The changing implications of research and development expenditures for future profitability

Asher Curtis University of Washington

abcurtis@uw.edu

Sarah McVay University of Washington

smcvay@uw.edu

Sara Toynbee University of Texas-Austin

sara.toynbee@mccombs.utexas.edu

November 2018

Abstract: We provide robust evidence of an economically and statistically significant decline in the association between current R&D expenditures and future profits over time. We consider a number of contributors to this decline, finding some evidence that potential shifts in the nature of R&D activities to safer projects and changes in the sample composition provide partial explanations for the lower profitability of R&D in recent years. Noting that the decrease in the average profitability of R&D also coincides with a significant increase in R&D spending, we also provide evidence that R&D investments experience diminishing marginal returns. Documenting that R&D profitability has reduced implications for future profitability in recent years, and providing initial evidence on the source of this decline, is important for financial statement users as R&D is an important investment of many firms. Keywords R&D; Earnings Implications; Capital Markets JEL Classification M41

* We appreciate comments and suggestions from two anonymous reviewers, the editor (Patricia Dechow), Bill Baber, Linda Bamber, Darren Bernard, Dave Burgstahler, Ed deHaan, Steve Fortin, Weili Ge, Frank Hodge, Allison Koester, Yaniv Konchitchki (FARS Discussant), Russell Lundholm, Mort Pincus, Bob Resutek, Edgar Rodriguez Vazquez, Holly Skaife, Theodore Sougiannis, Logan Steele (AAA Discussant), Ben Whipple, and participants from the 2013 UBCOW Conference, 2014 AAA Annual Meeting, 2015 FARS Mid-Year Meeting, 5th Annual INSEAD Accounting Symposium in 2015, 2016 University of Illinois-Chicago Accounting Conference, Georgetown University, McGill University, Oregon State University, Southern Methodist University, the University of California–Irvine and the University of Georgia.

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1. Introduction

Research and development (R&D) activities are an integral part of value creation for

many firms, and the economy more generally. As an indication that R&D costs are widely

viewed as a measure of economic growth, US GDP calculations have included these costs since

2013. Early academic research documents a positive association between R&D expenditures and

future profitability on average (Lev and Sougiannis 1996), highlighting the importance of

considering R&D expenditures when forecasting future performance.

There have been a number of changes in the economy as a whole as well as specifically

in the R&D domain that could impact the payoffs to R&D expenditures over time. These include

changes in the cost of conducting research, shifts from strategic to maintenance oriented R&D

activities, and substantial changes in the underlying composition of firms conducting R&D.

These changes also interact with the empirical observation that spending on R&D has increased

dramatically over time, both in aggregate and in terms of its importance in many firms’

investment portfolios. Specifically, aggregate R&D spending increased from $16 billion in 1979

to $465 billion in 2013 (Grueber and Studt 2013) and among firms reporting non-zero R&D, the

proportion of R&D to other investments (fixed assets, M&As and SG&A) has grown from 9.9%

percent in 1980 to 17% in 2011. Although these changes could influence the average profitability

of R&D, whether their effects cumulatively result in an increase or decrease is unclear ex ante.

We first investigate whether R&D profitability has changed over time. Specifically, we

measure R&D profitability using a linear forecasting framework in which we regress five

cumulative years of future net income on current R&D expense and other investment

expenditures (e.g., capital expenditures). Following Lev and Sougiannis (1996), we add back

R&D, depreciation and advertising expense to net income to avoid mechanical associations

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between R&D expenditures and the associated costs of the production and sale of any resulting

products. We examine R&D expenditures made between 1980-2011, with future profits

measured from 1981-2016. We interpret the coefficient on R&D as a measure of average firm-

level R&D profitability.

Our results illustrate an economically and statistically significant decline in average firm-

level R&D profitability over time. Specifically, we find a significant negative coefficient on the

interaction between R&D expenditures and a linear trend term. We further investigate the pattern

of this decline by estimating rolling window sample periods. We document a continuous decline

in R&D profitability from 1980 to the mid-1990s, after which average firm-level R&D

profitability appears to stabilize at a lower level.

We perform several additional analyses to examine whether our main inference is robust

at the aggregate level (versus firm-level), to the use of alternative measures of firm profitability

(e.g., sales, returns and performance measures adjusted for possible survivorship bias), over

alternative horizons (one, three and eight year horizons), and when measuring R&D expenditures

as a capitalized asset. Throughout these analyses we observe an economically significant and

pervasive decline in R&D profitability over time.

We identify and test several cpotential ontributors to this decline in R&D profitability.

First, it is possible that lower interest rates over time lead to lower required rates of return, which

would lower the average profitability of investments. Specifically, if firms invest in more

projects with successively lower expected rates of return (because more projects now meet the

lower required return), the average profitability of these investments would be lower. Because

this argument applies to all investments, that we do not see a similar decline for other

investments makes this explanation, as well as any other general macroeconomic trends that are

not unique to R&D, unlikely to be a major contributor to the decline in R&D profitability.

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Second, it is possible that R&D activities, on average, have switched to research projects

that require lower rates of return (e.g., because they are more maintenance oriented and thus are

less risky). We do find some evidence consistent with this possibility, as the association between

R&D and future earnings volatility, which proxies for the uncertainty of investment outcomes

(e.g., Kothari et al. 2002), has fallen over time. We also attempt to separate R&D expenditures

between those that are focused on maintenance activities (historical levels) versus strategic

initiatives (R&D changes), and provide evidence of a sharper decline in R&D profitability

among strategic R&D investments. The decline in average profitability is present for both types

of R&D activities, however, suggesting that changes in R&D activities do not provide a

complete explanation for the overall decline.

Third, we consider how shifts in the population of public firms conducting R&D (e.g.,

new cohorts, different industry representation, and different size representation) contribute to the

decline in the average profitability of R&D. Consistent with Srivastava and Tse (2016), we find

that successive cohorts tend to spend a higher than average amount on R&D. We also present

evidence that newer cohorts experience lower R&D profitability, suggesting that successive

cohorts have contributed to the decline in average R&D profitability. This is not the sole

contributor, however, as we continue to see a significant decline in profitability among our 1980s

cohort. Moreover, all but the oldest firms have similar levels of R&D profitability since the early

2000s. Similarly, we also find some evidence that changes in the industry composition of our

sample contributes to, but does not fully explain, the average decline in R&D profitability.

Finally, although prior literature demonstrates an increasing number of small firms over time

(Collins et al. 1997) and scale benefits to R&D profitability for large firms (Ciftci and Cready

2011), scale does not appear to be a major contributor to the decline in average R&D

profitability.

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Overall, we provide preliminary evidence of some potential, non-mutually exclusive,

contributors that partially explain the decline in the average profitability of R&D. We do not

consider an exhaustive set of contributors nor claim to provide evidence of a single cause of the

decline, which would arguably be too simplistic given the complex and dynamic setting we

examine.

We next explore the extent to which a broad explanation of diminishing marginal returns

applies to our evidence. Generally, our results demonstrate that the decline in the average

profitability of R&D coincides with an increase in aggregate R&D spending, and the subsequent

stabilization occurs when there is a steadying trend in R&D spending. This pattern is consistent

with the notion of diminishing marginal returns, which predicts that if spending increases at a

faster pace than increases in investment opportunities, the average return will decline (e.g.,

Stigler 1963; Fairfield et al. 2003). We explore this possibility by presenting cross-sectional tests

that illustrate variation in R&D profitability based on a prediction of diminishing marginal

returns. Specifically, firms with the most intensive spending and that are the least constrained in

their investment opportunities (due to low leverage) have lower R&D profitability on average.

We also find that the firms with the highest level of spending and lowest leverage experienced

the greatest average increases in spending over time, coinciding with these same firms

experiencing the largest decline R&D profitability.

Our paper shares some similarities with prior work but is distinct in terms of focus and

contribution. Specifically, we provide further evidence that R&D expenditures are associated

with future profitability (Griliches 1995; Lev and Sougiannis 1996), but also document

significant declines in R&D profitability over time. Our evidence suggests that changes in R&D

activities and the composition of US public firms contribute to the lower profitability of R&D in

more recent years, but do not fully explain the overall decline in average R&D profitability over

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the last three decades. These finding contribute to research in economics examining R&D

productivity (e.g., Bloom et al. 2017).

Our finding that the association between R&D expenditures and future accounting

income has declined significantly over time has implications for financial statement users and

accounting researchers examining pooled cross-sectional effects of R&D spending (or other

innovation related activities) on firm performance. Because historical estimates of the association

between R&D expenditures and future income do not generalize out-of-sample, forecasting the

outcomes from R&D activities is challenging for financial statement users. Similarly, researchers

examining coefficients on pooled data over time could be masking strong time trends. Our

findings also contribute to the growing body of evidence in the economics literature that R&D

productivity has been falling over time. For example, examining the number of researchers as the

input, and production output such as agricultural yields, concurrent work by Bloom et al. (2017)

conclude that research productivity is falling, implying more and more research effort is needed

to reach the same level of economic growth. By linking R&D expenditures to corporate profits

our study should be informative to users of financial statements wishing to understand the

implications of R&D for future profitability.1

2. Motivation and theoretical development

R&D activities are aimed at the discovery of new knowledge (ideas) and the refinement

of this knowledge into practical applications (including goods, services and processes).

Investments in R&D activities are an important source of value creation for individual firms and

1 Our results do not imply that managers’ actions are inefficient. There are a myriad of reasons to undertake R&D even if the profitability of these investments have fallen over time, such as to simply maintain a firm’s competitive position. Moreover, simply because it might take greater investment to reach the next technological breakthrough, it does not follow that this research should not be undertaken.

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the economy as a whole, and aggregate R&D expenditures are typically seen as a leading

indicator of economic growth (Lev 2001; Bloom et al. 2017). On average, approximately 40% of

US publicly traded firms between 1980–2011 disclose a non-zero amount of R&D expense in

their financial reports, allocating a median of 5% of their total assets annually to R&D expense

over this time period (Table 1). Thus, it is important for financial statement users to understand

the implications of R&D spending for future profitability (hereafter R&D profitability).

Among other things, the average profitability of R&D is a function of investment

opportunities and the expected and required rates of return, which in turn are a function of the

type and cost of R&D activities and the firms performing these activities. Early research

documents that investments in R&D are positively associated with future profits on average (Lev

and Sougiannis 1996) but are associated with greater future earnings volatility than capital

expenditures, suggesting R&D activities are riskier on average than investments in fixed assets

(Kothari et al. 2002). Since these early studies, however, there have been significant changes in a

number of the factors that influence the profitability of R&D investments and it is important to

understand what the cumulative effect of changes are on the average profitability of R&D.

We first consider how potential changes in the cost of R&D activities over time may be

associated with changes in the average profitability of R&D, holding investment opportunities

constant. There are at least two relevant factors that may have lowered the cost of R&D projects

over time: 1) lower interest rates and 2) better technology that makes innovation more efficient

and cheaper, “allowing companies to test new ideas at speeds and prices that were unimaginable

even a decade ago” (Brynjolfsson and Schrage 2009). All else equal, lower costs allow firms to

retain a greater portion of the payoffs from their R&D projects, resulting in a greater expected

return on these investments.

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To the extent the costs of R&D projects represent a binding constraint on firms’

investment decisions (i.e., firms invest in all projects for which the expected return meets the

required rate of return), the number of projects that meet the required rate of return would likely

increase as costs decline, leading to greater spending on R&D activities. If firms exploit their

most profitable projects first, new projects should have lower expected returns, resulting in a

corresponding decline in the average return on R&D expenditures as spending increases (e.g.,

Stigler 1963; Fairfield et al. 2003). Over time we observe a significant increase in R&D

spending, both at the aggregate level and for the median and average firm in our sample (see

Figure 1 and Table 1). Thus, it is possible that lower costs of R&D could contribute to the

increase in spending, which in turn would decrease the average profitability of R&D.

Survey evidence suggests that the nature of R&D activities has also changed over time

(Jaruzelski et al. 2014). Rather than being a highly speculative investment, scientific and other

economic changes suggest that R&D activities have become less effective at driving strategic

shifts and are now best characterized as maintenance activities for many firms. For example,

Jaruzelski et al. (2014) note “Innovation leaders say that 58 percent of R&D spending today

focuses on incremental or renewal innovations, i.e., those relating to current products, 28 percent

on new innovations and only 14 percent on breakthrough innovations.” Maintenance-like

activities could have shorter and less uncertain payoffs in recent decades and thus be more likely

to have lower expected and required rates of return than R&D activities conducted in earlier

decades. 2 To the extent that a greater proportion of R&D expenditures are allocated to

2 For instance, maintenance or incremental R&D activities could result in situations in which R&D output cannibalizes existing product lines, consistent with the idea of creative destruction (Aghion and Howitt 1992; Grossman and Helpman 1991), which would likely shorten and reduce the payoffs associated with a dollar of R&D spending.

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maintenance-oriented activities over time, average R&D profitability could fall as a result of the

lower average level of risk.

There have also been changes in the types of firms investing in R&D activities, which

could affect the average profitability of these expenditures even if an individual firm’s R&D

profitability is not changing. Srivastava and Tse (2016) demonstrate that successive cohorts of

firms spend increasingly more on R&D over time, contributing to the increase in the aggregate

and average level of R&D spending among US public firms. If the profitability of newer cohorts

is systematically different from existing cohorts, the average association between R&D and

future profitability will likely change.3 Moreover, Ciftci and Cready (2011) document that larger

firms benefit from scale and thus experience higher returns to R&D. Thus, if small firms

represent a larger proportion of US public firms over time (Collins et al. 1997), average R&D

profitability could decline simply as a result of a changing sample composition. Similarly, prior

studies investigate how changes in the industry composition of firms over time can influence

inferences about the average characteristics of firms, such as earnings quality (Dichev and Tang

2008; Srivastava 2014). Thus, given likely variation in R&D profitability across industries, shifts

in the industry composition of our sample could influence the average payoff to R&D even if

within an industry it has not changed.

Our discussion thus far explores how the profitability of R&D could have changed over

time given changes in the R&D setting, holding R&D-related investment opportunities constant.

It is possible, however, that the increase in R&D spending reflects management responding to

3 Srivastava and Tse (2016) provide evidence that these newer cohorts are riskier. It is unclear whether this would increase or decrease the profitability of R&D. It is possible that newer cohorts pursue riskier projects and thus require higher rates of return, resulting in higher R&D profitability. In contrast, to the extent these newer firms spend on more speculative investment opportunities, the higher failure rates could lead to lower R&D profitability. We explore these alternatives in section 5.

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greater investment opportunities (Eberhart et al. 2004).4 Moreover, an increase in R&D spending

could create new investment opportunities (e.g., Romer 1986; Shapiro et al. 1998). Specifically,

a unique feature of R&D activities relative to investments in fixed assets is that R&D activities

are generally non-rivalrous in nature, meaning that their output can typically be used by a

broader set of parties than the inventor herself. Thus, spending can result in positive spillover

effects in terms of investment opportunities (e.g., the invention of touchscreen technology

increased investment opportunities for both the inventor and other parties).5 It is unclear how on

average R&D profitability will change in response to the cumulative effects of changes in the

R&D setting. Generally, if investment opportunities increase at a faster (slower) rate than the

other effects, we expect the average profitability of R&D will increase (decrease) over time.

Research in economics provides some evidence that R&D profitability could have

changed over time in a manner consistent with diminishing marginal returns by documenting that

although research effort is rising substantially, research productivity is declining (Griliches 1990;

Jones 1995; Kortum 1997; Bloom et al. 2017).6 These studies do not attempt to measure firm

specific R&D profitability, however, but instead measure inputs such as the number of R&D

researchers and outputs such as the number of patents or bushels. Linking a decline in

productivity to profitability is not obvious, however, as the cost of each researcher, and the

4Eberhart et al. (2004) conduct portfolio tests by comparing the abnormal profits of firms with large unexpected increases in R&D to those that did not experience this increase. Their sample consists of 8,313 cases between 1951 and 2001. These large unexpected increases likely reflect firms taking advantage of promising new investment opportunities, but it does not immediately follow that R&D, on average, has increased in profitability. 5 It is also possible that the feature of non-rivalry could lower an individual firm’s R&D profitability, as others are able to benefit from the firm’s innovation output. We do not consider this possibility explicitly in our paper but do examine changes in aggregate profitability at the industry level over time in robustness analyses, which should capture the positive spillover effects to the future profitability of other firms in the industry. We continue to find an overall decline in R&D profitability at the aggregate industry level. 6 Other studies argue that the decline in productivity is a result of creative destruction, where new goods effectively replace old goods and the innovator captures the profit streams the original good would have provided the incumbent (Aghion and Howitt 1992; Grossman and Helpman 1991). Finally, many different firms might be racing to solve the same problem (e.g., cancer) thus leading to redundant expenditures (Dubois et al. 2015).

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profitability per patent (or any other R&D output) is not necessarily constant (Lev 2001). Thus,

whether and how R&D profitability has changed over time is unclear ex ante but has important

implications for financial statement users. Consequently, we state our hypothesis in null form:

H1: R&D profitability has not changed over time.

We test our hypothesis by examining whether the average association between R&D

expenditures for firm i in period t and net income over the subsequent five years has changed

over time. We also examine the effect of the potential contributors discussed above on R&D

profitability over time.

3. Sample and Descriptive Statistics

3.1. Sample

We obtain financial data from Compustat and data on mergers and acquisitions (hereafter

M&As) from Thomson SDC Platinum over the period 1980–2016; we begin in 1980 because

R&D expenditures are sparsely reported in Compustat before this point. Because we require five

years of subsequent earnings realizations, our sample period for R&D expenditures (i.e., year t)

is 1980–2011. We require firm-year observations to have positive R&D expenditures (Compustat

item XRD), sales (Compustat item SALE) and total assets (Compustat item AT) and have non-

missing book-to-market ratios in year t. We also require firms have non-missing net income

(Compustat NI) in years t – 1, t, and t + 1.7 We set missing values of our other variables equal to

7 Koh and Reeb (2015) examine the patenting behavior of firms with missing R&D expenditures on Compustat and argue that they may engage in strategic nondisclosure of R&D expenditures. We find that, although the proportion of firms with missing R&D expenditures has declined over time, there has been a similar proportionate increase in firms disclosing zero R&D expenditures, perhaps indicating greater compliance with ASC 730 (formerly SFAS 2), which requires separate disclosure of material amounts of R&D expenditures. To the extent that observations with missing R&D expenditures do not disclose this information for strategic reasons (i.e., because these activities are highly profitable), we would expect that greater proportions of missing R&D expenditures would bias our estimates of average R&D profitability downwards in the earlier part of our sample period. Thus, these reporting trends would bias in favor of finding an increase in R&D profitability over time.

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zero (e.g., M&A). We inflation-adjust all dollar values to be on a 2011 basis. After matching our

sample to CRSP, we obtain a final sample of 44,847 firm-year observations, although actual

sample sizes in our analyses vary due to availability of future periods of income.

3.2. Descriptive statistics

We illustrate the growth in R&D spending for our sample of firms in Figure 1.

Specifically, we plot the median proportion of sales (Panel A), total assets (Panel B), and total

investment (Panel C) spent on R&D over time. We define total investment expenditures as the

sum of R&D expense, capital expenditures, selling, general, and administrative (SG&A) expense

(excluding R&D so as not to double count these costs), and M&A. These plots show a significant

increase in R&D expenditures during the 1980s and 1990s before peaking in the early 2000s and

subsequently declining to similar levels as the late 1990s. We present the median, rather than the

mean, to prevent extreme observations from influencing this trend; however, mean values are

presented in the descriptive statistics in Table 1 and illustrate an even larger increase in spending.

We also present the aggregate level of spending on R&D (inflation adjusted) in Panel D.

Collectively, these figures demonstrate the significant increase in both the level of R&D

spending as well as the prominence of its role in firms’ investment portfolios over time.

We present descriptive statistics in Table 1. In Panel A, we present the full sample and, in

Panel B, descriptive statistics for the early (1980–1995) and later (1996–2011) parts of our

sample separately. The mean R&D/total assets over the full sample is 9% with a median of 5%.

In Panel B, we find evidence of a statistically significant increase in the mean (median)

R&D/total assets from 7% (4%) over 1980–1995 to 10% (6%) over 1996–2011. We see even

higher percentages when benchmarking R&D spending against sales, increasing from an average

of 16% in the early sample period to 48% in the more recent period. The average R&D/Sales,

however, is much higher than the median, indicating there are a significant number of firms that

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have very small sales, skewing these ratios and contributing to our decision to use assets as our

deflator in our regression analyses.

We also provide descriptive statistics for additional variables used in our analyses in

Table 1. Following Lev and Sougiannis (1996), we adjust net income by adding back R&D,

advertising, and depreciation expense (Compustat items NI + XRD + XAD + DP; hereafter, all

references to net income are to adjusted net income). Thus, future values of net income are not

mechanically reduced by future R&D expenditures, depreciation or advertising expenditures,

which could be a result of successful current R&D and capital expenditures. The average and

median firms are profitable (before depreciation, R&D expenditures and advertising expenses)

during the entire sample period.

4. Main analysis

We explore the profitability of R&D expenditures by measuring the association between

current R&D expense and net income over the subsequent five years. We use net income as our

measure of future profitability as this includes both the tax benefit of R&D expenditures and any

gains from the sale of R&D generated ideas and/or outputs, allowing us to capture a variety of

benefits to R&D, even if such benefits are realized in alternative ways over time (e.g., through

the sale of patents or in-process R&D).9 We consider a model of R&D profitability in which

R&D expenditures can be linked to cumulative future profits using a linear forecast (Doyle et al.

9 Although we could use patents as an alternative output measure, patents are the mechanism by which profits are earned (Lev 2001) and our focus is on the usefulness of financial statement information for forecasting future performance. Economists have acknowledged measurement error in patents as a measure of R&D productivity “because the costs of obtaining and enforcing patents have risen relative to alternative protection mechanisms” (Griliches 1994; Lanjouw and Schankerman 2004). In untabulated analyses, we find that the decline in R&D profitability is pervasive across firms with differing levels of patenting activity.

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2003). Specifically, we estimate the following OLS regression from which represents our

measure of R&D profitability:

, &

ɛ (1)

where , is (adjusted) net income summed over years t + 1 through t + 5. We

include several control variables to account for changes in a firm’s investment strategy over time

as well as other determinants of firm profitability. We include (Compustat item CAPX)

to control for investments in capital goods. To control for the effects of acquired innovation (Hitt

et al. 1991; Phillips and Zhdanov 2013), we include the total value of company M&As during the

fiscal year, as reported on Thomson SDC Platinum ( ).10 We also control for expenditures on

other investments in SG&A ( ; Compustat item XSGA + XRD), which represent an

important class of expenditures on intangible investments for many firms (Enache and Srivastava

2018). Missing values of investment variables other than R&D are set to zero. We include

current profitability , as positive earnings are highly persistent; growth in net income

from t to t-1 , as growth in net income is an important determinant of future

profitability and may affect firms’ investing decisions (e.g., Fairfield and Yohn 2001); and book-

to-market ( ), as growth opportunities are plausibly correlated with both R&D expenditures

and future earnings. All variables are scaled by total assets measured at the end of fiscal year t.

As we sample the same firm multiple times in the pooled regression and there are time effects

that affect all firms, we estimate robust standard errors by clustering our standard errors by firm

10Although we are restricted to publicly traded firms, Acharya and Xu (2017) provide evidence that profitability among private firms is not systematically higher than that of public firms, mitigating concerns that profits are simply realized sooner in firms’ life cycles, or that the most profitable R&D firms opt to remain private.

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and year (Gow et al. 2010). To limit the effects of outliers, we winsorize all variables at the 1%

and 99% levels by fiscal year.

To test whether R&D profitability has changed over time, we interact each of the

independent variables in Equation (1) with a trend variable ( ), which equals fiscal year t

less 1980 (i.e., = 2 when 1982 is fiscal year t). To aid with interpretability of over time

changes, we also examine whether the association between R&D expenditures and future profits

is significantly different in the early (1980–1995) and later (1996–2011) parts of our sample

period by estimating Equation (1) separately for these two subperiods.11

We present the results in Table 2. Over the full sample period, we provide evidence of

future benefits associated with R&D expenditures, consistent with prior research (Griliches

1995; Lev and Sougiannis 1996). Specifically, R&D expenditures have an association of 0.882

with future net income over the following five years, on average, suggesting that one dollar of

current R&D expenditure is associated with around 88 cents of earnings over the next five years

(the coefficient increases when extending the window to eight years; see the appendix). In

column 3 we find a significant negative coefficient on the interaction between R&D and the

trend term, indicating that the association between R&D and future profits has declined

significantly over time. When we examine the coefficients separately for the early and later parts

of the sample period, we see that the association between R&D expenditures and future

11 The tenor of the results is not affected by the year of the split. For example, in untabulated analyses we also considered a) three decade-long periods and b) beginning our sample in 1975 and partitioning the data at 1992 to examine the sample period after Lev and Sougiannis (1996). We opt to present the current results to minimize the subgroups (two versus three) and mitigate the concern of missing data items before 1980. Results are also similar if instead of clustering errors we estimate the regressions by year and present the mean coefficients and t-statistics using the Fama-MacBeth approach with a Newey-West adjustment for overlap in the dependent variable (not tabulated).

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profitability has fallen from 2.356 on average between 1980–1995, to 0.416 between 1996–2011;

the difference is statistically significant at the 1% level.12

The results in Table 2 demonstrate that the average association between R&D

expenditures and future profitability is significantly lower in the later part compared to the early

part of our sample period and this decline in the average profitability is significant enough to be

detected as a negative linear term. We next consider whether there are any non-linearities in the

decline over time. Specifically, in Figure 2, we graphically present the coefficients on R&D

estimated from five-year rolling-window regressions of Equation (1). The regression parameters

reflecting the association between R&D and future profitability show a steady decline from the

early 1980s to the late 1990s/early 2000s rather than a one-time structural change in the

association.13 The lower R&D profitability appears to have been relatively stable since the

2000s. This stabilization suggests that the decline is unlikely to be transitory, which makes it

particularly important to document, especially if market participants continue to expect the

higher profits realized in the earlier periods.

We explore the robustness of the main result in Table 2––that average R&D profitability

has declined significantly over time––to various research design choices in the appendix.

Including firm fixed effects leads to somewhat weaker results, consistent with changes in sample

composition likely contributing to the decline; we investigate this possibility more completely in

12 The lower Adjusted R2 in the later period (0.223 versus 0.316) is also consistent with R&D explaining a lesser amount of future profitability in the later time period. To assess the statistical significance of these differences over time, we estimate our main model by interacting all of our independent variables with an indicator variable equal to one for fiscal years occurring in our post period (i.e., 1996 onwards), as well as including the main effect of this indicator variable (POST). 13 For example, after the adoption of SFAS 123-R, R&D expenditures reported on the income statement can include amounts of stock compensation expense. This results in more costs being captured in R&D expense, but they are not expected to generate additional future net income, which could systematically lower the coefficient on R&D. Because the decline we document begins many years prior to the accounting rule change, this accounting difference does not provide an explanation for the overall decline in R&D profitability.

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the next section. We conduct our analyses at the aggregate level to consider both the effect of

equal-weighting firms in Table 2, as well as to identify whether positive spillovers outweigh the

firm-level decline in profitability. Specifically, we re-estimate Equation (1) and its variants after

aggregating data at the industry-year level, where industry is defined based on 3-digit SIC codes.

We find a similar decline in R&D profitability in this aggregate analysis. We also find that our

inferences are robust to creating a capitalized R&D variable and examining alternative payoff

windows, mitigating concerns that the benefits associated with R&D investments materialize

over alternative time horizons in more recent years. We continue to find evidence of a decline in

the payoffs associated with R&D when we consider alternative measures of future benefits,

including sales and operating income, market returns, and estimates of upper and lower bounds

of future operating income that adjust for survivorship bias and endogenous outcomes such as

being acquired. Our upper bound results allow us to examine whether investors are benefiting

from R&D via capital gains in their investments. Overall, these results indicate that the decline in

the average profitability of R&D expenditures is pervasive across various measures of future

benefits and robust to alternative model specifications.

5. Potential contributors to the decline

Our results thus far demonstrate that the profitability of R&D has fallen significantly over

time, on average. We next consider several potential, non-mutually exclusive, contributors to this

decline. Although we cannot provide causal evidence of a single or comprehensive explanation

for the lower profitability of R&D over time, we perform a number of analyses intended to

explore the extent to which the changes in the R&D landscape we discuss in section 2 contribute

to the decline.

17

Decline in interest rates. One possible reason for the decline in the average return to

R&D investments is falling interest rates, which could have reduced the required rate of return

on investment. Because lower interest rates likely lower the rate of return managers require for

all investment opportunities, we should observe a similar decline across other investment classes

that we consider in our model if lower interest rates are an important contributor to the decline in

R&D profitability, which we do not find in Table 2. We plot the rolling window coefficients for

the other investments we consider in our model compared to R&D in Panel B of Figure 2. The

coefficient on capital expenditures increases across time and there is no significant change in the

profitability associated with SG&A and M&A expenditures across time. Thus, the decline in the

coefficient on R&D expenditures is unlikely to be fully explained by lower interest rates or some

other macroeconomic factor not specific to R&D.

Nature of R&D activities: Maintenance spending versus strategic shifts. There is

evidence that an increasing amount of R&D activities focus on maintenance or incremental ideas

(Jaruzelski et al. 2014), while major disruptive innovations have become less common as

technological breakthroughs are more difficult to achieve (Kortum 1997). Simultaneously, some

degree of innovation has become necessary to remain competitive (Jaruzelski et al. 2005), as

demonstrated by the increasing percentage of investment funds allocated to R&D for many firms

(Figure 1, Panel C). If the purpose of R&D expenditures has become to maintain rather than

enhance a firm’s competitive position, this change could explain why the average profitability of

total R&D expenditures has fallen over time. Stated differently, the average profitability of R&D

could have changed over time because R&D activities have shifted toward those with lower risk

and therefore lower return.

Although we cannot observe firms’ underlying R&D activities, we consider whether the

volatility, or uncertainty, associated with R&D expenditures has changed over time in a manner

18

consistent with a shift in R&D activities becoming more maintenance oriented. Specifically, we

estimate the association between future earnings volatility over the subsequent five years and

R&D expenditures in the spirit of Kothari et al. (2002).14 In column 1 of Table 3, we find that the

association between current R&D expenditures and the standard deviation of future earnings has

declined significantly over time. We graphically present the coefficient on R&D in this model

using rolling window estimations in Figure 3. This decline parallels the decline in R&D

profitability, consistent with the notion of changing risk and return to R&D activities, which

could be due to different types of activities (i.e., maintenance versus strategic spending). We find

no similar evidence of a decline in the uncertainty associated with other investment types,

suggesting the results are unique to R&D and not a broader macroeconomic phenomenon (see

Figure 3, Panel B).

We investigate the potential for a change in R&D activities over time to contribute to the

decline in average profitability by decomposing current R&D expenditures into its lagged and

change components. We conjecture that changes in R&D are more likely to represent conscious

efforts by management to use R&D to change the firm’s competitive or strategic position

(hereafter strategic-oriented R&D), whereas prior year R&D spending is more likely to represent

R&D efforts toward maintaining their current portfolio (hereafter maintenance-oriented R&D).15

Therefore, we investigate the extent to which changes in the characteristics of these components

explain the decline. Specifically, we investigate the association between the lagged and change

components of current R&D expenditures and the level and standard deviation of future

earnings, and how these relations have changed over time. 14 Our methodology differs slightly to be consistent with our measurement choices in Table 2; our untabulated results are similar if we follow the exact specification of Kothari et al. (2002). 15 Untabulated results are extremely similar if we classify historical spending as the two-year average, and the change as the difference between current year R&D and the two-year historical average. We use prior R&D spending as historical spending in our main analysis so as not to further restrict our sample.

19

In Table 4, we present the results of estimating the profitability of R&D using this

decomposition. We find that the coefficient on change in R&D is significantly higher than the

coefficient on lagged R&D in the early part of our sample period (column 3; p-value on

difference = 0.031), highlighting the role R&D could play in affecting a firm’s competitive

position and driving growth in earlier decades. In contrast, the coefficient on lagged R&D is

visually larger than the coefficient on the change in R&D in more recent years, although not

significantly so (column 4; two-tailed p-value on difference = 0.106). Most notably, however, we

find that the average profitability of both the lagged and change components of current R&D

expenditures have fallen over time (column 2). We also present rolling window estimates of this

decomposition in Figure 4, Panel A, which demonstrates that the average profitability of the

lagged and change component converged around the late 1990s.

Finally, we estimate the association between the lagged and change components of R&D

and future earnings volatility. These results are presented in Table 3 and graphically presented in

Figure 4, Panel B. The figure demonstrates that early in the sample period, changes in R&D were

associated with higher future earnings volatility. Furthermore, although both components have

experienced a statistically significant decline over time, they have similar associations since the

late 1990s.

Collectively, these results suggest that one potential contributor to the decline in the

average future profitability associated with current R&D expenditures is a shift in the nature of

R&D spending. The decline in the profitability (and uncertainty) of changes in R&D is starker

than that of lagged R&D. These results could suggest that changes in R&D deliver smaller

improvements in competitive position in more recent years, perhaps because they represent safer

projects that deliver smaller incremental innovations that maintain a firm’s competitive position.

However, we also find a decline in the average profitability and uncertainty associated with

20

lagged R&D expenditures (our proxy for maintenance R&D), suggesting that changes in the

nature of R&D spending are unlikely to represent a complete explanation for the overall decline.

Changing sample composition. In our main analyses, we pool all firms in an equal-

weighted regression to estimate the average profitability of R&D expenditures. Prior studies

demonstrate that the characteristics of the population of listed firms has changed over time

(Collins et al. 1997; Srivastava 2014; Srivastava and Tse 2016; Donelson et al. 2011), however,

which could change average R&D profitability even if there have been little to no changes in the

R&D profitability of individual firms.16 For instance, Srivastava and Tse (2016) demonstrate that

successive cohorts of firms spend increasingly more on R&D (see also Figure 5, Panels A and

B). If the R&D profitability of new cohorts is systematically lower than that of the pre-existing

population, the lower average profitability of R&D over time could be explained by a changing

sample composition. We investigate the effect of successive cohorts on our main inferences

using cohorts based on the first year for which the firm has lagged, current, and one-year ahead

net income, and current total assets, book-to-market, and SG&A costs reported on Compustat.

Consistent with Srivastava and Tse (2016), we find that the average and median R&D

spending of each cohort is higher than its predecessor and the number of firms in the two most

recent cohorts in our sample (1990s and 2000s cohorts) is higher than the early sample (not

tabulated). In Figure 5, Panel C we present rolling window coefficient estimates of R&D expense

on future profitability for successive cohorts of firms using our main specification in which the

firm must have all five years of future net income to be included in the model. We find that the

oldest cohort (pre-1980s) actually exhibits a slight increase in R&D profitability. These firms are

16 Evidence in the appendix provides evidence consistent with this possibility. Specifically, the coefficient on the interaction between R&D and Trend is not significant at traditional two-tailed levels when we include firm fixed effects in the model. The coefficients are significantly different across time periods when we use POST to compare R&D profitability across the early and later parts of the sample period however.

21

subject to survivorship bias, however, which we address in the following paragraph. The

profitability of both the 1990s cohort and the 2000s cohort are notably lower than the pre-1980s

cohort, and thus the addition of these cohorts does appear to contribute to the overall decline in

average R&D profitability. That said, turning to the 1980s cohort, we see evidence of a decline

over time that is statistically significant (untabulated).

Because of potential survivorship issues associated with requiring firms to exist for five

years after R&D spending, we also estimate the rolling window analysis after adjusting the

future net income measure to set future missing values equal to zero (see the appendix for more

information on this procedure). These results are presented in Panel D of Figure 5. Using this

adjusted measure, we no longer see visual evidence of an increase in R&D profitability over time

for the pre-1980s cohort. The decline in R&D profitability for the 1980s cohort appears starker

and our overall inferences from this adjustment are similar. Specifically, although the addition of

the new cohorts contributes to the lower level of average R&D profitability, these new firms are

not the sole cause of this decline.

Given evidence in prior literature that smaller firms make up a larger proportion of the

Compustat population over time (e.g., Collins et al. 1997) and because larger firms have scale

advantages in R&D activities (Ciftci and Cready 2011), the decline in profitability could reflect a

change in the sample composition toward smaller firms that have lower R&D profitability on

average. We investigate the effects of these potential explanation by examining R&D

profitability across size groups and how the composition of our sample across these groups has

changed over time. We assign firms into groups on the basis of sales in year t – 1.17 To use a

17 We use lagged sales as our measure of firm size to avoid any association between R&D and market value influencing our categorization. Our inferences regarding the change in R&D profitability across time are unchanged if we instead use lagged market value of equity or lagged total assets to sort firms into size groups (not tabulated).

22

constant definition of small, medium, and large firms (which are inflation adjusted within our

data), we use cutoffs based on the minimum and maximum values in terciles formed in the first

five years of our sample period (1980-1984). Consistent with Ciftci and Cready (2011), we

expect and find that larger firms have the most profitable R&D on average. Interestingly,

however, we see that small firms comprised 38.52% of the sample in the early part of our sample

period and 31.33% in the most recent part of the sample period.18 Thus, changes in the sample

composition based on firm size between the early and later parts of our sample period do not

fully explain our evidence of a decline in the average profitability of R&D over time.

Finally, to consider whether industry composition impacts average R&D profitability, we

consider how our sample composition among the five sectors that spend the most on R&D has

changed over time. We present summary statistics for these sectors in Table 5. In the early part

of our sample period, the representation of these sectors ranged from 12.13% to 16.22% of the

sample. We observe substantial variation across sectors in the profitability of R&D expenditures

in this early time period, with firms in the Industrial Machinery and Equipment (SIC 2-digit =

35) and Electronic and Other Electric Equipment (SIC 2-digit = 36) sectors having the most

profitable R&D on average. In the later part of our sample period, however, we see changes in

the composition of our sample across these sectors. Most notably, we see a substantial increase

in the proportion of firms in the Business Services sector (SIC 2-digit = 73) and a moderate

decline in the Industrial Machinery and Equipment sector. The average profitability of R&D falls

in three of the five sectors we consider but there is no significant change in the average

profitability of firms in the Business Services sector, which is now the largest sector of the 18 In untabulated analyses, we find that the proportion of small firms in our sample does increase from 1980 until the late 1990s, after which it declines sharply and returns to similar levels as 1980 by 2009. Although this trend likely contributes to the average decline we see in Figure 2, the fact we observe a significant decline in the profitability of medium firms indicates that changes in sample composition do not provide a complete explanation for the decline in average R&D profitability.

23

sample (see also Srivastava 2014). These results indicate that there have been some shifts in the

industry composition over time that likely contribute to the lower profitability of R&D over time

but these shifts are unlikely to fully explain this decline.

Diminishing marginal returns. Thus far we provide evidence of several factors that

partially contribute to the overall decline in R&D. Given the numerous changes over time,

however, it is not surprising that we cannot identify a single complete explanation for the lower

profitability of R&D in recent years. The decline, however, suggests that the effect of any

increase in investment opportunities is outpaced by the increase in R&D spending and other

changes in the R&D setting. This finding is consistent with the broad notion of diminishing

marginal returns, which we next consider as a general explanation to our result of a decline in

R&D profitability. Specifically, we investigate whether R&D profitability varies cross-

sectionally in a manner consistent with diminishing marginal returns. We present the results of

these analyses in Table 6.

We first form terciles of firms each year on the basis of R&D intensity (R&D/Total

Assets). Consistent with diminishing marginal returns to R&D expenditures, we find firms with

the most intensive R&D spending (tercile 3) have the lowest average R&D profitability. This

result suggests that the more a firm spends on R&D, controlling for investment opportunities, the

more likely they are to be investing in projects with a lower rate of return.19 Moreover, these

firms had the greatest increase in spending and experienced the most significant decline in R&D

19 Evidence of diminishing marginal returns is also present in our earlier analyses. For example, the youngest cohorts spending the most on R&D tend to experience lower R&D profitability than the oldest cohorts, that are spending the least on R&D. Similarly, although the number of small firms is not a key driver of the decline in profitability, these firms do spend the most on R&D and experience lower, on average, returns to R&D. Looking over time in Table 5, smaller firms increased their spending the most (from 9.9% to 16.4%) and experienced a large decline in R&D profitability, especially relative to larger firms. Similarly, in our industry analysis, Chemical and Allied Products had the largest increase in spending and experienced the largest industry-level decline in R&D profitability over time.

24

profitability on average, providing further support for the existence of diminishing marginal

returns. The least R&D intensive firms (tercile 1) have a visual decline in R&D profitability

(from 5.109 to 2.393), but this decline is not statistically significant (two-tailed p-value = 0.221;

not tabulated), consistent with these firms maintaining spending at a pace more commensurate

with any changes in investment opportunities.

We expect that the profitability of R&D would also vary based on a firm’s leverage

position in the presence of diminishing marginal returns. In particular, leverage reduces the

availability of funds for investment and a firm’s ability to obtain external funds (Myers 1977;

Lang et al. 1996). We expect highly levered firms to face the greatest restrictions in investing in

R&D and those firms with the least outstanding debt to invest the most in R&D. If R&D

investments experience diminishing marginal returns and funds available represent a binding

constraint on investment, we expect firms with fewer restrictions to pursue projects with

successively lower expected returns. Thus, we sort firms into terciles based on their leverage

ratio at the beginning of year t.20 Consistent with this notion, we find that average R&D spending

is monotonically decreasing and average R&D profitability is increasing across terciles of

leverage. That is, the most highly levered firms (tercile 3) invest the least in R&D and have the

most profitable R&D on average. These results are consistent with firms making more judicious

investment decisions when they face greater financing constraints and support the general notion

of diminishing marginal returns to R&D spending. Turning to changes over time, we find

evidence of a significant decline in R&D profitability across all terciles of leverage, consistent

20 An alternative approach to examining this same notion would be to sort firms on the basis of free cash flow or cash availability. Because our sample pre-dates the requirement to present a statement of cash flows, we cannot measure free cash flow. We find similar inferences when we sort our sample into terciles on the basis of cash on hand at the beginning of year t. Specifically, we find that firms with the greatest cash on hand have lower R&D profitability on average.

25

with the decline in R&D profitability being a pervasive phenomenon.21 The decline, however, is

smaller among the most levered firms, which had the smallest increase in spending over time.

6. Conclusion

We examine how the association between current R&D expenditures and future income

(R&D profitability) has changed over time as various features of the R&D setting that influence

the profitability of these investments have changed. As a significant component of firm’s

investment portfolios, it is important for financial statement users to understand the implications

of R&D expenditures for future profitability. We find evidence that the profitability of R&D

expenditures fell significantly from the early 1980s through to the late 1990s and are notably

lower in recent years relative to the time periods studied in prior research (e.g., Lev and

Sougiannis 1996). This result is robust to numerous measures of future benefits and other model

specifications.

We provide some initial evidence on potential contributors to the decline in the average

profitability of R&D, drawing on evidence of changes in the cost and nature of R&D activities as

well as shifts in the sample composition of firms conducting R&D activities over time.

Specifically, we find support for the notion that the average profitability of R&D expenditures

has fallen over time as the underlying activities have become more maintenance-oriented,

focusing on safer and more incremental innovations, resulting in lower risk and lower returns to

total R&D expense. We also present evidence that changes in the sample composition, including

successive cohorts of firms and industry shifts, contribute to the lower average profitability of

R&D in more recent years. These effects provide only a partial explanation for the decline,

21 These results further mitigate concerns that lower interest rates over time explain the decline in R&D profitability because interest rates should have the greatest impact on the most highly levered firms but the decline exists across all firms.

26

however, as we the lower average R&D profitability in more recent years is pervasive across the

various analyses we perform. Thus, more research is required to fully investigate the underlying

causes and further implications of the decline in R&D profitability that we document.

Noting that the decline in R&D profitability coincides with a significant increase in

aggregate R&D spending, we also present evidence that diminishing marginal returns to R&D

investments could provide a general explanation for the decline (see also Bloom et al. 2017).

Specifically, we find that the profitability of R&D is lower for firms with higher level of R&D

spending and those that face financing constraints. We also find that high R&D intensity and low

leverage firms experienced both the greatest on average increase in R&D spending and greatest

on average decline in R&D profitability, providing further support for the notion of diminishing

marginal returns.

Our research is subject to limitations, most notably that we struggle with measurement

error. We must infer R&D profitability from the association between R&D and future net

income, which may not fully reflect the benefits of the R&D and could be subject to biased

estimation. We conduct numerous robustness checks in an attempt to mitigate this concern and

also corroborate evidence of the decline to changes in the R&D setting that we expect would

contribute to lower profitability of R&D over time. Any remaining bias would need to be time-

varying to present a valid alternative explanation for our results. Another limitation is that we are

unable to make conclusions about the efficiency of managers’ investment decisions, which

would be outside the scope of our research agenda. Similarly, our results indicate the average

characteristics of R&D activities have changed over time, but future work is required to

understand the consequences of these changes for the accounting for R&D activities under US

GAAP.

27

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29

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33

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34

Table 1 Descriptive statistics

Panel A: Full sample Variable N Mean Q1 Median Q3 Std. dev.

R&D 44,847 144.45 3.51 13.03 48.82 639.13 R&D/Total Assets 44,847 0.09 0.02 0.05 0.11 0.11 R&D/Sale 44,847 0.35 0.02 0.06 0.14 1.67 CAPEX 44,847 278.11 2.00 9.66 58.23 1553.00 CAPEX/Total Assets 44,847 0.05 0.02 0.04 0.07 0.05 SGA 44,847 481.24 12.55 44.63 196.15 1898.82 SGA/Total Assets 44,847 0.24 0.11 0.21 0.33 0.19 MA 44,847 87.15 0.00 0.00 0.00 1314.56 MA/Total Assets 44,847 0.03 0.00 0.00 0.00 0.09 NIAdj 44,847 631.69 5.67 31.47 174.06 2891.65 NIAdj/Total Assets 44,847 0.13 0.08 0.14 0.22 0.17 BM 44,847 0.59 0.26 0.46 0.76 0.50 NI , 36,060 0.99 0.42 0.82 1.36 1.15

σ NI , 36,060 0.11 0.04 0.07 0.14 0.13 Sale 44,847 3,816.97 53.31 215.48 1,216.85 17,219.92 Total Assets 44,847 4,615.75 67.70 236.96 1,268.34 24,376.19

Panel B: Means and medians by subsample periods 1980–1995 1996–2011 Variable N Mean Median N Mean Median

R&D 18,251 107.60 8.46 26,596 169.73*** 17.59*** R&D/Total Assets 18,251 0.07 0.04 26,596 0.10*** 0.06*** R&D/Sale 18,251 0.16 0.04 26,596 0.48*** 0.07*** CAPEX 18,251 266.59 10.30 26,596 286.02 9.21*** CAPEX/Total Assets 18,251 0.07 0.06 26,596 0.04*** 0.03*** SGA 18,251 432.54 38.32 26,596 514.66*** 48.86*** SGA/Total Assets 18,251 0.26 0.24 26,596 0.23*** 0.19*** MA 18,251 35.05 0.00 26,596 122.89*** 0.00*** MA/Total Assets 18,251 0.01 0.00 26,596 0.03*** 0.00*** NIAdj 18,251 482.84 27.17 26,596 733.83*** 34.94*** NIAdj/Total Assets 18,251 0.15 0.15 26,596 0.11*** 0.13*** BM 18,251 0.65 0.54 26,596 0.54*** 0.41*** NI , 15,473 1.15 0.89 20,587 0.87*** 0.76***

σ NI , 15,473 0.11 0.06 20,587 0.12** 0.07*** Sale 18,251 3,367.90 213.45 26,596 4,125.13*** 217.15 Total Assets 18,251 3,494.51 184.21 26,596 5,385.19*** 282.23***

35

Table 1 continued.

Panel C: Summary of variable definitions Variable Name Definition ∆NIAdj Change in net income before R&D, advertising, and depreciation from

fiscal year t−1 to fiscal year t

, Standard deviation of adjusted net income over fiscal years t + 1 through t + 5. Each future period of net income is scaled by total assets in year t and winsorized at the 1st and 99th percentile before calculating the standard deviation.

BM Book-to-market ratio (Compustat items (CEQ + TXDB) / PRCC_F × CSHO)

CAPEX Capital expenditures (Compustat item CAPX) LEV Leverage, measured as the book value of debt divided by the sum of

book value of debt and market value of equity (Compustat items (DLC + DLTT) / (DLC + DLTT + (CHSO×PRCC_F))

MA Total value of M&A deals that became effective during fiscal year t, as reported in SDC

NIAdj Net income before R&D, advertising and depreciation (Compustat items NI + XRD + XAD + DP)

, NIAdj aggregated from fiscal year t+1 through fiscal year t+n

, Operating income before R&D, advertising, and depreciation aggregated from fiscal year t+1 through fiscal year t+n (Compustat items OIBDP + XRD + XAD)

POST Indicator variable equal to one for fiscal years from 1996 onward and zero for fiscal years before and including 1995

R&D R&D expenditures (Compustat item XRD) Rett,t+n Market-adjusted buy-and-hold abnormal stock returns beginning 4

months after the end of fiscal year t – 1 for n years Sales Sales (Compustat item SALE) SGA Selling, general, and administrative expenditures, excluding R&D

(Compustat items XSGA + XRD) Total Assets Total assets (Compustat item AT) Total Investment R&D + CAPEX + MA + SGA Trend Time trend variable that equals the fiscal year less 1980

Notes: In this table, we provide descriptive statistics for our full sample (Panel A) and for the first and last half of our sample period (Panel B). We present raw values and scaled values for completeness. Variables in subsequent tables are typically scaled, as noted in the notes to each table. We provide variable definitions in Panel C. All variables are inflation adjusted to be on a 2011-dollar basis. All scaled variables are winsorized annually at the 1% and 99% levels after scaling. Total assets are measured at the end of year t. In Panel B, we provide two-tailed t-tests of the difference in the means from the early period (1980–1995) and the later period (1996–2011). We also perform a nonparametric test of equality of medians between the two periods. Significance at the 1%, 5%, and 10% levels are represented by *, **, and *** respectively. Variables are scaled in many of the analyses; please see table notes for details on the scale factor used.

36

Table 2 Estimates of R&D profitability over time

1980-2011 1980-1995 1996-2011 (1) (2) (3) (4) (5) (6)

NI , NI , NI , NI , NI , NI ,

R&Dt 0.760** 0.882*** 5.311*** 3.138*** 2.356*** 0.416**‡ (2.36) (3.84) (6.25) (5.65) (7.03) (2.01) R&Dt Trendt -0.218*** -0.112*** (-6.13) (-4.63) Controls: CAPEXt 1.731*** 0.415 0.799*** 1.545***‡ (6.85) (1.11) (3.13) (3.98) CAPEXt Trendt 0.056** (2.10) SGAt 0.943*** 0.750*** 0.689*** 0.903*** (8.40) (4.09) (5.72) (5.64) SGAt Trendt 0.006 (0.59) MAt -0.004 -0.205 0.283 0.009 (-0.04) (-0.54) (1.36) (0.08) MAt Trendt 0.010 (0.60)

NI 3.156*** 4.170*** 3.997*** 2.716***‡ (10.32) (10.82) (18.84) (8.16)

NI Trendt -0.057***

(-2.83)

∆NI -0.391** 0.084 -0.290** -0.388*

(-2.07) (0.33) (-2.33) (-1.71)

∆NI Trendt -0.022 (-1.63) BMt -0.241*** -0.349*** -0.362*** -0.174***‡ (-5.82) (-6.26) (-8.70) (-3.46) BMt Trendt 0.006* (1.85) Trendt 0.000 0.001 (0.07) (0.33) Intercept 0.927*** 0.328*** 0.878*** 0.328*** 0.386*** 0.343*** (38.41) (7.64) (17.19) (4.71) (8.21) (6.97) Observations 36,060 36,060 36,060 36,060 15,473 20,587 Adjusted R2 0.005 0.256 0.034 0.270 0.316 0.223 Firm-years are dropped from the model if net income is not available for all future years. See Table 1, Panel C for variable descriptions. All variables except BM, are scaled by total assets at the end of year t and winsorized at the 1% and 99% levels. t-statistics, calculated after clustering standard errors by firm and fiscal year, are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed). ‡ indicates a significant difference in the coefficient between column 5 and 6 at the p<0.1 level (two-tailed).

37

Table 3 Future earnings volatility associated with R&D and its components

(1) (2) (3) (4) (5)

1980-2011 1980-1995 1996-2011

σ NI , σ NI , σ NI , σ NI , σ NI ,

R&Dt 0.786*** 0.653*** 0.401*** (13.48) (12.82) (16.51) R&Dt Trendt -0.016*** (-5.83) Decomposing current R&D: R&Dt-1 0.448*** 0.735*** (17.72) (12.73) R&Dt-1 Trendt -0.014*** (-5.18) ∆R&Dt 0.597*** 1.109*** (12.00) (11.79) ∆R&Dt Trendt -0.028*** (-5.73) Controls: CAPEXt 0.017 0.157*** -0.013 0.057 0.265*** (0.34) (4.64) (-0.26) (1.55) (5.18) SGAt 0.087*** 0.104*** 0.090*** 0.095*** 0.097*** (4.40) (10.06) (4.55) (6.11) (8.26) MAt 0.018 0.010 -0.001 -0.028 0.029*** (0.45) (0.82) (-0.03) (-1.30) (2.81)

NI -0.178*** -0.075*** -0.178*** -0.106*** -0.073*** (-5.47) (-4.91) (-5.41) (-3.76) (-4.01)

∆NI 0.258*** 0.091*** 0.250*** 0.187*** 0.068*** (14.28) (5.85) (13.14) (12.26) (5.04) BMt -0.052*** -0.031*** -0.051*** -0.043*** -0.023*** (-6.26) (-5.55) (-6.16) (-7.35) (-3.33) Intercept 0.102*** 0.071*** 0.102*** 0.082*** 0.065*** (9.97) (12.15) (10.23) (13.27) (9.29) Controls interacted with Trend

No No Yes No No

Observations 36,060 35,535 35,535 15,473 20,587 Adjusted R2 0.217 0.203 0.219 0.233 0.200 Firm-years are dropped from the model if net income is not available for all future years. See Table 1, Panel C for a variable descriptions. All variables except BM, are scaled by total assets at the end of year t and winsorized at the 1% and 99% levels. t-statistics, calculated after clustering standard errors by firm and fiscal year, are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed). ‡ indicates a significant difference in the coefficient between column 3 and 4 at the p<0.1 level (two-tailed).

38

Table 4 Decomposing R&D into lag and changes

(1) (2) (3) (4) 1980-2011 1980-1995 1996-2011

NI , NI , NI , NI ,

R&Dt-1 0.839*** 2.708*** 2.087*** 0.430**‡ (3.68) (4.86) (6.09) (2.01) R&Dt-1 Trendt -0.091*** (-3.73) ∆R&Dt 1.088** 4.685*** 3.762*** -0.028‡ (2.38) (4.38) (5.28) (-0.08) ∆R&Dt Trendt -0.197*** (-3.91) Controls: CAPEXt 1.679*** 0.259 0.703*** 1.529***‡ (6.50) (0.69) (2.80) (3.82) SGAt 0.944*** 0.724*** 0.710*** 0.892*** (8.26) (3.78) (5.82) (5.52) MAt -0.030 -0.285 0.175 0.032 (-0.25) (-0.75) (0.78) (0.27)

NI 3.150*** 4.272*** 3.988*** 2.732***‡ (10.42) (11.33) (19.22) (8.38)

∆NI -0.400** -0.019 -0.314*** -0.419* (-2.09) (-0.08) (-2.62) (-1.85) BMt -0.246*** -0.364*** -0.359*** -0.183***‡ (-5.86) (-6.05) (-8.54) (-3.49) Intercept 0.338*** 0.354*** 0.395*** 0.351*** (7.85) (4.78) (8.33) (7.21) Controls interacted with Trend No Yes No No Observations 35,535 34,463 15,217 20,318 Adjusted R2 0.256 0.269 0.318 0.223 Firm-years are dropped from the model if net income is not available for all future years. See Table 1, Panel C for a variable descriptions. All variables except BM, are scaled by total assets at the end of year t and winsorized at the 1% and 99% levels. t-statistics, calculated after clustering standard errors by firm and fiscal year, are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed). ‡ indicates a significant difference in the coefficient between column 3 and 4 at the p<0.1 level (two-tailed).

39

Table 5 The effect of changes in the sample composition

Panel A: Firms assigned to size groups based on cutoffs from terciles of sales in year t – 1 in 1980-1984 (inflation adjusted)

Percentage of firms R&D profitability R&D/Total Assets

(1) (2) (3) (4) (5) (6)

Size group 1980-1995 1996-2011 1980-1995 1996-2011 1980-1995 1996-2011 Small 38.52% 31.33% 1.465*** -0.007‡ 0.099 0.164‡ Medium 32.93% 35.64% 2.539*** 1.256***‡ 0.061 0.093‡ Large 28.55% 33.03% 3.908*** 4.239*** 0.038 0.046‡ Panel B: Industry composition

Percentage of firms R&D profitability R&D/Total Assets

(1) (2) (3) (4) (5) (6)

1980-1995 1996-2011 1980-1995 1996-2011 1980-1995 1996-2011 Chemical and Allied Products 13.43% 16.39% 1.240*** -0.315‡ 0.102 0.180‡ Industrial Machinery and Equipment 16.22% 10.96% 4.908*** 2.095***‡ 0.073 0.081‡ Electronic and Other Electric Equipment 14.96% 14.82% 5.381*** 1.700***‡ 0.072 0.098‡ Instruments and Related Products 13.94% 12.70% 0.023 -0.252 0.080 0.099‡ Business Services 12.13% 21.72% 2.326** 2.183*** 0.123 0.120 In this table, we present the proportion of firm-years in each group (columns 1 and 2), estimates of R&D profitability using firm-level data (columns 3 and 4), and R&D intensity (R&D/Total Assets) (columns 5 and 6). In Panel A we assign firms into size groups on the basis of sales in year t – 1. To use a constant definition of small, medium, and large firms (which are inflation adjusted within our data), we use cutoffs based on the minimum and maximum values in terciles formed in the first five years of our sample period (1980-1984). In Panel B we assign firms into industries based on two-digit SIC codes, where Chemical and Allied Products (SIC 2-digit = 28), Industrial Machinery and Equipment (SIC 2-digit = 35), Electronic and Other Electric Equipment (SIC 2-digit = 36), Industrial Machinery and Equipment (SIC 2-digit = 38), Business Services sector (SIC 2-digit = 73). Estimates of R&D profitability are obtained through estimating Equation (1) for the time period listed and we suppress the coefficients on the control variables for parsimony. Note that firm-years are dropped from the model if

net income is not available for all future years. See Table 1, Panel C for a description of all other variables. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed). ‡ indicates a significant difference in the level of spending or R&D coefficient across the early and later parts of our sample period at the p<0.1 level (two-tailed) for R&D spending and R&D profitability.

40

Table 6 Diminishing marginal returns in R&D spending

Panel A: Terciles sorted on the basis of the firm’s R&D intensity (R&D/Total assets) in year t

R&D/Total Assets R&D profitability

(1) (2) (3) (4)

Tercile 1980-1995 1996-2011 1980-1995 1996-2011

1 (low) 0.012‡ 0.014‡ 5.109*** 2.393*

2 0.046‡ 0.062‡ 7.171*** 2.568***‡

3 (high) 0.147‡ 0.220‡ 1.017** -0.703**‡ Panel B: Terciles sorted on the basis of leverage at the beginning of year t

R&D/Total Assets R&D profitability

(1) (2) (3) (4)

Tercile 1980-1995 1996-2011 1980-1995 1996-2011

1 (low) 0.100‡ 0.139‡ 1.739*** -0.229‡

2 0.063‡ 0.107‡ 2.987*** 0.379‡

3 (high) 0.037‡ 0.047‡ 3.548*** 1.687***‡ In this table, we present R&D intensity (R&D/Total Assets) (columns 1 and 2) and estimates of R&D profitability using firm-level data (columns 3 and 4) across terciles formed annually on the basis of R&D intensity (Panel A) and leverage (Panel B). Estimates of R&D profitability are obtained through estimating Equation (1) for the time period listed and we suppress the coefficients on the control variables for parsimony. Note that firm-years are dropped from the model if net income is not available for all future years. See Table 1, Panel C for a description of all other

variables. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed). ‡ indicates a significant difference in the level of spending or R&D coefficient across the early and later parts of our sample period at the p<0.1 level (two-tailed).

41

Appendix to “The changing implications of research and development expenditures for future profitability”

Robustness tests

In this appendix, we explore the robustness of the main result in the paper––that average

R&D profitability has declined significantly over time––to various research design choices.

Firm fixed effects. We first estimate R&D profitability over time after including firm fixed

effects, which allows us to test whether there is an on average decline in profitability after

controlling for firm-specific and time invariant differences. We present the results in Table A1,

Panel A. In columns 2 and 3 we present the coefficients by period, similar to columns 5 and 6 of

Table 2. Compared to the main analyses, the coefficients on R&D are lower in the early part of

the sample (1.720 versus 2.356) and higher in the later part of the sample (0.909 versus 0.416),

highlighting a difference in the change in R&D profitability within versus between firms over

time. The lower magnitude of the decline is consistent with some of the decline stemming from

the changing composition in the sample, which we examine in Section 5. Moreover, this decline

is only statistically significant at a 10% level under a one-tailed test when using a trend variable

(column 1).

Industry aggregated data. We next aggregate our sample at the industry-year level to

address potential concerns that the equal weighting of firms in our main analysis drives our result

and as such, the lower R&D profitability in recent years is not economically significant. This

approach also allows us to capture the benefits to all firms’ spending in an industry, which is

important because as the previous example of touchscreen technology demonstrates, R&D

activities can benefit more firms than the innovating firm itself (i.e., there can be spillovers) and

thus, we may be under-identifying profits by restricting the relation between a single firm’s R&D

42

that that same firm’s future benefits.22 Specifically, we sum all firms’ R&D expenditures within

annual industry groups based on three-digit SIC codes provided there are at least 10 observations

in the industry-year. We aggregate all the other variables except BM using the same method and

scale by aggregate total assets at the end of year t. We calculate industry-year book-to-market as

the sum of book value divided by the sum of market value for all firms in the industry-year. The

results in Table A1, Panel B illustrate a decline in the association between industry-aggregated

R&D expenditures and industry-aggregated future net income over time. The coefficient on

aggregate R&D falls from 4.044 in the 1980–1995 period (column 2) to 1.531 for R&D

expenditures between 1996–2011 (column 3). We also find that the interaction between R&D

and the trend term is statistically significant (p-value < 0.01), consistent with our main results.

As previously noted, this aggregate analysis also allows us to capture the cumulative cross-

firm effects of positive spillovers within the industry. For example, if the investing firm benefits

less from touchscreen technology because of the spillover of knowledge to their industry

competitors, this could be a reason to see firm-level declines in R&D profitability, as some of the

profits are realized by peers. The aggregate analysis suggests that such spillovers are not the

main reason for the decline in R&D profitability at the firm level, since the decline is also

present at the aggregate level. Although positive spillovers can impact firms outside of the firm’s

industry, these spillovers are less likely to reduce the investing firm’s profits, which is our focus.

Alternative payoff horizons. Throughout our study, we measure future profitability over a

five-year future window. Because there is no pre-determined horizon over which R&D efforts

are expected to payoff, however, we also estimate our main associations using one-, three-, and

22 We focus on industry-year aggregations as we expect any effect of positive spillovers is more likely to occur within industries. Although this approach also has the benefit of increasing the sample size, it also provides equal weight to all industries. Inferences are unchanged if we aggregate across all sample firms by year (not tabulated). Because the purpose of this paper is to examine the firm-level implications of R&D profitability, our main analysis is conducted at the firm-level.

43

eight-year windows (see Table A2, Panel A). The results reveal a significant decline in the

average future profitability associated with firm-specific R&D expenditures across all three

alternative horizons, when the change over time is estimated either by comparing the early and

later parts of our sample period or by examining the change using the linear trend term,

suggesting our results are not attributable to our choice of payoff window.

Alternative measures of profitability. To estimate whether our results are robust to using

other profitability measures, we replace net income with sales, adjusted operating income before

depreciation (in which we also add back R&D and advertising similar to our main analyses),

adjusted operating cash flows (using cash flow data since 1987) and re-estimate Equation (1).

Consistent with our main results, we continue to find evidence of lower R&D profitability in

more recent years across these alternative profitability metrics (Table A2, Panels B-D). For

instance, the association between R&D and future sales has fallen significantly in recent years

(from 6.248 to 0.713 in the five-year aggregation window). We also consider a non-accounting

measure of profitability and regress buy-and-hold abnormal returns on R&D. We consider

various windows, where each begins four months into fiscal year t and cumulates the next one to

eight years. The results illustrate a significant positive association between R&D and future stock

returns in the first part of our sample period but no significant associations in more recent years

(Table A2, Panel E), although the interaction between R&D and the trend term is only

statistically significant for the eight year horizon. It is important to note, however, that the main

effect on R&D is not associated with returns over the one and three year horizons at conventional

levels.

Addressing survivorship bias. Throughout our analyses, we only include firm-years with

non-missing values of future net income for the entire aggregation period in each regression.

Because we do not expect missing observations to be exogenously determined, we make two

44

adjustments to the measurement of future income. First, because failing firms are more likely to

have missing income in future years, we replace missing values of future net income with zero

(provided the future period has occurred) and re-estimate a lower bound of R&D profitability. As

expected, the coefficient on R&D falls relative to the main analysis (e.g., 1.741 in Panel F of

Table A2 versus 2.356 in Table 2), but we continue to observe an economically and statistically

significant decline in the coefficient from the early to later parts of our sample period both across

the two distinct periods as well as with a trend term.

Second, to the extent that firm-years are dropped due to acquisitions, excluding these

observations from the estimation understates the benefits to R&D as it ignores the potential

income to shareholders when the company is sold. Therefore, we replace the first instance of

missing future net income with an estimate of the acquisition premium and set future periods to

zero. We estimate the acquisition premium as the difference between the proceeds from the sale

of the company less the book value of net assets.23 As expected, the returns to R&D increase

once we allow for potential benefits from M&As (4.177 versus the aforementioned 2.356). There

remains, however, evidence of a significant decline in recent years (from 4.177 to 1.383; see

Table A2, Panel G).

Measuring R&D as a capitalized asset. We also “capitalize” R&D expenditures over the

most recent five-year period using a 20% amortization rate and measure the association of this

R&D “asset” with future net income. We adjust capital expenditures using the same

23 This approach also helps capture the aggregate benefits from purchased R&D insofar as they are purchased from another public firm (e.g., Phillips and Zhdanov 2013). If both Firm A and Firm B spent $100 on R&D and then Firm A acquires Firm B, the firm-specific estimates of R&D productivity would overstate the benefits to Firm A’s $100 R&D expenditure, as the actual benefits relate to both firms’ R&D expenditures ($200). Without correcting for this possibility, the benefits to R&D would be biased upwards. One disadvantage of this correction method is that in some instances it double-counts R&D proceeds. If one of our sample firms is acquired by another of our sample firms, the future benefits to R&D will be measured both with the delisting return and with the future realizations of net income by the acquiring firm. Despite this limitation, this estimation procedure allows us to capture an upper bound of future benefits, mitigating concerns that benefits to R&D in the latter period are realized in alternative ways, such as through the acquisition of the firm.

45

methodology to allow both knowledge and physical assets some time to produce benefits to the

firm. Following Sougiannis (1994) we do not capitalize advertising due to the short-lived nature

of the benefits associated with it. Results of estimating Equation (1) with these alternative

independent variable definitions reveal a similar decline in the association between future

profitability and capitalized R&D expenditures; the coefficient on capitalized R&D falls from

0.815 in the early part of our sample to 0.149 in more recent years (Table A3). This decline is

statistically significant (p<0.10) and we also observe a similar decline when we use the linear

trend term to examine changes in profitability over time.

46

Table A1 Adjusting for equal weighting (firm fixed effects and industry aggregated data)

(1) (2) (3) 1980–2011 1980–1995 1996–2011 NI , NI , NI ,

Panel A: Firm fixed effects R&Dt 1.865*** 1.720*** 0.909***‡ (2.87) (3.77) (3.35) R&Dt Trendt -0.040 (-1.42) Observations 35,760 15,239 20,359 Adjusted R2 0.578 0.731 0.599 Panel B: Industry-aggregated data R&Dt 5.782*** 4.044*** 1.531***‡

(6.42) (6.71) (3.70) R&Dt Trendt -0.170*** (-3.60) Observations 789 353 436 Adjusted R2 0.668 0.710 0.607 Notes: In this table, we present results of alternative specifications to estimate R&D profitability. In Panel A, we adjust Equation (1) to include firm fixed effects (and a trend term in column (1)). Note that firm-years are dropped from the model if net income is not available for all future years. In Panel B we adjust the specification and estimate Equation (1) after aggregating all variables by industry and year, provided there are at least 10 observations in the industry-year. Coefficients on control variables are suppressed for parsimony. Note that firm-years are dropped from the model if net income is not available for all future years. All variables are winsorized at the 1% and 99% levels. T-statistics, calculated after clustering standard errors by firm and fiscal year in Panel A, are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed). ‡ indicates a significant difference in the coefficient between column (2) and (3) at the p<0.1 level (two-tailed).

47

Table A2 R&D profitability over various horizons and using alternative dependent variables

Panel A: Adjusted net income as the dependent variable 1980–2011 1980–1995 1996–2011

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

NI NI , NI , NI , NI NI , NI , NI , NI NI , NI , NI , R&Dt 0.190*** 1.235*** 3.138*** 9.296*** 0.130*** 0.870*** 2.356*** 7.026*** 0.032*‡ 0.200**‡ 0.416**‡ 1.685***‡ (4.27) (5.06) (5.65) (6.28) (4.31) (6.71) (7.03) (7.70) (1.92) (2.20) (2.01) (4.14) R&Dt Trendt -0.007*** -0.043*** -0.112*** -0.326*** (-3.06) (-3.54) (-4.63) (-5.04)

Observations 44,847 40,158 36,060 28,401 18,251 16,969 15,473 13,376 26,596 23,189 20,587 15,025 Adjusted R2 0.426 0.331 0.270 0.203 0.488 0.395 0.316 0.231 0.387 0.283 0.223 0.159 Panel B: Sales as the dependent variable 1980–2011 1980–1995 1996–2011

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Salet+1 Salet+1, t+3 Sale+1, t+5 Salet+1, t+8 Salet+1 Salet+1, t+3 Sale+1, t+5 Salet+1, t+8 Salet+1 Salet+1, t+3 Sale+1, t+5 Salet+1, t+8 R&Dt 0.210** 2.975*** 9.461*** 30.790*** 0.095* 1.704*** 6.248*** 20.096*** -0.117***‡ -0.012‡ 0.713‡ 4.581***‡ (2.44) (5.21) (6.30) (6.12) (1.75) (4.64) (6.38) (5.74) (-4.24) (-0.05) (1.59) (4.10) R&Dt Trendt -0.014*** -0.127*** -0.369*** -1.161*** (-3.86) (-4.86) (-5.76) (-5.09)

Observations 44,847 40,158 36,060 28,401 18,251 16,969 15,473 13,376 26,596 23,189 20,587 15,025 Adjusted R2 0.787 0.598 0.452 0.310 0.751 0.526 0.371 0.249 0.787 0.614 0.483 0.344 Panel C: Adjusted operating income as the dependent variable 1980–2011 1980–1995 1996–2011

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

OI OI , OI , OI , OI OI , OI , OI , OI OI , OI , OI , R&Dt 0.152*** 1.509*** 4.169*** 13.134*** 0.095*** 0.944*** 2.739*** 9.128*** -0.024**‡ 0.062‡ 0.318*‡ 1.810***‡ (4.40) (7.36) (8.38) (8.10) (3.67) (6.49) (6.71) (8.77) (-2.21) (0.78) (1.80) (4.12) R&Dt Trendt -0.007*** -0.061*** -0.162*** -0.492*** (-4.70) (-5.98) (-7.32) (-6.87)

Observations 44,726 40,014 35,903 28,225 18,224 16,925 15,414 13,291 26,502 23,089 20,489 14,934 Adjusted R2 0.597 0.447 0.340 0.230 0.608 0.437 0.335 0.234 0.581 0.440 0.325 0.209

48

Table A2 (Continued)

Panel D: Adjusted operating cash flows as the dependent variable 1980–2011 1980–1995 1996–2011

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

OCF OCF , OCF , OCF , OCF OCF , OCF , OCF , OCF OCF , OCF , OCF , R&Dt 0.185*** 1.400*** 3.599*** 12.010*** 0.129*** 0.900*** 2.373*** 8.620*** 0.031** 0.359*** 0.988*** 3.147*** (3.49) (6.78) (6.91) (5.78) (2.96) (6.93) (6.58) (7.76) (2.26) (4.25) (5.17) (6.80) R&Dt Trendt -0.006*** -0.044*** -0.111*** -0.388*** (-2.91) (-4.71) (-4.95) (-4.44)

Observations 36,117 31,767 28,074 21,226 9,588 8,663 7,574 6,277 26,529 23,104 20,500 14,949 Adjusted R2 0.443 0.376 0.300 0.216 0.438 0.377 0.311 0.224 0.444 0.372 0.287 0.201 Panel E: Current and future returns as the dependent variable 1980–2011 1980–1995 1996–2011

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Rett, t+1 Rett, t+3 Rett, t+5 Rett, t+8 Rett, f+1 Rett, t+3 Rett, t+5 Rett, t+8 Rett, t+1 Rett, t+3 Rett, t+5 Rett, t+8 R&Dt 0.029 0.158 0.421** 0.913** 0.126** 0.378*** 1.117*** 1.693*** 0.032 0.026‡ -0.063‡ -0.176‡ (0.52) (1.39) (2.10) (2.34) (2.33) (3.57) (2.95) (3.23) (0.25) (0.15) (-0.47) (-0.81) R&Dt Trendt 0.014 0.010 0.010 -0.058* (1.58) (0.84) (0.49) (-1.66) Observations 41,845 41,845 41,827 39,631 16,931 16,931 16,931 16,931 24,914 24,914 24,896 22,700 Adjusted R2 0.319 0.130 0.067 0.048 0.246 0.108 0.042 0.030 0.143 0.074 0.079 0.050 Panel F: Lower bound estimates (set missing future values to zero) 1980–2011 1980–1995 1996–2011

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

NI NI , NI , NI , NI NI , NI , NI , NI NI , NI , NI ,

R&Dt 0.190*** 1.062*** 2.396*** 6.657*** 0.130*** 0.732*** 1.741*** 4.182*** 0.032*‡ 0.113‡ 0.142‡ 0.250‡ (4.27) (4.71) (5.02) (6.04) (4.31) (5.56) (5.85) (5.54) (1.92) (1.43) (1.00) (0.83) R&Dt Trendt -0.007*** -0.040*** -0.093*** -0.282*** (-3.06) (-3.54) (-4.52) (-5.66)

Observations 44,847 44,847 44,847 41,005 18,251 18,251 18,251 18,251 26,596 26,596 26,596 22,754 Adjusted R2 0.426 0.320 0.248 0.160 0.488 0.376 0.280 0.188 0.387 0.273 0.206 0.115

49

Table A2 (Continued)

Panel G: Upper bound estimates (incorporate acquisition proceeds) 1980–2011 1980–1995 1996–2011

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

NI NI , NI , NI , NI NI , NI , NI , NI NI , NI , NI ,

R&Dt 0.190*** 2.199*** 5.448*** 13.189*** 0.130*** 1.333*** 4.177*** 9.558*** 0.032*‡ 0.729***‡ 1.383***‡ 3.207***‡

(4.27) (5.93) (6.41) (7.14) (4.31) (9.16) (8.22) (9.85) (1.92) (4.32) (5.06) (5.49) R&Dt Trendt -0.007*** -0.064*** -0.168*** -0.437*** (-3.06) (-4.13) (-5.05) (-5.37) Observations 44,847 44,847 44,847 41,005 18,251 18,251 18,251 18,251 26,596 26,596 26,596 22,754 Adjusted R2 0.426 0.158 0.145 0.138 0.488 0.251 0.186 0.171 0.387 0.121 0.117 0.104 Notes: In Panels A, B, C, and D we present coefficient estimates of R&D profitability using firm-level data in estimating Equation (1) in the paper when using future values of adjusted net income (our main measure of profitability), sales, adjusted operating income, and adjusted operating cash flows as the dependent variables respectively. Firm-years are dropped from the model if the future values are not available for all future years in the specific model. In Panel E, we present the coefficient estimate for R&D when regressing current and future stock returns on R&D, BM, market value of equity, firm beta, and momentum (controls not shown for succinctness), where we estimate the model using the quintile ranking for each independent variable, scaled to be between zero and one. In Panels F and G we adjust missing values of future net income for potential survivorship issues. In Panel F, we set missing values of future net income to zero provided the fiscal year to which they pertain was prior to and including 2016 (the end of data availability) and in Panel G, we add the difference between the delisting amount (as recorded in the CRSP delisting amount (DLAMT) times the number of shares outstanding) and book value of equity net assets (using the final pre-merger observation of common equity on Compustat Quarterly (CEQQ)) to the first instance of missing future net income and set all subsequent future years to zero provided the fiscal year to which they pertain was prior to and including 2016. Coefficients on control variables are suppressed for parsimony. Note that firm-years are dropped from the model if net income is not available for all future years. See Table 1, Panel C for a description of all other variables. All variables except BM, are scaled by total assets at the end of year t and winsorized at the 1% and 99% levels. T-statistics, calculated after clustering standard errors by firm and fiscal year, are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed). ‡  indicates a significant difference in the coefficient between the early and later part of the sample period at the p<0.1 level (two-tailed).

50

Table A3 Alternative definition of R&D investments (capitalized)

(1) (2) (3) (4)

NI , NI , NI , NI ,

Capitalized R&Dt 0.269*** 1.114*** 0.815*** 0.149*‡ (2.97) (4.49) (6.01) (1.69) Capitalized R&Dt Trendt -0.039*** (-3.78)

Controls:

Capitalized CAPEXt 0.715*** 0.190 0.247** 0.840***‡

(6.64) (1.12) (2.15) (5.45)

SGAt 0.885*** 0.733*** 0.677*** 0.835***

(8.68) (4.26) (5.87) (5.76)

MAt 0.313*** 0.285 0.566*** 0.290***

(3.20) (0.97) (2.87) (3.00)

NI 3.085*** 3.918*** 3.956*** 2.707***‡

(10.61) (9.73) (19.11) (9.04)

∆NI -0.458** -0.288 -0.618*** -0.392*

(-2.28) (-0.99) (-5.08) (-1.79) BMt -0.243*** -0.265*** -0.308*** -0.198***‡

(-6.41) (-5.63) (-8.46) (-3.77) Intercept 0.308*** 0.215*** 0.320*** 0.325***

(7.82) (2.91) (6.89) (7.54)

Controls interacted with Trend No Yes No No

Observations 30,578 30,578 12,691 17,887 Adjusted R2 0.280 0.290 0.347 0.248 Notes: In this table, we present estimates of R&D profitability using firm-level data in estimating Equation (1). We substitute out the level of R&D and Capital Expenditures in year t for a capitalized value of these expenditures over the current and prior four years using a 20% rate of depreciation (Capitalized R&D and Capitalized CAPEX respectively). Note that firm-years are dropped from the model if net income is not available for all future years. See Table 1, Panel C for a description of all other variables. All variables except BM, are scaled by total assets at the end of year t and winsorized at the 1% and 99% levels. T-statistics, calculated after clustering standard errors by firm and fiscal year, are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed). ‡ indicates a significant difference in the coefficient between the early and later part of the sample period at the p<0.1 level (two-tailed).