Innovation outcomes of public R&D support: A new approach to identifying output additionality

What difference does government support of business R&D make to the rate of innovation? Addressing this important question has deep theoretical roots and broadening practical applications in OECD countries. The analysis of output additionality has been hampered by incomplete data combined with adaption of problematic methodologies. In this light, we contribute to the formative literature in three main ways: we analyze comprehensive panel data of Norwegian enterprises over a 20-year period; we include trademarks and industrial designs as well as patents to broaden measures of innovation output; and we apply machine learning methods to estimate treatment effect functions, thereby addressing the problem of a practically unlimited number of potential confounding factors. Our findings support and elaborate earlier work that fiscal stimulus tends to have greatest impact on previously non-innovative firms. The impact of support measures, alone or in combination, is on the extensive rather than intensive margin. For previously R&D-active firms, our results indicate that public support has low additionality and even risks crowding-out private financing of R&D.