US economic policy uncertainty on Chinese economy: industry level analysis

ABSTRACT

This paper uses both Chinese aggregate and dis-aggregate level data to investigate the transmission mechanisms of spillover effects of US economic policy uncertainty on the Chinese macroeconomy activities. We find that different industries of China respond quite differently by using quasi-exogenous variation across industries. More specifically, increasing uncertainty reduces the Chinese stock market return, especially those industries closely connected to the US investors. FDI in China is positively associated with increased US policy uncertainties, and industries intensively depending on foreign investment would be affected more. Lastly, the export from China to US would decrease, and industries whose exports heavily relying on the US market would drop more in response to increases in the US policy uncertainties.

KEYWORDS:

• US economic policy uncertainty
• international transmission
• industry-level analysis
• Chinese macroeconomy

JEL CLASSIFICATION:

• F40
• F41
• F42
• F44

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

¹ For example, Bloom et al. (2018), Caggiano, Castelnuovo, and Figueres (2017), Bachmann, Elstner, and Sims (2013) suggests that uncertainty shocks can cause fluctuations in macroeconomic aggregates. Istrefi and Mouabbi (2018) study the impact of interest rate uncertainty on the economy.

² This idea is also explored in other literature (e.g., Bloom et al. (2018)).

³ $FD{I}^{cn}$, $SH$, $EX{P}^{us}$, $M{2}^{cn}$, $CP{I}^{cn}$ in SVAR models are in the format of year-to-year changing rate, and we take logarithm demean operation over policy uncertainty variables. We also consider the monthly volatility of Shanghai index as the proxy for the Chinese financial market, the results of which are shown in Figures (7-9) in the Appendix.

⁴ We also tried various specifications, such as changing causal ordering, inclusion of other exogenous variables. The results are in general consistent with main findings in the benchmark specification, which can be found in Figures (4–6) in the Appendix.

⁵ Datasource: WIND. The industry classification is based on GICS (Global Industrial Classification Standard). Data range: monthly data from January 1997 to June 2018. There are 10 primary industries of the Chinese stock market. Summary statistics are shown in Table (A4) in the Appendix.

⁶ The details of $D_i^s$ and corresponding industries can be found in Table (A1) in the Appendix.

⁷ We used different regression specifications to check the robustness of the estimation results, for example, adding or dropping exogenous variables. The results, including signs, significance and magnitude, are quite robust, and can be found in Tables (A5–A7) of Appendix.

⁸ Data source: CEIC. Data range: from January 2006 to June 2018. Classification standard is based on Standard International Trade Classification (SITC). We have 18 industries in total.

⁹ The details of $D_i^f$ and corresponding industries can be found in Table (A2) in the Appendix.

¹⁰ We also tried other regression specifications as robustness checks, the results of which are consistent with benchmark settings. Robustness check results can be found in Tables (A8–A10) of Appendix.

¹¹ Data source: CEIC. Data range: from January 1993 to June 2018. Classification standard is based on Harmonized Commodity Description and Coding Systems (HS). We have 22 industries in total.

¹² The details of $D_i^e$ and corresponding industries can be found in Table (A6) in the Appendix.

¹³ The results are quite robust when we change the specifications of panel regression, like adding or reducing exogenous variables, the results of which are list in Tables (A11–A13) of Appendix.

Additional information

Funding

Naijing Huang acknowledges the funding spport by  National Nature Science Foundation of China under project number 71850005 “Exchange Rate Market Presssure, Cross-border Capital Flow and Financial Risk Prevention„