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The authors employ a Bayesian Model Averaging (BMA) framework to study economic growth determinants of the 276 European regions during 2006– 2015 period. This framework allows one to address, as much as possible to date, the model uncertainty problem. Application of BMA provides a  possibility to run simultaneously numerous models to test each determinant in all possible variations. By controlling for top 500 higher education institutions over the world, the authors find statistical evidence that not only quantity of educational institutions does matter, but also the quality of each. In fact, it is one of the significant determinants of economic growth. Also, the model proves that higher education level, higher share of ICT patents, higher prime age population share, as well as higher manufacturing share are positively associated with subsequent economic growth; whereas, capital city regions tend to develop faster. The regions that tend to have a higher share of people with primary-only education have forecasted slower growth, as well as CO2 emissions and rapid population growth tend to have a  negative correlation with economic growth. The findings suggest that a high share of information and communication technologies patents and a high share of industry in gross value added (GVA) will positively affect economic development. The authors also found a  positive spillover effect from the neighbouring regions. Finally, the findings confirm a  conditional convergence process among European regions  – regions with higher initial income tend to develop slower if other factors remain unchanged.

Determinants of Economic Growth in the EU NUTS2 Regions