IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp2131.html
   My bibliography  Save this paper

Public Communication and Collusion: New Screening Tools for Competition Authorities

Author

Listed:
  • Tomaso Duso
  • Joseph E. Harrington Jr.
  • Carl Kreuzberg
  • Geza Sapi

Abstract

Competition authorities increasingly rely on economic screening tools to identify markets where firms deviate from competitive norms. Traditional screening methods assume that collusion occurs through secret agreements. However, recent research highlights that firms can use public announcements to coordinate decisions, reducing competition while avoiding detection. We propose a novel approach to screening for collusion in public corporate statements. Using natural language processing, we analyze more than 300, 000 earnings call transcripts issued worldwide between 2004 and 2022. By identifying expressions commonly associated with collusion, our method provides competition authorities with a tool to detect potentially anticompetitive behavior in public communications. Our approach can extend beyond earnings calls to other sources, such as news articles, trade press, and industry reports. Our method informed the European Commission’s 2024 unannounced inspections in the car tire sector, prompted by concerns over price coordination through public communication.

Suggested Citation

  • Tomaso Duso & Joseph E. Harrington Jr. & Carl Kreuzberg & Geza Sapi, 2025. "Public Communication and Collusion: New Screening Tools for Competition Authorities," Discussion Papers of DIW Berlin 2131, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp2131
    as

    Download full text from publisher

    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.969355.de/dp2131.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gaurab Aryal & Federico Ciliberto & Benjamin T Leyden, 2022. "Coordinated Capacity Reductions and Public Communication in the Airline Industry," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(6), pages 3055-3084.
    2. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    3. Ivaldi, Marc & Jullien, Bruno & Rey, Patrick & Seabright, Paul & Tirole, Jean, 2003. "The Economics of Tacit Collusion," IDEI Working Papers 186, Institut d'Économie Industrielle (IDEI), Toulouse.
    4. Tarek Alexander Hassan & Stephan Hollander & Aakash Kalyani & Laurence van Lent & Markus Schwedeler & Ahmed Tahoun, 2024. "Economic Surveillance using Corporate Text," NBER Working Papers 33158, National Bureau of Economic Research, Inc.
    5. Abrantes-Metz, Rosa M. & Froeb, Luke M. & Geweke, John & Taylor, Christopher T., 2006. "A variance screen for collusion," International Journal of Industrial Organization, Elsevier, vol. 24(3), pages 467-486, May.
    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. Tomaso Duso & Joseph E., Jr. Harrington & Carl Kreuzberg & Geza Sapi, 2025. "Public Communication and Collusion: New Screening Tools for Competition Authorities," CESifo Working Paper Series 12029, CESifo.
    2. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
    3. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    4. Juan Jiménez & Jordi Perdiguero, 2012. "Does Rigidity of Prices Hide Collusion?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 41(3), pages 223-248, November.
    5. Aaltio, Aapo & Buri, Riku & Jokelainen, Antto & Lundberg, Johan, 2025. "Complementary bidding and cartel detection: Evidence from Nordic asphalt markets," International Journal of Industrial Organization, Elsevier, vol. 98(C).
    6. Martin Huber & David Imhof & Rieko Ishii, 2022. "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
    7. Christian Lorenz, 2008. "Screening markets for cartel detection: collusive markers in the CFD cartel-audit," European Journal of Law and Economics, Springer, vol. 26(2), pages 213-232, October.
    8. Bovin, Andreas & Bos, Iwan, 2023. "Market Shares as Collusive Marker: Evidence from the European Truck Industry," Research Memorandum 011, Maastricht University, Graduate School of Business and Economics (GSBE).
    9. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
    10. Yue Cai, 2021. "Measuring Market Power in the IPO Underwriter," Working Papers 2108, Waseda University, Faculty of Political Science and Economics.
    11. Li Gan & Manuel A. Hernandez, 2013. "Making Friends with Your Neighbors? Agglomeration and Tacit Collusion in The Lodging Industry," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 1002-1017, July.
    12. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    13. Fourberg, Niklas, 2017. "Let's lock them in: Collusion under Consumer Switching Costs," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168097, Verein für Socialpolitik / German Economic Association.
    14. Severin Lenhard, 2025. "Random Pricing: Bertrand Competition with Uncontested Consumers," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 67(2), pages 191-208, August.
    15. Wallimann, Hannes & Sticher, Silvio, 2023. "On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement," Transport Policy, Elsevier, vol. 143(C), pages 121-131.
    16. Fourberg, Niklas, 2018. "Let's lock them in: Collusion under consumer switching costs," DICE Discussion Papers 296, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    17. Jeremy Bertomeu & John Harry Evans & Mei Feng & Ayung Tseng, 2021. "Tacit Collusion and Voluntary Disclosure: Theory and Evidence from the U.S. Automotive Industry," Management Science, INFORMS, vol. 67(3), pages 1851-1875, March.
    18. 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).
    19. David Imhof & Emanuel W Viklund & Martin Huber, 2025. "Catching Bid-rigging Cartels with Graph Attention Neural Networks," Papers 2507.12369, arXiv.org, revised Jul 2025.
    20. Clara Calini & Alessandra Catenazzo & Elisabetta Iossa, 2025. "Using Multiple Tools to Enhance Competition in Public Procurement," CEIS Research Paper 594, Tor Vergata University, CEIS, revised 25 Feb 2025.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L4 - Industrial Organization - - Antitrust Issues and Policies
    • L64 - Industrial Organization - - Industry Studies: Manufacturing - - - Other Machinery; Business Equipment; Armaments

    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:diw:diwwpp:dp2131. 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: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/diwbede.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.