Post-Brexit Power of European Union From the World Trade Network Analysis

Authors

DOI:

https://doi.org/10.52825/bis.v1i.48

Keywords:

International trade, Google matrix, Complex networks

Abstract

We develop the Google matrix analysis of the multiproduct world trade network obtained from the UN COMTRADE database in recent years. The comparison is done between this new approach and the usual Import-Export description of this world trade network. The Google matrix analysis takes into account the multiplicity of trade transactions thus highlighting in a better way the world influence of specific countries and products. It shows that after Brexit, the European Union of 27 countries has the leading position in the world trade network ranking, being ahead of USA and China. Our approach determines also a sensitivity of trade country balance to specific products showing the dominant role of machinery and mineral fuels in multiproduct exchanges. It also underlines the growing influence of Asian countries.

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References

European Union, https://europa.eu/european-union/about-eu/figures/economy_en#trade (Accessed February (2021)).

Brexit, https://en.wikipedia.org/wiki/Brexit (Accessed February (2021)).

UN Comtrade database, https://comtrade.un.org/ (Accessed February (2021)).

Ermann L. and Shepelyansky D.L.: Google matrix of the world trade network, Acta Physica Polonica A 120, A158 (2011).

Ermann L. and Shepelyansky D.L.: Google matrix analysis of the multiproduct world trade network, Eur. Phys. J. B 88, 84 (2015).

Coquide C., Ermann L., Lages J. and Shepelyansky D.L.: Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE data, Eur. Phys. J. B 92, 171 (2019).

Loye J., Ermann L. and Shepelyansky D.L.: World impact of kernel European Union 9 countries from Google matrix analysis of the world trade network, arXiv:2010.10962[cs.SI] (2020).

Ermann L., Frahm K.M. and Shepelyansky D.L.: Google matrix analysis of directed networks, Rev. Mod. Phys. 87, 1261 (2015).

Frahm K.M., Jaffres-Runser K. and Shepelyansky D.L.: Wikipedia mining of hidden links between political leaders, Eur. Phys. J. B 89, 269 (2016).

Brin S. and Page L.: The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems 30, 107 (1998).

Langville A.M. and Meyer C.D.: Google’s PageRank and beyond: the science of search engine rankings, Princeton University Press, Princeton (2006).

Post-Brexit trade power of EU, https://www.quantware.ups-tlse.fr/QWLIB/euwtn (Accessed February (2021)).

Frahm K.M., El Zant S., Jaffres-Runser K. and Shepelyansky D.L.: Multi-cultural Wikipedia mining of geopolitics interactions leveraging reduced Google matrix analysis, Phys. Lett. A 381, 2677 (2017).

Coquide C. and Lewoniewski W.: Novel version of PageRank, CheiRank and 2DRank for Wikipedia in multilingual network using social impact, In: Abramowicz W., Klein G. (eds)

Business Information Systems BIS, Lecture Notes in Business Information Processing 389, 319 (2020).

Lages J., Shepelyansky D.L. and Zinovyev A.: Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks, PLoS ONE 13(1), e0190812 (2018).

Frahm K.M. and Shepelyansky D.L.: Google matrix analysis of bi-functional SIGNOR network of protein-protein interactions, Physica A 559, 125019 (2020).

Published

2021-07-02