Frischseminar: Arvid Raknerud

Innovation outcomes of public R&D support: Can statistical learning inform causal inference?

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Abstrakt: We examine the impact of R&D tax credits and direct R\&D

subsidies on Norwegian firms' innovation activities, as measured by

patent and trademark applications. To address the problem of endogenous

selection, we apply machine learning methods to estimate average

treatment effects, which are applicable to situations where there is a

huge number of potential  confounding factors (p) relative to number of

observations (n) (possibly p > n), or equivalently, the "true" control

function is unknown and cannot be estimated consistently. We extend the

literature originally  developed by Belloni et al. (2014, 2016) and

Chernozhukov et al. (2018), by using statistical learning methods in the

context of an event study design, where treatments are sequential and

possibly repeated. Our results  show that both direct subsidies

and tax credits have significant positive effects on innovation

activities. However, the magnitude of the effects depend critically on

firms' pre-treatment characteristics. In particular, the statistically

significant estimates are all related to firms without prior  

innovations. Moreover, our results suggest that R\&D support should be

directed to promote innovations at the extensive margin, i.e. to firms

with a high potential of becoming innovative rather than to firms with a

record of being innovative.


Publisert 12. mars 2021 16:25 - Sist endret 25. mai 2021 09:01