Search Results (660)

Background/Objectives: This study examines individual perceptions of national healthcare system efficiency before and after the COVID-19 pandemic across 18 countries grouped into three clusters (the Anglo-world, Europe, East Asia). This paper aims to identify the demographic, socioeconomic, health-related, and macroeconomic healthcare drivers of public assessments, and explain changes in attitudes between 2011–2013 and 2021–2023. Methods: Using individual-level data from the International Social Survey Programme (ISSP) for 2011–2013 and 2021–2023, logistic regression models of perceived healthcare inefficiency are estimated. In addition, the Oaxaca–Blinder decomposition model is adopted in order to decompose the assessment gap between the two periods. Models include a range of individual demographic and socioeconomic characteristics and national healthcare controls (healthcare expenditure, potential years of life lost). Results: Health-related factors, especially self-assessed health and trust in doctors, consistently emerge as predictors of more favourable evaluations across regions and periods. Higher national healthcare expenditure is associated with more positive public views and is the single largest contributor to the improved assessments in 2021–2023. Demographic and socioeconomic variables show smaller regionally and temporally heterogeneous effects. Decomposition indicates that both changes in observed characteristics (notably, expenditure and trust) and unobserved behavioural, cultural, or institutional shifts account for the gap in public healthcare assessments between the two time periods. Conclusions: Public assessments of healthcare systems are primarily shaped by individual health status, trust in providers, and national spending rather than differential demographic and socioeconomic traits. Therefore, policymakers should couple targeted investments in the healthcare sector in order to address adequately public healthcare needs, and strengthen doctor–patient relationships in order to sustain public support. Future research should focus on disentangling the cultural and behavioural pathways influencing healthcare attitudes. Full article

Market indices, such as the S&P 500, serve as compressed representations of complex constituent-level dynamics. This study proposes a zero-shot forecasting framework capable of predicting index-level trajectories without direct supervision from index data. By leveraging a Variational AutoEncoder (VAE), the model learns a latent mapping from constituent-level price movements and macroeconomic factors to index behavior, effectively bypassing the need for aggregated index labels during training. Using hourly OHLC data of S&P 500 constituents, combined with the U.S. 10-Year Treasury Yield and the CBOE Volatility Index, the model is trained solely on disaggregated inputs. Experimental results demonstrate that the VAE achieves superior accuracy in index-level forecasting compared to models trained directly on index targets, highlighting its effectiveness in capturing the implicit generative structure of index formation. These findings suggest that constituent-driven latent representations can provide a scalable and generalizable approach to modeling aggregate market indicators, offering a robust alternative to traditional direct supervision paradigms. Full article

This study assesses the financial soundness of Sudanese commercial banks during escalating civil conflict by integrating Altman’s Z-score models with scenario-based stress testing. Using audited financial data from 2016 to 2022 (pre-war) and projections through to 2028, the analysis evaluates resilience under low- and high-intensity conflict scenarios. Altman’s Model 3 (for non-industrial firms) and Model 4 (for emerging markets) are applied to capture liquidity, retained earnings, profitability, and leverage dynamics. The findings reveal relative stability between 2017–2020 and in 2022, contrasted by significant vulnerability in 2016 and 2021 due to macroeconomic deterioration, sanctions, and political instability. Liquidity emerged as the most critical driver of Z-score performance, followed by earnings retention and profitability, while leverage showed a context-specific positive effect under Sudan’s Islamic finance framework. Stress testing indicates that even under low-intensity conflict, rising liquidity risk, capital erosion, and credit risk threaten sectoral stability by 2025. High-intensity conflict projections suggest systemic collapse by 2028, characterized by unsustainable liquidity depletion, near-zero capital adequacy, and widespread defaults. The results demonstrate a direct relationship between conflict duration and systemic fragility, affirming the predictive value of Altman’s models when combined with stress testing. Policy implications include the urgent need for enhanced risk-based supervision, Basel II/III implementation, crisis reserves, contingency planning, and coordinated regulatory interventions to safeguard the stability of the banking sector in fragile states. Full article

This paper investigates the time-varying efficiency of Western European stock markets and examines how macroeconomic events defined as endogenous and exogenous shocks influence the degree of efficiency by either long-range dependence or mean reverting. We apply a rolling-window detrended fluctuation analysis (DFA) with two window sizes, complemented by the Efficiency Index to synthetize multiple measures of market efficiency. The results confirm that efficiency evolves dynamically in response to macroeconomic disruptions. Specifically, endogenous shocks tend to generate anti-persistent behavior, while exogenous shocks are associated with long-memory effect. These shifts in efficiency are also reflected in rolling Kurtosis estimates, suggesting that only the most severe shocks produce spikes in Kurtosis, fat-tailed returns distributions, and structural inefficiencies. This dual approach allows us to classify shocks as major or minor based on their joint impact on both market efficiency and tail behavior. Overall, our findings support the adaptive market hypothesis and extend its implications through the fractal market hypothesis by underlining the role of heterogenous investment horizons during periods of turmoil. The combined use of dynamic DFA and Kurtosis offer a framework to assess how financial markets adapt to different types of macroeconomic shocks. Full article

With environmental and economic disruptions occurring faster than ever before, the link between green innovation and national competitiveness deserves further analysis. This article investigates how sustainability-oriented strategies (particularly investments in research and development (R&D), renewable energy, and innovation capacity) affect the performance of environmental goods exports and national economic resilience. An exploratory cross-sectional analysis is conducted using multiple linear regression models applied to a sample of 14 countries, including the seven most sustainability-oriented economies and seven countries whose economic growth relies predominantly on fossil fuels. The results suggest a strong positive relationship between R&D expenditure and green trade competitiveness, while renewable energy consumption indicators produce mixed or even negative short-term effects. Adjusted net savings emerge as a robust indicator of both growth and competitiveness. However, no significant associations were found between renewable energy indicators and economic resilience, highlighting transitional trade-offs and institutional barriers inherent in ecological transformation. The study contributes to the growing literature on green transitions by combining macroeconomic indicators of innovation and sustainability with export performance. Policy implications include aligning innovation strategies with trade objectives, improving the measurement of green competitiveness, and supporting institutional preparedness for the transition. Full article

The effectiveness of fiscal and monetary policy in sustaining growth and facilitating recovery from economic crises is increasingly considered to be significantly influenced by the quality of a country’s institutions. Strong institutions may determine how well macroeconomic policies perform under both stable and turbulent circumstances. This study examines how institutional quality (IQ) moderates the effects of fiscal and monetary policies on economic growth in Thailand from Q1:2003 to Q4:2023. Using a combination of BART and BASAD models, we find that voice and accountability and control of corruption are key institutional factors. Among macroeconomic indicators, exports, household debt, gold prices, and electricity generation emerge as the most important drivers of growth during the study period. The findings showed that IQ stabilizes and enhances the impact of policy interest rates and export growth while mitigating negative shocks from household debt and energy infrastructure challenges. Monetary policy effectiveness varies and depends on governmental institutions. Fiscal policy remains mostly neutral but shifts with institutional conditions. These results highlight that strong institutions improve the efficacy of macroeconomic policies and support sustainable growth. This study empirically examines the moderating role of IQ in economic resilience and policy design in an emerging economy using microdata from Thailand as a focus and the Time-varying Seemingly Unrelated Regression Equation (tvSURE) model. Full article

Although it is generally accepted that economic freedom stimulates economic growth, its effects in transitional economies are still up for debate. More empirical research is needed to examine the long-term effects of economic freedom on growth in the Western Balkans, a region characterised by uneven reform trajectories, fiscal pressures, and institutional fragility. This study examines the effects of seven fundamental factors on real GDP per capita growth (annual percentage change) in six Western Balkan nations between 2013 and 2023. These factors include property rights, government spending, government integrity, business freedom, monetary freedom, trade openness, and education spending. Importantly, in order to better capture macroeconomic constraints, it takes into account two fiscal burden indicators: the public debt and the government budget deficit. A triangulated analytical framework is used: Random Forest regression identifies non-linear patterns and ranks the importance of variables; Bayesian Vector Autoregression (VAR) models dynamic interactions and inertia; and the Generalised Method of Moments (GMM) handles endogeneity and reveals causal relationships. The GMM results show that while government integrity (β = −0.0820, p = 0.0206), government spending (β = −0.0066, p = 0.0312), and public debt (β = −0.0172, p = 0.0456) have negative effects on growth, property rights (β = 0.0367, p = 0.0208), monetary freedom (β = 0.0413, p = 0.0221), and the government budget deficit (β = 0.0498, p = 0.0371) have positive and significant effects on growth. Although the majority of economic freedom indicators are statistically insignificant, Bayesian VAR confirms strong growth persistence (GDP(−1) = 0.7169, SE = 0.0373). On the other hand, the Random Forest model identifies the most significant variables as property rights (3.72), public debt (5.88), business freedom (4.65), and government spending (IncNodePurity = 9.80). These results show that the growth effects of economic freedom depend on the context and are mediated by the state of the economy. Market liberalisation and legal certainty promote growth, but their advantages could be offset by inadequate budgetary restraint and difficulties with transitional governance. A hybrid policy approach, one that blends strategic market reforms with improved institutional quality, prudent debt management, and efficient public spending, is necessary for the region to achieve sustainable development. Full article

This study explores how FinTech and artificial intelligence (AI) adoption shape efficiency and financial stability in dual-banking systems. It focuses on 26 listed Islamic and conventional banks across 11 countries in the MENA and Southeast Asia regions between 2020 and 2024. To measure digital adoption, we create a seven-component FinTech Adoption Index. We use fixed-effects regressions to examine its impact on cost efficiency, profitability, solvency stability, and credit risk. This analysis also controls bank size, capitalization, and macroeconomic conditions. The results show a clear adoption gap. Conventional banks consistently score 0.5–0.8 points higher on the FinTech Index compared to Islamic banks. Each additional FinTech component raised operating costs by about 0.8%, but improved profitability slightly by only 0.03%. This suggests that technological integration creates upfront costs before any real efficiency gains are seen. However, the stability benefits are stronger. FinTech adoption increases the Z-score by 3.6 points and lowers the non-performing loan ratio by 0.1%. Islamic banks gain more stability benefits due to their risk-sharing contracts and asset-backed financing structures. Overall, an efficiency–stability trade-off emerges. Conventional banks focus more on profitability, while Islamic banks gain resilience, but face slower efficiency improvements. By combining the Resource-Based View and Financial Stability Theory, this study provides the first multi-country evidence of how governance structures shape digital transformation in dual-banking markets. The findings offer practical guidance for regulators and bank managers around balancing innovation, efficiency, and stability. Full article

Against the background of global climate change and increasing ecological vulnerability, enhancing ecosystem resilience has become a core task for coping with environmental shocks and achieving sustainable development. The urban energy structure plays a critical role in influencing the green development of the economy and the enhancement of environmental resilience. Existing studies have revealed the role of energy structure transformation in the identification of macroeconomic performance and environmental outcomes, but have neglected its impact on ecosystem resilience. This paper exploits the implementation of the New Energy Demonstration City pilot policy as a quasi-natural experiment. Using panel data of Chinese prefecture-level cities from 2010 to 2022, it constructs a multidimensional evaluation system of urban ecosystem resilience and employs a difference-in-differences (DID) model to empirically examine the impact of energy structure transformation on urban ecosystem resilience. It is found that energy structure transition significantly enhances urban ecosystem resilience, and this conclusion is verified through a series of robustness tests. Mechanism analysis shows that energy structure transformation comprehensively enhances urban ecosystem resilience through strengthening institutional regulation, optimizing resource allocation, promoting energy substitution, and enhancing public awareness. Heterogeneity analysis indicates that the strengthening effect of energy structure transition on urban ecosystem resilience is inclusive, and that this positive effect is greater in cities characterized by lower resource endowment and weaker governance capacity. This paper reveals the intrinsic mechanism of urban energy transition for ecological resilience enhancement, and provides an energy transition path for building more resilient urban ecosystems. Full article

The integration of artificial intelligence (AI) into environmental, social, and governance (ESG) disclosure represents a critical frontier for corporate transparency in emerging markets. This study investigates the relationship between AI adoption in ESG reporting, disclosure quality, and financial performance among 180 Saudi-listed companies (2021–2024) within Vision 2030’s transformative context. Using the System Generalized Method of Moments (GMM) estimation with panel unit root and cointegration testing to ensure stationarity assumptions and addressing endogeneity through bounding analysis, the study finds that AI adoption intensity significantly enhances ESG disclosure quality (β = 0.289, p < 0.001), with coefficient significance assessed through t-tests using firm-clustered robust standard errors. Enhanced disclosure quality translates into meaningful financial performance improvements: 0.094 percentage points in return on assets (ROA), 0.156 in return on equity (ROE), and 0.0073 units in Tobin’s Q. Mediation analysis reveals that 73% of AI’s total effect operates through improved ESG quality rather than direct operational benefits. The findings demonstrate parametric bounds robust to macroeconomic confounders, suggesting AI-enhanced transparency creates substantial shareholder value through strengthened stakeholder relationships and reduced information asymmetries. Full article

The stock market plays a crucial role in the financial system, with its price movements reflecting macroeconomic trends. Due to the influence of multifaceted factors such as policy shifts and corporate performance, stock prices exhibit nonlinearity, high noise, and non-stationarity, making them difficult to model accurately using a single approach. To enhance forecasting accuracy, this study proposes a hybrid forecasting framework that integrates wavelet denoising, multi-head attention-based BiLSTM, ARIMA, and XGBoost. Wavelet transform is first employed to enhance data quality. The multi-head attention BiLSTM captures nonlinear temporal dependencies, ARIMA models linear trends in residuals, and XGBoost improves the recognition of complex patterns. The final prediction is obtained by combining the outputs of all models through an inverse-error weighted ensemble strategy. Using the CSI 300 Index as an empirical case, we construct a multidimensional feature set including both market and technical indicators. Experimental results show that the proposed model clearly outperforms individual models in terms of RMSE, MAE, MAPE, and R². Ablation studies confirm the importance of each module in performance enhancement. The model also performs well on individual stock data (e.g., Fuyao Glass), demonstrating promising generalization ability. This research provides an effective solution for improving stock price forecasting accuracy and offers valuable insights for investment decision-making and market regulation. Full article

The spatial distribution mechanism of industrial agglomeration has long been a central topic in urban economic geography. With the increasing availability of street view imagery and built environment data, effectively integrating multi-source spatial information to identify key drivers of firm clustering has become a pressing research challenge. Taking Shanghai as a case study, this paper constructs a street-level Built Environment (BE) database and proposes an interpretable spatial analysis framework that integrates SHapley Additive exPlanations with Multi-Scale Geographically Weighted Regression. The findings reveal that: (1) building morphology, streetscape characteristics, and perceived greenness significantly influence firm agglomeration, exhibiting nonlinear threshold effects; (2) spatial heterogeneity is evident in the underlying mechanisms, with localized trade-offs between morphological and perceptual factors; and (3) BE features are as important as macroeconomic factors in shaping agglomeration patterns, with notable interaction effects across space, while streetscape perception variables play a relatively secondary role. This study advances the understanding of how micro-scale built environments shape industrial spatial structures and offers both theoretical and empirical support for optimizing urban industrial layouts and promoting high-quality regional economic development. Full article

This study aims to empirically investigate the contemporaneous, bidirectional causal relationship between innovation and entrepreneurial activities in China by constructing a dynamic simultaneous equation system. Using panel data from 31 provincial administrative regions from 2000 to 2022, our empirical results demonstrate a robust two-way causal relationship: vigorous innovation activities significantly stimulate the emergence and subsequent growth of entrepreneurial ventures, while entrepreneurial dynamism similarly promotes regional innovation. These findings remain stable and consistent after rigorous robustness checks. Further, employing a Panel Vector Autoregression (PVAR) approach in extended analyses, we find clear evidence of a stable positive feedback loop between innovation and entrepreneurship, characterized by progressive and cumulative effects. Additionally, regional heterogeneity analysis indicates that macroeconomic disparities significantly influence the bidirectional relationship between innovation and entrepreneurship. Specifically, differences in regional resource endowments and economic conditions largely account for variations in innovation–entrepreneurship dynamics across regions. Consequently, local governments should tailor innovation and entrepreneurship policies to regional contexts to maximize economic outcomes effectively under China’s current development paradigm. Full article

This study examines the role of algorithmic fairness in alleviating digital financial stress among consumers across OECD countries, utilizing panel data spanning from 2010 to 2023. By introducing a digital financial stress index—constructed from indicators such as household credit dependence, digital debt penetration, digital default rates, and financial complaint frequencies—the research quantitatively captures consumer financial anxieties within AI-driven e-commerce platforms. Employing two-way fixed-effects regression and system-GMM methods to address endogeneity and dynamic panel biases, findings robustly indicate that increased algorithmic fairness significantly reduces digital financial stress. Furthermore, the moderating analysis highlights digital literacy as a critical factor amplifying fairness effectiveness, revealing that digitally proficient societies derive greater psychological and economic benefits from equitable algorithmic practices. These results contribute to existing scholarship by extending discussions of algorithmic ethics from individual-level analyses to a macroeconomic perspective. Ultimately, this research underscores algorithmic fairness as a crucial policy lever for promoting consumer welfare, calling for integrated national strategies encompassing ethical algorithm governance alongside enhanced digital education initiatives within OECD contexts. Full article

This research seeks to evaluate the effects of the preceding cyclical indicators and macroeconomic variables on the performance of the Brazilian stock market from January 2011 to December 2022. The objective is to identify how these factors influence the behavior of the main index representing this market. In this way, it was analyzed how shocks in the composite leading indicator of the economy (IACE) as well as the basic interest rate of the economy (SELIC), the broad national consumer price index (IPCA), the nominal exchange rate (in reals per dollar—BRL/USD) and the central bank economic activity index (IBC-Br) impact the performance of Brazilian stock market index (IBOVESPA). Using the vector autoregression (VAR) model with vector error correction (VEC), positive shocks were simulated in the IACE and the aforementioned macroeconomic variables to identify and compare their impacts on the index. The results obtained, through generalized impulse response functions, indicated that the shocks to the IACE, the exchange rate, and the inflation variables influenced the IBOVESPA in different and statistically significant ways. However, shocks to the economic activity index and the interest rate did not exert a statistically significant influence on the index, partially confirming the hypothesis, which was initially raised, that these factors influence the stock index in different ways. Full article