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Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels

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

Abstract

We combine supervised machine learning techniques with statistical moments of the gasoline price distribution to detect cartels in the Brazilian retail market. Standard deviation, coefficient of variation, spread, skewness, and kurtosis are predictors that can help identify and predict anti-competitive market behavior. We evaluate each classifier and discuss the trade-offs related to false-positive (detect cartel when it does not exist) and false-negative (do not detect cartel when it does exist) predictions. The competition authority needs effective monitoring and often anticipating cartel movements. With this in mind, we test the algorithms’ performance in new datasets (ex-ante screening). Our results show that false-negative outcomes can critically increase when the main objective is to minimize false-positive predictions. The models’ overall average scoring rate for testing and predicting cartels in the same city is 96.22%. When we train the algorithms in one city and predict the cartel outcomes in other cities, on average, the overall scoring rate is equal to 73.75%. Our work suggests that machine learning classifiers have positive attributes and can provide valuable contributions to cartels’ deterrence. In addition, we offer a policy prescription discussion for antitrust authorities regarding the pros and cons of proactive tools for inhibiting collusive agreements in retail gasoline markets.

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  • 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).
  • Handle: RePEc:eee:eneeco:v:105:y:2022:i:c:s0140988321005594
    DOI: 10.1016/j.eneco.2021.105711
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    Cited by:

    1. Kurdoglu, Berkay & Yucel, Eray, 2022. "A Cointegration-based cartel screen for detecting collusion," MPRA Paper 113888, University Library of Munich, Germany.
    2. 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.
    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. 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).
    5. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    6. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).

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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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