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Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach

Author

Listed:
  • Maulana, Ardian
  • Hokky, Situngkir

Abstract

This research aims to elucidate the organizational patterns of interregional economic interdependence to enhance our comprehension of the national economy's structure at a regional scale. Employing a multilayer network model, this study represents economic interdependence among Indonesian regions, utilizing the InterRegional Input-Output (IRIO) table. Through the application of various metrics, such as degree and strength distribution, assortativity coefficient, and global and local rich club coefficient, to the multilayer IRIO network, we uncover the organizational patterns of economic exchanges between provinces and economic sectors within Indonesia. Our findings demonstrate that a multilayer network approach reveals the heterogeneous and complex structure of the national economy at the regional level. By analyzing the assortativity pattern and global rich-club coefficient, we illustrate that the IRIO network exhibits a hierarchical organization, where significant provincial-sector nodes are interconnected and form dense rich clubs, extending from a few structural cores to peripheral regions. Additionally, we identify distinct connectivity patterns of non-rich nodes based on their incoming and outgoing relations. The insights gained from this study have implications for the macro-control of regional development.

Suggested Citation

  • Maulana, Ardian & Hokky, Situngkir, 2024. "Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach," MPRA Paper 121129, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:121129
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    File URL: https://mpra.ub.uni-muenchen.de/121129/1/MPRA_paper_121129.pdf
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    References listed on IDEAS

    as
    1. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna & Luu, Duc Thi, 2022. "The multilayer architecture of the global input-output network and its properties," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 304-341.
    2. Jeff Alstott & Ed Bullmore & Dietmar Plenz, 2014. "powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    3. Luiz G. A. Alves & Giuseppe Mangioni & Francisco A. Rodrigues & Pietro Panzarasa & Yamir Moreno, 2018. "Unfolding the complexity of the global value chain: Strengths and entropy in the single-layer, multiplex, and multi-layer international trade networks," Papers 1809.07407, arXiv.org, revised Dec 2018.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • E0 - Macroeconomics and Monetary Economics - - General
    • H4 - Public Economics - - Publicly Provided Goods
    • H7 - Public Economics - - State and Local Government; Intergovernmental Relations
    • J1 - Labor and Demographic Economics - - Demographic Economics
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • P0 - Political Economy and Comparative Economic Systems - - General
    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • R5 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis
    • Z18 - Other Special Topics - - Cultural Economics - - - Public Policy

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