## 4 Results and Discussions
Thus, the model that is a better fit for variant D is the fixed effects model. For _Model E_, including only the innovation EU countries, the results of the Hausman test indicate that the H0 hypothesis (H0: random effects are preferred) is strongly rejected (\(p\) values = 0.000). After running the regression with fixed effects and applying the redundant fixed effects test, we observe that the null hypothesis is rejected (\(p\) values \(<\) 0.05) (H0: the fixed effects are redundant). Therefore, the fixed effects are statistically significant for _Model E_. For the model that includes the transition countries (_Model F_), we obtained similar results to those of the other models. Therefore, we conclude that fixed effects are the best fit for _Model F_. The results of the fixed effects models emphasise the existence of a significant relationship between some of the indicators measuring entrepreneurial performance and economic development. They also show that this relationship is different according to the economic situation of the countries analysed. These findings confirm our hypotheses (H3 and H4). In the following, we analyse each indicator in turn. Our findings emphasise that _enterprise birth rate_ has a positive relation with economic growth for all groups of countries considered, but this relation is statistically significant only for the transition ones. Our results are supported by the findings of other studies [59; 72; 73] which highlight that the entry of new enterprises on the market creates the premises for the introduction of new ideas and innovation which would represent a source of long-term economic growth. Additionally, entrepreneurs are considered agents of change and bring new ideas to markets and stimulate growth through a process of competitive firm selection [35]. The formation of new, independent firms is important for the development of regional economic well-being [74]. The indicator measuring _death rate_ resulted in a negative and statistically significant relationship with economic growth for the group of 27 countries. When grouping the countries, the results show a positive and statistically significant relationship between death rate and economic growth for both innovation and transition countries. The interpretation of the relationship between enterprise death rate and economic growth in the literature shows that there can be both a positive and a negative relationship. Thus, on one hand, higher exit rates determine a reduction in the number of competitors. The firms that remain on the market will face less competition, and will no longer have the same motivation to invest much in their development and innovation. All this will have negative effects on economic growth. On the other hand, higher exit rates can have beneficial effects on economic growth. If those companies that leave the market are non-performing companies, their maintenance in the economy would not help in any way. Thus, the relationship between exit rates and economic growth is positive when the death of enterprises in fact implies a cleansing of the economy of non-performing enterprises. Enterprise deaths are not harmful to the economy because they can be seen as a catalyst that ensures the continuous regeneration of the stock of enterprises in the economy. In addition, the dynamics of the firms on the