Close ties between government authorities and private firms are often the object of suspicion, but a systematic understanding of when they arise is still missing. This article uses machine learning tools to analyze a large dataset of public contracts from across Europe, in order to identify the conditions under which close connections, defined both in terms of repeated interaction, as well as geographical dispersion, appear. Previous theoretical results suggest that close ties should emerge as an enforcement mechanism in settings characterized by weak outside enforcement, such as those involving corruption. Results from random forest models show support for this hypothesis, along with identifying other structural determinants of the outcome. The most striking finding is that even after accounting for numerous potential confounders, major differences in terms of average diversity levels between countries persist, and these differences map onto an indicator of governance quality and corruption, but not at all on income per capita. These findings point to the centrality of the structure of interactions between private and public actors for understanding governance outcomes.

中文翻译：

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揭示公共采购交易的结构

政府当局与私营企业之间的密切联系往往是被怀疑的对象，但仍缺乏系统地了解它们何时出现。本文使用机器学习工具分析来自欧洲各地的公共合同的大型数据集，以确定在重复交互和地理分散方面定义的密切联系出现的条件。先前的理论结果表明，在外部执法薄弱的环境中，例如涉及腐败的环境中，密切联系应该作为一种执法机制出现。随机森林模型的结果支持这一假设，同时确定了结果的其他结构决定因素。最引人注目的发现是，即使在考虑了许多潜在的混杂因素之后，各国之间在平均多样性水平方面仍然存在重大差异，这些差异反映了治理质量和腐败的指标，但与人均收入完全无关。这些发现指出了私人和公共行为者之间的互动结构对于理解治理结果的核心作用。