του
Γιώργου Δελαστίκ
Γιώργου Δελαστίκ
Ποιος κυβερνά τον κόσμο; Το κλασικό και
σχεδόν γραφικό αυτό ερώτημα, που σε κάθε εποχή δέχεται διαφορετικές
απαντήσεις, έχει την απάντησή του και στις μέρες μας: μόλις 147
επιχειρήσεις! Μπορείτε να απαντήσετε και 737 επιχειρήσεις, καθώς οι 147
πρώτες ελέγχουν το 40% της παγκόσμιας οικονομίας, ενώ οι 737 (στις
οποίες συμπεριλαμβάνονται και οι 147) ελέγχουν το 80% της οικονομίας του
πλανήτη! Είναι απίστευτη πραγματικά η συγκέντρωση του κεφαλαίου και η
αλληλοδιασύνδεση των κολοσσιαίων επιχειρήσεων που κυριαρχούν στην
υδρόγειο.
Οι πάντες υπέθεταν ισχυρότατη συγκέντρωση
ελέγχου, αλλά τέτοιο πράγμα, μερικές εκατοντάδες επιχειρήσεις
αλληλοδιαπλεκόμενες να έχουν συμμετοχή σε εταιρείες που εκπροσωπούν το
80% της παγκόσμιας οικονομίας από πλευράς κύκλου εργασιών, κανένας δεν
το φανταζόταν. Γι’ αυτό και έχει προκαλέσει παγκόσμιο σάλο, αίσθηση και
συζητήσεις η πρωτοποριακή μελέτη τριών Ελβετών ερευνητών του
Πολυτεχνείου της Ζυρίχης, που αποκάλυψε τα στοιχεία αυτά.
Ο Τζέιμς Γκλάτφελντερ, ο Στέφανο
Μπατιστόν και η Στεφανία Βιτάλι, ειδικοί στα σύνθετα δίκτυα, ανέλαβαν
ένα εξαιρετικής σημασίας και τεράστιου όγκου δουλειάς έργο. Αντλησαν τα
στοιχεία της βάσης δεδομένων του ΟΟΣΑ για τις επιχειρήσεις (Οτβίς) για
το έτος 2007, το οποίο τότε περιλάμβανε στοιχεία για 37 εκατομμύρια
επιχειρήσεις σε όλον τον κόσμο (σήμερα περιλαμβάνει 44 εκατομμύρια
εταιρείες).
Από αυτά τα 37.000.000 ξεχώρισαν 43.060
επιχειρήσεις, οι οποίες ανταποκρίνονται στα κριτήρια που θέτει ο ΟΟΣΑ
για να οριστούν ως πολυεθνικές. Από εκεί και πέρα άρχισε η κοπιαστική
και πρωτότυπη δουλειά των ερευνητών: ερεύνησαν τι ποσοστό συμμετοχής
έχει η καθεμιά από τις μεγάλες αυτές επιχειρήσεις παγκόσμιας κλίμακας σε
άλλες επιχειρήσεις, μικρές ή μεγάλες.
Εκπληκτοι διαπίστωσαν ότι κάθε μία από
αυτές τις 43.000 επιχειρήσεις είχε κατά μέσο όρο πακέτα μετοχών (όχι
πλειοψηφικά, εννοείται) σε άλλες 20 επιχειρήσεις αυτής της κατηγορίας! Η
αλληλοδιαπλοκή μεταξύ τους δηλαδή ήταν τεράστιας έκτασης, πέρα φυσικά
από τις μετοχές εταιρειών μικρότερου μεγέθους που κατείχαν και οι οποίες
αποκάλυψαν ένα δίκτυο 600.000 αλληλεξαρτώμενων εταιρειών.
Η περαιτέρω επεξεργασία των στοιχείων
αυτών έφερε στο φως τις 147 προαναφερθείσες επιχειρήσεις (τα τρία
τέταρτα των οποίων ανήκουν στον χρηματοπιστωτικό τομέα, με πρώτη στον
κόσμο τη βρετανική τράπεζα Μπάρκλεϊς) που εκπροσωπούν το 40% της
παγκόσμιας οικονομίας. Διαπιστώνεται έτσι η ύπαρξη «μιας οικονομικής
υπερ-οντότητας στο παγκόσμιο δίκτυο των μεγάλων εταιρειών», όπως
επισημαίνουν οι Ελβετοί ερευνητές.
Η αλληλοδιασύνδεση αυτών των πανίσχυρων
επιχειρήσεων ενισχύεται ακόμη περισσότερο από δάνεια που χορηγούν η μία
στην άλλη, από ασφάλιστρα κινδύνου (CDS) και από άλλα υψηλού κινδύνου
χρηματοοικονομικά προϊόντα εντελώς αδιαφανή.
Το πολύ σημαντικό όμως στοιχείο επίσης
είναι ότι αυτή η στενότατη αλληλοδιασύνδεση αυξάνει τρομερά τους
κινδύνους μετάδοσης σε περιόδους οικονομικών κρίσεων, γιατί «σε άσχημες
εποχές οι επιχειρήσεις εμφανίζουν ταυτόχρονα προβλήματα» και έτσι δρουν
άκρως αποσταθεροποιητικά για το σύστημα.
Αυτό αποδείχτηκε περίτρανα το φθινόπωρο
του 2008 με την κατάρρευση της επενδυτικής τράπεζας Λίμαν Μπράδερς. Ετσι
εξηγείται γιατί η χρεοκοπία μίας και μόνης τράπεζας (34ης στη λίστα των
Ελβετών ερευνητών το 2007) πυροδότησε μια παγκόσμια χρηματοπιστωτική
κρίση – ακριβώς λόγω της ισχυρότατης αλληλοδιασύνδεσης αυτών των
γιγαντιαίων επιχειρήσεων.
Η μελέτη των ερευνητών του Πολυτεχνείου
της Ζυρίχης δεν μετράει φυσικά την τρομερή πολιτική ισχύ που δίνει σε
αυτές τις 147 εταιρείες η κολοσσιαία οικονομική τους δύναμη. «Στις ΗΠΑ
κατόρθωσαν πάνω απ’ όλα οι πρώην συνεργάτες της τράπεζας Γκόλντμαν Σαξ
που βρίσκονται στην αμερικανική κυβέρνηση και στο Κογκρέσο καθώς και οι
λομπίστες της Γουόλ Στριτ να εμποδίσουν κάθε πραγματικό έλεγχο του
χρηματοπιστωτικού τομέα… Επίσης στην Αγγλία, στην Ελβετία ή στη Γερμανία
πολύ λίγα έχουν γίνει στο θέμα αυτό», έγραφε η συντηρητική γερμανική
εφημερίδα «Ντι Βελτ».
Σκάνδαλο
Αντί για φόρους επιδοτήσεις
Ισχύς χωρίς οικονομικά ανταλλάγματα δεν
σημαίνει τίποτα στην εποχή μας. Γι’ αυτό και οι κολοσσιαίες επιχειρήσεις
που προαναφέραμε δεν πληρώνουν ουσιαστικά φόρους. Οπως γράφουν οι
«Τάιμς της Νέας Υόρκης», η Τζένεραλ Ελέκτρικ για παράδειγμα, με κέρδη
μέσα στις ΗΠΑ το 2010 ύψους 5 δισεκατομμυρίων δολαρίων, όχι μόνο δεν
πλήρωσε ούτε ένα δολάριο φόρο, αλλά πήρε κι από πάνω προνομιακές
επιδοτήσεις τριών δισεκατομμυρίων δολαρίων! Σκανδαλώδες, αλλά
συνηθισμένο πλέον. Αρκεί να φανταστεί κανείς ότι στις ΗΠΑ της δεκαετίας
του 1950, το κράτος εισέπραττε από τις επιχειρήσεις το 30% των εσόδων
του, ενώ το 2009 εισέπραξε μόλις το… 6,6%! Τώρα τα κράτη «γδέρνουν»
φορολογικά τους πολίτες τους. Οι εταιρείες κάνουν πάρτι.
EΘΝΟΣ 27.01.2012
——————————————
Παρακάτω μπορεί να δει κανείς την πηγή
που επικαλείται στο άρθρο του ο Γ. Δελαστίκ, μαζί με την παράθεση των
μεθόδων που ακολούθησε η μελέτη και συνοπτική παρουσίαση των
αποτελεσμάτων της στο αγγλικό πρωτότυπο:
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.
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 investigations 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 
denote the ownership matrix, where the component 
is the percentage of ownership that the owner (or shareholder) 
holds in firm 
. This corresponds to a directed weighted graph with firms represented as nodes and ownership ties as links. If, in turn, firm 
owns 
shares of firm 
, then firm 
has an indirect ownership of firm 
(Figure 1 A). In the simplest case, this amounts trivially to the product of the shares of direct ownership 
. If we now consider the economic value 
of firms (e.g., operating revenue in USD), an amount 
is associated to 
in the direct case, and 
in 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
denote the ownership matrix, where the component is the percentage of ownership that the owner (or shareholder) holds in firm . This corresponds to a directed weighted graph with firms represented as nodes and ownership ties as links. If, in turn, firm owns shares of firm , then firm has an indirect ownership of firm (Figure 1 A). In the simplest case, this amounts trivially to the product of the shares of direct ownership . If we now consider the economic value of firms (e.g., operating revenue in USD), an amount is associated to in the direct case, and in 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
Ownership and Control.
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 
in a firm, the larger is the associated control over it, denoted as 
.
Intuitively, control corresponds to the chances of seeing one's own
interest prevailing in the business strategy of the firm. Control 
is usually computed from ownership 
with a simple threshold rule: the majority shareholder has full control. In the example of Figure 1 C, D, this yields 
in the direct case and 
in 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: 
. This sums up the value controlled by 
through its shares in 
, plus the value controlled indirectly via the network control of 
. Thus, network control has the meaning of the total amount of economic value over which 
has an influence (e.g. 
in Figure 1 D).
in a firm, the larger is the associated control over it, denoted as .
Intuitively, control corresponds to the chances of seeing one's own
interest prevailing in the business strategy of the firm. Control is usually computed from ownership with a simple threshold rule: the majority shareholder has full control. In the example of Figure 1 C, D, this yields in the direct case and in 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: . This sums up the value controlled by through its shares in , plus the value controlled indirectly via the network control of . Thus, network control has the meaning of the total amount of economic value over which has an influence (e.g. in Figure 1 D).
Because
of indirect links, control flows upstream from many firms and can
result in some shareholders becoming very powerful. However, especially
in graphs with many cycles (see Figures 1 Band S4 in Appendix S1), the computation of 
,
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.
,
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
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.
Network topology.
Bow-tie statistics.
Notice that the cross-country analysis of [11]
found that only a few of the national ownership networks are bow-ties,
and, 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 
of top holders holding cumulatively 
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 
top holders accumulate 
of the control over the value of all TNCs (see also the list of the top 
holders in Table S1 of Appendix S1). The corresponding level of concentration is 
, to be compared with 
for operating revenue. Other sensible comparisons include: income distribution in developed countries with 
[22] and corporate revenue in Fortune1000 (
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.
of top holders holding cumulatively
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 top holders accumulate of the control over the value of all TNCs (see also the list of the top holders in Table S1 of Appendix S1). The corresponding level of concentration is , to be compared with for operating revenue. Other sensible comparisons include: income distribution in developed countries with [22] and corporate revenue in Fortune1000 (
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.
Concentration of network control and operating revenue.
Discussion
The
fact that control is highly concentrated in the hands of few top
holders does not determine if and how they are interconnected. 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 
chance of also being among the top holders, compared to, e.g., 
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 
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 
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 
of 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.
chance of also being among the top holders, compared to, e.g., 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
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
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 of 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 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 abbreviations, Data and TNC Network Detection,
Network Control, Degree and Strength Distribution Analysis, Connected
Components Analysis, Bow-Tie Component Size, Strongly Connected
Component Analysis, Network Control Concentration, Additional Tables.
(PDF)
Click here for additional data file.(716K, 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.
Footnotes
Competing Interests: The authors have declared that no competing interests exist.
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.
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