Journal of Quantitative & Technological Economics, 2023, Issue 1

Authors: LV Yue, GU Wei, WEI Yaning, BAO Qun

Summary: China has recently achieved rapid developments in its manufacturing industry, by maximizing the advantages of its labor force to introduce many processing and assembly jobs. It has also deeply integrated itself into the global value chain(GVC) network. However, this growth has come with increased labor costs. As such, many countries are seeking new technological upgrades and breakthroughs as a primary strategy for developing individual GVC networks. In this context, many countries have successively formulated transformation and upgrade plans for manufacturing industries, especially industrial robots using artificial intelligence(Al), considered critical forfuture manufacturing development, This highlights the fact that promoting high-quality development and increasing integration into the GVC network requires the high-tech support provided by Al.

This research empirically studies the impact of Al on the GVC network and analyzes the relevant mechanism associated with the impact. Based on Melitz(2003) and Bai et al.(2019), this paper introduces Al and misallocation of resources into the export decision-making model of heterogeneous enterprises, which describes the conditions of firms' export decision first introduced by Melitz(2003). The study also analyzes how Al influences enterprise value added exports. On this basis, we use industrial robot data from various countries, released by the International Robot Federation from 2000 to 2014, and measure the GVC network index from the perspective of value-added trade. We also empirically test the development of Al in different countries with respect to the deepening of GVC networks.

The results show the following. (1) The progress of the Al industry in different countries can significantly deepen GVC networks. (2) The positive impact of Al on the GVC network is mainly achieved through labor substitution and by mitigating the misallocation of resources. (3) Al has a stronger impact in deepening GVC networks in developing countries compared with developed countries, There is also a more prominent positive effect for low export-dependent countries compared to high export-dependent countries. In addition, the financial crisis may somewhat weaken the positive role of Al in deepening the GVC network, The effect of Al on GVC network centrality is more substantial in labor-intensive and technology intensive industries, and is not significant in capital-intensive industries. (4) Al can also extend the length of GVCs, and enhance GVC competitiveness and the degree of upstream reach.

Based on previous studies, this paper makes the following key contributions. (1)In terms of research themes, this paper explores the impact of Al on GVC networks. Previous studies have focused on analyzing specific facts about GVC networks. In contrast, this paper concentrates on the internal mechanism behind the deepening of GVC networks. The study also significantly enriches the examination of Al impacts. (2)In terms of theory, this paper extends the heterogeneous firm exports model of Melitz(2003) and analyzes the mechanism by which Al influences GVC networks, by introducing Al and misallocation into fims' export decisions. This enriches existing theory. (3)In terms of data, this paper combines industrial robot data released by the International Robot Federation(IRF) at the industry level in each country from 2000 to 2014 with the University of International Business and Economics (UlBE) GVC Indicators database, the World Development Indicators(WDl) database, and the Worldwide Governance Indicators(WGl) database. Based on the UlBE, GVC Indicators database, this paper constructs and measures country-industry level GVC network indicators, based on value-added trade, It also measures different dimensions of GVC network indicators, such as the average degree, global efficiency, centralization of eigenvector centrality, reciprocity, assortative characteristics, and the global clustering coefficient.

Keywords: Global Value Chain; Artificial Intelligence; Value added Trade; Social Network Analysis