## 5 Empirical Analysis
### Assessing the Matching Quality  
Ideal matching results can simultaneously satisfy the common support assumption and balancing assumption. The former requires distribution of propensity scores of individuals in the experience group and the control group to be as close as possible after matching. The latter demands no significant difference in observable variables in both groups after the matching. According to the nearest-neighbor matching within caliper (caliper \(\varepsilon=0.01,k=5\)), Figure 2a,b, respectively, represent the kernel density function figures of \"privacy fatigue (experience group)\" and \"non-privacy fatigue (control group)\" before and after matching. From Figure 2, it can be seen that there are clear differences in the probability distribution of propensity scores in both the experience and the control groups before matching, and, indeed, the zone of overlap is quite small. After matching, the distributions of propensity scores of samples in both groups are very close, better satisfying the common support conditions. Learning from the method in [PERSON] (2001) [46] the balance test is conducted. The results are shown in Table 5. The standardized bias (%bias) of most variables is in reasonable ranges after matching. Furthermore, t test results of all variables do not reject the original hypothesis of no systematic differences between the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Coef.** & **Std. Err.** & **Z** \\ \hline pro & 0.049 ** & 0.023 & 2.13 \\ sex & \(-\)0.246 ** & 0.138 & \(-\)1.78 \\ age & \(-\)0.271 *** & 0.064 & \(-\)4.24 \\ edu & 0.179 ** & 0.093 & 1.93 \\ freq & \(-\)0.014 & 0.056 & \(-\)0.26 \\ friends & \(-\)0.172 ** & 0.073 & \(-\)2.35 \\ \(-\)cons & \(-\)1.038 ** & 0.520 & \(-\)1.99 \\ \hline \hline \end{tabular}  
* Notes: **, ** and *** respectively denote significance on the 10%, 5% and 1% levels.  
\end{table}
Table 4: Results of the logit regression model.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Characteristic Variables** & **Variables** & **Choices** & **Number** & **Percentage (\%)** \\ \hline \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } & & Under one hour per & 193 & 11.1 \\  & Intensity of WeChat & 1–3 h daily & 556 & 32.1 \\  & usage & 3–5 h daily & 549 & 31.6 \\  & 5–8 h daily & 218 & 12.6 \\  & & Over 8 h per day & 218 & 12.6 \\ \cline{2-5}  & & Under 100 & 651 & 37.5 \\  & Number of friends on & 100–300 & 675 & 38.9 \\  & WeChat & 300–600 & 258 & 14.9 \\  & 600–1000 & 81 & 4.7 \\  & Over 1000 & 69 & 4.0 \\ \hline \hline \end{tabular}  
* Notes: **, ** and *** respectively denote significance on the 10%, 5% and 1% levels.  
\end{table}
Table 3: _Cont._experience group and control group. Figure 3 intuitively shows the common value range of propensity scores. The observed values of the control group are all within the common value range, while only four observations of the experience group do not fall in the range. Therefore, just a small number of samples will be lost during propensity score matching. Summing up the points above, matching significantly lowered differences in observations between the two groups, which can be seen as this paper having appropriately selected observable variables and that the matching method is suitable.