Measuring Trade Profile with Granular Product-level Data

The forthcoming article “Measuring Trade Profile with Granular Product-level Data” (https://doi.org/10.1111/ajps.12473) by In Song Kim, Steven Liao, and Kosuke Imai is summarized by the authors below.

This paper develops a new dynamic clustering method to effectively summarize two billion observations of product-level international trade data. The proposed method classifies a set of dyads into several clusters based on their similarities in the trade profile, i.e., the product composition of imports and exports. We show that typical dyadic trade relationships evolve from sparse trade to inter-industry trade and then to intra-industry trade while the specific timing for such transition varies significantly by dyads.  Furthermore, this paper develops two novel dyad-year level measures for International Relations research. First, the trade profile measure enables applied researchers to effectively account for changing trade relationships that manifest from product-level trade data. Second, we develop a measure of trade competition that incorporates the extent of competition that each country faces with all of its trading partners at the product level.  We apply these measures to examine the network effects of trade competition on the likelihood of signing Bilateral Investment Treaties (BITs). The open-source software, dynCluster: Dynamic Clustering Algorithm, is available as an R package for applying the proposed methods to various other dyadic panel data sets.

About the Author(s): In Song Kim is Associate Professor, Department of Political Science, Massachusetts Institute of Technology, Steven Liao is Assistant Professor, Department of Political Science, University of California, Riverside, and Kosuke Imai is Professor, Department of Government and Department of Statistics, Harvard University. Their research “Measuring Trade Profile with Granular Product-Level Data” (https://doi.org/10.1111/ajps.12473) is now available in Early View and will appear in a forthcoming issue of the American Journal of Political Science.