Speaker
Description
This paper offers methodological guidelines for the application of blockmodelling (BM), a clustering technique that historically informed heterodox analyses of trade but has since fallen out of favour, to the internataional trade network. It also puts these recommendations at work in a two-snapshot longitudinal case study into the the transformation of international trade under the first Trump administration (2017-2021). Namely, the paper examines the extent to which shifts in US trade policy (trade wars, renegotiated agreements, emphasis on economic nationalism) altered the structure of the ITN in those years.
The analysis compares existing BM approaches and assesses their suitability for capturing heterogenoeus trade patterns amidst exogenous shocks such as tariffs and sanctions. Furthermore, this study addresses critical methodological challenges relating to the ITN's scale-free properties and heterogeneities in relational capacities such as data normalisation. It also discusses the potential to integrate insights from the gravity model of trade, a standard econometric model, into BM. By bridging social-network analysis and international trade, this study both revitalises BM as a valuable tool in international economics and provides an empirical reassessment of the earlier 'Trump effect'.
Keywords/Topics
international trade, blockmodelling, dynamic networks, Donald Trump, tariffs
