A network analysis of countries' export flows: firm grounds for the building blocks of the economy

TL;DR

This paper analyzes the global trade network using bipartite graphs of countries and products, revealing socio-geographic clusters, proposing a new classification method, and improving ranking algorithms for countries and products.

Contribution

It introduces a novel filtering method for network analysis, reformulates the reflections method as a fixpoint problem, and proposes a biased Markov chain approach for consistent ranking.

Findings

Country clusters reveal socio-geographic links.

Product clustering enables bottom-up classification.

Non-linear interactions suggest new ranking algorithms.

Abstract

In this paper we analyze the bipartite network of countries and products from UN data on country production. We define the country-country and product-product projected networks and introduce a novel method of filtering information based on elements' similarity. As a result we find that country clustering reveals unexpected socio-geographic links among the most competing countries. On the same footings the products clustering can be efficiently used for a bottom-up classification of produced goods. Furthermore we mathematically reformulate the "reflections method" introduced by Hidalgo and Hausmann as a fixpoint problem; such formulation highlights some conceptual weaknesses of the approach. To overcome such an issue, we introduce an alternative methodology (based on biased Markov chains) that allows to rank countries in a conceptually consistent way. Our analysis uncovers a strong non-linear interaction between the diversification of a country and the ubiquity of its products, thus suggesting the possible need of moving towards more efficient and direct non-linear fixpoint algorithms to rank countries and products in the global market.