Abstract
International trade is stepping into the age of digitally enabled trade, driven by cross-border data flows and interoperability. 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 in services that seeks both to categorize data-related trade policies and to evaluate the extent to which they are similar to 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 that 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.
Recommended Citation
Huang, Keman; Chinnery, Samuel; and Madnick, Stuart, "Analysis of Cross-border Data Trade Restrictions using Mixture-based Clustering" (2019). AMCIS 2019 Proceedings. 6.
https://aisel.aisnet.org/amcis2019/digital_government/digital_government/6
Analysis of Cross-border Data Trade Restrictions using Mixture-based Clustering
International trade is stepping into the age of digitally enabled trade, driven by cross-border data flows and interoperability. 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 in services that seeks both to categorize data-related trade policies and to evaluate the extent to which they are similar to 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 that 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.