Corrupted Data and the Illicit Arms Trade

Abstract

With the ever-increasing advancements in weapons technology, the illicit arms trade has steadily become a greater threat to international security. The small arms trade, consisting of portable weapons and their parts, is not only a profitable good, but also a method of gaining power through violent and threatening means. Being able to identify when and what countries are engaging in illicit arms trade is essential in order to make informed policy decisions. The driving question behind this project is: how do we recognize corrupted network data and how does corrupted network data impact our statistical analyses? The arms trade takes the form of network data consisting of actors (nodes) and the relationship between them (edges). This analysis of methods initially looks at simulated data. We show that if data is sampled from a pre-specified model then increasing the amount of corrupt data present impacts posterior statistics such as the intercept and row and column coefficients, as well as posterior predictive descriptive statistics such as degree distributions, triangle counts, betweenness, and Eigen vector centrality. This analysis demonstrates if data is corrupt, then by replacing the corrupted values with NAs these missing values will be imputed from the true pre-specified model and thus will not impact inference. These methods are then applied to actual small arms trade data, to see what nations may be engaging in illicit arms trading.