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. Replication Materials: The data and materials required to verify the computational reproducibility of the results, procedures and analyses in this article are available on the American Journal of Political Science Dataverse within the Harvard Dataverse Network, at: https://doi.org/10.7910/DVN/SKPPLH. Since Ricardo, scholars have relied upon the concept of comparative advantage to explain why countries trade and to identify the winners and losers of trade (e.g., Hiscox 2002; Rogowski 1987; Scheve and Slaughter 2001). Although comparative advantage still plays a central role in explaining trade, consumer preferences for product variety and the use of global production chains by firms have dramatically altered patterns of international trade. The fast-growing political economy literature on productand firm-level theories demonstrates the importance of examining bilateral trade at the granular level in understanding the distributional consequences of international trade (e.g., Antràs and Staiger 2012; Grossman and Rossi-Hansberg 2012; Jensen, Quinn, and Weymouth 2015; Kim 2017; Osgood 2016). In Song Kim is Associate Professor, Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, E53-407, Cambridge, MA 02139 (insong@mit.edu). Steven Liao is Assistant Professor, Department of Political Science, University of California, Riverside, 2207 Watkins Hall, Riverside, CA 92521 (steven.liao@ucr.edu). Kosuke Imai is Professor, Department of Government and Department of Statistics, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138 (imai@harvard.edu). We are grateful to Tyson Chatagnier, Zachary Elkins, and Kerim Can Kavakli for providing us with replication code and data and answering numerous follow-up questions; to Hubert Jin and Radhika Saksena for programming assistance; to Tyler Simko for research assistance; and to Eric Arias, Leonardo Baccini, Kevin Esterling, Mark Fey, Michael Gibliseco, Hein Goemans, Joanne Gowa, Mark Handcock, Brenton Kenkel, David Lake, Gabriel Lopez-Moctezuma, Matto Mildenberger, Iain Osgood, Jack Paine, Molly Roberts, Curt Signorino, Leah Stokes, Randy Stone, Stephen Weymouth, Hye Young You, and Yiqing Xu for helpful comments. We also thank seminar participants at the American Political Science Association annual meeting, Korea University, the Midwest Political Science Association annual meeting, the MIT Statistics and Data Science Center, the Southern California Methods Workshop, the UCSD 21st Century China Center, and the University of Rochester. Despite the substantive importance of products and the massive amount of product-level bilateral trade flow data that are becoming available, most studies still rely on the total volume of trade aggregated across products (e.g., Carnegie 2014; Gartzke 2007; Mansfield, Milner, and Rosendorff 2000; Tomz, Goldstein, and Rivers 2007) or certain sets of aggregated products (e.g., Chatagnier and Kavaklı 2017; Dorussen 2006; Elkins, Guzman, and Simmons 2006; Goenner 2010). For many, computational and methodological challenges prohibit effective summaries of the massive amount of product-level data and preclude insights based on product composition. For example, our data set, which is based on the United Nations Comtrade Database, covers more than 600 products and 59,000 directed dyads over 53 years (1962–2014). As American Journal of Political Science, Vol. 64, No. 1, January 2020, Pp. 102–117 C ©2019, Midwest Political Science Association DOI: 10.1111/ajps.12473