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Liquidity distress can propagate through trade credit, contractual obligations, and financing dependencies in production networks. Existing network-contagion studies largely ask how distress spreads, but say much less about how scarce stabilization resources should be allocated across heterogeneous firms. This paper develops a heterogeneous network model in which firms are divided into structurally central large firms and small- and medium-sized enterprises (SMEs) with lower recovery capacity in the benchmark setting. The model incorporates recurrent healthy–distressed transitions, asymmetric contagion across firm types and network directions, decaying support stocks, preventive and curative support channels, and expectation-driven feedback linking aggregate distress to effective contagion and recovery probabilities. A reduced two-block approximation is used to characterize a local persistence threshold defined by the spectral radius of the Jacobian at the low-distress equilibrium. The analysis shows that targeted support need not dominate uniform support: its value depends on whether allocation priorities match the firm groups or network positions that generate the largest marginal reduction in persistent distress. Simulations on directed scale-free networks show that policy scale mainly determines peak containment, whereas allocation architecture primarily affects post-peak adjustment and long-run distress. Recovery-enhancing support plays a larger role in post-peak stabilization, and combined preventive–curative support yields stronger resilience than either channel alone. The framework provides a tractable basis for analyzing stabilization rules under limited support capacity in heterogeneous production networks.
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Kun Shuai
University of Electronic Science and Technology of China
Qian Qian
Sichuan Normal University
Mathematics
University of Electronic Science and Technology of China
Chengdu University of Technology
Sichuan Normal University
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Shuai et al. (Tue,) studied this question.
synapsesocial.com/papers/6a174f5695fbca339c8d86ea — DOI: https://doi.org/10.3390/math14111852