The economic complexity approach presents a shift from quantitative to qualitative measures of economic performance, while economic complexity refers to the accumulation of know-how. Economic complexity is considered a predictor of economic growth and research evidences a positive relationship between economic complexity and economic growth. In the EU countries, economic convergence is observed. Hence the question of economic complexity convergence arises, too. The paper aims to analyze the convergence of 27 EU countries considering their economic complexity from 1999 to 2021 computing the beta convergence. Using the Barro-type regressions, the econometric estimations focus on four indices of economic complexity—the economic complexity index published by Harvard’s Growth Lab, and economic complexity indices on research, trade, and technology published by the Observatory of Economic Complexity. The absolute beta convergence is observed in the EU except for the economic complexity index referring to trade. When including the dummy referring to the location of EU countries in the West or East of the EU considering their wealth, the conditional beta convergence is observed except for the trade-economic complexity index, again. When altering the condition of location by the GDP per capita and other controls, the conditional beta convergence of economic complexity in the EU is observed when estimating both fixed-effect models and dynamic panel data models based on the system generalized method of moments (GMM) estimator.

The increase in energy consumption is closely linked to environmental pollution. Healthcare spending has increased significantly in recent years in all countries, especially after the pandemic. The link between healthcare spending, greenhouse gas emissions and gross domestic product has led many researchers to use modelling techniques to assess this relationship. For this purpose, this paper analyzes the relationship between per capita healthcare expenditure, per capita gross domestic product and per capita greenhouse gas emissions in the 27 EU countries for the period 2000 to 2020 using Error Correction Westerlund, and Westerlund and Edgerton Lagrange Multiplier (LM) bootstrap panel cointegration test. The estimation of model coefficients was carried out using the Augmented Mean Group (AMG) method adopted by Eberhardt and Teal, when there is heterogeneity and cross-sectional dependence in cross-sectional units. In addition, Dumitrescu and Hurlin test has been used to detect causality. The findings of the study showed that in the long run, per capita emissions of greenhouse gases have a negative effect on per capita health expenditure, except from the case of Greece, Lithuania, Luxembourg and Latvia. On the other hand, long-term individual co-integration factors of GDP per capita have a positively strong impact on health expenditure per capita in all EU countries. Finally, Dumitrescu and Urlin’s causality results reveal a significant one-way causality relationship from GDP per capita and CO₂ emissions per capita to healthcare expenditure per capita for all EU countries.

This article examines how financial technology determines bank performance in different EU countries. The answer to that question would allow banks to choose their development policy. The paper focuses  on the main and most popular bank services that are linked to financial technology. A SWOT analysis of FinTech is also presented to show the benefits and drawbacks of FinTech. FinTech-based services are very diverse and are provided by financial  firms and banks alike. This paper looks at the financial technology provided by banks: internet usage (internet banking), number of ATMs, credit transfers in a country, percentage of the population in a country holding a debit or credit card and whether that population has received or made a digital payment. Using the multi-criteria assessment methods of CRITIC and EDAS, the authors analysed and compared the countries of the European Union and the financial technology used in them. As a result of the application of these methods, the EU countries under consideration were ranked in terms of the use of financial technology. Subsequently, three banks from different countries with different levels of the use of financial technology were selected for the study. For these banks, financial ratios of profitability were calculated to characterise their performance. Correlation and pairwise regression analyses between the banks’ profitability ratios and financial technology were used to assess the relationship and influence between these ratios. The main conclusion of the study focuses on the extent to which financial technology influences the performance of banks in the selected countries. It is likely that further research will try to take into account the size of the country’s population when analysing all financial technologies. Researchers also needed to find out what influence financial technologies have on the such financial indicators as operational efficiency (costs), financial stability, and capital adequacy.

This scientific study aims to thoroughly assess the current status and evaluate key indicators influencing healthcare and the workforce in selected European Union (EU) member states. Building upon this ambitious research agenda, we focused on a comprehensive descriptive analysis of selected indicators within the healthcare sector, including healthcare financing schemes, overall employment in healthcare and social care, the number of graduates in healthcare (including physicians and general practitioners), as well as migration patterns within the healthcare sector. The data forming the basis of this analysis were systematically gathered from Organization for Economic Co-operation and Development (OECD) and Eurostat databases. Subsequently, we conducted a robust correlation analysis to explore the intricate relationships among these indicators. Our research endeavour aimed to identify and quantify the impact of these indicators on each other, with a focus on their implications for overall healthcare and the workforce in the respective countries. Based on the findings obtained, we derived several significant conclusions and recommendations. For instance, we identified that increasing employment in the healthcare sector may be associated with the overall quality of healthcare provision in a given country. These findings have important implications for policymaking and decision-making at the EU level. Therefore, we recommend that policymakers in these countries consider implementing measures to further develop the healthcare sector while also helping to retain and attract qualified professionals in the healthcare industry. Such recommendations could include improving healthcare infrastructure, incentivizing professional education and further training in the healthcare sector, and implementing policies to support healthcare provision more broadly.