Abstract
We use methods from network science to analyze corruption risk in a large administrative dataset of over 4 million public procurement contracts from European Union member states covering the years 2008–2016. By mapping procurement markets as bipartite networks of issuers and winners of contracts, we can visualize and describe the distribution of corruption risk. We study the structure of these networks in each member state, identify their cores, and find that highly centralized markets tend to have higher corruption risk. In all EU countries we analyze, corruption risk is significantly clustered. However, these risks are sometimes more prevalent in the core and sometimes in the periphery of the market, depending on the country. This suggests that the same level of corruption risk may have entirely different distributions. Our framework is both diagnostic and prescriptive: It roots out where corruption is likely to be prevalent in different markets and suggests that different anti-corruption policies are needed in different countries.
Highlights
Ever since states have existed, they have sought to count and track their citizens in order to tax and control them [1]
With single-bidding rates below 10% in some countries and over 30% in others. How do these measures of corruption risk correlate with the survey-based perception indicators mentioned in the previous section? In Fig. 2, we correlate the average single-bidding rates from 2008 to 2016 with Transparency International’s Corruption Perception Index (TI Corruption Perceptions Index (CPI)) [32], the World Bank’s Control of Corruption Index (WB CoC) [33], Varieties of Democracy’s Corruption Index (V-Dem Corruption Index) [35], and the Quality of Government (QoG) Institute’s European Quality of Governance Index
We found that countries may have similar levels of corruption risk but significantly different distributions of that risk
Summary
Ever since states have existed, they have sought to count and track their citizens in order to tax and control them [1]. We mine a big administrative dataset with over 4 million public procurement contracts in the EU for insights on the organization of corruption. With micro-level data in hand, we apply the tools of network science to map and analyze the distribution and structure of corruption risks in different European countries. We represent markets as bipartite networks of the issuers (public institutions, sometimes referred to as buyers) and winners (firms, sometimes referred to as suppliers) of procurement contracts. These bipartite networks have many qualitative characteristics in common with other empirical networks [17] that distinguish them from random networks.
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