# Exploring Dependence Structures in the International Arms Trade Network

**Authors:** Michael Lebacher, G\"oran Kauermann

arXiv: 1803.03536 · 2018-03-12

## TL;DR

This paper analyzes the complex dependence structures in the international arms trade network from 1952 to 2016 using network disturbance models, revealing how trade flows relate to each other and are influenced by political and economic factors.

## Contribution

It introduces a network disturbance model to analyze dependence structures in the arms trade network, comparing multiple weight matrices to identify the most accurate representation.

## Key findings

- Dependence structures are complex and relate to all exports and imports of involved states.
- Political and economic variables significantly influence trade volumes.
- The best dependence model relates each trade flow to all exports and imports of involved states.

## Abstract

In the paper 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 relating the arms trade flows from the network of international arms exchange. Several different weight matrices are compared by means of the AIC in order to select the best dependence structure. It turns out that the dependence structure among the arms trade flows is rather complex and can be represented by a specification that, simply speaking, relates each arms trade flow to all exports and imports of the sending and the receiving state. By controlling for explanatory variables we are able to show the influence of political and economic variables on the volume traded.

## Full text

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## Figures

15 figures with captions in the complete paper: https://tomesphere.com/paper/1803.03536/full.md

## References

43 references — full list in the complete paper: https://tomesphere.com/paper/1803.03536/full.md

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Source: https://tomesphere.com/paper/1803.03536