Measuring Trade Profile with Granular Product-level Trade Data



The product composition of bilateral trade encapsulates complex relationships about comparative advantage, global production networks, and domestic politics. Yet, despite the availability of product-level trade data, most researchers rely on either the total volume of trade or certain sets of aggregated products. In this paper, we develop a new dynamic clustering method to effectively summarize this massive amount of product-level data. The proposed method classifies a set of dyads into several clusters based on their similarities in trade profile - the product composition of imports and exports - and captures the evolution of the resulting clusters over time. We apply this method to two billion observations of product-level annual trade flows. We show how typical dyadic trade relationships evolve from sparse trade to inter-industry trade and then to intra-industry trade. Finally, we illustrate the critical roles of our trade profile measure in International Relations research on trade competition and bilateral investment treaties. (Last Revised March, 2018)

© Kosuke Imai
 Last modified: Tue Mar 6 11:34:32 EST 2018