Bilateral Value Added in Trade between China and EU Countries

What is it about?

By applying stochastic frontier analysis, and replacing total export with value added in exports as a more accurate measure of domestic content embodied in exports, we estimate the trade efficiency between China and EU countries and analyze the determinant factors. The results show that the value added in trade between China and EU countries is running at a low level of efficiency, and the trade efficiency has an obvious imbalance between export and import. Our calculation of unexplored trade potential indicates that western EU countries, such as France, Germany, Italy, and Spain together are China’s top trading partner with the highest unexploited trade potential. Based on the principal component analysis of 14 trading-related variables, trade efficiency determinant results show that the tangible trade infrastructures of both exporter and importer matters most, including airports, container shipping, Internet, and broadband infrastructures. Meanwhile, intangible trade infrastructures, including customs procedures and domestic business environments, has a significant, but smaller influence on trade efficiency. Our important findings shed light on practical trade policy making to encourage Sino-EU trade collaboration.

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Photo by Kristijan Arsov on Unsplash

Photo by Kristijan Arsov on Unsplash

Why is it important?

This paper contributes related literature in two ways. we use a more accurate estimation results by considering the “double counting” problem arising from global production fragmentation. Not considering this “double counting” problem would result in biased trade potential analysis. Hence, we replace the dependent variable, i.e. total export, in gravity model with the value added in exports (VAE) as a more accurate measurement of domestic content embodied in exports, and thus obtain more precise trade efficiency estimation results. Furthermore, this paper does more than just estimation. We identify the determinants and the importance of them to trade efficiency, suggesting policy measures to increase bilateral trade efficiency. In this paper, we consider 14 variables impacting bilateral trade efficiencies, including tangible trade-related infrastructure, such as railways, airports, containers and internet usage; and intangible infrastructure, such as customs burdens and domestic institutions. The large number of control variables creates a collinearity problem when estimating, which we use a principal factor analysis strategy to tackle. In addition, we calculate the beta coefficient for each determinant to get a better understanding of which one is more important for trade efficiency.

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