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Corporate Network Governance: Power, Ownership and Control in Contemporary Global Capitalism

Periodic Reporting for period 3 - CORPNET (Corporate Network Governance: Power, Ownership and Control in Contemporary Global Capitalism)

Reporting period: 2018-09-01 to 2020-02-29

The character of global business networks has long fascinated but continues to divide scholars of global markets and governance. A well-established perspective looks at the changes in global networks and sees an emerging cohesive transnational capitalist class. However, a rival line of inquiry sees the rise of competing corporate elites. Scholars also disagree on the origins of emergent patterns of corporate networks. Do they reflect institutional preferences of corporate and political elites? Or are they unintended by-products of corporate conduct? Third, there are fundamental differences of opinion on how patterns of global corporate ownership relate to actual power in the governance of such networks.

Past research has been unable to adjudicate these debates in part due to insufficient data clarifying the full breadth of corporate interactions globally, and insufficient analytical tools for analysing that breadth. This project seeks to do what has so far eluded existing scholarship: to fully explore the global network of corporate ownership and control as a complex system. Network structures may appear to be the result of a grand design at macro level, but are the outcome of the sum of the actions of a large set of interdependent actors. Using cutting-edge network science methods, the project explores for the first time the largest database on ownership and control covering over 100 million firms. Exploiting the longitudinal richness of the new data in combination with state-of-the-art methods and techniques makes it possible to model and empirically test generating mechanisms that drive network formation.

By doing so the project bridges the hitherto disjoint fields of social network studies in socio-economics and political science on the one hand, and the growing body of literature on network science in physics, computer science and complexity studies on the other.
Inter and cross disciplinary work is at the heart of CORPNET’s ambition. This shows for instance in the publication strategy where we reach out to several disciplinary journals in computer science, network science, political science and socio economics. Nearly all of our publications are co-authored by team members with a background in computer and social science. The diversity and academic ethos of the CORPNET lab is key to its success. Our publication record is ahead of schedule, with contributions in academic journals such as Nature’s Scientific Reports; New Political Economy; Business & Politics; Global Networks; Social Network Analysis and Mining; The International Spectator, but also professional contributions in outlets as the Antitrust Chronicle, Goed Bestuur en Toezicht, en Economische Statistische Berichten. Firmly rooted in an open source approach, CORPNET publishes all its code through GITHUB (
Our recent paper on Offshore Financial Centers – a prime example of CORPNETs ambition and way of working – is ranked 24th of the 3,846 tracked articles of a similar age in Nature’s Scientific Reports. The publication was widely covered in national and international press, and the Dutch Minister of Finance was asked to respond to parliament regarding our findings the outstanding role of the Netherlands as OFC. Since publication in July 2017 we have been received visits from numerous government agencies interested in our findings and approach, including the Fiscal Information and Investigation Service (FIOD), Infobox on Criminal and unexplainable Assets (iCOV), and the Korean Tax Authority, as well as visits from international journalists.

Another example of our impact is our work on newly established concentration of corporate ownership through passive investors and its consequences for corporate governance. The findings led to question in EU parliament and our study together with two related studies triggered commissioner Vestager to closely monitor these new developments. Our outreach activities are popular as well: a short animation video we made based on this article received over 7.7k views on Facebook and 8k on Youtube.

Developing new methods and applying existing methods to novel applications is at the core of CORPNETs mission. For analyzing large scale networks with partitioned structures we developed centrality measure (Takes and Heemskerk 2016). In order to analyze networks at the meso level (instead of the micro level of actors or the macro level of the entire network) we invest in new motif detection algorithms (Takes, Kosters, Witte 2017). For this work Takes received the 2017 Young eScienctist award from the Netherlands Organisation of Scientific Research (NWO). Our work on network dynamics of elite formation (project 2) has for the first time applied the recently developed relational event modelling approach to the field of board interlock studies. The work on ownership chains and offshore finance builds on a novel method, developed by corpnet, to measure and count complex paths of global corporate ownership relations (parent-subsidiary relations) and the role of particular jurisdictions in these networks (project 3).
We lack an accurate understanding of the core organisational form in which the global economic order manifests itself: the global network of corporate ownership and control. As a result of theoretical and empirical nationalism, current scholarly work is not able to fully grasp recent important developments. This project’s ambition is to fill the gap by studying this subject without an a priori assumption of the appropriate level of analysis. This project’s complex social network approach will generate the first but much needed understanding of the properties, topology, interdependencies and generating mechanisms of the global network of ownership and control. The research strategy is fundamentally interdisciplinary. A specific goal is to bridge the hitherto disjoint fields of social network studies in socio-economics and political science on the one hand, and the growing body of literature on network science in physics, computer science and complexity studies on the other. The social sciences bring to the table good theories and a sound understanding of social and political processes that govern economic relations. In network science on the other hand, we see spectacular developments in techniques to analyse and investigate large, complex networks. However, the field has no social science theoretical interest in the objects they study. Thus we are left with theoretically informed speculation in the social sciences and advanced network analyses without substantial interpretation in physics and complexity science. Bringing these strands of work together combines the best of both and holds the promise of concrete scientific innovation.

Empirical innovation: The research team has conducted the first big data network analysis of the ownership ties and interlocking directorates on the largest dataset currently available, covering over 120 million firms worldwide. This endeavor required a significant investment in cleaning and organizing the data in such a way that it is suitable for social science network analysis. The data enhancing and quality assessment techniques form a first set of innovative outcomes with a large potential impact on how social scientists can deal with big data in the years to come. In addition, analysing the properties of the web of corporate governance relations uncovered new and unexpected properties, such as hithertho unnoticed levels of concentration of corporate ownership and hence power in the hands of passive asset managers, or the dominance of the UK and the Netherlands as conduits for Offshore Financial Centers.

Conceptual innovation: An important theoretical innovation comes from the conceptualisation of network formation as the interplay of strategies deployed by persons, corporations and owners. This will allow for a more precise modelling of complex network interactions and network formation. In particular, we have developed a novel conceptual and empirical approach to study the flow through networks, and the importance of particular (sets of) nodes such as countries or firms in these flows. Furthermore, the big data approach makes it possible to search for those regions and clusters that are empirically distinguishable and use these as a basis for comparative research, instead of using pre-defined categories (such as countries). We generate new classifications of global communities that go beyond fixed political-geographic lines.

Methodological innovation: A key goal is to transmute the advanced methodological toolbox from network science to social network analysis. This opens up a number of new and unexplored questions, for instance on the use of approximation algorithms, the differences between approaches in computer science and physics, and the use of the toolbox for social science applications. It will make advanced and computationally strong methods available to scholars that study political economy and socio-economics.