# Censored Regression for Modelling International Small Arms Trading and its "Forensic" Use for Exploring Unreported Trades

## TL;DR

This paper employs a censored regression model with network effects to analyze international small arms trade data, revealing endogenous network influences, path dependence, and enabling forensic analysis of unreported trades.

## Contribution

It introduces a novel spatial autocorrelation model with endogenous network effects for small arms trade analysis, including a forensic application to detect unreported transactions.

## Key findings

- Strong endogenous network effects identified
- Evidence of path dependence in trade flows
- Model effectively detects unreported trade tendencies

## Abstract

In this paper we use a censored regression model to investigate 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 not only rely on exogenous covariates but also estimate endogenous network effects. We apply a spatial autocorrelation (SAR) model with multiple weight matrices. The likelihood is maximized employing the Monte Carlo Expectation Maximization (MCEM) algorithm. Our approach reveals strong and stable endogenous network effects. Furthermore, we find evidence for a substantial path dependence as well as a close connection between exports of civilian and military small arms. The model is then used in a "forensic" manner to analyse latent network structures and thereby to identify countries with higher or lower tendency to export or import than reflected in the data. The approach is also validated using a simulation study.