Assessing Sanctions Motivations via Real-Time Shipping Data

Abstract

Contemporary sanctions literature is bogged by conflicting analysis of initiator goals and sanction efficacy in addition to numerous paradoxes and selection effect concerns. I believe there is greater potential for academic contributions via examining initiator-state motivations and I propose a new analysis framework which incorporates maritime Automatic Identification System (AIS) data (i.e. GPS tracking for ships) alongside official statements and subsequently published international trade data. My research adopts AIS data as a tool to help reveal initiator motivations by offering greater trade flow fidelity, credibility, and timeliness than is currently available via official customs reports. I establish a template for what will hopefully lead to a renaissance of real-time trade and sanctions policy analysis.

Chapter One reviews the current sanctions literature and establishes a framework to study initiator motivations by incorporating AIS analysis alongside official statements and subsequently published trade data. This framework improves academic understanding of the initial motivations and subsequent economic impacts of sanctions programs and can be used to test theories such as “bluster sanctions” or discover additional phenomena such as firm- or industry-level “self-sanctioning”. I address the limitations and concerns of AIS data, outline my personal research agenda, and conclude with a call to action for other academics.

In Chapter Two, I utilize AIS data to study baseline and subsequent China-Australia commodity trade flows during the 2020-2022 China-Australia ‘Coal War’ to reveal potential initiator motivations. My analysis provides sharp fidelity on port-to-port volumes and highlights potential trade flow rerouting or triangular masking. My analysis confirms a significant plunge in bilateral coal trade juxtaposed against negligible impact to bilateral iron ore and grain trades. This physical trade flow analysis, combined with official Chinese statements and synchronicity with shifting zero-COVID policies, drives a plausible conclusion that the coal ban was designed primarily as an international signaling device mixed with domestic political objectives. Although there is insufficient evidence to support the bluster sanctions theory, this case study proves the overall efficacy of AIS data for trade flow analysis.

In Chapter Three, I analyze energy trade flows immediately following the Russian invasion of Ukraine (pre-sanctions) in addition to post-sanction changes after the implementation of the oil ban and price cap regime in December 2022. The official sanctions regime appears to be primarily functioning as designed, at least through June 2023; however, significant profiteering and “sanctions rents” have been captured by several unaligned states including India, China, Saudi Arabia, and the UAE. AIS data also allows for enhanced firm-level analysis, and I find statistically significant post-invasion “self-sanctioning” by publicly listed firms which are not related to country of ownership characteristics such as EU or NATO membership nor related to size of fleet or other apparent company characteristics. This finding advances the nascent self-sanctioning literature and encourages a higher focus on publicly listed firms.

My thesis framework and research findings advance sanctions literature via a novel approach to determining initiator motivations and by improving the fidelity of related trade flow analysis. My findings bolster the emerging self-sanctioning literature and develop the groundwork for incorporating AIS data as an additional analysis tool for IR, IPE, and Trade scholars, particularly those who study how trade flows respond to major global catalysts such as conflict or sanctions.