# Getting Young People to Farm: How Effective Is Thailand's Young Smart Farmer Programme?
## 4 Results
### Impact Evaluation of the Young Smart Farmer Programme on Participants' Economic Viability  
#### 4.4.1 Binary Logistic Regression Model and Propensity Score Estimation  
The binary model had a reasonably good predictive power of farmer participation in the YSF programme, with a [PERSON]'s R-squared of 0.37 (Table 2). The model showed that farmers were more likely to participate in the programme if they were better educated and had less farming experience, confirming the result of the bivariate analysis (Section 4.1). Those with more farmland, not facing challenges relating to marketing and irregular weather, and those whose farms were far from district agricultural extension offices were more likely to participate. The binary model's propensity score estimate revealed adequate common support for further matching non-participants with participants (Figure S1 in Supplementary Materials). For every propensity score estimated for participants, there was the same or a close propensity score estimated for non-participants. 4.2 Matching and Estimating the Young Smart Farmer Programme Effect on Participants' Net Farm Income and Adoption of Innovative Farming Methods  
Using eight different matching algorithms, non-participants and participants with similar propensity scores were matched, and the matched sample was extracted from the total sample for each match for further estimation of the effect of the programme (Table 3). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Participation (_n_ = 176_)**} \\ \cline{2-4}  & **Coef.** & **SE** & **OR** \\ \hline Gender & \(-\)0.29 & 0.46 & 0.75 \\ Age & \(-\)0.001 & 0.04 & 1.00 \\ Education & 2.26 *** & 0.63 & 9.60 \\ Child & \(-\)0.005 & 0.44 & 1.00 \\ Size & 0.01 ** & 0.01 & 1.01 \\ Tenure & 0.005 & 0.48 & 1.01 \\ Activity & 0.84 & 0.60 & 2.31 \\ Experience & \(-\)0.09 ** & 0.04 & 0.91 \\ Off-farm income & \(-\)0.27 & 0.50 & 0.77 \\ Other support & \(-\)0.38 & 0.81 & 0.69 \\ Marketing problem & \(-\)2.31 *** & 0.59 & 0.10 \\ Pest problem & \(-\)0.80 & 0.51 & 0.45 \\ Weather problem & \(-\)0.92 * & 0.51 & 0.40 \\ Soil problem & 0.56 & 0.96 & 1.75 \\ Distance1 & 0.09 ** & 0.04 & 1.10 \\ Distance2 & \(-\)0.02 & 0.02 & 0.98 \\ Hosmer and Lemeshow & & 8.99 & \\ goodness of fit test (Chi-squared) & & & \\ _p_-value & & 0.34 & \\ McFadden’s R-squared & & 0.37 & \\ \hline \hline \end{tabular} 10.08. OR = odds ratio; *, **, *** significant at 10%, 5% and 1% level.  
\end{table} 
Table 2: Result of a binary logistic regression model of farmers’ participation in the Young Smart Farmer programme. The simple linear regression model after each match showed no effect of the programme on participants' net farm income. We found that, in general, although the estimated average net farm income difference between non-participants and participants differed across all matches, they had the same direction (Table 3). Participants tended to have a lower net farm income than non-participants, at between 4589 and 8818 baht/rai. However, the estimated net farm income difference was statistically insignificant for all matches (\(p\)-value > 0.1), meaning that participants had a similar net farm income to non-participants. Hypothesis 1 was, therefore, rejected. The binary logistic regression model after each match also revealed no effect of the programme on participants' adoption of innovative farming methods other than common machinery and chemicals. We found that, generally, although the estimated probability of adopting innovative farming methods had the same direction for all matches, their values were different (Table 3). Participants tended to have 45% to 146% higher probability of adopting innovative farming methods than non-participants, although, again, the estimated probability of adopting innovative farming methods was statistically insignificant for all matches (\(p\)-value > 0.1), meaning that participants and non-participants adopted the same innovative farming methods. Therefore, hypothesis 2 was also rejected.