Exploring dependence structures in the international arms trade network: A network autocorrelation approach. (April 2020)

• Record Type: Journal Article
• Authors: Lebacher, Michael; Thurner, Paul W; Kauermann, Göran
• Is Part Of: Statistical modelling. Volume 20:Number 2(2020)
• Page Start: 195
• Page End: 218
• Publication Date: 2020-04
• Subjects: Arms trade -- dependence structures -- model selection -- network disturbance model -- spatial econometrics; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X18817673
• Languages: English
• ISSNs: 1471-082X

Abstract

In this article, we analyse dependence structures among international trade flows of major conventional weapons from 1952 to 2016. We employ a Network Disturbance Model commonly used in inferential network analysis and spatial econometrics. The dependence structure is represented by pre-defined weight matrices that allow for correlating flows from the network of international arms exchange. Three dependence structures are proposed, representing sender-, receiver- and sender–receiver-related dependencies. The appropriateness of the presumed structures is comparatively assessed using the Akaike Information Criterion (AIC). It turns out that the dependence structure among the arms trade flows is complex and can be represented best by a specification that relates each arms trade flow to all exports and imports of the sending and the receiving state. Controlling for exogenous variables, we find that the trade volume increases with the GDP of the sending and the receiving state while the impact of geographical distance, regime dissimilarity and formal alliance membership is rather small.