Back
 AJIBM  Vol.10 No.5 , May 2020
Changes in Global Trade Patterns in Manufacturing, 2001-2018
Abstract: This paper used trade data with the year 2001-2018 to construct the global manufacturing multi-layer trade network, analyzed the characteristics of the network and predicted the development trend of the network. The results show that global manufacturing trade has been on the rise and focus on the increase of trade flow; trade be more likely to cooperate with core economic or trade organization; the orientation of returning to manufacturing makes a positive impact on manufacturing trade; network accessibility and compactness are strong, and it tends to be mature. Core-periphery analysis reveals that the United States and China will be the core countries of high, medium and low technology products. The trade forecast results show that the United States and China will conduct fierce manufacturing competition, and the world will form three manufacturing trade dominant regions of North American three countries, Asia-pacific cluster and European cluster.
Cite this paper: Jiang, J., & Qu, L. C. (2020) Changes in Global Trade Patterns in Manufacturing, 2001-2018. American Journal of Industrial and Business Management, 10, 876-899. doi: 10.4236/ajibm.2020.105059.
References

[1]   Almog, A., Squartini, T., & Garlaschelli, D. (2015). A GDP-Driven Model for the Binary and Weighted Structure of the International Trade Network. New Journal of Physics, 17, 169-174.
https://doi.org/10.1088/1367-2630/17/1/013009

[2]   Barabasi, A. L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286, 509-512.
https://doi.org/10.1126/science.286.5439.509

[3]   Barabasi, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the Social Network of Scientific Collaborations. Physica A Statistical Mechanics and Its Applications, 311, 590-614.
https://doi.org/10.1016/S0378-4371(02)00736-7

[4]   Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gomez-Gardenes, J., Romance, M., Zanin, M. et al. (2014). The Structure and Dynamics of Multilayer Networks. Physics Reports—Review Section of Physics Letters, 544, 1-122.
https://doi.org/10.1016/j.physrep.2014.07.001

[5]   De Domenico, M., Solè-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Arenas, A. et al. (2014). Mathematical Formulation of Multi-Layer Networks. Physical Review X, 3, 4192-4195.
https://doi.org/10.1103/PhysRevX.3.041022

[6]   Federico, B., Vincenzo, N., & Vito, L. (2014). Structural Measures for Multiplex Networks. Physical Review E Statistical Nonlinear & Soft Matter Physics, 89, Article ID: 032804.
https://doi.org/10.1103/PhysRevE.89.032804

[7]   Gemmetto, V., Squartini, T., Picciolo, F., Ruzzenenti, F., & Garlaschelli, D. (2015). Multiplexity and Multireciprocity in Directed Multiplexes. Physical Review E, 94, Article ID: 042316.
https://doi.org/10.1103/PhysRevE.94.042316

[8]   Girvan, M., & Newman, M. E. J. (2001). Community Structure in Social and Biological Networks. Proceedings of the National Academy of Sciences of the United States of America, 99, 7821-7826.

[9]   Halu, A., Mukherjee, S., & Bianconi, G. (2015). Emergence of Overlap in Ensembles of Spatial Multiplexes and Statistical Mechanics of Spatial Interacting Network Ensembles. Physical Review E Statistical Nonlinear & Soft Matter Physics, 89, 152-152.
https://doi.org/10.1103/PhysRevE.89.012806

[10]   IMF (2019). Global Manufacturing Downturn, Rising Trade Barriers. Washington DC.
https://www.imf.org/en/Publications/WEO/Issues/2019/03/28/world-economic-outlook-april-2019

[11]   Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer Networks. SSRN Electronic Journal, 2, 261-268.
https://doi.org/10.1093/comnet/cnu016

[12]   Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985-98. Oxford Development Studies, 28, 337-369.
https://doi.org/10.1080/713688318

[13]   Lee, K. M., & Goh, K. I. (2016). Strength of Weak Layers in Cascading Failures on Multiplex Networks: Case of the International Trade Network. Scientific Reports, 6, Article No. 26346.
https://doi.org/10.1038/srep26346

[14]   Matteo, B., Giorgio, F., & Diego, G. (2010). Multinetwork of International Trade: A Commodity-Specific Analysis. Physical Review E Statistical Nonlinear & Soft Matter Physics, 81, Article ID: 046104.
https://doi.org/10.1103/PhysRevE.81.046104

[15]   Roberts, B. (2004). A Gravity Study of the Proposed China-Asean Free Trade Area. The International Trade Journal, 18, 335-353.
https://doi.org/10.1080/08853900490518208

[16]   Saracco, F., Clemente, R. D., Gabrielli, A., & Squartini, T. (2015). Detecting the Bipartite World Trade Web Evolution across 2007: A Motifs-Based Analysis. Riccardo Di Clemente, 21, 88-94.

[17]   Shi, P., Zhang, J. et al. (2014). Hierarchicality of Trade Flow Networks Reveals Complexity of Products. PLoS ONE, 9, e98247.
https://doi.org/10.1371/journal.pone.0098247

[18]   Solé-Ribalta, A., Domenico, M. D., Gómez, S., & Arenas, A. (2016). Random Walk Centrality in Interconnected Multilayer Networks. Physica D Nonlinear Phenomena, 323-324, 73-79.
https://doi.org/10.1016/j.physd.2016.01.002

[19]   Squartini, T., Fagiolo, G., & Garlaschelli, D. (2011). Randomizing World Trade. I. A Binary Network Analysis. Physical Review E Statistical Nonlinear & Soft Matter Physics, 84, Article ID: 046117.
https://doi.org/10.1103/PhysRevE.84.046117

[20]   Tzekina, I., Danthi, K., & Rockmore, D. N. (2008). Evolution of Community Structure in the World Trade Web. European Physical Journal B, 63, 541-545.
https://doi.org/10.1140/epjb/e2008-00181-2

[21]   Vázquez, A., Pastorsatorras, R., & Vespignani, A. (2002). Large-Scale Topological and Dynamical Properties of the Internet. Physical Review E Statistical Nonlinear & Soft Matter Physics, 65, Article ID: 066130.
https://doi.org/10.1103/PhysRevE.65.066130

[22]   Watts, D. J., & Strogatz, S. H. (1998). Collective Dynamics of “Small-World” Networks. Nature, 393, 440-442.
https://doi.org/10.1038/30918

[23]   Zhang, X. H., Cui, H. Y., Zhu, J., Du, Y., Wang, Q., & Shi, W. H. (2017). Measuring the Dissimilarity of Multiplex Networks: An Empirical Study of International Trade Networks. Physica A Statistical Mechanics and Its Applications, 467, 380-394.
https://doi.org/10.1016/j.physa.2016.10.024

 
 
Top