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Asymmetric Relatedness from Partial Correlation.

Carlos Saenz de Pipaon Perez1, Andrea Zaccaria2,3, Tiziana Di Matteo1,3,4

  • 1Department of Mathematics, King's College London, The Strand, London WC2R 2LS, UK.

Entropy (Basel, Switzerland)
|March 25, 2022
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Summary
This summary is machine-generated.

This study introduces a new asymmetric relatedness measure for economic complexity, analyzing temporal export correlations. The findings reveal intuitive clusters and assortative mixing of economic sectors by complexity.

Keywords:
complex systemseconomic complexitypartial correlationplanar graphproducts and servicesrelatedness

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Area of Science:

  • Economics
  • Network Science
  • Data Science

Background:

  • Economic complexity relies on sector similarity for development strategies.
  • Existing relatedness measures lack explicit consideration of export time correlation structures.

Purpose of the Study:

  • To introduce a novel asymmetric relatedness measure incorporating temporal export correlations.
  • To apply this measure to a comprehensive database of goods and services exports.
  • To analyze the resulting economic activity network structure and properties.

Main Methods:

  • Developed an asymmetric relatedness definition using statistically significant partial correlations.
  • Generalized a correlation-filtering algorithm (partial correlation planar graph) for bipartite temporal networks.
  • Employed bootstrapping for statistical confidence assessment of network edges.

Main Results:

  • Constructed a network of economic activities where links signify temporal correlation influence.
  • Observed the formation of intuitively related clusters of economic sectors.
  • Found strong assortative mixing of sectors based on their economic complexity.
  • Identified that hub nodes exhibit more robust connections than peripheral nodes.

Conclusions:

  • The new asymmetric relatedness measure effectively captures temporal dynamics in economic activities.
  • The network analysis reveals meaningful structures in economic complexity, including clustering and assortativity.
  • The findings support the use of time-correlated relatedness for informed economic development strategies.