## 4 Results and Discussion
Moreover, in the case of the male respondents and for married ones, the influence exerted by _dsv_recog4 my_work_ on the outcome in both its forms _compl_satisf_with_my_job_ and _satisf_with_my_job_ is stronger (Table 1, scenarios a, b, e and f), meaning that receiving recognition for work done counts more in terms of the impact on job satisfaction for the married people than for unmarried ones and much more for males rather than for females. The overall scenario (all 2052 distinct responses in Table 2) also confirms the structure of this powerful and significant dual-core and the order of importance already identified when we analyzed the regional subsets (MB, TBCM and OMD) and the six derived after we filtered on different types of respondents (Table 1). The accuracy of classification when we considered only the dual-core in all ten scenarios in Tables 1 and 2 is remarkably high (excellent or good to excellent--ROC over or near the threshold of 0.9) and the maximum VIF is well below 10 (value less than 6.75), indicating good chances to identify some other particular influences. The values of the pseudo R-square also indicate a good explanatory power, even when we took into account only the dual-core in all ten cases above. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Outcome** & **Dataset/Subset** & **Overall** & **MB** & **TBCM** & **OMD** \\ \hline \multirow{5}{*}{**Full job**} & **Variables** & **(a)** & **(b)** & **(c)** & **(d)** \\ \cline{2-6}  & own work recognition & 0.0825 *** & 0.0708 *** & 0.0906 *** & 0.0767 *** \\  & _(dsv\_recog4 my\_work)_ & (0.0056) & (0.0144) & (0.0090) & (0.0083) \\ \cline{2-6}  & workplace atmosphere & 0.2137 *** & 0.1843 *** & 0.2118 *** & 0.2228 *** \\
**(4 from the** & _(colleag\_good\_atmosph_) & (0.0079) & (0.0193) & (0.0128) & (0.0115) \\
**0-4 scale)** & Pseudo R-square & 0.3839 & 0.3546 & 0.3709 & 0.3982 \\  & AUROC & 0.8893 & 0.8873 & 0.8798 & 0.8961 \\ \hline \multirow{5}{*}{**Job satisfaction**} & own work recognition & 0.0859 *** & 0.0607 *** & 0.077 *** & 0.0953 *** \\  & _(dsv\_recog4 my\_work)_ & (0.0073) & (0.0233) & (0.0115) & (0.0100) \\ \cline{1-1}  & workplace atmosphere & 0.2589 *** & 0.272 *** & 0.2431 & 0.257 *** \\ \cline{1-1}  & _(colleag\_good\_atmosph_) & (0.0028) & (0.0095) & (0.0037) & (0.0060) \\ \cline{1-1}  & Pseudo R-square & 0.505 & 0.3673 & 0.4841 & 0.5914 \\ \cline{1-1}  & AUROC & 0.9309 & 0.9004 & 0.9251 & 0.9573 \\ \hline \multirow{5}{*}{Source: Own calculations in Stata 16. Note: Standard errors in parentheses. ***, *** indicate significance at 1%, and 1‰, respectively.} & \multicolumn{1}{c}{} & & \\ \cline{1-1}  & \multicolumn{1}{c}{} & & & & \\ \end{tabular}
\end{table} 
Table 2: The second round of robustness checks (residence) for both components of the core, as average marginal effects on job satisfaction based on binary logistic regressions for both forms of the outcome. Consequently, these very promising results encouraged us to try more in terms of exploration and identify specific patterns for each of those three aforementioned historical regions. Therefore, another consistent part of the binary logistic regressions served for validating the common and particular influences, as indicated when using the LASSO variable selection procedure for the regional subsets (Table 3, scenarios from a to c). For reasons concerning better comparability inside and across the regional models built using both types of binary logistic and Poisson regressions, we have not reported the corresponding raw coefficients, but only the average marginal effects for both. The results above (Table 3) clearly indicate that for the respondents from the second region (TBCM), the fact of receiving the observed recognition of one's own work counts the most (almost double as magnitude) when compared with the other two regions, when we took into consideration one's state of being fully satisfied with their job as the outcome. We expected this in light of our previous research results.