European Procurement Markets as Bipartite Networks

EU Procurement Market Network Data

Johannes Wachs
January 2020
*In case you use this data, please reference: 

• Wachs, J., Fazekas, M. & Kertész, J. Corruption risk in contracting markets: a network science perspective. Int J Data Sci Anal (2020). 10.1007/s41060-019-00204-1
• Available open access at: https://link.springer.com/article/10.1007%2Fs41060-019-00204-1

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.

Nodes with the suffix "_i" are issuers (sometimes referred to as buyers) of public contracts, for instance public hospitals, ministries, local governments. Nodes with the suffix "_w" are winners (sometimes called suppliers) of public contracts, generally private-sector firms. Identities have been statistically deduplicated, as described in the paper. 

Each network is bipartite: edges only exist between issuers and winners. Edges represent contracting relationships and have two attributes: 

• ``count'' 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.
• ``pctSingleBid'' 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.

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.

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 as an example):

    import networkx as nx
    G=nx.read_gml('country_year_networks/PT_2010.gml')