Strategic partner election: proposal for a Binary Logistic Model for the European Union

The European Union's foreign policy faces numerous challenges, necessitating stronger partnerships. The EU has granted strategic partner status to ten countries, but lacks an official definition or criteria for this designation. This research aims to define strategic partnership and develop a data-driven model for partner selection, using a range of economic, political, and socio-cultural indicators. The study expands beyond the initial 'Special Ten' to analyze a broader pool of 143 potential partner countries.

The concept of 'strategic partnership' is examined chronologically, revealing diverse interpretations across countries. Lessa (1998) views them as priority relations, while Hubbell (1999) highlights their use in US-China relations. Ismael and Kreutz (2001) note their use in Soviet-Iraqi cooperation, emphasizing its post-Cold War emergence. Kay (2000) connects it to the end of bipolarity and the rise of the USA. Emerson (2001) describes it as involving powerful actors taking joint strategic action, while other definitions highlight economic intensification, pursuit of multipolarity, pragmatic approaches, and the importance of shared values and mutual benefits. The lack of an official definition and the diversity in interpretations motivate the study to propose its own operational definition: long-term bilateral cooperation for mutual benefit, equality, and respect between significant economic and geopolitical actors, ideally based on shared interests, values, and historical roots, aiming at common strategic objectives.

The study employs a multi-stage methodology. First, indicators from the European Strategic Partnerships Observatory (ESPO) and existing literature are categorized into economic, commercial, political, social, geographical-cultural, common values, jurisdictional basis, institutional base, and discriminative factors. Categorical Principal Component Analysis (CATPCA) is used to analyze these indicators for 143 countries (2010-2019 data). This is followed by Principal Component Analysis (PCA) to reduce dimensionality, and finally, Binary Logistic Regression is used to model the probability of a country becoming an EU strategic partner. The model uses three principal components: 'Strategic Component', 'Partnership in Spirit Component', and 'Good Neighbor Component'.

CATPCA yielded 14 dimensions, which were then analyzed using PCA. Three principal components were extracted, explaining approximately 67% of the total variance. The Binary Logistic Model revealed that the first two components ('Strategic Component' and 'Partnership in Spirit Component') significantly predicted the likelihood of strategic partnership. The 'Strategic Component' positively correlated with economic and political weight, commercial interests, and social development. The 'Partnership in Spirit Component' negatively correlated with sustainable governance, economic freedom, and shared values, indicating a mismatch between rhetoric and practice in EU partner selection. The 'Good Neighbor Component' reflected geographical proximity. Applying the model, eight of the 'Special Ten' were confirmed as highly probable strategic partners (USA, China, Russia, Japan, India, Brazil, Canada, Mexico). Argentina emerged as a strong potential partner, while South Africa and South Korea showed lower probabilities. The model identified a core group of six countries (USA, China, Russia, Japan, India, Brazil) as the most strategically important partners for the EU.

The findings highlight the dominance of economic and political factors in the EU's strategic partner selection, despite the stated importance of shared values. The negative correlation between the 'Partnership in Spirit Component' and the likelihood of partnership demonstrates a discrepancy between the EU's expressed commitment to common values and its actual selection criteria. This challenges the EU's approach, suggesting a potential need for greater alignment between rhetoric and practice. The model's prediction of Argentina as a potential partner and the downplaying of South Africa's strategic importance reveals the limitations of the existing criteria and offers scope for a more objective and transparent approach.

This study provides a novel methodology for identifying EU strategic partners using a data-driven approach. The Binary Logistic Model reveals the significant role of economic and political factors, challenging the rhetoric of shared values. The findings suggest the need for a more transparent and objective framework for selecting strategic partners, balancing geopolitical priorities with the stated values of the EU. Future research should continuously monitor the strategic landscape and adapt the model to incorporate new indicators and changing geopolitical realities.

The study relies on readily available data, which may not fully capture the complexities of international relations. The indicators used are quantitative and may not fully capture qualitative aspects of partnerships. The model's predictions are based on past data and may not accurately reflect future developments. The reliance on specific datasets may limit the generalizability of the findings.