Inferring firm-level supply chain networks with realistic systemic risk from industry sector-level data

TL;DR

This paper evaluates maximum-entropy algorithms to infer detailed firm-level supply chain networks from sector-level data, demonstrating that incorporating firm-specific input profiles yields realistic systemic risk estimates, validated against Ecuadorian data.

Contribution

It introduces a method to accurately infer firm-level supply chain networks with systemic risk features using minimal sector-level data, validated on real-world Ecuadorian networks.

Findings

Maximum-entropy models can replicate systemic risk features of real networks.

Incorporating firm-specific input data improves inference accuracy.

Minimal sector-level data suffices for realistic systemic risk modeling.

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.