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Innovation quality of firms with the research and development tax credit

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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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Notes

  1. 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.

  2. 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.

  3. 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.

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

  5. 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.

  6. 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.

  7. 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”.

  8. 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).

  9. 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.

  10. 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.

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

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

  13. 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.

  14. 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).

  15. 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.

  16. 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).

  17. 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”.

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

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

  20. 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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Acknowledgements

This paper is based on my dissertation. I am grateful for the funding provided by MOST 105-2410-H-002-044- and thank for the comments from the editor and the anonymous reviewers. I especially thank Kumar Sivakumar (Chair), Krishnagopal Menon, Donald J. Smith, Edward Riedl, Alison Kirby Jones, Timothy Simcoe, and Moshi Hagigi for their guidance and advice. I also thank the editor, anonymous referees, Lynn Lei Li, Nopmanee Tepalagul, Wasinee Thammasiri, Pavinee Manowan, Mahfujia Malik, Nacy Chun Feng, seminar participants in Boston University, UMass-Boston, SUNY-Brockport, NTU, and NCCU, and conference participants in 2013 Rookie Camp, 2014 ATA midyear Meeting, and 2015 AAA Annual Meeting for their helpful comments and suggests. I thank Monte J. Shaffer for providing the data in CRIE. This is substantially revised version of the paper that was previously circulated as “Characteristics and Performance of R&D Tax Users.”

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  1. Department of Accounting, College of Management, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei City, 106, Taiwan

    Wei-Chuan Kao

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Appendix

Appendix

Variable name

Measurement

Age

The natural logarithm of the number of years that the firm has been listed on CRSP monthly returns tape or the Compustat Fundamental Annual, whichever occurs first

BHAR

Buy and hold abnormal returns during the current fiscal year, measured as the geometrically summation of the 12-monthly returns (Ret) minus the value weighted returns (vwret)

CapInt

The natural logarithm of the ratio of net property, plant, and equipment (ppent) to the number of employees (emp) in the current year

Cash

The ratio of the current cash and cash equivalent (che) to the current total assets (at)

CashShort

The ratio of the negative free cash flows before R&D to total assets in the current year, − [cash flow from operations (oancf) + R&D (xrd) − capital expenditures (capx)]/total assets (at)

CashShortD

Equal to one for a firm with top ten percent of cash shortfall in the current year; zero otherwise

CR

Equal to one for a firm that disclose earned R&D tax credits in the current year; zero otherwise

ExcQRE

The ratio of the current R&D tax credit amounts divided by [.2 × (1 − statutory tax rate)] to the current sales (sale)

FCite

The natural logarithm of one plus the average of the adjusted forward citation count 4-year-ahead (FCite4), 1-year-ahead (FCite1), until 2012 (FCite2012) across all patents applied for by the firm during the current year. Each patent’s forward citation count is scaled by the average citation count of all patents in the same technology class and year

FCite_exSelf

Same as FCite but excluding self-citations for 1-year-ahead (FCite1_exSelf), 4-year-ahead (FCite4_exSelf), and until 2012 (FCite2012_exSelf)

FE

The ratio of operating income before depreciation (oibdp), advertising (xad) and R&D expenditures (xrd) to sales (sale) over 1-year-ahead (FE1) and over 5 subsequent years (FE5).

FE_ indadj

Future earnings per sale weighted by the industry average over 1-year-ahead (FE1_indadj) and over 5 subsequent years (FE5_indadj)

GDP

Natural logarithm of real gross domestic product (gdpr1) in the current year

InstOwn

Percentage of total institutional shareholdings to shares outstanding in the current year

Lev

The ratio of the sum of short-term debt (dlc) and long-term debt (dltt) to total assets (at) in the current year

LevD

An indicator for a firm ranked in the top deciles of current leverage

MTB

Market value of equity to book value of equity in the current year (csho × prcc_f)/seq

MV

Natural logarithm of current market value of equity (csho × prcc_f)

OthRD

The current R&D expenditure (xrd) minus ExcQRE divided by the current sales (sale)

PatRnk

The natural logarithm of the summation of a firm’s total patent scores estimated by using the marginal-combined (mc) Patent Rank specification in Shaffer (2011)

PatSuccess t1

The natural logarithm of one plus patent application counts in the prior year

PatSuccess t1–5

The natural logarithm of one plus the average patent application counts during the past 5 years

RDLead

Equal to one when a firm is ranked in the top 25% of firms in terms of the number of patent applications within its industry; zero otherwise

RDStart

Equal to one for a firm starting to spend R&D (xrd) less than 10 years ago; zero otherwise

ROA

Ratio of current net income (ib) to current total assets (at)

SALE

The natural logarithm of current sales (sale)

Seg

The natural logarithm of the number of segments in the current year

StateAdoption

Equal to one if the state adopts R&D tax credits in the current year and afterwards; zero otherwise

StateRDCrtRate

The state’s R&D tax credit rate

StateRDUserCost

The state R&D after-tax user cost from Wilson (2009)

Std_FE

The standard deviation of future earnings per sale, including 3-year-ahead earnings per sale from year t + 1 to t + 3 (Std_FE3), 5-year-ahead earnings per sale from year t + 1 to t + 5 (Std_FE5), 3-year earnings per sale from year t to t + 2 (Std_cFE3), and 5-year earnings per sale from year t to t + 4 (Std_cFE5). I use quarterly data to measure earnings per sale here, quarterly operating income before depreciation (oibdpy) and before R&D (xrdq) divided by sales (saleq). The standard deviation of future earnings per sale is the difference between earnings per sale for the same quarter of the previous year, computed over a 3-year (or 5-year) period, requiring a minimum of four quarters of data and annualized by multiplying by \(\sqrt 4\)

Std_RET

The standard deviation of the 12-monthly stock returns (ret) during the current year

StkOption

Equal to one for a firm with nonzero executive options outstanding in the current year (opt_unex_exer_num + opt_unex_unexer_num) or with nonzero executive options granted (option_awards_num) if the current options are exercised in the same year; zero otherwise

TanATS

The ratio of tangible assets to sales in the current year, calculated as the sum of plant property and equipment (ppent), inventory (invt), investments and advances–equity, and investment and advances–other (ivao) divided by sales (sale)

TASSET

The natural logarithm of current total assets (at)

TaxProf

Equal to one for a firm with nonzero current tax expense (txc) and no operating loss carryforwards (tlcf); zero otherwise

TechInt

Equal to one for high-tech industries using the SIC two-digit classifications of technology areas developed by the Organization for Economic Cooperation and Development (2003); zero otherwise

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Kao, WC. Innovation quality of firms with the research and development tax credit. Rev Quant Finan Acc 51, 43–78 (2018). https://doi.org/10.1007/s11156-017-0661-x

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