With the convenience and timeliness of information communication, as well as the low cost of cross-border transportation, Global Value Chain (GVC) led by intra-product division emerged in the 1980s. During that period, China embedded in GVC quickly by relying on the advantages of labor endowment. However, China is still in the low-end of GVC. There is a serious asymmetry between trade volumes and trade gains brought by GVC, and more and more developed countries gradually transfer the division of labor that originally belonged to China in GVC to countries such as India where labor costs are relatively low because of China’s increasing labor costs. Global economic uncertainty has increased recently. The limitations of China’s long-term embedding in GVC’s low value-added links have become more apparent. Facing the above situations, how to seek a new upgrade path based on GVC, and how to use technology spillover effects brought by FDI to promote healthy and sustainable development of the Chinese economy? Based on the existing research, this paper attempts to explore these issues from a new perspective of global FDI flow networks. This paper uses bilateral FDI flow data of 40 countries or regions published by UNCTAD from 2001 to 2012 to build a global FDI sum, inflow networks and outflow networks, and innovatively adopts social network analysis methods to study the overall patterns of these three kinds of global FDI flow networks. It finds that the density values of global FDI flow networks show an increasing trend from 2001 to 2012, and FDI flow connections among countries are gradually strengthened. Furthermore, to the network characteristics of countries or regions, it points out that global FDI flow networks are skewed, and the distributions of network intensiveness and network extensiveness are right-biased, but the trends of changes are different. Based on the above analysis, this paper then empirically tests how one country’s FDI flow network characteristics affect its GVC division of labor. Results show that the increase of one country’s FDI flow network intensiveness and extensiveness can significantly improve this country’s GVC division of labor. Furthermore, in view of the fact FDI flow networks only bring value chain upgrade advantages rather than value chain upgrade resources, this paper then verifies the existence of transformation process of " value chain upgrade advantages—value chain upgrade resources” from technology absorption capacity, points out strengthening the technology absorption capacity of developing countries can more heavily enhance this transformation and be more conducive to improve GVC division of labor, especially in outflow networks. In short, this paper mainly expands the existing research from the following three aspects. First, the application of social network analysis methods in FDI analyses enriches the research on technology spillover effects of FDI, which is the demand for continuous development of global FDI flows. Second, the distinction of " value chain upgrade advantages” and " value chain upgrade resources” can more accurately evaluate promotion value chain upgrade effects brought by FDI flow networks. Third, the higher the technology absorption capacity of developing countries is, the more it can strengthen the impact of FDI network characteristics on its GVC division of labor, especially in capital outflow networks, which has certain guiding significance on how to regulate FDI flows.
/ Journals / Journal of Finance and Economics
Journal of Finance and Economics
LiuYuanchun, Editor-in-Chief
ZhengChunrong, Vice Executive Editor-in-Chief
YaoLan BaoXiaohua HuangJun, Vice Editor-in-Chief
FDI Flow and the Division of Labor in GVC: An Understanding from the Perspective of Social Network Analyses
Journal of Finance and Economics Vol. 45, Issue 03, pp. 100 - 113 (2019) DOI:10.16538/j.cnki.jfe.2019.03.008
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Cite this article
Liu Jingqing, Yu Jiawen, Che Weihan. FDI Flow and the Division of Labor in GVC: An Understanding from the Perspective of Social Network Analyses[J]. Journal of Finance and Economics, 2019, 45(3): 100-113.
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