Censored Regression for Modelling International Small Arms Trading and its "Forensic" Use for Exploring Unreported Trades
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In this paper we use a censored regression model to analyse data on the international trade of small arms and ammunition (SAA) provided by the Norwegian Initiative on Small Arms Transfers (NISAT). Taking a network based view on the transfers, we do not only rely on exogenous covariates but also estimate endogenous network effects, including a measure for reciprocity as well as the weighted out- and indegree. We rely on a spatial autocorrelation (SAR) model with multiple weight matrices. The likelihood is maximized employing the Monte Carlo Expectation Maximization (MCEM) algorithm. The fitted coefficients reveal strong and stable endogenous network effects. Furthermore, we find evidence for substantial path dependence as well as a close connection between exports of civilian and military small arms. Interestingly, the binary occurrence of major conventional weapons exports is positively related to SAA exports, whereas the volumes have a negative impact. The model is then used in a "forensic" manner to analyse latent network structures and identify countries with higher or lower tendency to export or import than mirrored in the data.
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