Why does Europe follow such divergent globalization paths?

Europe is a patchwork of economies moving at different speeds in globalization. A recent study mapped these paths using an algorithm that blends Dynamic Time Warping with k‑means, allowing comparison of not only current levels but also their evolution over time. By analysing the KOF Globalization Index of 40 countries, researchers identified four distinct groups.

The first gathers Western European economies, highly globalized and stable. The second includes Central and Eastern European nations, converging toward higher levels. The third consists of micro‑states adopting niche integration models, while the fourth clusters Southeast European countries facing greater challenges.

This core‑periphery split shows that a one‑size‑fits‑all EU policy is insufficient. To cut inequalities and promote sustainable development, tailored strategies for each cluster are needed, turning globalization into a benefit for all. The new clustering method also provides a robust tool for studying other long‑term socioeconomic processes.

The proposed method combines Dynamic Time Warping (DTW) and k‑means to overcome the limitations of traditional clustering approaches based on Euclidean distance. DTW aligns time series with temporal offsets, capturing shape similarities even when peaks occur at different moments. After alignment, k‑means groups countries based on the average distance of their transformed series.

The KOF Globalization Index, used as the main variable, integrates four dimensions: economic, social, political, and technological. Selecting 40 countries covers the full spectrum of territorial sizes, from micro‑states like San Marino to large economies such as Germany and France.

Results reveal a stable Western cluster with minimal variation over the past two decades, indicating mature trade networks and knowledge flows. The Central‑Eastern cluster shows rapid growth, especially in the technological dimension, driven by digital infrastructure investments and market liberalization. Micro‑states, despite low absolute globalization scores, display niche specialization patterns (e.g., offshore finance, luxury tourism). The Southeast group exhibits marked fluctuations linked to political instability and reliance on traditional sectors like agriculture.

Policy‑wise, the model suggests targeted interventions—digitalization programs for Southeast countries or innovation incentives for micro‑states—can narrow the core‑periphery gap. Moreover, the methodology can be extended to other sustainable development indicators, such as CO₂ emissions or well‑being indices, to assess globalization’s impact across environmental and social dimensions.