Data Driven Regulation: Theory and Application to Missing Bids

Chassang, Sylvain; Kawai, Kei; Nakabayashi, Jun; Ortner, Juan

doi:10.3386/w25654

Cited by 9 publications

(24 citation statements)

References 26 publications

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“…This testimonial evidence is consistent with the cartel behaviour described in Section 7 of Chassang et al (2020) and in Ortner et al (2020). As mentioned, they find isolated winning bids in their sample of Japanese procurement auctions, but they also point out that bids are somewhat clustered with a large mass of bids within 2% of the winning bid.…”

Section: Introductionsupporting

confidence: 90%

“…In the appendix we present results in which we do not drop the entrants and our results are largely unchanged. We also show that results are unchanged if we drop auctions from 2010, which features more contracts than in other years in the full sample with entrants, and Chassang et al (2020) document missing bids around 0 in the distribution of bid differences for public works procurement auctions in Japan. The measure they focus on is the difference between a given bidder's own bid and the most competitive bid in the auction.…”

Section: Datamentioning

confidence: 66%

“…In this section we explain how isolated winning bids and clustered bidding could coexist as part of a collusive arrangement featuring complementary bidding. In their sample of procurement auctions from Japan, Chassang et al (2020) find evidence that winning bids are isolated, but that at the same time bids are somewhat clustered with a large mass of bids within 2% of the winning bid. As mentioned above, the authors explain that a cartel's main objective is to ensure the stage-game optimality of play.…”

Section: Complementary Bidding Isolated Winning Bids and Clustered Bmentioning

confidence: 99%

“…In other words, bid differences capture bidders' margins of victory or defeat. Chassang et al (2020) are interested in the distribution of…”

Section: Datamentioning

confidence: 99%

“…A.1 Normalization with average winning bid in Montreal preinvestigation Chassang et al (2020) are interested in the distribution of…”

Section: A Appendixmentioning

confidence: 99%

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Complementary Bidding and the Collusive Arrangement: Evidence from an Antitrust Investigation

Clark

Coviello

2020

SSRN Journal

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Section: Introductionsupporting

confidence: 90%

Section: Datamentioning

confidence: 66%

Section: Complementary Bidding Isolated Winning Bids and Clustered Bmentioning

confidence: 99%

“…In other words, bid differences capture bidders' margins of victory or defeat. Chassang et al (2020) are interested in the distribution of…”

Section: Datamentioning

confidence: 99%

“…A.1 Normalization with average winning bid in Montreal preinvestigation Chassang et al (2020) are interested in the distribution of…”

Section: A Appendixmentioning

confidence: 99%

See 3 more Smart Citations

Complementary Bidding and the Collusive Arrangement: Evidence from an Antitrust Investigation

Clark

Coviello

2020

SSRN Journal

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Pricing Patterns in Wholesale Electricity Markets: Unilateral Market Power or Coordinated Behavior?*

Brown

Eckert

2022

The J Industrial Economics

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We examine allegations that firms in Alberta's electricity industry manipulated public information to coordinate in the wholesale market. We investigate whether bids by firms who employed unique pricing patterns were consistent with unilateral expected profit maximization. Our results suggest that these firms could have increased expected profits through unilateral deviations. For one firm, the potential to increase profits is greater on days when certain offer patterns are observed, providing support for the claim that such patterns may have assisted coordination on high-priced outcomes. These results suggest that regulators should exercise caution when designing information disclosure policies in concentrated electricity markets.

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Transnational Machine Learning with Screens for Flagging Bid-Rigging Cartels

Huber

Imhof

Ishii³

2022

Journal of the Royal Statistical Society Series A: Statistics in Society

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We investigate the transnational transferability of statistical screening methods originally developed using Swiss data for detecting bid‐rigging cartels in Japan. We find that combining screens with machine learning (either a random forest or an ensemble method consisting of six different algorithms) to classify collusive versus competitive tenders entails (depending on the model) correct classification rates of 88%–97% when training and testing the method on the Okinawa bid‐rigging cartel. As in Switzerland, bid rigging in Okinawa reduced the variance and increased the asymmetry in the distribution of bids. When training the models in data from one country to test their performance in the data from the other country, imbalance increases between the correct prediction of truly collusive and competitive tenders for all machine learners and classification rates go down substantially when using the random forest as machine learner, due to some screens for competitive Japanese tenders being similar to those for collusive Swiss tenders. Demeaning the screens reduces such distortions due to institutional differences across countries such that correct classification rates based on training in one and testing in the other country amount to 85% and to 90% when using the ensemble method as machine learner, which generally outperforms the random forest.

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Data Driven Regulation: Theory and Application to Missing Bids

Cited by 9 publications

References 26 publications

Complementary Bidding and the Collusive Arrangement: Evidence from an Antitrust Investigation

Complementary Bidding and the Collusive Arrangement: Evidence from an Antitrust Investigation

Pricing Patterns in Wholesale Electricity Markets: Unilateral Market Power or Coordinated Behavior?*

Transnational Machine Learning with Screens for Flagging Bid-Rigging Cartels

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