Our activity is in the track of a long-standing interaction between economics and physical sciences in order to explain, extend and complement a recent analysis introduced to describer the network of trades between nations. Hidalgo and Hausmann  addressed the problem of competitiveness and robustness of different countries in the global economy by studying the differences in Gross Domestic Product and assuming that the development of country is related to different capabilities

While countries cannot directly trade capabilities, it is the specific combination of those capabilities that results in different products traded. More capabilities are supposed to bring higher returns and the accumulation of new capabilities provides an exponentially growing advantage. Therefore the origin of the differences in the wealth of countries can be inferred by the record of trading activities analyzed as the expressions of the capabilities of the countries. The information is available in the form of an NcXNp rectangular matrix M giving the different production of the possible Np goods for each of the Nc countries. The matrix M corresponds to a bipartite graph, the country-product network, that can be projected into the country-country network C=MMT and the product-product network P=MTM.

In this unit we 

  • collaborate with University of Zurich and European Central Bank in order to study the trees of production from input/output matrices;
  • extend traditional gravity model by means of statistical physics models of networks based on fitnesses;
  • reconstruct trading relations by means of statistical methods.

  Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - systems. Nestedness, for example, has been observed in plants-pollinator as well as in country-product trade networks. This has raised questions about the significance of these patterns, which are often believed to constitute a genuine signature of self-organization. Here, we review several methods that have been developed for the analysis of such evidence.

Due to the interdisciplinary character of complex networks, tools developed in one field, for example ecology, can greatly enrich other areas of research, such as economy and finance, and vice versa. With this in mind, we briefly review several entropy-based bipartite null models that have been recently proposed and discuss their application to several real-world systems. The focus on these models is motivated by the fact that they show three very desirable features: analytical character, general applicability and versatility. In this respect, entropy-based methods have been proven to perform satisfactorily both in providing benchmarks for testing evidence-based null hypotheses and in reconstructing unknown network configurations from partial information. On top of that, entropy-based models have been successfully employed to analyze ecological as well as economic systems, thus representing an ideal, interdisciplinary tool to approach the study of bipartite complex systems.

From ecology to finance (and back?): recent advancements in the analysis of bipartite networks M. Straka, G. Caldarelli, T. Squartini, F. Saracco, arXiv 1710.10143

  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

Grand canonical validation of the bipartite international trade network Mika J. Straka, Guido Caldarelli, and Fabio Saracco Phys. Rev. E 96, 022306