Aggregate R&D Expenditures and Firm-Level Profitability of R&D †

Asher Curtis University of Washington

abcurtis@uw.edu

Sarah McVay University of Washington

smcvay@uw.edu

Sara Toynbee University of Washington

stoynbee@uw.edu

August 28, 2016

ABSTRACT: We examine the effect of increased aggregate spending on R&D on the aggregate and firm-level profitability of R&D expenditures. Theory provides conflicting predictions on how increased aggregate spending affects the payoffs to firm-level R&D expenditures. The ambiguity arises from the different effects of rivalrous and non-rivalrous innovations of the firm and competitors. Empirically we estimate that the profitability of R&D has declined by 57 percent (in aggregate) and 47 percent (within firm) between the early and later parts of our sample period. This decline does not appear limited to any particular subset of our sample and is robust to alternative model specifications. We provide evidence of diminishing marginal returns to aggregate and firm-level R&D investments. We document peer effects from positive knowledge spillovers and negative duplication or crowding effects. Despite the importance of aggregate R&D spending to firm-specific profitability, we provide evidence that analysts do not fully incorporate this information into their long-term growth forecasts. Our results inform market participants and researchers assessing the implications of R&D for future earnings, specifically, we document the mechanisms through which aggregate spending by peers’ impacts R&D profitability.

Keywords: R&D; Competition; Spillovers; Forecasting.

JEL Codes: M40.

Data Available: All data are available from the sources described in the text.

† An earlier version of this paper benefited from comments made by participants and discussants at conferences and workshops. We look forward to comments on this substantially revised draft. An appendix with additional discussion and results can be found at: http://faculty.washington.edu/abcurtis/wordpress/wp-content/uploads/2016/08/CMT_DMR_20160828_appendix.pdf

1

1. Introduction

We examine the effect of increased aggregate spending on research and development (R&D) on the

aggregate and firm-level profitability of R&D expenditures. Understanding whether the usefulness of

reported R&D for forecasting earnings is affected by aggregate R&D expenditures is important given the

sharp increase in aggregate R&D spending over the past 30 years. For instance, aggregate R&D

expenditures in the U.S. increased from $15.9 billion in 1979 to $465 billion in 2013, an annual nominal

growth rate of 10.14 percent, which is twice as large as the nominal GDP growth rate over the same

period (Grueber and Studt 2013). R&D activities are considered to be an important contributor to

economic growth, as the recent inclusion of these expenditures in U.S. GDP calculations indicates. The

output of R&D activities, however, often includes ideas, which are nonrivalrous inputs to production

(Romer 1990).1 Theory suggests that nonrivalrous inputs create dependencies across time and firms in

R&D spending, which leads to an ambiguous effect on the profitability of R&D at both the aggregate- and

firm-level.

To determine the effect of increases in aggregate R&D spending on R&D profitability we draw on

economic theory to develop predictions based on various dependencies in R&D spending. We first define

our null hypothesis based on theory assuming that the productivity of R&D is independent of the

aggregate level of R&D spending, i.e., R&D spending is independent both across time and firms. Under

this condition, the productivity of R&D is expected to exhibit constant returns to scale (hereafter “CRS”),

which implies a constant linear association between R&D spending and future profitability. We then draw

on the semi-endogenous and endogenous growth theories from macroeconomics to develop alternative

hypotheses. An important theme in these theories is that R&D activities are often related, which

influences the productivity of R&D expenditures at the aggregate- and firm-level. The mechanisms

1 That is, adding one new factory benefits the factory owner (a rivalrous input), whereas adding one new idea potentially benefits everyone (or some population) in the economy (a nonrivalrous input). For example, the invention of wireless technology has provided economy-wide benefits. In other words, ideas generated by R&D activities benefit those who can make use of the idea, not just the inventor.

2

through which R&D activities are related, however, lead to different predictions about how the

productivity of R&D is associated with the level of spending, both in the past and contemporaneously.

One potentially important effect of related R&D activities is that they could lead to knowledge

spillovers, both intertemporally and cross-sectionally. For instance, the ideas generated from R&D efforts

could enhance the productivity of subsequent R&D activities. Moreover, because access to these ideas is

often not restricted to the inventor and utilization of the idea does not necessarily restrict others ability to

also exploit the idea, R&D expenditures could exhibit increasing returns to scale (hereafter “IRS”).

However, if subsequent R&D efforts lead to less impactful ideas, the payoffs associated with R&D

expenditures could exhibit diminishing marginal returns (hereafter “DMR”). Furthermore, if R&D

activities are related because they reflect duplicative efforts to discover similar ideas or business stealing

motives, greater spending on R&D could lead to lower returns (i.e., DMR). As such, how increases in

aggregate R&D affect the productivity of these investments is an empirical question.

To test our hypotheses, we examine both the aggregate and firm-specific association between R&D

activities and future profitability in a linear forecasting framework. It is important to examine both levels

of analysis because it is possible that aggregate R&D exhibits constant returns to scale even if firm-

specific estimates differ (Theil 1954). If R&D exhibits constant returns to scale, the linear association

between R&D and future profits should not change over time. However, our estimates of R&D

profitability indicate significant positive returns to these investments in the early years of our sample,

consistent with prior literature (Griliches 1995; Lev and Sougiannis 1996), but we find evidence of an

economically and statistically significant decline in R&D profitability. We document a decline in

profitability of 57 percent at the aggregate level and 47 percent at the firm level with firm-fixed effects,

where profitability is assessed by regressing the five cumulative years of earnings on R&D expenditures.

Given the large increase in spending over time, this evidence is suggestive of DMR to R&D

expenditures. Nonetheless, we directly model the possibility of a non-linear relation between R&D and

future profits by supplementing our forecasting model with a quadratic term for R&D expenditures

3

(Wooldridge 2013). Consistent with the existence of DMR to R&D, our results reveal a nonlinear

(concave) relation between R&D spending and future profitability, both on an aggregate- and firm-level.

We also conduct our firm-level analysis using rolling-window sample periods to infer whether the

decline is due to a shock or is more gradual. We note a continuous decline in the profitability of R&D

from 1980 to the mid-1990s, after which the average profitability of R&D appears to stabilize at a lower

level.2 We observe that this stabilization also appears to coincide with a steadying trend in R&D spending

over the same time period.

We next estimate an empirical model that directly incorporates peer interactions within a

competitive model of R&D activities. In particular, we introduce peer firms’ R&D activities into our

forecasting model, allowing for a non-linear relation between peer R&D spending and firm-specific

profitability. We define peer spending as spending of those firms in the same three-digit SIC industry

group. Consistent with the cross-sectional spillovers to R&D in Bloom et al. (2013), we find a positive

main effect of peer R&D spending on firm profitability. Furthermore, our results reveal a concave relation

between peer R&D expenditures and firm profitability. We interpret this association as providing

evidence of creative destruction, which negatively impacts firm profitability and intensifies with greater

amounts of R&D spending in a peer group. Creative destruction reflects the replication and improvement

of an idea that subsumes the profit stream associated with the original idea and can lead to similar effects

on R&D productivity as the business stealing and duplication efforts described by Jones (1995).3 This

2 We present a number of robustness analyses in an Online Appendix (see section C) where we consider sales, operating income and future returns as alternate profitability measures and estimate upper and lower bounds of future profitability to incorporate possible survivorship bias (due to both performance delisting and acquisitions). We also conduct numerous cross-sectional analyses of the association between firm-specific R&D and future profitability. We examine the profitability of R&D quintiles sorted on the basis of firm size, profitability, leverage, patent filing activity, and the proportion of firms reporting R&D in the industry. These analyses reveal that although some firms appear more successful in generating profits from R&D investments, the lower profitability of R&D is not driven by a particular subset of firms. 3 For example, the development of Netflix and other video streaming services resulted in significant disruption to traditional video rental services such as Blockbuster, reducing the profitability of this original subscription service. This effect is similar to the notion of sales cannibalization, which is an important consideration in strategic decision-making (i.e., opening a new Starbucks store on the same block as an existing store is likely to result in reduced sales for the existing store).

4

analysis provides additional insights into how the various interactions between firms lead to DMR on

average.

To investigate the practical implications of our findings, we examine whether a particular group of

sophisticated capital market participants – specifically, analysts – appear to impound the changing

implications of R&D expenditures for future profitability into their earnings expectations. We focus on

analysts’ forecasting behavior as it has the distinct advantage of being able to match expectations with

realizations. In contrast, investors’ expectations, as reflected in market prices, reflect all future periods for

which the firm is expected to be operational. Consequently, we examine the association between analysts’

long-term growth forecasts and R&D expenditures. Our results illustrate that, in the more recent period,

the ratio of R&D/total assets (hereafter R&D intensity) is associated with optimism in analysts’ long-term

growth forecasts, consistent with analysts overestimating the earnings implications of R&D. Hence, it

appears that analysts did not anticipate the effect of the increase in aggregate R&D spending on the

profitability of firm-specific R&D, but rather scale their long-term growth forecasts with R&D changes.

This finding highlights the practical importance of our evidence, and is distinct from the findings that link

analysts’ forecasting of innovative companies over short horizons. For example, Donelson and Resutek

(2012) find that analysts’ forecast errors are not statistically associated with short-term analysts forecasts,

and DeFond et al. (2013) find that analysts quickly revise their forecasts following innovation awards.

Our results highlight that over longer periods, analysts face additional complexity when predicting how

innovation at the firm level is expected to affect earnings growth.

We contribute to the forecasting literature by documenting significant reductions in the profitability

of firm-specific R&D over time and illustrating several important mechanisms that link the decline in

firm-specific profitability to increases in aggregate spending. First, the increasing investment in R&D

activities is associated with lower average R&D profitability, both at the aggregate- and firm-level,

suggesting DMR. Second, we provide evidence of both positive spillover effects across firms within an

industry, but also evidence of duplication of efforts or business stealing as spending increases, where the

latter appears to outweigh the former, limiting the positive externalities of R&D spending and

5

culminating in DMR to R&D expenditures. Overall, this evidence is consistent with theories that describe

how relations between R&D activities can affect their productivity.

Our paper shares some similarities with recent work in both microeconomics and macroeconomics

but is distinct in terms of focus and contribution. For example, whereas Bloom et al. (2013) provide

estimates of static spillover effects, we are interested in how these spillover effects link to changes in the

aggregate level of R&D spending. Our spillover results complement Bloom et al. (2013) as we find a

positive effect but also provide evidence of a negative quadratic term, consistent with creative destruction

resulting from increased peer spending. Our study also complements Jones and Williams (2000) as our

empirical tests complement their simulation results for business stealing. Our paper also shares some

similarities with recent accounting research that examines the effects of macroeconomics on the

interpretation of accounting estimates. For example, Konchitchki (2011) and Curtis et al. (2015) examine

how inflation affects the interpretation of accounting information and Konchitchki et al. (2016) examine

how the pricing of earnings relates to macroeconomic risk. We focus on how the increase in aggregate

R&D expenditures affects the future profitability of a firm’s R&D expenditures.

Finally, our results have important implications for researchers who are examining pooled cross-

sectional effects of R&D spending (or other innovation related activities) on firm outcomes. In our

primary results, we identify a strong nonstationarity in the coefficient that links firm-specific R&D

profitability to future income. Further, we document that this effect is pervasive in many traditional cross-

sectional cuts of the data. As such our results highlight an important caveat for interpreting coefficients on

pooled data with strong time trends. In these cases reporting only a full sample estimate can be

misleading, and will lead to inference that will not generalize out-of-sample. In addition, depending on

the research question, future researchers could consider supplementing R&D with controls for knowledge

spillovers due to the R&D spending of peers, as the association between this measure and future income

appears less affected by aggregate changes in R&D spending.

6

2. Hypothesis development

2.1. The nature of R&D activities and the role of aggregate R&D spending

In this study, we examine whether the increase in aggregate R&D expenditures affects the

profitability of firm-specific R&D expenditures. Specifically, given our interest in forecasting earnings

and the growing importance of R&D as an input in firms’ production functions, it is important to

understand the productivity of these investments. Economic theory motivates the importance of

examining the productivity of R&D as aggregate spending increases. In particular, unlike other factors of

production such as capital goods, ideas, which are the output of R&D activities, are often considered to be

nonrivalrous inputs into the production function (Romer 1990).4 Conceptually, new ideas that are

generated by R&D activities are shared among those who can make use of the idea, not just the inventor.

This implies that aggregate R&D expenditures could be associated with IRS.5 However, because multiple

firms contribute to aggregate R&D expenditures, how these firms interact also likely impacts the

productivity of these investments.

Jones (1995) identifies three channels through which firms’ interactions in R&D activities influence

the creation of new knowledge at the aggregate level: (1) intertemporal knowledge spillover effects, (2)

duplication of knowledge production efforts, and (3) business stealing. Although the duplication of

knowledge production or business stealing effects predict diminishing marginal returns to R&D at the

aggregate level, the impact that intertemporal knowledge spillovers have on productivity is ambiguous.

Either prior innovation enhances the productivity of current research activities, or prior innovation leads

to diminishing marginal productivity due to the best ideas being discovered first. In the following section,

4 In contrast, capital goods are rivalrous in that a firm generally has exclusive rights to that asset (e.g., a factory, land). 5 For instance, consider the simple production function � = ���, �� where � is output, � is the stock of knowledge or ideas, and � is a vector of the remaining inputs to production, typically capital goods and labor. To increase output by the factor , we could replicate an existing idea (such as the plans to build a smartphone) and increase the capital goods (factories and machinery) and labor by the factor , such that production becomes: � = ���, ��. Thus the economy achieves constant returns to scale for the (rivalrous) inputs �. However, because the stock of knowledge did not need to change in order to achieve the increased production, increases in knowledge will amplify the returns to the economy, i.e., ���, �� > �.

7

we use the notion that productivity is increasing in the stock of knowledge (i.e., ideas) to develop our

hypotheses with respect to the productivity of R&D.

2.2. Implications of the increase in aggregate R&D spending on R&D productivity

In developing our testable predictions, we take a macro-to-micro approach by drawing on the semi-

endogenous and endogenous growth theory literatures in macroeconomics (Jones 1995; Romer 1990) We

first consider a simplified model of R&D productivity at the aggregate level, where R&D can be linked to

future profitability using a linear forecast such as:

� �� = �� + ��� + ���� + � �� (1)

where � is an accounting measure of income, �� is R&D expenditures, and �� reflects the productivity

of R&D activities.6 We next discuss several possible interactions between firms’ R&D efforts and how

these interactions may lead to differences in the productivity of R&D as aggregate expenditures change.

In the special case where the productivity of R&D activities is independent of the level of aggregate

spending (both across time and firms), theory suggests that R&D will exhibit CRS. That is, the increase in

aggregate R&D expenditures should not affect the productivity of these investments. However, as

discussed above, because R&D activities primarily generate ideas, which are a nonrivalrous good, it is

feasible that the productivity of R&D activities is not independent across time and firms.

Two important interactions proposed by Jones (1995) are the duplication of knowledge production

efforts and business stealing, both of which predict that greater R&D spending is associated with lower

returns to these investments. For instance, consider an extreme scenario in which firms’ R&D activities

reflect a duplication of efforts to produce the same idea (e.g., many firms seeking a vaccine for Ebola).

Because these expenditures are all focused on generating the same idea, it is likely that each dollar

invested in generating new knowledge produces a lower expected return. In other words, there would be

DMR to R&D at the aggregate level.

6 Note that empirically we will include specific controls and provide specifications that allow for the identification of �� under certain assumptions. We discuss the model and these additional assumptions in greater detail in the Online Appendix Section A.

8

Another important interaction between R&D activities and the productivity of these investments is

the intertemporal knowledge spillover effect (Jones 1995). Intuitively, the idea with this effect is that the

productivity of research efforts is a function of the prior stock of knowledge (see Online Appendix

Section A). Unlike the implications of duplication and business stealing, how this effect impacts the

productivity of R&D is ambiguous. On the one hand, ideas generated from prior R&D efforts could

enhance the productivity of current R&D activities, which would lead to IRS to R&D expenditures (e.g.,

the invention of touchscreen technology). On the other hand, if the ideas generated from prior R&D

efforts were the highest projected net present value, ideas that derive from subsequent R&D activities will

be less productive; leading to DMR to R&D (e.g., treatments for common diseases are generally

developed first).

2.3. Hypotheses

Given the above discussion, we posit CRS as our null hypothesis and compare this to the

alternatives of IRS or DMR, which result from different variations of interactions in R&D activities.

There are multiple ways to test these hypotheses. Because CRS models imply a stationary association

between R&D activities and future profitability, we consider a simple test and split a regression model

into two sub-periods based on time, the first T observations and the remaining m observations:

� �� = ��� + ��� + ���� + � ��, � = 1,… , �.��� + ���� + ����� + � ��, � = � + 1,… , � +�. , (2)

Thus, our null hypothesis of CRS model can be stated as follows:

H0: R&D expenditure exhibits constant returns to scale ��� = ����. Similarly, we can state our alternative hypotheses with respect to the coefficient estimates in

Equation (2):

HA: R&D expenditure exhibits increasing returns to scale ��� > ����. HB: R&D expenditure exhibits diminishing marginal ��� < ����.

9

This approach represents a simple test of our predictions due to the empirical observation that R&D

has been increasing over time. An alternative is to examine the productivity of R&D expenditures using a

non-linear function (Wooldridge 2013); specifically, by introducing a quadratic term for R&D

expenditures into the forecasting model described in Equation (1):

� �� = �� + ��� + ���� + ���� � + � �� (3)

where �� � is the square of R&D expenditures. Under the null hypothesis of CRS, future profits are a

linear function of R&D expenditures (H0: �� = 0). In contrast, IRS and DMR suggest non-linear payoffs

to R&D, with IRS predicting �� > 0, and DMR predicting �� < 0. Note that under both of these

empirical approaches, we may fail to reject the null even when it is false if both positive and negative

externalities are increasing with aggregate R&D expenditure but through different channels. For example,

if positive intertemporal knowledge spillovers offset the negative effects of duplication and business

stealing, we may fail to pick up these effects.

Although our discussion to this point has focused on aggregate effects, our hypotheses also apply

on firm-level basis. Linking the firm and aggregate, however, is much more complex. For example, the

effects of the R&D spending of others could have no effect on the profitability of R&D activities of the

firm (i.e., cross-sectional independence of R&D productivity), but individual firms could experience

either DMR or IRS to their R&D activities (i.e., intertemporal dependence of R&D productivity). In

contrast, the R&D activities of new entrants could affect firm-specific profitability via business stealing,

but in aggregate, profitability to R&D activities could be constant. These complexities and ambiguities

suggest that tests at both levels will be informative.

At the firm-level we are able to examine how the interactions between firms affect the productivity

of R&D activities. Specifically, we introduce peer firms’ R&D activities into our forecasting model.

Given the nonrivalrous nature of the ideas and inventions that arise from R&D activities, firms could

benefit from the R&D efforts of others, which may facilitate more productive products or services. Thus,

as long as firms R&D efforts are not duplicated, a given firm could benefit from the R&D expenditures of

peer firms. This expected effect is similar to the cross-sectional spillover effect described by Bloom et al.

10

(2013), and suggests a positive correlation between future profitability and the ideas, or R&D

expenditures, of others.

Despite the potential for positive spillover effects to R&D activities, R&D efforts could also lead to

creative destruction. Intuitively, creative destruction is the replication (i.e., business stealing) and

improvement of an idea that subsumes the profit stream associated with the original idea. Creative

destruction can result in an effect that is similar to the duplication of R&D efforts discussed by Jones

(1995). For instance, to the extent that peer firms’ R&D efforts are intended to replicate a firm’s initial

idea (i.e., R&D reflects a business stealing incentive), these expenditures effectively represent a

duplication of resources to generate the same idea. Because creative destruction likely intensifies with

greater amounts of R&D spending in a peer group (e.g., Dubois et al. 2015), we expect to see a concave

relation between future profitability and peer firms’ R&D expenditures.

To summarize, although other firms’ R&D activities could result in positive spillover effects, the

potential for creative destruction suggests a non-linear relation between peer firm spending on R&D and

firm-level profitability. To examine this implication, we use the following forecasting specification:

�! �� = �� + ���! + ����! + ����! � + �"��_$%%�& + �'��_$%%�& � + � �� (4)

where ( subscripts relate to the firm and ) subscripts to variables that sum other firms in the same 3-digit

SIC code industry group (i.e., ( ≠ )). Our null hypothesis is that peer R&D spending does not

incrementally impact R&D profitability (�" = �' = 0). In contrast, the existence of cross-sectional

knowledge spillovers implies �" > 0, and �" < 0 implies that the spending of others directly affects the

market share of the firm. For the quadratic term, �' > 0 implies that cross-sectional spillovers have

increasing returns to scale (ideas amplify productivity among peers) whereas �' < 0 implies that creative

destruction and duplication of efforts are increasing as peer spending increases.

3. Sample and descriptive statistics

3.1. Sample

11

We obtain financial data from Compustat and data on mergers and acquisitions (hereafter M&A)

from Thomson SDC Platinum over the period 1980–2015; we begin in 1980 because R&D is sparsely

reported in Compustat before this point. However, because we require five years of subsequent earnings

realizations, our sample period for R&D expenditures is 1980-2010. 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 and net income (Compustat NI) in the

prior and current year.7 We inflation-adjust all dollar values to be on a 2012 basis. After matching our

sample to CRSP, we obtain a final sample of 44,464 firm-year observations for our main analyses. We

also obtain analysts’ long-term growth forecasts from I/B/E/S, which yields a sample of 16,044 firm-year

observations.

3.2. Descriptive statistics

We illustrate the growth in R&D spending for the median firm in Figure 1. We present the median,

rather than the mean, to prevent extreme observations from influencing this trend; mean values are

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

1 plots the median proportion of total assets (solid line) and sales (dashed line) spent on R&D over time

among Compustat-covered firms that report R&D expenditures. The plot presents a significant increase in

the 1980s and 1990s. R&D investment then declines in the 2000s, potentially consistent with managers

becoming more judicious in their spending after the dot.com bust. In Figure 2 we compare R&D

expenditures to total investment-related expenditures (which we measure as the sum of R&D, capital

expenditures, advertising, and M&A). Among firms disclosing R&D expenditures, the median proportion

of investment funds allocated to R&D has increased significantly over time. In sum, R&D expenditures

7 Koh and Reeb (2015) examine the patenting behavior of firms with missing R&D on Compustat and argue that they may engage in strategic nondisclosure of these 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, perhaps indicating greater compliance with SFAS 2, which requires separate disclosure of material amounts of R&D. To the extent that observations with missing R&D 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 would bias our estimates of average R&D profitability downwards in the earlier part of our sample period. Thus, these reporting trends should bias against finding our main results.

12

have become a more prominent feature of the median sample firm’s investment portfolio. This trend

could reflect firms reallocating resources away from investments with relatively lower expected returns

(e.g., capital expenditures) to investments with relatively higher returns (e.g., R&D).

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

the results separately for two periods: 1980–1995 and 1996–2010.8 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%) in the 1980–1995 period to 10% (6%) in the 1996–

2010 period.

We also provide descriptive statistics for additional variables used in our analysis in Table 1. We

adjust net income by adding back R&D, advertising, and depreciation (Compustat items NI + XRD +

XAD + DP; hereafter, all references to net income are to adjusted net income).9 Thus, future values of net

income are not mechanically reduced by future expenditures of R&D, depreciation associated with capital

expenditures, or advertising expenditures, which could be a result of successful R&D projects. Net

income is higher at both the mean and median in more recent years. However, when we scale net income

by total assets, the ratio is higher at both the mean and median in the earlier period. Nonetheless, the

results indicate that the average and median firms are profitable (before depreciation, R&D expenditures

and advertising expenses) in both periods.

There are other noteworthy changes across the two periods. First, the number of firm-year

observations increases from 18,554 in the earlier period to 25,910 in the later period. This difference is

driven by an increase in the number of firms in the sample rather than a significant increase in the average

number of available years per firm in more recent years (not tabulated). This increase in unique firms is

8 The tenor of the results is not affected by the year of the split. For example, 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. In Figure 3, we provide rolling-window estimates to mitigate concerns about the arbitrary nature of the sample period split. 9 Following Lev and Sougiannis (1996), we add back advertising and depreciation expenses to avoid mechanical associations that might affect our inferences. For example, if the R&D leads to a new product launch, that launch may be associated with high advertising expenses, which would mechanically lower near-term net income. Depreciation could mechanically introduce a negative association between net income and capital expenditures, which we include in our main model.

13

consistent with prior studies investigating changes in the characteristics of publicly traded firms (e.g.,

Collins et al. 1997). Second, for the subset of our sample firms that have non-missing long-term growth

forecasts and three-year ahead EPS realizations reported on I/B/E/S, we find that analysts make higher

average long-term growth forecasts in more recent years (18.08 versus 16.63 percent growth). Although

the magnitude of the increase is small, the difference is statistically significant. We also find that the

annualized five-year-ahead realized growth rate in EPS for these firms is significantly higher in the later

part of our sample period (5.55 versus 1.73), although the magnitudes fall short of the analysts’ forecasts,

on average.

4. Empirical analysis

4.1. Aggregate analyses

As discussed in section 2.3, there are different empirical approaches to test how the increase in

aggregate R&D expenditures affect the productivity of these investments (i.e., whether there are constant

returns to scale). The first approach we employ is to divide our sample period into subsamples and

examine the stationarity of the association between R&D expenditures and the sum of future profitability.

Specifically, we estimate the following regression model for the early (1980-1995) and later (1996-2010)

parts of our sample period and test for the similarity of the coefficient +�over time:

-. ��, �'/0& =+� + +�-. /0& + +�1&3 + 4567��689 +ɛ (5)

where-./0&is net income plus depreciation, R&D, and advertising expenses (Compustat items NI +

XRD + XAD + DP). We sum net income over the subsequent five years.10 We include controls to account

for the effect of other factors of production. We include 5;<=�! (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&A during the fiscal year, as reported on

Thomson SDC Platinum (>;! ). We also control for investments in advertising (;3?! ; Compustat item

XAD), which may be correlated with successful R&D efforts. We include growth in net income from t to

10 We also report windows of one, three and eight years in the Online Appendix (see Appendix C).

14

t-1 (@-.! /0&), 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 (A>! ), as growth

opportunities may be correlated with both the profitability of R&D and future earnings. All variables are

adjusted for inflation and (except BM) scaled by total assets measured at the end of fiscal year t. To

estimate Equation (5) at the aggregate level, we simply sum all firms’ R&D within annual industry groups

based on three-digit SIC codes provided there are at least 10 observations in the industry-year.11 We

aggregate all the other variables using the same method and scale by aggregate total assets at the end of

year t.

The results, presented in Table 2, illustrate a decline in the association between aggregate R&D

expenditures and aggregate future net income over time. In particular, the coefficient on aggregate R&D

falls from 5.128 in the 1980-1995 period, to 2.192 for R&D expenditures between 1996-2010. This

decline of approximately 57% is significant at the 1% level (two-tailed). Thus, based on this evidence, we

reject our null hypothesis that R&D investments exhibit constant returns to scale at the aggregate level.

Furthermore, given the observation that R&D spending is significantly higher in the latter part of our

sample period, the results in Table 2 suggest that R&D exhibits diminishing marginal returns.

We explore the possibility of diminishing marginal returns to R&D spending more directly by

introducing a quadratic term into the specification in Equation (5).12 We present this specification in

column 4 of Table 2. As expected, the coefficient on the main effect of R&D is positive and significant

(3.697), whereas the coefficient on the squared R&D term is negative and significant (–16.978). This

result is consistent with diminishing marginal returns at the aggregate level and links the increased

aggregate R&D spending to the lower levels of profitability observed in recent periods.

11 We also aggregate across all firms in the sample, across quintiles of R&D/total assets, two-digit SIC codes, and two-digit NAICS codes. We continue to find evidence of a decline in the profitability of aggregate R&D using each approach (not tabulated). 12 Because we include quadratic terms, we mean-adjust each independent variable to reduce issues with collinearity. Mean-adjusting these variables also allows us to directly interpret the value of the coefficients on each investment to determine the shape of its production function. Specifically, if investments are characterized by diminishing marginal returns, we expect to see a positive main effect on the investment variable but a negative quadratic term.

15

4.2. Firm analyses

Because the productivity of firm-level R&D expenditures may differ from those at the aggregate

level, we also estimate Equation (5) using firm-level data. We estimate the coefficients of Equation (5)

using OLS under the identifying assumptions that the error term, ɛ! , is uncorrelated with the independent

variables. Similar approaches to estimating R&D profitability are found in the prior literature (Ciftci and

Cready 2011; Pandit et al. 2011). Technological differences, either in initial endowments or in time-

varying opportunities, may cause ɛ! to be correlated with the investments made by the firm (including

R&D). We address these two concerns as follows. First, if the correlation is due to the time-varying

opportunities of the firm, including the firm’s book-to-market ratio in the panel regression provides a

control for this endogeneity. Second, we also present results including firm fixed effects in the second

specification to control for the time-invariant effects of differences in initial technological endowments.

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 and year.13

Moreover, to limit the effects of outliers, we winsorize all scaled variables at the 1% and 99% levels by

fiscal year.

We present the results in Table 3. Over the full sample period, we provide evidence of future

benefits of R&D, consistent with prior research. Specifically, R&D has an association of 0.973 with

future net income over the following five years, on average, suggesting that one dollar of current R&D is

associated with 97 cents of earnings over the next five years. When we examine the coefficients by period

to test our hypothesis of constant returns to scale, we see that the future profitability of R&D has fallen

significantly in more recent years, consistent with the results in Table 2 for aggregate R&D expenditures.

For example, in the specification without firm fixed effects, R&D has an association of 2.529 with future

13 Gow et al. (2010) suggest that robust-standard-error clustering on two dimensions is appropriate in many settings where accounting data is used and when the sample consists of both large N and large T. Our test statistics are similar when we cluster on firm alone, which lowers the concern that the relatively small T dimension biases the two-way-clustered standard errors downwards. An alternative would be to 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. Our results are robust to this alternative estimation (not tabulated).

16

net income from 1980–1995, which declines to 0.492 from 1996–2010, and the difference is statistically

significant at the 1% level.14 Inferences are similar when including firm fixed effects in the estimation,

although the decline is less stark, at only 47 percent, relative to the 81 percent decline when firm fixed

effects are excluded.

Although our results are presented in two distinct periods for ease of interpretation, in Figure 3, we

graphically represent the coefficients on R&D estimated from five-year rolling-window regressions. The

regression parameters reflecting the association between R&D and future profitability show a clear trend

rather than a one-time structural change.15 This provides evidence that our results are not affected by the

specific time at which we divide our sample period but in fact reflect a systematic decline over time in the

profitability of R&D. Moreover, the lower R&D profitability appears to have been relatively stable since

the early 2000s, which also matches the plateau, and even small decline, in R&D spending in Figure 1.

Figure 3 also suggests that the decline is unlikely to be transitory.

We next investigate whether the non-linear relation between R&D and future profits that we

document on an aggregate basis also exists at the firm-level. We follow the same approach as discussed in

section 4.1 and introduce a quadratic term for R&D expenditures. The results of this specification,

presented in column 1 of Table 4 indicate that firm-level R&D expenditures also exhibit diminishing

marginal returns. We also examine how the interactions between firms (i.e., peers’ R&D activities) affect

the profitability of R&D. Specifically, due to the nonrivalrous nature of ideas, we hypothesize that peer

R&D has a concave association with firm-level profitability. To test this empirically, we augment

14 By the eighth year following the initial investment, the association of R&D with future net income exceeds one in both periods, which could be considered as a rough proxy for recouping the initial investment (see Online Appendix: Appendix C). 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). For parsimony, we only present the p-value on the R&D×POST coefficient in Table 3. 15 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. As a result, in addition to the rolling-window analysis, we also examine the robustness of our results to the exclusion of post-2005 data. After this exclusion, there remains a significant decline in the profitability of R&D expenditures in recent years (not tabulated).

17

Equation (5) with two additional terms: R&Dt, peer and R&D2t, peer, which capture contemporaneous R&D

spending by all other firms in the same three-digit SIC code industry group.

We present the results in column 2 of Table 4 and continue to find evidence of diminishing

marginal returns to firm-specific R&D spending. We also find evidence of both positive knowledge

spillovers and creative destruction. Specifically, firm profitability is positively associated with peer R&D

spending but at a decreasing rate (i.e., a negative coefficient on the quadratic term for peer R&D

spending). This suggests that as more is spent on R&D within a peer group, these increased expenditures

could reflect duplication or creative destruction of other R&D efforts (or both).

5. Additional analyses

5.1. Analysts’ growth forecasts and R&D spending

We next investigate the importance and practical implications of our results. In particular, we

examine whether analysts – an important class of capital market participants – appear to recognize the

economically significant decline in the association between R&D expenditures and future net income

over time. We focus on analysts’ forecasts as it is more straightforward to match analyst expectations

with subsequent realization relative to assessing how investors’ expectations map to ex post realizations.

In Table 5, we regress long-term growth forecasts, actual EPS growth, and forecast errors on R&D and

our other control variables for the subset of our sample firms that have non-missing long-term growth

forecasts in I/B/E/S and five-year ahead EPS realizations. We estimate a fully interacted model, where

POST is an indicator variable equal to one for fiscal years 1996 and later. We use the annual quintile

ranking minus one, scaled by four to range from zero to one, of each independent variable (except POST)

to alleviate concerns of outliers, where the ranking is based on our main sample. The tenor of our results,

however, remains unchanged using continuous variables (not tabulated).

In column 1, we find that in the early part of our sample period, after controlling for other

investments and the current level of and growth in net income, analysts forecast long-term growth to be

approximately 9.412% higher for firms in the highest quintile of R&D/total assets than firms in the lowest

18

quintile. These results are consistent with analysts viewing R&D as a leading indicator of growth. We

find no evidence that they lower their growth forecasts in recent periods (the coefficient on R&D × POST

is not statistically significant). We next report the association between R&D and future realized growth

for this subset of our sample, where future realized growth is calculated as the compounded annualized

growth rate in EPS over the next five years in column 2 of Table 5. We measure growth over five years as

analysts typically make their long-term growth forecasts over this horizon and it matches our prior

horizon. In the earlier period, we find that R&D intensity is significantly associated with future growth,

with an average difference of 10.685% for the five-year growth rate between firms in the lowest and

highest quintiles of R&D intensity. In the later part of our sample, however, the average difference in the

realized growth rates between the lowest and highest quintiles of R&D intensity is significantly lower at

approximately 2.696% (10.685–7.989). Thus, consistent with Table 2, we find a decline in the association

between R&D and future EPS growth in more recent years.

Finally, in column 3 of Table 5, we examine analysts’ forecast errors of long-term growth.

Specifically, we calculate the difference between the realized annualized growth rate over five years less

the median analyst forecast of long-term growth. Forecast errors are positive (negative) when the analyst

consensus forecast was pessimistic (optimistic) regarding future earnings growth. In the earlier period, we

find that R&D intensity is associated with neutral forecasts of growth on average over the subsequent five

years. In the recent period, however, we find a negative association between analyst forecast errors and

R&D intensity, suggesting that analysts overestimate the implications of R&D for future growth on

average. This finding suggests that sophisticated users of financial statements do not fully incorporate the

lower R&D profitability into their growth expectations.

5.2. Cross-sectional analysis

In our main results, we provide evidence of a decline in the association between R&D and future

profitability at both the aggregate and firm-level. We also provide evidence of diminishing marginal

returns to R&D investments, linking the decline to the increase in aggregate R&D spending over time.

19

We also conduct a series of descriptive cross-sectional analyses to assess the pervasiveness of this

decline. In particular, we consider whether the decline is concentrated among certain types of firms.

Although our cross-sectional analyses indicate that some types of firms are better at pursuing profitable

R&D investments, there does not appear to be any one type of firm driving the decline in the association

between R&D expenditures and future profitability. The results of these analyses are presented and

discussed in the Online Appendix (see Appendix C).

6. Conclusion

We investigate the effect of increased aggregate spending on R&D on the firm-level profitability of

R&D expenditures. Given the growth in the importance of innovation in the US and global economies, as

evidenced by the large increase in R&D investments over time, it is important to understand the

implications of these expenditures for future profitability. We consider this task from the perspective of a

financial statement user who aims to infer the profitability of R&D investments made today. Economic

theory provides conflicting predictions on how increased aggregate spending affects the payoffs to firm-

level R&D expenditures with the ambiguity arising from the different effects of interactions between the

firm and their peers. How these peer effects aggregate could also lead to different inference at the firm

and aggregate level. As such, we estimate a number of specifications that aid in assessing how aggregate

R&D affects firm-specific and aggregate profitability.

Empirically we estimate that the profitability of R&D has declined significantly, with estimates of

over 47 percent within firm and 57 percent in aggregate, between the early (1980-1995) and later (1996-

2010) years of our sample. We provide additional evidence of diminishing marginal returns to both firm-

level and aggregate R&D investments by estimating a quadratic term in the model that links R&D with

future profitability. We also document peer effects from positive knowledge spillovers and also from

negative duplication or crowding effects, due to increased aggregate spending within these peer groups.

We contribute evidence that the effects we document – of the increase in aggregate R&D spending

on the profitability of firm-specific R&D – was not anticipated ex-ante. Specifically, despite the apparent

20

importance of aggregate R&D spending on firm-specific profitability, we find that the long-run growth

forecasts of analysts scale with R&D, rather than anticipate diminishing marginal returns. This finding

highlights the practical importance of our evidence, and is distinct from findings in prior research that link

analysts’ forecasting of innovative companies over short horizons. Our results highlight the difficulties

associated with forecasting over longer periods, especially when analysts’ face additional complexity due

to the effects of competition for peers.

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

must infer returns to R&D 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 by, for example, incorporating the acquisition

premium into our estimate of the upper bound of profitability. Any remaining bias would need to be time-

varying to present a valid alternative explanation for our results. Our results inform market participants

and researchers assessing the implications of R&D for future earnings, specifically, we document the

mechanisms through which aggregate spending by peers’ impacts R&D profitability. More research is

required, however, to fully understand other causes and implications of the decline, especially in terms of

predicting how the accounting measurement of R&D could be made more useful.

21

References

Bloom, N., M. Schankerman, and J. Van Reenen. 2013. Identifying Technology Spillovers and Product

Market Rivalry. Econometrica 81 (4):1347-1393.

Ciftci, M., and W. M. Cready. 2011. Scale effects of R&D as reflected in earnings and returns. Journal of

Accounting and Economics 52 (1):62-80.

Collins, D. W., E. L. Maydew, and I. S. Weiss. 1997. Changes in the value-relevance of earnings and book

values over the past forty years. Journal of Accounting and Economics 24 (1):39-67.

Curtis, A., M. F. Lewis-Western, and S. Toynbee. 2015. Historical cost measurement and the use of

DuPont analysis by market participants. Review of Accounting Studies 20 (3):1210-1245.

DeFond, M. L., Y. Konchitchki, J. L. McMullin, and D. E. O'Leary. 2013. Capital markets valuation and

accounting performance of Most Admired Knowledge Enterprise (MAKE) award winners.

Decision Support Systems 56:348-360.

Donelson, D. C., and R. J. Resutek. 2012. The effect of R&D on future returns and earnings forecasts.

Review of Accounting Studies 17 (4):848-876.

Dubois, P., O. de Mouzon, F. Scott-Morton, and P. Seabright. 2015. Market size and pharmaceutical

innovation. The RAND Journal of Economics 46 (4):844-871.

Fairfield, P. M., and T. L. Yohn. 2001. Using Asset Turnover and Profit Margin to Forecast Changes in

Profitability. Review of Accounting Studies 6 (4):371-385.

Gow, I. D., G. Ormazabal, and D. J. Taylor. 2010. Correcting for Cross-Sectional and Time-Series

Dependence in Accounting Research. The Accounting Review 85 (2):483-512.

Griliches, Z. 1995. R&D and Productivity: Econometric Results and Measurement Issues, in “Handbook of

the Economics of Innovation and Technological Change”, ed. Paul Stoneman: Blackwell, Oxford.

Grueber, M., and T. Studt. 2013. Global R&D Funding Forecast. R&D Magazine

136:http://www.battelle.org/docs/tpp/2014_global_rd_funding_forecast.pdf.

Hitt, M. A., R. E. Hoskisson, R. D. Ireland, and J. S. Harrison. 1991. Effects Of Acquisitions on R&D Inputs

and Outputs. Academy of Management Journal 34 (3):693-706.

Jones, C. I. 1995. R & D-Based Models of Economic Growth. Journal of Political Economy 103 (4):759-

784.

Jones, C. I., and J. C. Williams. 2000. Too Much of a Good Thing? The Economics of Investment in R&D.

Journal of Economic Growth 5 (1):65-85.

Koh, P.-S., and D. M. Reeb. 2015. Missing R&D. Journal of Accounting and Economics 60 (1):73-94.

Konchitchki, Y. 2011. Inflation and Nominal Financial Reporting: Implications for Performance and Stock

Prices. The Accounting Review 86 (3):1045-1085.

Konchitchki, Y., Y. Luo, M. L. Z. Ma, and F. Wu. 2016. Accounting-based downside risk, cost of capital,

and the macroeconomy. Review of Accounting Studies 21 (1):1-36.

Lev, B., and T. Sougiannis. 1996. The capitalization, amortization, and value-relevance of R&D. Journal of

Accounting and Economics 21 (1):107-138.

Pandit, S., C. E. Wasley, and T. Zach. 2011. The Effect of Research and Development (R&D) Inputs and

Outputs on the Relation between the Uncertainty of Future Operating Performance and R&D

Expenditures. Journal of Accounting, Auditing & Finance 26 (1):121-144.

Phillips, G. M., and A. Zhdanov. 2013. R&D and the Incentives from Merger and Acquisition Activity.

Review of Financial Studies 26 (1):34-78.

Romer, P. M. 1990. Endogenous Technological Change. Journal of Political Economy 98 (5):S71-S102.

Theil, H. 1954. Linear aggregation of economic relations.

Wooldridge, J. 2013. Introductory econometrics: A Modern Approach.

22

23

Figure 1 Median R&D intensity over time

Notes: Figure 1 plots the time-series of the average R&D/Assets and R&D/Sales ratios for our sample.

24

Figure 2 Median proportion of R&D to total investment over time

Notes: This figure plots the time-series of the median R&D/Total Investment ratio for our sample, where Total Investment is the sum of R&D, capital expenditures, advertising, and the total value of M&A transactions in year t.

25

Figure 3 Rolling window regression coefficients of R&D on future net income for five-year accumulation

period

Notes: This figure plots the time-series of the coefficient on R&D from Equation (5).

26

Table 1 Descriptive statistics

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

R&D 44,464 144.68 3.42 12.79 47.73 643.33 R&D/Total Assets 44,464 0.09 0.02 0.05 0.11 0.11 CAPEX 44,464 283.10 1.94 9.50 57.86 1,587.86 CAPEX/Total Assets 44,464 0.05 0.02 0.04 0.07 0.05 MA 44,464 90.64 0.00 0.00 0.00 1,353.35 MA/Total Assets 44,464 0.03 0.00 0.00 0.00 0.09 ADV 44,464 47.31 0.00 0.00 1.20 315.17 ADV/Total Assets 44,464 0.01 0.00 0.00 0.01 0.04 NIAdj 44,464 634.75 5.37 30.65 170.00 2,989.69 NIAdj/Total Assets 44,464 0.13 0.07 0.14 0.21 0.18 BM 44,464 0.59 0.26 0.46 0.76 0.50 Sales 44,464 3,808.65 51.78 211.39 1,201.30 16,951.76 Total Assets 44,464 4,619.86 66.09 231.86 1,247.95 24,491.51 MVE 44,464 4,899.98 85.28 324.24 1,569.28 22,297.62 LTG Forecast 16,044 17.47 11.00 15.00 20.00 13.21 5-year AGR 16,044 3.96 -7.16 2.69 12.63 21.39

Panel B: Means and medians by subsample periods 1980-1995 1996-2010 Variable N Mean Median N Mean Median

R&D 18,554 109.55 8.58 25,910 169.83*** 17.11*** R&D/Total Assets 18,554 0.07 0.04 25,910 0.10*** 0.06*** CAPEX 18,554 273.26 10.40 25,910 290.15 8.89*** CAPEX/Total Assets 18,554 0.07 0.06 25,910 0.04*** 0.03*** MA 18,554 36.33 0.00 25,910 129.53*** 0.00*** MA/Total Assets 18,554 0.01 0.00 25,910 0.03*** 0.00*** ADV 18,554 44.66 0.00 25,910 49.21 0.00*** ADV/Total Assets 18,554 0.02 0.00 25,910 0.01*** 0.00*** NIAdj 18,554 492.73 27.38 25,910 736.45*** 33.21*** NIAdj/Total Assets 18,554 0.15 0.15 25,910 0.11*** 0.13*** BM 18,554 0.65 0.54 25,910 0.54*** 0.41*** Sales 18,554 3,424.27 216.37 25,910 4,083.90*** 207.95 Total Assets 18,554 3,568.15 186.45 25,910 5,372.98*** 270.42*** MVE 18,554 2,549.41 210.04 25,910 6,583.20*** 441.73*** LTG Forecast 6,702 16.63 14.98 9,342 18.08*** 15.00*** 5-year AGR 6,702 1.73 0.05 9,342 5.55*** 4.84*** 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). All variables are inflation adjusted to be on a 2012-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. See Appendix B for a description of all other variables. 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 (1996–2010). 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.

27

Table 2 Estimates of industry-aggregated R&D profitability

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

1980–2010 1980–1995 1996–2010 1980–2010 NID��,D�'EFG NID��,D�'EFG NID��,D�'EFG NID��,D�'EFG

R&D t 3.899*** 5.128*** 2.192*** 3.697*** (10.33) (8.27) (5.11) (9.45) Test of difference between R&D coefficients across time (p-value) (p=0.01)

R&D t2 -16.978*

(-1.94) Controls CAPEXt 1.745*** 2.337*** -0.508 1.891*** (4.71) (3.70) (-1.17) (5.01) MA t -0.690*** 0.227 -0.740*** -0.683*** (-3.20) (0.50) (-3.50) (-3.18) ADV t 3.358*** 3.387*** 2.844*** 3.434*** (7.57) (5.05) (5.31) (7.73)

NIDEFG 3.251*** 3.404*** 2.865*** 3.108*** (12.71) (8.13) (9.77) (11.69)

∆NIDEFG -0.729*** -0.808 -0.874*** -0.649** (-2.80) (-1.47) (-3.39) (-2.46) BM t -0.140*** -0.229*** -0.314*** -0.148*** (-3.08) (-3.61) (-3.97) (-3.24) Intercept 0.098** 0.056 0.373*** 1.011*** (2.37) (0.83) (6.18) (70.51) Observations 778 362 416 778 Adjusted R2 0.661 0.724 0.625 0.663 Notes: In this table we aggregate all variables by industry and year, provided there are at least 10 observations in the industry-year. Note that firm-years are dropped from the model if net income is not available for all future yearsAll variables except BM, are scaled by aggregate total assets at the end of year t and winsorized at the 1% and 99% levels. See Appendix B for a description of all other variables. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed).

Table 3 Estimates of firm-specific R&D profitability

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

1980–2010 1980–1995 1996–2010 1980–2010 1980–1995 1996–2010 NID��,D�'EFG NID��,D�'EFG NID��,D�'EFG NID��,D�'EFG NID��,D�'EFG NID��,D�'EFG

R&D t 0.973*** 2.529*** 0.492** 1.419*** 2.102*** 1.115*** (4.07) (7.19) (2.41) (4.47) (4.38) (3.10) Test of difference between R&D coefficients across time (p-value)

(p<0.01) (p<0.01)

Controls CAPEXt 1.706*** 0.782*** 1.522*** 1.313*** -0.196 0.727 (6.53) (3.08) (4.11) (4.24) (-0.80) (1.12) MA t -0.060 0.228 -0.022 -0.603*** -0.864*** -0.385*** (-0.50) (1.12) (-0.18) (-4.96) (-5.16) (-2.90) ADV t 3.240*** 2.674*** 2.829*** 2.544*** 3.040*** 0.890 (5.84) (2.89) (5.26) (3.15) (2.60) (1.05)

NIDEFG 2.978*** 3.644*** 2.632*** 0.912*** 0.416 0.506* (9.26) (14.70) (7.13) (4.29) (1.60) (1.88)

∆NIDEFG -0.388** -0.253* -0.396* 0.193** 0.343** 0.208** (-2.05) (-1.68) (-1.72) (2.35) (2.38) (2.07) BM t -0.239*** -0.380*** -0.151*** -0.132*** -0.220*** -0.066* (-5.26) (-8.95) (-2.91) (-3.67) (-7.95) (-1.65) Intercept 0.528*** 0.576*** 0.503*** 0.730*** 1.056*** 0.694*** (9.99) (12.42) (7.72) (15.85) (23.12) (12.15) Firm Fixed Effects No No No Yes Yes Yes Observations 35,846 15,746 20,100 35,846 15,746 20,100 Adjusted R2 0.246 0.306 0.216 0.554 0.731 0.594 Notes: In this table we present the results of using the level of R&D (scaled by total assets) in year t as the primary independent variable of interest. Note that firm-years are dropped from the model if net income is not available for all future years. All variables except BM, are scaled by total assets at the end of year t and winsorized at the 1% and 99% levels. See Appendix B for a description of all other variables. Standard errors, clustered by firm and fiscal year, are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed).

Table 4 Estimates of IRS or DMR and peer effects on firm-specific R&D profitability

(1) (2) 1980–2010

Firm-Specific 1980–2010

Firm-Specific NID��,D�'EFG NID��,D�'EFG R&D t 2.806*** 2.339*** (8.28) (7.51) R&D t

2 -5.211*** -4.592*** (-6.53) (-6.16) R&D t, peer 1.912*** (4.22) R&D2

t, peer -1.593*** (-3.82) Controls CAPEXt 1.902*** 1.985*** (7.05) (7.13) MA t -0.087 -0.095 (-0.75) (-0.80) ADV t 3.342*** 3.383*** (6.14) (5.87)

NIDEFG 2.840*** 2.841*** (9.21) (9.20)

∆NIDEFG -0.350* -0.362** (-1.95) (-1.96) BM t -0.212*** -0.208*** (-5.07) (-5.07) Intercept 1.039*** 1.912*** (26.53) (4.22) Observations 35,846 34,694 Adjusted R2 0.263 0.265 Notes: In Column 1 we use the firm-specific level of R&D (scaled by total assets), both in year t, allowing R&D to experience increasing or decreasing returns to scale with the inclusion of a quadratic term. In Column 2 we allow for peer spending in the firm-level regressions. Note that firm-years are dropped from the model if net income is not available for all future years. All variables except BM, are scaled by total assets at the end of year t and winsorized at the 1% and 99% levels. See Appendix B for a description of all other variables. Standard errors (clustered by firm and fiscal year in columns 2 and 3) are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed).

30

Table 5 R&D intensity and analyst long-term growth forecasts

(1) (2) (3) LTG Forecast 5-year AGR 5-year FE R&D t 9.412*** 10.685*** 1.273 (17.93) (6.80) (0.74) R&D t × POST 2.620* -7.989*** -10.609*** (1.86) (-3.65) (-3.63) CAPEXt 0.001 0.334 0.333 (0.00) (0.34) (0.36) CAPEXt × POST 0.145 -2.724* -2.869** (0.19) (-1.82) (-2.00) MA t -1.371*** -2.902*** -1.531** (-5.74) (-4.11) (-2.02) MA t × POST 0.584 1.453 0.869 (1.30) (1.37) (0.71) Advt -0.043 2.376*** 2.419*** (-0.11) (3.20) (3.16) Advt × POST 0.851 0.052 -0.799 (1.29) (0.04) (-0.55)

NIDEFG -8.151*** -10.215*** -2.064 (-9.84) (-4.95) (-1.02)

NIDEFG× POST -2.819 10.566*** 13.385*** (-1.55) (3.70) (3.81)

∆NIDEFG 7.788*** -2.887*** -10.675*** (17.69) (-2.83) (-9.80)

∆NIDEFG× POST 0.250 1.057 0.807 (0.34) (0.70) (0.50) BM t -10.401*** 2.517 12.919*** (-16.55) (1.37) (6.80) BM t × POST 3.325*** -1.126 -4.450* (3.06) (-0.52) (-1.91) POST 0.283 2.751 2.469 (0.17) (1.44) (1.01) Intercept 18.367*** 2.827** -15.540*** (22.57) (2.13) (-10.86) Observations 16,044 16,044 16,044 Adjusted R2 0.155 0.025 0.054 Notes: See Appendix B for a description of all other variables. Standard errors, clustered by firm and fiscal year, are in parentheses. *** p<0.01, ** p<0.05, * p<0.10 (two-tailed).