Staffordshire University logo
STORE - Staffordshire Online Repository

The relative effectiveness of R&D tax credits and R&D subsidies: A comparative Meta-Regression Analysis

Dimos, Chris, PUGH, Geoff, Hisarciklilar, Mehtap, TALAM, Ema and Jackson, Ian (2022) The relative effectiveness of R&D tax credits and R&D subsidies: A comparative Meta-Regression Analysis. Technovation. ISSN 0166-4972

[img] Text
Dimos_et_al_2019_Technovation_revised_mh_gp.docx - AUTHOR'S ACCEPTED Version (default)
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

Download (286kB)

Abstract or description

There are large primary literatures that evaluate the effectiveness of either R&D tax credits or R&D subsidies in promoting private R&D. However, this Meta-Regression Analysis, by investigating these literatures jointly, is the first study that systematically measures and compares the effectiveness of these two policy instruments. After controlling for publication selection and sources of heterogeneity, we find that both tax credits and subsidies induce additional private R&D and that neither instrument systematically outperforms the other. However, whereas subsidy effects are increasing over time tax credit effects are not. Although their respective effects are “small”, they are not negligible: in round terms, an additional $1 of public R&D support of either type induces 7.5 cents of additional private R&D expenditure. Sources of heterogeneity in the reported effects include: tax credits are most effectively delivered as “incremental” schemes, are more effective in economies with a balanced “policy-mix” regime, and are generally less effective for micro firms and SMEs than for large firms; while subsidies are more effective for manufacturing firms, although not for high-tech firms, and are more effective than tax credits in economies predominantly using subsidies. Finally, we argue for the importance of statistical power in the design of evaluation studies.

Item Type: Article
Uncontrolled Keywords: R&D tax credits; R&D subsidies; Meta-regression analysis; Publication bias; Policy evaluation; Additionality
Faculty: Staffordshire Business School > Accounting and Finance
Depositing User: Geoff PUGH
Date Deposited: 24 Jan 2022 11:50
Last Modified: 01 Mar 2024 01:38

Actions (login required)

View Item View Item

DisabledGo Staffordshire University is a recognised   Investor in People. Sustain Staffs
Legal | Freedom of Information | Site Map | Job Vacancies
Staffordshire University, College Road, Stoke-on-Trent, Staffordshire ST4 2DE t: +44 (0)1782 294000