## 5 Empirical Test and Result Analysis
### Endogenous Treatment
#### 5.3.4 PSM-OLS and PSM-DID Estimation  
Following the research methodology of [PERSON] [59] et al. (2021), this paper argues that companies should gradually promote digital transformation rather than accomplish it overnight, so the disclosure of the first digital transformation can be regarded as a quasi-natural experiment. At the same time, in order to avoid 'false' digital transformation, disclosure of fewer than 3 years and discontinuous disclosure of samples, this paper argues that the enterprise did not really implement digital transformation. In order to solve the problem that the digital transformation is not a random distribution, which leads to the biased estimation of the difference-in-differences model, this paper attempts to use the Mahalanobis distance of the propensity score method (PSM) to perform 1:1 matching. The selected matching covariates include enterprise size, asset-liability ratio, operating income growth rate, total asset profit rate, enterprise age and other characteristic variables. The matching samples are obtained, and the OLS and difference-in-differences methods are used again to estimate, making the empirical design closer to random distribution.  
The balance test of PSM in Figure 1 shows that there is no significant difference between the mean values of each variable before and after matching. Figure 2 shows the common support test graph. The comparison of the kernel density function graph before  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **(1)** & **(2)** \\ \hline \multirow{3}{*}{_Lev_} & **First** & **Second** \\ \cline{2-3}  & 0.0600 & \(-\)0.1020 *** \\ \cline{2-3}  & (0.0770) & (0.018) \\ \hline \multirow{3}{*}{_Growth_} & 0.0210 * & \(-\)0.0030 \\ \cline{2-3}  & (0.0120) & (0.0030) \\ \hline \multirow{3}{*}{_Roa_} & \(-\)0.2780 & \(-\)0.1590 *** \\ \cline{2-3}  & (0.2170) & (0.0510) \\ \hline \multirow{3}{*}{_Cashflow_} & \(-\)0.1580 & 0.1700 *** \\ \cline{2-3}  & (0.1820) & (0.0430) \\ \hline \multirow{3}{*}{_Mb_} & \(-\)0.1410 *** & 0.0030 \\ \cline{2-3}  & (0.0320) & (0.0080) \\ \hline \multirow{3}{*}{_Fsh1_} & 0.0100 & 0.0210 \\ \cline{2-3}  & (0.0760) & (0.0180) \\ \hline \multirow{3}{*}{_Dlzb_} & \(-\)0.6530 *** & 0.0710 \\ \cline{2-3}  & (0.2000) & (0.0470) \\ \hline \multirow{3}{*}{_Big4_} & 0.0790 ** & 0.1350 *** \\ \cline{2-3}  & (0.0380) & (0.0090) \\ \hline \multirow{3}{*}{_Lnage_} & 0.0720 ** & 0.1210 *** \\ \cline{2-3}  & (0.0330) & (0.0080) \\ \hline \multirow{3}{*}{_.cons} & \(-\)0.1960 & 0.7870 *** \\ \cline{2-3}  & (0.3010) & (0.0710) \\ \hline Industry & control & control \\ \hline Year & control & control \\ \hline \(N\) & 9766 & 9766 \\ \hline R-squared & 0.2526 & 0.289 \\ \hline adj. \(R^{2}\) & 0.2480 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._and after matching shows that the two lines after matching are very similar, indicating that the matching effect is good and meets the common support test.  
Using the sample weight is not empty, the sample to meet the common support hypothesis, frequency weighted regression after three matching samples to re-OLS and DID estimation. In the frequency-weighted regression, due to the frequency weighting based on the weight, the samples actually participating in the regression will be copied according to the weight; that is, the regression samples will increase. The estimated results are as follows according to Table 7: the coefficients of the digital transformation (_Lndigital_) in columns (1), (2) and (3) and the coefficients of the quadratic difference term (_DID_) in columns (4), (5) and (6) are significantly positive, indicating that the matched estimation results are consistent with the basic regression, which can eliminate the influence of endogeneity. The results of digital transformation and ESG performance are still robust.  
Figure 1: Balance test of PSM.  
Figure 2: Common support test diagram; (**a**) Kernel density map of propensity score before matching; (**b**) Kernel density map of propensity score after matching.