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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.
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The Network of Global Corporate Control
Stefania Vitali, James B. Glattfelder, Stefano Battiston*
Chair of Systems Design, ETH Zurich, Zurich, Switzerland
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:
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.
Ownership refers to a person or a firm owning another firm
entirely or partially. Let Wdenote the ownership matrix, where
the component Wij [½0, 1is 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
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.
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.
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.
The Network of Global Corporate Control
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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
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.
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).
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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
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.
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
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.
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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
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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Supplementary resource (1)

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ABSTRACT In this study we investigate the creation and persistence of interfirm ties in a large-scale business transaction network. Transaction ties, firms buying or selling products or services can be the outcome of pure business motivations, but the social connections of owners or the geographical location of companies may also influence their development. We build the transaction and the ownership networks of firms in Hungary for 2016 and 2017 from two administrative datasets and identify multi-layer network motifs to predict the creation and persistence of business transactions. We show that direct or indirect relationships in this two-layered network contribute to both the creation and persistence of business transaction ties. We find a positive, but smaller impact of geographic proximity and industrial similarity of firms. For our estimations, we utilize loglinear models and emphasize their efficiency in predicting links in such large networks. We contribute to the literature by illustrating business connection patterns in a nationwide multilayer interfirm network.
... We call the owning/controlling entity the "parent" and the owned/controlled entity the "offspring." Direct ownership relationships can typically be composed transitively to generate indirect relationships; for example, if A owns B directly, and B owns C directly, then A owns C indirectly [26]. ...
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We consider the challenges and benefits of ontologies for information management for regulatory reporting from bank holding companies (BHCs). Many BHCs, especially the largest and most complex firms, have multiple federal supervisors who oversee a diverse array of subsidiaries. This creates a federated data management problem that disperses information across many firms and regulators. We prototype an ontology for the Federal Reserve's public National Information Center (NIC) database. The NIC identifies all BHCs, their subsidiaries, and the ownership and control relationships among them. It is a basic official source on the structure of the industry. A formal ontology can capture this expert-curated knowledge in a coherent, structured format. This could assure data integrity and enable non-experts to more readily integrate and analyze data about complex organizations. We test the design and development of federated prototype ontologies in OWL/RDF to provide and integrate the NIC data with precise semantics for transparency and consistency. Our preliminary results indicate that this is feasible in practice for data search and analysis, and that the ontologies can facilitate semantic integration and improve the integrity of data and metadata.
... While power-related connections between corporations play an important role in understanding our global corporate system (Vitali et al. 2011), few papers have investigated such networks. For example, Nakamoto et al. (2019) employed the so-called Orbis database (Dijk 2018) to identify and analyze high-risk intermediate companies used for international profit shifting. ...
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Social network analysis is increasingly applied to modeling regional relationships. However, in this scenario, we cannot ignore the geographical economic and technological nature of the relationships. In this study, the tools of social network analysis and the gravity model are combined. Our study is based on the Amadeus database of European organizations, which includes 24 million companies. The ownership of parent subsidiaries was modeled using economic, technological, and geographic factors. Ownership was aggregated to the NUTS 3 regional level, to which average corporate profitability indicators, the GDP per capita characterizing the economic environment, and the number of patents, which is a proxy of the technological environment, were assigned to NUTS 3 regions. The formation of the ownership network between 2010 and 2018 was characterized using this dataset. As the proposed model accurately describes the formation of ownership relationships marked with edges, it is possible to estimate network properties, such as modularity and centrality.
La responsabilidad social empresaria (RSE), concepto centenario, asume actualmente un importante papel en el mundo empresarial. Luego de que a sus contenidos fundamentales se les incorporasen conceptos de la teoría de los stakeholders y del desarrollo sostenible, se acrecentó su complejidad, así como también su interés académico y empresarial. Este trabajo refleja una investigación que busca analizar la evolución conceptual de los cuatro conceptos mencionados mediante revisión bibliográfica, complementándola con reflexiones sobre resultados de cuatro investigaciones de campo realizadas en empresas de la República Argentina. Luego de examinar las ideas iniciales desde sus orígenes y de constatar su mutua interrelación e interacción, se concluye que su combinación expresa una nueva orientación en la conducción de las empresas y representa el sustrato esencial que contiene las directrices clave de un nuevo paradigma de gestión socioambientalmente responsable y sostenible.
This paper investigates whether the global trend of ownership con-centration of international financial institutions can also be observed for the Deutschland AG, which is the informal designation for the historically grown and largely isolated network of German stock listed companies. Using network analysis, capital linkages of German HDAX and SDAX companies in both 2006 and 2018 are analyzed and the results are compared. The network analysis enables a systematic presentation of the capital linkages and also helps to analyze link strengths and make supposedly hidden relationships visible. This has been made possible in recent years by the further development of powerful IT hardware and the development of corresponding network analysis software. The results show a noticeable increase in the concentration of internationally active investment companies in the ownership structures with a simultaneous decline in the participation rates of German investors in German companies. Therefore, both the trend of ownership concentration of international financial institutions and the erosion of the Deutschland AG can be confirmed.KeywordsNetwork analysisCapital linkagesDAXOwnership structuresCorporate networkCapital marketCorporate governance
In my young times I came across two professional disappointments: my professors of economics and business spoke about the desired goals of human activities and about the hierarchy of subordination, instead of well-grounded goals and interdisciplinary cooperation in process-steps/succession hierarchy and interdependence. Then I learned to know systems theory and my second disappointment was its prevailing limitation to using the word system instead of the word denoting what was covered under the word system, more concretely, e.g. house, hospital, mister So and So, electrical installation, market place, social order, method, etc. My first solution was my Dialectical Systems Theory as a methodology of systemic, i.e. as holistic as possible and necessary, called requisitely holistic, behavior. My second solution was using my Dialectical Systems Theory for my perception of the Social Responsibility concept, leading to the sustainable socially responsible society model, i.e. as a non-technological innovation process aimed at humankind’s search for the way out from the global socio-economic crisis caused by the globally prevailing neoliberalism’s monopolies destroying both market and democracy and natural precondition of humankind’s survival, to the benefit of a very small percentage of the current humankind. MOTTO: Thinking is the most important human behavior (De Bono (2005 & 2006) Šest klobukov razmišljanja.(in Slovene; Six Thinking Hats).Ljubljana: New Moment (In: New Moment, 28, all-journal issue)(Original ‘Six Thinking Hats’ published in 1985; new Slovene publication in 2021) ). Let us make things as simple as possible, but no more (Wikipedia on Einstein). The General Systems Theory was created against over-specialization (Bertalanffy (1968, edition 1979): General Systems Theory. Foundations, Development, Applications. Revised Edition. Sixth Printing. Braziller, New York.): VII). Cybernetics resulted from interdisciplinary creative cooperation of Norbert Wiener (Wikipedia on Norbert Wiener). Both Bertalanffy (philosophy, arts history, theoretical biology) and Wiener (mathematics, philosophy, zoology) were interdisciplinary personalities, others need teams in creative interdisciplinary cooperative work, to follow them. Hence, systems theory, systemic thinking and systemic behavior cover, like bridges, areas left un-covered by other/usual sciences and practices, in order to help humans to overcome the lack of holism of approach and wholeness of outcomes, in order to resolve resulting failures, reaching all way to world wars and destroyed natural preconditions and conditions of humankind’s survival. Who tries, may lose; who does not try, has lost already.KeywordsDialectical Systems TheoryEthic of interdependenceGoalsISO 26000Systemic behaviorRequisite holismSocial responsibilitySustainable socially responsible society
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Hinter digitalen Technologien liegen mathematische Modelle, die einer 1- oder 0-Logik folgen. Diese Modelle sind keine Naturgesetze, sondern der Versuch, die Realität in einer solchen Logik abzubilden. Demnach sind Technologien nicht neutral, sondern bilden bestehende Machtverhältnisse ab oder begünstigen gar ihre Verfestigung. In der Coronakrise wurde ein Großteil des beruflichen wie privaten gesellschaftlichen Geschehens in den digitalen Raum verlagert. Dadurch lassen sich zentrale Konfliktlinien der Digitalisierung, die Interessensvertretungen beschäftigen, zugespitzt beobachten. Zentrale Spannungsfelder betreffend die geschlechtliche Arbeitsteilung und die Verkehrung von öffentlich vs. privat, die Macht- und Kapitalkonzentration bei den IKT-Konzernen und eine Krise der Organisierung in der digitalen Arbeitswelt. Diesen Herausforderungen zu begegnen erfordert, dass Interessensvertretungen den technologischen Wandel als sozialen Prozess erfassen, diese Machtverhältnisse begreifen und entsprechende Antworten finden.
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We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.
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The boards of directors of the largest corporations of a country together with the directors form a dense bipartite network. The board network consists of boards connected through common directors. The director network is obtained taking the directors as nodes, and a membership in the same board as a link. These networks are involved in the decision making processes relevant to the macro-economy of a country. We present an extensive and comparative analysis of the statistical properties of the board network and the director network for the first 1000 US corporations ranked by revenue (Fortune 1000) in the year 1999 and for the corporations of the Italian Stock Market. We find several common statistical properties across the data sets, despite the fact that they refer to different years and countries. This suggests an underlying universal formation mechanism which is not captured in a satisfactory way by the existent network models. In particular we find that all the considered networks are Small Worlds, assortative, highly clustered and dominated by a giant component. Several other properties are examined. The presence of a lobby in a board, a feature relevant to decision making dynamics, turns out to be a macroscopic phenomenon in all the data sets.
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This paper argues that efficiency-oriented approaches to corporate governance and law are limited in their ability to explain the politics of corporate control and, in particular, the rise of shareholder activism. Politics, like other social action, is embedded in social structures that influence whether, when, and how collective action is accomplished by interest groups. We use a social movement framework to explain the changing capacities of shareholders and managers-as members of classes-to act on their interests in control at the firm, state, and federal level. We illustrate this framework by showing how activist shareholders increased their influence in corporate governance in the early 1990s.
This study analyzes organization of economic activity within and between markets and hierarchies. It considers the transaction to be the ultimate unit of microeconomic analysis, and defines hierarchical transactions as ones for which a single administrative entity spans both sides of the transaction, some form of subordination prevails and, typically, consolidated ownership obtains. Discusses the advantages of the transactional approach by examining three issues: price discrimination, insurance, and vertical integration. Develops the concept of the organizational failure framework, and demonstrates why it is always the combination of human with environmental factors, not either taken by itself, that causes transactional problems. The study also describes each of the transactional relations of interest, and presents the advantages of internal organization with respect to the transactional condition. The analysis explains why primary work groups of the peer group and simple hierarchy types arise. The same transactional factor which impede autonomous contracting between individuals also impede market exchange between technologically separable work groups. Peer groups can be understood as an internal organizational response to the frictions of intermediate product markets, while conglomerate organization can be seen as a response to failures in the capital market. In both contexts, the same human factors, such as bounded rationality and opportunism, occur. Examines the reasons for and properties of the employment relation, which is commonly associated with voluntary subordination. The analysis attempts better to assess the employment relation in circumstances where workers acquire, during the course of the employment, significant job-specific skills and knowledge. The study compares alternative labor-contracting modes and demonstrates that collective organization is helpful in enhancing the acquisition of idiosyncratic knowledge and skills by the work force. The study then examines more complex structures -- the movement from simple hierarchies to the vertical integration of firms, then multidivisional structures, conglomerates, monopolies and oligopolies. Discusses the market structure in relation to technical and organizational innovation. The study proposes a systems approach to the innovation process. Its purpose is to permit the realization of the distinctive advantages of both small and large firms which apply at different stages of the innovation process. The analysis also examines the relation of organizational innovation to technological innovation. (AT)
This paper is a review of the post-war literature on income distribution and development. It argues that the literature has cycled from one consensus to another, responding to emerging policy issues and new analysis. On the basis of the review, the paper identifies five areas that will command the attention of analysts in the coming two decades: (i) country case studies rather than cross-country regression analysis; (ii) the phenomenon of increasing inequality; (iii) different levels of disaggregation, particularly distribution between broadly defined groups; (iv) intra-household allocation; and (v) alternative modes of redistribution in face of inequality increasing tendencies.
Interpersonal links in sociometric structures are interpreted as input-output channels for the transmission of influence. Magnitudes of influence flow generate objective indices of cohesiveness, which serve as the basis for the identification of cliques. This stands in contrast to the factor-analytic rationale of similarity of incoming and outgoing choice patterns. The model departs from the classical sociometric tradition by permitting links to have fractional and/or negative strength, and by taking simultaneous account of direct and indirect linkages. The cohesiveness index is useful for inter-group and diachronic comparisons of structures.
The globalization of financial markets and the concomitant restructuring decisions of firms challenge the historical legacy of national systems of governance. German corporate ownership patterns and restructuring events in the 1990's are examined here in this light. The results show that ownership links among German firms constitute a "small world" that has consequences for understanding mergers and acquisitions. Ownership links form closely-knit clusters of firms that are nonetheless highly connected across the network as a whole. Restructuring events fall squarely in the center of this structure. Despite increasing global competition, the German small world tends to replicate itself. To illustrate this robustness, potential disruptions to the observed German network are simulated. This simulation shows that the properties of the small world remain intact even when ownership ties are changed. These findings suggest that a more global economy in Germany need not lead to the dissolution of the ownership structure, but rather may be associated with a deepening of network ties.