Detecting corruption in single-bidder auctions via positive-unlabelled learning
Machine Learning
2021-02-11 v1 Computer Science and Game Theory
Abstract
In research and policy-making guidelines, the single-bidder rate is a commonly used proxy of corruption in public procurement used but ipso facto this is not evidence of a corrupt auction, but an uncompetitive auction. And while an uncompetitive auction could arise due to a corrupt procurer attempting to conceal the transaction, but it could also be a result of geographic isolation, monopolist presence, or other structural factors. In this paper we use positive-unlabelled classification to attempt to separate public procurement auctions in the Russian Federation into auctions that are probably fair, and those that are suspicious.
Cite
@article{arxiv.2102.05523,
title = {Detecting corruption in single-bidder auctions via positive-unlabelled learning},
author = {Natalya Goryunova and Artem Baklanov and Egor Ianovski},
journal= {arXiv preprint arXiv:2102.05523},
year = {2021}
}