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IQ and the R&D Market Value Puzzle

Anne Marie Knott

Olin Business School Washington University in St. Louis

knott@wustl.edu

Carl Vieregger Olin Business School

Washington University in St. Louis vieregger@wustl.edu

James C. Yen Olin Business School

Washington University in St. Louis yenc@wustl.edu

Abstract

The dominant measures of R&D effectiveness in studies of innovation are patent counts and R&D expenditures. Patent counts are problematic because they are neither universal (less than 50% of firms conducting R&D have any patents) nor uniform (10% of patents account for 85% of economic value). R&D expenditures are problematic because they are an input rather than an output. Moreover both measures exhibit anomalies in models of market value. Thus they are unreliable We propose and test a novel measure of R&D effectiveness (IQ) that appears to be universal, uniform and reliable.

August 22, 2011

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1. Introduction

R&D is important both to firms and the economy. R&D expenditures comprise 5.8% of

annual firm expenditures and the associated intangible assets comprise 5.6% of firm market

value.1 Moreover, R&D is believed responsible for 7% of real GDP growth.2 However two

things suggest R&D is failing to deliver on its promise. At the economic level, for the past fifty

years GDP growth has been slowing despite increasing R&D intensity. Similarly at the firm-

level a Booz-Allen study3 shows little link between R&D spending and performance.

One reason government policy and firm strategy are failing to deliver on R&D’s promise

is a dearth of measures of its effectiveness. The two dominant measures in empirical studies

have been R&D spending (an input measure) and patent counts (an intermediate measure, which

is sometimes treated as an input and other times treated as an output).

Although an input measure (R&D expenditures) can’t directly test effectiveness, it can

test whether firm investment behavior is consistent with behavioral models. To date however

empirical tests of the market value of R&D lead to an empirical puzzle. In particular, tests

indicate increases in R&D increase firm market value, which shouldn’t be true in equilibrium.

In contrast, the intermediate measure (patent counts) can be used as a coarse measure of

effectiveness (patent counts/expenditures), but is neither universal, uniform nor reliable: 1) less

than 50% of firms conducting R&D have any patents; 2) there is substantial variance in patents'

economic value, e.g., Scherer and Harhoff (2000) report that 10% of U.S patents account for 81-

85% of the economic value of all US patents; 3) patent counts are a poor predictor of firm market

value (Hall, Jaffe and Trajtenberg 2005).

1 estimates derived from firms in the 25 most R&D intensive industries 2 http://www.nsf.gov/news/news_summ.jsp?cntn_id=110139 (downloaded May 4, 2009) 3 http://www.strategy-business.com/media/file/sb41_05406.pdf

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What firms, policy makers and academics need is a reliable measure of R&D

effectiveness that matches constructs in formal models (so empiricists can test them) and

facilitates derivation of optimal R&D, so firms can both choose investment levels and determine

if that investment generates the desired output. In addition the measure should be universal

such that it can be generated for all firms engaged in R&D, and uniform such that it can be

compared across firms, or within the firm over time.

A good test of whether a measure of R&D effectiveness satisfies the reliability property

is its ability to predict firm investment behavior and firm market value (since all models of R&D

investment assume firms maximize the net present value (NPV) of long run profits--precisely

what market value captures). In addition the measure should pass simple tests of face validity.

We propose that firm-specific output elasticity of R&D (organizational IQ) (Knott 2008)

satisfies the above criteria, and in addition resolves the empirical anomalies associated with other

measures. Like individual IQ, organizational IQ is normally distributed across firms. In

principle, it captures firms’ technical problem solving capability in much the same way that

individual IQ captures individual analytical problem solving capability: those with higher IQ

solve more problems per unit of input (dollars for firms, minutes for individuals) than those with

lower IQ.

We estimate IQ for publicly traded US manufacturing firms engaged in R&D. We then

demonstrate 1) that IQ predicts firm R&D investment behavior and 2) that IQ resolves the

empirical puzzle of non-decreasing market value to R&D: It is not that increasing R&D

increases market value; it is that firms differ in their IQ, and those with higher IQ both have

higher optimal R&D and higher market value. Finally, we show that R&D spending beyond the

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optimum prescribed by IQ is wasted—market value is non-increasing in R&D above the

optimum.

Although the primary value of the measure is the academic implications for reinterpreting

past empirics and guiding future empirics, there are managerial implications as well. First,

increasing R&D will not increase market value; however increasing IQ will (and this appears

possible). Second, whereas firms seem to have a relative sense of their IQ (those with higher IQ

spend more on R&D), they lack an absolute sense—they tend to deviate from the optimum. We

believe this stems from the fact that firms don't know their IQ (in large part because the measure

hasn't been available). Finally, and most importantly, IQ offers a more universal and precise

measure of R&D effectiveness than existing alternatives. Attention to the IQ measure may

therefore improve R&D effectiveness just as Total Quality Management (TQM) improved

product quality (Zbaracki 1998) and hospital report cards reduced hospital mortality (Dranove,

Kessler, McClellan and Satterthwaite 2003).

The article begins by characterizing the empirical puzzle of non-decreasing market value

of R&D. We articulate how the IQ measure potentially resolves the puzzle. We then empirically

test propositions arising from the "IQ hypothesis" across the set of publicly traded US firms

engaged in R&D.

2. The empirical puzzle of market value to R&D

Theories of innovation, particularly within industrial organization economics (IO)

typically assume a) firms in an industry share a common R&D production function, and b)

market structure and firm R&D behavior are endogenously determined by three exogenous

conditions: demand, technological opportunity and appropriability (See for example Levin and

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Reiss 1988). If all firms in an industry share these conditions, then whether we employ game

theoretic logic (where each firm chooses its R&D investment assuming optimal investment by

rivals), or decision theoretic logic (where the firm chooses a research stream to maximize its

market value), investing beyond optimal levels should be penalized. In other words, if firms

have been investing optimally, then increases in R&D should decrease market value (as should

decreases in R&D).4

This is not borne out by empirical studies. In fact the empirical record consistently

demonstrates the opposite—increases in R&D increase firms’ market value. This was true for

Grabowski and Mueller (1978) who tested the impact of R&D on profits in an effort to examine

the potential role of R&D as an industry entry barrier, for Connolly and Hirschey (1984) who

tested the simultaneous effects of profits, R&D, and market concentration, for Pakes (1985) who

examined the impact of R&D on inventive output, for Jaffe (1988) who examined the impact of

technological opportunity and spillovers on R&D productivity, for Cockburn and Griliches

(1988) who examined the impact of patent protection on firm performance, for Chan,

Lakonishok and Sougiannis (2001) who examined whether the stock market fully values

intangible assets, for Hall, Jaffe and Trajtenberg (2005) who examined whether patent citations

improved estimates for the market value of R&D, and for Nicholas (2008) who examined the

stock market’s changing valuation of corporate patentable assets.

3. The IQ hypothesis

We propose the empirical puzzle of non-decreasing returns to R&D arises from the

theoretical and empirical assumption of a common production function across firms in an 4 One possible explanation for the anomaly is that firms systematically underinvest in R&D. This could occur for two reasons—1) they don’t know the optimal level, or 2) they face budget constraints (however such constraints shouldn’t persist if financial markets are efficient)

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industry. Under such an assumption differences in R&D investment are interpreted as responses

to exogenous shocks. We propose instead that firms differ in their R&D production functions.

Although all inputs are likely to exhibit heterogeneity in their output elasticities, we are

particularly interested in the elasticity of R&D. Following prior work (Knott 2008), we refer to

the firm-specific output elasticity of R&D as organizational IQ. This is captured as in Equation

1:

Y = K LR SA (1)

where:

Y = output

K = capital

L = labor

R = R&D

S = spillovers

A = advertising

Organizational IQ is obtained by estimating firms’ R&D production functions using a

random coefficients model. This allows the researcher to recover firm specific error terms and

interpret them as something meaningful. Thus firm differences are not something to be cleaned

out, but rather are something to lean on (Griliches and Mairesse 1998). This approach to

interpreting random coefficients is similar to Henderson and Cockburn’s (1996) interpretation of

firm fixed effects as capturing flexible managerial integration processes.

Because the firm specific error terms are normally distributed by construction, firm IQ

resembles individual IQ. Both capture problem solving capability. For individuals, IQ is

captured as the speed and accuracy of solving problems of increasing difficulty--within any

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given time constraint, individuals with higher IQ solve more problems correctly than those with

lower IQ. For firms, IQ is efficiency solving new problems. For any given level of R&D

spending, high IQ firms will generate more innovations, or for any given innovation, high IQ

firms will invest less developing it.

If, as we propose, firms differ in R&D elasticity, those IQ differences will have two

effects on market value: a direct effect and an indirect effect. The direct effect is that higher

output elasticity corresponds to higher revenues per dollar of R&D and accordingly higher

market value (net present value of revenues minus cost of R&D). The indirect effect is that the

elasticity endogeneously determines investment. In particular, firms with higher IQ have higher

optimal investment, R*:

1 2 4 5 0 ( )IQK L R S A e dK L R AR R

(2)

1

1

1 2 4 5 0

1*

IQ

RIQK L S A e

(3)

A sample combination of direct and indirect effects of IQ is depicted in Figure 1. The

indirect effect is captured in the lower curve showing the relationship between IQ and

investment: As raw IQ increases from 0.10 to 0.16, optimal R&D increases from $12 million to

16 million (left hand scale). The direct effect is captured in the upper curve showing the

relationship between IQ and market value: as IQ increases from 0.10 to 0.16, and R&D

increases accordingly, then market value increases from 1.3 to 1.6 (right-hand scale). The figure

presents an alternative logic to the empirical puzzle of non-decreasing returns to R&D: It is not

that increasing R&D increases market value, but rather, IQ jointly increases optimal R&D and

market value per dollar of R&D.

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-----------------------------------

Insert Figure 1 about here

-----------------------------------

To test the IQ hypothesis, we estimate three models of market value: simple OLS, fixed

effects (FE), and simulated fixed effects (SFE) (which treats deviations from the firm’s optimal

R&D, R*). Expectations for the market value of R&D differ across the models. Under an

generalized least squares (GLS) model, market value should increase with R&D because greater

R&D reflects higher IQ and accordingly higher output and market value per dollar of R&D.

Under an FE model, the effects of R&D are indeterminate because there is an optimal

level of R&D spending. If on average firms invest at the optimal level, deviations above the

optimum should have negative coefficients, whereas deviations below the optimum should have

positive coefficients. Since a standard FE model will treat excess investment as the inverse of

underinvestment, the net effect will depend on the extent of positive deviations relative to

negative deviations.

Resolving the indeterminacy requires a model that treats deviations above and below R*

symmetrically. Accordingly we construct a "simulated fixed effects" estimation which models

market value as a function of deviations from the firm's optimal R&D investment, R*. Under the

IQ hypothesis, market value should be non-increasing with deviations from R* (both

overinvestment and underinvestment).

4. Empirical approach.

Our empirical test of the IQ hypothesis has two components. The first component tests

whether the IQ measure predicts firm behavior. The second component tests the hypothesized

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relationships between market value and R&D across the three models. Before conducting either

test, we first estimate firms' IQs.

4.1 Estimating Firm IQ5

We derive firm R&D elasticities (IQ) by estimating the firm’s final goods production

function with a random coefficients model that allows for heterogeneity in the output elasticity

for R&D (as well as all other inputs). A random coefficients model represents a general

functional form model which treats coefficients as being non-fixed (across members of a cross-

section or over time) and potentially correlated with the error term. Random coefficient models

are those in which each coefficient has two components: 1) the direct effect of the explanatory

variable and 2) the random component that proxies for the effects of omitted variables. Our use

of a random coefficients specification follows from the need to capture firm specific estimates

for R&D elasticity. Equation 4 models output (value-added, Y) for firm i in year t with random

coefficients for all inputs (capital, K, labor, L, R&D, R, spillovers, S, and advertising, A) as well

as the intercept:

0 0 1 1 2 2 3 3

4 4 5 5

ln ( ) ( ) ln ( ) ln ( ) ln

( ) ln ( ) lnit i i it i it i it

i it i it it

Y K L R

S A

(4)

We estimate Equation 3 using the Stata program, xtmixed. xtmixed fits linear mixed

models (both fixed effects and random effects) using maximum likelihood estimation. The

5 This discussion closely follows Knott 2008 which estimates the R&D production function using revenues as the output variable together with the conventional four inputs (capital, labor, R&D and spillovers). Because we use ultimately examine market value, we changed the output variable to value-added and included the other important intangible asset (brand--captured via advertising) to the production function in Stage 1. In addition, we employ a newer Stata program xtmixed, rather than xtrc. xtmixed utilizes maximum likelihood estimation, which is superior to estimation via generalized least squares (the method in xtrc) (Beck and Katz 2007). Given these changes, estimated coefficients differ slightly from those in Knott 2008.

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random effects, _i, are not directly estimated, but we form best linear unbiased predictions

(BLUPs) of them (and standard errors) using xtmixed postestimation.

4.2 Behavioral impact of IQ

The behavioral tests check that firm R&D behavior corresponds with firm IQ. In

particular, if firms differ in their IQ and if their investment behavior conforms to equation 2, then

R&D should increase with IQ. Note this test assumes firms have an intuitive feel for their

productivity of R&D investments—that firms get them right on average.

4.3 Market value of R&D

The traditional functional form for estimating the market value of R&D was established

by Griliches (1981), and is largely retained in more recent tests (Hall et al. 2005, Nicholas 2008):

'

ln ln 1'it i t it

RQ m d u

A

(5)

This equation is derived from a simple definitional model of market value:

' 'V q A R (6)

where:

Q = V/A' (equivalent to Tobin's Q)

V = firm market value (equity plus debt)

A' = value of conventional assets (plant, equipment, inventories and financial assets)

R' = value of firm's intangible 'stock of knowledge'

q = market valuation coefficient of firm's assets, defined as exp(mi+dt+uit)

This model imposes some restrictive assumptions:

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1) assets exhibit constant returns to scale (CRS)

2) assets are additive in levels

3) the market value coefficient, q, is common across asset classes6

Given concerns with these restrictions, we estimate a more general functional form and

test whether the restrictions hold. To build our model we revert to first principles. The market

value, V, of the firm represents the net present value of future profits, where NPV of profits is

defined as revenues, Y, minus costs, c, divided by the firm’s discount rate, d, minus its growth, g:

( )Y c

V NPVd g

(7)

Substituting for revenues using the firm's production function and specifying costs yields:

1 2 3 4 5 0 /V K L R S A e c d g (8)

We make the assumption that in steady state flows represent the depreciation rates of

asset stocks for both R&D and advertising. This assumption relies on Knott, Bryce, and Posen

(2003) which characterized the knowledge accumulation function in the pharmaceutical industry

and found that R&D stocks reached steady state within three years. Thereafter, spending was

largely that required to compensate for obsolescence and to grow at the industry rate. This

finding of steady-state explains two empirical regularities: econometric equivalence between

stock and flow models and econometric equivalence of models with different lags (Griliches and

Mairesse 1984, Adams and Jaffe 1996). Accordingly, we can approximate stocks using flows7:

1 2 3 4 5 0k l r s a e (9)

We can then estimate market value using equation 10:

6 In some models the coefficient for R&D assets is allowed to be a scalar multiple of q 7 this is true because: Kt=(1-d)(Kt-1) + k Kt= Kt-1-dKt-1 + k in steady state, Kt= Kt-1 therefore k = dKt-1

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0 1 2 3 4 5 6ln ln ln ln ln ln lnV k l r s A d (10)

This approach yields some notable differences from the Griliches specification. First, inputs are

multiplicative (additive in logs). Second, the specification includes advertising. Third, assets are

not constrained to contribute equally to market value. Fourth, there is no CRS assumption.

We estimate equation 10 using three specifications: OLS, FE and SFE. In all cases we

cluster standard errors to account for the possible dependence among observations within firms.

If firms are investing optimally in R&D, then under the IQ hypothesis we expect to find that 3 is

positive and significant in GLS specifications, but non-increasing in simulated FE specifications.

Our primary tests use R&D expenditures as the knowledge asset. However we replicate

tests using patent counts as the knowledge asset. If patents are an output, they should be

significant in GLS and FE regressions of market value. If however they are an input, results

should match expectations for R&D expenditures.

4.4 Data and variables

We estimate firm IQ and the market value of R&D using a twenty-five year panel of all

publicly traded US firms engaged in R&D. Data for the study comes from the Compustat

industrial annual file for all active and inactive firms over the period 1981 through 2006.

Excluded from this data set are firms that are publicly traded subsidiaries of other publicly traded

firms (since their results would have already been reported within their parent firm’s results) as

well as firms trading on non-major stock exchanges (since the data are often pro forma rather

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than realized), firms with headquarters located outside of the US and firms with no advertising

expenditures.8

Firm level data items include (in $MM unless otherwise stated): market value (MVit)9,

value added as revenues minus cost of goods sold (Yit), capital as net property, plant and

equipment (Kit), labor as full-time equivalent employees (1000)(Lit), advertising (Ait), and R&D

(Rit). From these primary data, we derive a secondary measure: firm specific spillovers (Sit)

which is computed as the sum of the differences in knowledge between focal firm i and rival

firm j for all firms in the respective industry with more knowledge than the focal

firm: it jt it jt itj

S R R R R .10

Within the 25-year period, we restricted the sample to firms with at least seven years of

complete data. The final dataset comprises an unbalanced panel of 1177 firms and 13372 firm-

year observations. Summary statistics for these data are presented in Table 1a.

-----------------------------------

Insert Table 1a about here

-----------------------------------

In a secondary dataset we merge Compustat data with patent data collected by the NBER

Patent Data Project (PDP) under the leadership of Bronwyn Hall.11 This dataset includes patent

8 In principle we could include firms who report zero advertising expenditures. However there is a commonly held belief that some zero observations are instances of not reporting. We tested all propositions using an alternative data set that included firms with zero advertising. Whereas results for propositions are robust for this data set, the coefficient estimates for other inputs increase--suggesting an omitted variable problem 9 Market value is calculated as (stock price*common shares outstanding) + book value of preferred stocks + short term and long-term debt. 10 This functional form matches the probability density form in endogenous growth models (Jovanovic and Rob 1989, Jovanovic and MacDonald 1994, and Eeckhout and Jovanovic 2002), which captures the likelihood of obtaining superior knowledge in a random encounter with a rival. Empirical tests show this form to best match empirical reality in US manufacturing firms (Knott, Posen and Wu 2009). We test both 2-digit and 4-digit industry definitions for spillover pools. While results are simlar for both, we use 2-digit pools because they have greater explained variance--suggesting that firms utilize knowledge outside their narrowly defined industry. 11 http://elsa.berkeley.edu/~bhhall/patents.html

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data from 1976 to 2006. To merge the patent data with the Compustat data we follow Besssen

(2008). First, since owners of the patents change over time, patents must be matched to owners

dynamically. These dynamic matches are recorded in DYNAMIC data, and we thus merge

DYNAMIC data with the patent counts data. We then use DYNAMIC data to find the

appropriate gvkey (firm identifier) to assign the patents because patents change ownership when

firms are merged or acquired. Second, we sum over multiple assignees to get patents for each

company and merge this patent data with our Compustat firm data by matching the gvkey’s.

Finally we distinguish the firms that have zero patents from those whose number of patents are

missing. The merged data set (summarized in Table 1b) contains 753 unique firms and 5652

firm-year observations with non-zero patents, thus reducing our total observations by 58%. The

reduction directly illustrates the concern raised in the introduction that patents are not a universal

measure of R&D effectiveness.

-----------------------------------

Insert Table 1b about here

-----------------------------------

5. Results

5.1 Estimating firm IQ

Estimation results for the random coefficients specification of the R&D production

function (equation 3) are presented in Table 2. Our primary interest is in the firm specific

coefficients of R&D, 3i. However we first discuss the production function estimates more

generally. To do so, we compare the random coefficients (RC) estimates (Model 2) to those

from a GLS specification (Model 1).

-----------------------------------

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Insert Table 2 about here

-----------------------------------

Both the RC and GLS models exhibit constant returns to scale for purchased inputs (i.e.,

ignoring spillovers). The sum of purchased inputs is 0.992 in RC:

(0.130+0.478+0.154+0.239=0.992), while it is 1.002 for GLS:

(0.182+0.330+0.232+0.255=1.002). Conventionally we expect non-increasing returns to scale

for own inputs, but increasing returns with the inclusion of spillovers. Thus both specifications

conform to expectations.

Figure 2 presents a histogram of the 3i values for all 1170 firms with sufficient

observations to form estimates. For comparability with individual IQ we have rescaled the

values such that the mean value of 0.230 is set to 100 and the standard deviation of 0.181 is set to

15. Of the 1177 firms, 9.9% have elasticities that differ significantly from the value for 3.12

Thus we reject the conventional view of homogeneous firms.

-----------------------------------

Insert Figure 2 about here

-----------------------------------

To provide intuition for the measure, we present Figure 3 which shows the range of IQs

(min, max and mean) for the twenty industries with the greatest number of firms engaged in

R&D. A few things are worth noting. First, industries vary in their mean IQ. The surgical and

medical instruments and apparatus industry has the lowest mean (91), whereas the electronic

computers industry has the highest mean (108). The second thing worth noting is there is greater

variance of IQ within industries than across industries. On average the IQ range within industry

12 Note that xtmixed generates firm specific standard errors for each 3i. These differ from (and in general exceed) the standard error for 3. Significant IQs are defined using the firm specific standard error.

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is 61.6, though the range is lowest for electronic measurement equipment (26), and highest for

computer programming and data processing (97). This result of greater heterogeneity within

than across industries matches that for other measures of firm performance (Rumelt 1991,

McGahan and Porter 2002)

-----------------------------------

Insert Figure 3 about here

-----------------------------------

5.2 Behavioral impact of IQ

Next we test whether firm R&D investment corresponds with expectations under the IQ

hypothesis. In particular, if firms differ in their IQ and if their investment behavior conforms to

equation 2, then R&D should increase with IQ (lower plot figure 1). Table 3 presents results

from test of a reduced form of equation 213. The results confirm expectations. The coefficient

on IQ is 1.431 and significant at the 0.001 level after controlling for effects of the other inputs.

The economic impact of a standard deviation increase in IQ is to increase R&D spending 93%.

In addition to these formal tests we also present the deviations graphically in Figure 4. In

general firms tend to underinvest. Of the 12354 firm-year observations where we are able to

calculate the optimal R&D (R*), 1569 observations overinvest by more than 50%, while 7738

underinvest by the same amount. Interestingly, the firms most likely to underinvest are those

with an IQ of 110. This suggests perhaps these firms are using a strategy of matching rival R&D

intensity, when their higher IQ calls for higher investment.

-----------------------------------

Insert Table 3 about here 13 We test the equation: ln R = lnIQ + lnK + lnL +lnA

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Insert Figure 4 about here

-----------------------------------

5.3 Market value of R&D

The market value tests estimate R&D elasticity under three empirical specifications of

Equation 10. Table 4 presents results for these tests.

-----------------------------------

Insert Table 4 about here

-----------------------------------

Model 1 presents an GLS specification of equation 10. The coefficient on R&D is

positive and significant. This merely replicates results generating the empirical puzzle. The

empirical puzzle interpretation of the result is that R&D exhibits non-decreasing returns. The IQ

interpretation of the result is that the correlation between R&D and returns is due to an omitted

variable problem—higher IQ firms have both higher optimal R&D and higher market value per

dollar of R&D.

Model 3 presents an FE specification of equation 10. The coefficient on R&D is again

positive and significant. This test provides additional support for the empirical puzzle, but

provides no information on the IQ hypothesis because the impact of R&D on market value is

indeterminate in FE under the hypothesis.

To solve the indeterminacy problem we test a simulated FE specification of equation 10

(Model 5). To do this we decompose R&D spending into two multiplicative components:

optimal R&D, R* and deviation from optimum, R&D/R* (expressed as a ratio to preserve the

functional form of the production function). Results for that test indicate the coefficient on R*

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(.307) is significantly higher than the coefficient on R&D in model 1 (.256). This suggests firms

are better off at the optimum (which of course is by definition). In addition the coefficient on

deviations is significantly different from that on R*. To interpret the results, consider the case of

a firm investing at the optimum. The deviation ratio is 1, so the market value of R&D is (R*).307.

If however firms underinvest by 20%, then the ratio is 0.8, and the contribution from R is

(R*).307* (0.8).240. Thus the market value is 94.8% that at R*. Conversely at spending 20%

above R*, market value is only 4.5% above that at R*.

In sum, results for the behavioral and market value tests are all consistent with the

propositions for the IQ hypothesis. Firms differ in their IQ; firm R&D spending responds to

those differences; and the market value of R&D reflects those differences. Finally, R&D

investment above the optimum has a non-increasing impact on market value.

5.4 Patent tests

The above tests utilize R&D as the knowledge input. Because we are interested in

measures of R&D effectiveness, we also adopt specifications using patent counts as the

knowledge input. Before doing so, we first treat patents as an intermediate output and examine

the impact of R&D on patent counts. The results for this test are presented in Table 5. Table 5

presents the impact of R&D on the expected intermediate output.14 The results indicate that

R&D is highly significant and explains 39% of the variance in patent counts. A 10% increase in

R&D increases patents by 4.7%.

-----------------------------------

Insert Table 5 about here

----------------------------------- 14 We test the equation ln(PatentCounts) = lnR + ε

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Next we replicate the tests of market value replacing the R&D input with patents as the

intermediate input. Again we test GLS and FE specifications of equation 10.15 Results for the

market value of patent counts mimic those for R&D expenditures (both are shown in Table 4).

The coefficient on patent counts is positive and significant in the GLS specification (model 2),

insignificant in the FE (model 4). Thus patents appear to behave like an intermediate input

rather than as an output with intrinsic market value. In particular there appears to be an optimum

level of patents (just as there is an optimal level of R&D). Thus patent counts as a measure of

R&D effectiveness shares all the problems of R&D intensity, but compounds them with

problems of universality and uniformity.

5.5 Robustness checks

In addition to the models presented in Table 4, we performed a number of robustness

checks. With regard to IQ estimation (which flowed through all subsequent analyses) we tested

two different output variables (revenues and value added), and several sample constructions: 10

year versus 25 year, each with and without firms reporting advertising. With regard to functional

form of the market value model, we tested OLS specifications in addition to the GLS

specification, we also tested alternative configurations of the simulated fixed effects model

examining deviations from the optimum. Results for all such checks (available from the

corresponding author) confirm the basic results, but exhibit weaker measures of fit.

5.6 Test of Griliches restrictions

15 We present results for contemporaneous patent counts. However we also tested specifications using patent stocks. Results for patent stocks match those for patent counts: patent stocks are significant in the GLS specification (though less so than patents) and insignificant in the FE specification.

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Finally we tested the three Griliches restrictions. Results indicate the constant returns to

scale restriction is upheld. As noted in the discussion comparing the GLS and the RC

specifications for the production function (Table 2), the sum of purchased input coefficients is

not significantly different from 1.0. The other two restrictions are not upheld. The elasticity of

the assets differs across asset classes (capital, advertising, and R&D) in the market value models

(Table 4). The mean market value of capital is 0.420, whereas that for advertising is 0.145, and

that for R&D is .256. Third, and accordingly, assets are not additive in levels.

5.7 Mutability of IQ.

An obvious follow-on question given the significant impact of IQ on market value is

whether IQ is mutable. If R&D spending only affects market value to the extent it deviates from

the optimum, then a firm wanting to increase its market value through R&D, must first change its

IQ. Is this possible?

To test the mutability of IQ, we reduced the original sample to the set of firms present in

the data for all years between 1987 and 2006, resulting in 478 firms16. We then subdivided that

sample into two ten-year periods (1987-1996 and 1997-2006). We estimated IQ for each firm in

each ten year period according to a modified equation 317. Figure 5a crossplots the estimated

IQs for the late period versus the early period. The diagonal line represents the constant IQ line.

The figure indicates that IQ changes, however, those changes appear to be random.

-----------------------------------

Insert Figure 5 about here

-----------------------------------

16 We have to exclude advertising expenditures from our production model in the following tests because no firm has consecutive advertising data for 20 years. 17 Modified equation 3 becomes ln ( ) ( )ln ( )ln ( )ln ( )ln0 0 1 1 2 2 3 3 4 4Y K L R Sit i i it i it i it i it it

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Figure 5b summarizes these changes. The table reveals that of the 478 firms in the comparison

subsample, 163 increased their IQ. The mean value of the increase in the raw IQ was 0.114 (of

which 61 changes were statistically significant). The remaining 315 firms decreased their IQ.

The mean value of the decrease was -0.109 (of which 105 changes were statistically significant).

The fact that IQ changes randomly is consistent with the idea that firms don’t fully understand

their IQ nor the factors contributing to it. Again this is not surprising since the measure hasn't

been available.

6. Discussion.

R&D is important both to firms and the economy. Despite its importance, research and

practice are handicapped by the lack of reliable measures for R&D effectiveness. What firms,

policy makers and academics need is a measure of R&D effectiveness that matches constructs in

formal models (so empiricists can test them) and facilitates derivation of optimal R&D, so firms

can both choose investment levels and determine if that investment generates the desired output.

Ideally the measure should also be universal and uniform such that it can be compared across all

firms engaged in R&D.

We proposed that organizational IQ not only satisfied these criteria, but also resolved the

empirical anomalies associated with other measures. We estimated IQ for publicly traded US

manufacturing firms engaged in R&D then tested its implications in models of firm behavior and

market value. Our results indicate that firm behavior and market value conform to expectations

under the IQ hypothesis.

As a byproduct of our main inquiry we also examined the functional form for the market

value of intangible assets. In particular we tested a general form to see whether the restrictions

Page 22

in the more prevalent Griliches form were upheld. We found that while the constant returns to

scale (CRS) assumption was upheld, the common value of assets was not. By extension neither

is additivity of asset classes. Accordingly we advocate use of the more general form in tests of

market value to R&D.

In sum, the academic implications of these findings are 1) a resolution to the empirical

puzzle of market value to seemingly suboptimal behavior, 2) a better understanding of R&D

capability, optimal R&D behavior and the market value of R&D, and 3) perhaps most

importantly a universal, uniform and reliable measure of R&D effectiveness to replace the

proxies of R&D expenditures and patent counts. The IQ measure allows academics to compare

R&D effectiveness across all firms as well as within a firm over time.

There are managerial implications to these results as well. First, increasing R&D will not

increase market value, however increasing IQ will. Moreover, changes in IQ seem feasible.

Future work could examine the firm structures and processes distinguishing high IQ firms from

their lower IQ counterparts to inform what firms need to do to increase their IQ. Again, as with

the academic implications, perhaps the most important managerial implication is the new

measure of R&D effectiveness. Our results suggest firms have a relative sense of their IQ (R&D

spending increases with IQ), however they lack an absolute sense: 1) their R&D investment

tends to deviate from the optimum prescribed by IQ, and 2) IQ seems to change randomly rather

than purposefully. Neither of these results is surprising given the measure hasn’t exist

previously. Our hope is that adoption of the measure will improve R&D effectiveness just as

TQM improved manufacturing quality and hospital report cards improved hospital morbidity.

Page 23

References Adams, J. D., A. B. Jaffe. 1996. Bounding the effects of R&D: An investigation using matched establishment-firm data. RAND Journal of Economics 27 700–721. Beck, N. and J. Katz. 2007. Random coefficient Models for Time-Series-Cross-Section Data: Monte Carlo Experiments, Political Analysis 15: 182-195. Bessen, J. 2008. Matching Patent Data to Compustat Firms, NBER PDP Project User Documentation. Chan, L., J. Lakonishok and T. Sougiannis 2001 The Stock Market Valuation of Research and Development Expenditures. Journal of Finance, 56 (6): 2431-2456. Cockburn, I. and Z. Griliches 1988. Industry Effects and Appropriability Measures in the Stock Market's Valuation of R&D and Patents. American Economic Review, 78 (2): 419-423. Cohen, W., R. Nelson, and J. Walsh. 2000. Protecting Their Intellectual Assets: Appropriability Conditions and Why U.S. Manufacturing Firms Patent (Or Not). NBER Working Paper 7552. Connolly, R. and M. Hirschey 1984. R & D, Market Structure and Profits: A Value-Based Approach. Review of Economics & Statistics, 66 (4): 682-686. Dranove, D., D. Kessler, M. McClellan and M. Satterthwaite, 2003. Is More Information Better? The Effects of Health Care Quality Report Cards, Journal of Political Economy, 111:555-88. Eeckhout, J. & B. Jovanovic. 2002. “Knowledge Spillovers and Inequality.” American Economic Review, 92: 1290-1307. Grabowski, H. and D. Mueller 1978 Industrial research and development, intangible capital stocks, and firm profit rates. Bell Journal of Economics, 9 (2): 328-343. Griliches, Z. and J. Mairesse. 1984. Productivity and R&D at the firm level, in Z. Griliches, ed. R&D, Patents, and Productivity. University of Chicago Press, Chicago, IL. Hall, B., A. Jaffe, and M. Trajtenberg. 2005. Market Value and Patent Citations, RAND Journal of Economics, 36 (1): 16-38. Henderson, R. and I. Cockburn. 1996. Scale, Scope, and Spillovers: The Determinants of Research Productivity in Drug Discovery, RAND Journal of Economics, 27 (1): 32-59. Jaffe, A. 1988. Demand and Supply Influences in R&D Intensity and Productivity Growth. Review of Economics and Statistics, 70: 431-437. Jovanovic, B. & G. MacDonald. 1994. “Competitive Diffusion.” Journal of Political Economy, 102: 24-52.

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Jovanovic, B. & R. Rob. 1989. “The Growth and Diffusion of Knowledge.” Review of Economic Studies, 56: 569-582. Knott, A.M. 2008. R&D/Returns Causality: Absorptive Capacity or Organizational IQ, Management Science, 54: 2054 - 2067. Knott, A.M., D. J. Bryce, and H. E. Posen, 2003. On the Strategic Accumulation of Intangible Assets, Organization Science, 14 (2): 192-207. Knott, A.M., H. E. Posen, and B. Wu. 2009. Spillover Asymmetry and Why It Matters, Management Science, 55: 373 - 388. Levin, R. and P. Reiss. 1988. “Cost-Reducing and Demand-Creating R&D with Spillovers.” RAND Journal of Economics, 19(4): 538-56. McGahan, A. and M. Porter. 2002. What do we know about Variance in Accounting Profitability, Management Science, 48(7): 834-851. Nicholas, T. 2008. Does Innovation Cause Stock Market Runups? Evidence from the Great Crash, American Economic Review, 98 (4): 1370–1396. Pakes, A. 1985. On Patents, R & D, and the Stock Market Rate of Return. Journal of Political Economy, 93 (2):390-409. Rumelt, R. 1991. How Much Does Industry Matter? Strategic Management Journal, 12(3): 167-185. Swamy, P., and G. Tavlas. 1995. Random coefficient models: Theory and applications. Journal of Econometric Surveys, 9(2) 165–196. Zbaracki, M. 1998. The Rhetoric and Reality of Total Quality Management, Administration Science Quarterly, 43: 602-636.

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Table 1a. Summary for full data set

Variable Obs Mean Std. Dev. Min Max

ln(value add) 13372 4.128 2.465 -4.510 11.100ln(capital) 13372 3.178 2.784 -5.809 11.626ln(labor) 13372 0.044 2.284 -6.215 6.776ln(advertising) 13372 1.019 2.755 -6.908 8.821ln(R&D) 13372 1.863 2.490 -6.908 9.408ln(spillovers) 13372 7.319 4.365 -9.210 10.832Firm-level IQ 1177 0.230 0.181 -1.296 1.009

Table 1b. Summary for patent subset

Variable Obs Mean Std. Dev. Min Max

ln(patents) 5652 1.791 1.776 -1.609 8.367ln(patent stock) 5652 4.839 2.100 0.000 10.635ln(value add) 5652 5.498 2.260 -2.323 10.814ln(capital) 5652 4.745 2.536 -2.996 11.340ln(labor) 5652 1.333 2.121 -5.298 6.776ln(advertising) 5652 2.426 2.607 -6.215 8.821ln(R&D) 5652 3.245 2.308 -4.135 9.408ln(spillovers) 5652 7.017 4.796 -9.210 10.774ln(R*) 5652 4.673 2.843 -45.252 11.615Firm-level IQ 753 0.270 0.130 0.001 1.009

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Table 2. Estimates of equation 1

GLS RC

ln(capital) 0.182*** 0.130***(0.007) (0.012)

ln(labor) 0.330*** 0.478***(0.008) (0.016)

ln(advertising) 0.232*** 0.154***(0.004) (0.010)

ln(R&D) 0.255*** 0.230***(0.004) (0.011)

ln(spillovers) 0.003** 0.051***(0.001) (0.004)

Constant 2.800*** 2.545***(0.022) (0.047)

N 13979 13979

Standard errors in parentheses

* p<0.10 ** p<0.05 *** p<0.01

Dependent variable = ln(Value add)

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Table 3. Behavioral test of IQ

GLS

R&D Beta (IQ) 1.431***(0.061)

ln(sales) 0.847***(0.019)

ln(capital) 0.147***(0.014)

ln(labor) -0.262***(0.019)

ln(advertising) 0.074***(0.009)

Constant -3.222***(0.085)

N 13979

Standard errors in parentheses

* p<0.10 ** p<0.05 *** p<0.01

Dependent variable = ln(R&D)

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Table 4. Results of Market Value Tests

1 2 3 4 5

GLSa GLS Patents FEb FE

PatentsGLS with

R*

ln(capital expenditures) 0.420*** 0.593*** 0.249*** 0.285*** 0.410***(0.008) (0.013) (0.013) (0.024) (0.008)

ln(labor) 0.116*** 0.077*** 0.366*** 0.402*** 0.090***(0.009) (0.015) (0.033) (0.057) (0.010)

ln(advertising) 0.145*** 0.191*** 0.104*** 0.142*** 0.127***(0.005) (0.008) (0.015) (0.034) (0.006)

ln(R&D) 0.256*** 0.116***(0.006) (0.021)

ln(patents) 0.023*** 0.015(0.007) (0.013)

ln(R*) 0.307***(0.007)

ln(R&D / R*) 0.240***(0.006)

year effects included included included included included

Constant 3.862*** 4.841*** 4.708*** 4.861*** 3.901***(0.048) (0.129) (0.058) (0.095) (0.050)

N 13372 5915 13372 5915 12354

R2 NA NA 0.585 0.662 NA

Standard errors in parentheses and are adjusted in FE models for clusters in firms

* p<0.10 ** p<0.05 *** p<0.01

a: The R2 is not reported for GLS models

b: The R2 for each FE model is within groups

Dependent variable = ln(Market value)

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Table 5. Impact of R&D on intermediate output

OLS

ln(R&D) 0.465***(0.030)

Constant 0.284***(0.063)

N 5915

R2 0.386

Standard errors in parentheses are adjusted for clusters in firms

* p<0.10 ** p<0.05 *** p<0.01

Dependent variable = ln(patent count)

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Figure 1. Optimal investment versus R&D productivity (IQ) (Assumes joint contribution from all other inputs=100)

0

10

20

30

40

50

60

70

80

0.020.

030.

040.

050.

060.

070.

080.

090.10.

110.

120.1

30.

140.

150.

160.1

70.

180.

190.20.

210.

220.

230.

240.

25

Op

tim

al R

&D

sp

en

din

g

0.0

0.5

1.0

1.5

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2.5

3.0

3.5

R&

D^I

Q optimal R

Rd IQ

RD for IQ

MV for “IQ”

Real source of MV (IQ)

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Figure 2. Histogram of firm IQ

050

100

150

200

250

Num

ber

of F

irms

50 100 150Firm IQ

Distribution of Firm IQ

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Figure 3. Intra-industry versus cross-industry variance in IQ

IQs by Industry

30

50

70

90

110

130

150

2834

2835

2844

3571

3572

3576

3577

3651

3661

3663

3674

3679

3825

3826

3841

3842

3845

7370

7372

7373

Industry SIC

Fir

m-L

evel

IQ

IQ Mean

IQ Max

IQ Min

SIC Mean Max Min Industry2834 105 149 55 Pharmaceutical Preparations2835 96 129 41 In Vitro & In Vivo Diagnostic Substances2844 99 128 76 Perfumes, Cosmetics & Other Toilet Preparations3571 108 127 96 Electronic Computers3572 99 118 65 Computer Storage Devices3576 103 121 81 Computer Communications Equipment3577 97 129 47 Computer Peripheral Equipment, Nec3651 93 108 56 Household Audio & Video Equipment3661 104 142 80 Telephone & Telegraph Apparatus3663 97 120 67 Radio & Tv Broadcasting & Communications Equipment3674 99 125 57 Semiconductors & Related Devices3679 97 120 63 Electronic Components, Nec3825 106 120 94 Instruments For Meas & Testing Of Electricity & Elec Signals3826 103 116 80 Laboratory Analytical Instruments3841 91 114 73 Surgical and Medical Instruments and Apparatus3842 104 128 81 Orthopedic, Prosthetic & Surgical Appliances & Supplies3845 104 147 62 Electromedical & Electrotherapeutic Apparatus7370 106 150 53 Services-Computer Programming, Data Processing, Etc.7372 103 162 68 Services-Prepackaged Software7373 101 142 68 Services-Computer Integrated Systems Design

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Figure 4. Actual R&D vs R* (Optimal R&D)

020

0040

0060

0080

0010

000

Act

ual R

&D

0 20000 40000 60000 80000Optimal R&D (R*)

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Figure 5a. Cross plot of old IQ versus new IQ

Figure 5b. Summary of changes

Number of Firms Mean ChangeStatistically Significant

Increasing IQ 163 0.114 61Decreasing IQ 315 -0.109 105

050

100

150

200

Firm

IQ

199

7-20

06

0 50 100 150 200Firm IQ 1987-1996

Cross Plot of Old IQ versus New IQ