Comments on Economic Integration and Network Trade: A Comparison of East Asia and the European Union

Abstract

Fukunari Kimura, Keio University and Economic Research Institute for ASEAN and East Asia: The comparison between East Asia (EA) and the European Union (EU) in the development of network trade is an important topic to be investigated. Nguyen and Wu conducted a gravity equation exercise with the global trade data and with EA/EU regional export data (to the world), focusing on trade in intermediate goods based on Broad Economic Categories (BEC) categories. The careful introduction of variables for “economic mass” and “multilateral resistance” may be a methodological contribution to the literature.The major findings from the regression analysis are as follows. First, with the global trade data, trade in intermediate goods is more sensitive to distance and further influenced by the economic mass of exporters and importers than the total trade. Although the sensitivity in distance has already been detected by many authors, including one of my papers with old-fashioned gravity (Kimura, Takahashi, and Hayakawa 2007), the confirmation of the result with new analytical technique is meaningful. Second, in comparison with the EU, intra-EA trade in intermediate goods is less sensitive to distance and further influenced by the economic mass of exporters and importers. This may give us a clue for examining differences between the two regions.I believe that the interpretation of these results can be more general than the authors claim. As for the first point, the authors note that trade in intermediate goods is more sensitive to “trade barriers,” but distance may mean more than trade barriers, including physical difficulty due to geographical distance. Furthermore, we have to be careful that a larger coefficient for distance does not necessarily mean higher physical and policy barriers if the scope of traded intermediate goods is expanded. As for the second point, the interpretation of the authors is that EA is still strongly dependent on outside regions. However, less sensitivity to distance, together with coefficients for remoteness, may also come from biases in traded intermediate goods. EA has become a strong exporter of electronic parts and components, which is easily traded in distance beyond the region by air. On the other hand, in the EU, we observe a massive trade in automobile parts, which is traded by trucks in relatively short distance. This would thus indicate the international competitiveness of EA rather than the “dependency” on outside regions. The authors could investigate these points further by more thoroughly expanding regression analyses.Unfortunately, the authors did not seem to follow my comments at the Asian Economic Panel Meeting on the basic data handling. First, it is customary to use mirror import data, rather than directly using export data, in gravity equation exercises, but the revised version of the paper still seems to use export data. We know that the quality of data is much better for imports than exports. Second, it is safer to merge the trade data of China (Hong Kong) and China (Macao) with the data of mainland China, though the authors do not seem to recognize the importance of such a treatment. I am also wondering why Taiwan is included in the East Asian group even though UN Comtrade does not provide its data. Finally, this was also pointed out in my original comments but is not clear whether the authors responded properly or not in the revised version: in using time series data, the authors should apply deflators or alternatively introduce year dummies. Because of these elementary problems in data handling, the regression results became somewhat dubious.It is again a pity that the authors did not follow another comment of mine; they did not decompose “intermediate goods” into two distinctive categories. The first category includes intermediate goods in food processing, light industries, and the energy sector, which are traded relatively slowly in a traditional manner. Baldwin (2016) would call it “the first unbundling.” The second category contains intermediate goods in machinery industries, the trade of which is time-sensitive, synchronized, and sometimes based on relation-specific transactions or “the second unbundling.” Buyer-driven production networks and producer-driven production networks, as Athukorala (2019) argues, roughly correspond to these two categories. The nature of international division of labor and international trade is quite different between these two categories. Therefore, in BEC categories, it is worth classifying into two categories: 111+121+21+22+32 and 42+53.Lastly, we can easily imagine that the difference between EA and the EU is substantially due to industrial biases, particularly electronics for EA and automobiles for the EU. We may also want to see overtime changes in the trade pattern, which can be explored with this data set.Overall, although the paper still leaves ample room for the improvement, it is successful in setting up a good starting point for further research on the topic.