A longitudinal analysis of local innovation in Italy: How do proximity measures matter?

This paper investigates the factors influencing local innovation from a longitudinal perspective while assessing geographical, economic and technological proximity. The research hypotheses concern spatial interactions, spillover effects and proximity measures that best fit innovation patterns and territorial interactions in Italy. The estimation strategy is the spatial Durbin panel model. The optimal specification to handle cross-sectional dependence in the data was derived from statistical tests evaluating (i) individual-specific effects, (ii) time-specific effects and (iii) both individual and time effects. The model was estimated using data from 107 Italian provinces over 2010–2019. The results show that both endogenous and exogenous interaction effects drive innovation processes and the underlying spillovers are global. Economic proximity explains local innovation patterns more effectively than geographical contiguity and technological proximity.