Advertisement

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

  • Katherine AbramskiEmail author
  • Natallia Katenka
  • Marc Hutchison
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)

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.

Keywords

Networks GERGM Refugees Migration 

References

  1. 1.
    Akee, R.K.Q., Basu, A.K., Chau, N.H., Khamis, M.: Ethnic fragmentation, conflict, displaced persons and human trafficking: an empirical analysis. IZA Discussion Paper 5142, Institute for the Study of Labor (IZA) (2010)Google Scholar
  2. 2.
    Altai Consulting: Migration trends across the Mediterranean: connecting the dots. IOM MENA Regional Office, June 2015Google Scholar
  3. 3.
    Cranmer, S.J., Desmarais, B.A.: Inferential network analysis with exponential random graph models. Polit. Anal. 19(1), 66–86 (2011)CrossRefGoogle Scholar
  4. 4.
    Cranmer, S.J., Desmarais, B.A., Menninga, E.J.: Complex dependencies in the alliance network. Confl. Manage. Peace Sci. 29(3), 279–313 (2012)CrossRefGoogle Scholar
  5. 5.
    Day, K., White, P.: Choice or circumstance: the UK as the location of asylum applications by Bosnian and Somali refugees. GeoJournal 55, 15–26 (2001)Google Scholar
  6. 6.
    de Haas, H.: The determinants of international migration: conceptualizing policy, origin and destination effects. IMI Working Paper No 32. International Migration Institute, Oxford (2011)Google Scholar
  7. 7.
    Dedeoğlu, D., Deniz Genç, H.: Turkish migration to Europe: a modified gravity model analysis. IZA J. Dev. Migr. 7, 17 (2017)CrossRefGoogle Scholar
  8. 8.
    Denny, M.: The importance of generative models for assessing network structure. Pennsylvania State University (2016)Google Scholar
  9. 9.
    Desmarais, B.A., Cranmer, S.J.: Statistical inference for valued-edge networks: the generalized exponential random graph model. PLoS ONE 7(1), e30136 (2012)CrossRefGoogle Scholar
  10. 10.
    Desmarais, B.A., Cranmer, S.J.: Statistical inference in political networks research. In: The Oxford Handbook of Political Networks. Oxford University Press (2017)Google Scholar
  11. 11.
    Görlach, J.S., Motz, N.: Refuge and refugee migration: how much of a pull factor are recognition rates? Bocconi University and Universidad Carlos III de Madrid (2017)Google Scholar
  12. 12.
    Greenwood, M.J.: Modeling migration. In: Kempf-Leonard, K. (ed.) Encyclopedia of Social Measurement, vol. 2, pp. 725–734. Elsevier, New York (2005)CrossRefGoogle Scholar
  13. 13.
    Hoff, P.D., Raftery, A.E., Handcock, M.S.: Latent space approaches to social network analysis. J. Am. Stat. Assoc. 97(460), 1090–1098 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Holland, P.W., Leinhardt, S.: An exponential family of probability distributions for directed graphs. J. Am. Stat. Assoc. 76(373), 33–50 (1981)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Iqbal, Z.: The geo-politics of forced migration in Africa, 1992–2001. Confl. Manage. Peace Sci. 24, 105–119 (2007)CrossRefGoogle Scholar
  16. 16.
    Karemera, D., Iwuagqu Oguledo, V., Davis, B.: A gravity model analysis of international migration to North America. Appl. Econ., 1745–1755 (2010)CrossRefGoogle Scholar
  17. 17.
    Krackardt, D.: QAP partialling as a test of spuriousness. Soc. Netw. 9(2), 171–186 (1987)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Langley, S., Vanore, M., Siegel, M., Roosen, I., Rango, M., Leonardelli, I., Laczko, F.: The push and pull factors of asylum related migration: a literature review. European Asylum Support Office (2016)Google Scholar
  19. 19.
    Lee, E.S.: A theory of migration. Demography 3(1), 47–57 (1996)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Leifeld, P., Cranmer, S.J.: A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model. In: The 7th Political Networks Conference, McGill University, May 2014Google Scholar
  21. 21.
    Neumayer, E.: Bogus refugees? The determinants of asylum migration to Western Europe. Int. Stud. Quart. 49(3), 389–410 (2005)CrossRefGoogle Scholar
  22. 22.
    Poot, J., Alimi, O., Cameron, M.P., Maré, D.C.: The gravity model of migration: the successful comeback of an ageing superstar in regional science. Institute for the Study of Labor. Discussion Paper No. 10329 (2016)Google Scholar
  23. 23.
    Ramos, R.: Gravity models: a tool for migration analysis. IZA World Labor 239, 1–10 (2016)Google Scholar
  24. 24.
    Ramos, R., Suriñach, J.: A gravity model of migration between ENC and EU. Research Institute of Applied Economics (2013)Google Scholar
  25. 25.
    Snijders, T.A.B.: The statistical evaluation of social network dynamics. Sociol. Methodol. 31, 361–395 (2001)CrossRefGoogle Scholar
  26. 26.
    Snijders, T.A.B., van de Bunt, G.G., Steglich, C.E.G.: Introduction to stochastic actor-based models for network dynamics. Soc. Netw. 32(1), 44–60 (2010)CrossRefGoogle Scholar
  27. 27.
    UNHCR. The UN Refugee Agency. Population Statistics, 30 August 2019. http://popstats.unhcr.org/en/overview
  28. 28.
    Wasserman, S., Pattison, P.: Logit models and logistic regressions for social networks: I. An introduction to markov graphs and p*. Psychometrika 61(3), 401–425 (1996)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Wilson, J., Denny, M., Bhamidi, S., Cranmer, S.J., Desmarais, B.A.: Stochastic weighted graphs: flexible model specification and simulation. Soc. Netw. 49 (2016)Google Scholar
  30. 30.
    Windzio, M.: The network of global migration 1990–2013: using ERGMs to test theories of migration between countries. Soc. Netw. 53, 20–29 (2017). SON-1045CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Katherine Abramski
    • 1
    Email author
  • Natallia Katenka
    • 1
  • Marc Hutchison
    • 1
  1. 1.University of Rhode IslandKingstonUSA

Personalised recommendations