European Procurement Markets as Bipartite Networks

<p><strong>EU Procurement Market Network Data</strong></p> <p>Johannes Wachs<br> January 2020<br> <strong>*In case you use this data, please reference:</strong>&nbsp;</p> <ul> <li>Wachs, J., Fazekas, M. &amp; Kert&eacute;sz, J. Corruption risk in contracting markets: a network science perspective. Int J Data Sci Anal (2020). 10.1007/s41060-019-00204-1</li> <li>Available open access at: <a href="https://link.springer.com/article/10.1007%2Fs41060-019-00204-1">https://link.springer.com/article/10.1007%2Fs41060-019-00204-1</a></li> </ul> <p>These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.</p> <p>Nodes with the suffix &quot;_i&quot; are issuers (sometimes referred to as buyers) of public contracts, for instance public hospitals, ministries, local governments. Nodes with the suffix &quot;_w&quot; are winners (sometimes called suppliers) of public contracts, generally private-sector firms. Identities have been statistically deduplicated, as described in the paper.&nbsp;</p> <p>Each network is bipartite: edges only exist between issuers and winners. Edges represent contracting relationships and have two attributes:&nbsp;</p> <ul> <li>``count&#39;&#39; measures the volume of contracts between the issuer and winner in the given year. This attribute can be interpreted as a weight or strength of the relationship.</li> <li>``pctSingleBid&#39;&#39; describes the share of contracts between the issuer and winner awarded without competition, i.e. with the winner as single bidder or sole-supplier. Note: missing data on single-bidding is imputed. This is an elementary indicator of corruption risk of the contract. For more information consult the paper referenced above.</li> </ul> <p>Ids of issuers and winners are consistent across time and within countries. Node ids have been randomly generated and do not correspond to any official statistics.</p> <p>The networks are stored in the GML format. For example: they can be read in directly by Gephi or by the Python NetworkX library with the following commands (using the 2010 Portuguese market&nbsp;as an example):</p> <blockquote> <p>&nbsp;&nbsp; &nbsp;import networkx as nx<br> &nbsp;&nbsp; &nbsp;G=nx.read_gml(&#39;country_year_networks/PT_2010.gml&#39;)</p> </blockquote>