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The Relation Between Migration And Fdi In The Oecd From A Complex Network Perspective

Author

Listed:
  • ANTONIOS GARAS

    (Chair of Systems Design ETH Zurich, Weinbergstrasse 56/58, CH-8092 Zürich, Switzerland)

  • ATHANASIOS LAPATINAS

    (Department of Economics, University of Ioannina, P. O. Box 1186, 45110, Ioannina, Greece)

  • KONSTANTINOS POULIOS

    (Department of Economics, University of Ioannina, P. O. Box 1186, 45110, Ioannina, Greece)

Abstract

We explore the relationship between human migration and OECD’s foreign direct investment (FDI) using a gravity equation enriched with variables that account for complex-network effects. Based on a panel data analysis, we find a strong positive correlation between the migration network and the FDI network, which can be mostly explained by countries’ economic/demographic sizes and geographical distance. We highlight the existence of a stronger positive FDI relationship in pairs of countries that are more central in the migration network. Both intensive and extensive forms of centrality are FDI enhancing. Illuminating this result, we show that bilateral FDI between any two countries is further affected positively by the complex web of ‘third party’ corridors/migration stocks of the international migration network (IMN). Our findings are consistent whether we consider bilateral FDI and bilateral migration figures, or we focus on the outward FDI and the respective inward migration of the OECD countries.

Suggested Citation

  • Antonios Garas & Athanasios Lapatinas & Konstantinos Poulios, 2016. "The Relation Between Migration And Fdi In The Oecd From A Complex Network Perspective," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(06n07), pages 1-20, September.
    • Handle: RePEc:wsi:acsxxx:v:19:y:2016:i:06n07:n:s0219525916500090
    DOI: 10.1142/S0219525916500090
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    Cited by:

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    2. Feifan Wang & Baihai Zhang & Senchun Chai & Yuanqing Xia, 2018. "An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks," Complexity, Hindawi, vol. 2018, pages 1-10, August.
    3. Roberto Antonietti & Giulia De Masi & Giorgio Ricchiuti, 2020. "Linking FDI Network Topology with the Covid-19 Pandemic," Working Papers - Economics wp2020_18.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    4. Filippo Santi & Giorgia Giovannetti & Margherita Velucchi, 2021. "Migrants know better: Migrants' networks and FDI," Working Papers - Economics wp2021_17.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    5. Berezka Kateryna & Kovalchuk Olha, 2019. "Modelling Factors Connected with the Effect of International Migration for Security and Economy," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(4), pages 30-42, December.
    6. Shuzhong Ma & Mengheng Liu, 2020. "Spatial correlation effect of China's outward foreign direct investment in countries along the One Belt and One Road," Pacific Economic Review, Wiley Blackwell, vol. 25(2), pages 228-249, May.
    7. Giulia Masi & Giorgio Ricchiuti, 2020. "From FDI network topology to macroeconomic instability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 133-158, January.

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