Abstract:
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Understanding the determinants of migration is central to anticipating and mitigating the adverse effects of large-scale human displacement. Traditional models quantify the influence of different factors on migration but fail to consider the interdependent nature of human displacement. Network models take into account interdependencies and are ideal for modeling migration. Here, we apply a Generalized Exponential Random Graph Model (GERGM) to two weighted-edge networks of international refugee migration from 2015, centered around Syria and the Democratic Republic of Congo (DRC). The GERGM quantifies the influence of push and pull factors on out-migration and in-migration. Our results indicate that both factors drive migration within the DRC network, while migration within the Syria network is predominately driven by push factors. The reason for this 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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