A Structural Break Cartel Screen for Dating and Detecting Collusion

Crede, Carsten J.

doi:10.1007/s11151-018-9649-5

Cited by 20 publications

(15 citation statements)

References 42 publications

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“…Considering the framework linked to our research, collusive patterns based on retail price variations stand out. The economic intuition is that the reduced retail price variance across time or within geographical clusters is an indicator of collusion (Abrantes-Metz, 2012;Crede, 2019;Harrington, 2008). The literature on behavioral cartel screens has grown significantly in the last decade.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2022

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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 falsepositive predictions. We offer a discussion regarding the pros and cons of our approach for antitrust authorities aiming at detecting and avoiding gasoline cartels.

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Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2022

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“…For this, we take advantage of our outlet-level panel to analyze price changes within and across markets during the 2009-2018 period, which includes both the last seven years of the coordination phase 20 The test was developed by Quandt (1960) and distributional properties were established by Andrews (1993). This test has been suggested and used in previous work involving collusive behaviour (see for instance Harrington (2008), Clark and Houde (2014), Boswijk, Bun, and Schinkel (2018), Crede (2019)). 21 We have redone our difference-in-differences analysis below using this alternative break timing and our results are comparable, although the average price decline is slightly larger.…”

Section: Evidence Of Collusion At Both Ends Of the Supply Chainmentioning

confidence: 99%

Hub and Spoke Cartels: Theory and Evidence from the Grocery Industry

Clark¹,

Horstmann²,

Houde³

2021

SSRN Journal

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“…There are a number of other large brands with over 350 stations each: Esso, Total, Avia, Jet, Star, BFT, Agip, Raiffeisen, and Hem. 18 In terms of market shares, ARAL, Shell, Jet, BFT, Total and Esso together account for 84 per cent of fuel sales in the German retail gas market. 19 There are 5,781 5-digit ZIP codes in our data.…”

Section: Station-level Descriptive Statisticsmentioning

confidence: 99%