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on Network Economics |
| By: | Ugo Bolletta; Paolo Pin |
| Abstract: | Polarization is a well-documented phenomenon across a wide range of social issues. However, prevailing theories often compartmentalize the examination of herding behavior and opinion convergence within different contexts. In this study, we delve into the micro-foundations of how individuals strategically select reference groups, offering insight into a dynamic process where both individual opinions and the network evolve simultaneously. We base our model on two parameters: people's direct benefit from connections and their adaptability in adjusting their opinions. Our research highlights which conditions impede the network from achieving complete connectivity, resulting in enduring polarization. Notably, our model also reveals that polarization can transiently emerge during the transition towards consensus. We explore the connection between these scenarios and a critical network metric: the initial diameter, under specific conditions related to the initial distribution of opinions. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.01341&r= |
| By: | Elliott, M.; Jackson, M. O. |
| Abstract: | We introduce a parsimonious multi-sector model of international production and use it to study how a disruption in the production of intermediate goods propagates through to final goods, and how that impact depends on the goods’ positions in, and overall structure of, the production network. We show that the short-run disruption can be dramatically larger than the long-run disruption. The short-run disruption depends on the value of all of the final goods whose supply chains involve a disrupted good, while by contrast the long-run disruption depends only on the cost of the disrupted goods. We use the model to show how increased complexity of supply chains leads to increased fragility in terms of the probability and expected short-run size of a disruption. We also show how decreased transportation costs can lead to increased specialization in production, with lower chances for disruption but larger impacts conditional upon disruption. |
| Keywords: | Supply Chains, Globalization, Fragility, Production Networks, International Trade |
| JEL: | D85 E23 E32 F44 F60 L14 |
| Date: | 2024–05–14 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camjip:2415&r= |
| By: | Bernardo J. Zubillaga; Mateus F. B. Granha; Andr\'e L. M. Vilela; Chao Wang; Kenric P. Nelson; H. Eugene Stanley |
| Abstract: | This work investigates the effects of complex networks on the collective behavior of a three-state opinion formation model in economic systems. Our model considers two distinct types of investors in financial markets: noise traders and fundamentalists. Financial states evolve via probabilistic dynamics that include economic strategies with local and global influences. The local majoritarian opinion drives noise traders' market behavior, while the market index influences the financial decisions of fundamentalist agents. We introduce a level of market anxiety $q$ present in the decision-making process that influences financial action. In our investigation, nodes of a complex network represent market agents, whereas the links represent their financial interactions. We investigate the stochastic dynamics of the model on three distinct network topologies, including scale-free networks, small-world networks and Erd{\"o}s-R\'enyi random graphs. Our model mirrors various traits observed in real-world financial return series, such as heavy-tailed return distributions, volatility clustering, and short-term memory correlation of returns. The histograms of returns are fitted by coupled Gaussian distributions, quantitatively revealing transitions from a leptokurtic to a mesokurtic regime under specific economic heterogeneity. We show that the market dynamics depend mainly on the average agent connectivity, anxiety level, and market composition rather than on specific features of network topology. |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2404.18709&r= |
| By: | Agathe Sadeghi; Zachary Feinstein |
| Abstract: | In this paper, we introduce a novel centrality measure to evaluate shock propagation on financial networks capturing a notion of contagion and systemic risk contributions. In comparison to many popular centrality metrics (e.g., eigenvector centrality) which provide only a relative centrality between nodes, our proposed measure is in an absolute scale permitting comparisons of contagion risk over time. In addition, we provide a statistical validation method when the network is estimated from data, as is done in practice. This statistical test allows us to reliably assess the computed centrality values. We validate our methodology on simulated data and conduct empirical case studies using financial data. We find that our proposed centrality measure increases significantly during times of financial distress and is able to provide insights in to the (market implied) risk-levels of different firms and sectors. |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2404.14337&r= |
| By: | Elena Panova (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - 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) |
| Abstract: | We consider the problem of sharing the cost of a fixed tree-network among users with differentiated willingness to pay for the good supplied through the network. We find that the associated value-sharing problem is convex, hence, the core is large and we axiomatize a new, computationally simple core selection based on the idea of proportionality. |
| Keywords: | Sharing network cost, Core, Proportional allocation |
| Date: | 2023–11 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04556220&r= |
| By: | Hsieh, Chih Sheng; Deer, Lachlan (Tilburg University, School of Economics and Management); Koenig, Michael; Vega-Redondo, Fernando |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:d03852a7-c5e7-4be0-8583-1cee0d69bea7&r= |
| By: | Adelia Fatikhova; Fabrizio Fusillo; Sandro Montresor; |
| Abstract: | This work investigates the role of external exchanges of green knowledge on the regional development of new green technological specializations. We extend the recombinant knowledge framework to commodity-embodied knowledge and posit that inter-industry inter-regional flows of commodities, in which new green knowledge gets incorporated, are a channel through which regions can increase their opportunities of specializing in new green technologies and diversify in a more exploratory manner. We further expect these dynamics to be stronger when foreign rather than domestic embodied flows are concerned. By combining the EUREGIO input-output database with patent data, we test our hypotheses on a sample of 237 EU (NUTS2) regions over the period 2000-2019. We measure the regions’ centrality in the network of inter-regional flows of embodied green knowledge (GreenFlowNet) and exploit regional network centrality in a model of related diversification for green technologies. Results show that the centrality of regions in the network is positively associated with green diversification, making this process more exploratory. We also find that the regional ability to acquire new green-techs is mainly associated with the centrality in outward flows of green knowledge towards other regions rather than inward ones. Lastly, we find that regions’ green-tech diversification seems to be enabled (at the extensive margin) primarily by their centrality in the foreign network and accelerated (at the intensive margin) by their centrality in the domestic one. Policy implications are drawn accordingly. |
| Keywords: | green technologies, diversification, relatedness, knowledge networks |
| JEL: | R11 R15 O52 O33 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2413&r= |
| By: | Antonio Scala; Marco Delmastro |
| Abstract: | Networks have always played a special role for human beings in shaping social relations, forming public opinion, and driving economic equilibria. Nowadays, online networked platforms dominate digital markets and capitalization leader-boards, while social networks drive public discussion. Despite the importance of networks in many economic and social domains (economics, sociology, anthropology, psychology, ...), the knowledge about the laws that dominate their dynamics is still scarce and fragmented. Here, we analyse a wide set of online networks (those financed by advertising) by investigating their value dynamics from several perspectives: the type of service, the geographic scope, the merging between networks, and the relationship between economic and financial value. The results show that the networks are dominated by strongly nonlinear dynamics. The existence of non-linearity is often underestimated in social sciences because it involves contexts that are difficult to deal with, such as the presence of multiple equilibria -- some of which are unstable. Yet, these dynamics must be fully understood and addressed if we aim to understand the recent evolution in the economic, political and social milieus, which are precisely characterised by corner equilibria (e.g., polarization, winner-take-all solutions, increasing inequality) and nonlinear patterns. |
| Date: | 2022–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2208.04813&r= |
| By: | Dyer, Travis; Köchling, Gerrit; Limbach, Peter |
| Abstract: | We show that investors acquire more public information about firms to which they are more socially proximate. On average, a standard deviation increase in the Social Connectedness Index (Bailey et al., 2018) between a firm's headquarter county and a searcher county is associated with 30% more EDGAR filing downloads from the searcher county. The effect of social proximity on traditional investment research is distinct from the effect of geographic proximity. We find similar results studying headquarter relocations, investor-level data, and EDGAR downloads from European regions, for which physical distance should be irrelevant. Social proximity matters more during times of high market-wide uncertainty and for firms with weaker information environments. Finally, information gathered by socially proximate investors predicts short-term earnings and stock returns, but also heightened volatility. Collectively, the evidence indicates that social networks mitigate informational frictions and foster information acquisition in financial markets. |
| Keywords: | Corporate disclosures, EDGAR, Geography, Information acquisition, Social networks, Social connections |
| JEL: | D80 D83 G10 G41 M40 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cfrwps:294838&r= |
| By: | Peter Devine (Boston College); Sumit Joshi (George Washington University); Ahmed Saber Mahmud (Virginia Polytechnic Institute and State University) |
| Abstract: | We propose a multilayer network approach to alliance formation. In a signed affinity layer, agents are partitioned into clusters, with friendly relations within and hostile connections across clusters. Agents then form defensive collaborations in an alliance layer as follows: Agents in the same cluster form a nested split graph with degree inversely correlated to the level of hostility, and agents from disparate clusters with high-degree and low-hostility form cliques. Within cliques, agents from a cluster that is "intermediate" in terms of discord serve as a bridge to interconnect agents from more "extreme" clusters. |
| Keywords: | Alliance formation, signed graphs, nested split graphs, pairwise stability, cliques |
| JEL: | C72 D74 D85 |
| Date: | 2024–05–10 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1071&r= |
| By: | Claudio Bellei; Muhua Xu; Ross Phillips; Tom Robinson; Mark Weber; Tim Kaler; Charles E. Leiserson; Arvind; Jie Chen |
| Abstract: | Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup level (multiple connected nodes) rather than at a node level of abstraction. We posit that certain domain applications, such as anti-money laundering (AML), are inherently subgraph problems and mainstream graph techniques have been operating at a suboptimal level of abstraction. This is due in part to the scarcity of annotated datasets of real-world size and complexity, as well as the lack of software tools for managing subgraph GNN workflows at scale. To enable work in fundamental algorithms as well as domain applications in AML and beyond, we introduce Elliptic2, a large graph dataset containing 122K labeled subgraphs of Bitcoin clusters within a background graph consisting of 49M node clusters and 196M edge transactions. The dataset provides subgraphs known to be linked to illicit activity for learning the set of "shapes" that money laundering exhibits in cryptocurrency and accurately classifying new criminal activity. Along with the dataset we share our graph techniques, software tooling, promising early experimental results, and new domain insights already gleaned from this approach. Taken together, we find immediate practical value in this approach and the potential for a new standard in anti-money laundering and forensic analytics in cryptocurrencies and other financial networks. |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2404.19109&r= |
| By: | Tingguo Zheng; Hongyin Zhang; Shiqi Ye |
| Abstract: | This paper introduces a novel multi-moment connectedness network approach for analyzing the interconnectedness of green financial market. Focusing on the impact of monetary policy shocks, our study reveals that connectedness within the green bond and equity markets varies with different moments (returns, volatility, skewness, and kurtosis) and changes significantly around Federal Open Market Committee (FOMC) events. Static analysis shows a decrease in connectedness with higher moments, while dynamic analysis highlights increased sensitivity to event-driven shocks. We find that both tight and loose monetary policy shocks initially elevate connectedness within the first six months. However, the effects of tight shocks gradually fade, whereas loose shocks may reduce connectedness after one year. These results offer insight to policymakers in regulating sustainable economies and investment managers in strategizing asset allocation and risk management, especially in environmentally focused markets. Our study contributes to understanding the complex dynamics of the green financial market in response to monetary policies, helping in decision-making for sustainable economic development and financial stability. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.02575&r= |
| By: | Jamie Fogel; Bernardo Modenesi |
| Abstract: | Recent advances in the literature of decomposition methods in economics have allowed for the identification and estimation of detailed wage gap decompositions. In this context, building reliable counterfactuals requires using tighter controls to ensure that similar workers are correctly identified by making sure that important unobserved variables such as skills are controlled for, as well as comparing only workers with similar observable characteristics. This paper contributes to the wage decomposition literature in two main ways: (i) developing an economic principled network based approach to control for unobserved worker skills heterogeneity in the presence of potential discrimination; and (ii) extending existing generic decomposition tools to accommodate for potential lack of overlapping supports in covariates between groups being compared, which is likely to be the norm in more detailed decompositions. We illustrate the methodology by decomposing the gender wage gap in Brazil. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.04365&r= |