## 4 Data and Methodology
### Methodologies  
The propensity score matching (PSM) method is used that is quasi-experimental with a random assignment nature. This method further estimates the causal treatment effect with a matching approach. \(PUBPRO_{i}\) is the treatment indicator equals one if company \(i\) has a public contract and zero otherwise. \(ECOINNO_{i}(1)\) is the potential eco-innovation outcomes for company \(i\) when it has a public contract, and \(ECOINNO_{i}(0)\) is the potential eco-innovation outcome when it has no public contracts. \[ATT=E[ECOINNO_{i}(1)|PUBPRO_{i}=1]-E[ECOINNO_{i}(0)|PUBPRO_{i}=1] \tag{1}\]  
\[PSM\ ATT=E[ECOINNO(1)|PUBPRO=1,\ P(x_{i})] \tag{2}\]  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Categories** & **Indicators in Theory** & **Indicators in This Research** & **CONT** \\ \hline \multirow{4}{*}{Technological push} & R\&D investment activities in 2014 & Expenditure on different innovation activities in 2014 & RD \\  & High-qualified human resource & Percentage of the enterprise’s employees in 2014 who have a tertiary degree & \multirow{4}{*}{EMPRD} \\ \hline \multirow{4}{*}{Organizational capability} & \multirow{4}{*}{Environmental management} & Procedures in place to regularly identify and reduce environmental impacts (for example, environmental audits, environmental performance goals, ISO 14001 certification, etc.) & ENVMG \\  & CSR & “ & \\  & & Cooperate on innovation with other enterprises or organizations (in): 1-within the enterprise group; 2-suppliers; 31-clients/customers from the private sector; 32-clients/customers from the public sector; 4-competitors or other enterprises in the sector; 5-consultants or or other enterprises or other physical labs; 6-universities or other higher education institutes; 7-research institutes; A-your country; B-other Europe; C-United States; D-China or India; E-all other countries & **CO**: CO1 CO2 CO31 CO32 CO4 CO5 CO6 CO7 COA COB COB COC COC COC CO  
The average treatment effect on the treated group (_ATT_) is the difference between expected eco-innovation outcomes with and without public contracts for companies who actually participated in a public contract (Equation (1)). To solve the counterfactual problem and selection bias, the matching approach has been utilized to produce a matched control group, based on a set of covariates \(x_{i}\), in which the distribution of covariates is similar to that of the treated group. The propensity score \(P\) combines the information of all covariates. The PSM estimator for ATT is the mean difference in eco-innovation outcomes over the common support, appropriately weighted by the propensity score distribution of participants [61; 62] (Equation (2)). We chose the Logit model to estimate the propensity score. The first step for the PSM analysis is the selection of covariates \(x_{i}\) to estimate the propensity score \(P\). The data for companies with and without a public contract all come from the same source, the CIS survey questionnaire, which is a backing condition for the PSM analysis. We tested on four Logit regressions with different covariates. As indicated in Table 3, regression (1) includes all general basic variables, in which the prediction rate is 75.6% and the goodness of fit is 0.076. Regression (2) further includes the sub-variables of _public financial support_ and _cooperation_, where both the prediction rate and goodness of fit are improved. Moreover, regression (3) replaces the unordered categorical variable (employee number, sectors, and nations) with their dummies, which also makes the prediction rate (77.8%) and the model fitness (0.126) better. Based on regression (3), we added the square terms of each variable in regression (4), and the two indices of prediction and fitness almost do not change. What is more, for all square terms except that of _largest market_, their coefficients are not statistically significant, and the significance of the basic variables stays the same with regression (3).