Decarolis Francesco, Giorgiantonio Cristina
Department of Economics, Bocconi University, Milano, Italy.
IGIER, Milano, Italy.
EPJ Data Sci. 2022;11(1):16. doi: 10.1140/epjds/s13688-022-00325-x. Epub 2022 Mar 18.
This paper contributes to the analysis of quantitative indicators (i.e., or ) to detect corruption in public procurement. It presents an approach to evaluate corruption risk in public tenders through standardized ML tools applied to detailed data on the content of calls for tenders. The method is applied to roadwork contracts in Italy and three main contributions are reported. First, the study expands the set of commonly discussed indicators in the literature to new ones derived from operative practices of police forces and the judiciary. Second, using novel and unique data on firm-level corruption risk, this study validates the effectiveness of the indicators. Third, it quantifies the increased corruption-prediction ability when indicators that are known to be unavailable to the corruption-monitoring authority are included in the prediction exercise. Regarding the specific red flags, we find a systematic association between high corruption risk and the use of multi-parameter awarding criteria. Furthermore, predictability of the red flag makes them ineffective as prediction tools: the most obvious and scrutinized red flags are either uncorrelated with corruption or, even, negatively associated with it, as it is the case for invoking special procedures due to "urgency," or the extent of publicity of the call for tender.
本文有助于对检测公共采购腐败行为的定量指标（即 或 ）进行分析。它提出了一种通过应用于招标内容详细数据的标准化机器学习工具来评估公共招标中腐败风险的方法。该方法应用于意大利的道路工程合同，并报告了三个主要贡献。首先，该研究将文献中通常讨论的指标集扩展到了源自警察部队和司法机构操作实践的新指标。其次，利用关于公司层面腐败风险的新颖且独特的数据，本研究验证了这些指标的有效性。第三，它量化了在预测过程中纳入已知腐败监测机构无法获取的指标时，腐败预测能力的提升。关于具体的警示信号，我们发现高腐败风险与使用多参数授标标准之间存在系统性关联。此外，警示信号的可预测性使其作为预测工具无效：最明显且经过仔细审查的警示信号要么与腐败无关，甚至与腐败呈负相关，例如因“紧急情况”而援引特殊程序，或招标公告的范围就是如此。
