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Grand canonical validation of the bipartite international trade network

Overview of attention for article published in Physical Review E, August 2017
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Title
Grand canonical validation of the bipartite international trade network
Published in
Physical Review E, August 2017
DOI 10.1103/physreve.96.022306
Pubmed ID
Authors

Mika J. Straka, Guido Caldarelli, Fabio Saracco

Abstract

Devising strategies for economic development in a globally competitive landscape requires a solid and unbiased understanding of countries' technological advancements and similarities among export products. Both can be addressed through the bipartite representation of the International Trade Network. In this paper, we apply the recently proposed grand canonical projection algorithm to uncover country and product communities. Contrary to past endeavors, our methodology, based on information theory, creates monopartite projections in an unbiased and analytically tractable way. Single links between countries or products represent statistically significant signals, which are not accounted for by null models such as the bipartite configuration model. We find stable country communities reflecting the socioeconomic distinction in developed, newly industrialized, and developing countries. Furthermore, we observe product clusters based on the aforementioned country groups. Our analysis reveals the existence of a complicated structure in the bipartite International Trade Network: apart from the diversification of export baskets from the most basic to the most exclusive products, we observe a statistically significant signal of an export specialization mechanism towards more sophisticated products.

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Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 40%
Student > Ph. D. Student 4 27%
Professor 2 13%
Student > Master 2 13%
Other 1 7%
Other 0 0%
Readers by discipline Count As %
Physics and Astronomy 7 47%
Mathematics 3 20%
Unspecified 2 13%
Social Sciences 2 13%
Economics, Econometrics and Finance 1 7%
Other 0 0%