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).
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, TSE.
Selected Work in Progress
Who Bears the Brunt of Cost Shocks? Analyzing Pass-Through on Dual-Mode E-Commerce Platforms (with Jun Yan and Li Yu)
Presentations: Stockholm School of Economics, NHH Bergen, MaCCI (Mannheim), EARIE (Amsterdam), Xiamen University.