Measuring Trade Profile with Granular Product-level Data


The product composition of bilateral trade encapsulates complex relationships about comparative advantage, global production networks, and domestic politics. 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 article, we develop a new dynamic clustering method to effectively summarize this massive amount of product-level information. 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 interindustry 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.

American Journal of Political Science
In Song Kim
Associate Professor of Political Science
Steven Liao
Steven Liao
Assistant Professor of Political Science
Kosuke Imai
Professor, Department of Government and Department of Statistics