Zhanat Khishauyeva

As health systems worldwide increasingly focus on mitigating the burden of non-communicable diseases, the strategic role of insurance schemes in facilitating early detection and preventive care, thereby reducing the substantial costs associated with advanced-stage treatment, has become a critical area of policy and research attention. This study aims to evaluate the impact of various health financing models, specifically voluntary, compulsory, and social insurance, on the burden of cardiovascular diseases and neoplasms, measured by Disability-Adjusted Life Years (DALYs), across working-age and older populations. The analysis is based on unbalanced panel data from 51 countries covering the period 2000–2021, drawing from the Global Burden of Disease database for DALY rates and the OECD and WHO Global Health Expenditure Database for health financing indicators. Fixed and random effects panel regression models with clustered robust standard errors were employed to estimate the associations. Results show that voluntary private insurance significantly reduces DALY rates from cardiovascular diseases, by approximately 19-28%, among working-age (15-49) and older adults (50-69). Compulsory and social insurance models also exhibit protective effects, but of smaller magnitude. Government health financing schemes similarly correlate with improved outcomes. In contrast, enterprise-based financing is positively associated with higher DALY rates, especially in older age groups. Insurance schemes demonstrate weaker and more inconsistent associations for neoplasms, with compulsory insurance and government schemes showing the most stable links to reduced burden among older adults.

Type of the article: Research Article

The global recovery from recent economic, health, and geopolitical crises, including the COVID-19 pandemic and the Russia–Ukraine war, increasingly depends on how economies mobilize human capital, migration, and financial flows. This article examines how human capital and its reallocation through migration and remittances, under different institutional conditions and economic system types, relate to configurations of human capital, net migration, political stability, and remittances that support sustained post-crisis economic recovery. Using a panel of 73 economies from 2010 to 2023, the empirical strategy combines descriptive rankings, multiple linear regression (MLR), and decision-tree classification, with an 80/20 split of the sample, primarily drawing on the WDI. Descriptive patterns highlight large asymmetries in both migration and growth: some advanced and emerging economies combine sizeable net migration inflows with robust GDP growth, whereas conflict-affected and fragile states, including the Syrian Arab Republic, Yemen, and Ukraine, experience substantial net outflows alongside persistent output losses. However, regression results indicate that differences in human capital primarily drive cross-country variation in post-crisis growth: HCI is the only statistically significant predictor, while net migration, political stability, and remittances display small and insignificant linear effects (R² ≈ 0.15; adjusted R² ≈ 0.10; n = 73). DTC reveals complex, non-linear relationships between migration, human capital, financial flows, and economic recovery, with outcomes concentrated in economies that combine higher skills and sizable remittances with stable institutions, effectively converting them into productive human capital.

Acknowledgments
The research was carried out with funds from the budget of the Ministry of Education and Science of Ukraine on the topic of the research project "Modeling educational transformations in wartime to preserve the intellectual capital and innovative potential of Ukraine" (0123U100114).

Type of the article: Research Article

Abstract
Central bank digital currency (CBDC) programs have rapidly shifted from experimentation to policy-critical infrastructure decisions, yet countries show strikingly uneven progress from research to pilots and implementation. This study aims to identify and explain the key structural, macroeconomic, technological, and ecosystem-related factors that differentiate CBDC initiatives advancing to pilot or implementation stages from those remaining in early research or being discontinued across countries worldwide. Using 161 CBDC projects across 109 countries (as of December 2024) and 10 project-, public interest-, technology-, and macroeconomic indicators, we estimate a Support Vector Machines classifier with GridSearchCV (5-fold) tuning and interpret the results using Shapley Additive exPlanations explainability. The raw outcome distribution was strongly imbalanced (83.85% in the early/cancelled class), so ADASYN balancing was applied, producing 270 observations with equal class shares and an 85/15 train–test split (229/41). The optimized SVM (RBF; C = 10, gamma = 10) achieved 93.90% cross-validated accuracy and 0.88 accuracy on the test set, indicating strong predictive performance on unseen data. Test-set metrics show an informative error profile: for class 1 (advanced projects), recall = 1.00 and F1 = 0.89, while for class 0 (early/cancelled), precision = 1.00 with recall = 0.75 (macro/weighted F1 = 0.88), implying that the model identifies all advanced projects but may misclassify around one-quarter of early/cancelled cases. SHAP ranks the strongest drivers as use-case direction, inflation, crypto adoption ranking, CBDC-related research output, and international participation, with mixed/wholesale projects, higher inflation, stronger scientific attention, and greater international involvement generally increasing the likelihood of advancement.