Paper: arXiv 2408.02467

Authors: Massimiliano Fessina, Giulio Cimini, Tiziano Squartini, Pablo Astudillo-Estévez, Stefan Thurner, Diego Garlaschelli

Abstract

Production networks constitute the backbone of every economic system. They are inherently fragile as several recent crises clearly highlighted. Estimating the system-wide consequences of local disruptions (systemic risk) requires detailed information on the supply chain networks (SCN) at the firm-level, as systemic risk is associated with specific mesoscopic patterns. However, such information is usually not available and realistic estimates must be inferred from available sector-level data such as input-output tables and firm-level aggregate output data. Here we explore the ability of several maximum-entropy algorithms to infer realizations of SCNs characterized by a realistic level of systemic risk. We are in the unique position to test them against the actual Ecuadorian production network at the firm-level. Concretely, we compare various properties, including the Economic Systemic Risk Index, of the Ecuadorian production network with those from four inference models. We find that the most realistic systemic risk content at the firm-level is retrieved by the model that incorporates information about firm-specific input disaggregated by sector, indicating the importance of correctly accounting for firms’ heterogeneous input profiles across sectors. Our results clearly demonstrate the minimal amount of empirical information at the sector level that is necessary to statistically generate synthetic SCNs that encode realistic firm-specific systemic risk.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a rigorous mathematical framework using maximum-entropy models and Exponential Random Graphs to infer supply chain networks from aggregated sector data. It is exceptionally well-validated against a rare, complete country-level firm transaction dataset (Ecuador), offering actionable insights for systemic risk assessment.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Infer firm-level supply chain networks (SCNs)<br>with realistic systemic risk"] --> B["Empirical Inputs:<br>Input-Output tables, firm output data,<br>& actual Ecuadorian SCN benchmark"]
    B --> C["Apply Maximum-Entropy Algorithms:<br>Test 4 inference models with varying levels<br>of sector-level disaggregation"]
    C --> D["Network & Risk Evaluation:<br>Reconstruct SCN topologies & compute<br>Economic Systemic Risk Index (ESRI)"]
    D --> E["Key Outcome:<br>Models using firm-specific sectoral inputs<br>best match real firm-level systemic risk"]

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2 of 159 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.