We lacked 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, scholarly work was not well able to fully grasp recent important developments. This project filled the gap by studying this subject without an a priori assumption of the appropriate level of analysis. This project’s complex social network approach generated the first coherent understanding of the properties, topology, interdependencies and generating mechanisms of the global network of ownership and control.
Empirical innovation: We conducted the first big data network analysis of the ownership ties and interlocking directorates on the largest dataset currently available, covering over 200 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, analyzing the properties of the web of corporate governance relations uncovered new and unexpected properties, such as hitherto unnoticed levels of concentration of corporate ownership and hence power in the hands of passive asset managers; 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 allowed 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 was to transmute the advanced methodological toolbox from network science to social network analysis. This opened 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. This has made advanced and computationally strong methods available to scholars that study political economy and socio-economics.
