Randomizing world trade. I. A binary network analysis

TL;DR

This paper demonstrates that the structure of the international trade network's binary projections is fully determined by the degree sequence, highlighting the importance of local properties in understanding higher-order trade patterns.

Contribution

It introduces a randomization method to show that all binary network properties of the ITN are fully explained by the degree sequence, emphasizing its significance in trade modeling.

Findings

Binary projections are fully determined by degree sequences.

Degree sequence explains all higher-order patterns.

Trade models should focus on degree sequences.

Abstract

The international trade network (ITN) has received renewed multidisciplinary interest due to recent advances in network theory. However, it is still unclear whether a network approach conveys additional, nontrivial information with respect to traditional international-economics analyses that describe world trade only in terms of local (first-order) properties. In this and in a companion paper, we employ a recently proposed randomization method to assess in detail the role that local properties have in shaping higher-order patterns of the ITN in all its possible representations (binary/weighted, directed/undirected, aggregated/disaggregated by commodity) and across several years. Here we show that, remarkably, the properties of all binary projections of the network can be completely traced back to the degree sequence, which is therefore maximally informative. Our results imply that explaining the observed degree sequence of the ITN, which has not received particular attention in economic theory, should instead become one the main focuses of models of trade.