Evidence map›Paper›PMID 32240201›Full record

ArticlePloS one2020

Core-periphery structure in sectoral international trade networks: A new approach to an old theory.

Olivera Kostoska, Sonja Mitikj, Petar Jovanovski, Ljupco Kocarev

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In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Core-periphery structure in directed networks.Proceedings. Mathematical, physical, and engineering sciences · 2020
    Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Olivera KostoskaFaculty of Economics-Prilep, "St. Kliment Ohridski" University, Bitola, North Macedonia.
Sonja MitikjMacedonian Academy of Sciences and Arts, Skopje, North Macedonia.
Petar JovanovskiMacedonian Academy of Sciences and Arts, Skopje, North Macedonia.ORCID 0000-0002-3990-8775
Ljupco KocarevMacedonian Academy of Sciences and Arts, Skopje, North Macedonia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The research on core-periphery structure of global trade from a complex-network perspective has shown that the world system is hierarchically organized into blocks and that countries play different roles in the world economy. Yet, little attention has been paid to investigating whether the sectoral international trade networks conform to a core-periphery structure, hence what is the role of different levels of processing in creating and maintaining structural inequality. This issue is of particular importance given the contemporary focus upon global production networks and reshaping of the international division of labor. With this in mind, we propose a model (LARDEG) from network science to reexamine old theories in economics, such as core-periphery structures in sectoral international trade networks and test whether the global value chains have changed structural positions in terms of the level of processing. The economic background of our model permitting a more accurate sorting of countries into structural positions and the general stability of results have provided for a more solid measurements than has hereto been possible. Our algorithm naturally produces networks with hierarchically nested block structure obtained from an iterative decomposition of the network periphery such that each block represents a vertex set of a maximal size sub-graph existing at different levels. The results not only lend support to the previous hierarchical model of the world-system (core, semi-periphery, and periphery) but also find that, depending on particular industry, the number of analytically identifiable blocks could be more than three. We show that 'size effect' is the one that prevails for core block membership at the first hierarchical level, while the GNI per capita is a much poorer proxy for the world-system status. Moreover, the patterns of blocks we label as the second- or third-level 'core' are strongly dependent on distance and geographical proximity. Overall, the various configurations of asymmetrical trade patterns between our blocks and the remarkably stable position of core countries at the top of structure clearly indicate that the rise of global production networks has actually restored a huge and unequal international division of labor splitting the world into 'headquarter' and 'factory' economies.

Indexed as

InternationalityModels, EconomicAlgorithmsCommerceHumansIndustrySocioeconomic Factors

Identifiers

PMID32240201
PMCPMC7117750

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.