@Holland_Leinhardt_1976; @Rao_etal_1996; @Roberts_2000; @Newman_2001; @Maslov_etal_2004_rewiring_physa; @Ansmann_Lehnertz_2011; @Bargigli_Gallegati_2011]. In economics, the use of purely random models is not new and spans from industrial agglomeration [@Ellison_Glaeser_1997; @Rysman_Greenstein_2005] to international trade [@Armenter_Miklos_2010]. Identifying the observed properties of real economic networks carrying non-trivial information allows one to select the ‘target quantities’ that economic models should aim at explaining or reproducing. In particular, if a random network model where only a set of local node-specific properties are enforced turns out to reproduce a real-world network satisfactorily well, then there is no need to know additional information besides the chosen constraints. The latter are therefore maximally informative, and the relevant (economic) models should aim at reproducing the observed values of the constraints themselves. On the contrary, a bad agreement indicates the need for additional information.

An important economic case study is the World Trade Web (WTW) or International Trade Network (ITN), where nodes are world countries and links represent international trade relationships. At the aggregate level, various authors have focused on the binary version of the WTW [@SeBo03; @Garla2004; @Garla2005], showing (among several other patterns) the presence of a ‘disassortative’ pattern and a stable negative correlation between node degree and clustering coefficient. The relevant role played 