Incentive Effects of R&D Tax Incentives: A Meta-Analysis Focusing on R&D Tax Policy Designs - Carla Pöschel (Arbeitskreis Quantitative Steuerlehre (arqus), 2020)
Despite the growing literature on the effectiveness of research and development (R&D) tax incentives, little is known about the differing design aspects of the underlying tax policies. In this paper, I apply meta-regression analysis (MRA) to separate the distinct provisions through which various tax schemes affect firms' R&D expenditures. Using 192 estimates from 19 studies exploiting the direct approach, the results indicate, on average, greater input additionality effects of hybrid regimes in comparison to volume-based and incremental ones. MetaForest, a novel machine learning algorithm, confirms these results: the moderator for hybrid schemes is the most important variable in explaining the heterogeneity among estimates. Unlike previous MRA, I find only weak evidence for publication bias in this stream of literature. Overall, the relation between tax incentives and R&D expenditures is positive, on average, but the strength varies with methodological variations across studies.
Keywords: 330; R&D; tax incentives; additionality effects; direct approach; meta-regression analysis; random forest