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The Role of Comparative Advantage in Enhancing Trade in Value-Added Using a Dynamic GMM Model

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  • Josephine Wuri

    (Department of Economics, Faculty of Economics, Sanata Dharma University, Yogyakarta 55281, Indonesia)

Abstract

Currently, international trade has evolved into international production fragmentation captured in GVCs. Countries must enhance intermediate exports in comparative advantage sectors to increase their trade in value-added (TVA) in global production chains. However, traditional measurements of revealed comparative advantage (RCA) based on gross exports need to be updated due to overvaluation, double counting, and implicit distortions in international trade. This study uses a new comparative advantage measure, “new revealed symmetric comparative advantage” (NRSCA). Using a dynamic General Method of Moment (GMM) approach, we investigate the role of comparative advantage in driving TVA regarding backward and forward linkages and examine the impact of the COVID-19 pandemic. We use data from the current Asian Development Bank multi-regional input–output database for 2010–2020. Our findings reveal that comparative advantage significantly impacted international TVA, along with the support of quality institutional services in each country. Implementing a new comparative advantage measure, NRSCA, provided accurate estimation results to overcome the overvaluation problem. Moreover, the COVID-19 pandemic disrupted value-added trade.

Suggested Citation

  • Josephine Wuri, 2024. "The Role of Comparative Advantage in Enhancing Trade in Value-Added Using a Dynamic GMM Model," Economies, MDPI, vol. 12(7), pages 1-21, July.
    • Handle: RePEc:gam:jecomi:v:12:y:2024:i:7:p:187-:d:1438222
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    References listed on IDEAS

    as
    1. Alessandro Borin & Michele Mancini, 2019. "Measuring What Matters in Global Value Chains and Value-Added Trade," World Bank Publications - Reports 31533, The World Bank Group.
    2. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
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