A method for estimating driving factors of illicit trade using node embeddings and clustering

Abstract

The trade on illegal goods and services, also known as illicit trade, is expected to drain 4.2 trillion dollars from the world economy and put 5.4 million jobs at risk by 2022. These estimates reflect the importance of combating illicit trade, as it poses a danger to individuals and undermines governments. To do so, however, we have to first understand the factors that influence this type of trade. Therefore, we present in this article a method that uses node embeddings and clustering to compare a country based illicit supply network to other networks that represent other types of country relationships (e.g., free trade agreements, language). The results offer initial clues on the factors that might be driving the illicit trade between countries.