## 4 Results
### Research Questions 1 and 2
#### 4.1.2 Recession and Non-Recession Model 2--Each Sector Separately  
Model 2 works with the same data as Model 1, but it examines each sector of the Czech economy separately. Therefore, it is no longer necessary to use the logarithm function. Model 2 (separate results for each sector) is given in Equation (2):  
\[\#\text{subjects}=\beta_{0}+\beta_{1}\left(\text{t}\right)+\beta_{2}\left( \text{recession}\right)+\epsilon_{\text{i}}, \tag{2}\]  
where \(\#\text{subjects}\) are number of subjects. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sector** & **n** & **Mean** & **Sd** & **Median** & **Min** & **Max** & **Se** \\ \hline Business enterprise s. & 11 & 2187.91 & 285.03 & 2299 & 1736 & 2628 & 85.94 \\ Government s. & 11 & 206.91 & 12.64 & 199 & 195 & 224 & 3.81 \\ Higher education s. & 11 & 205.55 & 17.03 & 203 & 185 & 229 & 5.14 \\ Non-gov. non-profit s. & 11 & 62.27 & 5.5 & 62 & 53 & 72 & 1.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics of each sector. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Value** & **Std. Error** & **DF** & **t-Value** & **p-Value** \\ \hline intercept & 5.557 & 0.6861 & 38 & 8.09836 & 0.0000 \\ t & 0.00954 & 0.0131 & 38 & 0.7309 & 0.4693 \\ recession & 0.00585 & 0.0184 & 38 & 0.318373 & 0.7519 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of linear mixed effect model 1. The results of Model 2 (Business enterprise sector) are presented in Table 4.  
The results of linear regression (Table 4) show that the p-value is lower than 0.05 for intercept and time variable. As can be seen, each year the number of subjects in this sector rises by a value of 82. It also can be seen that the dummy variable (recession) is insignificant. The R\({}^{2}\) of this model is 0.89, which can be interpreted as this model explaining 89.9% of variation. Results of Model 2 (Non-government non-profit sector) are presented in Table 5.  
The results of linear regression (Table 5) show that the p-value is lower than 0.05 for the intercept only. This model has very poor explanatory power. It can be seen from the data that this sector has a very subtle number of subject fluctuations, which can be explained by the intercept itself. The result shows that the number of subjects can be predicted by intercept, which is 64 subjects with no growth or descent annually. Results of Model 2 (Government sector) are presented in Table 6.  
The results of linear regression (Table 6) show that the p-value is lower than 0.05 for the intercept and time variable. As can be seen, each year the number of subjects in this sector decreases by a value of 3. It also can be seen that the dummy variable (recession) is insignificant. The R\({}^{2}\) of this model is 0.82, which can be interpreted as this model explaining 82% of variation. Results of Model 2 (Higher education sector) are presented in Table 7.  
The results of linear regression (Table 7) show that the p-value is lower than 0.05 for the intercept and time variable. As can be seen each year, the number of subjects in this sector rises by a value of 5. It also can be seen that the dummy variable (recession) is insignificant. The R\({}^{2}\) of this model is 0.96, which can be interpreted that this model explains 96% of variation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictors** & **Estimates** & **CI** & **p** \\ \hline (Intercept) & 229.07 & 220.85–237.29 & \(<\)0.001 \\ t & \(-\)3.47 & \(-\)4.60—2.34 & \(<\)0.001 \\ Recession & \(-\)4.89 & \(-\)12.94–3.16 & 0.268 \\ Observations & \(11\) & & \\ R\({}^{2}\)/adjusted R\({}^{2}\) & 0.819/0.774 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model 2 results for Government sector.