Abstract
AbstractEmerging in the twenty-first century, Network Science provides practical measures to interpret a system’s interactions between the components and their links. Literature has focused on countries’ interconnections on the final goods, but its application on the value-added from a network perspective in trade is still imitated. This paper applies network science properties and a multi-regional input–output analysis by using the UNCTAD-Eora Global Value Chain Database on the Transport Equipment value added on 2017 to unwrap the specific structural characteristics of the industry. Results show that the industry is highly centralized. The center of the network is dominated by developed countries, mainly from Europe, the United States, and Japan. Emerging countries such as China, Mexico, Thailand, and Poland also have an important position. In addition, the structure reveals two sub-hubs located in East Europe and North America. By extending to community detection, the network consists of three different communities led by Germany, the United States, and the United Kingdom, associated with more significant value-added flows. The study concludes that flows are not always consistent with the economy’s geographical location as usually final goods analysis suggests, and highlight the need to continue using the complex network to reveal the world trade structure.
Publisher
Springer Science and Business Media LLC
Subject
Economics, Econometrics and Finance (miscellaneous),Economics and Econometrics
Reference49 articles.
1. Amador J, Cabral S (2016) Networks of value-added trade. World Econ 40(7):1291–1313. https://doi.org/10.1111/twec.12469
2. Artuc E, Christiaensen L, Winkler HJ (2019) Does automation in rich countries hurt developing ones. Evidence from the US and Mexico. The World Bank. http://hdl.handle.net/10986/31279
3. Azmeh S, Nguyen H, Kuhn M (2022) Automation and industrialization through global value chains: North Africa in the German automotive wiring harness industry. Struct Change Econ Dyn 63:125–138. https://doi.org/10.1016/j.strueco.2022.09.006
4. Barabási LA (2016) Network science. Cambridge University Press, Cambridge
5. Borgatti SP, Everett MG, Johnson JC (2018) Analyzing social networks, 2nd edn. SAGE Publications Ltd, Los Angeles