Abstract

The article focuses on the leniency programme, the key mechanism to strengthen the public enforcement of competition law, and its compatibility with the debarment mechanism and self-cleaning measures, which are both procurement remedies. As the article will show, procurement remedies interfere with cartel enforcement and the debarment mechanism undermines leniency in public procurement. The fact that firms may be banned from bidding, where there are plausible indications for their participation in agreements aiming at distorting competition, discourages infringing companies from coming forward and self-reporting. Even the self-cleaning measures under the current procurement Directive 2014/24/EU, which aim to help debarred firms to avoid exclusion or minimize its risk, undermine leniency in public procurement. This is particularly true after the recent judgment of the European Court of Justice in c-124/17 Vossloh Laeis GmbH v Stadtwerke München GmbH case. In this case, it was held that a contracting authority must be able to ask a leniency applicant to provide the decision of the competition authority concerning it. This must apply even if there is a pending private action for damages for breach of competition law by the contracting authority against that leniency applicant. In view of the above conflicting policy objectives, a number of proposals are discussed in order to better align leniency programmes with the mechanism of debarment and self-cleaning policy in public procurement. In this way, the article contributes to the optimal design of enforcement policies.

中文翻译：

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加强公开市场的宽大处理计划

摘要

文章重点介绍了宽大程序，这是加强竞争法公共执法的关键机制，以及它与禁止机制和自洁措施的兼容性，这两者都是采购补救措施。正如本文将表明的那样，采购补救措施会干扰卡特尔的执法，而禁止机制会破坏公共采购的宽大处理。公司可能会被禁止投标，如果有合理的迹象表明它们参与旨在扭曲竞争的协议，则不鼓励侵权公司挺身而出和自我报告。即使是现行采购指令 2014/24/EU 下的自我清洁措施，旨在帮助被禁止的公司避免被排除在外或将其风险降至最低，也会破坏公共采购的宽大处理。c-124/17 Vossloh Laeis GmbH v Stadtwerke München GmbH案。在本案中，订约当局必须能够要求宽大处理申请人提供竞争当局有关它的决定。即使订约当局针对宽大处理申请人违反竞争法而提起的未决私人诉讼，也必须适用这一规定。鉴于上述相互冲突的政策目标，为了更好地将宽大处理方案与公共采购中的禁止和自清洁政策机制相结合，讨论了一些建议。通过这种方式，本文有助于执行政策的优化设计。