Illicit Small Arms Prices: Introducing Two New Datasets

Marsh, Nicholas; McDougal, Topher L.

doi:10.1080/10242694.2020.1757348

Cited by 4 publications

(8 citation statements)

References 14 publications

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“…This is plausible because countries that export massive amounts of civilian arms also have the capabilities to produce military arms. The data is also provided by NISAT (Marsh & McDougal, 2016). Furthermore it seems plausible that there is a connection between the volume of small arms traded and the volume of MCW.…”

Section: Application To the Datamentioning

confidence: 99%

“…However, only the Norwegian Initiative on Small Arms Transfers (NISAT, see Marsh, 2017) provides truly relational data necessary for applying network analysis. The NISAT database contains relational information on the trade of small arms, light weapons and ammunition (see also Marsh & McDougal, 2016). This information is collected from different sources as described in Haug et al.…”

Section: Data Descriptionmentioning

confidence: 99%

“…A major reason behind this research gap are the notorious data deficiencies due to non‐reporting and illicit trafficking (see Holtom & Pavesi, 2018, p. 29–46). Based on the only large‐scale data base for small arms (Marsh & McDougal, 2016), we aim to analyse for the first time the small arms trading network. To do so, we combine gravity models with network statistics to apply a ‘forensic’ statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Censored Regression for Modelling Small Arms Trade Volumes and Its ‘Forensic’ Use for Exploring Unreported Trades

Lebacher

Thurner

Kauermann

2021

Journal of the Royal Statistical Society Series C: Applied Statistics

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In this paper, we use a censored regression model to investigate data on the international trade of small arms and ammunition provided by the Norwegian Initiative on Small Arms Transfers. Taking a network‐based view on the transfers, we do not only rely on exogenous covariates but also estimate endogenous network effects. We apply a spatial autocorrelation gravity model with multiple weight matrices. The likelihood is maximized employing the Monte Carlo expectation maximization 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.

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Section: Application To the Datamentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Censored Regression for Modelling Small Arms Trade Volumes and Its ‘Forensic’ Use for Exploring Unreported Trades

Lebacher

Thurner

Kauermann

2021

Journal of the Royal Statistical Society Series C: Applied Statistics

View full text Add to dashboard Cite

show abstract

“…However, only the Norwegian Initiative on Small Arms Transfers (NISAT, see Marsh, 2017) provides truly relational data necessary for applying network analysis. The NISAT database contains relational information on the trade of small arms, light weapons and ammunition (see also Marsh and McDougal, 2016). This information is collected from different sources as described in Haug et al (2002).…”

Section: Data Descriptionmentioning

confidence: 99%

“…A major reason behind this research gap are the notorious data deficiencies due to non-reporting and illicit trafficking (see Holtom and Pavesi, 2018, p. 29-46). Based on the only large-scale data base for small arms (Marsh and McDougal, 2016) we aim to analyse for the first time the small arms trading network. We integrate gravity models in a statistical network design to apply a forensic statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Lebacher,

Thurner,

Kauermann

2019

Preprint

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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.

show abstract