IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2310.08193.html
   My bibliography  Save this paper

Are sanctions for losers? A network study of trade sanctions

Author

Listed:
  • Fabio Ashtar Telarico

Abstract

Studies built on dependency and world-system theory using network approaches have shown that international trade is structured into clusters of 'core' and 'peripheral' countries performing distinct functions. However, few have used these methods to investigate how sanctions affect the position of the countries involved in the capitalist world-economy. Yet, this topic has acquired pressing relevance due to the emergence of economic warfare as a key geopolitical weapon since the 1950s. And even more so in light of the preeminent role that sanctions have played in the US and their allies' response to the Russian-Ukrainian war. Applying several clustering techniques designed for complex and temporal networks, this paper shows that a shift in the pattern of commerce away from sanctioning countries and towards neutral or friendly ones. Additionally, there are suggestions that these shifts may lead to the creation of an alternative 'core' that interacts with the world-economy's periphery bypassing traditional 'core' countries such as EU member States and the US.

Suggested Citation

  • Fabio Ashtar Telarico, 2023. "Are sanctions for losers? A network study of trade sanctions," Papers 2310.08193, arXiv.org.
    • Handle: RePEc:arx:papers:2310.08193
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2310.08193
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vladimir Mau, 2016. "Between crises and sanctions: economic policy of the Russian Federation," Post-Soviet Affairs, Taylor & Francis Journals, vol. 32(4), pages 350-377, July.
    2. T. Clifton Morgan & Constantinos Syropoulos & Yoto V. Yotov, 2023. "Economic Sanctions: Evolution, Consequences, and Challenges," Journal of Economic Perspectives, American Economic Association, vol. 37(1), pages 3-30, Winter.
    3. Catherine Matias & Vincent Miele, 2017. "Statistical clustering of temporal networks through a dynamic stochastic block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1119-1141, September.
    Full references (including those not matched with items on IDEAS)

    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. Fabio Ashtar Telarico, 2023. "Are Sanctions for Losers? [Les sanctions sont-elles destinées aux perdants ?]," Post-Print hal-04238902, HAL.
    2. Chen, Mo & Xue, Wei-Xian & Zhao, Xin-Xin & Chang, Chun-Ping & Liu, Xiaoxia, 2024. "The impact of economic sanctions on the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 163-174.
    3. Felbermayr Gabriel & Janeba Eckhard, 2024. "Improving Supply Security: Guidelines and Policy Proposals," Intereconomics: Review of European Economic Policy, Sciendo, vol. 59(3), pages 146-153.
    4. Jerg Gutmann & Matthias Neuenkirch & Florian Neumeier, 2024. "Political Economy of International Sanctions," Research Papers in Economics 2024-07, University of Trier, Department of Economics.
    5. Dmitriy Aleksandrovich Endovitskiy & Nikolay Petrovich Lyubushin & Nadezhda Evaldovna Babicheva & Tatyana Alekseevna Pozhidaeva, 2017. "The Quantitative Assessment of the Cyclical Development in Modern Conditions," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 13(4), pages 109-119.
    6. Juan de Lucio & Raúl Mínguez & Asier Minondo & Francisco Requena, 2024. "Reducing trade with Russia: Sanctions vs. reputation," Working Papers 2406, Department of Applied Economics II, Universidad de Valencia.
    7. Jiang, Binyan & Li, Jialiang & Yao, Qiwei, 2023. "Autoregressive networks," LSE Research Online Documents on Economics 119983, London School of Economics and Political Science, LSE Library.
    8. Thorben Funke & Till Becker, 2019. "Stochastic block models: A comparison of variants and inference methods," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-40, April.
    9. Ludkin, Matthew, 2020. "Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    10. Lee, Kevin H. & Xue, Lingzhou & Hunter, David R., 2020. "Model-based clustering of time-evolving networks through temporal exponential-family random graph models," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    11. Jun Liu & Jiangzhou Wang & Binghui Liu, 2020. "Community Detection of Multi-Layer Attributed Networks via Penalized Alternating Factorization," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    12. Ovielt Baltodano L'opez & Roberto Casarin, 2022. "A Dynamic Stochastic Block Model for Multi-Layer Networks," Papers 2209.09354, arXiv.org.
    13. Mario Larch & Jeff Luckstead & Yoto V. Yotov, 2024. "Economic sanctions and agricultural trade," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(4), pages 1477-1517, August.
    14. Zhiwei Yang & Weigang Wu & Yishun Chen & Xiaola Lin & Jiannong Cao, 2018. "(Q, S)-distance model and counting algorithms in dynamic distributed systems," International Journal of Distributed Sensor Networks, , vol. 14(1), pages 15501477187, January.
    15. Juliet Johnson & Seçkin Köstem, 2016. "Frustrated Leadership: Russia's Economic Alternative to the West," Global Policy, London School of Economics and Political Science, vol. 7(2), pages 207-216, May.
    16. Wei Zhao & S.N. Lahiri, 2022. "Estimation of the Parameters in an Expanding Dynamic Network Model," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 261-282, June.
    17. Elias Dinopoulos & Constantinos Syropoulos & Theofanis Tsoulouhas, 2023. "Global Innovation Contests," Games, MDPI, vol. 14(1), pages 1-24, February.
    18. Borer, Daniel & Perera, Devmali & Fauzi, Fitriya & Chau, Trinh Nguyen, 2023. "Identifying systemic risk of assets during international financial crises using Value at Risk elasticities," International Review of Financial Analysis, Elsevier, vol. 90(C).
    19. Anastasia Kazun, 2017. "Agenda-Setting in Russian Media," HSE Working papers WP BRP 49/PS/2017, National Research University Higher School of Economics.
    20. Farzanegan Mohammad Reza & Batmanghelidj Esfandyar, 2023. "Understanding Economic Sanctions on Iran: A Survey," The Economists' Voice, De Gruyter, vol. 20(2), pages 197-226, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2310.08193. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.