Analysis of Cross-Border Data Trade Restrictions Using Mixture-Based Clustering

Cross-border data flows and interoperability have created an increasingly digital global trade environment. Concerns for cybersecurity, privacy, and digital sovereignty have prompted the widespread implementation of trade policies regulating cross-border data flows. At present, there is a deficit of information characterizing such policies and analyzing their role in the global economy. We address this deficit through a quantitative analysis of trade policies in services that seeks both to categorize data-related trade policies and to evaluate the extent to which they are different from other restrictive measures. We propose a mixture-based clustering pipeline to group trade restrictiveness data and a method for quantifying the difference between the cross-border data flow regulations and other traditional regulations. Our analysis reveals a significant localization effect among data flow restrictions and that, while highly restrictive data flow policies generally do not overlap with other policies, there is a significant similarity in moderate to liberal policies