Date of Award

2018

Degree Type

Thesis

Degree Name

Master of Science in Statistics

Department

Computer Science and Statistics

First Advisor

Natallia Katenka

Abstract

The world is currently facing the worst migration crisis on record. Violent conflicts around the globe have forced over 65 million people to flee their homes, and receiving countries are struggling to support the massive influx of refugees. Lack of preparation and disorganization have only worsened the situation, and there is a pressing need to better understand refugee migration patterns in order to inform policy decisions and improve humanitarian efforts. In previous migration research, gravity models have been one of the classical methods for investigating determinants of migration, however this approach fails to take into account the interdependent nature of migration. To address this weakness, we apply statistical network analysis, which takes into account this interdependency, in order to quantify the influence of certain economic, political, social, and geographical factors on refugee migration. We create four different networks in order to investigate forced migration patterns surrounding four countries that are currently experiencing violent conflicts: Syria, Ukraine, the Democratic Republic of Congo (DRC), and Myanmar. Each network includes 12 nodes: the respective country of interest and the eleven countries hosting the most refugees from that country in 2015. Weighted directed edges represent the number of refugees from the origin country living in the host country in 2015. In order to quantify the influence of chosen factors on refugee migration in the context of the specific countries and conflicts of interest, we apply two different network models to each of the four networks —the exponential random graph model (ERGM) adapted for binary edges, and the generalized exponential random graph model (GERGM) adapted for weighted edges. Our results indicate that the ERGM is a poor choice for modeling this specific problem since the thresholding required to coerce weighted edges into binary edges results in a failure to capture the vastly different magnitudes of refugee migration present in the networks. The GERGM proved to be a much better model. Our final GERGM produced vastly different results for each of the four networks, suggesting that refugee migration patterns differ greatly for different countries and conflicts. Our results also suggest that the influence of determinants of migration on refugee flow patterns differs greatly for outmigration and in-migration. We speculate that determinants of migration have a greater influence on out migration than in-migration in the context of more recent conflicts. On the other hand, we speculate that determinants of migration have an influence on both out-migration and in-migration in the context of conflicts that have been ongoing for many years. This is likely a result of organized migration routes that have been established over many years.

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