Maxine Leis: Signals of Violence, Patterns of Flight: Predicting and Explaining Conflict-Related Mobility

Abstract

This dissertation contributes to the literature on conflict-related mobility by examining how and under what conditions political violence shapes mobility. Each of the four essays approaches this question from a different angle by analysing how the temporal, spatial, and actor-related characteristics of political violence shape mobility decisions and outcomes.  Essay I shows that disaggregated temporal dynamics (persistence, escalation, volatility), spatial patterns (scope, location), and actor-related characteristics (relative strength, fragmentation, government targeting of civilians) substantially improve one-year-ahead predictions of refugee and asylum seeker outflows, especially for large outflows. Essay II develops a theoretical and empirical framework distinguishing observed violence from violence risk, modelled via spatio-temporal and network-weighted proximity, and provides evidence of anticipatory mobility in Somalia, suggesting that civilians move in response to violence occurring in other districts. Essay III quantifies the longer-term demographic consequences of organised violence by using a machine learning-based hurdle model with counterfactual simulations to estimate how state-based and non-state conflict reshape five-year net migration across Africa and the Middle East; the analysis indicates that state-based conflict is associated with an estimated 2.2 million net migration differences between 2015 and 2020 relative to a counterfactual peace scenario, corresponding to about 3.9% of total absolute predicted net migration. Essay IV examines compound pressures at the household level in Bangladesh and shows, using interpretable machine learning, that models incorporating both political violence and natural hazards outperform simpler specifications, with effects conditioned by household resources in ways that generate both mobility and immobility. The dissertation advances research on conflict-related mobility theoretically by demonstrating that mobility is shaped by how different forms of violence unfold and are perceived, rather than by their mere presence, and methodologically by integrating predictive and explanatory approaches through theory-informed, machine learning–based forecasting frameworks. By examining refugee and asylum seeker outflows, internal displacement, net migration patterns, and household-level responses within a unified conceptual lens, it bridges refugee studies and migration research and reveals how conflict reshapes population distributions through multiple, often overlapping, pathways.