This article investigates the income and expenditure patterns of individuals, with a specific focus on investments in luxury items, real estate, and expensive modes of transportation. Using global databases such as “Luxury Goods—Worldwide/Statista Market Forecast” and “Data—WID—World Inequality Database”, the authors explore the correlation between high demand for luxury items and economic inequality. The study emphasizes the role of luxury tax as essential for implementing a progressive personal income tax system in Russia. By examining country-specific factors, particularly in China and Russia, and conducting a comparative analysis of progressive tax systems globally, the research highlights the potential of luxury tax to enhance the efficacy of income tax in reducing inequality.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Comparative studies of national values are becoming increasingly important in the context of contemporary globalization processes. An essential condition for the shaping of national values in learners is the enrichment of pedagogical technology with components of digital technology. Both qualitative and quantitative approaches were used in the current study. The purpose of this research is to examine the efficacy of mobile learning in shaping the national values of prospective teachers. The experiment included 180 participants. Diagnostics of the levels of national values formation in the initial stage confirmed the assumption about the low formation of national values among teacher candidates and, consequently, the need for targeted work on their formation. This study demonstrates that significant advances in students’ national values have occurred following the introduction and testing of mobile learning with experimental group (EG) participants to shape national values. The data from this study can serve as the basis for creating strategies for shaping the national values of learners in universities and as a methodological basis for adapting mobile learning for the shaping of national values.

In Ghana, youth unemployment remains significant challenges, with technical and vocational education and training (TVET) emerging as a potential solution to equip young people with practical skills for the job market. However, the uptake of TVET programmes among Ghanaian youth remains low, particularly among females. This study therefore explores the determinants that influence TVET choices among Ghanaian youth, with the goal of informing policy development to enhance participation in vocational education. Applying an enhanced multinomial logistic regression (MLR) model, this research examines the influence of socio-economic, demographic, and attitudinal factors on career decisions. The enhanced model accounts for class imbalances in the dataset and improves classification accuracy, making it a robust tool for understanding the drivers behind TVET choices. A sample of 1600 Ghanaian youth engaged in vocational careers was used, ensuring diverse representation of the population. Key findings reveal that males are approximately three times more likely to choose TVET programs than females, despite females making up 50.13% of Ghana’s population. Specific determinants influencing TVET choices include financial constraints, parental influence, peer influence, teacher influence, self-motivation, and vocational limitations. In regions with limited vocational options, youth often pursue careers based on availability rather than preference, which highlights a gap in vocational opportunities. Parental and teacher influences were found to play a dominant role in steering youth towards specific careers. The study concludes with recommendations for policymakers, instructors, and stakeholders to increase the accessibility, relevance, and quality of TVET programmes to meet the socio-economic needs of Ghanaian youth.

To fight inflation, European Central Bank (ECB) announced 10 successive interest rate hikes, starting on 27 July 2022, igniting an unprecedented widening of interest rate spreads in the euro area (ΕΑ). Greek banks, however, recorded among the highest interest rate spreads, far exceeding ΕΑ median and weighted average. Indeed, we document a strong asymmetric response of Greek banks to ECB interest rate hikes, with loan interest rates rising immediately, whilst deposit interest rates remained initially unchanged and then rose sluggishly. As a result, the interest rate spread hit one historical record after another. Greek systemic banks, probably taking advantage of the high concentration and low competition in the domestic sector benefited from key ECB interest rate hikes, recording gigantic increases in net interest income (NII), and consequently, substantial profits (almost €7.4 billion in the 2022–2023 biennium). Such excessive accumulation of profits (that deteriorates the living conditions of consumers) by the banking system could be called the inflation of “banking greed”, or bankflation. This new source of inflation created by the oligopolistic structure of the Greek banking sector counterworks the very reason for ECB interest rate increases and requires certain policy analysis recommendations in coping with it.