SASCAT: Natural language processing approach to the study of economic sanctions

Existing datasets of economic sanctions rely primarily on secondary sources and do not tend to take full advantage of government documents related to economic coercion. Such data may miss sanctions, and do not capture important details in how coercive measures are threatened, imposed and removed. The latter processes often have much to do with the domestic politics in sender countries. Understanding these processes may be necessary in order to fully account for sanctions’ effectiveness. We present a natural language processing (NLP) approach to retrieving sanctions-related government documents. We apply our method to the case of US sanctions. The United States is the world’s pre-eminent user of sanctions. Our method can be applied to other cases. We collect all sanctions events originating in the office of the US president, and all congressional sanctions, for 1988–2016. Our approach has three advantages: (1) by design, it captures all sanctions-related documents; (2) the resulting data are disaggregated by imposing branch of government; (3) the data include the original language of the measures. These features directly shed light on interbranch delegation, domestic (partisan) conflict, and policy priorities. We show that our data record more episodes than most existing sanctions’ data, and have features that other datasets lack. The availability of the original text opens up new avenues for research and analysis.