Stealed-bid Auctions: Detecting Bid Leakage via Semi-Supervised Learning

TL;DR

This paper introduces a semi-supervised learning method to detect bid leakage in auctions, estimating the prevalence and impact of such corruption using data from Russian procurement auctions.

Contribution

It formulates bid leakage detection as a positive-unlabeled classification problem and provides a scalable approach to estimate bid leakage probabilities without labeled data.

Findings

Approximately 9% of auctions are affected by bid leakage.

Bid leakage increases auction prices by about 1.5%.

Higher risk of bid leakage in auctions with high reserve prices or certain participant patterns.

Abstract

Bid leakage is a corrupt scheme in a first-price sealed-bid auction in which the procurer leaks the opponents' bids to a favoured participant. The rational behaviour of such participant is to bid close to the deadline in order to receive all bids, which allows him to ensure his win at the best price possible. While such behaviour does leave detectable traces in the data, the absence of bid leakage labels makes supervised classification impossible. Instead, we reduce the problem of the bid leakage detection to a positive-unlabeled classification. The key idea is to regard the losing participants as fair and the winners as possibly corrupted. This allows us to estimate the prior probability of bid leakage in the sample, as well as the posterior probability of bid leakage for each specific auction. We extract and analyze the data on 600,000 Russian procurement auctions between 2014 and 2018. We find that around 9% of the auctions are exposed to bid leakage, which results in an overall 1.5% price increase. The predicted probability of bid leakage is higher for auctions with a higher reserve price, with too low or too high number of participants, and if the winner has met the auctioneer in earlier auctions.