## 2 Methodology and Data
### Probit Regression  
We combine Probit regression with fixed effects regression in our analysis, country fixed term and month fixed term, as well as the robustness-controlled error term, to estimate whether the factors have effects on green contracts or green public procurement. The regression equation is shown below.  
\[Y_{it}=\alpha+\beta CON+\lambda PUIR+\
u TEN+\gamma SEC+\delta_{i}+\delta_{t}+ \varepsilon_{it} \tag{1}\]  
\(Y_{it}\) is dummy for green contracts, CON is a vector of contract properties (OPE, value, subject matter, framework agreement, GPA, EU funds, and electrical auction, award method), PUR includes purchasers' characteristics: CPB and joint procurement. TEN is a bunch of tenderers' characteristics (dummy of SMEs in offering, number of awarded SMEs, awarded to a group, number of offers and awards). SEC contains dummies for ten important sectors of objectives of the contracts. \(\delta_{i}\) is the country fixed term controlling omitted variables that vary in countries, but were constant within the year 2018. \(\delta_{t}\) is the month fixed term representing unobservable variables changing over time, but not across countries. \(\varepsilon_{it}\) is error term, which is robust standard error.  
In the Pobit regressions, the magnitude of the coefficients is not meaningful, so we need to calculate marginal effects to get the effect magnitude. We use average marginal effect in this analysis. The significance, signs of the coefficients and the marginal effects are checked. Furthermore, with the logit regression and Probit regression with normal standard error (rather than robust one), we cross check the verification of our results. We also regress on three subsamples on products, services and works to verify our analysis.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **Variable Name** & **Value Assignment** \\ \hline \hline  & \multicolumn{2}{c}{**Tenderers**} \\ \hline Dummy & SMEs & The suppliers of a contract include at least one SMEs \\ \hline Numerical & Number of SMEs & The number of SMEs awarded by the purchaser \\ \hline Dummy & Awarded to a group & The contract has been awarded to a group of operators or suppliers \\ \hline Numerical & Number of offers & Number of tenders received \\ \hline Numerical & Number of awards & The number of contract awards for a given award notice \\ \hline  & \multicolumn{2}{c}{**Business sectors**} \\ \hline Dummy & Construction work & Construction work \\ \hline Dummy & Medical equipment & “Medical equipment, pharmaceuticals and personal care products” \\ \hline Dummy & Construction service & “Architectural, construction, engineering and inspection services” \\ \hline Dummy & Health & “Health and social work services” \\ \hline Dummy & Office supplies & “Office and computing machinery, equipment and supplies except \\  & furniture and software packages” \\ \hline Dummy & Business service & “Business services: law, marketing, consulting, recruitment, \\  & printing and security” \\ \hline Dummy & Transport & “Transport equipment and auxiliary products to transportation” \\ \hline Dummy & Transport service & “Transport services (excl. Waste transport)” \\ \hline Dummy & Food & “Food, beverages, tobacco and related products” \\ \hline Dummy & Electrical product & “Electrical machinery, apparatus, equipment and consumables; \\  & lighting” \\ \hline \hline \end{tabular} Source: authors’ elaboration based on coding information of the database [15] and the TED website [21].  
\end{table}
Table 2: Cont.