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Distance-varying assortativity and clustering of the international trade network

Published online by Cambridge University Press:  08 June 2018

ANGELA ABBATE ,
LUCA DE BENEDICTIS ,
GIORGIO FAGIOLO  and
LUCIA TAJOLI
ANGELA ABBATE
Affiliation:
Economic Affairs Division, Swiss National Bank, Bern/Zürich, Switzerland (e-mail: [email protected])
LUCA DE BENEDICTIS
Affiliation:
DED, University of Macerata, Via Crescimbeni 20, Macerata 62100, Italy (e-mail: [email protected])
GIORGIO FAGIOLO
Affiliation:
Institute of Economics, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa 56127, Italy (e-mail: [email protected])
LUCIA TAJOLI
Affiliation:
Dipartimento di Ingegneria Gestionale, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano 20133, Italy (e-mail: [email protected])
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Abstract

In this paper, we study how the topology of the International Trade Network (ITN) changes in geographical space, and along time. We employ geographical distance between countries in the world to filter the links in the ITN, building a sequence of subnetworks, each one featuring trade links occurring at similar distance. We then test if the assortativity and clustering of ITN subnetworks changes as distance increases, and we find that this is indeed the case: distance strongly impacts, in a non-linear way, the topology of the ITN. We show that the ITN is disassortative at long distances, while it is assortative at short ones. Similarly, the main determinant of the overall high-ITN clustering level are triangular trade triples between geographically close countries. This means that trade partnership choices and trade patterns are highly differentiated over different distance ranges, even after controlling for the economic size and income per capita of trading partners, and it is persistent over time. This evidence has relevant implications for the non-linear evolution of globalization.

Keywords

international tradenetwork analysisdistance
Type
Research Article
Information
Network Science , Volume 6 , Issue 4 , December 2018 , pp. 517 - 544
Copyright
Copyright © Cambridge University Press 2018 

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