The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems

Abstract

In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries in international and domestic economies and show that these networks are significantly nested. This means that the less filled rows and columns of these networks' adjacency matrices tend to be subsets of the fuller rows and columns. Moreover, we show that their nestedness remains constant over time and that it is sustained by both, a bias for industries that deviate from the networks' nestedness to disappear, and a bias for the industries that are missing according to nestedness to appear. This makes the appearance and disappearance of individual industries in each location predictable. We interpret the high level of nestedness observed in these networks in the context of the neutral model of development introduced by Hidalgo and Hausmann (2009). We show that the model can reproduce the high level of nestedness observed in these networks only when we assume a high level of heterogeneity in the distribution of capabilities available in countries and required by products. In the context of the neutral model, this implies that the high level of nestedness observed in these economic networks emerges as a combination of both, the complementarity of inputs and heterogeneity in the number of capabilities available in countries and required by products. The stability of nestedness in industrial ecosystems, and the predictability implied by it, demonstrates the importance of the study of network properties in the evolution of economic networks.

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

“…This would be evidenced by compartmentalized (i.e., modular), rather than nested, country-food production graphs [25]). In partial agreement with what is found for industrial and economic products [29], Fig 3(a) shows that the bipartite country-food product networks have NODF consistently higher than those found in randomly assembled networks (P-value < 0.001) for all the years analyzed (1992-2011). This result indicates that food commodities produced by countries that are only able to produce a small basket of different food products, are also produced by nations that are characterized by large and diversified food production.…”

Corruption risk in contracting markets: a network science perspective

“…Public procurement markets can be described as bipartite networks because of the clear division of their participants into issuers and winners. Bipartite networks have been applied to the study of systems including two kinds of actors including flowers and their pollinators [54], cities and industries present in them [55], and buyers and sellers in markets [56]. We apply the same paradigm to our data on procurement markets: for each country and year in our dataset, we create bipartite networks in which issuers and winners are connected by a weighted edge, with weight counting the number of contracts between the two entities.…”

Structure and dynamics of core/periphery networks

“…Bastolla et al [84] introduced an explicit definition of nestedness similar to that of NODF. However, it is very important to note that the measure of Bastolla et al [79] is the only nestedness measure, which is directly linked to the dynamics (in particular: to the interspecies competition) of the complex system and thus to the development of the mutualistic plant/pollinator and plant/seed-dispersal networks.…”