## 4 Results
### Driving Forces of the CCD
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multicolumn{2}{c}{**Random Spatial Effects, Fixed Time-Period Effects**} \\ \cline{2-4}  & **Coef** & **\(T\)-Test** & \(p\)**-Value** \\ \hline \(W^{*}\)Log (\(C\)) & 0.0670 & 2.40 & 0.016 \\ Log (\(C\)\(U\)) & 0.0648 & 0.77 & 0.443 \\ Log (\(RE\)) & 0.2841 & 2.50 & 0.012 \\ Log (\(VH\)) & 0.1154 & 0.92 & 0.359 \\ Log (\(SE\)) & 0.3160 & 4.65 & 0.000 \\ Log (\(CR\)) & \(-\)0.0249 & \(-\)0.94 & 0.349 \\ Log (\(EX\)) & \(-\)0.0328 & \(-\)0.22 & 0.823 \\ Log (\(TE\)) & 0.0678 & 1.12 & 0.265 \\ W\({}^{*}\)Log (\(CL\)) & \(-\)0.0425 & \(-\)1.45 & 0.147 \\ W\({}^{*}\)Log (\(RE\)) & \(-\)0.1008 & \(-\)2.03 & 0.042 \\ W\({}^{*}\)Log (\(VH\)) & \(-\)0.0170 & \(-\)0.34 & 0.735 \\ W\({}^{*}\)Log (\(SE\)) & \(-\)0.0906 & \(-\)2.59 & 0.010 \\ W\({}^{*}\)Log (\(CR\)) & 0.0088 & 0.36 & 0.719 \\ W\({}^{*}\)Log (\(EX\)) & 0.0236 & 0.71 & 0.475 \\ W\({}^{*}\)Log (\(TE\)) & \(-\)0.0119 & \(-\)0.37 & 0.713 \\ \(\sigma^{2}\) & 0.0984 & 8.55 & 0.000 \\ \(R^{2}\) & & 0.3504 & \\ Log likelihood & & \(-\)77.9577 & \\  & & chi2 (7) = 0.49 & \\ Wald test spatial error & & \(p=0.9995\) & \\  & & chi2 (7) = \(-\)35.97 & \\  & & \(p=1.0000\) & \\ Wald test spatial lag & & chi2 (7) = 46.53 & \\  & & \(p=0.0000\) & \\ LR test spatial lag & & chi2 (7) = 33.73 & \\  & & \(p=0.0000\) & \\ Hausman test & & chi2 (7) = 13.80 & \\  & & \(p=0.0549\) & \\ Observations & & 110 & \\ Number of id & & 22 & \\ \hline \hline \end{tabular} Note: Coef denotes the coefficient. The Wald test was used to test whether the SDM could be simplified to the SAR or SEM [93]. The LR test is the likelihood ratio test, which was used to test whether the SDM could be simplified to the SAR and SEM [94].  
\end{table} 
Table 4: The regression results of the spatial Durbin model (SDM) with time-period specific effects. In the SDM with random spatial effects and fixed time-period effects (Table 5), the direct effect of the \(RE\) was 0.2952 and of the \(SE\) was 0.3122, which implies that the \(RE\) elasticity of 0.0602 in the nonspatial model was underestimated by 23.5%, and the \(SE\) elasticity of 0.3872 was overestimated by 7.5%. Because the direct effect of the \(RE\) was 0.2952 and its coefficient estimate 0.2841 (the second column of Table 4), its feedback effect was 0.0111, or 1.11% of the direct effect. Similarly, the feedback effect of the SE was \(-0.0038\), or \(-0.38\%\) of the direct effect. Therefore, these feedback effects were relatively small. In contrast, the indirect effects in the nonspatial model were set to zero by construction. However, the indirect effect of the \(RE\) in the SDM was approximately 34.89% of the direct effect of the \(RE\). Similarly, the indirect effect of the \(SE\) was approximately \(-13.83\%\) of the direct effect of the \(SE\). However, based on \(T\)-statistics, these two indirect effects were insignificantly different from zero. Therefore, the coefficient of the spatial lagged value of the \(RE\) was significantly positive, but its indirect effect was insignificantly positive. Similarly, the coefficient of the spatial lagged value of the SE was significantly positive, but its indirect effect was insignificantly negative. This implies that if the secondary industry share and the registered enterprises per thousand of the urban population in an Aimag increased, the CCD would only increase in this Aimag (elasticity 5.28 and 2.92, respectively), but would not change in its neighboring Aimags.