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  5. Pricing and Contract Design for Digital Platforms
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Pricing and Contract Design for Digital Platforms

Paper Session

Saturday, Jan. 6, 2024 2:30 PM - 4:30 PM (CST)

Grand Hyatt, Travis D
Hosted By: Econometric Society
  • Chair: Karthik Sastry, Princeton University

Contracting and Vertical Control by a Dominant Platform

Zi Yang Kang
,
Stanford University
Ellen Muir
,
Harvard University

Abstract

We study a platform that sells productive inputs (such as e-commerce and distribution services) to a fringe of producers in an upstream market, while also selling its own output in the corresponding downstream market. The platform faces a tradeoff: any output that it sells downstream increases competition with the fringe of producers and lowers the downstream price, which in turn reduces demand for the platform's productive inputs and decreases upstream revenue. Adopting a mechanism design approach, we characterize the optimal menu of contracts the platform offers in the upstream market. These contracts involve price discrimination in the form of nonlinear pricing and quantity discounts. If the platform is a monopoly in the upstream market, then we show that the tradeoff always resolves in favor of consumers and at the expense of producers. However, if the platform faces competition in the upstream market, then it has an incentive to undermine this competition by engaging in activities, such as "killer" acquisitions and exclusive dealing, that harm both consumers and producers.

Optimally Coarse Contracts

Roberto Corrao
,
Massachusetts Institute of Technology
Joel Peter Flynn
,
Massachusetts Institute of Technology
Karthik Sastry
,
Princeton University

Abstract

We study a principal-agent model in which actions are imperfectly contractible and the principal chooses the extent of contractibility at a cost. If contractibility costs satisfy a monotonicity property---which is implied by costs that come from difficulties in distinguishing actions when writing the contract---then optimal contracts are necessarily coarse: they specify finitely many actions out of a continuum of possibilities. This result holds even if contractibility costs are arbitrarily small. Applying our results to a nonlinear pricing model, we study how changes in consumer demand, production costs, and informational asymmetries affect the optimally coarse set of quality options.

Informational Intermediary, Market Feedback, and Welfare Losses

Kai Hao Yang
,
Yale University

Abstract

This paper examines the welfare implications of third-party informational intermediation. A seller sets the price of a product that is sold through an informational intermediary. The intermediary can disclose information about the product to consumers and earns a fixed percentage of sales revenue in each period. The intermediary's market base grows at a rate that increases with past consumer surplus. We characterize the stationary equilibria and the set of subgame perfect equilibrium payoffs. When market feedback (i.e., the extent to which past consumer surplus affects future market bases) increases, welfare may decrease in the Pareto sense.

Engagement Maximization

Benjamin Hebert
,
Stanford University
Weijie Zhong
,
Stanford University

Abstract

We consider the problem of a Bayesian agent receiving signals over time and then
taking an action. The agent chooses when to stop and take an action based on her
current beliefs, and prefers (all else equal) to act sooner rather than later. The signals
received by the agent are determined by a principal, whose objective is to maximize
engagement (the total attention paid by the agent to the signals). We show that engage-
ment maximization by the principal minimizes the agent’s welfare; the agent does no
better than if she gathered no information. Relative to a benchmark in which the agent
chooses the signals, engagement maximization induces excessive information acqui-
sition and extreme beliefs. An optimal strategy for the principal involves “suspensive
signals” that lead the agent’s belief to become “less certain than the prior” and “deci-
sive signals” that lead the agent’s belief to jump to the stopping region.

Discussant(s)
Daniel T. Chen
,
Princeton University
Piotr Dworczak
,
Northwestern University
Joel Peter Flynn
,
Massachusetts Institute of Technology
Ian Ball
,
Massachusetts Institute of Technology
JEL Classifications
  • D8 - Information, Knowledge, and Uncertainty