Vugar Nazarov

The low level of the nation’s health and physical activity highlights the need to find additional mechanisms for the development of the sports sphere, one of which is to increase its financing. This paper aims to investigate how an increase in general government expenditure on recreation and sports affects sports entrepreneurship (turnover or gross premium written in the sports industry; value added at factor cost of sports enterprises; sports industry market size) and the share of the population involved in sports (as an indicator characterizing the development of sports). The study used the panel unit root test and fixed and random effects models. Modeling proved that the increase in general government expenditure on recreation and sports by 1% largely determines the increase in value added at factor cost of sports enterprises (by 5.48%). A significantly less effect is for turnover or gross premium written in the sports industry (by 0.85%), and the smallest is for the sports industry market size (by 0.4%). A 1% increase in general government expenditure on recreation and sport has the greatest impact in the Czech Republic (by 2.37%) and Slovakia (by 2.44%) and the least – in Australia (by 0.4%). The share of the population involved in sports is almost independent of general government expenditure on recreation and sports in all 10 OECD countries.

Type of the article: Research Article

Abstract
The accelerating digital and green transition has intensified the role of financial centers as investment conduits, making the relationship between financial strength and smart city development both timely and policy-relevant. This study aims to examine whether and to what extent the Global Financial Centres Index (GFCI) explains variation in the Smart Centres Index (SCI) across a global sample of cities. The analysis relies on panel data covering 78 cities from 2019 to 2025, with all calculations performed in R Studio using fixed effects, random effects, and robust error-corrected estimators. The findings reveal a sharp contrast between specifications: while the fixed-effects model detects no significant relationship (β = 0.0013, p = 0.963), the random-effects model identifies a positive and statistically significant link (β = 0.0858, p < 0.001), explaining about 56% of SCI variation (R² = 0.557). Robustness checks with clustered and Driscoll–Kraay standard errors confirm the stability of this result. City-level effects highlight London (+60.24), New York (+54.39), and Singapore (+39.46) as leading overperformers, while New Delhi (–74.49) and Mumbai (–68.15) emerge as underperformers. These outcomes demonstrate that financial strength matters for smart city advancement, but local governance, infrastructure, and innovation ecosystems critically shape whether financial capacity translates into smart development.