Research

Entry and Acquisitions in Software Markets (Job Market Paper)

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 create a product-level dataset of enterprise software, and use textual analysis to identify competing firms. Motivated by new stylized facts on startup acquisitions in software, I build and estimate a dynamic model of startups’ entry decisions in the face of these acquisitions. In the model, acquisitions can affect returns to entry (1) by affecting market structure, and (2) by providing an entry-for-buyout incentive to potential entrants. Using the parameter estimates, I simulate how startup entry would evolve over time if merger control was tightened. The simulations reveal that, if all startup acquisitions were blocked, entry would decline on the order of 8-20% in some markets. In contrast, I find suggestive evidence that blocking mergers between established industry players and more mature startups might increase entry. These findings indicate that case-by-case merger review can best foster sustained startup entry.

Selected Presentations: CEPR-JIE Summer School (Cambridge, UK), HEC Lausanne, HEC Montreal, McGill, ifo Institute, DICE (Düsseldorf), Royal Holloway (London), Sciences Po (Paris), HEC Paris, CREST (Paris), KU Leuven (MSI), 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).

How do Online Product Rankings Influence Sellers’ Pricing Behavior?

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, LED Young Economist Seminar (UC Louvain), Competition and Innovation Summer School, HEC Lausanne, TSE.

Work in Progress

Dual Mode E-commerce Platforms and Pass-Through (with Jun Yan and Li Yu)