Table 2 presents the descriptive statistics for the variables used in the study, including the number of observations (Obs), mean, standard deviation (Std. Dev.), minimum (Min) and maximum (Max) values for each. The variables encompass key aspects of the research, such as regulatory adjustments (RA, in €1,000), TIER1, TCR, RC, CRO, CFO, TITLE, SENIOR, BI, CEOAD, BS, SIZE (in €1,000) and LNSIZE. These statistics illustrate the data spread and central tendencies, providing a comprehensive understanding of the data set. The data set, comprising 14,596 bank-director years from 2001 to 2020, reflects individual directors’ experiences within banks over this period, offering a detailed “bank-director years” level of analysis. This approach enhances the understanding of the interplay between risk governance characteristics and regulatory adjustments. Notably, the RGI (Risk Governance Index) is not included in this table. The RGI, derived through PCA, is a composite measure aggregating individual risk governance characteristics. It captures the shared variance of these characteristics, providing a consolidated measure of a bank’s overall risk governance strength. As a derived measure, the RGI is crucial in regression analysis for assessing the collective impact of risk governance characteristics on regulatory adjustments. The inclusion of both size and lnsize (natural logarithm of size) in the analysis serves distinct purposes. Size represents the actual size of the bank, assessing the direct linear relationship with the dependent variables. In contrast, lnsize captures nonlinear relationships and the percentage change in the dependent variable for a 1% change in the bank’s size. This dual approach ensures a comprehensive understanding of the impact of bank size on the dependent variables, capturing both linear and nonlinear relationships and reinforcing the robustness of the findings