Drug Smuggling Seizures: The Effects of Reporting Consistency and Quality on the Observed Transnational Structure

Abstract

If it is possible to overcome significant data challenges, social network analytics could be used to expose structural vulnerabilities in transnational drug smuggling operations, offering clear targets for crime control efforts that aim to disrupt transhipment. This study explores the extent to which data inclusion decisions might distort the emergent structure of nation-to-nation smuggling networks mapped with aggregate intelligence using United Nations Office on Drugs and Crime (UNODC) incident level seizure data (2010–2016). Bivariate exponential random graph models (ERGM) show that relaxing data inclusion standards exposes illicit backchannels (reciprocity) and a more complete picture of major transhipment activity (activity and popularity spread) than would be otherwise undetected. Relaxed data inclusion standards may help to adjust for the data limitations associated with the detection of rare events and inconsistent reporting practices, if usage rules are followed.