Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market

Abstract

Economic theory provides ambiguous and conflicting predictions about the association between algorithmic pricing and competition. In this paper we provide the first empirical analysis of this relationship. We study Germany's retail gasoline market where algorithmic-pricing software became widely available by mid-2017, and for which we have access to comprehensive, highfrequency price data. Because adoption dates are unknown, we identify gas stations that adopt algorithmic-pricing software by testing for structural breaks in markers associated with algorithmic pricing. We find a large number of station-level structural breaks around the suspected time of large-scale adoption. Using this information we investigate the impact of adoption on outcomes linked to competition. Because station-level adoption is endogenous, we use brand headquarter-level adoption decisions as instruments. Our IV results show that adoption increases margins by 9%, but only in non-monopoly markets. Restricting attention to duopoly markets, we find that market-level margins do not change when only one of the two stations adopts, but increase by 28% in markets where both do. These results suggest that AI adoption has a significant effect on competition.

Platform Design When Sellers Use Pricing Algorithms

“…33 This shift to algorithmic pricing has also raised regulatory concerns; see OECD (2017b), CMA (2018), and DOJ (2018). Simulation‐based work by Calvano, Calzolari, Denicolò, and Pastorello (2020) and Klein (2021) shows that algorithms may learn to play collusive strategies, while empirical work by Clark, Assad, Ershov, and Xu (forthcoming) and Musolff (2022) finds that adopting AI algorithms may lead to higher prices. Brown and MacKay (2023) show that adopting (non‐AI) algorithms allows firms to achieve a form of price commitment that raises overall prices. …”