Volodymyr Shalimov

This study examined how multinational corporations adapted their corporate social responsibility strategies while operating in russia during the ongoing war against Ukraine. Specifically, the analysis investigated the impact of different corporate social responsibility approaches on financial performance, stakeholder trust, and corporate reputation for multinational corporations operating in russia during the war. A game-theoretic model evaluated three distinct strategies: minimal corporate social responsibility engagement (Strategy 1), increased corporate social responsibility involvement (Strategy 2), and a complete exit from the russian market (Strategy 3). The quantitative analysis showed that companies choosing the exit strategy (Strategy 3) gained the highest payoffs for financial performance, stakeholder trust, and reputation. In contrast, minimal corporate social responsibility engagement (Strategy 1) resulted in negative outcomes, including reputational damage and potential exposure to sanctions. On the other hand, increased corporate social responsibility involvement (Strategy 2) produced neutral outcomes, offering short-term benefits but still leaving companies vulnerable to ongoing risks. The sensitivity analysis confirmed the stability of these outcomes. The study concludes that exiting the russian market not only aligns with ethical standards but also ensures long-term sustainability, offering critical insights for corporations navigating corporate social responsibility challenges in war zones.

Acknowledgments
We sincerely express our gratitude to Prof. Dr. Dr. h.c. Marko Sarstedt, the LMU Fellowship Grant (LMU Ukraine support-fund) for the support of Ukrainian scientists.

Type of the article: Research Article

Abstract
This study aims to examine how different EU economic models mediate the relationship between post-crisis economic restructuring and migration pressures by analyzing the co-evolution of immigration, public finance, social protection, and labor market indicators, and to identify which institutional configurations most effectively harness migration to support resilient and inclusive growth. The analysis employs a panel of EU member states, combining harmonized indicators (immigration, GDP per capita, at-risk-of-poverty rates, public finances, and labor market conditions) and two-way fixed-effects regressions with interactions for economic models (social market, neoliberal, and mixed) and predictive margins. The results indicate that immigration is associated with modest but statistically significant gains in GDP per capita in social market economies. A 1 percentage point increase in the share of immigrants corresponds to a rise of around 0.3–0.4% in GDP per capita (p < 0.05). The effect is smaller and only weakly significant in neoliberal economies, and approaches zero in mixed economies. The direct impact of immigration on at-risk-of-poverty rates is limited in all three models, with coefficients close to zero, and country-time effects explain the bulk of the variation in poverty. Neoliberal economies combine relatively higher average GDP with greater dispersion and higher poverty risks, whereas mixed economies exhibit lower GDP levels and more volatile poverty dynamics. The findings indicate that institutional design and welfare-labor market architectures condition whether migration supports resilient and inclusive post-crisis restructuring, implying that migration policy must be integrated with broader social, labor, and fiscal reforms.

Acknowledgments
The project was funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V01-00023 and the Ministry of Education, Research, Development and Youth of the Slovak Republic, and the Slovak Academy of Sciences (VEGA 2/0172/2). Oleksandr Matsenko acknowledges that his input to the publication was prepared within the framework of the research project “Restructuring of the national economy in the direction of digital transformations for sustainable development” (№0122U001232) funded by the National Research Foundation of Ukraine.