Xing Lizhi, Ai Xi, Ren Jiaqi, Wang Dawei
College of Economics & Management, Beijing University of Technology, Beijing 100124, China.
International Business School, Beijing Foreign Studies University, Beijing 100089, China.
Entropy (Basel). 2021 Sep 29;23(10):1276. doi: 10.3390/e23101276.
Network science has been widely applied in theoretical and empirical studies of global value chain (GVC), and many related articles have emerged, forming many more mature and complete analytical frameworks. Among them, the GVC accounting method based on complex network theory is different from the mainstream economics in both research angle and content. In this paper, we build up global industrial value chain network (GIVCN) models based on World Input-Output Database, introduce the theoretical framework of Social Capital, and define the network-based indicators with economic meanings. Second, we follow the econometric framework to analyze the hypothesis and test whether it is true. Finally, we study how the three types of capital constituted by these indicators interact with each other, and discuss their impact on the social capital (economic development level, i.e., GDP). The results prove that the structural capital (industrial status) has a positive impact on the social capital; the relational capital (industrial correlation) has a positive impact on both social capital and structural capital; the cognitive capital (industrial structure) has a small impact on the social capital, structural capital, and relational capital.
网络科学已广泛应用于全球价值链（GVC）的理论和实证研究，相关文章不断涌现，形成了许多更为成熟和完善的分析框架。其中，基于复杂网络理论的全球价值链核算方法在研究角度和内容上均有别于主流经济学。本文基于世界投入产出数据库构建全球产业价值链网络（GIVCN）模型，引入社会资本理论框架，并定义具有经济意义的网络指标。其次，遵循计量经济学框架分析假设并检验其真伪。最后，研究这些指标构成的三种资本如何相互作用，并探讨它们对社会资本（经济发展水平，即GDP）的影响。结果表明，结构资本（产业地位）对社会资本有正向影响；关系资本（产业关联）对社会资本和结构资本均有正向影响；认知资本（产业结构）对社会资本、结构资本和关系资本的影响较小。
