Abstract

Fraud in public funding can have deleterious consequences for societies’ economic, social, and political well-being. Fraudulent activity associated with public procurement contracts accounts for losses of billions of euros every year. Thus, it is of utmost relevance to explore analytical frameworks that can help public authorities identify agents that are more susceptible to irregular activities. Here, we use standard network science methods to study the co-bidding relationships between firms that participate in public tenders issued by the 184 municipalities of the State of Ceará (Brazil) between 2015 and 2019. We identify 22 groups/communities of firms with similar patterns of procurement activity, defined by their geographic and activity scopes. The profiling of the communities allows us to highlight organizations that are more susceptible to market manipulation and irregular activities. Our work reinforces the potential application of network analysis in policy to unfold the complex nature of relationships between market agents in a scenario of scarce data.

Highlights

  • Despite the weight of public procurement in governmental budgets (OECD.Stat 2017), procurement activity is still one of the most vulnerable vehicles open to corruption (Murray 2014; OECD 2015)

  • By matching firms with similar bidding patterns, we have inferred a firm–firm network comprising a total of 1141 nodes and 10,630 edges

  • We showed that we were able to identify communities of firms with similar bidding patterns

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Summary

Introduction

Despite the weight of public procurement in governmental budgets (OECD.Stat 2017), procurement activity is still one of the most vulnerable vehicles open to corruption (Murray 2014; OECD 2015). Can communities of firms obtained from co-bidding patterns allow us to highlight groups that are more susceptible to collusion and market manipulation? The use of network analysis to study the relationship between firms in procurement bids is a relatively new venture (Reeves-Latour and Morselli 2017).

Results
Conclusion
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