Asymmetric Relatedness from Partial Correlation
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 corre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/3/365 |
| _version_ | 1797471736090853376 |
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| author | Carlos Saenz de Pipaon Perez Andrea Zaccaria Tiziana Di Matteo |
| author_facet | Carlos Saenz de Pipaon Perez Andrea Zaccaria Tiziana Di Matteo |
| author_sort | Carlos Saenz de Pipaon Perez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:52:26Z |
| format | Article |
| id | doaj.art-b3fdbdd0815141148ae07b06881174e2 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T19:52:26Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-b3fdbdd0815141148ae07b06881174e22023-11-24T01:07:21ZengMDPI AGEntropy1099-43002022-03-0124336510.3390/e24030365Asymmetric Relatedness from Partial CorrelationCarlos Saenz de Pipaon Perez0Andrea Zaccaria1Tiziana Di Matteo2Department of Mathematics, King’s College London, The Strand, London WC2R 2LS, UKIstituto dei Sistemi Complessi (ISC)—CNR, UoS Sapienza, P.le A. Moro, 2, 00185 Rome, ItalyDepartment of Mathematics, King’s College London, The Strand, London WC2R 2LS, UKRelatedness 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.https://www.mdpi.com/1099-4300/24/3/365complex systemseconomic complexityrelatednessproducts and servicesplanar graphpartial correlation |
| spellingShingle | Carlos Saenz de Pipaon Perez Andrea Zaccaria Tiziana Di Matteo Asymmetric Relatedness from Partial Correlation Entropy complex systems economic complexity relatedness products and services planar graph partial correlation |
| title | Asymmetric Relatedness from Partial Correlation |
| title_full | Asymmetric Relatedness from Partial Correlation |
| title_fullStr | Asymmetric Relatedness from Partial Correlation |
| title_full_unstemmed | Asymmetric Relatedness from Partial Correlation |
| title_short | Asymmetric Relatedness from Partial Correlation |
| title_sort | asymmetric relatedness from partial correlation |
| topic | complex systems economic complexity relatedness products and services planar graph partial correlation |
| url | https://www.mdpi.com/1099-4300/24/3/365 |
| work_keys_str_mv | AT carlossaenzdepipaonperez asymmetricrelatednessfrompartialcorrelation AT andreazaccaria asymmetricrelatednessfrompartialcorrelation AT tizianadimatteo asymmetricrelatednessfrompartialcorrelation |