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A Network-Based Analysis of International Refugee Migration Patterns Using GERGMs

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 882))

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Abstract

Understanding determinants of migration is central to anticipating and mitigating the adverse effects of large-scale human displacement. Traditional migration models quantify the influence of different factors on migration but fail to consider the interdependent nature of human displacement. In contrast, network models inherently take into account interdependencies in data, making them ideal for modeling relational phenomena such as migration. In this study, we apply one such model, a Generalized Exponential Random Graph Model (GERGM), to two different weighted-edge networks of international refugee migration from 2015, centered around Syria and the Democratic Republic of Congo (DRC), respectively. The GERGM quantifies the influence of various factors on out-migration and in-migration within the networks, allowing us to determine which push and pull factors are largely at play. Our results indicate that both push factors and pull factors drive migration within the DRC network, while migration within the Syria network is predominately driven by push factors. We suspect the reason for this difference may lie in that the conflict in Syria is relatively recent, in contrast to the conflict in the DRC, which has been ongoing for almost two decades, allowing for the establishment of systematic migration channels, migration networks, and resettlement, all which are related to pull factors, throughout the years.

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Authors and Affiliations

  1. University of Rhode Island, Kingston, RI, 02882, USA

    Katherine Abramski, Natallia Katenka & Marc Hutchison

Authors
  1. Katherine Abramski
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  2. Natallia Katenka
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  3. Marc Hutchison
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Corresponding author

Correspondence to Katherine Abramski .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Abramski, K., Katenka, N., Hutchison, M. (2020). A Network-Based Analysis of International Refugee Migration Patterns Using GERGMs. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_32

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