Abstract
Industrial Transfer is an inevitable trend in the process of vertical specialization. The traditional industrial transfer theory tends to adopt partial data and methodologies from reductionism, and thus can not tackle with the highly non-linear systematic problems, such as the evolutionary mechanism and path of global economic system. With the properties of structural complexity, dynamic evolution and multiple linkages, complex networks can better reflect the interdependent and mutually restricted relations between different levels and components of the industrial structure, pinpoint the key to optimization and control. Currently, there are only a few available studies on such weighted, directed, and dense networks, which reflect the topological complexity of GVC with the results being unsystematic and impractical. This chapter utilizes the binary GISRN model to describe the trajectory of the most crucial value stream on the GVC, making it possible to find the transfer paths between economies. Also, methods of defining and measuring the networks’ redundancies are devised to figure out the fundamental laws of worldwide industrial transfer pattern based on Link Prediction, thus blazing a new trial for the evolutionary economics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Niche advantage is the comprehensive resource advantage of a region, i.e., the favorable condition or superior position in terms of economic growth. It mainly comprises natural resources, geographical location, and social, economic, scientific, management, political, cultural, educational, and tourism factors.
References
Hayter R. The dynamics of industrial location: the factory, the firm, and the production system. Chichester: Wiley; 1997.
An H. The principle of new economic geography. Beijing: Economic Science Press; 2009.
Chen X, Zhang K. Regional economic theory. Beijing: The Commercial Press; 2003.
Martin R. Critical survey. The new geographical turn in economics: some critical reflections. Cambridge J Econ. 1999;23(1):65–91.
Thompson B. Behavior and location, foundations for a geographic and dynamic location theory. Part I. Econ Geogr. 1969;45 183–84.
Simon HA. A behavioral model of rational choice. Quar J Econ. 1955:99–118.
Cyert RM, March JG. A behavioral theory of the firm. Syst Res Behav Sci. 2004;4(2):81–95.
Van Dijk J, Pellenbarg PH. Firm relocation decisions in The Netherlands: an ordered logit approach. Pap Reg Sci. 2000;79(2):191–219.
Rosenthal SS, Strange WC. Geography, industrial organization, and agglomeration. Rev Econ Stat. 2003;85(2):377–93.
Granovetter M. Economic institutions as social constructions: a framework for analysis. Acta Sociologica. 1992;35(1):3–11.
Miao C. Three approaches in contemporary economic geographies. Sci Geogr Sinica. 2007;27(5):617–23.
Pellenbarg PH, Van Wissen LJG, Van Dijk J. Firm relocation: state of the art and research prospects. Groningen: University of Groningen; 2002.
Martin R. Path dependence and the spatial economy: a key concept in retrospect and prospect. In: Handbook of regional Science, 2014:609–29.
Manjón-Antolín MC, Arauzo-Carod JM. Locations and relocations: determinants, modelling, and interrelations. Ann Reg Sci. 2011;47(1):131–46.
Wooldridge JM. Econometric analysis of cross section and panel data. Cambridge: MIT Press; 2010.
Kim H, Waddell P, Shankar VN, et al. Modeling micro- spatial employment location patterns: a comparison of count and choice Approaches. Geogr Anal. 2008;40(2):123–51.
Yu P, Chen J. Research on the location selection of multinational corporations in China under the framework of new economic geography. J World Econ. 2012;11:31–58.
Hu A, Sun J. Migration of manufacturing industries in China: whether, how and where. China Econ Quar. 2013;13(4):1533–56.
Liu HG, Liu WD, Liu ZG. The quantitative study on inter-regional industry transfer. China Ind Econ. 2011;6:79–88.
Gao X, Hewings G, Yang CH. Measuring the generalized global industry relocation. In: Discussion paper, 26th international input-output conference; 2019.
Lü LY. Link prediction on complex networks. J Univ Electron Sci Technol China. 2010;39(5):651–61.
Zhu J, Hong J, Hughes JG. Using Markov Chains for link prediction in adaptive web sites. Berlin Heidelberg: World. Springer; 2002.
Popescul A, Ungar LH. Statistical relational learning for link prediction. In: IJCAI workshop on learning statistical models from relational data, 2003.
O’Madadhain J, Hutchins J, Smyth P. Prediction and ranking algorithms for event-based network data. ACM SIGKDD Explor Newsl. 2005;7(2):23–30.
Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks. J Am Soc Inform Sci Technol. 2007;58(7):1019–31.
Zhou T, Lü L, Zhang YC. Predicting missing links via local information. Eur Phys J B. 2009;71(4):623–30.
Guimerà R, Sales-Pardo M. Missing and spurious interactions and the reconstruction of complex networks. Proc Natl Acad Sci. 2009;106(52):22073–8.
Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701.
Ohlin BG. Interregional and international trade. Cambridge: Harvard University Press; 1935.
Lü L, Zhou T. Link prediction in complex networks: a survey. Physica A. 2011;390(6):1150–70.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Xing, L. (2022). Identify the Worldwide Industrial Transfer Pattern. In: Complex Network-Based Global Value Chain Accounting System. Springer, Singapore. https://doi.org/10.1007/978-981-16-9264-2_11
Download citation
DOI: https://doi.org/10.1007/978-981-16-9264-2_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9263-5
Online ISBN: 978-981-16-9264-2
eBook Packages: Business and ManagementBusiness and Management (R0)