the algorithm. Our framework offers insights into content production and matching in digital markets, giving rise to potential regulatory interventions.

Discussant(s)
Mingzi Niu
,
Hebrew University of Jerusalem
Heng Liu
,
Rensselaer Polytechnic Institute
Harry Pei
,
Northwestern University
Udayan Vaidya
,
Duke University
JEL Classifications
  • D8 - Information, Knowledge, and Uncertainty