Evaluating crypto regulation stringency and its impact on digital asset market adoption

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Type of the article: Research Article

Abstract
The article provides a comprehensive empirical assessment of the legal and operational regimes of digital asset circulation and their real impact on the financial market. To quantitatively measure the degree of state control, a composite Crypto Regulation Stringency Index (CRSI) was applied, constructed based on the OECD-JRC methodology. The index integrates 16 parameters across five fundamental areas (legal status, anti-money laundering, taxation, licensing, and consumer protection) for 61 countries (jurisdictions) as of 2025. The developed tool demonstrated high internal consistency and factor structure reliability (Cronbach’s α = 0.955).
To determine the market consequences of legal regulation, the index values were compared with the Chainalysis 2025 Global Crypto Adoption Index. The direct unconditional correlation between the stringency of rules and the scale of digital asset usage proved to be weak. However, a multivariate regression analysis conditional on a set of macroeconomic and institutional covariates (income level, digital infrastructure development, financial freedom, quality of the rule of law, and the specifics of the MiCA regulation) revealed a robust and statistically significant positive relationship. The reversal of the effect is consistent with a Simpson-type compositional effect: within groups of countries with comparable levels of economic development, clear and strict regulatory rules stimulate market activity. The findings extend the literature on comparative financial law and demonstrate that state control does not suppress the crypto-economy but rather serves as its stable institutional foundation.

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    • Figure 1. Heatmap of pairwise Pearson correlations among the five CRSI dimension scores
    • Figure 2. Two-dimensional scatter plot of CRSI_EW and Chainalysis 2025 inverse adoption rank (n = 61); the red line represents the linear approximation
    • Figure 3. Distribution of CRSI by region (Panel A); CRSI vs. adoption differentiated by MiCA status (Panel B)
    • Figure 4. Regression diagnostics: residuals vs. fitted values, normal Q–Q plot, residual distribution, and comparison of the CRSI coefficient across different specifications (95% confidence intervals)
    • Table 1. Structure of the CRSI index by key dimensions
    • Table 2. Descriptive statistics of dimensions and the CRSI 2025 (n = 61)
    • Table 3. Top 15 jurisdictions by CRSI 2025*
    • Table 4. Pairwise Pearson correlations among the CRSI dimension scores (n = 61)
    • Table 5. OLS regressions of the Chainalysis 2025 inverse adoption rank on CRSI and control variables
    • Table A1. Complete CRSI 2025 ranking with scores across five dimensions and the original 2025 Chainalysis rank
    • Table B1. Sub-indicator system and evaluation criteria of the CRSI index
    • Conceptualization
      Maksym Ivasenko, Natalia Pavlova
    • Data curation
      Maksym Ivasenko, Serhiy Frolov, Natalia Pavlova
    • Investigation
      Maksym Ivasenko
    • Methodology
      Maksym Ivasenko, Serhiy Frolov, Viktoriia Datsenko, Tetiana Tereshchenko
    • Software
      Maksym Ivasenko
    • Visualization
      Maksym Ivasenko, Tetiana Tereshchenko, Natalia Pavlova
    • Writing – original draft
      Maksym Ivasenko
    • Formal Analysis
      Serhiy Frolov, Tetiana Tereshchenko
    • Project administration
      Serhiy Frolov, Viktoriia Datsenko
    • Resources
      Serhiy Frolov, Tetiana Tereshchenko, Natalia Pavlova
    • Supervision
      Serhiy Frolov, Viktoriia Datsenko, Natalia Pavlova
    • Writing – review & editing
      Serhiy Frolov, Viktoriia Datsenko, Tetiana Tereshchenko, Natalia Pavlova
    • Funding acquisition
      Viktoriia Datsenko
    • Validation
      Viktoriia Datsenko, Tetiana Tereshchenko