Headlines Published on 17 November 2011

ICT
Title Researchers spotlight global corporate control

The control network structure of multinational companies impacts market competition as well as financial stability on a global level. Researchers in Switzerland investigated the architecture of the international ownership network, as well as the computation of the control held by each global player. Their results, published in the journal PLoS ONE, show that multinational companies are part of a huge 'bow-tie' structure, and that a large piece of the control pie is maintained by a small yet tight 'core' of financial institutions. The researchers call this core an economic 'superentity'. The study was funded in part by the FOC-II ('Forecasting financial crises') project, which is backed with almost EUR 1.9 million under the 'Information and communication technologies' (ICT) Theme of the EU's Seventh Framework Programme (FP7).

The 1 318 transnational corporations that form the core of the economy. Super-connected companies are red, very connected companies are yellow. The size of the dot represents revenue size  © PLoS One
The 1 318 transnational corporations that form the core of the economy. Super-connected companies are red, very connected companies are yellow. The size of the dot represents revenue size
©  PLoS One

While most people have long believed that a handful of companies dominates the global economy, no study has been able to confirm or even reject this theory. Getting the quantitative information is no easy task; companies could control other firms either directly or indirectly.

In order to find the structure of control and its implications, particularly how they impact the global economy, researchers from the Chair of Systems Design at the Swiss Federal Institute of Technology Zurich (ETH Zurich) identified a small group of firms that have disproportionate power over the world economy. The team achieved this by investigating the relationship between 43 000 multinational companies.

'We start from a list of 43 060 translational corporations identified according to the Organisation for Economic Cooperation and Development (OECD) definition, taken from a sample of about 30 million economic actors contained in the Orbis 2007 database,' the authors write. 'We then apply a recursive search which singles out, for the first time to our knowledge, the network of all the ownership pathways originating from and pointing to transnational corporations. The resulting network includes 600 508 nodes and 1.1 million ownership ties.'

In their study, they discovered a core of 1 318 companies with interlocking ownerships. Each of the 1 318 had connections to at least 2 if not more companies. On average, each company was linked to 20 others.

The team points out that while the core accounts for 20% of the world operating revenues, the 1 318 seemingly hold, through their shares, the majority of the globe's large blue chip and manufacturing companies. The latter is considered by many to be the 'real economy', making up another 60% of the global revenues. They also found that only 147 companies control 40% of the network's wealth, what most people know as the '1%'.

'This remarkable finding raises at least two questions that are fundamental to the understanding of the functioning of the global economy,' write the authors of the study. 'Firstly, what are the implication for global financial stability? It is known that financial institutions establish financial contracts, such as lending or credit derivatives, with several other institutions. This allows them to diversify risk, but, at the same time, it also exposes them to contagion. Unfortunately, information on these contracts is usually not disclosed due to strategic reasons. However, in various countries, the existence of such financial ties is correlated with the existence of ownership relations. Thus, in the hypothesis that the structure of the ownership network is a good proxy for that of the financial network, this implies that the global financial network is also very intricate.'

The researchers say their methodology can be used to identify significant nodes in any real-world network in which a scalar quantity, like resources, flows along directed weighted links.









More information:

  • PLoS ONE
  • FOC-II
  • ETH Zurich







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