## 3 Results
### Results of Regression Analysis  
#### 3.2.1 Results of Total Sample  
Table 5 shows the Probit regression results and marginal effects of the total sample. The first and second columns are without fixed effect controls. As a more precise analysis for causal relations, we added country and month fixed effects controls in the third and fourth columns, which are the main results. Based on Probit and fixed effect regression outcomes, OPE, a framework agreement, being funded by the EU, the medical equipment sector, sector of health and social services, and sector of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **Observations** & **Mean** & **Standard Deviation** & **Max.** & **Min.** \\ \hline Green contracts & 234071 & 0.09 & 0.29 & 0 & 1 \\ \hline \multicolumn{5}{c}{**Contracts and procedures**} \\ \hline Award criteria & 123310 & 0.44 & 0.50 & 0 & 1 \\ \hline OPE & 234071 & 0.79 & 0.41 & 0 & 1 \\ \hline Log contract value & 206281 & 11.52 & 4.28 & \(-\)4.61 & 25.33 \\ \hline Product & 234071 & 0.36 & 0.48 & 0 & 1 \\ \hline Service & 234071 & 0.49 & 0.50 & 0 & 1 \\ \hline Framework agreement & 234071 & 0.15 & 0.35 & 0 & 1 \\ \hline GPA & 234071 & 0.54 & 0.50 & 0 & 1 \\ \hline Funded by EU & 234071 & 0.044 & 0.21 & 0 & 1 \\ \hline Electrical auction & 234071 & 0.020 & 0.14 & 0 & 1 \\ \hline \multicolumn{5}{c}{**Purchasers**} \\ \hline CPB & 208991 & 0.056 & 0.23 & 0 & 1 \\ \hline Joint procurement & 234071 & 0.029 & 0.17 & 0 & 1 \\ \hline \multicolumn{5}{c}{**Tenderers**} \\ \hline SMEs & 234071 & 0.42 & 0.49 & 0 & 1 \\ \hline Number of SMEs & 66925 & 2.57 & 9.46 & 0 & 998 \\ \hline Awarded to a group & 234071 & 0.061 & 0.24 & 0 & 1 \\ \hline Number of offers & 196702 & 3.87 & 8.57 & 0 & 998 \\ \hline Number of awards & 234071 & 3.00 & 9.97 & 0 & 742 \\ \hline \multicolumn{5}{c}{**Business sectors**} \\ \hline Construction work & 234071 & 0.14 & 0.35 & 0 & 1 \\ \hline Medical products & 234071 & 0.10 & 0.30 & 0 & 1 \\ \hline Construction service & 234071 & 0.082 & 0.27 & 0 & 1 \\ \hline Health & 234071 & 0.059 & 0.24 & 0 & 1 \\ \hline Office supplies & 234071 & 0.031 & 0.17 & 0 & 1 \\ \hline Business service & 234071 & 0.055 & 0.23 & 0 & 1 \\ \hline Transport & 234071 & 0.048 & 0.21 & 0 & 1 \\ \hline Transport service & 234071 & 0.030 & 0.17 & 0 & 1 \\ \hline Food & 234071 & 0.020 & 0.14 & 0 & 1 \\ \hline Electrical product & 234071 & 0.015 & 0.12 & 0 & 1 \\ \hline \hline \end{tabular} Source: authors’ elaboration with Stata.  
\end{table} 
Table 4: Descriptive analysis results. business services are negatively related with whether a contract being green, with 5% or 1% significance levels. On the other hand, log of the contract value, GPA coverage, joint procurement, the construction service sector, the transport equipment sector, and the food sector can positively correlate with green contract, or these factors increase the possibility of a contract being green. With column (4), we can compare the magnitude of the effects. On the negative side, in descending order we find the health and social services sector, OPE, medical equipment sector, business services sector, framework agreements, and being funded by EU. On the positive side, our results in descending order are the food sector, the transport equipment sector, joint procurement, the construction services sector, log of the contract value, and GPA coverage. In column (4), the coefficient of OPE means that the contracts applying open procedure possess a lower possibility of being green contracts by 5.6% compared with contracts with other procurement procedures, including competitive dialogue, negotiation, and restricted procedure, etc. The estimated results of a framework agreement, GPA, being funded by the EU, joint procurement, and the sector dummies can be explained in similar way. The contracts without framework agreements have a higher possibility of being green, by 3.1%, than those within a framework. The contracts under GPA are more likely (1.6%) to be green, while contracts funded by EU are less likely to be green (2.2%).