## 3 Results
### Regression and Causality Analysis  
To understand more deeply the connection between DfG and economic development, we have employed the Pooled OLS model. The results are summarized in Table 1. Here, we select 60 countries which have continuous data from 1996 to 2019. We thus have a total of 1440 observations. We have first run a pooled OLS regression for the whole period. The results are shown in columns 1 to 3 of Table 1, where the 1 st column is for the model with only DfG considered, the 2 nd column for the results with only ECI considered, and the 3 rd column for both DfG and ECI considered. We call these models 1-3. We observe that the regression coefficients for models 1-3 are significant at the 1% level. By comparing the columns 1 and 2, we find that DfG can explain 57.3 percent of the variance in GDP, while ECI accounts for 45.7 percent, as shown by the \(R^{2}\) of the regression. This suggests that the explanatory power of DfG on GDP is stronger than that of ECI. After both DfG and ECI are considered, the model explains 58.7 percent of the variance in GDP, which is slightly better than model 1. Considering that DfG has a higher correlation with GDP than ECI since the global financial crisis of 2008, we have also divided the whole time period into two, one from the year 1996 to 2007, the other from 2008 to 2019. The results are shown in the columns 4-6 and 7-9 of Table 1, for the models 1-3 explained earlier. By comparing the results of regression models for these two groups, we find: (1) in both time periods DfG has a stronger explanatory power on the variance of GDP than ECI, (2) the explanatory power of DfG and ECI combined on the variance of GDP in first group is stronger that that of the second group. It is worth noting that ECI does not significantly improve the explanatory power of the model on the variance of GDP in these three scenarios of regression models, especially in the period after the global financial crisis of 2008. Therefore, DfG better explains the variance in GDP than ECI. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variables** & \multicolumn{3}{c}{**Model 1-3 (1996–2019)**} & \multicolumn{3}{c}{**Model 4–6 (1996–2007)**} & \multicolumn{3}{c}{**Model 7-9 (2008–2019)**} \\ \hline \hline  & **In GDP** & **In GDP** & **In GDP** & **In GDP** & **In GDP** & **In GDP** & **In GDP** & **In GDP** & **In GDP** \\ \hline \multirow{3}{*}{DG} & \(-18.97\)***1 & \(-15.04\)*** & \(-19.78\)*** & & \(-13.65\)*** & \(-18.48\)*** & & \(-16.88\)*** \\  & \((-43.95)\) & & \((-21.29)\) & \((-33.74)\) & & \((-14.34)\) & \((-33.05)\) & & \((-14.34)\) \\ \hline \multirow{3}{*}{ECI} & & 1.26 *** & 0.37 *** & & 1.43 *** & 0.58 *** & & 1.11 *** & 0.14 *** \\  & \((34.78)\) & & (6.95) & & (29.2) & (7.98) & & (22.73) & (2.16) \\ \hline \multirow{3}{*}{Constant} & 31.94 *** & 25.16 *** & 30.48 *** & 31.75 *** & 24.67 *** & 29.49 *** & 32.22 *** & 25.64 *** & 31.64 *** \\  & \((221.73)\) & \((593.36)\) & \((120.6)\) & \((163.24)\) & \((442.62)\) & \((86.8)\) & \((171.91)\) & \((437.45)\) & \((96.43)\) \\ \hline Observations & 1440 & 1440 & 1440 & 720 & 720 & 720 & 720 & 720 & 720 \\ Adjusted \(R^{2}\) & 0.573 & 0.457 & 0.587 & 0.623 & 0.543 & 0.645 & 0.603 & 0.418 & 0.605 \\ F-Statistics & 1931.66 & 1209.56 & 1021.59 & 1138.19 & 852.59 & 650.51 & 1092.18 & 516.62 & 551.22 \\ Prob \textgreater{} F & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}  
* **Note: *** \(p<0.01\), ** \(p<0.05\).**  
\end{table} 
Table 1: Regression results for GDP, DfG and ECI  
Figure 6: Variation of the Pearson correlation with time, where the red and the blue curves are for the absolute value of the correlation coefficient between DfG and the logarithm of GDP, and the correlation coefficient between ECI and the logarithm of GDP. We have also performed a panel VAR analysis. LLC test indicates that the three variables with one period lag and trend are stationary.