|
on Network Economics |
| By: | Van Wolleghem, Pierre; Soares, Marta Bruno; Puga-Gonzalez, Ivan; Shults, LeRon |
| Abstract: | As climate change intensifies, European local authorities (LAs) face growing pressure to adapt effectively. This article explores how LAs acquire and disseminate climate and policy knowledge, with a focus on their participation in EU-funded Research and Innovation (R&I) projects and Transnational Municipal Networks (TMNs). We map over 500 LAs involved in climate-related R&I projects and nearly 14, 000 LAs participating in 12 TMNs. Social Network Analysis (SNA) is used to identify influential hubs, LAs that have potential to both generate and spread adaptation knowledge. We find considerable variation in participation across LAs, both in R&I projects and TMN membership. Cities like Lisbon, Milan, and Tampere emerge as potential “super-spreaders”, displaying high centrality and the potential to bridge otherwise disconnected parts of the European network. |
| Date: | 2025–11–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:erxqg_v1 |
| By: | Sadegh Shirani; Mohsen Bayati |
| Abstract: | Causal effect estimation in networked systems is central to data-driven decision making. In such settings, interventions on one unit can spill over to others, and in complex physical or social systems, the interaction pathways driving these interference structures remain largely unobserved. We argue that for identifying population-level causal effects, it is not necessary to recover the exact network structure; instead, it suffices to characterize how those interactions contribute to the evolution of outcomes. Building on this principle, we study an evolution-based approach that investigates how outcomes change across observation rounds in response to interventions, hence compensating for missing network information. Using an exposure-mapping perspective, we give an axiomatic characterization of when the empirical distribution of outcomes follows a low-dimensional recursive equation, and identify minimal structural conditions under which such evolution mappings exist. We frame this as a distributional counterpart to difference-in-differences. Rather than assuming parallel paths for individual units, it exploits parallel evolution patterns across treatment scenarios to estimate counterfactual trajectories. A key insight is that treatment randomization plays a role beyond eliminating latent confounding; it induces an implicit sampling from hidden interference channels, enabling consistent learning about heterogeneous spillover effects. We highlight causal message passing as an instantiation of this method in dense networks while extending to more general interference structures, including influencer networks where a small set of units drives most spillovers. Finally, we discuss the limits of this approach, showing that strong temporal trends or endogenous interference can undermine identification. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.21675 |
| By: | Ryota Ishikawa (Graduate School of Economics, Waseda University) |
| Abstract: | Bramoull´e et al. (2009) considered a linear social interaction model with network structures under complete information. However, their model is not appropriate for the case where the individual outcome is not completely observed or not precisely predictable by the other individuals in the same group. In this paper, we consider a linear social interaction model with network structures under incomplete information and derive the efficiency bound. The efficiency bound for the model considered in this paper had not been derived before. We also provide a sufficient condition for the existence of the efficiency bound. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:wap:wpaper:2524 |
| By: | Castells, Pau; Zagdanski, Jakub |
| Abstract: | We investigate the economic justification for market-based payments from large internet traffic generators (LTGs) to network operators and internet service providers (ISPs) to support network investments, connectivity, and digital society objectives. Our analysis addresses ongoing debates about the LTG-ISP relationship. First, we confirm that traffic volume significantly influences network costs, countering claims to the contrary. Second, we frame telecommunications as a two-sided market where consumers access content and content providers reach consumers via networks, with payment structures varying based on market dynamics, as seen in other two-sided markets. We argue that extending incentives for efficient network use solely to consumers is ineffective due to their limited control over data consumption and transmission. In contrast, LTGs possess the technical expertise and capability to manage data flows, including optimizing their services' traffic generation, making them better candidates for such incentives. Despite this, market-based payment solutions have not gained traction. We identify regulatory constraints, such as net-neutrality rules, universal service obligations, and peering/interconnection regulations, as key factors reducing network operators' bargaining power. This asymmetry hinders their ability to negotiate agreements that effectively incentivize LTGs to use networks efficiently, limiting the adoption of such payment models. |
| Keywords: | Telecommunications economics, Large traffic generators (LTGs), Internet service providers (ISPs), Two-sided markets, Interconnection agreements |
| JEL: | L96 L51 L13 O33 D62 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itse25:331256 |
| By: | Hui Gong; Akash Sedai; Francesca Medda |
| Abstract: | Classical correlation matrices capture only linear and pairwise co-movements, leaving higher-order, nonlinear, and state-dependent interactions of financial markets unrepresented. This paper introduces the Quantum Network of Assets (QNA), a density-matrix based framework that embeds cross-asset dependencies into a quantum-information representation. The approach does not assume physical quantum effects but uses the mathematical structure of density operators, entropy, and mutual information to describe market organisation at a structural level. Within this framework we define two structural measures: the Entanglement Risk Index (ERI), which summarises global non-separability and the compression of effective market degrees of freedom, and the Quantum Early-Warning Signal (QEWS), which tracks changes in entropy to detect latent information build-up. These measures reveal dependency geometry that classical covariance-based tools cannot capture. Using NASDAQ-100 data from 2024-2025, we show that quantum entropy displays smoother evolution and clearer regime distinctions than classical entropy, and that ERI rises during periods of structural tightening even when volatility remains low. Around the 2025 US tariff announcement, QEWS shows a marked pre-event increase in structural tension followed by a sharp collapse after the announcement, indicating that structural transitions can precede price movements without implying predictive modelling. QNA therefore provides a structural diagnostic of market fragility, regime shifts, and latent information flow. The framework suggests new directions for systemic risk research by linking empirical asset networks with tools from quantum information theory. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.21515 |
| By: | HARA, Yasushi |
| Abstract: | This study revisits the impact of customer concentration on the performance and survival of Small and Medium-sized Enterprises (SMEs) by proposing an integrated “Quantity-Quality-Structure” framework. Utilizing a large-scale panel dataset of Japanese manufacturing SMEs, we employ rigorous empirical methods—including two-way fixed-effects models with controls for export status, Cox proportional hazards models, and dynamic event studies—to disentangle the complex effects of inter-firm relationships. While the static relationship between customer concentration (Quantity) and sales growth is found to be inconsistent across industries, our survival analysis reveals a robust and critical finding: high concentration significantly increases the risk of firm exit, supporting the vulnerability tenet of Resource Dependency Theory. Conversely, simple network connectivity (Degree Centrality) acts as a powerful buffer, significantly reducing exit risk and functioning as “structural insurance, ” whereas network brokerage (Betweenness Centrality) can exacerbate risks in certain assembly industries. Furthermore, dynamic analyses of strategic change reveal that firms “decoupling” from major customers face a multiyear “danger zone” of increased vulnerability before achieving diversification. Successful growth strategies are shown to be driven not by expanding existing B2B ties, but by a strategic pivot to new market types, specifically direct-to-consumer (B2C) segments. These findings reframe the debate on customer concentration from one of performance optimization to one of existential risk management and dynamic adaptation. |
| Keywords: | Customer Concentration, Firm Survival, Inter-firm Networks, Strategic Adaptation, SMEs |
| JEL: | L14 L25 M10 C23 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:hit:tdbcdp:e-2025-02 |
| By: | Junlin Yang |
| Abstract: | This paper investigates how institutional learning and regional spillovers shape volatility dynamics in ASEAN equity markets. Using daily data for Indonesia, Malaysia, the Philippines, and Thailand from 2010 to 2024, we construct a high-frequency institutional learning index via a MIDAS-EPU approach. Unlike existing studies that treat institutional quality as a static background characteristic, this paper models institutions as a dynamic mechanism that reacts to policy shocks, information pressure, and crisis events. Building on this perspective, we introduce two new volatility frameworks: the Institutional Response Dynamics Model (IRDM), which embeds crisis memory, policy shocks, and information flows; and the Network-Integrated IRDM (N-IRDM), which incorporates dynamic-correlation and institutional-similarity networks to capture cross-market transmission. Empirical results show that institutional learning amplifies short-run sensitivity to shocks yet accelerates post-crisis normalization. Crisis-memory terms explain prolonged volatility clustering, while network interactions improve tail behavior and short-horizon forecasts. Robustness checks using placebo and lagged networks indicate that spillovers reflect a strong regional common factor rather than dependence on specific correlation topologies. Diebold-Mariano and ENCNEW tests confirm that the N-IRDM significantly outperforms baseline GARCH benchmarks. The findings highlight a dual role of institutions and offer policy insights on transparency enhancement, macroprudential communication, and coordinated regional governance. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.19824 |
| By: | Veraart, Luitgard A. M.; Zhang, Yuliang |
| Abstract: | We analyse how post-trade netting in over-the-counter derivatives markets affects systemic risk. In particular, we focus on two post-trade netting services that rely on multilateral netting techniques: portfolio rebalancing and portfolio compression. First, we provide mathematical characterisations of their netting mechanisms and explain their relationship. Then, we analyse the effects of post-trade netting from a network perspective by considering contagion arising from defaults on variation margin payments. We provide sufficient conditions for post-trade netting to reduce systemic risk and show that post-trade netting can be harmful. We also explore the implications, particularly when institutions strategically react to liquidity stress by delaying their payments. |
| JEL: | F3 G3 |
| Date: | 2025–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129549 |
| By: | Yanina Domenella (Universidad Autónoma de Madrid) |
| Abstract: | During economic downturns, governments often provide business grants to stimulate entrepreneurship. However, in societies where kinship ties play a significant role, policy design may be suboptimal if spillover effects are not accounted for. This paper examines the role of family ties in shaping entrepreneurship and the effectiveness of business support measures during economic crises. Using a randomized controlled trial in Kenya, I find that entrepreneurs with larger families coped better with the crisis. However, when external funding was available, strong family ties reduced the positive effects on entrepreneurship.The analysis identifies mutual assistance, crowding-out effects, and managerial interference as key mechanisms. These findings highlight the dual role of family networks, acting as both a safety net and a constraint, with implications for the design of business support policies in developing economies. |
| Keywords: | Entrepreneurship, kinship networks, private transfers, social norms, business support, crisis, field experiment, Kenya. |
| JEL: | L26 O12 O15 Z13 C93 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:cmf:wpaper:wp2025_2529 |