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

51 Pages Posted: 26 Jan 2021

See all articles by Douglas Silveira

Douglas Silveira

Federal University of Juiz de Fora; University of Alberta; Pontifical Catholic University of Rio de Janeiro (PUC-Rio)

Silvinha Vasconcelos

Universidade Federal de Juiz de Fora - Department of Economics

Marcelo Resende

Universidade Federal do Rio de Janeiro (UFRJ); CESifo (Center for Economic Studies and Ifo Institute)

Daniel O. Cajueiro

Universidade de Brasília (UnB)

Multiple version iconThere are 2 versions of this paper

Date Written: 2021

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.

JEL Classification: C210, C450, C520, K400, L400, L410

Suggested Citation

Silveira, Douglas and Vasconcelos, Silvinha and Resende, Marcelo and Cajueiro, Daniel O., Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels (2021). CESifo Working Paper No. 8835, Available at SSRN: https://ssrn.com/abstract=3770457

Douglas Silveira (Contact Author)

Federal University of Juiz de Fora ( email )

Juiz de Fora, Minas Gerais
Brazil

University of Alberta ( email )

Edmonton, Alberta T6G 2R6
Canada

Pontifical Catholic University of Rio de Janeiro (PUC-Rio) ( email )

Rua Marquas de Sao Vicente, 225
Rio De Janeiro, RJ 22453-900
Brazil

Silvinha Vasconcelos

Universidade Federal de Juiz de Fora - Department of Economics

Juiz de Fora, Minas Gerais
Brazil

Marcelo Resende

Universidade Federal do Rio de Janeiro (UFRJ) ( email )

Rua Sao Francisco Xavier, 524
Instituto de Economia
Rio de Janeiro RJ 21949
Brazil

CESifo (Center for Economic Studies and Ifo Institute)

Poschinger Str. 5
Munich, DE-81679
Germany

Daniel O. Cajueiro

Universidade de Brasília (UnB) ( email )

Campus Universitário Darcy Ribeiro
Asa Norte
Brasília, Distrito Federal 70910-900
Brazil

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