Vitaliia Koibichuk

This study aims to examine whether the labor productivity of the US population directly depends on public or private insurance coverage of people, employment level, life expectancies, spending on the public health system as a percentage of GDP, and spending on the public health system in natural terms.
Empirical testing was carried out on the USА statistical data for 1987–2021 using a regression model with the fitting procedure backward stepwise selection (in Statgraphics software), and a multivariate adaptive regression spline MARS (using Salford Predictive Modeler software). The research hypothesis was confirmed for only two indicators: life expectancies and spending on the public health system in natural terms. Their impact on labor productivity appeared to be directly proportional. As an indicator, spending on the public health system has a greater impact on the change in productivity (0.0058%), whereas life expectancy has a lesser effect (0.0047%). The study showed that the MARS model provides more objective and accurate results compared to the regression model with the fitting procedure – backward stepwise selection. This conclusion is based on a comparison of real data modeled by both methods. The study proved that labor productivity in the USA grew yearly from 1987 to 2021 (the constant term in the MARS model’s regression equation is +0.48428). To calculate the specific values of labor productivity for each year, a model was developed depending on the optimal basic functions (automatically generated by the MARS model depending on the current values of life expectancies and spending on the public health system in natural terms).

Acknowledgment
This study is funded by Department of Applied Social Sciences of the Faculty of Organization and Management of the Silesian University of Technology for the year 2023 (grant number 13/020/BK_23/0081).

As cities increasingly adopt smart technologies and seek to foster innovation-driven economies, it is vital to understand how smart city development relates to the strength of local startup ecosystems. This study investigates whether a statistically significant relationship exists between a city’s performance in the smart city ranking and the strength of its startup ecosystem. The study employed available data from the Global Startup Ecosystem Report (by Startup Genome) and the Smart City Index (SCI by the IMD World Competitiveness Center). A balanced panel regression analysis was conducted on a dataset comprising 77 cities across the years 2020, 2021, and 2023 (2022 is excluded as the SCI was not published). The findings reveal that the Random Effects model yielded statistically significant results, indicating a weak (R² = 25.63%) but significant inverse relationship between SCI and startup ecosystem development, which means cities that rank higher on smart city metrics tend to show lower levels of startup ecosystem performance. This counterintuitive result challenges the assumption that technologically advanced cities automatically provide fertile ground for entrepreneurial activity. One possible explanation is that smart cities, dominated by large tech players and rigid governance structures, may present entry barriers for emerging startups. High operational costs, regulatory constraints, and a focus on large-scale infrastructure projects may disincentivize startups from localizing their innovations within these environments. Although the R² suggests that other variables beyond the smart city ranking influence startup development. This study highlights the need for urban policies that actively integrate startup-supportive mechanisms into smart city strategies.

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.