Abstract
Relatedness is a key concept in economic complexity, since the assessment of the similarity between industrial sectors enables policymakers to design optimal development strategies. However, among the different ways to quantify relatedness, a measure that takes explicitly into account the time correlation structure of exports is still lacking. In this paper, we introduce an asymmetric definition of relatedness by using statistically significant partial correlations between the exports of economic sectors and we apply it to a recently introduced database that integrates the export of physical goods with the export of services. Our asymmetric relatedness is obtained by generalising a recently introduced correlation-filtering algorithm, the partial correlation planar graph, in order to allow its application on multi-sample and multi-variate datasets, and in particular, bipartite temporal networks. The result is a network of economic activities whose links represent the respective influence in terms of temporal correlations; we also compute the statistical confidence of the edges in the network via an adapted bootstrapping procedure. We find that the underlying influence structure of the system leads to the formation of intuitively-related clusters of economic sectors in the network, and to a relatively strong assortative mixing of sectors according to their complexity. Moreover, hub nodes tend to form more robust connections than those in the periphery.
Highlights
In the past few years, the use of bipartite networks for the representation of realworld complex systems has become widespread in a variety of fields and applications.These networks are usually constructed using multi-sample, multi-variate structured data used to model complex systems such as biological networks, movies and actors [4,5], authors and papers [5,6], board of directors members and companies [7,8], companies and technologies they patent [9], members of peer-to-peer networks and data provided [10], internationalNGO branches and cities hosting them [11], supreme court judges and their votes [12], and legislators and bills they sponsor [13].A prominent example is the bipartite network formed by countries and the products they export
We find that the underlying influence structure of the system leads to the formation of intuitively-related clusters of economic sectors in the network, and to a relatively strong assortative mixing of sectors according to their complexity
To do so we introduce a framework that generalises a network generation method based on correlation-filtering called the partial correlation planar graph (PCPG) algorithm [30] in order to allow for its use with multisample multi-variate datasets
Summary
In the past few years, the use of bipartite networks for the representation of realworld complex systems has become widespread in a variety of fields and applications.These networks are usually constructed using multi-sample, multi-variate structured data used to model complex systems such as biological networks (enzymes and reactions [1], genes and diseases [2], plants and pollinators [3]), movies and actors [4,5], authors and papers [5,6], board of directors members and companies [7,8], companies and technologies they patent [9], members of peer-to-peer networks and data provided [10], internationalNGO branches and cities hosting them [11], supreme court judges and their votes [12], and legislators and bills they sponsor [13].A prominent example is the bipartite network formed by countries and the products they export. In the past few years, the use of bipartite networks for the representation of realworld complex systems has become widespread in a variety of fields and applications. These networks are usually constructed using multi-sample, multi-variate structured data used to model complex systems such as biological networks (enzymes and reactions [1], genes and diseases [2], plants and pollinators [3]), movies and actors [4,5], authors and papers [5,6], board of directors members and companies [7,8], companies and technologies they patent [9], members of peer-to-peer networks and data provided [10], international. With respect to the datasets implemented in the literature up to now, the dataset we use in this paper adds the inclusion of services to the set of tangible products traditionally considered in the EC literature [23,24]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
