Revealing production networks from firm growth dynamics

Paper: arXiv 2302.09906

Authors: Luca Mungo, José Moran

Abstract

We study the correlation structure of firm growth rates. We show that most firms are correlated because of their exposure to a common factor but that firms linked through the supply chain exhibit a stronger correlation on average than firms that are not. Removing this common factor significantly reduces the average correlation between two firms with no relationship in the supply chain while maintaining a significant correlation between two firms that are linked. We then investigate if this observation can be used to reconstruct the topology of a supply chain network using Gaussian Markov Models.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10 (capped from 7.0: no out-of-sample test; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces an innovative method to infer supply chain networks using firm growth rate correlations and Gaussian Markov Models. It effectively strips out common macro factors to isolate structural firm-level interactions with solid mathematical and empirical grounding.

Research Flowchart

  flowchart TD
    A[Goal: Reconstruct Supply Chain Network from Firm Growth Rates] --> B[Data: Firm Growth Rates & Equity Data]
    B --> C[Analyze Correlation Structure of Growth Rates]
    C --> D[Apply Common Factor Model to Filter Market Effects]
    D --> E[Use Gaussian Markov Random Fields on Residuals]
    E --> F[Outcome: Discovered Supply Chain Topology & Disentangled Networks]