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Who's who in global value chains? A weighted network approach

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  • João Amador
  • Sónia Cabral
  • Rossana Mastrandrea
  • Franco Ruzzenenti

Abstract

This paper represents global value chains (GVCs) as weighted networks of foreign value added in exports, which allows for the identification of the specific roles of countries and for the quantification of their relative importance over time. A major structural change occurred in the beginning of the century as GVCs steadily turned into global networks, amid an unprecedented growth of value-added flows and the rise of China as a major player. First-order network metrics highlight the vital but also distinct roles of Germany, the US, China and Japan in the international organisation of production. Germany is very relevant both as a user and as a supplier of foreign inputs, while the US acts mostly as a supplier of value added to other countries. Second-order properties of networks shed light on the complex architecture of GVCs, notably in terms of cyclical triangular relationships. Germany's GVCs mostly root in direct relationships, while Japanese ones typically involve more than two countries.

Suggested Citation

  • João Amador & Sónia Cabral & Rossana Mastrandrea & Franco Ruzzenenti, 2017. "Who's who in global value chains? A weighted network approach," Working Papers w201707, Banco de Portugal, Economics and Research Department.
  • Handle: RePEc:ptu:wpaper:w201707
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    3. Zaclicever, Dayna, 2020. "A network analysis approach to vertical trade linkages: the case of Latin America and Asia," Comercio Internacional 147, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    4. Xing, Lizhi & Wang, Dawei & Li, Yan & Guan, Jun & Dong, Xianlei, 2020. "Simulation analysis of the competitive status between China and Portuguese-speaking countries under the background of one belt and one road initiative," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
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    7. Kobylianska, A.V., 2019. "Enterprises’ openness to global economy: mega-regional framework," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 8(4), pages 189-195, December.
    8. Filipa Lima & Flávio Pinheiro & João Falcão Silva & Pedro Matos, 2020. "Foreign direct investment – using network analysis to understand the position of Portugal in a global FDI network," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Bridging measurement challenges and analytical needs of external statistics: evolution or revolution?, volume 52, Bank for International Settlements.
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    10. Wu, Gang & Pu, Yue & Shu, Tianran, 2021. "Features and evolution of global energy trade network based on domestic value-added decomposition of export," Energy, Elsevier, vol. 228(C).

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    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F15 - International Economics - - Trade - - - Economic Integration

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