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

Douglas Silveira, Silvinha Vasconcelos, Marcelo Resende and Daniel Cajueiro

No 8835, CESifo Working Paper Series from CESifo

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.

Keywords: cartel screens; price dynamics; fuel retail market; machine learning (search for similar items in EconPapers)
JEL-codes: C21 C45 C52 K40 L40 L41 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-com, nep-ene and nep-law
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Journal Article: Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_8835

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