Identifying Bid Leakage in Procurement Auctions: Machine Learning Approach

P. 69–70.

Dmitry Ivanov, Alexander Nesterov

We propose a novel machine-learning-based approach to detect bid leakage in first-price sealed-bid auctions. We extract and analyze the data on more than 1.4 million Russian procurement auctions between 2014 and 2018. As bid leakage in each particular auction is tacit, the direct classification is impossible. Instead, we reduce the problem of bid leakage detection to 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 find that at least 16% of auctions are exposed to bid leakage. Bid leakage is more likely in auctions with a higher reserve price, lower number of bidders and lower price fall, and where the winning bid is received in the last hour before the deadline.

Keywords: коррупция, госзакупки, запрос котировок, First-price auction, Машинное обучение и анализ данных, positive-unlabeled learning, corruption in public procurement, bid leakage, procurement auctions

Publication based on the results of: Allocation and social choice mechanisms: axioms, incentives, algorithms (2020)

In book

EC '19 Proceedings of the 2019 ACM Conference on Economics and Computation

Association for Computing Machinery (ACM), 2019.