## 3 Materials and Methods
### Heterogeneity Analysis
Thus, the degree of promotion, policy sustainability, and implementation effect can affect the satisfaction with GPPs among farmers in different age groups, while the policy support intensity only significantly affected the satisfaction of old farmers. With the development of part-time production, compared to old farmers, young farmers were more likely to engage in non-agricultural production during their leisure time, and their income was significantly higher than that from agricultural production and operation. Therefore, the impact of the agricultural policy support intensity on young farmers was relatively low. (3) Analysis of educational heterogeneity. The impact of educational differences on the satisfaction of farmers with GPPs was also explored. Based on the education level of the respondents, the sample was divided into two sub-samples, namely, the low education group and the high education group. Among them, farmers with a junior high school education or below were classified as the low education group (N = 932), while farmers with a senior high school education or above were classified as the high education group (N = 257). The analysis results of education heterogeneity are presented in Table 6. As previously studied [9; 14], Chinese farmers are mainly educated in junior high school or below. Models (1) and (2) showed that the variables of service, financial, and subsidy policies were significant and had positive coefficients, indicating that the satisfaction of both highly and lowly educated farmers with GPPs was influenced by these policies. Technical and supervision policies were only significant in the group of lowly educated farmers, indicating that these policies only had significant impacts on the satisfaction of lowly educated farmers with GPPs. The results of Models (3) and (4) showed that the satisfaction of both lowly and highly educated farmers with GPPs was significantly affected by the support intensity, degree of promotion, policy sustainability, and implementation effect. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Model (1): Male** & **Model (2): Female** & **Model (3): Male** & **Model (4): Female** \\ \hline
**Policy structure** & & & & \\ Service policy & 0.310 *** & 0.603 *** & & \\  & (0.0743) & (0.163) & & \\ Financial policy & 0.315 *** & 0.612 *** & & \\  & (0.0638) & (0.169) & & \\ Technical policy & 0.394 *** & 0.199 * & & \\  & (0.0565) & (0.104) & & \\ Subsidy policy & 0.431 *** & 0.323 *** & & \\  & (0.0570) & (0.109) & & \\ Supervision policy & 0.133 ** & 0.148 & & \\  & (0.0518) & (0.103) & & \\
**Policy implementation** & & & 0.253 *** & 0.417 *** \\  & Support intensity & & & (0.0601) & (0.162) \\ Degree of promotion & & & 0.193 *** & 0.321 ** \\  & & & (0.0616) & (0.160) \\ Policy sustainability & & & 0.163 *** & 0.381 *** \\  & & & (0.0534) & (0.135) \\ Implementation effect & & & 0.439 *** & 0.649 *** \\  & & & (0.0495) & (0.112) \\ \hline Control variable & Yes & Yes & Yes & Yes \\ \hline N & 944 & 236 & 944 & 236 \\ Pseudo R\({}^{2}\) & 0.2655 & 0.3227 & 0.1905 & 0.3554 \\ \hline \hline \end{tabular} Notes: *, ***, *** Statistically significant at 10%, 5%, 1% levels, respectively.  
\end{table} 
Table 4: The estimation results of gender heterogeneity.