The Network of Global Corporate Control

Abstract

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic "super-entity" that raises new important issues both for researchers and policy makers.

Full text

The Network of Global Corporate Control
Stefania Vitali, James B. Glattfelder, Stefano Battiston*
Chair of Systems Design, ETH Zurich, Zurich, Switzerland
Abstract
The structure of the control network of transnational corporations affects global market competition and financial stability.
So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We
present the first investigation of the architecture of the international ownership network, along with the computation of the
control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large
portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic ‘‘super-
entity’’ that raises new important issues both for researchers and policy makers.
Citation: Vitali S, Glattfelder JB, Battiston S (2011) The Network of Global Corporate Control. PLoS ONE 6(10): e25995. doi:10.1371/journal.pone.0025995
Editor: Alejandro Raul Hernandez Montoya, Universidad Veracruzana, Mexico
Received March 29, 2011; Accepted September 15, 2011; Published October 26, 2011
Copyright: ß2011 Vitali et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors acknowledge financial support from the ETH Competence Center ‘‘Coping with Crises in Complex Socio-Economic Systems’’ (CCSS)
through ETH Research Grant CH1-01-08-2; the European Commission FP7 FET Open Project ‘‘FOC’’ No. 255987. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: sbattiston@ethz.ch
Introduction
A common intuition among scholars and in the media sees the
global economy as being dominated by a handful of powerful
transnational corporations (TNCs). However, this has not been
confirmed or rejected with explicit numbers. A quantitative
investigation is not a trivial task because firms may exert control
over other firms via a web of direct and indirect ownership
relations which extends over many countries. Therefore, a
complex network analysis [1] is needed in order to uncover the
structure of control and its implications. Recently, economic
networks have attracted growing attention [2], e.g., networks of
trade [3], products [4], credit [5,6], stock prices [7] and boards of
directors [8,9]. This literature has also analyzed ownership
networks [10,11], but has neglected the structure of control at a
global level. Even the corporate governance literature has only
studied small national business groups [12]. Certainly, it is
intuitive that every large corporation has a pyramid of
subsidiaries below and a number of shareholders above.
However, economic theory does not offer models that predict
how TNCs globally connect to each other. Three alternative
hypotheses can be formulated. TNCs may remain isolated,
cluster in separated coalitions, or form a giant connected
component, possibly with a core-periphery structure. So far, this
issue has remained unaddressed, notwithstanding its important
implications for policy making. Indeed, mutual ownership
relations among firms within the same sector can, in some cases,
jeopardize market competition [13,14]. Moreover, linkages
among financial institutions have been recognized to have
ambiguous effects on their financial fragility [15,16]. Verifying
to what extent these implications hold true in the global economy
is per se an unexplored field of research and is beyond the scope of
this article. However, a necessary precondition to such investi-
gations is to uncover the worldwide structure of corporate
control. This was never performed before and it is the aim of the
present work.
Methods
Ownership refers to a person or a firm owning another firm
entirely or partially. Let Wdenote the ownership matrix, where
the component Wij [½0, 1is the percentage of ownership that the
owner (or shareholder)iholds in firm j. This corresponds to a
directed weighted graph with firms represented as nodes and
ownership ties as links. If, in turn, firm jowns Wjl shares of firm l,
then firm ihas an indirect ownership of firm l(Figure 1 A). In the
simplest case, this amounts trivially to the product of the shares of
direct ownership Wij Wjl . If we now consider the economic value v
of firms (e.g., operating revenue in USD), an amount Wij vjis
associated to iin the direct case, and Wij Wjl vlin the indirect case.
This computation can be extended to a generic graph, with some
important caveats [17], Appendix S1, Sections 3.1 and 3.2.
Each shareholder has the right to a fraction of the firm revenue
(dividend) and to a voice in the decision making process (e.g.,
voting rights at the shareholder meetings). Thus the larger the
ownership share Wij in a firm, the larger is the associated control
over it, denoted as Cij . Intuitively, control corresponds to the
chances of seeing one’s own interest prevailing in the business
strategy of the firm. Control Cij is usually computed from
ownership Wij with a simple threshold rule: the majority
shareholder has full control. In the example of Figure 1 C, D,
this yields Cij vj~1vjin the direct case and Cij Cjl vl~0in the
indirect case. As a robustness check, we tested also more
conservative models where minorities keep some control (see
Appendix S1, Section 3.1). In analogy to ownership, the extension
to a generic graph is the notion of network control:cnet
i~
XjCij vjzXjCijcnet
j. This sums up the value controlled by i
through its shares in j, plus the value controlled indirectly via the
network control of j. Thus, network control has the meaning of the
total amount of economic value over which ihas an influence (e.g.
cnet
i~vjzvkin Figure 1 D).
Because of indirect links, control flows upstream from many
firms and can result in some shareholders becoming very powerful.
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However, especially in graphs with many cycles (see Figures 1
Band S4 in Appendix S1), the computation of cnet , in the basic
formulation detailed above, severely overestimates the control
assigned to actors in two cases: firms that are part of cycles (or
cross-shareholding structures), and shareholders that are upstream
of these structures. An illustration of the problem on a simple
network example, together with the details of the method are
provided in Appendix S1, Sections 3.2–3.4. A partial solution for
small networks was provided in [18]. Previous work on large
control networks used a different network construction method
and neglected this issue entirely [11], Appendix S1, Sections 2 and
3.5. In this paper, by building on [11], we develop a new
methodology to overcome the problem of control overestimation,
which can be employed to compute control in large networks.
Results
We start from a list of 43060 TNCs identified according to the
OECD definition, taken from a sample of about 30 million
economic actors contained in the Orbis 2007 database (see
Appendix S1, Section 2). We then apply a recursive search (Figure
S1 and Section 2 in Appendix S1) which singles out, for the first
time to our knowledge, the network of all the ownership pathways
originating from and pointing to TNCs (Figure S2 in Appendix
S1). The resulting TNC network includes 600508 nodes and
1006987 ownership ties.
Notice that this data set fundamentally differs from the ones
analyzed in [11] (which considered only listed companies in
separate countries and their direct shareholders). Here we are
interested in the true global ownership network and many TNCs
are not listed companies (see also Appendix S1, Section 2).
Network Topology
The computation of control requires a prior analysis of the
topology. In terms of connectivity, the network consists of many
small connected components, but the largest one (3/4 of all nodes)
contains all the top TNCs by economic value, accounting for
94.2% of the total TNC operating revenue (Table 1). Besides the
usual network statistics (Figures S5 and S6 in Appendix S1), two
topological properties are the most relevant to the focus of this
work. The first is the abundance of cycles of length two (mutual
cross-shareholdings) or greater (Figure S7 and Section 7 in
Appendix S1), which are well studied motifs in corporate
governance [19]. A generalization is a strongly connected component
(SCC), i.e., a set of firms in which every member owns directly
and/or indirectly shares in every other member. This kind of
structures, so far observed only in small samples, has explanations
such as anti-takeover strategies, reduction of transaction costs, risk
sharing, increasing trust and groups of interest [20]. No matter its
origin, however, it weakens market competition [13,14]. The
second characteristics is that the largest connect component
Figure 1. Ownership and Control. (A&B) Direct and indirect ownership. (A) Firm ihas Wij percent of direct ownership in firm j. Through j, it has
also an indirect ownership in kand l. (B) With cycles one has to take into account the recursive paths, see Appendix S1, Section 3.1. (C&D) Threshold
model. (C) Percentages of ownership are indicated along the links. (D) If a shareholder has ownership exceeding a threshold (e.g. 50%), it has full
control (100%) and the others have none (0%). More conservative model of control are also considered see Appendix S1, Section 3.1.
doi:10.1371/journal.pone.0025995.g001
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contains only one dominant strongly connected component (1347
nodes). Thus, similar to the WWW, the TNC network has a bow-tie
structure [21] (see Figure 2 A and Appendix S1, Section 6). Its
peculiarity is that the strongly connected component, or core,is
very small compared to the other sections of the bow-tie, and that
the out-section is significantly larger than the in-section and the
tubes and tendrils (Figure 2 B and Table 1). The core is also very
densely connected, with members having, on average, ties to 20
other members (Figure 2 C, D). As a result, about 3/4 of the
ownership of firms in the core remains in the hands of firms of the
core itself. In other words, this is a tightly-knit group of
corporations that cumulatively hold the majority share of each
other.
Notice that the cross-country analysis of [11] found that only a
few of the national ownership networks are bow-ties, and,
Figure 2. Network topology. (A) A bow-tie consists of in-section (IN), out-section (OUT), strongly connected component or core (SCC), and tubes
and tendrils (T&T). (B) Bow-tie structure of the largest connected component (LCC) and other connected components (OCC). Each section volume
scales logarithmically with the share of its TNCs operating revenue. In parenthesis, percentage of operating revenue and number of TNCs, cfr. Table 1.
(C) SCC layout of the SCC (1318 nodes and 12191 links). Node size scales logarithmically with operation revenue, node color with network control
(from yellow to red). Link color scales with weight. (D) Zoom on some major TNCs in the financial sector. Some cycles are highlighted.
doi:10.1371/journal.pone.0025995.g002
Table 1. Bow-tie statistics.
TNC (#)SH(#)PC(#)OR(%)
LCC 15491 47819 399696 94.17
IN 282 5205 129 2.18
SCC 295 0 1023 18.68
OUT 6488 0 318073 59.85
T&T 8426 42614 80471 13.46
OCC 27569 29637 80296 5.83
Percentage of total TNC operating revenue (OR) and number (#) of nodes in
the sections of the bow-tie (acronyms are in Figure 2). Economic actors types
are: shareholders (SH), participated companies (PC).
doi:10.1371/journal.pone.0025995.t001
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importantly, for the Anglo-Saxon countries, the main strongly
connected components are big compared to the network size.
Concentration of Control
The topological analysis carried out so far does not consider the
diverse economic value of firms. We thus compute the network
control that economic actors (including TNCs) gain over the
TNCs’ value (operating revenue) and we address the question of
how much this control is concentrated and who are the top control
holders. See Figure S3 in Appendix S1 for the distribution of
control and operating revenue.
It should be noticed that, although scholars have long
measured the concentration of wealth and income [22], there is
no prior quantitative estimation for control. Constructing a
Lorenz-like curve (Figure 3) allows one to identify the fraction g
of top holders holding cumulatively 80% of the total network
control. Thus, the smaller this fraction, the higher the
concentration. In principle, one could expect inequality of
control to be comparable to inequality of income across
households and firms, since shares of most corporations are
publicly accessible in stock markets. In contrast, we find that only
737 top holders accumulate 80% of the control over the value of
all TNCs (see also the list of the top 50 holders in Table S1 of
Appendix S1). The corresponding level of concentration is
g
1~0:61%,tobecomparedwithg
2~4:35% for operating
revenue. Other sensible comparisons include: income distribution
in developed countries with g
3*5%{10% [22] and corporate
revenue in Fortune1000 (g
4*30% in 2009). This means that
network control is much more unequally distributed than wealth.
In particular, the top ranked actors hold a control ten times
bigger than what could be expected based on their wealth. The
results are robust with respect to the models used to estimate
control, see Figure 3 and Tables S2 and S3 in Appendix S1.
Discussion
The fact that control is highly concentrated in the hands of few
top holders does not determine if and how they are interconnect-
ed. It is only by combining topology with control ranking that we
obtain a full characterization of the structure of control. A first
question we are now able to answer is where the top actors are
located in the bow-tie. As the reader may by now suspect, powerful
actors tend to belong to the core. In fact, the location of a TNC in
the network does matter. For instance, a randomly chosen TNC in
the core has about 50% chance of also being among the top
holders, compared to, e.g., 6% for the in-section (Table S4 in
Appendix S1). A second question concerns what share of total
control each component of the bow-tie holds. We find that, despite
its small size, the core holds collectively a large fraction of the total
network control. In detail, nearly 4=10 of the control over the
economic value of TNCs in the world is held, via a complicated
web of ownership relations, by a group of 147 TNCs in the core,
which has almost full control over itself. The top holders within the
core can thus be thought of as an economic ‘‘super-entity’’ in the
global network of corporations. A relevant additional fact at this
point is that 3=4of the core are financial intermediaries. Figure 2
D shows a small subset of well-known financial players and their
links, providing an idea of the level of entanglement of the entire
core.
This remarkable finding raises at least two questions that are
fundamental to the understanding of the functioning of the global
economy. Firstly, what are the implication for global financial
stability? It is known that financial institutions establish financial
Figure 3. Concentration of network control and operating revenue. Economic actors (TNCs and shareholders) are sorted by descending
importance, as given by cnet. A data point located at (g,h) corresponds to a fraction gof top economic actors cumulatively holding the fraction hof
network control, value or operating revenue. The different curves refer to network control computed with three models (LM, TM, RM), see Appendix
S1, Section 3.1, and operating revenue. The horizontal line denotes a value of hequal to 80%. The level of concentration is determined by the gvalue
of the intersection between each curve and the horizontal line. The scale is semi-log.
doi:10.1371/journal.pone.0025995.g003
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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 [15]. 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 [23]. 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. Recent works have shown that when a financial network
is very densely connected it is prone to systemic risk [16,24].
Indeed, while in good times the network is seemingly robust, in
bad times firms go into distress simultaneously. This knife-edge
property [25,26] was witnessed during the recent financial turmoil.
Secondly, what are the implications for market competition?
Since many TNCs in the core have overlapping domains of
activity, the fact that they are connected by ownership relations
could facilitate the formation of blocs, which would hamper
market competition [14]. Remarkably, the existence of such a core
in the global market was never documented before and thus, so
far, no scientific study demonstrates or excludes that this
international ‘‘super-entity’’ has ever acted as a bloc. However,
some examples suggest that this is not an unlikely scenario. For
instance, previous studies have shown how even small cross-
shareholding structures, at a national level, can affect market
competition in sectors such as airline, automobile and steel, as well
as the financial one [13,14]. At the same time, antitrust institutions
around the world (e.g., the UK Office of Fair Trade) closely
monitor complex ownership structures within their national
borders. The fact that international data sets as well as methods
to handle large networks became available only very recently, may
explain how this finding could go unnoticed for so long.
Two issues are worth being addressed here. One may question
the idea of putting together data of ownership across countries
with diverse legal settings. However, previous empirical work
shows that of all possible determinants affecting ownership
relations in different countries (e.g., tax rules, level of corruption,
institutional settings, etc.), only the level of investor protection is
statistically relevant [27]. In any case, it is remarkable that our
results on concentration are robust with respect to three very
different models used to infer control from ownership. The second
issue concerns the control that financial institutions effectively
exert. According to some theoretical arguments, in general,
financial institutions do not invest in equity shares in order to
exert control. However, there is also empirical evidence of the
opposite [23], Appendix S1, Section 8.1. Our results show that,
globally, top holders are at least in the position to exert
considerable control, either formally (e.g., voting in shareholder
and board meetings) or via informal negotiations.
Beyond the relevance of these results for economics and policy
making, our methodology can be applied to identify key nodes in
any real-world network in which a scalar quantity (e.g., resources
or energy) flows along directed weighted links. From an empirical
point of view, a bow-tie structure with a very small and influential
core is a new observation in the study of complex networks. We
conjecture that it may be present in other types of networks where
‘‘rich-get-richer’’ mechanisms are at work (although a degree
preferential-attachment [1] alone does not produce a bow-tie).
However, the fact that the core is so densely connected could be
seen as a generalization of the ‘‘rich-club phenomenon’’ with
control in the role of degree [3,28], Appendix S1, Section 8.2.
These related open issues could be possibly understood by
introducing control in a ‘‘fitness model’’ [29] of network evolution.
Supporting Information
Appendix S1 Supporting material: Acronyms and abbrevia-
tions, Data and TNC Network Detection, Network Control,
Degree and Strength Distribution Analysis, Connected Compo-
nents Analysis, Bow-Tie Component Size, Strongly Connected
Component Analysis, Network Control Concentration, Additional
Tables.
(PDF)
Acknowledgments
Authors acknowledge F. Schweitzer and C. Tessone for valuable feedback,
D. Garcia for generating the 3D figures, and the program Cuttlefish used
for networks layout.
Author Contributions
Conceived and designed the experiments: SB. Analyzed the data: SV JBG.
Wrote the paper: SB SV JBG.
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