Post-crisis economic restructuring in the context of the EU migration crisis: The role of diverse economic models

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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.

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    • Figure 1. (a) Social contributions by type of contributors in 2022; (b) Expenditure on social protection in the EU by type of expenditure in 2007 and 2022
    • Figure 2. GDP trends over time by country
    • Figure 3. Mean GDP trends by economic model over time for each model type
    • Figure 4. Pairwise scatter plots (GDP, government debt, migration)
    • Figure 5. Box plot of mean GDP by economic model
    • Figure 6. Average immigration vs. Average GDP by economic model
    • Figure 7. Predicted GDP by economic model (predictive margins)
    • Figure 8. Kernel density estimate of GDP within (a) social market, (b) neoliberal, and (c) mixed economic models
    • Table 1. The EU countries with different economic models
    • Table 2. The correlation matrix between economic and social variables in the EU countries
    • Table 3. Fixed-effects regression on GDP
    • Table 4. Fixed-effects regression on the at-risk-of-poverty indicator
    • Table 5. Predictive margins by economic models
    • Conceptualization
      Olha Yeremenko, Ruslan Aliyev, Liudmyla Saher, Volodymyr Shalimov, Oleksandr Matsenko, Oleksandr Hrytsenko, Serhiy Lyeonov
    • Investigation
      Olha Yeremenko
    • Methodology
      Olha Yeremenko
    • Writing – original draft
      Olha Yeremenko, Ruslan Aliyev, Liudmyla Saher, Volodymyr Shalimov, Oleksandr Matsenko, Oleksandr Hrytsenko, Serhiy Lyeonov
    • Writing – review & editing
      Olha Yeremenko, Ruslan Aliyev, Liudmyla Saher, Volodymyr Shalimov, Oleksandr Matsenko, Oleksandr Hrytsenko, Serhiy Lyeonov
    • Visualization
      Ruslan Aliyev
    • Funding acquisition
      Liudmyla Saher
    • Resources
      Liudmyla Saher
    • Software
      Volodymyr Shalimov
    • Data curation
      Oleksandr Matsenko
    • Formal Analysis
      Oleksandr Hrytsenko
    • Project administration
      Serhiy Lyeonov
    • Supervision
      Serhiy Lyeonov