## 4 Results and Discussions
However, such high growth is mostly driven by the entry of very small enterprises, which is offset by job destruction of a similar magnitude [68]. The indicator measuring the _age of the enterprises_ (3-year-old enterprises) has a positive and statistically significant relationship with national competitiveness for all three models. The higher number of young enterprises will determine an increase in national competitiveness, regardless of the group of countries we are considering. This is explained by the fact that young enterprises, which are usually SMEs, are the largest contributor to job creation and employment growth with positive effects on the economic development of countries [43; 44]. In older enterprises, the owners might lower their commitment and involvement compared to young enterprises. Thus, an enterprise's performance is usually diminished as the enterprise ages [45; 46; 47; 69; 70]. The indicator measuring the _average size of five-year-old enterprises_ represents the ratio between the number of employees in young companies that have survived for three years and the number of companies that have survived for 5 years. According to our results, this indicator appears to have a positive and statistically significant relation with national competitiveness in the case of innovation countries and a negative relation for the transition countries. These results can be explained by the fact that, in developed countries, larger enterprises can spread investment costs over greater output so that returns of those investments will enhance competitiveness [71]. In the countries in transition, taking into account the business environment characteristics (such as lack of access to certain resources and capabilities), even if the enterprises were larger, no positive effects on national competitiveness would be felt. The other variables such as _net business population growth_ and _5-year-old enterprises_ do not result in a statistically significant influence on global competitiveness index. From the control variables included, education and population have a positive and statistically significant effect on the global competitiveness index, regardless of the group of countries analysed. The variable measuring resource endowment did not significantly influence the competitiveness of countries. The value of R-squared adjusted for _Model A_ is between 0.50 and 0.61. This result shows that between 50 and 61% of the change in the competitiveness of EU countries can be explained by the variation in the indicators measuring entrepreneurial performance. For _Model B_, around 97%% of the variation in competitiveness in innovation-driven EU countries can be explained by the changes in entrepreneurship performance. For the transition countries, around 70% of the variation in competitiveness can explained by the entrepreneurial performance dynamics. Moving on to _the second part_ of our empirical analysis, for identifying the best model that explains the link between entrepreneurial performance and economic growth, we ran the regression analysis by applying on our sample three different models: ordinary least squares, fixed effects model and random effects model. We obtained three models, each one analysing a different group of countries: total 27 EU countries--_Model D_--innovative economies--_Model E_--and economies in transition--_Model F_. As summarised in Table 6, the results are different for each model. Therefore, we have tested to see which model best describes the relation between variables. We ran two tests: the Hausman test and the redundant fixed effects test. For _Model D_, the results of the Hausman test point out that the H0 hypothesis (H0: random effects are preferred) is strongly rejected, because the \(p\) value is equal to 0.000. On the other hand, the results for the redundant fixed effects test show that the fixed effects are preferred because \(p\) values are less than 0.05 and we reject the null hypothesis (H0: the fixed effects are redundant).