Asymmetric trade costs, domestic value chains and global value chains in China: mechanism and effect assessment

Abstract

Previous research mostly evaluates global value chain embeddedness at the country level rather than considering the expansions of global value chains at the regional level within a country, and pays little attention to the mechanism linking global value chains and domestic value chains. Therefore, a substantial part of the variation in global value chain embeddedness at the regional level remains unexplained. Using Chinese interregional input-output tables in 2002, 2007, 2010, and 2012, this study investigates the impact of the asymmetric trade cost on global value chain embeddedness in China, and the underlying interaction mechanisms between domestic value chains and global value chains. The findings show that the asymmetric trade cost may widen the gap in the degree of global value chain embeddedness between bilateral trade regions, which unveils the variation in global value chain embeddedness at the regional level from the perspective of the asymmetric trade cost. Furthermore, domestic value chains may promote global value chains through improving collaboration, reducing transaction costs, and enhancing industrial competitiveness. In closing, this study reveals that the exacerbating effect of asymmetric trade costs on the disparity in global value chain embeddedness among regions becomes more pronounced in the presence of a significant gap in domestic value chain integration.

Keywords:

• Asymmetric trade costs
• global value chains
• domestic value chains

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 Data source: the UNCTAD-Eora Global Value Chain (GVC) database.

2 Some research found that market segmentation within China is intensifying, and the interprovincial trade costs in China are significantly high (Poncet, 2003). In fact, local protectionism and market segmentation within China are substantial factors contributing to asymmetric trade costs. However, since 2013, the comprehensive and deepening process of socialist market economy reform in China has been accelerating, and there is a clear trend towards market integration from segmentation. What’s more, taking into account the consistency in the principles of compiling interregional input-output tables in China, this study covers the years 2002, 2007, 2010, and 2012.

3 This paper focuses on domestic interregional trade to reflect the existence of a large number of product inflow and outflow activities in China.

4 Considering that the manufacturing sectors account for the vital part of the specialized manufacturing of intermediate products, which is influenced by trade costs mostly when participating in value chains, and the characteristics of agricultural, manufacturing, and service sectors participating in value chains are different, this paper focuses on the impact of asymmetric trade costs on value chain embeddedness of the manufacturing sectors.

5 In this paper, the elasticity of substitution is set to 9, which is consistent with Allen & Arkolakis (2014).

6 This study closely follows Tombe & Zhu (2019) to estimate asymmetries by using a standard gravity regression to infer asymmetric components of trade costs from fixed effects.

7 Specifically, the eight regions in this paper are defined as Northeast region (including Heilongjiang, Jilin, Liaoning), North Municipality region (including Beijing, Tianjin), North Coast region (including Hebei, Shandong), East Coast region (including Jiangsu, Shanghai, Zhejiang), South Coast region (including Fujian, Guangdong, Hainan), Central Region (including Shanxi, Henan, Anhui, Hubei, Hunan, Jiangxi), Northwest Region (including Inner Mongolia, Shaanxi, Ningxia, Gansu, Qinghai, Xinjiang), Southwest region (including Sichuan, Chongqing, Yunnan, Guizhou, Guangxi). The eight regions include 22 provinces, four autonomous regions, and four municipalities of China, and Tibet, Taiwan, Hong Kong, and Macao are excluded due to lack of data.

8 Due to the inconsistent industry classification in the Chinese interregional input-output tables, this paper refers to Industry Classification of National Economy (GB/T4754-2002, GB/T4754-2011) to adjust the industries in the Chinese interregional input-output tables, and retain manufacturing related sub-industries, including:(1) mining and dressing industry, (2) food manufacturing and tobacco processing industry, (3) textile and clothing industry, (4) papermaking, printing and cultural and educational supplies manufacturing industry, (5) chemical industry, (6) non-metallic mineral product manufacturing industry, (7) metal smelting and product manufacturing industry, (8) machinery industry, (9) transportation equipment manufacturing industry, (10) electrical machinery and electronic communication equipment manufacturing industry, (11) utilities industry, (12) wood processing and furnishing industry, (13) other manufacturing industry.

9 The world input-output tables (WIOD 2016) were used to estimate the returned domestic value added and the value added of imported inputs.

10 China’s National Bureau of Statistics makes Chinese interregional input-output tables (for 2002, 2007, 2010 and 2012) publicly available, which are semi-official and have been widely used for the analyses of interindustry linkages and their consequences within and between Chinese regions (Duan et al., 2021; Zhang et al., 2019).

Additional information

Funding

This research receives financial support from the National Natural Science Foundation of China (72303166) and the R&D Program of Beijing Municipal Education Commission (SM202310038014). The author is solely responsible for any errors or omissions herein.

Notes on contributors

Jinran Chen

Jinran Chen is currently an Assistant Professor at School of Economics, Capital University of Economics and Business, China. She received her doctoral degree in economics at Renmin Business School, Renmin University of China, China, in 2021. Her main research interests include domestic trade and value chains, and digital economics. She has received funding from the National Natural Science Foundation of China, Beijing Social Science Foundation Project, and the R&D Program of Beijing Municipal Education Commission.