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

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  • Pöschel, Carla

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.

Suggested Citation

  • Pöschel, Carla, 2020. "Incentive Effects of R&D Tax Incentives: A Meta-Analysis Focusing on R&D Tax Policy Designs," arqus Discussion Papers in Quantitative Tax Research 243, arqus - Arbeitskreis Quantitative Steuerlehre, revised 2020.
    • Handle: RePEc:zbw:arqudp:243
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    Keywords

    R&D; tax incentives; additionality effects; direct approach; meta-regression analysis; random forest;
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