WORKING PAPERS
Competition in Digital Advertising Auctions with Asymmetric Consumer Information (joint with A. Veneri).
Presented at Paris Conference on Digital Economics (2026), CRESSE (Crete 2025), Lisbon Meetings (2025), 36th Stony Brook International Conference on Game Theory (2025)
We study how asymmetric access to consumer data shapes competition and data management strategies in real-time bidding advertising markets. In our model, two demand-side platforms (DSPs) bid for a single ad slot in a first-price auction. Each DSP privately values the impression according to the consumer’s expected interaction with its ad. The DSPs are asymmetrically informed about their values. One DSP is vertically integrated within the ad-tech stack and has privileged access to consumer data: with probability α , it observes its value before bidding; otherwise, it receives the same noisy signal as its non-integrated rival. We characterize the unique Bayesian Nash equilibrium and show that increased access to data softens competition and raises both DSPs' expected payoff. At the same time, consumers benefit from improved targeting. We then allow the integrated platform to license data access to its rival through a take-it-or-leave-it offer. We characterize when the platform optimally preserves a closed ecosystem, offering a formal explanation for the persistence of data silos.
WORK IN PROGRESS
Cross Market Incidence of Digital Services Taxes
EUI Second-Year Best Paper Award 2024
#MyEUIResearch interview on this project available here.
Presented at Jornadas de Economia Industrial (JEI Santander 2025), Oligo Workshop (Cambridge, 2025)
National authorities and international organisations are adopting ad-hoc tax regulations targeting large digital platforms. The introduction of tax policies creates distortions in markets where platforms operate as intermediaries and generate revenue through commission fees. This issue is particularly relevant in sectors where major digital marketplaces are closely integrated with other regulated markets, raising concerns about the potential pass-through effects of taxation across coexisting marketplaces. I develop a model to study the incidence of digital taxes across different marketplaces for the same good.
Algorithmic Rankings, Contestability and Monetization in Digital Marketplaces
Digital marketplaces shape consumer choice through algorithmic rankings that allocate visibility among sellers. This paper studies how platforms jointly design ranking systems and monetization when consumer preferences are imperfectly observed. We develop a model with two horizontally differentiated sellers, noisy consumer queries, seller advertising, and platform commission fees. Rankings combine query relevance and seller-side promotion, with a relevance weight governing how contestable product prominence is. More relevance-based rankings improve consumer-product matching and increase aggregate trade volume. At the same time, they reduce sellers’ incentives to advertise by making visibility less contestable. The platform therefore faces a tradeoff between expanding transactions and preserving seller-side monetization. The model shows that relevance-based ranking and commission fees are complements. Platforms that rely more on organic relevance optimally charge higher commissions, while more sponsored rankings rely more on advertising revenue. The results highlight that ranking transparency, sponsored placement, and commission regulation should be analyzed jointly in digital marketplaces.