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A Structural Break Cartel Screen for Dating and Detecting Collusion

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  • Carsten J. Crede

    (Bundeskartellamt (Federal Cartel Office))

Abstract

In this article, a new empirical screen for detecting cartels is developed. It can also be used to date the beginning of known conspiracies, which is often difficult in practice. Structural breaks that are induced by cartels in the data-generating process of industry prices are detected by testing reduced-form price equations for structural instability. The new screen is applied to three European markets for pasta products, in which it successfully reports the cartels that were present in the Italian and Spanish markets, but finds no suspicious patterns in the French market, which was not cartelised.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:revind:v:54:y:2019:i:3:d:10.1007_s11151-018-9649-5
    DOI: 10.1007/s11151-018-9649-5
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    Cited by:

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    2. 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).
    3. Kurdoglu, Berkay & Yucel, Eray, 2022. "A Cointegration-based cartel screen for detecting collusion," MPRA Paper 113888, University Library of Munich, Germany.
    4. Wallimann, Hannes & Imhof, David & Huber, Martin, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers 513, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    5. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," CESifo Working Paper Series 8521, CESifo.
    6. 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.
    7. Chowdhury, Subhasish M. & Crede, Carsten J., 2020. "Post-cartel tacit collusion: Determinants, consequences, and prevention," International Journal of Industrial Organization, Elsevier, vol. 70(C).
    8. 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.
    9. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for Collusion in Wholesale Electricity Markets: A Review of the Literature," Working Papers 2023-7, University of Alberta, Department of Economics.
    10. Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).
    11. Holler, Emanuel & Rickert, Dennis, 2022. "How resale price maintenance and loss leading affect upstream cartel stability: Anatomy of a coffee cartel," International Journal of Industrial Organization, Elsevier, vol. 85(C).
    12. 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).

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    More about this item

    Keywords

    Antitrust; Cartel; Detection; Empirical screen;
    All these keywords.

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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