SASCAT: Natural Language Processing Approach to the Study of Economic Sanctions

25 Pages Posted: 25 Mar 2021 Last revised: 2 Aug 2021

See all articles by Ashrakat Elshehawy

Ashrakat Elshehawy

University of Oxford

Nikolay Marinov

University of Houston - Department of Political Science

Federico Nanni

Data and Web Science Group

Jordan Tama

American University

Date Written: February 22, 2021

Abstract

Existing datasets of economic sanctions do not tend to take full advantage of government documents related to economic coercion. They may miss some economic sanctions, and do not capture some important details in how measures are threatened, imposed and removed. The latter often have to do with the domestic politics in sender countries, and understanding them may be necessary to fully account for sanctions' effectiveness. We present a natural-language processing (NLP) approach to retrieving sanctions-related government documents for the US case. 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 is disaggregated by imposing branch; (3) the data includes the original language of the measures. These features directly shed light on inter-branch delegation, domestic (partisan) conflict, and policy priorities.

Keywords: sanctions, nlp

Suggested Citation

Elshehawy, Ashrakat and Marinov, Nikolay and Nanni, Federico and Tama, Jordan, SASCAT: Natural Language Processing Approach to the Study of Economic Sanctions (February 22, 2021). Available at SSRN: https://ssrn.com/abstract=3790866 or http://dx.doi.org/10.2139/ssrn.3790866

Ashrakat Elshehawy

University of Oxford ( email )

Oxford
United Kingdom

Nikolay Marinov (Contact Author)

University of Houston - Department of Political Science ( email )

TX 77204-3011
United States

HOME PAGE: http://www.nikolaymarinov.com

Federico Nanni

Data and Web Science Group ( email )

Germany

Jordan Tama

American University ( email )

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