nep-net New Economics Papers
on Network Economics
Issue of 2022‒03‒07
sixteen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Mapping intra firm trade in the automotive sector: a network approach By Matthew Smith; Yasaman Sarabi
  2. Optimism leads to optimality: Ambiguity in network formation By Peter Bayer; Ani Guerdjikova
  3. The Network Structure of Occupations: Fragmentation, Differentiation, and Contagion By Lin, Ken-Hou; Hung, Koit
  4. ClearingPayments in Dynamic Financial Networks By Giuseppe C. Calafiore; Giulia Fracastoro; Anton V. Proskurnikov
  5. Origins and consequences of long ties in social networks By Jahani, Eaman; Fraiberger, Samuel P.; Bailey, Michael; Eckles, Dean
  6. Clubs and Networks By Ding, S.; Dziubinski, M.; Goyal, S.
  7. Best-response dynamics in directed network games By Peter Bayer; György Kozics; Nora Gabriella Szöke
  8. Learning with latent group sparsity via heat flow dynamics on networks By Subhroshekhar Ghosh; Soumendu Sundar Mukherjee
  9. The Northern Ireland Longitudinal Study (NILS) as a resource for investigating neighbourhood peer effects: A case study using natural experiment(s) in fertility and labour market participation By Zhang, Meng Le
  10. The effect of the pandemic on complex socio-economic systems: community detection induced by communicability By Gian Paolo Clemente; Rosanna Grassi; Giorgio Rizzini
  11. When do you Stop Supporting your Bankrupt Subsidiary? By Maxim Bichuch; Nils Detering
  12. Stochastic Consensus and the Shadow of Doubt By Emilien Macault
  13. Understanding European Integration with Bipartite Networks of Comparative Advantage By Riccardo Di Clemente; Bal\'azs Lengyel; Lars F. Andersson; Rikard Eriksson
  14. Opinion Dynamics in Financial Markets via Random Networks By Mateus F. B. Granha; Andr\'e L. M. Vilela; Chao Wang; Kenric P. Nelson; H. Eugene Stanley
  15. The Impact of Connectivity on the Production and Diffusion of Knowledge By Gustavo Manso; Farzad Pourbabaee
  16. Propagation of disruptions in supply networks of essential goods: A population-centered perspective of systemic risk By William Schueller; Christian Diem; Melanie Hinterplattner; Johannes Stangl; Beate Conrady; Markus Gerschberger; Stefan Thurner

  1. By: Matthew Smith; Yasaman Sarabi
    Abstract: Intra-firm trade describes the trade between affiliated firms and is increasingly important as global production is fragmented. However, statistics and data on global intra-firm trade patterns are widely unavailable. This study proposes a novel multilevel approach combining firm and country level data to construct a set of country intra-firm trade networks for various segments of the automotive production chain. A multilevel network is constructed with a network of international trade at the macro level, a firm ownership network at the micro level and a firm-country affiliation network linking the two, at the meso level. A motif detection approach is used to filter these networks to extract potential intra-firm trade ties between countries, where the motif (or substructure) is two countries linked by trade, each affiliated with a firm, and these two firms linked by ownership. The motif detection is used to extract potential country level intra-firm trade ties. An Exponential Random Graph Model (ERGM) is applied to the country level intra-firm trade networks, one for each segment of the automotive production chain, to inform on the determinants of intra-firm trade at the country level.
    Date: 2022–02
  2. By: Peter Bayer (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Ani Guerdjikova (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)
    Abstract: We analyze a model of endogenous two-sided network formation where players are affected by uncertainty in their opponents' decisions. We model this uncertainty using the notion of equilibrium under ambiguity. Unlike the set of Nash equilibria, the set of equilibria under ambiguity does not always include underconnected and thus inefficient networks such as the empty network. On the other hand, it may include networks with unreciprocated, one-way links, which comes with an efficiency loss as linking efforts are costly. We characterize equilibria under ambiguity and provide conditions under which increased player optimism comes with an increase in connectivity and realized benefits in equilibrium. Next, we analyze network realignment under a myopic updating process with optimistic shocks, and derive a global stability condition of efficient networks. Under this condition, called ‘aligned preferences', a subset of the Pareto optimal equilibrium networks is reached, specifically, networks that maximize the players' total benefits of connections.
    Date: 2022–01–25
  3. By: Lin, Ken-Hou; Hung, Koit
    Abstract: Occupational structure is commonly viewed as either hierarchical or organized around stable classes. Yet, recent studies have proposed to describe occupational structure as a network, where the mobility of workers demarcates boundaries. Moving beyond boundary detection, this article develops occupational network as a dynamic system in which between-occupation exchange is shaped by occupational similarities, and occupational attributes are in turn responsive to mobility patterns. We illustrate this perspective with the exchange networks of detailed occupations. Our analysis shows that the U.S. occupational structure has become more fragmented. The division was in part associated with the emerging importance of age composition, as well as those of quantitative, creative, and social tasks. The fragmentation reduced wage contagion and therefore contributed to a greater between-occupation wage dispersion. These results indicate that occupational attributes and mobility are co-constitutive, and that a network perspective provides a unifying framework for the study of stratification and mobility.
    Date: 2021–10–19
  4. By: Giuseppe C. Calafiore; Giulia Fracastoro; Anton V. Proskurnikov
    Abstract: In this paper, we propose a novel dynamical model of clearing in a financial network, which stems from the classical Eisenberg- Noe model of financial contagion. The Eisenberg-Noe model assumes that at one point in time (say, at the end of a day), all liabilities are claimed and due simultaneously, and that the entire network of banks becomes aware of the claims and possible defaults and instantaneously agrees on the clearing payments. The motivation for the dynamic model we propose in this paper is that one may expect that if financial operations are allowed for a given number of time periods after the initial theoretical defaults, some nodes may actually recover and eventually manage to fulfill their obligations. We prove that the proposed model obeys the standard requirement known as the priority of debt claims, that is, each node either pays its liabilities in full, or it pays out all its balance. We also show that the requirements of ro-rata payments determines the solution uniquely.
    Date: 2022–01
  5. By: Jahani, Eaman; Fraiberger, Samuel P.; Bailey, Michael; Eckles, Dean
    Abstract: Social networks play a predominant role in determining how information spreads between individuals. Previous works suggest that long ties, which connect people who do not share any mutual contact, provide access to valuable information on economic opportunities. However, no population-scale study has determined how long ties relate to economic outcomes and how such ties are formed. Using a novel dataset from Facebook, we reconstruct the network of interactions between users and we uncover a strong relationship between the share of long ties and economic outcomes at the local level in the United States and in Mexico. Administrative units with a higher proportion of long ties have higher incomes, higher economic mobility, lower unemployment rates and higher wealth, even after adjusting for potential confounders of these outcomes. In contrast to the weak tie theory, we find that having stronger long ties is associated with better economic outcomes. Furthermore, we discover that users with a higher proportion of long ties are more likely to have migrated between US states, to have transferred to a different high school, and to have attended college outside of their home state. Taken together, these results suggest that long ties contribute to economic prosperity and highlight the role played by disruptive life events in the formation of these ties.
    Date: 2022–01–08
  6. By: Ding, S.; Dziubinski, M.; Goyal, S.
    Abstract: A recurring theme in the study of society is the concentration of influence and power that is driven through unequal membership of groups and associations. In some instances these bodies constitute a small world while in others they are fragmented into distinct cliques. This paper presents a new model of clubs and networks to understand the sources of individual marginalization and the origins of different club networks. In our model, individuals seek to become members of clubs while clubs wish to have members. Club value is increasing in its size and in the strength of ties with other clubs. We show that a stable membership profile exhibits marginalization of individuals and that this is generally not welfare maximizing. Our second result shows that if returns from strength of ties are convex (concave) then stable memberships support fragmented networks with strong ties (small worlds held together by weak ties). We illustrate the value of these theoretical results through case studies of inter-locking directorates, boards of editors of journals, and defence and R&D alliances.
    Date: 2021–10–25
  7. By: Peter Bayer (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); György Kozics (Unknown); Nora Gabriella Szöke (Unknown)
    Abstract: We study public goods games played on networks with possibly non-recip-rocal relationships between players. Examples for this type of interactions include one-sided relationships, mutual but unequal relationships, and par-asitism. It is well known that many simple learning processes converge to a Nash equilibrium if interactions are reciprocal, but this is not true in general for directed networks. However, by a simple tool of rescaling the strategy space, we generalize the convergence result for a class of directed networks and show that it is characterized by transitive weight matrices and quadratic best-response potentials. Additionally, we show convergence in a second class of networks; those rescalable into networks with weak exter-nalities. We characterize the latter class by the spectral properties of the absolute value of the network's weight matrix and by another best-response potential structure.
    Keywords: Potential games,Local public goods,Externalities,Networks,Non-reciprocal relations
    Date: 2022–01
  8. By: Subhroshekhar Ghosh; Soumendu Sundar Mukherjee
    Abstract: Group or cluster structure on explanatory variables in machine learning problems is a very general phenomenon, which has attracted broad interest from practitioners and theoreticians alike. In this work we contribute an approach to learning under such group structure, that does not require prior information on the group identities. Our paradigm is motivated by the Laplacian geometry of an underlying network with a related community structure, and proceeds by directly incorporating this into a penalty that is effectively computed via a heat flow-based local network dynamics. In fact, we demonstrate a procedure to construct such a network based on the available data. Notably, we dispense with computationally intensive pre-processing involving clustering of variables, spectral or otherwise. Our technique is underpinned by rigorous theorems that guarantee its effective performance and provide bounds on its sample complexity. In particular, in a wide range of settings, it provably suffices to run the heat flow dynamics for time that is only logarithmic in the problem dimensions. We explore in detail the interfaces of our approach with key statistical physics models in network science, such as the Gaussian Free Field and the Stochastic Block Model. We validate our approach by successful applications to real-world data from a wide array of application domains, including computer science, genetics, climatology and economics. Our work raises the possibility of applying similar diffusion-based techniques to classical learning tasks, exploiting the interplay between geometric, dynamical and stochastic structures underlying the data.
    Date: 2022–01
  9. By: Zhang, Meng Le
    Abstract: Our neighbours can potentially influence our behaviour. For instance, poor health behaviours amongst neighbours may normalise and reinforce our poor health behaviours. This is an example of a peer effect. Imitative behaviour can cause small initial changes in individual behaviour to spread amongst their social networks and result in a social multiplier effect. Understanding the size and mechanisms behind the social multiplier effect allows for more effective health interventions. It also helps us understand why persistent health inequalities exist across different neighbourhoods and social groups. In an ideal experiment, we would randomly allocate people into treatment and control groups and change the behaviours of persons A in the treatment group (directly or through incentives). Then we would observe the effects of changes in person A's behaviour on their neighbour person B. This ideal experiment is practically and (possibly) ethically unfeasible. Furthermore, data on a large enough sample of people and their neighbours is very expensive to collect. Instead of a normal experiment, we can use natural experiments which change person A's behaviour. In this paper, we use a well-known natural experiment that affects women's fertility and, indirectly, their labour market participation. Furthermore, data on people and their neighbours are available from the Northern Ireland Longitudinal Study (NILS). The Northern Ireland Longitudinal Study (NILS) is a longitudinal study consisting of 28% of the NI census and roughly 50% of all households. The NILS is linked to a database of all addressable properties in NI which include the coordinates of residences. In theory, NILS contains a large sample of households in NI and their close neighbours that can be used for studying peer effects. This project is a proof of concept for studying peer effects using NILS. If any random intervention exists (e.g. natural, quasi- or actual randomised trial) then NILS can always be used to study peer effects (amongst neighbours). This is significant because peer effects are notoriously hard to study due data limitations and a large number of credible natural experiments exist in health research. This project's contribution is the discovery that NILS almost uniquely placed as resource for studying peer effects in the UK.
    Date: 2021–12–03
  10. By: Gian Paolo Clemente; Rosanna Grassi; Giorgio Rizzini
    Abstract: The increasing complexity of interrelated systems has made the use of multiplex networks an important tool for explaining the nature of relations between elements in the system. In this paper, we aim at investigating various aspects of countries' behaviour during the coronavirus pandemic period. By means of a multiplex network we consider simultaneously stringency index values, COVID-19 infections and international trade data, in order to detect clusters of countries that showed a similar reaction to the pandemic. We propose a new methodological approach based on the Estrada communicability for identifying communities on a multiplex network, based on a two-step optimization. At first, we determine the optimal inter-layer intensity between levels by minimizing a distance function. Hence, the optimal inter-layer intensity is used to detect communities on each layer. Our findings show that the community detection on this multiplex network has greater information power than classical methods for single-layer networks. Our approach better reveals clusters on each layer with respect to the application of the same approach on each single-layer. Moreover, detected groups in the multiplex case benefit of a higher cohesion, leading to identifying on each layer a lower number of communities with respect to the ones obtained in the single-layer cases.
    Date: 2022–01
  11. By: Maxim Bichuch; Nils Detering
    Abstract: We consider a network of bank holdings, where every holding has two subsidiaries of different type. A subsidiary can trade with another holding's subsidiary of the same type. Holdings support their subsidiary up to a certain level when they would otherwise fail to honor their financial obligations. We investigate the spread of contagion in this banking network when the number of bank holdings is large, and find the final number of defaulted subsidiaries under different rules for the holding support. We also consider resilience of this multilayered network to small shocks. Our work sheds light onto the role that holding structures can play in the amplification of financial stress. We find that depending on the capitalisation of the network, a holding structure can be beneficial as compared to smaller separated entities. In other instances it can be harmful and actually increase contagion. We illustrate our results in a numerical case study and also determine the optimal level of holding support from a regulator perspective.
    Date: 2022–01
  12. By: Emilien Macault
    Abstract: We propose a stochastic model of opinion exchange in networks. A finite set of agents is organized in a fixed network structure. There is a binary state of the world and each agent receives a private signal on the state. We model beliefs as urns where red balls represent one possible value of the state and blue balls the other value. The model revolves purely around communication and beliefs dynamics. Communication happens in discrete time and, at each period, agents draw and display one ball from their urn with replacement. Then, they reinforce their urns by adding balls of the colors drawn by their neighbors. We show that for any network structure, this process converges almost-surely to a stable state. Futher, we show that if the communication network is connected, this stable state is such that all urns have the same proportion of balls. This result strengthens the main convergence properties of non-Bayesian learning models. Yet, contrary to those models, we show that this limit proportion is a full-support random variable. This implies that an arbitrarily small proportion of misinformed agents can substantially change the value of the limit consensus. We propose a set of conjectures on the distribution of this limit proportion based on simulations. In particular, we show evidence that the limit belief follows a beta distribution and that its average value is independent from the network structure.
    Date: 2022–01
  13. By: Riccardo Di Clemente; Bal\'azs Lengyel; Lars F. Andersson; Rikard Eriksson
    Abstract: Core objectives of the European integration are convergence and economic growth, but these are challenged by competition and value chain asymmetries within the common market. A difficult challenge for the EU is how to harmonize specialization of industries and countries to reach global competitiveness, and at the same time bridge productivity differences across more and less developed countries. Here, we develop a novel bipartite network approach and trace pairwise co-specialization, by applying the widely used revealed comparative advantage (RCA) method, within and between EU15 and Central and Eastern European (CEE) member states from 2000. This new co-specialization approach can be used to assess redundancies and division in the system as a whole, and at the level of industries and countries as well. This latter feature enables us to investigate how co-specialization across countries impact economic growth. We find significant overlap of RCA among CEE countries but a diverging RCA structure between EU15 and CEE. Our econometric analysis indicates that productivity increases in those CEE industries that have co-specialized with other CEE countries after EU accession, while co-specialization across CEE and EU15 countries is less related to productivity growth. These results inform European policy that a division of sectoral specialization can lead to productivity convergence between EU15 and CEE member states.
    Date: 2022–02
  14. By: Mateus F. B. Granha; Andr\'e L. M. Vilela; Chao Wang; Kenric P. Nelson; H. Eugene Stanley
    Abstract: We investigate the financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize the individuals in a financial market by their trading strategy, namely noise traders and fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the fundamentalist agents' decisions. We introduce a noise parameter $q$ to represent a level of anxiety and perceived uncertainty regarding the market behavior, enabling the possibility for an adrift financial action. We place the individuals as nodes in an Erd\"os-R\'enyi random graph, where the links represent their social interaction. At a given time, they assume one of two possible opinion states $\pm 1$ regarding buying or selling an asset. The model exhibits such fundamental qualitative and quantitative real-world market features as the distribution of logarithmic returns with fat-tails, clustered volatility, and long-term correlation of returns. We use Student's t distributions to fit the histograms of logarithmic returns, showing the gradual shift from a leptokurtic to a mesokurtic regime, depending on the fraction of fundamentalist agents. We also compare our results with the distribution of logarithmic returns of several real-world financial indices.
    Date: 2022–01
  15. By: Gustavo Manso; Farzad Pourbabaee
    Abstract: We study a social bandit problem featuring production and diffusion of knowledge. While higher connectivity enhances knowledge diffusion, it may reduce knowledge production as agents shy away from experimentation with new ideas and free ride on the observation of other agents. As a result, under some conditions, greater connectivity can lead to homogeneity and lower social welfare.
    Date: 2022–02
  16. By: William Schueller; Christian Diem; Melanie Hinterplattner; Johannes Stangl; Beate Conrady; Markus Gerschberger; Stefan Thurner
    Abstract: The Covid-19 pandemic drastically emphasized the fragility of national and international supply networks (SNs),leading to significant supply shortages of essential goods for people, such as food and medical equipment. Severe disruptions that propagate along complex SNs can expose the population of entire regions or even countries to these risks. A lack of both, data and quantitative methodology, has hitherto hindered us to empirically quantify the vulnerability of the population to disruptions. Here we develop a data-driven simulation methodology to locally quantify actual supply losses for the population that result from the cascading of supply disruptions. We demonstrate the method on a large food SN of a European country including 22,938 business premises, 44,355 supply links and 116 local administrative districts. We rank the business premises with respect to their criticality for the districts' population with the proposed systemic risk index, SRIcrit, to identify around 30 premises that -- in case of their failure -- are expected to cause critical supply shortages in sizable fractions of the population. The new methodology is immediately policy relevant as a fact-driven and generalizable crisis management tool. This work represents a starting point for quantitatively studying SN disruptions focused on the well-being of the population.
    Date: 2022–01

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