## 3 Results
### Results of Regression Analysis
#### 3.2.2 Results of Subsamples on Subject Matters
For some factors, the estimated effects we got in the integrated regression are supported only by one subgroup's analysis. For example, the OPE procedure negatively affected the probability of a green contract in the total sample. The only result in the service subsample is in line with it, while there are no significant results in product and work procurement. For variables of GPA, joint procurement, the medical equipment sector, transport equipment sector, and food sector, the signs of their coefficients in the product group are the same with those in the total sample, but we find no significant results in service and work subsamples. On the other hand, for factors of OPE, funded by EU, the health sector, and the business service sector, their estimated effects' signs only in the service group are consistent with those in the total sample. That means it is tested that different types of subject matters have various influential factors, and one factor may display effects to a different extent in different subsamples. There are three variables for which there are no significant results in the total sample, but significant ones in subsamples. The number of SMEs is one such variable, whose coefficients in product, service, and work subsamples are all statistically significant. The number of SMEs awarded in one contract decreases the possibility of the contract being green for products and works, while increasing the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Regressors**} & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** \\  & **Product** & **Marginal** & **Service** & **Marginal** & **Work** & **Marginal** \\  & & **Business sectors** & & & & \\ \hline Construction work & & & \(-0.27\) * & \(-0.064\) * & & \\  & & & (0.16) & (0.036) & & \\ \hline Medical equipment & \(-0.19\) ** (0.074) & \(-0.034\) ** (0.014) & & & & \\ \hline Construction service & & & & & & \\ \hline Health & & & \(-0.30\) *** & \(-0.071\) *** & \\  & & & (0.088) & (0.021) & \\ \hline Office supplies & & & & & & \\ \hline Business service & \(0.52\) * (0.29) & \(0.096\) * (0.052) & \(-0.19\) *** (0.057) & \(-0.044\) *** (0.013) & \\ \hline Transport equipment & \(0.38\) *** (0.069) & \(0.070\) *** (0.013) & & & & \\ \hline Transport service & & & & & & \\ \hline Food & \(0.23\) ** (0.091) & \(0.043\) ** (0.017) & & & & \\ \hline Electrical product & & & & & & \\ \hline Constant & & & & & & \\ \hline Country fixed term & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Month fixed term & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Observations & 6111 & 6111 & 8408 & 8408 & 1230 & 1230 \\ \hline Pseudo R2 & 0.2328 & – & 0.2120 & – & 0.2611 & – \\ \hline \hline \end{tabular}  
* Source: authors’ elaboration with Stata. Robust standard deviation are shown in the parentheses.
* \(p<0.1\), **\(p<0.05\), ***\(p<0.01\). All regressors are included in each regression, and only significant results are shown.  
\end{table} 
Table 6: _Cont._possibility for services. What is more, the number of offers negatively correlates with the green contracts only for services, and the number of awards positively correlates with the green contracts only for works.