Summary:
At present, momentous changes of a like not seen in a century are accelerating across the world, “black swan” and “gray rhino” events are frequent, global uncertainty has increased significantly, and preventing imported risks is a key task to safeguard China's national security. US uncertainty is an important external shock, and its international spillover effect cannot be ignored. Therefore, in the context of continuous promotion of China's high-standard opening up, it is of great significance for China to prevent and mitigate major risks and safeguard national security by clarifying the impact of US uncertainty on China, grasping the key types of US uncertainty shocks needed to be prevented, and further clarifying the internal mechanism of US uncertainty shocks affecting China's economy from the perspective of production network. Based on the production network perspective, this paper uses the newly developed heterogeneous spatial auto-regressive (HSAR) model to measure the direct and network effects of US uncertainty on the tail risk of China's industries and comparatively analyzes the differential impacts of the US economic and financial and economic policy uncertainty shocks. On this basis, this paper further compares industry forward and backward linkages to clarify the main transmission direction of each type of shock along the industrial chain and investigates the changes in the effects of the three types of shocks during the Sino-US trade friction. It is found that, firstly, the rise in US economic and financial uncertainty will exacerbate China's industry tail risk, while the rise in economic policy uncertainty suppresses China's industry tail risk. Moreover, compared with financial uncertainty, US economic uncertainty has a greater effect on the tail risk of China's industries. Secondly, production network plays a role in amplifying the impact of US uncertainty on the tail risk of China's industries. And more than 40% of the total effect is the network effect brought about by input-output linkages among China's industries. The basic chemical and social service industries suffer the highest network effects. Thirdly, US uncertainty shocks can cause an overall homogeneous change in China's industry tail risk through the production network. US economic and financial uncertainty has a greater network effect on China's real economy industries, and US economic policy uncertainty has a greater network effect on China's financial industries. Fourthly, US economic and financial uncertainty shocks are mainly transmitted from downstream industries to upstream industries, and economic policy uncertainty shocks are mainly transmitted from upstream industries to downstream industries. Fifthly, the impact of US financial uncertainty on China's industry tail risks during the Sino-US trade friction period was greater compared to other periods. This paper provides empirical evidence and useful insights for Chinese policymakers to accurately grasp and effectively deal with external shocks. Based on the above conclusions, this paper puts forward the following policy recommendations. First, we need to monitor all types of US uncertainty separately and accurately grasp the sources of risks. Second, the production network of China's industries should be included in the monitoring and prevention system of external shocks, and the path of risk contagion should be clarified according to the production network, to block the cross-industry contagion of risks in a timely manner. Third, efforts should be made to enhance the resilience of industrial and supply chains, improve the ability of various industries to defend against external shocks, and especially accelerate the diversification of supply chains in the basic chemical and social service industries. The marginal contributions of this paper are mainly reflected in the following three aspects. First, this paper expands the research on the spillover effect of US uncertainty shocks to the industry level and uses tail risk to accurately characterize the industry performance under the shocks. Second, this paper introduces the production network when exploring the impact of US economic, financial and economic policy uncertainty on China's industries, uses the newly developed heterogeneous spatial auto-regressive model to quantify the direct and network effects of the three types of shocks, and examines the main transmission direction of the shocks along the industrial chain, and clarifies the internal mechanism of US uncertainty shocks affecting China's economy. Third, it reveals the changes in the effects of US uncertainty shocks during the Sino-US trade friction, and further enriches the research on the economic consequences of Sino-US trade friction.
李政, 李薇, 李丽雯. 美国三类不确定性冲击、生产网络传导与中国行业尾部风险[J]. 金融研究, 2024, 530(8): 39-57.
LI Zheng, LI Wei, LI Liwen. Three Types of US Uncertainty Shocks, Production Network Transmission and China's Industry Tail Risks. Journal of Financial Research, 2024, 530(8): 39-57.
[1]邓创、吴健和吴超,2022a,《外部经济、金融不确定性与我国的宏观经济下行风险》,《统计研究》第6期,第36~51页。 [2]邓创、赵珂和吴超,2022b,《中国政策不确定性会加剧经济与金融不确定性吗》,《系统工程理论与实践》第3期,第559~574页。 [3]李政、李丽雯和刘淇,2024a,《我国行业间尾部风险溢出的测度及时空驱动因素研究》,《统计研究》第2期,第64~76页。 [4]李政和李薇,2024,《美国不确定性冲击对全球股市波动的影响研究》,《财经理论与实践》第2期,第48~55页。 [5]李政、刘淇和梁琪,2019,《基于经济金融关联网络的中国系统性风险防范研究》,《统计研究》第2期,第23~37页。 [6]李政、武坤和石晴,2024b,《外部输入性冲击与中国行业波动风险——基于四类不确定性的研究》,《北京工商大学学报(社会科学版)》第4期,第116~128页。 [7]谭小芬、曹倩倩和赵茜,2021,《全球风险偏好、美国经济政策不确定性与跨境资本流动——基于新兴经济体基金数据的证据》,《南开经济研究》第5期,第80~99页。 [8]杨子晖、陈雨恬和张平淼,2020,《股票与外汇市场尾部风险的跨市场传染研究》,《管理科学学报》第8期,第54~77页。 [9]杨子晖、王姝黛和梁方,2023,《产业链结构新视角下的尾部风险跨行业传染》,《经济学(季刊)》第1期,第212~227页。 [10]张成思、孙宇辰和阮睿,2023,《经济政策不确定性、企业货币政策感知与实业投资》,《财贸经济》第7期,第75~90页。 [11]周颖刚和肖潇,2022,《汇率波动、生产网络与股市风险——基于中美贸易摩擦背景的分析》,《金融研究》第7期,第115~134页。 [12]Acemoglu, D.,U. Akcigit and W. Kerr, 2016, “Networks and the Macroeconomy: An Empirical Exploration”, NBER Macroeconomics Annual, 30, pp. 273~335. [13]Acemoglu, D.,V. M. Carvalho, A. Ozdaglar and A. Tahbaz-Salehi, 2012, “The Network Origins of Aggregate Fluctuations”, Econometrica, 80(5), pp. 1977~2016. [14]Baker, S. R.,N. Bloom and S. J. Davis, 2016, “Measuring Economic Policy Uncertainty”, Quarterly Journal of Economics, 131(4), pp. 1593~1636. [15]Bhattarai, S.,A. Chatterjee and W. Y. Park, 2020, “Global Spillover Effects of US Uncertainty”, Journal of Monetary Economics, 114, pp. 71~89. [16]Bloom, N.,2009, “The Impact of Uncertainty Shocks”, Econometrica, 77(3), pp. 623~685. [17]Carvalho, V. M.,2014, “From Micro to Macro via Production Networks”, Journal of Economic Perspectives, 28(4),pp. 23~48 [18]Choi, S.,2018, “The Impact of US Financial Uncertainty Shocks on Emerging Market Economies: An International Credit Channel”, Open Economies Review, 29(1),pp.89~118. [19]Di Giovanni, J. and G. Hale, 2022, “Stock Market Spillovers via the Global Production Network: Transmission of U.S. Monetary Policy”, Journal of Finance, 77(6), pp. 3373~3421. [20]Engle, R. and S. Manganelli, 2004, “CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles”, Journal of Business & Economic Statistics, 22(4), pp. 367~381. [21]Fogli, A. and F. Perri, 2015, “Macroeconomic Volatility and External Imbalances”,Journal of Monetary Economics, 69, pp. 1~15. [22]Hu, Z.,A. M. Kutan and P. Sun, 2018, “Is U.S. Economic Policy Uncertainty Priced in China's A-Shares Market? Evidence from Market, Industry, and Individual Stocks”, International Review of Financial Analysis, 57, pp. 207~220. [23]Huang, Z.,C. Tong, H. Qiu and Y. Shen, 2018, “The Spillover of Macroeconomic Uncertainty between the US and China”, Economics Letters, 171, pp. 123~127. [24]LeSage, J. and R. K. Pace, 2009, Introduction to Spatial Econometrics, New York: Chapman and Hall/CRC. [25]Long, J. B. and C. I. Plosser, 1983, “Real Business Cycles”, Journal of Political Economy, 91(1), pp. 39~69. [26]Ludvigson, S. C.,S. Ma and S. Ng, 2021, “Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?”, American Economic Journal: Macroeconomics, 13(4), pp. 369~410. [27]Martin, J.,I. Mejean, and M. Parenti, 2023, “Relationship Stickiness, International Trade, and Economic Uncertainty”, Review of Economics and Statistics, forthcoming. [28]Novy, D. and A. M. Taylor, 2020, “Trade and Uncertainty”, Review of Economics and Statistics, 102(4), pp. 749~765. [29]Ozdagli, A. and M. Weber, 2017, “Monetary Policy through Production Networks: Evidence from the Stock Market”, NBER Working Paper, No. 23424. [30]Yildirim, Z.,2023, “Spillover Effects of US Uncertainty: Does the Type of US Uncertainty Matter?”, Applied Economics, 55(29), pp. 3365~3389.