摘要
准确评估产业链韧性是维护产业链平稳运行的重要前提,也是百年未有之大变局下防范化解外部风险的重要保障。本文将宏观大数据、在险增长研究框架与产业链韧性的基本理念有机结合,提出了一种兼具时效性与稳健性的产业韧性测度方法,并进一步从时变关联网络和中国产业结构出发,考察了产业韧性的关联以及产业间韧性差异的成因。测度结果表明,多数产业韧性在2011—2019年间呈现波动上升趋势,并且在2020年后出现U型特征,美国和越南的经济态势以及中国房地产业状况对中国产业平稳运行有着重要影响,国内居民收入、欧盟债务、利率、外商投资等成为影响产业韧性的关键因素。关联分析表明,以供给侧结构性改革和新发展格局为时间节点,中国内部产业间韧性的总关联性呈现“先升—后降—再升”态势。归因分析表明,上游供给来源集中度可以有效解释产业间韧性差异,产业间韧性的有向关联与产业链上下游结构之间也存在紧密联系。本文的研究结果为产业链韧性监测和预测、产业链风险评估以及大数据赋能产业高质量发展提供了有益参考。
Accurate measurement of industrial chain resilience is a crucial prerequisite for maintaining the stable operation of industrial chains and serves as an important safeguard for mitigating external risks under profound changes unseen in a century.This paper constructs a big data set reflecting the operation status of China's industries,integrates the definition of industrial chain resilience with the Growth at Risk(GaR)research framework,and proposes a method for measuring industrial chain resilience using big data.The method examines the impact of extreme events in crisis scenarios,incorporating both high frequency and timeliness features.Furthermore,this paper investigates the correlation between industrial resilience and the causes of differences in resilience between industries,based on time-varying correlation networks and China's industrial structure.The results indicate that the resilience of most industries showed a fluctuating upward trend from 2011 to 2019,with a U-shaped characteristic after 2020.The economic conditions of the United States and Vietnam,as well as the status of China's real estate industry,significantly influence the stable operation of China's industries.Variables such as the income of China's residents,European Union(EU)debt,interest rates,and foreign investment have successively become key factors impacting industrial resilience.In terms of dynamic changes in the correlation between industrial resilience,before the implementation of supply-side structural reform policies,the internal correlation of industrial resilience fluctuated and increased.From the implementation of the supply-side structural reform policies to the occurrence of public health emergencies,the internal correlation of industrial resilience gradually declined.Since the occurrence of public health emergencies and the implementation of the new“dual circulation”development pattern,the internal correlation between industries has risen again,with a noticeable increase in the correlation between manufacturing industries highly dependent on intermediate goods imports and other industries.In terms of the input-output structure of the industrial chain,there is a negative correlation between the concentration of supply sources and resilience.The more concentrated the supply sources,the lower the resilience level.Additionally,the directed correlations between industrial resilience are closely linked to the input-output structure of the industrial chain,with such correlations mainly manifested in the influence of upstream sectors on downstream sectors.The findings provide important insights for economic policies.Given the multiple challenges facing the operation of industries,China needs to maintain policy coherence and stability and provide a favorable investment environment and predictable returns for foreign enterprises.It should strengthen policy communication and interpretation to reduce information asymmetry,thereby enhancing policy transparency and predictability.Given the close interconnection between the resilience of upstream and downstream sectors in the industrial chains,industrial policies should promote the deep integration and collaborative development of the upstream and downstream sectors to enhance the overall competitiveness of industries.Furthermore,more efforts should be made to explore diversified markets to reduce reliance on a single market,strengthen regional cooperation,actively integrate into global industrial chains,supply chains,and value chains,and engage proactively in international economic cooperation and competition.This paper introduces big data methods into the field of resilience measurement,demonstrating the capacity of big data techniques to accurately capture industrial operational trends and providing valuable references for monitoring and forecasting industrial chain resilience,industrial chain risk assessment,and leveraging big data to empower high-quality industrial development.
作者
赵宇
叶仕奇
杨翠红
洪永淼
ZHAO Yu;YE Shi-qi;YANG Cui-hong;HONG Yong-miao(Academy of Mathematics and Systems Science,Chinese Academy of Sciences;College of Business,City University of Hong Kong,China)
出处
《中国工业经济》
北大核心
2025年第2期61-79,共19页
China Industrial Economics
基金
国家自然科学基金基础科学中心项目“计量建模与经济政策研究”(批准号71988101)
国家自然科学基金青年学生基础研究项目“多矩阵自回归模型扩展及在经济领域中的应用”(批准号723B2020)
中国博士后科学基金面上项目“得分驱动混频动态因子模型及其应用”(批准号2024M763465)。
关键词
产业链韧性
宏观大数据
在险增长
关联网络
industrial chain resilience
macro big data
Growth at Risk
connectedness network
