Loan maturity aggregation in interbank lending networks obscures mesoscale structure and economic functions

Since the 2007-2009 financial crisis, substantial academic effort has been dedicated to improving our understanding of interbank lending networks (ILNs). Because of data limitations or by choice, the literature largely lacks multiple loan maturities. We employ a complete interbank loan contract dataset to investigate whether maturity details are informative of the network structure. Applying the layered stochastic block model of Peixoto (2015) and other tools from network science on a time series of bilateral loans with multiple maturity layers in the Russian ILN, we find that collapsing all such layers consistently obscures mesoscale structure. The optimal maturity granularity lies between completely collapsing and completely separating the maturity layers and depends on the development phase of the interbank market, with a more developed market requiring more layers for optimal description. Closer inspection of the inferred maturity bins associated with the optimal maturity granularity reveals specific economic functions, from liquidity intermediation to financing. Collapsing a network with multiple underlying maturity layers or extracting one such layer, common in economic research, is therefore not only an incomplete representation of the ILN's mesoscale structure, but also conceals existing economic functions. This holds important insights and opportunities for theoretical and empirical studies on interbank market functioning, contagion, stability, and on the desirable level of regulatory data disclosure

Social Stability and Extended Social Balance - Quantifying the Role of Inactive Links in Social Networks

Structural balance in social network theory starts from signed networks with active relationships (friendly or hostile) to establish a hierarchy between four different types of triadic relationships. The lack of an active link also provides information about the network. To exploit the information that remains uncovered by structural balance, we introduce the inactive relationship that accounts for both neutral and nonexistent ties between two agents. This addition results in ten types of triads, with the advantage that the network analysis can be done with complete networks. To each type of triadic relationship, we assign an energy that is a measure for its average occupation probability. Finite temperatures account for a persistent form of disorder in the formation of the triadic relationships. We propose a Hamiltonian with three interaction terms and a chemical potential (capturing the cost of edge activation) as an underlying model for the triadic energy levels. Our model is suitable for empirical analysis of political networks and allows to uncover generative mechanisms. It is tested on an extended data set for the standings between two classes of alliances in a massively multi-player on-line game (MMOG) and on real-world data for the relationships between countries during the Cold War era. We find emergent properties in the triadic relationships between the nodes in a political network. For example, we observe a persistent hierarchy between the ten triadic energy levels across time and networks. In addition, the analysis reveals consistency in the extracted model parameters and a universal data collapse of a derived combination of global properties of the networks. We illustrate that the model has predictive power for the transition probabilities between the different triadic states.Comment: 21 pages, 10 figure

Statistical physics of balance theory

Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively capture the dynamics of the four types of triadic relationships in a network. Central to our model are the triads’ incidence rates and the idea that those can be modeled by assigning a specific triadic energy to each type of triadic relation. We emphasize the role of the degeneracy of the different triads and how it impacts the degree of frustration in the political network. In order to account for a persistent form of disorder in the formation of the triadic relationships, we introduce the systemic variable temperature. In order to learn about the dynamics and motives, we propose a generic Hamiltonian with three terms to model the triadic energies. One term is connected with a three-body interaction that captures balance theory. The other terms take into account the impact of heterogeneity and of negative edges in the triads. The validity of our model is tested on four datasets including the time series of triadic relationships for the standings between two classes of alliances in a massively multiplayer online game (MMOG). We also analyze real-world data for the relationships between the “agents” involved in the Syrian civil war, and in the relations between countries during the Cold War era. We find emerging properties in the triadic relationships in a political network, for example reflecting itself in a persistent hierarchy between the four triadic energies, and in the consistency of the extracted parameters from comparing the model Hamiltonian to the data

Addressing socioeconomic challenges with micro-level trace data

This dissertation is part of a growing specialism within Applied Empirical Economics. The specialism leverages novel sets of trace data originating from the recent trend in digitalization across public and private sectors. Trace data originates from the natural usage of (digital) products or services and offers a sharp contrast to directly collected data. The greatest strength of trace data is that it is unobtrusive and non-reactive. The collection of trace data does not interfere with the natural flow of behavior and events in the given context. The first chapter gives an overview of the challenges and opportunities that these new datasets present to economic research and how they are utilized in the dissertation. From challenges concerning privacy to the potential to explore novel heterogeneity in economic behavior. It also introduces the datasets that are utilized, namely, the population of Russian interbank contracts, client-level financial data from a large European bank, and data on inhabitants of the EVE Online virtual world. The second chapter analyses the population of Russian interbank contracts with a layered stochastic block model rooted in Bayesian inference and Network Science, a novel feat in this literature. We find that loan maturity details, up till now rarely included in studies of interbank networks, are informative of the lending and borrowing patterns and economic functions present in the interbank lending market. For the third chapter, we obtained access to client-level data of over 3 million Belgian clients of a large European bank covering the period 2006-2016. We utilize this data to explore possible mechanisms contributing to the perpetuation of wealth inequality. We find evidence for a transmission channel via human capital allocation, in the form of higher quality job-matchings. The fourth chapter also utilizes the client-level bank data but focuses on consumption dynamics. We find an asymmetric consumption response to anticipated income changes which the different theories in the literature are unable to explain. We propose the well-known myopic loss-aversion model from behavioral economics as an explanation. The fifth chapter details my contribution and yet to be explored possibilities on a dataset containing the behavior of individuals in a virtual world (EVE Online). I explain the findings of two other projects I co-authored and collaborated on in a more supporting and advisory role. For these projects, I provided my skills and expertise in data collection and processing, and advised on the research and the methodology. The first project detailed is published in PLOS ONE, and the second project in Physica A: Statistical Mechanics and its Applications and are on testing and extending Social Balance Theory (SBT) utilizing a statistical physics approach. SBT is the political science theory behind the principle of ``the enemy of my enemy is my friend''. I end by discussing future possibilities with such virtual world trace data

Financial wealth and early income mobility

We study the interaction between financial wealth and early income growth. Using banking data on career starters in Belgium, we find higher income growth for individuals with higher financial wealth as early as 3 years into a career. While the roles of social capital and innate abilities appear limited, our results suggest that individuals with higher disposable wealth are more likely to find a job matching their human capital, in turn, boosting their chances of higher performance and consequent income growth. Policies addressing individuals’ capacity to accommodate frictions in the market for first jobs could therefore substantially promote economic mobility

Network control by a constrained external agent as a continuous optimization problem

Social science studies dealing with control in networks typically resort to heuristics or solely describing the control distribution. Optimal policies, however, require interventions that optimize control over a socioeconomic network subject to real-world constraints. We integrate optimisation tools from deep-learning with network science into a framework that is able to optimize such interventions in real-world networks. We demonstrate the framework in the context of corporate control, where it allows to characterize the vulnerability of strategically important corporate networks to sensitive takeovers, an important contemporaneous policy challenge. The framework produces insights that are relevant for governing real-world socioeconomic networks, and opens up new research avenues for improving our understanding and control of such complex systems

Network control by a constrained external agent as a continuous optimization problem

Social science studies dealing with control in networks typically resort to heuristics or solely describing the control distribution. Optimal policies, however, require interventions that optimize control over a socioeconomic network subject to real-world constraints. We integrate optimisation tools from deep-learning with network science into a framework that is able to optimize such interventions in real-world networks. We demonstrate the framework in the context of corporate control, where it allows to characterize the vulnerability of strategically important corporate networks to sensitive takeovers, an important contemporaneous policy challenge. The framework produces insights that are relevant for governing real-world socioeconomic networks, and opens up new research avenues for improving our understanding and control of such complex systems

Time series of the entropy for the triadic relationships among countries during the Cold War era.

<p>Time series of the entropy for the triadic relationships among countries during the Cold War era.</p