🤖 AI Summary
This study addresses why minor shocks often trigger disproportionately large macroeconomic crises by introducing Forman–Ricci curvature from graph theory into macroeconomics to characterize local substitutability within evolving production networks under supply-chain disruptions. The framework reveals how increasing specialization drives economies toward a critical state prone to cascading failures, thereby establishing a non-ergodic, path-dependent theory of crisis generation that transcends the limitations of representative-agent models. Empirical analysis demonstrates that the global input–output network has persistently exhibited negative curvature, which significantly predicts three-year-ahead output growth and outperforms conventional network metrics in explaining cross-country differences in economic resilience.
📝 Abstract
Why do capitalist economies recurrently generate crises whose severity is disproportionate to the size of the triggering shock? This paper proposes a structural answer grounded in the evolutionary geometry of production networks. As economies evolve through specialization, integration, and competitive selection, their inter-sectoral linkages drift toward configurations of increasing geometric fragility, eventually crossing a threshold beyond which small disturbances generate disproportionately large cascades.
We introduce Sandpile Economics, a formal framework that interprets macroeconomic instability as an emergent property of disequilibrium production networks. The key state variable is the Forman--Ricci curvature of the input--output graph, capturing local substitution possibilities when supply chains are disrupted. We show that when curvature falls below an endogenous threshold, the distribution of cascade sizes follows a power law with tail index $α\in (1,2)$, implying a regime of unbounded amplification.
The underlying mechanism is evolutionary: specialization reduces input substitutability, pushing the economy toward criticality, while crisis episodes induce endogenous network reconfiguration and path dependence. These dynamics are inherently non-ergodic and cannot be captured by representative-agent frameworks.
Empirically, using global input--output data, we document that production networks operate in persistently negative curvature regimes and that curvature robustly predicts medium-run output dynamics. A one-standard-deviation increase in curvature is associated with higher cumulative growth over three-year horizons, and curvature systematically outperforms standard network metrics in explaining cross-country differences in resilience.