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Volume XXI, Spring, Issue 2(92), 2026
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In this work, Malthus is recognized as the true discoverer of the idea of effective demand. The power to produce and the power to consume represent the principles of the Malthusian concept of effective demand which, within the capitalist logic of unequal exchange, becomes the limit within which the mechanism of accumulation can operate. However, this mechanism cannot be left to operate spontaneously, nor can it be manipulated without running the risk of destroying it. Effective demand is assigned the role of major determinant of the price level and, therefore, of distribution and development.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 25th of January, 2026; Revised 12th of February, 2026; Accepted 8th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This study investigates the structural relationships between Most Favoured Nation Weighted Average (MFNWA) tariffs and the Logistics Performance Index (LPI) using graph theoretical analysis. While these metrics are fundamental to global trade, they are rarely analysed as intertwined networks. Utilizing World Bank data from 2007 to 2022, we construct independent association networks to measure how nations align with global tariff and logistics trends. Through measures of node degree and betweenness centrality, the research identifies nations that serve as pivotal hubs or outliers in the global trade ecosystem. Findings reveal a strong negative correlation between average MFNWA rates and LPI scores, yet a lack of correlation between their respective network degrees, suggesting that the factors driving logistics excellence and tariff alignment are governed by distinct economic drivers. The study provides a novel framework for policymakers to assess a nation’s connectivity and strategic positioning within global trade networks.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 14th of January, 2026; Revised 11th of February, 2026; Accepted 2nd of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This study decodes the "Trump Trade" phenomenon by investigating the intersection of political sentiment and cryptocurrency market dynamics during the 2024 US presidential election. Focusing on major digital assets, (Bitcoin, Ethereum, and Dogecoin) we employ a dual-methodology approach that integrates Natural Language Processing (NLP) via the FinBERT model with traditional event study analysis and GARCH (1,1) volatility modelling. The analyse of financial news sentiment and price volatility, results show a rapid increase in volatility and positive abnormal returns following the election, correlated with Trump's pro-crypto rhetoric. A comparative analysis with the 2016 and 2020 elections reveals an intensification of the market's sensitivity to political signals, indicating that cryptocurrencies are maturing into assets responsive to policy communication. The study contributes to the growing literature on the intersection of politics and digital finance.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 25th of January, 2026; Revised 12th of February, 2026; Accepted 8th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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Interest Rates, Yield Curves, and Bank Profitability: Evidence from the United States Banking Sector
This paper investigates the effect of monetary policy on the profitability of US banks from 2004 to 2023, a period that includes the Financial Crisis, a prolonged low-rate environment, and the rate hikes following the COVID-19 pandemic. Using annual data for all US banks, we analyse how the Federal Funds Rate and the yield curve impact net interest income, non-interest income, loan loss provisions, and return on assets with a focus on differences across bank sizes and rate environments. We find that in normal rate environments, increases in the federal funds rate and the yield curve benefit smaller banks but reduce profitability for larger institutions. In low-rate environments, large banks show stronger gains in net interest income, likely due to more diversified funding and asset strategies. The results highlight the importance of accounting for both interest rate regimes and bank size in making and assessing monetary policy.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 25th of January, 2026; Revised 12th of February, 2026; Accepted 8th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This study examines the structural drivers of energy import dependency among EU member states over the period 2015–2024, with a focus on how energy transition promotes long-term financial sustainability. As transitional and emerging economies within the EU face increasing pressure to decouple growth from fossil fuel imports, understanding the structural determinants of energy security is crucial for fiscal resilience. Drawing on energy security theory and demand–supply frameworks, the research employs panel data models to empirically assess the impact of energy intensity, renewable energy share, gross available energy, final energy consumption, and economic development on energy import dependency. The results reveal that the share of energy from renewable sources is the most robust and consistent factor reducing energy import dependency, confirming the role of renewables for reducing external vulnerability.
The paper demonstrates that lowering energy import dependency is not merely a security concern but a fundamental requirement for financial sustainability, as it reduces trade deficits and mitigates the impact of global price volatility on national budgets. The results provide evidence-based recommendations for policymakers to prioritise energy-based structural reforms as a mechanism for achieving macroeconomic stability and institutional resilience in a changing global landscape.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 10th of January, 2026; Revised 31st of January, 2026; Accepted 6th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This research examines the impact of Environmental, Social, and Governance (ESG) disclosures on firm value within the Indian pharmaceutical sector from 2015 to 2025. Utilizing a panel data regression framework with fixed-effects estimation, the study analyses how composite ESG scores and individual pillars correlate with market performance as measured by Tobin’s Q. Controlling for firm size, leverage, and age, the empirical results indicate that while aggregate ESG performance shows a statistically insignificant correlation with market value, the 'Social' pillar emerges as a significant positive driver of firm valuation.
These findings suggest that investors in emerging markets prioritise social impact and labour practices over environmental disclosures in specialized sectors like healthcare. The study provides critical insights for corporate managers and policymakers, suggesting that structural reforms in sustainability reporting should emphasise sector-specific metrics to enhance transparency and financial sustainability in transitional economies.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 15th of January, 2026; Revised 9th of February, 2026; Accepted 7th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, 2(92), March, 2026.
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This study investigates the impact of Foreign Direct Investment (FDI) on economic growth across diverse European regions from 2000 to 2023. The analysis into Northern, Central/Eastern, and Southern Europe, the research identifies how structural and socio-economic disparities moderate the FDI-growth relationship. Utilizing Panel Least Squares and Panel EGLS regression models, the findings indicate a robust positive correlation between FDI and GDP growth in Northern and Central/Eastern Europe, while revealing a statistically insignificant impact in the Southern region. A central contribution of the paper is the identification of a "Human Capital Threshold," demonstrating that higher education levels significantly amplify the positive spillovers of FDI. The results suggest that for transitional and emerging European economies, investment in human capital is a prerequisite for translating foreign capital inflows into sustainable economic development. The study provides a regional roadmap for structural reforms aimed at enhancing investment absorptive capacity.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 13rd of January, 2026; Revised 19th of February, 2026; Accepted 5th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This study evaluates the impact of local taxation on the financial self-sufficiency of territorial communities in Ukraine within the context of ongoing fiscal decentralisation. As transitional economies seek to empower local governance, the ability to generate autonomous revenue becomes a prerequisite for sustainable regional development. Utilizing a structural-empirical analysis of budget data, the research identifies the specific tax instruments, primarily property and land taxes, that most effectively drive community autonomy. The findings demonstrate a significant correlation between tax base diversification and the reduction of vertical fiscal imbalances. The study highlights the challenges posed by martial law and economic volatility, proposing a model for strengthening local fiscal resilience through digital administrative reforms and optimised tax incentive structures. The research concludes with policy recommendations for harmonising Ukrainian local tax frameworks with European standards to ensure long-term financial stability at the municipal level.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 5th of January, 2025; Revised 9th of February, 2026; Accepted 2nd of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, 2(92), March, 2026.
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This study evaluates the efficiency of public debt management across European Union (EU) member states within the broader framework of public finance modernisation. As the Eurozone navigates post-pandemic recovery and shifting monetary policies, the ability of states to optimise debt structures has become a critical determinant of macroeconomic stability. Utilizing a multidimensional cluster analysis, the research categorizes EU countries based on debt-to-GDP ratios, servicing costs, and institutional fiscal strength.
The findings identify three distinct archetypes: "Fiscal Anchors" with high management efficiency, "Modernising Transnationals" with moderate leverage, and "Vulnerable Peripheral" economies facing structural debt pressures. The research demonstrates that modernizing public finance institutions is positively correlated with lower debt-servicing burdens and enhanced market credibility. The study concludes with policy recommendations for harmonising debt management strategies across the EU to ensure long-term fiscal sustainability and economic resilience.Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 15th of December, 2025; Revised 19th of January, 2026; Accepted 1st of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This paper examines the role of internal control systems (ICS) in ensuring the reliability of management reporting under conditions of economic turbulence. Using a panel dataset of 52 Ukrainian enterprises over the period 2020–2024, the research applies fixed-effects and dynamic panel (System GMM) models to evaluate both the direct and moderating effects of internal control and macroeconomic instability. The results indicate that internal control systems have a significant positive impact on reporting reliability, while economic turbulence negatively affects the quality of management information. Importantly, the interaction between internal control and turbulence reveals a buffering effect, demonstrating that mature control systems partially mitigate the adverse impact of external shocks. Firm size and profitability further enhance reporting reliability, whereas financial leverage increases the risk of distortions. The study contributes to the literature by providing empirical evidence on the stabilising role of internal control systems in volatile environments and offers practical implications for strengthening risk-oriented governance and digital control mechanisms to support resilient decision-making.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 30th of November, 2025; Revised 29th of December, 2025; Accepted 29th of February, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), 2026.
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Machine learning (ML) offers powerful capabilities for large-scale data processing, predictive modelling, and decision support in complex legal and economic systems. This study investigates the application of ML techniques in judicial and economic governance, focusing on their role in optimizing institutional performance, improving case-flow management, and strengthening judicial predictability.
Using judicial case data from Kazakhstan (2014–2024), during which investigative and security services nearly quadrupled, supervised and unsupervised learning algorithms are employed to model caseload dynamics, forecast procedural duration, and assess efficiency-enhancing scenarios. The results indicate that ML-based interventions can reduce case processing time by 5%–15% and increase judicial throughput by 5%–18%, significantly improving predictive accuracy, resource allocation, and operational planning. These findings highlight the potential of ML to enhance institutional efficiency, reduce procedural uncertainty, and support sustainable judicial modernisation.Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 30th of December, 2025; Revised 31st of January, 2025; Accepted 2nd of March, 2025; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This study is an attempt to compare the effectiveness of Agile (Scrum) and Waterfall methodologies in IT project management. The primary focus has been given to the Ukrainian software development framework. The investigation covers four weeks and two matched teams of 6-8 IT professionals. The selection is based on comparable skill levels and expertise. The task of identical software development was assigned under meticulous conditions. The Agile group tracked a Scrum framework, while the Waterfall group observed a linear phase model. Jira and Microsoft Project have been employed as tools of Project management, along with automated testing tools and standardized Likert-scale satisfaction surveys. The findings of the study reveal that the Agile team is more significant (mean = 20.14 days) compared to the Waterfall team (mean = 25.29 days; p < 0.05). Moreover, the satisfaction score of Agile is also reportedly higher alongside the production of software with fewer and less severe bugs (mean = 5.14 vs. 7.86; p < 0.05). Similarly, the outcomes of correlation analysis divulge strong associations between delivery time, team morale, and product quality. On the other side, qualitative ripostes further reinforced these outcomes. The findings offer worthwhile visions for Ukrainian information technology firms navigating a highly competitive and export-driven global market. Agile methodologies not only provide faster delivery and improve quality but also foster internal team cohesion. This study provides empirical evidence for organisational leaders to invest in Agile adoption and training as a strategic lever for performance enhancement and global competitiveness.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 15th of January, 2025; Revised 13th of February, 2026; Accepted 12th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This paper examines the impact of innovative technologies on the economic efficiency of food manufacturing enterprises under conditions of market volatility and sustainability pressures. Addressing the lack of empirical evidence in existing literature, the research develops an integrated framework combining technological clustering and econometric modelling.
Based on a dataset of 20 technologies (2020–2025), the study estimates β-coefficients for key performance indicators, including return on investment (ROI), cost efficiency, and productivity. The results confirm a strong and statistically significant relationship between technological adoption and economic performance (R² = 0.713). Digital technologies, particularly Machine Vision, Big Data analytics, and Smart Packaging, demonstrate the highest aggregated impact.
The findings indicate that optimized technology portfolios can increase ROI by 19–26%, reduce unit costs by up to 18%, and improve labour productivity by approximately 25%. The study contributes a quantitative, multi-criteria framework linking technological innovation with economic efficiency and supporting strategic investment decision-making.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 28th of January, 2025; Revised 4th of March, 2025; Accepted 19th of March, 2025; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This paper examines the impact of innovation management on the economic development and resilience of the tourism business in Ukraine. In the face of unprecedented geopolitical risks and the aftermath of global health crises, innovation has transformed from a competitive advantage into a fundamental survival mechanism for service enterprises. Utilizing a structural-analytical framework, the research evaluates how the adoption of digital service models, safety-oriented product innovations, and adaptive marketing strategies influences firm performance and market stability.
The findings demonstrate that effective innovation management significantly mitigates the negative economic impacts of external shocks by optimizing resource allocation and enhancing consumer confidence. The study concludes that for tourism businesses in transitional and high-risk environments, the integration of technological and managerial innovations is essential for ensuring long-term financial sustainability and contributing to regional economic recovery.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 15th of December, 2025; Revised 19th of January, 2026; Accepted 28th of February, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
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This research paper addresses the critical challenge of financing Smart City infrastructure through the optimization of Public-Private Partnership (PPP) mechanisms. As municipalities face increasing fiscal constraints, the transition to digital urban environments requires innovative capital allocation strategies that balance public oversight with private sector efficiency. Using a comparative, correlation, and cluster analysis framework, this research evaluates their capacity to mitigate information asymmetry and distribute financial risks. The findings indicate that well-structured partnerships serve as essential economic instruments for expanding 'fiscal space' and ensuring the long-term financial sustainability of urban digital transformations. The results offer evidence-based insights for policymakers in transitional economies seeking to bridge the infrastructure funding gap while maintaining macroeconomic stability.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 25th of January, 2026; Revised 27th of February, 2026; Accepted 24th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.