A growing interest in R&D tax incentives as a way to sustain research and innovation efforts has given rise to a large number of evaluations. The absence of consensus in the literature about their impact on R&D is intertwined with the variety of underpinning R&D tax incentives designs. Our meta-analysis aims at explaining this heterogeneity by the designs characteristics of R&D tax incentives. We find that the type of design has a distinct impact on R&D demand in the short run. We argue that these distinct effects are the results of managing a trade-off between providing strong incentives for R&D and simplicity to claim R&D deduction. In this respect, incremental and volume-based designs find a balance between both dimensions while hybrid designs lack clarity and predictability in the short run. Their respective effect can be moderated by additional features (i.e. generosity, targeting rules) even if the latter increases complexity and decreases predictability. We conclude by highlighting the importance of having a stable, clear, and simple framework to enhance the effect of R&D tax incentives.

Blandinières, Florence, Daniela Steinbrenner and Bernd Weiß (2020), Which Design Works? A Meta-Regression Analysis of the Impacts of R&D Tax Incentives, ZEW Discussion Paper No. 20-010, Mannheim. Download


Blandinières, Florence
Steinbrenner, Daniela
Weiß, Bernd


Meta-analysis - research and innovation policies - tax incentives