## 4 Analysis
### OLS Results
 } & \multirow{2}{*}{0.037} & \multirow{2}{*}{0.158} & \multirow{2}{*}{1.000} & \multirow{2}{*}{0.052} & \multirow{2}{*}{0.165} & \multirow{2}{*}{0.955} & \multirow{2}{*}{1.05} & \multirow{2}{*}{1.055} \\  & & & & & & & & & & \\ \hline \multicolumn{11}{l}{**Lpjm1\_Princeton} & **Model 1** & **Model 2** & \multicolumn{8}{c}{**Model 3**} & \multicolumn{8}{c}{**Model 1**} & \multicolumn{8}{c}{**Model 2**} & \multicolumn{8}{c}{**Model 3**} \\ \hline MeanYield & 0.235 *** & 1.474 & 0.226 *** & 0.001 & 1.535 & 0.102 ** & 1.100 & 0.122 *** & 1.103 & \(-0.010\) & 1.113 \\ MeanYield & (0.000) & 1.474 & 0.026 *** & 0.000 & 1.666 & 0.001 & 0.000 & (0.000) & 1.103 & \(-0.016\) & 1.113 \\ MeanFlow & 0.035 & 1.472 & 0.033 & \multirow{2}{*}{1.472} & \multirow{2}{*}{\(-0.001\)**} & \multirow{2}{*}{1.474} & \multirow{2}{*}{0.063 **} & \multirow{2}{*}{1.074} & \multirow{2}{*}{0.052 *} & \multirow{2}{*}{1.075} & \multirow{2}{*}{0.022 **} \\  & (0.311) & 1.472 & 0.0307 & \multirow{2}{*}{1.472} & \multirow{2}{*}{0.075} & \multirow{2}{*}{1.042} & \multirow{2}{*}{0.042} & \multirow{2}{*}{1.074} & \multirow{2}{*}{0.072} & \multirow{2}{*}{1.075} & \multirow{2}{*}{1.076} \\  & (0.311) & 1.006 & \(-0.189\)** & 1.007 & \multirow{2}{*}{0.001} & \multirow{2}{*}{1.043} & \multirow{2}{*}{\(-0.126\)**} & \multirow{2}{*}{1.079} & \multirow{2}{*}{-0.109} & \multirow{2}{*}{1.082} & \multirow{2}{*}{-0.019} \\ \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{1.020} & \multirow{2}{*}{(0.000)} & \multirow{2}{*}{(0.216)} & \multirow{2}{*}{(0.000)} & \multirow{2}{*}{(0.000)} & \multirow{2}{*}{(0.000)} & \multirow{2}{*}{1.082} & \multirow{2}{*}{-0.019} \\  & (0.000) & & & & & & & & \\ \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{1.010} & \multirow{2}{*}{\(-0.032\)} & \multirow{2}{*}{1.010} & \multirow{2}{*}{(0.039)} & \multirow{2}{*}{1.011} & \multirow{2}{*}{(0.000)} & \multirow{2}{*}{1.048} & \multirow{2}{*}{(0.000)} & \multirow{2}{*}{1.048} & \multirow{2}{*}{(0.000)} & \multirow{2}{*}{1.054} \\  & (0.377) & & & & & & & & & \\ \multirow{2}{*}{\ \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{\(0.360\)**} & \multirow{2}{*}{1.002} & \multirow{2}{*}{1.002} & \multirow{2}{*}{\(0.000\)} & \multirow{2}{*}{1.002} & \multirow{2}{*}{(0.000)} & \multirow{2}{*}{1.000} & \multirow{2}{*}{1.007} & \multirow{2}{*}{1.036} \\ \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{0.095} & \multirow{2}{*}{0.224} & \multirow{2}{*}{1.000} & \multirow{2}{In Ethiopia, we firstly examined the correlation between population redistribution and environmental change factors (Model 1). MeanYield and MeanFlow had positive correlations with population redistribution, and ConsecLowField and ConsecLowFlow had negative correlations with population redistribution (Table 4). Although the correlation results were consistent with our expectations, the correlations were not consistently significant across two crop-water datasets in two decades. When Built-up, which reflects urbanization level, was included in the model as a control variable (Model 2), the adjusted \(R^{2}\) for Mexico was significantly increased to approximately 0.37, whereas Ethiopia's adjusted \(R^{2}\) was approximately 0.16. Compared with that in Ethiopia, the degree of urbanization in Mexico has a greater impact on population redistribution. In Ethiopia, urbanization is still in the primary development stage, and population redistribution is only weakly influenced by Built-up. However, when Built-up was introduced as a control variable, population redistribution and crop yield deviation (MeanYield) in Ethiopia were significantly and positively correlated in both decades and with both datasets. When Pop was included as the control variable (Model 3), the adjusted \(R^{2}\) value of Ethiopia was much higher than that of Mexico. When the influence of Pop on population redistribution was controlled, in Ethiopia, there was no correlation between crop yield deviation (MeanYield) and population redistribution under Model 3.