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. |