Multiple shocks and the external market structure of China’s manufacturing industry

Abstract

This study examines multiple shocks to the external market of China’s manufacturing industry based on a trade model with multi-level unobserved factors. We find that the global external market of main industries is suffering constant downward shocks since 2008, while the traditional (non-traditional) markets are further affected by downward (upward) shocks. Private enterprises are more sensitive to shocks. Further, the adjustment of US trade policy toward China has caused China’s exports to the US to deviate from the normal path by around 10% from 2015 to 2019, while having no significant effect on China’s exports to the other traditional markets.

KEYWORDS:

• Manufacturing industry
• external shocks
• market divergence
• US trade policy toward China

JEL Classifications:

• F14
• L60

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Global shock is the unobserved composite shock from all uncertain events in global market of China’s manufacturing industry, such as the 2008 international financial crisis and the COVID-19 pandemic.

2 Group-specific shocks are the composite shocks from all uncertain events in specific group of markets, such as the technological monopoly policies in traditional markets, or the infrastructure boom in non-traditional markets.

3 The data are from the Chinese Customs Trade Statistics (CCTS).

4 Please refer to http://i-tip.wto.org/goods/Forms/TableView.aspx#. Between 2000 and 2020, the US initiated 389 Non-Tariff Measures (NTMs) on China, including 253 anti-dumping measures, 126 countervailing measures, and 10 sanitary and phytosanitary measures.

5 For the two-level factor model, the factors are categorized into two types: one is pervasive and affects all economic sectors, the other is nonpervasive and affects a specific economic sector.

6 The classification criteria of the two markets should satisfy the following three conditions: first, it should be in accordance with the economic meaning of traditional and non-traditional countries; second, it should be exogenously given and not affected by the external shocks during the sample period; third, it should not be dynamic and varying over time.

According to those conditions, we classify the market by China’s export value because the traditional countries are the main (major) trading partners of China (Aisyah and Renggani 2021; Destiarni, Triyasari, and Jamil 2021). Specifically, traditional market is a group of countries to which China exported in large quantities, while non-traditional market is the opposite. Moreover, we classify the market by the export value in 2001, because 2001 is the beginning of our sample period and the export value in 2001 is unaffected by the external shocks during the sample period.

The classification results show that traditional countries are dominated by OECD developed countries such as Canada, Germany, France, the United Kingdom, Japan, and South Korea, which account for approximately half of China’s external market. While non-traditional markets are represented by Africa and Latin America, which account for approximately a fifth of China’s external market. The detailed definitions of traditional and non-traditional market are reported in Appendix Table A1.

7 $PGDPDIF$ represents factor endowment differences. Scholars usually use the absolute value of the difference between bilateral GDP per capita to represent factor endowment differences, because GDP per capita is an appropriate measure for capital-labor ratios and thus relative factor endowment (Kaldor 1961). Many literatures use this indicator in trade models to determine whether the H-O model or the Linder hypothesis explains the pattern of bilateral trade flows (Batra 2006; Baskaran et al. 2011).

$OPENNESS$ represents trade openness. Trade openness is important in international trade considerations, trade is likely to improve considerably with the liberalization of trade and removal of barriers in trading partners. For instance, Rahman (2003) uses the trade-GDP ratio in a gravity model to analyze trade flows between Bangladesh and its trading partners. Hence, we also use this variable as a proxy for trade openness.

$PI$ and $EI$ represent institutions, while $PI$ represents political institutional quality and $EI$ represents economic institutional quality. Numerous studies find the correlation between institutions and trade (Bilgin, Gozgor, and Demir 2018; Ranjan and Lee 2007; Lin et al. 2021).

8 Considering the different sensitivity of traditional and non-traditional countries to the US trade policy toward China, we assume the coefficient of $\text{lnE}{\text{X}}_{\text{us},\text{t}}$ are different for traditional and non-traditional countries.

9 The estimation results of the regression coefficients are reported in the Appendix Table A4.

10 The estimation results of the regression coefficients are reported in the Appendix Table A5.

11 In a specific year, the ratio of export losses = (the actual exports – the potential exports) / the potential exports.

12 Specifically, the countries selected include Germany, France, Spain, Japan, the Netherlands, Belgium, Switzerland, and South Korea for the textile industry; Germany, France, Spain, Japan, the Netherlands, Belgium, the UK, and Sweden for the machinery industry; Germany, France, Spain, Japan, the UK, Canada, South Korea, and Sweden for the electronics industry.

13 I-TIP Database of World Trade Organization includes integrated analysis and retrieval of notified non-tariff measures, http://i-tip.wto.org/goods.

14 Non-Tariff Measures (NTMs) include Antidumping (ADP), Countervailing (CV), Sanitary and Phytosanitary (SPS), and Quantitative Restrictions (QR).

15 Major traditional countries include the European Union, Canada, Japan, the Republic of Korea, and the Russian Federation.

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [grant number 71773032]; The Fundamental Research Funds for the Central Universities: [grant number HUST: 2020JYCXJJ031].