Reconstructing the world trade multiplex: The role of intensive and extensive biases

Abstract

In economic and financial networks, the strength of each node has always an important economic meaning, such as the size of supply and demand, import and export, or financial exposure. Constructing null models of networks matching the observed strengths of all nodes is crucial in order to either detect interesting deviations of an empirical network from economically meaningful benchmarks or reconstruct the most likely structure of an economic network when the latter is unknown. However, several studies have proved that real economic networks and multiplexes topologically differ from configurations inferred only from node strengths. Here we provide a detailed analysis of the world trade multiplex by comparing it to an enhanced null model that simultaneously reproduces the strength and the degree of each node. We study several temporal snapshots and almost 100 layers (commodity classes) of the multiplex and find that the observed properties are systematically well reproduced by our model. Our formalism allows us to introduce the (static) concept of extensive and intensive bias, defined as a measurable tendency of the network to prefer either the formation of extra links or the reinforcement of link weights, with respect to a reference case where only strengths are enforced. Our findings complement the existing economic literature on (dynamic) intensive and extensive trade margins. More generally, they show that real-world multiplexes can be strongly shaped by layer-specific local constraints.

Jan Tinbergen’s Legacy for Economic Networks: From the Gravity Model to Quantum Statistics

“…For the first time, we find a very close agreement for all these topological properties simultaneously. This result is very robust, as it holds for different snapshots and different commodities [24]. Two main conclusions can be drawn:…”

“…In the previous section we discussed some coincidences and deeper similarities behind the use of the gravity law in physics and economics. As another interesting similarity, recent results [16,17,18,24] suggest that the limitations of the Gravity Model can only be overcome after a change of paradigm which is not dissimilar from the one that accompanied two revolutions in physics, namely the advent of statistical mechanics and that of quantum physics. The new paradigm assumes that, in order to predict the presence of a link (and not only its weight), probabilistic models of networks need to be considered.…”

Multiplex networks with heterogeneous activities of the nodes

“…Networks of networks are formed by different interacting networks, as the molecular networks in the cell, in which every node is a different type of biological molecule. Multiplex networks [6,7,[30][31][32][33][34][35], instead, are multilayer networks in which the same set of nodes interact through different networks. Examples of multiplex networks are social networks, in which individual are connected by different types of social ties, transportation networks, like airport networks in which each airport is connected to other airports though connections of different airline companies, or collaboration networks in which scientists collaborate on different topics and eventually cite each other.…”

“…Examples of multiplex networks are social networks, in which individual are connected by different types of social ties, transportation networks, like airport networks in which each airport is connected to other airports though connections of different airline companies, or collaboration networks in which scientists collaborate on different topics and eventually cite each other. Several works have focused on modeling multilayer networks [31][32][33][34][35]. In particular a very useful approach employs statistical ensembles which are able to generate a large variety of multiplex network topologies with a desired level of structural correlation [31,33,34].…”

“…Several works have focused on modeling multilayer networks [31][32][33][34][35]. In particular a very useful approach employs statistical ensembles which are able to generate a large variety of multiplex network topologies with a desired level of structural correlation [31,33,34]. In networks of networks as well as in multiplex networks the possible correlations existent in this structure can strongly affect their robustness properties [1,9,10,17].…”