Paper: arXiv 2506.21426

Authors: Anna Mancini, Balázs Lengyel, Riccardo Di Clemente, Giulio Cimini

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a rigorous empirical study of systemic risk in production networks using granular VAT data from Hungary across an eight-year span. It innovatively benchmarks empirical shock propagation against a maximum entropy null model (s-GM) to reveal structural adaptation and resilience shifts post-COVID-19.

Research Flowchart

  flowchart TD
    A["Research Goal: Assess firm-level systemic risk & supply chain rewiring dynamics (2015-2022)"] --> B["Data Input: Hungarian production network & international trade data"]
    B --> C["Methodology: Compare Empirical Network vs. Max Entropy Null Model (preserved sector demand/supply)"]
    C --> D["Computational Process: Calculate firm-level systemic risk scores pre- and post-COVID-19"]
    D --> E1["Finding 1: Structural shift during COVID-19 as trade-enabling firms became key systemic players"]
    D --> E2["Finding 2: Post-pandemic adaptive rewiring reduced overall systemic risk below null model predictions"]
    D --> E3["Finding 3: Imports and exports exert opposing effects on local systemic risk"]

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4 of 214 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.