A dynamic separable network model with actor heterogeneity: An application to global weapons transfers

Michael Lebacher, Paul W. Thurner and Göran Kauermann

Journal of the Royal Statistical Society Series A, 2021, vol. 184, issue 1, 201-226

Abstract: In this paper, we analyse the network of international major conventional weapons (MCW) transfers from 1950 to 2016, based on data from the Stockholm International Peace Research Institute (SIPRI). The dataset consists of yearly bilateral arms transfers between pairs of countries, which allows us to conceive of the individual relationships as part of an overall trade network. For the analysis, we extend the separable temporal exponential random graph model (STERGM) to account for time‐varying effects on both the network level (trade network) and the actor level (country effects). Our investigation enables the identification of potentially differing driving forces that influence the formation of new trade relationships versus the persistence of existing ones. In accordance with political economy models, we expect security‐ and network‐related covariates to be most important for the formation of transfers, whereas repeated transfers should prevalently be determined by the importers’ market size and military spending. Our proposed modelling approach corroborates the hypothesis and quantifies the corresponding effects. Additionally, we subject the time‐varying heterogeneity effects to a functional principal component analysis. This analysis serves as an exploratory tool and allows us to identify countries with exceptional increases or decreases in their tendency to import and export weapons.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1111/rssa.12620

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:184:y:2021:i:1:p:201-226

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X

Access Statistics for this article

Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples

More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().