Technology shape, distance to frontier, or frontier shift? Modeling the determinants of TFP growth

We investigate the determinants of TFP growth of Italian manufacturing firms. Using stochastic frontier techniques, we consider three approaches to take into account the influence of external factors, i.e., the determinants of growth. First, external factors may affect the shape of the technology. Second, they may influence the distance from the frontier. Third, in a novel approach, the external factors influence the technological progress, that is the shift of the frontier. Using a sample of manufacturing firms in 1998-2003, we find that the exports, technological investments and spillovers, public infrastructures, and banking efficiency all have a positive effect on TFP growth. We also find that the first model best fits the data.TFP, growth accounting, stochastic frontiers, R&D spillovers, banking efficiency, infrastructures

Be productive or face decline. On the sources and determinants of output growth in Italian manufacturing firms

We investigate the sources and determinants of output growth of Italian manufacturing firms. Applying stochastic frontier techniques, we decompose output growth into factor accumulation and TFP growth from 1998 to 2003. TFP growth is further decomposed into technological change, efficiency change, and scale effects.We find that both input accumulation and TFP growth are important in explaining output growth. In addition, efficiency change (technological catch-up) is the most significant component of TFP growth. Finally, using a specific formulation of the asymmetric error component, we find that R&D spillovers, banking efficiency and public infrastructures have statistically significant and economically relevant effects on the technological catch-up.growth accounting, stochastic frontiers, TFP, R&D spillovers, banking efficiency, infrastructure, Italian manufacturing firms

Rule Changes and Competitive Balance in Formula One Motor Racing

This paper provides an economic explanation of the frequent rule changes in the Formula One (F1) motor racing series. In a two-stage model, the FIA (as the organizer of the F1) first decides whether to change the rules or not, and then the racing teams compete in a contest. It turns out that a rule change reduces the teams' performances, but also improves competitive balance between the teams. The rule change is implemented, if the FIA's revenue gain from the latter effect overcompensates the FIA's revenue loss from the former effect. We provide empirical evidence from F1 seasons in the period 1950-2003 which supports the main implications of the model