Entry and Acquisitions in Software Markets

How do acquisitions of young, innovative, Venture Capital funded firms (startups) affect the entry of new firms? To answer this question, I collect product-level data on enterprise software and merge it with companies’ histories of acquisitions. I apply a machine learning algorithm to cluster products that are likely substitutable into markets, and provide novel stylized facts on startup acquisitions in software markets. In ongoing work, I develop a structural model of startups’ entry decisions and acquisitions. The goal is to simulate how entry would evolve if antitrust authorities were to prevent the acquisition of software startups.

(Draft out soon.)

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. A product’s default positioning, however, may depend on the seller’s pricing decision. By conditioning a product’s position on its price, ranking algorithms can thus intensify, or weaken the extent of price competition between sellers. Using scraped data from hotels displayed on Expedia, I find that for a given hotel, a lower price implies a more prominent position in the ranked list of results. I provide a structural framework that allows to simulate how changes in the ranking algorithm influence hotel and platform profits, consumer surplus, and welfare. To perform these counterfactual simulations, I employ my estimates jointly with demand parameters obtained from a sequential search model by Ursu (2018).

(Paper available upon request.)