Abstract
INTRODUCTION: In the context of heightened economic uncertainty and frequent extreme events, enhancing the resilience of pharmaceutical supply chains, safeguarding their security and stability, and promoting high-quality development in China's pharmaceutical industry have become pressing issues requiring in-depth research. METHODS: This study takes China's pharmaceutical industry from January 1, 2012, to March 31, 2023, as the research subject. The TENET method is employed to construct a tail risk network for the pharmaceutical supply chain. We examine its structural characteristics and dynamic temporal patterns, while analyzing variations in risk spillover effects across different tail risk events. Results: At the overall supply chain level, tail risks exhibit notable time-varying characteristics, with total connectedness rising significantly during risk events. At the module level, the production module serves as the primary source of both risk input and output. Cross-module analysis reveals clustering characteristics in risk spillovers between the production and distribution modules. Additionally, bidirectional spillovers are observed between the service and distribution modules, as well as between these modules and the production module. At the institutional level, the in-degree and out-degree of pharmaceutical institutions are not correlated with market capitalization. Hengrui Pharmaceuticals, Aier Eye Hospital, and Fosun Pharma are identified as systemically important institutions in the supply chain. Furthermore, the characteristics of the risk network vary under different tail risk events: financial crises elevate the overall risk level of the supply chain, whereas public health events do not significantly impact the overall risk level. Nonetheless, tail events universally increase the frequency of risk propagation within the supply chain. DISCUSSION: While the TENET model employed in this study serves as a powerful tool for analyzing tail risk networks, it possesses inherent limitations. Future research could integrate structured econometric models, such as the introduction of exogenous instrumental variables, or adopt high-frequency data causal discovery techniques. These approaches would help disentangle intrinsic causal pathways and further reveal the "topology of risk transmission.