Effectiveness of Fiscal Incentives for R&D: Quasi-Experimental Evidence

Abstract

With growing academic and policy interest in research and development (R&D) tax incentives, the question about their effectiveness has become ever more relevant. In the absence of an exogenous policy reform, the simultaneous determination of companies' tax positions and their R&D spending causes an identification problem in evaluating tax incentives. To overcome this identification challenge, we exploit a U.K. policy reform and use the population of corporation tax records that provide precise information on the amount of firm-level R&D expenditure. Using difference-indifferences and other panel regression approaches, we find a positive and significant impact of tax incentives on R&D spending , and an implied user cost elasticity estimate of around-1.6. This translates to more than a pound in additional private R&D for each pound foregone in corporation tax revenue.

Will the real R&D employees please stand up? Effects of tax breaks on firm-level outcomes

“…The findings of this paper suggest that the reform in the R&D tax relief that changed the status of the enterprises in the treatment group from 'large' to 'SME' caused these firms to increase their R&D spending by 15-20%. Guceri and Liu (2015) estimate that the drop in the user cost of R&D capital for the treated group of firms thanks to the 2008 reform of the SME tax relief eligibility criteria was around 17%. In the BERD data, I do not have information on the tax position of each firm and therefore cannot calculate individual values for the user cost of R&D, but in this section, I refer to the point estimate of 17% in order to infer the implied user cost elasticity.…”

“…The use of information on total R&D spending (as opposed to only qualifying R&D) and the possibility to differentiate between 'price' and 'quantity' effects distinguishes this paper from other recent working papers that use the UK tax returns data on qualifying R&D. The BERD dataset that I use in this study and the corporate tax returns data cannot be matched due to legal restrictions. Regarding the papers that use the tax returns data, Guceri and Liu (2015) estimates the effect of the R&D tax relief on R&D spending that qualifies for the tax breaks and Dechezlepretre et al (2016) focuses on the patenting behaviour of firms, again referring to the data on changes in 'qualifying' R&D. Both these latter studies find very high elasticities of qualifying R&D with respect to its user cost. In fact, comparing the elasticity of total R&D with respect to its user cost from the present paper (−1.18) with the elasticity of qualifying R&D with respect to its user cost found in Guceri and Liu (2015) (−2.34), we can infer that all of the impact comes from the qualifying expenditure response, confirming Rao (2016).…”

“…3 A recent wave of studies use administrative data in a quasi-experimental setup (examples are Agrawal et al 2014;Guceri and Liu 2015;Rao 2016;Dechezlepretre et al 2016). These studies generally find a positive and significant effect of tax incentives on R&D spending by businesses.…”

Banking Consolidation and Small Firm Financing for Research and Development

“…Data on R&D obligations come from the NSF's WebCASPAR database (?). 17 Other examples of research into R&D tax incentives include Chang (2014) and Guceri and Liu (2015). 18 To determine which types of patents to include in this measure, I sample the largest SBIR agency's (Department of Defense) award winners from the SBA Tech-Net Database.…”

“…18 To determine which types of patents to include in this measure, I sample the largest SBIR agency's (Department of Defense) award winners from the SBA Tech-Net Database. I present results where the patent count control variable includes total patent applications for the following two-digit Hall, Jaffe, and Trajtenberg (2001) technology categories: gas (13), communications (21), computer hardware and software (22), computer peripherals (23), information storage (24), electrical devices (41), electrical lighting (42), measuring and testing (43), nuclear and x-rays (44), power systems (45), semiconductor devices (46), miscellaneous electronics (49), materials processing and handling (51), metalworking (52), motors (53), optics (54), transportation (55), miscellaneous mechanical (59), and heating (66). I also check the results using all patent applications, and the results are nearly identical.…”