Evolution and determinants of firm-level systemic risk in local production networks

2025-06-26

AI Summary

This study investigates the dynamic evolution and drivers of firm-level systemic risk within Hungary’s domestic production network from 2015 to 2022, with emphasis on how supply chain restructuring—triggered by external shocks such as COVID-19—affects network resilience. Employing a sector-level supply-demand-constrained maximum-entropy null model, complemented by network topology analysis and panel regression, we quantify firm-level systemic risk and identify its determinants. Our key contributions are threefold: first, we document a structural shift induced by the pandemic, wherein highly connected firms assume markedly elevated risk roles; second, we uncover asymmetric predictive effects of international trade volume and direction (imports vs. exports) on domestic systemic risk via supply- and demand-side channels; third, empirical evidence shows that firms’ adaptive rewiring reduces realized systemic risk significantly below static benchmarks, establishing dynamic supply chain reconfiguration as a critical mechanism for enhancing regional industrial resilience.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Recent crises like the COVID-19 pandemic and geopolitical tensions have exposed vulnerabilities and caused disruptions of supply chains, leading to product shortages, increased costs, and economic instability. This has prompted increasing efforts to assess systemic risk, namely the effects of firm disruptions on entire economies. However, the ability of firms to react to crises by rewiring their supply links has been largely overlooked, limiting our understanding of production networks resilience. Here we study dynamics and determinants of firm-level systemic risk in the Hungarian production network from 2015 to 2022. We use as benchmark a heuristic maximum entropy null model that generates an ensemble of production networks at equilibrium, by preserving the total input (demand) and output (supply) of each firm at the sector level. We show that the fairly stable set of firms with highest systemic risk undergoes a structural change during COVID-19, as those enabling economic exchanges become key players in the economy -- a result which is not reproduced by the null model. Although the empirical systemic risk aligns well with the null value until the onset of the pandemic, it becomes significantly smaller afterwards as the adaptive behavior of firms leads to a more resilient economy. Furthermore, firms' international trade volume (being a subject of disruption) becomes a significant predictor of their systemic risk. However, international links cannot provide an unequivocal explanation for the observed trends, as imports and exports have opposing effects on local systemic risk through the supply and demand channels.

Problem

• Assessing firm-level systemic risk in production networks
• Understanding firm adaptability during supply chain disruptions
• Analyzing international trade's impact on local systemic risk

Innovation

• Maximum entropy model for production networks
• Firm-level systemic risk dynamics analysis
• COVID-19 impact on supply chain resilience