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

Justin Loye,Katia Jaffrès-Runser,Dima L Shepelyansky

doi:10.52825/bis.v1i.48

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.

Highlights

The European Union (EU) is composed from 27 countries and is considered as a major world leading power [1]
Our study shows that the Google matrix approach (GMA) allows to characterize in a more profound manner the trade power of countries compared to the usual method relying on import and export analysis (IEA) between countries
GMA shows that United Kingdom (UK) position is significantly weakened compared to IEA description ( UK moves from K∗ = 7 in IEA to K∗ = 10 in GMA)

Summary

Introduction

The European Union (EU) is composed from 27 countries and is considered as a major world leading power [1]. We use the UN COMTRADE data [3] for years 2012, 2014, 2016 and 2018 to construct the trade flows of the multiproduct WTN following the procedure detailed in [5, 6]. (5); Basic manufactures (6); Machinery, transport equipment (7); Miscellaneous manufactured articles (8); Goods not classified elsewhere (9) (product index p is given in brackets) They belong to the Standard International Trade Classification (SITC Rev. 1) the total Google matrix G size is given by all system nodes N = NcNp = 1680 including countries and products. This algorithm takes into accounts all transitions of direct and indirect pathways happening in the full Google matrix G between Nr nodes of interest We use this GR matrix to construct a reduced network of most strong transitions (“network of friends”) between a selection of nodes representing countries and products. We note that GMA allows to obtain interesting results for various types of directed networks including Wikipedia [13, 14] and protein-protein interaction [15, 16] networks

CheiRank and PageRank of countries

Trade balance and its sensitivity to product prices

Network structure of trade from reduced Google matrix

Discussion