English

Hall-Like Transversal Stress and Sandpile Criticality on Real Production Networks

Econometrics 2026-05-05 v1 Machine Learning Physics and Society

Abstract

This paper develops a Hall-Sandpile model of economic instability that combines a Hall-like transversal stress mechanism with sandpile threshold dynamics on a real production-network substrate. In analogy with the physical Hall effect, where exposed flows under an external field generate stress in a transversal direction, we model economic shocks as fields that act on flow-intensive, low-redundancy, low-capacity nodes and produce systemic stress through a multiplicative conversion function. The accumulated stress drives a discrete toppling rule and an avalanche dynamics whose effective activation threshold declines with transversal exposure. The model is calibrated on annual World Input--Output Database (WIOD) production networks for 2000--2014 and simulated on the 2014 substrate (2{,}283 country--sector nodes) under three alternative propagation normalisations to avoid mechanical near-criticality from row-stochastic operators. Controlled Monte Carlo experiments over external field intensity and redundancy stress generate four ordered regimes: stable absorption, latent fragility, critical transition, and avalanche regime. Mean avalanche size and the probabilities of finite-size systemic events Pr(S ⁣ ⁣5)\Pr(S\!\geq\!5), Pr(S ⁣ ⁣10)\Pr(S\!\geq\!10) and Pr(S ⁣ ⁣20)\Pr(S\!\geq\!20) rise jointly with field intensity and redundancy stress. Tail diagnostics show regime-dependent thickening of the avalanche distribution, but the estimated tail indices remain too high to interpret as evidence of universal power-law criticality. The contribution is therefore a finite-size, real-network description of how transversal stress activates structural fragility, not a claim of self-organised criticality in the global economy.

Keywords

Cite

@article{arxiv.2605.01561,
  title  = {Hall-Like Transversal Stress and Sandpile Criticality on Real Production Networks},
  author = {Diego Vallarino},
  journal= {arXiv preprint arXiv:2605.01561},
  year   = {2026}
}