## 4 Results and Discussions
These results point out the fact that in less developed countries, more people are looking to enter into entrepreneurship, as an escape from unemployment. Meanwhile, in more developed countries, there is greater stability of the economic and business environment, which gives the companies better prospects for development and survival. The population and also the percentage of people who receive basic education are larger in more developed countries. Meanwhile, the resource endowment is higher in less developed countries. The correlation matrix of the variables (see Table 4) shows that there exists correlation between some of the considered variables (correlation coefficient above 0.70). Thus, business churn is highly correlated with birth and death rate. Survival rate at 3 years is highly correlated with survival rate at 5 years. Employment share at 3 years is highly correlated with employment share at 5 years. Average size of 3-year-old enterprises is highly correlated with average size of 5-year-old enterprises. To ensure that we obtained accurate results, we eliminated from the analysis some of the highly correlated variables (business churn, the survival rate at 5 years, employment share at 5 years and average size of 3-year-old enterprises). In addition, because other variables have quite high correlation coefficients (above 0.40), we ran alternative regressions with these variables to eliminate the problem of multicollinearity. Our empirical analysis has a dual purpose: (i) to identify the link between entrepreneurial performance and competitiveness; (ii) to identify the link between entrepreneurial performance and economic growth. _The first part_ of the empirical analysis considers as a dependent variable the national competitiveness (see Table 5). Therefore, to find the best model fitting to our sample we ran the regression analysis by applying three different models: _ordinary least squares_ (OLS), _fixed effects model_ (FE) and _random effects model_ (RE). We used the three models for each group  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & **GCI** & **GDPC** & **Birth** & **Death** & **Churn** & **Bpop** & **Sur3** & **Sur5** & **Empl3** & **Empl5** & **Old3** & **Size5** & **Size3** & **Educ** & **Lpop** & **Res** \\ \hline \hline \multirow{2}{*}{GCI} & 1.000 & & & & & & & & & & & & & & \\ GDPC & \(-0.121\) & 1.000 & & & & & & & & & & & & & \\  & \(-0.411\) & 0.230 & 1.000 & & & & & & & & & &  
of countries: a total of 27 EU countries, including innovative economies (19 countries) and economies in transition (eight countries). As the results are different for each model applied, we have tested to see which model better describes the relationships between variables. We ran two tests: the Hausman test and the redundant fixed effects test. For _Model A_, which comprises all the 27 EU countries, the results of the Hausman test point out that the H0 hypothesis (random effects are preferred) is strongly accepted because \(p\) values = 1.000. On the other hand, the results for the redundant fixed effects test show that the fixed effects are preferred. As \(p\) values are less than 0.05, this means that we reject the null hypothesis (H0: the fixed effects are redundant). The contradiction between the results of the two tests points out that the pooled OLS regression is a better fit for _Model A_[57]. For _Model B_, including only the innovation EU countries, the results of the Hausman test indicate that the H0 hypothesis (random effects are preferred) is strongly accepted (\(p\) values = 1.000). After running the regression with fixed effects and applying the redundant fixed effects test, we observe that the null hypothesis is firmly rejected for cross section and combined fixed effects (\(p\) values < 0.05, H0: the fixed effects are redundant). When testing only for period fixed effects, the result shows that H0 is accepted (\(p\) values > 0.05), and that period fixed effects are redundant.