A Data Driven Network Approach to Rank Countries Production Diversity and Food Specialization

Abstract

The easy access to large data sets has allowed for leveraging methodology in network physics and complexity science to disentangle patterns and processes directly from the data, leading to key insights in the behavior of systems. Here we use country specific food production data to study binary and weighted topological properties of the bipartite country-food production matrix. This country-food production matrix can be: 1) transformed into overlap matrices which embed information regarding shared production of products among countries, and or shared countries for individual products, 2) identify subsets of countries which produce similar commodities or subsets of commodities shared by a given country allowing for visualization of correlations in large networks, and 3) used to rank country fitness (the ability to produce a diverse array of products weighted on the type of food commodities) and food specialization (quantified on the number of countries producing a specific food product weighted on their fitness). Our results show that, on average, countries with high fitness produce both low and high specializion food commodities, whereas nations with low fitness tend to produce a small basket of diverse food products, typically comprised of low specializion food commodities.

Reconciling contrasting views on economic complexity

“…1 should be mainly attributed to the fact that eigenvectors of different order are considered in the two approaches. Hence, the two metrics bring different information; albeit different, this information is relevant for both metrics, as demonstrated by numerous practical applications of the two approaches [20][21][22]24,[30][31][32][33] .…”

Grand canonical validation of the bipartite international trade network

“…As a matter of fact, it has been shown that the degree sequence is responsible for the main characteristics of the trade network, such as the triangular structure of the biadjacency matrix between countries and products, see Fig. 8, [11][12][13][14][15][16][17]41] Using the BiCM as a filter permits to uncover structures of the network not explained by node degrees. The application of the BiCM to the ITN as a statistical null-model reveals communities of countries with similar economic development, namely developed, newly industrialized, and developing countries, and raw material (e.g.…”

“…As a matter of fact, it has been shown that the degree sequence is responsible for the main characteristics of the trade network, such as the triangular structure of the biadjacency matrix between countries and products, see Fig. 8, [11][12][13][14][15][16][17]41] Using the BiCM as a filter permits to uncover structures of the network not explained by node degrees.…”

“…The relative focus of country communities on specific product groups has strong implications. Evidence presented in studies on the bipartite representation of international trade [11][12][13][14][15][16][17]41] connect productive capabilities to the triangular shape of the country-product biadjacency matrix, advocating that the most developed countries export even the least complex products. This stands in contrast to standard economic theories expressed by Ricardo [42]: according to his hypothesis, every country should specialize on the production of the most sophisticated goods its resources can support, even if they would be able to export less elaborate items as well.…”

Nestedness Maximization in Complex Networks through the Fitness-Complexity Algorithm

“…Originally introduced to rank countries and products in the bipartite country-product export network [ 25 ], the fitness-complexity algorithm has been applied to diverse systems including ecological mutualistic networks [ 33 ], knowledge production networks [ 36 ], food production networks [ 37 ]. In its formulation for countries and products [ 25 ], the algorithm aims to find a vector of “fitness” scores for countries and “complexity” scores for products, respectively.…”