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Explaining destinations and volumes of international arms transfers: A novel network Heckman selection model
What determines the volumes of international weapon transfers? And why do countries establish such arms trading relationships in the first place? We propose an innovative statistical strategy that builds on the gravity approach and combines a Heckman model with a network analysis. This allows us, fo...
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Published in: | European Journal of Political Economy 2021-09, Vol.69, p.102033, Article 102033 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | What determines the volumes of international weapon transfers? And why do countries establish such arms trading relationships in the first place? We propose an innovative statistical strategy that builds on the gravity approach and combines a Heckman model with a network analysis. This allows us, for the first time, to analyze the impact of network structures on both the extensive and the intensive margins of the international arms trade simultaneously. We argue that the structure of the arms transfer network conveys important information for exporting and importing countries. Therefore, past topological properties of the trade network play a central role in its future evolution. Using data on the trade of major conventional weapons between 1955 and 2018, our estimation results and out-of-sample predictions show that network structures have considerable explanatory power with respect to the creation of trade links. They are far less relevant for the explanation of trade volumes, which are mainly determined by demand factors.
•We estimate the effects of network properties on the international arms trade.•Informational barriers and link costs affect the evolution of the arms trade.•Network structures are essential in explaining the choice of export destinations.•Supply constraints and demand patterns more important for trade volumes.•Out-of-sample predictions highlight the importance of network structures. |
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ISSN: | 0176-2680 1873-5703 |
DOI: | 10.1016/j.ejpoleco.2021.102033 |