Research

Entry and Acquisitions in Software Markets (October 2025)

Best Paper Award, Doctoral Workshop on the Economics of Digitization, 2023. Schumpeter Prize, Best Student Paper on Antitrust (ITIF), 2022. Finalist, Lear Young Talent Competition Award, 2021.

How do acquisitions of young, innovative, venture capital-funded firms (startups) affect firms’ incentives to enter a market? I build a novel product-level dataset of enterprise software, and apply text-as-data methods to identify competing firms. I document new empirical patterns on startup acquisitions in software, including who acquires whom, and how these acquisitions relate to subsequent entry. To interpret these patterns and to quantify the underlying mechanisms, I build and estimate a dynamic model of startup entry in the face of these acquisitions. In the model, acquisitions influence returns to entry (1) by affecting market structure, and (2) by creating an entry-for-buyout incentive for potential entrants. Using the estimated model, I simulate how entry would evolve under alternative merger policies. The simulations reveal that, if all startup acquisitions were blocked, entry would decline by about 16% in the average market. By contrast, blocking acquisitions conducted by established incumbents at high transaction prices increases entry slightly. Accordingly, enforcement that prioritizes review of incumbent-led, high-value acquisitions is likely to sustain startup entry.

Selected Presentations: CEPR-JIE Summer School (Cambridge, UK), HEC Lausanne, HEC Montreal, McGill, Royal Holloway (London), Sciences Po (Paris), HEC Paris, CREST (Paris), KU Leuven (MSI), ifo Institute, DICE (Düsseldorf), University of Mannheim, Boston University (Questrom Strategy Brown Bag & IO Reading Group), Technology & Policy Research Initivative (TPRI), ZEW (Mannheim), IIOC (Washington D.C.), EARIE (Vienna), APPAM (Washington D.C.), MFA (Chicago), SFI Research Days (Gerzensee), University of Zürich (Department of Business Administration), Bridging Theory and Empirical Research in Finance (Boston College).

Pricing Patterns on Dual-Mode E-Commerce Platforms (with Jun Yan and Li Yu)

Many of today’s e-commerce platforms operate under a dual mode: they not only provide online marketplaces where third-party sellers compete and offer products, but also act as retailers selling products of their own. We study how dual-mode operation changes price competition on e-commerce platforms. Using a stylized model that incorporates some of the key features of Amazon, we show that, if the platform both earns commissions on third-party sales and retails on its own marketplace, price competition changes along two margins: platform retail presence can raise or lower rivals’ price levels, and common cost shocks need not be passed through symmetrically across seller types. Using data from Amazon U.S., we provide event-study evidence on third-party price dynamics around temporary changes in Amazon’s retail availability. We find a 6\% price decrease (5\% price increase) in the days after Amazon enters (exits) as a reseller alongside third parties. Consistent with the model, we find asymmetric pass-through of commodity-cost shocks: third-party prices respond more strongly than Amazon’s own retail prices, with pass-through approaching full transmission only at longer horizons.

Presentations: Stockholm School of Economics, NHH Bergen, Toulouse School of Economics “Frontiers in IO’’ Conference, Paris Conference on Digital Economics, Bristol Empirical IO Workshop, Zurich Workshop on Digital Platforms, MaCCI (Mannheim), EARIE (Amsterdam), Xiamen University.

How do Online Product Rankings Influence Sellers’ Pricing Behavior? (December 2023)

Products that are displayed more prominently on e-commerce platforms are more likely to be found and purchased by consumers. The algorithms ranking these products, however, may condition a product’s position in a listings page on its price. Using web-scraped data from hotels displayed on Expedia and an instrumental variable identification strategy, I find that the ranking algorithm tends to display hotels at less favorable positions at times at which they are priced higher. I provide a framework that employs these estimates jointly with demand parameters obtained from a sequential search model. I simulate a counterfactual scenario, and reveal that Expedia’s ranking algorithm tends to intensify price competition between sellers compared to a random ranking. This increases consumer welfare, but reduces seller profits. My finding has consequences for two-sided platforms’ optimal design of ranking algorithms: in order to foster adoption, platforms should carefully trade off benefits arising to the two sides, and consider equilibrium effects.

Presentations: EARIE (Barcelona), LED Young Economist Seminar (UC Louvain), Competition and Innovation Summer School, HEC Lausanne, Toulouse School of Economics.

Selected Work in Progress

M&As and Privacy Regulation (draft available upon request)

• Presentations: Swiss Finance Institute; University of Lausanne; Toulouse School of Economics.

When the Merger is Reviewed by Consumers: Stealth Acquisitions and Online Ratings (with Reinhold Kesler and Ulrich Kaiser)

• Presentations: MaCCI (Mannheim), SSES Annual Conference (St. Gallen).