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Université de Fribourg

Independent and competing agencies : An effective way to control government

Schelker, Mark ; Eichenberger, Reiner

In: Public Choice, 2007, vol. 130, p. 79-98

Controlling government is a primary focus of the politico-economic literature. Recently, various political institutions have been analyzed from this perspective, most importantly balanced budget rules, fiscal federalism, and direct democracy. However, one type of institution has been neglected so far: elected competitors to the government. Such institutional competition between the government...

Université de Fribourg

Machine learning with screens for detecting bid-rigging cartels

Huber, Martin ; Imhof, David

In: International Journal of Industrial Organization, 2019, vol. 65, p. 277-301

We combine machine learning techniques with statistical screens computed from the distribution of bids in tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample performance of this approach and find it to correctly classify more than 84% of the total of bidding processes as collusive or non-collusive. We also discuss...

Université de Fribourg

Transnational machine learning with screens for flagging bid-rigging cartels

Huber, Martin ; Imhof, David ; Ishii, Rieko

(Working Papers SES ; 519)

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 for the distribution of bids in tenders with machine learning to classify collusive vs. competitive tenders entails a correct classification rate of 88% to 93% when training and testing the method...

Université de Fribourg

Machine learning approach for flagging incomplete bid-rigging cartels

Wallimann, Hannes ; Imhof, David ; Huber, Martin

(Working Papers SES ; 513)

We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. Our approach combines screens, i.e. statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids...

Université de Fribourg

Empirical Methods for Detecting Bid-rigging Cartels

Imhof, David ; At, Christian (Dir.) ; Madiès, Thierry (Dir.)

Thèse de doctorat : Université de Fribourg, 2018.

Organisée en cinq chapitres, cette thèse de doctorat vise à étudier et à développer des méthodes empiriques pour détecter les cartels de soumission. Elle en propose également une étude économique et étudie leur fonctionnement notamment en se fondant sur l’expérience professionnelle de l’auteur. Chaque chapitre correspond à un article indépendant s’intégrant de façon...

Université de Fribourg

Machine learning with screens for detecting bid-rigging cartels

Huber, Martin ; Imhof, David

(Working Papers SES ; 494)

We combine machine learning techniques with statistical screens computed from the distribution of bids in tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample performance of this approach and find it to correctly classify more than 80% of the total of bidding processes as collusive or non-collusive. As the correct...