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Review of Quantitative Finance and Accounting

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19-08-2017 | Original Research

Innovation quality of firms with the research and development tax credit

Author: Wei-Chuan Kao

Published in: Review of Quantitative Finance and Accounting | Issue 1/2018

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Abstract

This paper examines innovation quality of U.S. research tax credit users (i.e., firms with currently earned research tax credits). Prior literature reports that the research tax credit is effective in increasing research and development (R&D) expenditures and reducing managers’ myopic behavior. However, little is known about the real (or economic) effect of R&D tax credits, as most of these findings have been based on estimated R&D tax credits rather than actual R&D tax credits. Additionally, some researchers and the government still have concerns about the real effect of R&D tax credits by criticizing the ambiguity and complexity of the tax codes (IRC Section 41). Therefore, I use actual R&D tax credits identified in firms’ 10-K and state R&D tax credits as identification tests to reduce endogeneity issues. My results indicate that research generating R&D tax credits contributes to better innovation quality and higher return volatility but lower pre-tax profitability. Overall, these findings imply that enacting the R&D tax credit provisions would trigger better innovation.

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Hereafter, I define “R&D tax credit users” as firms with currently earned R&D tax credits, while “non-R&D tax credit users” or “non-users” refers to firms without these credits.

Specifically, R&D tax credits equal 20% of excess QREs net of tax, so excess QREs are equivalent to less costly expenses than other R&D expenses.

This paper focuses on innovation quality, and therefore does not measure a firm’s innovation quality using tech proximity in Balsmeier et al. (2017) or innovation originality in Hirshleifer et al. (2017). Unlike patent rank or patents’ forward citations, these two convey additional information of patents’ complexity or diversity.

R&D tax credits for states generally follow the above definition for U.S. federal R&D tax credits.

For example, utilities and overhead related to research in the experimental or laboratory sense are § 174 expenditures but are not QREs under § 41. Legal and patent expenses, including attorney fees in making and perfecting the application, are research and experimentation expenditures under § 174, but are neither QREs under § 41 nor R&D expenses under FAS 2.

For example, different from R&D expenses under FAS 2, QREs under § 41 exclude research conducted outside the U.S., research in the social sciences or humanities, and funded research.

This paper investigates regular R&D tax credits because the regular credit rate is relatively higher than other types of R&D tax credit rates. In addition, most firms choose to use regular R&D tax credits (GAO 2009). The IRS now calls the tax credit the corporate research credit, although the official name on IRS Form 6765 is “Credit for Increasing Research Activities”.

For instance, firms’ development costs accounted for 80% ($226.6 billion) of their R&D in 2009, while their applied research took 14.5% ($41.1 billion) and basic research 5.2% ($14.82 billion).

There is a 2-year gap between R&D expenditures (simultaneously occurred with patent application) and patents granted as it takes, on average, 2 years for the United States Patent and Trademark Office to grant a patent application (Hall et al. 2001, 2005). Hall et al. (2001) also show that in 1990, most patents start receiving citations in 5 years. Recent papers (Hirshleifer et al. 2012; Lyandres and Palazzo 2016) use a 3-year gap between R&D expenditures and patent citations to calculate innovation efficiency. Therefore, I use the average 4-year citation lag, resulting in my sample period from 1997 to 2007.

Keywords include “research and development tax credit,” “research and development credit,” “R&D tax credit,” “R&D credit,” “research and experimentation tax credit,” “research and experiment tax credit,” “research and experimentation credit,” “research and experiment credit,” “tax credit from research activities,” “R&E tax credit,” “research tax credit,” and “research credit.” I use Boolean and wildcard analysis to increase the probability of identifying R&D tax credit users.

I also do 1 and 99% winsorization and the results are not affected by this alternative winsorization.

Firm and year subscripts are suppressed in my equations and discussions.

I use the classification developed by the Organization for Economic Cooperation and Development (2003) using the first-two SIC code of technology areas. Firms classified by the OECD as high technology and medium–high tech are re-classified as high-tech.

I perform the following alternative matching criteria: (1) matching an R&D tax credit user by its firm size, prior patents(PatSuccess _t1 or PatSuccess _t1–5), industry and year; (2) replace the dummy variables, RDLead, TaxProf, RDStart, CashD and LevD, with the with the continuous variables and do the same PSM matching; (3) using alternative PSM, including radius matching, Kernel matching, Mahalanobis matching of the first R&D tax credit users, as well as finding the matching firm-years of R&D tax credit firm-years. The results with alternative matching or without matching remain consistent (available upon requests).

This database includes patent rank of each patent and other patent data in NBER or Patent Network Dataverse at Harvard Business School. Additionally, this database updates the data frequently and thus contains more updated patent data than the previous two databases.

The R&D tax credit users in top five industries are computer software and data services (SIC codes of 73); chemical, biotech and drug (SIC codes of 28); electrical and electronic components (SIC codes of 36), medical and scientific instruments (SIC codes of 38), and machinery and computer equipment (SIC codes of 35).

All the results of additional tests are untabulated and can be obtained upon request. Please also see the detailed measurements of alternative (additional) variables in the “Appendix”.

I also use the seemingly unrelated regression to include state impacts on excess QREs. The untabulated results remain consistent (available upon request).

My sample size is reduced by 1 year because the data of state R&D user costs from Wilson (2009) stop in 2006.

I also use Heckman two-stage regressions to control for the potential self-selection. In the first stage, I employed a probit regression, Eq. (1), which models the likelihood of earning R&D tax credits on firm characteristics identified from IRC Section 41, including research ability, tax advantage, and year-industry fixed effects. In the second stage, I employed main regressions (2) and (3) that further include inverse Mill’s ratio calculated from the first stage to control for selection bias. The results remain consistent and are available upon request.

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Title: Innovation quality of firms with the research and development tax credit
Author: Wei-Chuan Kao
Publication date: 19-08-2017
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 1/2018
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-017-0661-x

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