Unfolding the complexity of the global value chain: Strengths and entropy in the single-layer, multiplex, and multi-layer international trade networks

TL;DR

This paper compares single-layer, multiplex, and multi-layer network models of international trade to understand their ability to capture trade complexity, heterogeneity, and the evolution of entropy, especially during economic downturns.

Contribution

It introduces a multi-layer network framework for international trade, revealing how complex representations improve understanding of trade heterogeneity and entropy dynamics.

Findings

Multi-layer networks better distinguish intra- and cross-industry trade roles.

Entropy peaks correlate with global economic downturns.

Complex network models reveal heterogeneity in trade strength distribution.

Abstract

The worldwide trade network has been widely studied through different data sets and network representations with a view to better understanding interactions among countries and products. Here we investigate international trade through the lenses of the single-layer, multiplex, and multi-layer networks. We discuss differences among the three network frameworks in terms of their relative advantages in capturing salient topological features of trade. We draw on the World Input-Output Database to build the three networks. We then uncover sources of heterogeneity in the way strength is allocated among countries and transactions by computing the strength distribution and entropy in each network. Additionally, we trace how entropy evolved, and show how the observed peaks can be associated with the onset of the global economic downturn. Findings suggest how more complex representations of trade, such as the multi-layer network, enable us to disambiguate the distinct roles of intra- and cross-industry transactions in driving the evolution of entropy at a more aggregate level. We discuss our results and the implications of our comparative analysis of networks for research on international trade and other empirical domains across the natural and social sciences.