Dymytrii Grytsyshen

The formation of the country’s tax competitiveness and countering the shadowing of the economy depend on several factors. One of them is an effective public administration. It defines state policy vectors, institutions’ critical tasks, and business and society development priorities. The aim of the paper is to analyze the impact of good governance on the country’s tax competitiveness and the level of the shadow economy of 11 EU countries and Ukraine in 2011–2021. The study employs statistical analysis of data series. It constructs a correlation field of points of relationship between indicators (to determine the general trends of changes in the level of the shadow economy, tax competitiveness, and the Good Governance Index). Linear mathematical model and Fishburn formula are used to construct an integral indicator of the level of efficiency of public management (Good Governance Index). Structural modeling methods formalize the influence of government management on the level of the shadow economy and tax competitiveness. It was found that the Government Efficiency Index and the Corruption Control Index exert the most significant correlation with the level of tax competitiveness and the shadow economy. Its increase of 1% leads to a rise in the level of tax competitiveness of Slovakia by 7.015%, Croatia by 6.889%, the Czech Republic by 6.606%, and Romania by 5.773%. At the same time, the smallest correlation with the level of tax competitiveness performs an Index of Political Stability and Absence of Violence/Terrorism.

Acknowledgment
The study is funded by the project VEGA – 1/0392/23 “Changes in the approach to the creation of companies’ distribution management concepts influenced by the effects of social and economic crises caused by the global pandemic and increased security risks.”
The authors are grateful to the participants of projects 0123U101945 and 0122U000777 for numerous discussions and comments.

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).

Type of the article: Theoretical Article

Abstract
Virtual assets are integrated into financial markets, but legal recognition and tradability do not determine whether an instrument can support regulated liabilities in non-bank financial institutions (NBFIs). This theoretical study develops the Conceptual Prudential Compatibility Index (CPCI) as an ordinal framework for prudential screening. It combines peer-reviewed research with primary-source analysis of the European Union, the United States, and Ukraine, using a regulatory cut-off date of August 4, 2026. To avoid unsupported precision, the CPCI does not assign scalar scores. It separates a core economic-prudential profile – liquidity, value stability, and predictability/valuation reliability – from a non-compensatory regulatory overlay, R(a, j, s, u), defined by instrument, jurisdiction, NBFI sector, and intended prudential use. Each dimension is classified through fixed documentary anchors as high, moderate, limited, very low, or insufficient evidence. Traditional reference archetypes show stronger core profiles than large-cap unbacked crypto-assets, with stress-sensitive liquidity and very low value stability and predictability. Fiat-backed stablecoins require separate assessment of redemption rights, reserve quality, and segregation. Tokenized securities and real-world claims require look-through assessment of the underlying asset and legal, custody, and operational risks. The regulatory comparison confirms that market regulation does not create reserve eligibility: MiCA is distinct from Solvency II and IORP rules; U.S. insurance accounting treats directly held crypto-assets as non-admitted under the cited NAIC guidance; and reviewed Ukrainian sectoral rules do not expressly identify virtual assets as eligible prudential assets. The CPCI is a documentary screening framework, not an externally validated quantitative risk model.