IDEAS home Printed from https://ideas.repec.org/a/sae/jocore/v63y2019i7p1736-1764.html

Network Interdependencies and the Evolution of the International Arms Trade

Author

Listed:
  • Paul W. Thurner
  • Christian S. Schmid
  • Skyler J. Cranmer
  • Göran Kauermann

Abstract

Since few states are able to produce all of their own military hardware, a majority of countries’ military systems rely on weapon imports. The structure of the international defense technology exchange network remains an important puzzle to understand, along with the factors that drive its evolution. Drawing on a political economy model of arms supply, we propose a new network-oriented explanation for the worldwide transactions of major conventional weapons in the period after World War II. Using temporal exponential random graph models, our dynamic approach illustrates how network dependencies and the relative weighting of economic versus security considerations vary over time. One of our major results is to demonstrate how security considerations started regaining importance over economic ones after 2001. Additionally, our model exhibits strong out-of-sample predictive performance, with network dependencies contributing to model improvement especially after the Cold War.

Suggested Citation

  • Paul W. Thurner & Christian S. Schmid & Skyler J. Cranmer & Göran Kauermann, 2019. "Network Interdependencies and the Evolution of the International Arms Trade," Journal of Conflict Resolution, Peace Science Society (International), vol. 63(7), pages 1736-1764, August.
  • Handle: RePEc:sae:jocore:v:63:y:2019:i:7:p:1736-1764
    DOI: 10.1177/0022002718801965
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0022002718801965
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0022002718801965?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Brzoska, Michael & Ohlson, Thomas, 1987. "Arms Transfers to the Third World, 1971-85," OUP Catalogue, Oxford University Press, number 9780198291169.
    2. Ward, Michael D. & Ahlquist, John S. & Rozenas, Arturas, 2013. "Gravity's Rainbow: A dynamic latent space model for the world trade network," Network Science, Cambridge University Press, vol. 1(1), pages 95-118, April.
    3. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael C Horowitz & Joshua A Schwartz, 2025. "To compete or strategically retreat? The global diffusion of reconnaissance strike," Journal of Peace Research, Peace Research Institute Oslo, vol. 62(4), pages 847-862, July.
    2. Weidong Guo & Debin Du & Tingzhu Li & Qiang Zhang, 2025. "The Vulnerability of the Global Arms Trade: A Network Perspective," Networks and Spatial Economics, Springer, vol. 25(3), pages 593-612, September.
    3. Rajwani, Tazeeb & Hartwell, Christopher A. & Stephens, Melodena & Chen, Tao, 2026. "Corporate military activities and key frontiers in international business research," Journal of World Business, Elsevier, vol. 61(1).
    4. Tsintsaris, Dimitris & Ioannidis, Evangelos, 2025. "Modeling structural power in hypergraphs: An application to the interstate alliances network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
    5. Marius Mehrl & Daniel Seussler & Paul W. Thurner, 2025. "Sharing rivals, sending weapons: Rivalry and cooperation in the international arms trade, 1920–1939," The Review of International Organizations, Springer, vol. 20(1), pages 59-85, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    2. Peter R. Herman, 2022. "Correction to: Modeling complex network patterns in international trade," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(2), pages 713-714, May.
    3. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    4. Lomi, Alessandro & Fonti, Fabio, 2012. "Networks in markets and the propensity of companies to collaborate: An empirical test of three mechanisms," Economics Letters, Elsevier, vol. 114(2), pages 216-220.
    5. Shu Takahashi & Kento Yamamoto & Shumpei Kobayashi & Ryoma Kondo & Ryohei Hisano, 2024. "Dynamic Link and Flow Prediction in Bank Transfer Networks," Papers 2409.08718, arXiv.org, revised Oct 2024.
    6. Liu, Jie & Ge, Huilin, 2022. "Collaboration mechanisms and community detection of statisticians based on ERGMs and kNN-walktrap," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    7. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    8. Michael Brzoska †, 2004. "The economics of arms imports after the end of the cold war," Defence and Peace Economics, Taylor & Francis Journals, vol. 15(2), pages 111-123, April.
    9. Krivitsky, Pavel N., 2017. "Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 149-161.
    10. Michael Brzoska & Frederic S. Pearson, 1994. "Developments in the Global Supply of Arms: Opportunity and Motivation," The ANNALS of the American Academy of Political and Social Science, , vol. 535(1), pages 58-72, September.
    11. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    12. Patience Pokuaa Gambrah & Qian Yu, 2025. "Smallholder farmers’ network structure: a case study in Ghana," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(3), pages 1-17, March.
    13. Alessandro Lomi & Philippa Pattison, 2006. "Manufacturing Relations: An Empirical Study of the Organization of Production Across Multiple Networks," Organization Science, INFORMS, vol. 17(3), pages 313-332, June.
    14. Wong, Ling Heng & Pattison, Philippa & Robins, Garry, 2006. "A spatial model for social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 99-120.
    15. Guo, Yaoqi & Zhao, Boya & Zhang, Hongwei, 2023. "The impact of the Belt and Road Initiative on the natural gas trade: A network structure dependence perspective," Energy, Elsevier, vol. 263(PD).
    16. Yan, Jingjing & Guo, Yaoqi & Zhang, Hongwei, 2024. "The dynamic evolution mechanism of structural dependence characteristics in the global oil trade network," Energy, Elsevier, vol. 303(C).
    17. David Kinsella, 2011. "The Arms Trade," Chapters, in: Christopher J. Coyne & Rachel L. Mathers (ed.), The Handbook on the Political Economy of War, chapter 12, Edward Elgar Publishing.
    18. Michael Lebacher & Paul W. Thurner & Göran Kauermann, 2021. "Censored regression for modelling small arms trade volumes and its ‘Forensic’ use for exploring unreported trades," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 909-933, August.
    19. David Dekker & Dimitirs Christopoulos & Heather McGregor, 2025. "Dynamic Structures of Knowledge Production: Citation Rates in Hydrogen Technologies," Papers 2502.00797, arXiv.org.
    20. Li, Yonglin & Zuo, Zhili & Cheng, Jinhua & Xu, Deyi, 2024. "Evolutionary characteristics and structural dependence determinants of global lithium trade network: An industry chain perspective," Resources Policy, Elsevier, vol. 99(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jocore:v:63:y:2019:i:7:p:1736-1764. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://pss.la.psu.edu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.