IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3289-d1064928.html
   My bibliography  Save this article

A Graph-Based Network Analysis of Global Coffee Trade—The Impact of COVID-19 on Trade Relations in 2020

Author

Listed:
  • Zsuzsanna Bacsi

    (Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary)

  • Mária Fekete-Farkas

    (Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary)

  • Muhammad Imam Ma’ruf

    (Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary
    Development Economics Study Program, Economic Sciences Department, Faculty of Economics, Universitas Negeri Makassar (UNM), Makassar 90221, Indonesia)

Abstract

International trade relations have been considerably affected by the coronavirus pandemic. Our analysis was aimed at identifying its effect on the global trade network of green coffee beans, comparing the COVID-year 2020 to the pre-COVID year 2018. The methodology applied was that of social network analysis using trade value data for the above two years. Our results show that between the pre-pandemic and the pandemic years, the role of some major actors considerably changed, and many trade relationships were disrupted. Overall trade value decreased, and the number of trade connections also changed—some countries gained, but more countries lost compared to their former positions. The network measures, i.e., degree distribution, betweenness, closeness and eigenvector centralities, modularity-based clustering and the minimum spanning tree, were suitable for quantifying these changes and identifying differences between affected countries. The changes found between the two years are assumed to be due to the effects of the pandemic, but further analysis is needed to reveal the actual mechanisms leading to these results.

Suggested Citation

  • Zsuzsanna Bacsi & Mária Fekete-Farkas & Muhammad Imam Ma’ruf, 2023. "A Graph-Based Network Analysis of Global Coffee Trade—The Impact of COVID-19 on Trade Relations in 2020," Sustainability, MDPI, vol. 15(4), pages 1-32, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3289-:d:1064928
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3289/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/4/3289/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    2. Wagner, Stephan M. & Neshat, Nikrouz, 2010. "Assessing the vulnerability of supply chains using graph theory," International Journal of Production Economics, Elsevier, vol. 126(1), pages 121-129, July.
    3. Arita, Shawn & Grant, Jason & Sydow, Sharon & Beckman, Jayson, 2022. "Has global agricultural trade been resilient under coronavirus (COVID-19)? Findings from an econometric assessment of 2020," Food Policy, Elsevier, vol. 107(C).
    4. Suci Wulandari & Fadjry Djufry & Renato Villano, 2022. "Coping Strategies of Smallholder Coffee Farmers under the COVID-19 Impact in Indonesia," Agriculture, MDPI, vol. 12(5), pages 1-18, May.
    5. Rebeca Utrilla-Catalan & Rocío Rodríguez-Rivero & Viviana Narvaez & Virginia Díaz-Barcos & Maria Blanco & Javier Galeano, 2022. "Growing Inequality in the Coffee Global Value Chain: A Complex Network Assessment," Sustainability, MDPI, vol. 14(2), pages 1-27, January.
    6. Luca De Benedictis & Lucia Tajoli, 2011. "The World Trade Network," The World Economy, Wiley Blackwell, vol. 34(8), pages 1417-1454, August.
    7. J.-P. Onnela & K. Kaski & J. Kertész, 2004. "Clustering and information in correlation based financial networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 353-362, March.
    8. M. Angeles Serrano & Marian Boguna & Alessandro Vespignani, 2007. "Patterns of dominant flows in the world trade web," Papers 0704.1225, arXiv.org.
    9. Zhang, Si Ying, 2021. "Using equity market reactions and network analysis to infer global supply chain interdependencies in the context of COVID-19," Journal of Economics and Business, Elsevier, vol. 115(C).
    10. Pinior, Beate & Conraths, Franz J. & Petersen, Brigitte & Selhorst, Thomas, 2015. "Decision support for risks managers in the case of deliberate food contamination: The dairy industry as an example," Omega, Elsevier, vol. 53(C), pages 41-48.
    11. Pinior, Beate & Conraths, Franz J. & Petersen, Brigitte & Selhorst, Thomas, 2015. "Reprint of “Decision support for risks managers in the case of deliberate food contamination: The dairy industry as an example”," Omega, Elsevier, vol. 57(PA), pages 114-122.
    12. Ji, Qiang & Fan, Ying, 2016. "Evolution of the world crude oil market integration: A graph theory analysis," Energy Economics, Elsevier, vol. 53(C), pages 90-100.
    13. N. Vandewalle & F. Brisbois & X. Tordoir, 2001. "Non-random topology of stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(3), pages 372-374, March.
    14. Li, Xiang & Ying Jin, Yu & Chen, Guanrong, 2003. "Complexity and synchronization of the World trade Web," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 328(1), pages 287-296.
    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. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
    2. Výrost, Tomáš, 2012. "Country effects in CEE3 stock market networks: a preliminary study," MPRA Paper 43481, University Library of Munich, Germany.
    3. Rosanna Grassi & Paolo Bartesaghi & Stefano Benati & Gian Paolo Clemente, 2021. "Multi-Attribute Community Detection in International Trade Network," Networks and Spatial Economics, Springer, vol. 21(3), pages 707-733, September.
    4. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2020. "Community structure in the World Trade Network based on communicability distances," Papers 2001.06356, arXiv.org, revised Jul 2020.
    5. Shi, Huai-Long & Chen, Huayi, 2024. "Understanding co-movements based on heterogeneous information associations," International Review of Financial Analysis, Elsevier, vol. 94(C).
    6. Marcos Duenas & Rossana Mastrandrea & Matteo Barigozzi & Giorgio Fagiolo, 2017. "Spatio-Temporal Patterns of the International Merger and Acquisition Network," LEM Papers Series 2017/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. Xu, Helian & Cheng, Long, 2016. "The QAP weighted network analysis method and its application in international services trade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 91-101.
    8. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2013. "Null models of economic networks: the case of the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 75-107, April.
    9. Hübler, Michael, 2016. "A new trade network theory: What economists can learn from engineers," Economic Modelling, Elsevier, vol. 55(C), pages 115-126.
    10. Leonidas Sandoval Junior, 2011. "A Map of the Brazilian Stock Market," Papers 1107.4146, arXiv.org, revised Mar 2013.
    11. Bilal Ahmed Memon & Rabia Tahir, 2021. "Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 329-344.
    12. He, Chengying & Huang, Ke & Lin, Jianwu & Wang, Tianqi & Zhang, Zuominyang, 2023. "Explain systemic risk of commodity futures market by dynamic network," International Review of Financial Analysis, Elsevier, vol. 88(C).
    13. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    14. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
    15. Liu, Linqing & Shen, Mengyun & Sun, Da & Yan, Xiaofei & Hu, Shi, 2022. "Preferential attachment, R&D expenditure and the evolution of international trade networks from the perspective of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    16. Elena Farahbakhsh Touli & Hoang Nguyen & Olha Bodnar, 2022. "Monitoring the Dynamic Networks of Stock Returns," Papers 2210.16679, arXiv.org.
    17. Giorgio Fagiolo, 2010. "The international-trade network: gravity equations and topological properties," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 5(1), pages 1-25, June.
    18. Tristan Millington & Mahesan Niranjan, 2020. "Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation," Papers 2005.03963, arXiv.org, revised Nov 2020.
    19. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
    20. He, Chengying & Wen, Zhang & Huang, Ke & Ji, Xiaoqin, 2022. "Sudden shock and stock market network structure characteristics: A comparison of past crisis events," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

    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:gam:jsusta:v:15:y:2023:i:4:p:3289-:d:1064928. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.