Incentive Effects of R&D Tax Incentives: A Meta-Analysis Focusing on R&D Tax Policy Designs

Carla Pöschel

No 243, arqus Discussion Papers in Quantitative Tax Research from arqus - Arbeitskreis Quantitative Steuerlehre

Abstract: 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: R&D; tax incentives; additionality effects; direct approach; meta-regression analysis; random forest (search for similar items in EconPapers)
Date: 2020, Revised 2020
New Economics Papers: this item is included in nep-acc, nep-cfn and nep-tid
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:arqudp:243

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