Working papers SES

Working papers SES
The Working Papers SES collection is a series of research papers authored by members of the Faculty of Economics and Social Sciences of the University of Fribourg (Switzerland). This series exists since 1980 and the themes investigated reflect the different scientific orientations of the Faculty: economics, business administration, computer management, quantitative methods, social sciences and media and communication sciences. The contents of the research papers are the sole responsibility of their authors.
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

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...