This study investigates factors influencing migrants’ decisions to enter Europe via Bulgaria or Greece along the Balkan route, using logistic regression and machine learning models on data from the International Organization for Migration (IOM) Flow Monitoring Survey (August 2022–June 2025,
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This study investigates factors influencing migrants’ decisions to enter Europe via Bulgaria or Greece along the Balkan route, using logistic regression and machine learning models on data from the International Organization for Migration (IOM) Flow Monitoring Survey (August 2022–June 2025,
). We examine demographic variables (age), push factors (economic reasons, war/conflict, personal violence, limited access to services, and avoiding military service), and governance clusters derived from the World Bank’s Worldwide Governance Indicators (WGIs). An adapted migration gravity model incorporates corruption control as a key push–pull factor. Key findings indicate that younger migrants are significantly more likely to choose Bulgaria (
,
), and governance clusters show that migrants from high-corruption origins (e.g., Syria and Afghanistan) prefer Bulgaria, likely due to governance similarities and facilitation costs. The Cluster Model achieves a slight improvement in fit (McFadden’s
, AIC = 7367) compared to the Base (AIC = 7374) and Interaction (AIC = 7391) models. Machine learning extensions using LASSO and Random Forests on a subset of data (
) yield similar moderate performance (AUC: LASSO = 0.524, RF = 0.515). These insights highlight corruption’s role in route selection, offering policy recommendations for origin, transit, and destination phases.
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