Uncovering the structure of public procurement transactions

Mircea Popa*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
174 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)351-384
Number of pages34
JournalBusiness and Politics
Volume21
Issue number3
Early online date10 May 2019
DOIs
Publication statusPublished - 1 Sep 2019

Keywords

  • machine learning
  • corruption
  • governance
  • public contracting
  • public procurement

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