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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.

The adoption of e-procurement may reduce bid rigging in public auctions by limiting in-person meetings of bidders. Using the data from construction auctions tendered by a Japanese local government where paper-based manual procurement is replaced by e-procurement, we find that the adoption of e-procurement reduced bids in a section of the market where the bids were initially higher than the other section of the market. The degree of reduction was smaller in an auction when the bidders were likely to be in the same industrial community, suggesting that the effect of e-procurement by limiting in-person meetings is smaller when the bidders have chances to communicate through other than the procurement processes.

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