The Network of Global Corporate Control, Media Digital Cyber

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S. Vitali, J.B. Glattfelder, and S. Battiston:
The network of global corporate control
Thenetworkofglobalcorporatecontrol
StefaniaVitali
1
,JamesB.Glattfelder
1
,andStefanoBattiston
1?
1
Chair of Systems Design, ETH Zurich, Kreuzplatz 5, 8032 Zurich, Switzerland,
?
corresponding author, email: sbattiston@ethz.ch
Abstract
The structure of the control network of transnational corporations aects global market com-
petition 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 struc-
ture 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.
Introduction
A common intuition among scholars and in the media sees the global economy as being domi-
nated 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 rela-
tions 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 litera-
ture 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 oer 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 eects on their ﬁnancial fragility [15, 16]. Verifying to
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S. Vitali, J.B. Glattfelder, and S. Battiston:
The network of global corporate control
A
B
C D
Figure 1:OwnershipandControl.(A&B) Direct and indirect ownership. (A) Firm i has W
ij
percent of direct ownership in ﬁrm j. Through j, it has also an indirect ownership in k and l. (B)
With cycles one has to take into account the recursive paths, see SI Appendix, Sec. 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 SI Appendix, Sec. 3.1.
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 inves-
tigations 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 W denote
the ownership matrix, where the component W
ij
2[0;1]is the percentage of ownership that the
owner (or shareholder) i holds in ﬁrm j. This corresponds to a directed weighted graph with
ﬁrms represented as nodes and ownership ties as links. If, in turn, ﬁrm j owns W
jl
shares of ﬁrm
l, then ﬁrm i has an indirect ownership of ﬁrm l (Fig. 1 A). In the simplest case, this amounts
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S. Vitali, J.B. Glattfelder, and S. Battiston:
The network of global corporate control
trivially to the product of the shares of direct ownership W
ij
W
jl
. If we now consider the economic
value v of ﬁrms (e.g., operating revenue in USD), an amount W
ij
v
j
is associated to i in the direct
case, and W
ij
W
jl
v
l
in the indirect case. This computation can be extended to a generic graph,
with some important caveats [17, SI Appendix, Secs. 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 W
ij
in a ﬁrm, the larger is the associated control over it, denoted as C
ij
.
Intuitively, control corresponds to the chances of seeing one’s own interest prevailing in the
business strategy of the ﬁrm. Control C
ij
is usually computed from ownership W
ij
with a simple
threshold rule: the majority shareholder has full control. In the example of Fig. 1 C, D, this yields
C
ij
v
j
=1v
j
in the direct case and C
ij
C
jl
v
l
=0in the indirect case. As a robustness check, we
tested also more conservative models where minorities keep some control (see SI Appendix, Sec.
3.1). In analogy to ownership, the extension to a generic graph is the notion of network control:
c
net
i
=
P
j
C
ij
v
j
+
P
j
C
ij
c
ne
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 i has an inﬂuence (e.g. c
net
i
=v
j
+v
k
in Fig. 1 D).
Because of indirect links, control ﬂows upstream from many ﬁrms and can result in some share-
holders becoming very powerful. However, especially in graphs with many cycles (see Figs. 1 B
and S4), the computation of c
net
, 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 prob-
lem on a simple network example, together with the details of the method are provided in SI
Appendix, Secs. 3.2 – 3.4. A partial solution for small networks was provided in [18]. Previous
work on large control networks used a dierent network construction method and neglected this
issue entirely [11, SI Appendix, Secs. 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 SI
Appendix, Sec. 2). We then apply a recursive search (Fig. S1 and SI Appendix, Sec. 2) which
singles out, for the ﬁrst time to our knowledge, the network of all the ownership pathways
originating from and pointing to TNCs (Fig. S2). The resulting TNC network includes 600508
nodes and 1006987 ownership ties.
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S. Vitali, J.B. Glattfelder, and S. Battiston:
The network of global corporate control
B
A
C
D
Figure 2:Networktopology.(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 struc-
ture 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 paren-
thesis, 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.
Notice that this data set fundamentally diers from the ones analysed in [11] (which considered
only listed companies in separate countries and their direct shareholders). Here we are interested
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S. Vitali, J.B. Glattfelder, and S. Battiston:
The network of global corporate control
Table 1:Bow-tiestatistics.Percentage of total TNC operating revenue (OR) and number (#)
of nodes in the sections of the bow-tie (acronyms are in Fig. 2). Economic actors types are:
shareholders (SH), participated companies (PC).
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
in the true global ownership network and many TNCs are not listed companies (see also SI
Appendix, Sec. 2).
NetworkTopology
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 (Tbl. 1). Besides the usual network statistics (Figs. S5, S6), 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 (Fig. S7 and SI Appendix, Sec. 7), 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 contains only one dominant
strongly connected component (1347 nodes). Thus, similar to the WWW, the TNC network has
a bow-tie structure [21] (see Fig. 2 A and SI Appendix, Sec. 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 (Fig. 2
B and Tbl. 1). The core is also very densely connected, with members having, on average, ties
to 20 other members (Fig. 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 net-
works are bow-ties, and, importantly, for the Anglo-Saxon countries, the main strongly connected
components are big compared to the network size.
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