Webinars
MSI Webinar: Algorithmic Pricing and Competition
In recent years, machine learning/artificial intelligence based algorithmic pricing tools have become substantially more sophisticated and autonomous. This increase in sophistication and their widespread adoption prompted concerns about the potential for algorithms to behave anti-competitively and cooperate to raise consumer prices. There is an extensive debate in the academic literature about the plausibility of such effects, but scant empirical evidence. In this webinar, we will discuss the various mechanisms through which algorithmic pricing can soften competition. We will then examine novel empirical evidence of the impact of algorithmic pricing adoption in Germany’s retail gasoline market, where such software became widely available in 2017. Adoption in this market increased margins and prices, but only for non-monopoly stations. In two or three station markets, margins increased by 30% if all competitors adopted, suggesting that interaction between pricing algorithms can have a significant effect on competition. We will then conclude with some managerial implications.
speaker
Daniel Ershov is an Assistant Professor of Marketing & Analytics at the University College London School of Management. He specializes in quantitative marketing and empirical industrial organization, studying competition and the effects of digital technology on the operation of markets and on consumer/firm interactions. His main research areas include the effects of algorithms on retail prices, competition in the mobile app market, and influencer advertising. Daniel’s research has been published in top academic journals like the Journal of Political Economy, the RAND Journal of Economics, the Economic Journal and the American Economics Journal: Microeconomics. Before joining UCL, Daniel was a professor at the Toulouse School of Economics, and he holds a PhD in Economics from the University of Toronto.