Measuring the impact of determinant factors on innovation

The coefficients obtained for variables expressing the GDP level, education level, research development status, scientific articles published, or internet users’ number are significant. The Hausman test value indicated that the most appropriate estimate is the model with fixed effect. The results obtained for F-test and Lagrange Multiplier also confirm that the fixed effects model explains the analyzed data better.

Other results show that GDP per employed person and education years impact the number of patent applications, their role being essential. In other words, labor productivity and education are essential in explaining cross-continent differences. Furthermore, our finding suggests that, in the future, there may be an unexpected increase in labor productivity not directly but through improved performance of other determinants of patent applications. In these conditions we conclude that, in a developed economy, priority should be given to an increase in the education and skill training of the working population.