five

Replication data for: IGO Membership, Network Convergence, and Credible Signaling in Militarized Disputes

收藏
DataONE2015-04-11 更新2024-06-27 收录
下载链接:
https://search.dataone.org/view/sha256:bf32cb8503270e021ae1d87eb909f58ef1c8f80d4e6795ed247cfa52eecf5ea7
下载链接
链接失效反馈
官方服务:
资源简介:
Existing studies of intergovernmental organizations (IGOs) and militarized conflict focus on dyadic counts of shared IGO membership. However, dyadic approaches are inconsistent with the basic properties of IGOs. Because IGOs are multilateral organizations, shared membership necessarily involves ties to third parties. This article employs network analytics to develop a novel explanation of how third-party IGO ties reduce militarized conflict. The analysis first examines the \"structural similarity\" of states, defined by the extent to which states share similar patterns of IGO membership with relevant third parties. High levels of structural similarity indicate that states interact with a common set of IGO collaborators. The analysis then shows that micro-level changes in IGO membership effect changes in structural similarity, leading to the macro-level phenomenon of \"network convergence,\" wherein states increasingly collaborate with the same third parties over time. Substantively, convergence results in increased overlap and integration between states' respective local networks of IGO partners. Because network convergence is costly, involving a combination of IGO-based accession, sovereignty, and alignment costs, it is unlikely to be pursued by purely exploitative state types. Consequently, convergence provides cooperative types with a mechanism for signaling a preference for cooperation over conflict. These credible signals in turn establish mutual trust among cooperators and effectively reduce the risk of militarized conflict. Extensive empirical analysis shows that, in fact, network convergence strongly correlates with a decline in militarized dispute initiations. The more that states collaborate with one another's IGO partners, the less likely they are to fight.
创建时间:
2023-11-21
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作