Search Results (173)

This paper studies the topic that has been rather less explored until now—the internal diversification of trading. Unlike looking at aggregate measures of financial development such as market capitalization and liquidity, the study focuses on trading diversification, defined as the portion of trading volume attributed to firms other than the ten most actively traded (VTX). The empirical analysis is based on the World Bank’s Global Financial Development database. It covers an unbalanced cross-country dataset of 2004–2021. Due to limited data availability, the resulting database became smaller and has an unbalanced panel structure. Four main independent variables in the core regression specification are related to financial structure (bank deposits) and financial integration (remittances, international public debt), as well as external measures of financial development (market capitalization, excluding firms within VTX). A broad range of control variables are introduced into the model to account for macroeconomic conditions, financial development, market size, liquidity, and participation. Lagged regressors are introduced to address persistence, delays, and potential endogeneity issues. The methodology relies on panel data econometrics, hierarchical clustering, and machine learning. The findings show that market structure and remittances positively affect trading diversification, whereas banks’ dominance and international public debt contribute to its concentration. The results persist across alternative specifications and robustness tests. The country-level analysis shows a core–periphery pattern, while machine learning demonstrates the critical importance of market structure. Full article

Despite the growing literature on entrepreneurial ecosystems and Industry 4.0, few studies systematically link ecosystemic dynamics to structural transformation in the Global South; even fewer compare BRICS Plus economies with G7 benchmarks using a unified econometric framework. This study addresses that gap. Its objective is to assess how the configuration of such ecosystems shapes the trajectory of sustainable industrialization, comparing the BRICS Plus group with the G7 economies over 2002–2021, a period determined by the most recent harmonized data available across all panel countries from the World Bank, UNIDO and OECD. Methodologically the study employs a balanced panel applying panel unit root and cointegration tests, Vector Error Correction Models, Generalized Method of Moments estimation and Granger causality analysis applied to manufacturing value added (MVA), GDP excluding manufacturing (GDPNOTMVA), industrial employment and productivity indices. The results indicate a long-run interaction between manufacturing and non-manufacturing output in BRICS Plus economies that is consistent with—though not a direct measurement of—entrepreneurial ecosystem dynamics, whereas G7 economies show no cointegration and only a weak unidirectional Granger causality from non-manufacturing to manufacturing (significant at the 10% level), consistent with post-industrial ecosystemic autonomy driven by AI and knowledge-intensive services. The contribution of this work is threefold: (i) theoretically, it operationalizes entrepreneurial ecosystems as macro-structural outcomes within a unified econometric framework; (ii) empirically, it identifies a systematic divergence between BRICS Plus and G7 ecosystemic regimes; and (iii) for policy, it shows that industrial upgrading in emerging economies depends on the coherent integration of industrial policy, digital infrastructure, and entrepreneurship, rather than on manufacturing scale alone. Full article

Aligning mental health research with territorial health needs remains a critical goal, yet the global distribution, coherence, and impact of scientific output across income groups remain poorly understood. We conducted a meta-research study combining scientometric analyses with longitudinal data on 60 health and development indicators. Over 386,000 peer-reviewed publications were retrieved from five major databases. Linear regressions, meta-analyses, and meta-regressions were performed, stratified by World Bank income classification. We find that high-income countries (HICs) accounted for 67% of publications, exhibiting the highest research density but the lowest potential marginal health returns. In contrast, low-income countries (LICs) showed the strongest associations between research volume and improvements in life expectancy (β = 0.13; p < 0.01) and child mortality (β = −1.38; p < 0.01). Structural moderators such as governance quality, health expenditure, and education explained up to 48% of between-group variance. In conclusion, the global landscape of mental health research remains unequal. While scientific production is concentrated in HICs, its population-level association is greatest in LICs. These findings underscore the need to redirect investments and enhance research coherence with health needs, particularly through governance safeguards and capacity building in underrepresented regions. Full article

Background: Bladder cancer disproportionately affects non-high-income countries, yet clinical trials underrepresent global diversity. We assessed global availability of bladder cancer trials, their alignment with disease burden, and barriers to equitable care. Methods: We queried ClinicalTrials.gov for adult bladder cancer trials from June 2019 to June 2024, excluding observational and non-oncologic trials. Trial characteristics were summarized descriptively, and country data came from the Global Cancer Observatory. Countries were classified per World Bank Ranking (WBR) into high-income (HICs), upper middle-income (UMICs), lower middle-income (LMICs), and low-income countries (LICs). Trials were categorized as HIC-only, non-HIC, or mixed-income trials. Fisher’s exact and Kruskal–Wallis tests compared groups. Multivariable logistic regression assessed associations between trial availability and WBR, national health expenditure, and gross national income (GNI). Univariable linear regression and ANOVA assessed the association between the mortality-to-incident ratio and WBR. Results: Of 611 trials, 75.1% were HIC-only, 16.9% non-HIC, and 8.0% mixed-income trials. Non-HIC trials were mainly academic-sponsored (80.6%), while all mixed-income trials had pharmaceutical sponsorship (p < 0.001). Non-HIC trials had lower enrollment, less pharmaceutical funding, fewer multinational collaborations, and fewer basket, multi-arm, early-phase designs (all p < 0.001). Mixed-income trials were larger, led by HICs, had broader eligibility criteria, more novel therapies, and more frequent use of overall survival endpoints. Trial availability was lower in UMICs (p = 0.011), LMICs (p = 0.024), and absent in LICs, and positively associated with higher national health expenditure (p = 0.007) and GNI (p = 0.001). Conclusions: Bladder cancer trials remain concentrated in HICs. Mixed-income trials expand access in non-high-income countries, but are exclusively led by HICs and require balanced sponsorship, early-phase research, and lasting local benefits. Full article

The current study investigates the relationships among environmental variables, energy sector characteristics, and the resilience of the financial sector using a panel dataset of OECD countries covering 2004–2021. For that purpose, information from the World Bank Global Financial Development Database and Sovereign ESG Data was used, along with the indicator of financial stability—bank capitalization, represented by the capital-to-asset ratio. This work uses an integrated empirical framework that includes panel regressions, clustering techniques, and machine learning models. The findings from fixed-effects panel regression indicate that methane emissions, PM2.5 air pollution, and energy dependence are negatively correlated with bank capitalization, whereas renewable energy consumption is positively correlated. Contrariwise, fossil fuel consumption is positively correlated with the dependent variable, perhaps indicating the financial conditions prevailing at the moment, but not accounting for the long-run sustainable perspective. Robustness checks, such as excluding major economies, using lagged specifications, and adding control variables, confirm the robustness of the main empirical relationships, yet the results need to be interpreted conditionally. Through clustering analysis, various regimes are observed across the sample, each characterized by different combinations of environmental, energy, and financial features. On the other hand, the machine learning results obtained using K-Nearest Neighbors and Random Forest algorithms are consistent with the regression analysis, revealing non-linearities in the data. Full article

Integrated water resources management uses decision-making and planning techniques in developing long-term strategies to ensure the sustainability of water resources and the resulting water security of future generations. Policy formulation through such integrated planning interlinks with indicators serving as an information channel to decision-makers. The present effort aims to develop a specific methodology using technical, environmental, and social indicators, formulating composite indices to identify vulnerability to changing water conditions. Thus, a set of indices developed through a multiyear research effort in Latin America, namely Drought Vulnerability Index (DVI), Water Stress Vulnerability Index (WSTVI), Water Scarcity Vulnerability Index (WSCVI), and Water Changing Conditions Vulnerability Index (WCCVI). Time series analysis covered the years 1991–2020, whereas the reference period was 1961–2020. Climate and water resources information is mainly obtained from ERA5-Land reanalysis; social, economic, infrastructure, and institutional data derived from harmonized sources (COROADO Project-EU, FAO, The World Bank, WHO/UNICEF JMP). Statistical tests and Principal Component Analysis (PCA) identified the indicators included in the equations for each index. Expert knowledge played an important role in the development as data were collected according to known local specificities and global trends, as well as scientific criteria and methodological rigor regarding the proposed new indices. Finally, application of such a framework for spatially explicit analysis indicated higher levels of vulnerability to changing water conditions in the northern part of Mexico, the Andes, Bolivia, Paraguay, and Central America, and lower levels in Chile, Brazil, Uruguay, and Argentina. This application demonstrates that the produced composite indices may be implemented with matching success all over Latin America and, therefore, in diversified natural, technical, environmental, social and economic conditions. Full article

Nowadays, Global Value Chains (GVCs) play a vital role in job creation, income generation, knowledge diffusion, and productivity growth. However, significant disparities exist across countries in terms of their integration into GVCs, and Tunisia is no exception to this pattern. In this regard, the question about factors that influence GVCs’ participation is yet to be discussed, to formulate and implement appropriate strategies and reforms. Thus, using firm-level data from the 2025 World Bank Enterprise Survey, this paper examines the role of female leadership and innovation in determining Tunisian firms’ participation in GVCs. Participation in GVCs is captured by a dummy variable indicating the firm’s export and import status. Estimation results from the logit model show that female representation in decision-making positions significantly increases the likelihood of firms’ participation in GVCs. The results also highlight the importance of process innovation in GVC participation, while product innovation appears to have no significant effect. Notably, when firms combine both types of innovation, their likelihood of joining GVCs increases further. Regarding control variables, firm size appears to be an important determinant, as larger firms display a greater tendency to participate in GVCs. The findings further indicate that firm certification and foreign equity participation significantly promote integration into GVCs, while corruption constitutes a major constraint on the integration of Tunisian firms. From a policy perspective, these findings highlight the need to rethink industrial policies, with a stronger focus on process innovation as a key lever of productive sector modernization. Achieving this transformation also requires the development of an inclusive policy ecosystem that supports meaningful and sustainable progress in female’s leadership representation. Full article

Background: Childhood obesity is a growing global public health concern influenced by early-life conditions and broader environmental factors. Although industrial food systems shape dietary patterns, cross-national ecological evidence on how these environments influence childhood obesity through population-level pathways related to women’s health remains limited. Objective: This study aims to examine whether industrial food systems influence childhood obesity through population-level pathways involving women’s health, and to assess whether female obesity mediates the association between national sugar availability and childhood obesity prevalence. Methods: A cross-national ecological study was conducted using publicly available data from 46 countries (2015–2020). Childhood obesity (ages 5–19 years) and female obesity (≥18 years) were obtained from the WHO Global Health Observatory. Food system indicators were derived from FAOSTAT, and socioeconomic variables from the World Bank. Pearson correlations, multivariable regression models, and mediation analysis with bootstrapping (5000 resamples) were performed. Results: Female obesity was strongly associated with childhood obesity prevalence (r = 0.71, p < 0.001), explaining approximately 50% of its variance. Sugar availability was positively associated with both female obesity and childhood obesity (r = 0.49 for both associations; p < 0.001). In multivariable models, female obesity remained the only significant predictor (β = 0.61, p < 0.001), with the model explaining 59% of the variance (R² = 0.59; adjusted R² = 0.49). Mediation analysis showed a significant indirect effect of sugar availability on childhood obesity through female obesity (B = 0.013, p = 0.003), with no significant direct effect. Conclusions: These findings support a population-level framework in which industrial food systems may influence intergenerational obesity through pathways involving women’s health. Female obesity may act as an integrative marker linking food environments to childhood obesity risk. Full article

Prices and quantities in agricultural commodity and food product markets are subject to constant changes due to evolving supply and demand conditions. Big and sudden shifts in supply or demand may lead to price movements that bring negative consequences for food producers or consumers. Factors causing such movements can be of different natures, but substantial changes in the world energy price levels are supposed to be one of the most important. The purpose of the study was to investigate the effect of global energy price shocks on the evolution of food commodities and food consumer prices. Using the World Bank data on the respective price indices, we looked for shocks in these data series by utilizing statistical tools. Having identified three global energy price shocks in the period 2000–2024 induced by the financial crisis of 2008, the COVID-19 pandemic, and the outbreak of war in Ukraine, their influence on the world agricultural commodity prices and food consumer prices was assessed. It was found that the series of energy, food commodity, and food consumer price indices were related in the long term. Also, the occurrence of global energy price shocks to a visible extent translated into global food commodity and food consumer price shocks. Applying various statistical and econometric techniques, including Chow tests and MS-VAR modelling, enables the identification of which breaking points led to regime changes between the analysed variables. The most sensitive to the structural breaking points appeared to be the relation between energy and consumer food prices. This discovery can be considered our major contribution. Full article

This paper examines the effect of trade openness on corporate tax revenue in the Middle East and North Africa (MENA) region, where increased economic integration might incite more business activity and expand taxable corporate income but also intensify losses due to practices such as profit shifting. The study follows a quantitative empirical approach and applies a panel ARDL model to secondary data collected from international databases (World Bank and IMF), such as GDP, trade openness (exports and imports as % of GDP), inflation, corporate tax revenues, foreign direct investment inflows and tax evasion via informal economies, for a sample of ten developing countries from the MENA region, including Morocco, Tunisia, Egypt, Jordan, Lebanon, Algeria, Saudi Arabia, Oman, the United Arab Emirates, and Bahrain, over the period 2010–2023. We employ a PMG ARDL model to study our panel data, allowing the analysis of both short-run and long-run effects to investigate the relationship between trade openness and tax revenues. Our results show that in the long run, export-driven economies generate higher corporate tax revenues by expanding profitability and the tax base, and imports also positively affect revenues, indicating that trade openness stimulates economic activity. Conversely, FDI inflows reduce corporate tax revenues, consistent with profit shifting and tax incentives in developing countries. GDP growth does not necessarily increase tax receipts, likely due to tax elasticity effects and growth-oriented tax structures. Also, tax evasion appears to decline, likely reflecting improved compliance, and no significant short-run effects are observed. The results contribute to the literature on tax compliance and economic integration in the case of open economies in developing countries. From a practical perspective, our findings have implications for policymakers and tax regulators in the MENA region, as they highlight the dual nature of globalization for developing countries and their tax systems and underscore the need for effective compliance measures in trade and investment policies. Full article

The COVID-19 pandemic caused stark global mortality disparities, influenced by a complex interplay of demographic, economic, and health factors. This ecological study investigates associations between country macroscopic variables and COVID-19 accumulated mortality ratio (AMR) across 174 countries and may serve as a preparation for new pandemics. Methods: The study applies bidirectional stepwise multiple linear regression. To ensure statistical validity, we conducted diagnostic tests for multicollinearity and heteroscedasticity, applying robust M-estimation where necessary to minimize root mean squared error. The analysis covered six distinct stratifications based on development status (developed, developing, least developed, and combinations), and incorporated temporal analyses across three specific annual periods: 21 January 2020–20 January 2021; 21 January 2021–20 January 2022; and 21 January 2022–10 January 2023. Data: AMR per country values, accumulated between 21 January 2020 and 10 January 2023, and data on the prevalence of health conditions, and socioeconomic descriptive variables were extracted from Our World in Data (OWID) and other public data sites, like the World Bank. Results: The percentage of population aged over 65 years has the most consistent association with increased AMR globally. Obesity prevalence and income inequality (Gini index) were positively associated with AMR regardless of country development status. Conversely, the study finds a consistent negative correlation with diabetes prevalence, while the prevalence of respiratory diseases is a significant association only for developed nations. Socioeconomic factors were significantly associated with AMR, but this influence is stronger in developed countries than in the developing and least developed countries. Conclusions: While population aging is the primary association with increased AMR, the mortality impact of comorbidities and socioeconomic factors is heavily conditioned by a country’s development stage, pointing to the necessity of development-status-aware public health strategies for incoming pandemics. Full article

Predicting loan defaults is a significant challenge for financial institutions; however, current machine learning techniques often encounter issues in areas such as data privacy, cross-institutional cooperation, and model transparency. The restrictions on the practical implementation of advanced predictive models are centralized training paradigms, which limit the application of advanced models because of regulatory and confidentiality issues, and black-box decision making, which diminishes confidence in automated credit risk tools. This study mitigates these problems by adopting a federated-inspired decentralized ensemble learning model combined with explainable artificial intelligence (XAI) in predicting loan defaults. Various machine learning classifiers are trained on partitioned institutional data without the need to share any data; they include K-Nearest Neighbors, support vector machine, random forest, and XGBoost. They use a prediction-level aggregation strategy to simulate the collaborative decision-making process without losing locality of data. SHAP and LIME are used to promote model interpretability by giving both global and local explanations of the consequences of prediction. The proposed framework was tested on a large public dataset of loans that contains more than 116,000 records, including various financial and borrower-related features. The experimental findings show that XGBoost has high and reliable predictive accuracy in both centralized and decentralized scenarios, achieving 99.7% accuracy under federated-inspired evaluation. The explanation analysis shows interest rate spread and upfront charges as the most significant predictors of loan default risk. The main contributions of this research are as follows: (i) a privacy-preserving decentralized ensemble learning framework that is applicable in multi-institutional financial contexts, (ii) a detailed analysis of centralized and decentralized predictive performances, and (iii) the pipeline of the XAI, which can be used to increase its transparency and regulatory confidence in automated credit risk evaluation. These results prove that decentralized learning combined with explainable AI can provide high-performing, transparent and privacy-sensitive loan default prediction systems in practice in real-world banking systems. Full article

In today’s world, climate change has become a global predicament. The implications for financial sector activities have given rise to ample literature on the climate change and banking system stability nexus in developing economies. However, there still remain important knowledge gaps pertaining to areas such as the asymmetric impact of climate change on banking system relationships, threshold effects, and transmission channels. Therefore, this research investigated the impact of climate change on banking system stability in the Southern Africa Development Communities (SADC). The study employed a panel data estimation technique, analysing fixed and random effects to test these hypotheses in SADC. In doing so, it not only explored how climate-related risks affect banking stability but also assessed how economic, environmental, and institutional dynamics mediate this relationship. The findings contribute to informing regional policy on financial resilience and adaptive climate strategies within fragile banking environments. Full article

This study investigates whether long-term global warming is associated with fertility decline across 195 countries from 1960 to 2023, and whether this relationship varies by economic development and adaptive capacity. We analyze Total Fertility Rate (TFR) data from the World Bank alongside temperature anomaly measures from NOAA and NASA using Pearson correlations and ordinary least squares (OLS) regression models. Regional analyses include Africa, Asia, Europe, the Middle East, and the Arctic, with GDP per capita serving as a proxy for economic development and adaptive capacity. Globally, temperature anomalies and fertility exhibit a strong negative correlation ($r\approx -0.90$, $p<0.001$). However, substantial regional heterogeneity emerges after controlling for GDP. In Africa ($r=-0.89$) and the Middle East, temperature anomalies remain statistically significant predictors of fertility decline even after GDP adjustment ($\beta =-0.99$, $p<0.001$; $\beta =-1.27$, $p<0.001$, respectively). In contrast, temperature effects become statistically insignificant in South Asia, East Asia, Europe, and the Arctic once GDP is controlled, indicating that fertility decline in these regions is driven primarily by socioeconomic modernization rather than climatic stress. These findings suggest that global warming functions as a conditional demographic stressor whose impact depends critically on adaptive capacity. In regions with limited infrastructure, including constrained access to air conditioning, healthcare, and occupational heat protection, rising temperatures remain significant predictors of fertility decline, potentially mediated through heat-sensitive biological mechanisms such as impaired spermatogenesis. By contrast, in higher-income regions, high adaptive capacity appears to buffer reproductive systems from thermal stress, allowing socioeconomic factors to dominate fertility dynamics. Full article

Background: Tuberculosis (TB) remains a major global public health challenge, with substantial variation across countries. South Africa has one of the highest TB incidence and mortality rates globally, while Bulgaria, a low-incidence country, faces a persistent TB burden among vulnerable populations. Objectives: To compare national trends in TB incidence and mortality in South Africa and Bulgaria from 2000 to 2023 and explore associations with selected socioeconomic indicators and health system coverage. Methods: An ecological, descriptive, analytical study used national-level data from the WHO, World Bank, and official statistics. TB trends were analyzed alongside income, poverty, unemployment, and Universal Health Coverage indicators. Time series measures and Pearson correlation were used descriptively to summarize co-variation over time. Results: Between 2000 and 2023, TB incidence declined by approximately 44% in the Republic of South Africa and 69% in Bulgaria. In both countries, TB incidence co-varied strongly with unemployment (RSA: r = 0.805; BG: r = 0.723). In Bulgaria, TB incidence was also strongly negatively associated with GDP per capita (r = −0.910), whereas no significant association with GDP was observed in South Africa. These findings indicate that TB trends co-varied more closely with labour market conditions in both contexts, while broader economic growth co-occurred with declining TB incidence only in Bulgaria. Conclusions: TB trends co-occurred with changes in socioeconomic conditions and health system coverage, with differing patterns across contexts. Findings highlight the relevance of equity-oriented, context-specific TB control strategies integrated with social and economic policies. Full article