FOC-platform

TNC



The Network of Global Corporate Control


Stefania Vitali, James B. Glattfelder, Stefano Battiston*
Chair of Systems Design, ETH Zurich, Zurich, Switzerland

Transnational corporations (TNC) are the main players in today's economy. The control over a corporation measures the potential influence that a shareholder has in its business strategy. This work is the first global analysis of the control of transnational corporations. In particular, we analyze the architecture of their whole ownership network, which enables us to compute the control held by each global player.

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Download the 737 top holders list as an Excel Spreadsheet or explore it with Google document . Read the full article on PlosOne

The work was supported by the FET-OPEN project FOC "Forecasting Financial Crises"
The diagram shows the network of the 295 TNCs among the top holders

1) THE BASICS OF THE METHOD TO ESTIMATE CONTROL: In the simplest scenario we have used, we estimate the control of a shareholder as the fraction of its shares weighted by the value of the company. E.g., if A owns 20% of B, then we say A has a control over 20% of the revenue. This is the basic method. We then extend the computation to take into account longer chains, multiple paths and loops. We used also other more complicated scenarios to test the robustness of the results



2) WHAT THE PAPER CLAIMS: The basics of the recipe are simple and the results show that there is a group of 1300 companies, the core, that are all connected to each other by relations of direct or indirect ownership, the shares of which remains for 3/4 within the core. This fact alone indicate a very high level of interconnectedness. Moreover, altogether, this core controls (in the above meaning of “control”) 50% of the total TNC operating revenue in the world (By the way, funds are a tiny minority in the core. The fund issue is a minor issue that diverts the attention from the major facts). Depending on where we cut the crop of the cream, we obtain various figures. The one has circulated on the web is that 147 topholders TNC in the core of the network, control nearly 40% of the TNC value. Overall, we observe high concentration of control in a core of actors connected by high level of mutual control. These are the main results of the study



3) WHAT THE PAPER DOES NOT CLAIM: Our study does NOT claim that the actors in the core are colluding. NOR it claims that this structure is the result of some intentional design. We actually think that it probably emerges “naturally” as a result of simple mechanisms that are at work in the market. However, “naturally”, does not necessarily means “good” for our economies and societies. What we claim is that further studies are needed to investigate the implications of such a structure, because it is very well possible that it is engenders market competition and financial stability. We do not say it certainly engenders. We say that, based on what is known, we should better check.

4) IMPLICATIONS The findings raise legitimate questions in two domains which are today in all newspapers: market competition and systemic risk. a) Market competition: Previous studies (on small samples) have shown that cross-shareholdings (e.g. when firm mutually hold shares of each other) significantly reduce competition. b) Financial systemic risk: For years economic theory has supported the idea that more connected networks are more stable. In contrast, recently, the work of some scholars as well as the view of some authoritative policy makers predict that higher level of interconnections among financial institutions can lead to higher systemic risk. In a nutshell, if the global financial sector is all tied up together, then financial distress propagates like a contagious disease to all the major institutions. It follows that, in the debate on financial stability, our result is of great interest, especially because, surprisingly, there is no prior investigation of how entangled the global financial sector is.

5) GENERAL RELEVANCE Our study is also relevant to the field of complex networks in general, since our methodology can be applied to discover influential nodes in any network where resources are flowing along weighted directed edges (e.g. food webs and supply networks).