This paper aims to review the different impacts of income inequality drivers on the Gini coefficient, depending on institutional specificities. In this context, we divided the European Union member states in two clusters (the cluster of member states with inclusive institutions / extractive institutions) using the institutional pillar as a clustering criterion. In both cases, we assesed the impact of income inequality drivers on Gini coefficient by using a fixed effects model in order to examine the role and importance of the institutions in the dynamics of income disparities.The models were estimated by applying the Panel Estimated Generalized Least Squares (EGLS) method, this being weighted by Cross-section weights option. The separate assessment of the income inequality reactivity to the change in its determinants according to the institutional criterion represents a new approach in this field of research and the results show that the impact of moderating income inequality strategies is limitedin the case of member states with extractive institutions.

In this paper, we examined the relationship between income inequality and economic growth from the perspective of each country’s level of development in the European Union, this linkage being reviewed using the median of GDP per capita expressed in the purchasing power standard to split the European Union Member States into two clusters of 14 countries each. Furthermore, we estimated the impact of income inequality on economic growth during the 2010–2018 period at the level of both clusters using the Estimated Generalized Least Squares with a fixed effects method, reinforced by the cross-section weights option. Our results show that income inequality is positively linked to economic growth in the case of developed EU Member States, while for developing EU countries, income inequality is detrimental to growth. This also demonstrates that income gaps may have positive and negative effects on growth depending on the stage of development, this providing important evidence for the need to promote an optimum level of income inequality.

The concepts of economic and social security are very complex and dynamic, closely related to many new challenges that characterize today’s societies. Our research aims at investigating the economic and social security considering the current vulnerabilities, using an integrated multicriteria approach to this issue, taking into account five pillars (dimensions): demographic, climate, globalization, and ITC adaptation, social, and economic. Based on these dimensions, we developed an Economic and Social Security Index across the Euro-Atlantic countries and we found that the European Union (EU) is better positioned compared to the other country-groups analysed. Furthermore, we estimated the effects of duration of schooling and climate degradation on economic growth, by using the Panel EGLS method over the period 2010–2019, and we compared the results obtained for EU countries to the ones obtained for the other groups of countries in EuroAtlantic space: EU neighbours and candidate/potential candidate countries and other Eastern EU neighbourhood countries. We found a positive impact of the duration of schooling on the economic growth, which is greater in countries registering high levels of socio-economic security, the effect of the duration of schooling varying depending on the quality of education. In terms of the effects of CO2 emissions on economic growth, we found a negative impact, considering the one year lag.

This paper examines the impact of education and labour market challenges on the income inequality in European Union (27 Member States) within the period 2012-2022, this being calculated using the Panel EGLS method. Even if the effects are clearly visible from a theoretical point of view, in the latest years there were not many authors focusing their studies on the effects of the unemployment and early dropout from school and training on income inequality. In this regard, updating the figures, the impact coefficients and the theoretical background increase the understanding of the statistical processes and their results in the new economic context. Our results confirmed a positive relationship between unemployment rate and income inequality (measured by Gini coefficient), this being also the highest impact found, but also a positive link between the early leavers from education and training rate and income inequality. In addition, we used additional variables to catch the current economic challenges that are related to demographic changes and high energy prices. In this context, we found positive effects exerted by housing cost overburden rate and old-age dependency ratio on income inequality. Even if the model is limited to four income inequality drivers, we have demonstrated that the calculated coefficients are the best linear unbiased estimators.