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Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels

Author

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  • Douglas Silveira
  • Silvinha Vasconcelos
  • Marcelo Resende
  • Daniel O. Cajueiro

Abstract

In this article, we combine machine learning techniques with statistical moments of the gasoline price distribution. By doing so, we aim to detect and predict cartels in the Brazilian retail market. In addition to the traditional variance screen, we evaluate how the standard deviation, coefficient of variation, skewness, and kurtosis can be useful features in identifying anti-competitive market behavior. We complement our discussion with the so-called confusion matrix and discuss the trade-offs related to false-positive and false-negative predictions. Our results show that in some cases, false-negative outcomes critically increase when the main objective is to minimize false-positive predictions. We offer a discussion regarding the pros and cons of our approach for antitrust authorities aiming at detecting and avoiding gasoline cartels.

Suggested Citation

  • Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021. "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series 8835, CESifo.
    • Handle: RePEc:ces:ceswps:_8835
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    Cited by:

    1. 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).
    2. Kurdoglu, Berkay & Yucel, Eray, 2022. "A Cointegration-based cartel screen for detecting collusion," MPRA Paper 113888, University Library of Munich, Germany.
    3. 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).
    4. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    5. 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.

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    More about this item

    Keywords

    cartel screens; price dynamics; fuel retail market; machine learning;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General
    • L40 - Industrial Organization - - Antitrust Issues and Policies - - - General
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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