IDEAS home Printed from https://ideas.repec.org/p/qed/wpaper/1438.html
   My bibliography  Save this paper

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

Author

Listed:
  • Stephanie Assad
  • Robert Clark

    (Queen's University)

  • Daniel Ershov
  • Lei Xu

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.

Suggested Citation

  • Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Working Paper 1438, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1438
    as

    Download full text from publisher

    File URL: https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1438.pdf
    File Function: First version 2020
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Robert Clark & Jean-Fran?ois Houde, 2013. "Collusion with Asymmetric Retailers: Evidence from a Gasoline Price-Fixing Case," American Economic Journal: Microeconomics, American Economic Association, vol. 5(3), pages 97-123, August.
    2. Francesco Decarolis & Gabriele Rovigatti, 2021. "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising," American Economic Review, American Economic Association, vol. 111(10), pages 3299-3327, October.
    3. Severin Boreinstein & Andrea Shepard, 1996. "Dynamic Pricing in Retail Gasoline Markets," RAND Journal of Economics, The RAND Corporation, vol. 27(3), pages 429-451, Autumn.
    4. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, March.
    5. Michael Gelman & Yuriy Gorodnichenko & Shachar Kariv & Dmitri Koustas & Matthew D. Shapiro & Dan Silverman & Steven Tadelis, 2023. "The Response of Consumer Spending to Changes in Gasoline Prices," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 129-160, April.
    6. Hal Varian, 2018. "Artificial Intelligence, Economics, and Industrial Organization," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 399-419, National Bureau of Economic Research, Inc.
    7. Robert Clark & Jean-François Houde, 2014. "The Effect of Explicit Communication on pricing: Evidence from the Collapse of a Gasoline Cartel," Journal of Industrial Economics, Wiley Blackwell, vol. 62(2), pages 191-228, June.
    8. Carsten J. Crede, 2019. "A Structural Break Cartel Screen for Dating and Detecting Collusion," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(3), pages 543-574, May.
    9. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    10. Jeanine Miklós-Thal & Catherine Tucker, 2019. "Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?," Management Science, INFORMS, vol. 65(4), pages 1552-1561, April.
    11. Ben Shiller & Joel Waldfogel, 2011. "Music for a Song: An Empirical Look at Uniform Pricing and Its Alternatives," Journal of Industrial Economics, Wiley Blackwell, vol. 59(4), pages 630-660, December.
    12. Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
    13. Slade, Margaret E, 1987. "Interfirm Rivalry in a Repeated Game: An Empirical Test of Tacit Collusion," Journal of Industrial Economics, Wiley Blackwell, vol. 35(4), pages 499-516, June.
    14. David P. Byrne & Nicolas de Roos, 2019. "Learning to Coordinate: A Study in Retail Gasoline," American Economic Review, American Economic Association, vol. 109(2), pages 591-619, February.
    15. Benjamin Reed Shiller, 2013. "First Degree Price Discrimination Using Big Data," Working Papers 58, Brandeis University, Department of Economics and International Business School, revised Jan 2014.
    16. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, December.
    17. Zhongmin Wang, 2009. "(Mixed) Strategy in Oligopoly Pricing: Evidence from Gasoline Price Cycles Before and Under a Timing Regulation," Journal of Political Economy, University of Chicago Press, vol. 117(6), pages 987-1030, December.
    18. Zhongmin Wang, 2008. "Collusive Communication and Pricing Coordination in a Retail Gasoline Market," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 32(1), pages 35-52, February.
    19. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    20. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2015. "Learning and Pricing with Models That Do Not Explicitly Incorporate Competition," Operations Research, INFORMS, vol. 63(1), pages 86-103, February.
    21. Can Erutku & Vincent A. Hildebrand, 2010. "Conspiracy at the Pump," Journal of Law and Economics, University of Chicago Press, vol. 53(1), pages 223-237, February.
    22. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
    23. Margaret E. Slade, 1992. "Vancouver's Gasoline-Price Wars: An Empirical Exercise in Uncovering Supergame Strategies," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 59(2), pages 257-276.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Robert Clark & Jean-François Houde, 2014. "The Effect of Explicit Communication on pricing: Evidence from the Collapse of a Gasoline Cartel," Journal of Industrial Economics, Wiley Blackwell, vol. 62(2), pages 191-228, June.
    3. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    4. Andreoli-Versbach, Patrick & Franck, Jens-Uwe, 2015. "Endogenous price commitment, sticky and leadership pricing: Evidence from the Italian petrol market," International Journal of Industrial Organization, Elsevier, vol. 40(C), pages 32-48.
    5. Clark, Robert & Coviello, Decio & de Leverano, Adriano, 2020. "Complementary bidding and the collusive arrangement: Evidence from an antitrust investigation," ZEW Discussion Papers 20-052, ZEW - Leibniz Centre for European Economic Research.
    6. Robert Clark & Ignatius Horstmann & Jean-François Houde, 2021. "Hub and Spoke Cartels: Theory and Evidence from the Grocery Industry," NBER Working Papers 29253, National Bureau of Economic Research, Inc.
    7. Stephanie Assad & Emilio Calvano & Giacomo Calzolari & Robert Clark & Vincenzo Denicolò & Daniel Ershov & Justin Johnson & Sergio Pastorello & Andrew Rhodes & Lei Xu & Matthijs Wildenbeest, 2021. "Autonomous algorithmic collusion: economic research and policy implications," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 459-478.
    8. Zhang, Xiao-Bing & Fei, Yinxin & Zheng, Ying & Zhang, Lei, 2020. "Price ceilings as focal points to reach price uniformity: Evidence from a Chinese gasoline market," Energy Economics, Elsevier, vol. 92(C).
    9. Tveito, Andreas, 2019. "Coordination and price leadership in an unregulated environment," Working Papers in Economics 4/19, University of Bergen, Department of Economics.
    10. González, Xulia & Moral, María J., 2019. "Effects of antitrust prosecution on retail fuel prices," International Journal of Industrial Organization, Elsevier, vol. 67(C).
    11. Eibelshäuser, Steffen & Wilhelm, Sascha, 2017. "Markets Take Breaks: Dynamic Price Competition with Opening Hours," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168247, Verein für Socialpolitik / German Economic Association.
    12. David P. Byrne, Gordon W. Leslie, and Roger Ware, 2015. "How do Consumers Respond to Gasoline Price Cycles?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    13. Haucap, Justus & Heimeshoff, Ulrich & Siekmann, Manuel, 2016. "Selling gasoline as a by-product: The impact of market structure on local prices," DICE Discussion Papers 240, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    14. Nicolas de Roos & Vladimir Smirnov, 2020. "Collusion with intertemporal price dispersion," RAND Journal of Economics, RAND Corporation, vol. 51(1), pages 158-188, March.
    15. Mariano Tappata & Jing Yan, 2017. "Competition in Retail Petrol Markets," Australian Economic Papers, Wiley Blackwell, vol. 56(3), pages 201-218, September.
    16. Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
    17. Jorge Lemus & Fernando Luco, 2021. "Price Leadership and Uncertainty About Future Costs," Journal of Industrial Economics, Wiley Blackwell, vol. 69(2), pages 305-337, June.
    18. Isakower, Sean & Wang, Zhongmin, 2014. "A comparison of regular price cycles in gasoline and liquefied petroleum gas," Energy Economics, Elsevier, vol. 45(C), pages 445-454.
    19. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    20. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.

    More about this item

    Keywords

    Artificial Intelligence; Pricing-Algorithms; Collusion; Retail Gasoline;
    All these keywords.

    JEL classification:

    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L71 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Hydrocarbon Fuels

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qed:wpaper:1438. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mark Babcock (email available below). General contact details of provider: https://edirc.repec.org/data/qedquca.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.