Work in progress

Competition in Digital Advertising with Asymmetric Consumer Information (joint with R. A. Salonia)

We study how preferential access to consumer data shapes competition and data management strategies in digital display advertising markets. We develop a model in which two demand-side platforms (DSPs) compete to purchase a single advertising slot through a first-price auction. The two DSPs differ in their access to consumer data: one is vertically integrated within the ad-tech stack and consequently observes consumer preferences with positive probability, while the other does not. We characterize the unique Bayesian Nash equilibrium of the auction and show that greater data access for the integrated DSP softens competition, increasing expected payoffs for both bidders. At the same time, enhanced data availability improves ad relevance for consumers. We then derive conditions under which the integrated platform optimally maintains a closed data ecosystem rather than licensing consumer information to rivals, offering a formal explanation for the persistence of data silos in digital advertising markets.

Screening with Aggregate Quality Spillovers in Two-Sided Markets

This paper studies optimal screening in two-sided markets with aggregate quality spillovers, where consumer participation depends on platform-wide quality and sellers' quality provision responds to market size. I show that the standard revelation principle fails when multiple equilibria exist for a given quality profile, though any direct mechanism remains implementable through nonlinear pricing. I provide primitive conditions ensuring fixed-point uniqueness that restores equivalence between direct and indirect mechanisms. I establish that two-sided instruments yield systematically different allocations than one-sided instruments due to amplification effects.