This paper explores the relationships between migration and trade using a complex-network approach. We show that: (i) both weighted and binary versions of the networks of international migration and trade are strongly correlated; (ii) such correlations can be mostly explained by country...

In the last years, non-linear dynamics and complexity gained an important role in economics. Non-linear models are the analytical foundation of complex dynamic theory. This class of models is able to capture more conveniently and more naturally economic behaviors that are non-regular, such as...

This paper begins to explore the determinants of the topological properties of the international - trade network (ITN). We fit bilateral-trade flows using a standard gravity equation to build a “residual” ITN where trade-link weights are depurated from geographical distance, size, border...

In this paper we compare the patterns of trade and financial integration by exploiting network analysis. Our results show that, by combining binary and weighted network analysis, it is possible to deliver more precise and thorough insights on the topological structure and properties of the...

In this paper we study a class of evolutionary models of industrial agglomeration with local positive feedbacks, which allow for a wide set of empirically-testable implications. Their roots rest in the Generalized Polya Urn framework. Here, however, we build on a birth-death process over a...

This paper studies how the interplay between technological shocks and financial variables shapes the properties of macroeconomic dynamics. Most of the existing literature has based the analysis of aggregate macroeconomic regularities on the representative agent hypothesis (RAH). However, recent...

This work explores the spatial distribution of productive activities in the Italian manufacturing industry. We propose an econometric model which tries to disentangle locationspecific from sectoral drivers in the dynamic process of spatial agglomeration. The basic idea is that the former...