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

Stephanie Assad (), Robert Clark, Daniel Ershov and Lei Xu

No 1438, Working Paper from Economics Department, Queen's University

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, high-frequency 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 nd 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.

Keywords: Artificial Intelligence; Pricing-Algorithms; Collusion; Retail Gasoline (search for similar items in EconPapers)
JEL-codes: D43 D83 L13 L41 L71 (search for similar items in EconPapers)
Pages: 73 pages
Date: 2020-08
New Economics Papers: this item is included in nep-big, nep-com, nep-ind and nep-tre
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (47)

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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1438

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