In this paper we develop a data-driven hierarchical clustering methodology to group the economic sectors of a country in order to highlight strongly coupled groups that are weakly coupled with other groups. Specifically, we consider an input-output representation of the coupling among the sectors and we interpret the relation among sectors as a directed graph; then we recursively apply the spectral clustering methodology over the graph, without a priori information on the number of groups that have to be obtained. In order to do this, we resort to the eigengap criterion, where a suitable number of groups is selected automatically based on the intensity and structure of the coupling among the sectors. We validate the proposed methodology considering a case study for Italy, inspecting how the coupling among clusters and sectors changes from the year 1995 to 2011, showing that in the years the Italian structure underwent deep changes, becoming more and more interdependent, i.e., a large part of the economy has become tightly coupled.
|Titolo:||Critical clusters in interdependent economic sectors: A data-driven spectral clustering analysis|
|Data di pubblicazione:||2016|
|Citazione:||Oliva, G., Setola, R., & Panzieri, S. (2016). Critical clusters in interdependent economic sectors: A data-driven spectral clustering analysis. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS, 225(10), 1929-1944.|
|Appare nelle tipologie:||1.1 Articolo in rivista|