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on Network Economics |
| By: | Yue Zhang (Audencia Business School); Haozhi Chen; Xiaolei He (Guangzhou University) |
| Abstract: | This study develops a multilayer dynamic network framework to evaluate the systemic importance of 348 firms listed in China's A-share market over the period 2010-2021. By employing the maximum mutual information coefficient (MIC), the model captures both linear and nonlinear interdependencies, integrating firm-specific tail risk indicators and tradingbased metrics. Topological analysis of the network, including connectivity, clustering, and centrality measures, reveals structural drivers of systemic risk propagation. The results show that firms with high centrality and interconnectedness disproportionately amplify systemic vulnerabilities, underscoring their critical roles in financial stability. The multilayer dynamic framework significantly enhances the precision of systemic risk assessment compared to traditional single-layer models. This study contributes to systemic risk literature by extending advanced network methodologies to emerging markets and offers actionable insights for policymakers and regulators to design effective risk mitigation strategies. |
| Keywords: | Systemic risk, Topological theory, Multilayer financial network, Systemically important corporations |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05306848 |
| By: | Zhao, Xialing; Fan, Linlin; Xu, Yilan |
| Abstract: | Understanding how climate change beliefs are shaped by social networks is critical for designing effective climate communication strategies, yet the degree of peer influence across spatial and political contexts remains insufficiently understood. This study examines the influence of peer counties on local climate change beliefs using a spatial autoregressive (SAR) model. We construct geographic, political, and economic peer networks at the county level and quantify the magnitude of peer effects. Results show that a 10% increase in climate change beliefs among peer counties is associated with a 4.2% to 9.2% increase in average beliefs within the focal county, depending on the network type. The geographic peer network exerts the strongest influence, with estimated effects ranging from 6.7% to 9.2%, followed by the political network, with effects between 4.2% and 7.5%. Counterfactual simulations reveal that targeting interventions at top key opinion leader (KOL) counties—those most connected in a network—is more effective than targeting counties with extreme belief levels or KOL counties with below-average beliefs. These findings provide actionable insights for policymakers seeking to promote climate belief formation and encourage climate-friendly behaviors through network-informed interventions. |
| Keywords: | Environmental Economics and Policy |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360759 |
| By: | Alejandra Martinez; Dennis Novy; Carlo Perroni |
| Abstract: | A large literature has documented transitivity as a key feature of social networks: individuals are more likely connected with each other if they share common connections with other individuals. We take this idea to trading relationships between firms: firms are more likely to trade with each other if they share common trading partners. Transitivity leads to a clustered pattern of relationship formation and break-up. It is therefore important for understanding how firms meet and how shocks propagate through firm networks. We describe a method for detecting and quantifying transitivity in firm-to-firm transactions, based on systematic deviations from conditional independence across firm-to-firm relationships. We apply the method to Colombia-U.S. exporter-importer data and show in counterfactuals that transitivity is a significant and economically meaningful factor in how firm networks adjust to cost shocks. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.18893 |
| By: | Coen Hutters; Max B. Mendel |
| Abstract: | In this paper, we demonstrate how multiport network theory can be used as a powerful modeling tool in economics. The critical insight is using the port concept to pair the flow of goods (the electrical current) with the agent's incentive (the voltage) in an economic interaction. By building networks of agents interacting through ports, we create models with multiple levels of abstraction, from the macro level down to the micro level. We are thereby able to model complex macroeconomic systems whose dynamical behavior is emergent from the micro level. Using the LTSpice circuit simulator, we then design and analyze a series of example systems that range in complexity from the textbook Robinson Crusoe economy to a model of an entire economy. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.20600 |
| By: | Gian Jaeger; Wang Ngai Yeung; Renaud Lambiotte |
| Abstract: | Although the automation and digitisation of anti-financial crime investigation has made significant progress in recent years, detecting insider trading remains a unique challenge, partly due to the limited availability of labelled data. To address this challenge, we propose using a data-driven networks approach that flags groups of corporate insiders who report coordinated transactions that are indicative of insider trading. Specifically, we leverage data on 2.9 million trades reported to the U.S. Securities and Exchange Commission (SEC) by company insiders (C-suite executives, board members and major shareholders) between 2014 and 2024. Our proposed algorithm constructs weighted edges between insiders based on the temporal similarity of their trades over the 10-year timeframe. Within this network we then uncover trends that indicate insider trading by focusing on central nodes and anomalous subgraphs. To highlight the validity of our approach we evaluate our findings with reference to two null models, generated by running our algorithm on synthetic empirically calibrated and shuffled datasets. The results indicate that our approach can be used to detect pairs or clusters of insiders whose behaviour suggests insider trading and/or market manipulation. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.18918 |
| By: | Chen, Xi; Zhang, Xiaobo |
| Abstract: | Blood donation with compensation is considered as a social stigma. However, more people in the reference group donate blood often leads to less moral concern and more followers. Therefore, the behavior is likely to be influenced through one’s interactions with neighbors, friends and relatives. Meanwhile, relative income may affect the motives for blood donation through increasing mistrust and stress. The motives might be stronger for households of lower social rankings. Utilizing three-wave census-type panel data in 18 villages in rural western China, two identification strategies, instrumental variable and network-based identification, are implemented to estimate the effect of social interactions. Both community-specific and household-specific relative income measures are employed to test whether blood donation is more sensitive towards the less well-off in a society. We find strong evidence in support of the effects of social interactions, no matter whether instrumental variables or network centrality measures are adopted. Household-specific measures of relative income show more salient effects on blood donation than community-specific inequality. |
| Keywords: | Agricultural and Food Policy, Institutional and Behavioral Economics, Political Economy |
| URL: | https://d.repec.org/n?u=RePEc:ags:iamo10:90796 |
| By: | Sandeep Neela |
| Abstract: | Financial crises emerge when structural vulnerabilities accumulate across sectors, markets, and investor behavior. Predicting these systemic transitions is challenging because they arise from evolving interactions between market participants, not isolated price movements alone. We present Systemic Risk Radar (SRR), a framework that models financial markets as multi-layer graphs to detect early signs of systemic fragility and crash-regime transitions. We evaluate SRR across three major crises: the Dot-com crash, the Global Financial Crisis, and the COVID-19 shock. Our experiments compare snapshot GNNs, a simplified temporal GNN prototype, and standard baselines (logistic regression and Random Forest). Results show that structural network information provides useful early-warning signals compared to feature-based models alone. This correlation-based instantiation of SRR demonstrates that graph-derived features capture meaningful changes in market structure during stress events. The findings motivate extending SRR with additional graph layers (sector/factor exposure, sentiment) and more expressive temporal architectures (LSTM/GRU or Transformer encoders) to better handle diverse crisis types. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.17185 |
| By: | Daniel Czarnowske; Amrei Stammann |
| Abstract: | Inference for fixed effects estimators of linear and nonlinear panel models is often unreliable due to Nickell- and/or incidental parameter biases. This article develops new inferential theory for (non)linear fixed effects M-estimators with data featuring a three-dimensional panel structure, such as sender x receiver x time. Our theory accommodates bipartite, directed, and undirected network panel data, integrates distinct specifications for additively separable unobserved effects with different layers of variation, and allows for weakly exogenous regressors. Our analysis reveals that the asymptotic properties of fixed effects estimators with three-dimensional panel data can deviate substantially from those with two-dimensional panel data. While for some specifications the estimator turns out to be asymptotically unbiased, in other specifications, it suffers from a particularly severe inference problem, characterized by a degenerate asymptotic distribution and complex bias structures. We address this atypical inference problem, by deriving explicit expressions to debias the fixed effects estimators. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.18678 |
| By: | Hern\'an Ezequiel Ben\'itez; Claudio Oscar Dorso |
| Abstract: | The analysis of financial markets using models inspired by statistical physics offers a fruitful approach to understand collective and extreme phenomena [3, 14, 15] In this paper, we present a study based on a 2D Ising network model where each spin represents an agent that interacts only with its immediate neighbors plus a term reated to the mean field [1, 2]. From this simple formulation, we analyze the formation of spin clusters, their temporal persistence, and the morphological evolution of the system as a function of temperature [5, 19]. Furthermore, we introduce the study of the quantity $1/2P\sum_{i}|S_{i}(t)+S_{i}(t+\Delta t)|$, which measures the absolute overlap between consecutive configurations and quantifies the degree of instantaneous correlation between system states. The results show that both the morphology and persistence of the clusters and the dynamics of the absolute sum can explain universal statistical properties observed in financial markets, known as stylized facts [2, 12, 18]: sharp peaks in returns, distributions with heavy tails, and zero autocorrelation. The critical structure of clusters and their reorganization over time thus provide a microscopic mechanism that gives rise to the intermittency and clustered volatility observed in prices [2, 15]. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.17925 |
| By: | João Carlos Lopes; Vítor Escária |
| Abstract: | The main purpose of this paper is to analyse the evolution of the Portuguese productive structure between the end of the 1950s and 2020, based on the relationship between the productive sectors, i.e. the so-called intersectoral (or input-output) flows. Firstly, the history of the construction of Input-Output tables in Portugal is presented. Secondly, to analyse the evolution of the density of the industrial network over time based on quantitative indicators of the most relevant flows, all the tables are harmonized and made compatible (same number of sectors, as homogeneous as possible). Thirdly, the key sectors of the Portuguese economy are identified, using several indicators and fourthly, the evolution of “economic complexity” in Portugal between 1959 and 2020 is studied using two distinct input-output based quantitative measures. |
| Keywords: | Productive structure; Key sectors; Economic complexity; Input-output flows; Portugal |
| JEL: | C67 D57 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp04012026 |
| By: | Katharina Ledebur. Ladislav Bartuska; Klaus Friesenbichler; Peter Klimek |
| Abstract: | The automotive industry is undergoing transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends blur the boundaries between the automotive sector and other industries. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominated, competitiveness in electric vehicle (EV) production increasingly depends on expertise in electronics, batteries, and software. This study investigates whether and how firms' ability to leverage cross-industry diversification contributes to competitive advantage. We develop a country-level product space covering all industries and an industry-specific product space covering over 900 automotive components. This allows us to identify clusters of parts that are exported together, revealing shared manufacturing capabilities. Closeness centrality in the country-level product space, rather than simple proximity, is a strong predictor of where new comparative advantages are likely to emerge. We examine this relationship across industrial sectors to establish patterns of path dependency, diversification and capability formation, and then focus on the EV transition. New strengths in vehicles and aluminium products in the EU are expected to generate 5 and 4.6 times more EV-specific strengths, respectively, than other EV-relevant sectors over the next decade, compared to only 1.6 and 4.5 new strengths in already diversified China. Countries such as South Korea, China, the US and Canada show strong potential for diversification into EV-related products, while established producers in the EU are likely to come under pressure. These findings suggest that the success of the automotive transformation depends on regions' ability to mobilize existing industrial capabilities, particularly in sectors such as machinery and electronic equipment. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.13178 |