On the Strategic Accumulation of
Intangible Assets
Anne Marie Knott • David J. Bryce • Hart E. Posen
The Wharton School, University of Pennsylvania, 2023 Steinberg Hall-Dietrich Hall,
Philadelphia, Pennsylvania 19104-6370
Marriott School of Management, Brigham Young University, 790 TNRB, Provo, Utah 84602
The Wharton School, University of Pennsylvania, 2000 Steinberg Hall-Dietrich Hall,
Philadelphia, Pennsylvania 19104-6370
knott@wharton.upenn.edu • dbryce@byu.edu • hposen@wharton.upenn.edu
Abstract
The resource-based view holds that firms can earn supranormal returns if and only if they have superior resources and
those resources are protected by some form of isolating mechanism preventing their diffusion throughout industry. One isolating mechanism that has been proposed for intangible assets
is their accumulation process. The hypothesis is that intangible assets are inherently inimitable because would-be imitators
need to replicate the entire accumulation path to achieve the
same resource position. Thus, entrants can never catch up to
incumbents.
An interesting challenge to this hypothesis is counterfactual evidence that entrants sometimes outperform incumbents.
Such counterfactual evidence should not exist if the theory
is strictly correct. This paper attempts to reconcile resource
accumulation theory with the counterfactual evidence. We do
so by building an intermediate good-production function for a
firm’s intangible asset stocks. We test the contribution of the
intangible asset stock to the firm’s final good-production function and examine the extent to which that asset stock deters
rival mobility in the pharmaceutical industry.
We find that the asset accumulation process itself cannot
deter rivals, because asset stocks reach steady state rather
quickly. Entrants can achieve an incumbent’s intangible asset
stock merely by matching its investment until steady state.
Thus, we conclude that the accumulation process per se is
not an isolating mechanism. While this is perhaps the most
important contribution, another contribution is an empirical
methodology for characterizing the accumulation function.
(Resource-Based View; Intangible Assets; Asset Stocks; R&D Productivity; Mobility Deterrence)
1.
Introduction
The resource-based view holds that firms can earn
supranormal returns if and only if they have superior
resources, and those resources are protected by some
Organization Science © 2003 INFORMS
Vol. 14, No. 2, March–April 2003, pp. 192–207
form of isolating mechanism that prevents their diffusion throughout industry. One issue that arises, given that
resources are protected, is: How do firms obtain those
resources without dissipating the supranormal returns?
One answer (Rumelt 1984, 1987) is that firms are
lucky—they stumble upon the resources before their
value is known.
A second stream imparts a greater role to managers.
That stream holds that valuable resource positions are
developed over long periods of time (Itami 1987, Winter
1987, Dierickx and Cool 1989, Ghemawat 1991, Teece
et al. 1997), and that they are inherently inimitable
because would-be imitators need to replicate the entire
accumulation path to achieve the same resource position.
Dierickx and Cool (1989) provide the most fully articulated model of intangible asset accumulation, from
which they conclude that relative resource positions are
sustainable. The sustainability arises from asset mass
efficiencies and time compression diseconomies. Asset
mass efficiencies imply that the more assets a firm has,
the lower the marginal cost of producing further additions to the asset stock. Time compression diseconomies
imply that asset accumulation can’t be rushed. Even if an
entrant invests in one year the total sum of the incumbent
investments made over several years, it won’t achieve
the same resource position.
Resource accumulation theory is appealing because
it both identifies a role for managers and appears to
explain persistent heterogeneity of firms. Further, it provides intuition for the general tendency of incumbents
to prevail (Makadok 1998, Lieberman and Montgomery
1998). However, the theory is controverted by evidence that entrants in some instances have outperformed
incumbents (Teece 1987, Klepper 1999), and that R&D
at small firms is more productive than that at large firms
(see review in Cohen and Levin 1989). Neither of these
1047-7039/03/1402/0192$05.00
1526-5455 electronic ISSN
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
is possible if resource accumulation theory is strictly
correct. The fact that there are counterexamples means
that refinements are necessary in either its assumptions
or its mechanics.
This paper attempts to decompose the assumptions
and mechanics of resource accumulation theory so that
we can make progress refining it: (1) How do resources
accumulate, and (2) under what conditions will that
accumulation engender persistent heterogeneity? The
approach we take combines theory and empiricism. We
begin by translating the Dierickx and Cool (1989) model
of accumulation into an intermediate good-production
function for a firm’s intangible asset stock. We embed
that intermediate good-production function into the
firm’s final good-production function. We conduct an
empirical test in the pharmaceutical industry, examining
first the contribution of the intangible asset stock to the
firm’s final good-production function, and second, the
extent to which those asset stocks deter rival mobility.
2.
Intangible Asset Stock
Accumulation Model
Dierickx and Cool (1989) argue that some strategic factors cannot be traded; they can only be accumulated.
They give as examples, reputations and R&D capability:
“The strategic asset is the cumulative result of adhering to a consistent set of policies over a period of time”
(p. 1506). Dierickx and Cool (1989) develop a descriptive model of asset stocks which they define as “flows
in” minus the “flows out,” where inflows are instantaneous investments in an asset stock, and the outflows are
the erosion of existing asset stocks. At first glance, this
appears to mimic the conventional time-series model for
capital stock accumulation:
Kt+1 = 1 − Kt + It (1)
where
Kt = intangible asset stock at time t
= annual erosion of the asset stock
It = investment in intangible asset stock in period t.
However, inherent in resource accumulation theory is a more complex process for intangible asset
accumulation than for physical capital accumulation.
The intangible asset accumulation process described
in the literature resembles an internal intermediate
good-production function—firms produce their intangible asset stocks from existing asset stocks and current
Organization Science/Vol. 14, No. 2, March–April 2003
period investments. In contrast, physical capital is normally accumulated through purchase. Thus this simple
model needs some revision.
Dierickx and Cool (1989) define five features of
intangible asset accumulation that confer sustainable
advantage, and that seem to distinguish intangible asset
accumulation from physical capital accumulation: time
compression diseconomies (Scherer 1967, Mansfield
1968), asset mass efficiencies (scale economies), interconnectedness of asset stocks (Teece 1987), asset erosion (depreciation), and causal ambiguity (Nelson and
Winter 1982, Lippman and Rumelt 1982).
Time compression diseconomies are described in Deirickx and Cool (1989) as diminishing returns to current
period investments. The phenomenon is referred to as
convex adjustment costs, wherein the cost of expansion
increases if the rate of expansion is accelerated.
Asset mass efficiencies are economies of scale in
the production of intangible asset stock from existing
asset stock, such that the productivity of investments
in the current period increases with larger asset stocks
(Dierickx and Cool 1989).
Asset erosion is the intangible asset equivalent of
physical capital depreciation. There is a distinction
between the two constructs, however: Depreciation is an
annual reduction in the useful life of physical capital
associated with its consumption in use. Intangible assets
are not consumed in use, but their value may nevertheless erode, either because the firm is unable to maintain
proprietary rights, or because new assets or technological advances render them obsolete. In essence, they are
consumed even if not used.
To capture these features, we build an intermediate good-production function for intangible asset stocks:
Intangible asset stocks at the beginning of a period
combine with current period investments to create additional intangible assets. We build the intermediate goodproduction function by modifying Equation (1), which
captures asset erosion (depreciation), to incorporate time
compression diseconomy and asset mass efficiency. Time
compression diseconomy is modeled as a concave function of current period investments, It , 0 < < 1. As
a firm increases its investment, the contribution to the
asset stock increases, but at a decreasing rate. Asset mass
efficiency is modeled as an accelerator of current period
investments, (Kt . The larger a firm’s asset stock in the
prior period, the greater the productivity of the current
period investments in building the asset stock. The accelerator is assumed to exhibit diminishing returns to asset
stock size, 0 < < 1.
Thus a firm’s asset stock at time t + 1 comprises the
eroded asset stock from the prior period (the first term
193
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
on the right-hand side) plus the new investments, conditioned by time compression diseconomy and asset mass
efficiency (the second term on the right-hand side).
Kt+1 = 1 − Kt + It Kt (2)
where
∈ 0 1 = time compression diseconomy
∈ 0 1 = asset mass efficiency
This formulation ignores two features of Dierickx and
Cool’s (1989) model. This is done in the interest of isolating the accumulation process itself. The first feature
we ignore is interconnectedness of asset stocks. This is
not because interconnectedness is unimportant. We know
from Levinthal (1997), Levinthal and Warglien (1999),
Rivkin (2000), and Knott (2001) that interconnectedness
is important. It is rather that interconnectedness adds
dimensionality. Because heterogeneity can arise from
interconnectedness alone (without accumulation), a theory asserting that firms obtain heterogeneity by combining the two is superfluous. Our objective here is to
determine whether resource accumulation alone (without
interconnectedness) will produce heterogeneity.
The second feature we ignore is causal ambiguity. Again, this is not because causal ambiguity is
unimportant, but rather because causal ambiguity, like
interconnectedness, adds dimensionality. We know from
Lippman and Rumelt (1982) that fixed costs plus
irreducible ex ante uncertainty regarding the level of
efficiency achieved by entrants (causal ambiguity) will
produce heterogeneity. Thus, we can be certain that
accumulation combined with causal ambiguity can produce heterogeneity. Again, we are trying to determine
whether resource accumulation alone (without causal
ambiguity) will produce heterogeneity.
3.
Empirical Model
There are two implicit hypotheses set forth by resource
accumulation theory. The first of these (H1) is that rents
accrue from asset stocks rather than asset flows (current period investments). Thus, asset stocks should be a
significant factor in the firm’s production function. The
second hypothesis (H2) is that characteristics of the asset
accumulation process inhibit rival mobility (sustain privileged asset positions). We test both hypotheses via a
single model.
Because we don’t know a priori how knowledge accumulates, we need to estimate each of the terms in the
intermediate good-production function (Equation 2). We
do so by embedding the intermediate good-production
194
function inside the firm’s final good-production function. Thus, we simultaneously estimate the accumulation
coefficients as well as the contribution of the asset stock
in use.
Following convention in R&D productivity studies
(see Griliches 1984), we model the effects of the knowledge stock Kit using a generalized Cobb-Douglas production function for firm i:
Yit = Kit Cit Lit St expit (3)
where Yit is output of firm i in year t, Kit is the knowledge stock, Cit is physical capital, and Lit is labor. We
add spillovers of industry knowledge, St , as a separate
factor, since these assets are available on a nonrival basis
to all firms within the industry. We ignore materials. This
imposes an assumption that materials are used in fixed
proportions to output.
Because we not only want to test the significance of
intangible asset stocks, but also want to characterize the
accumulation process, we need to replace Kit in Equation (3) with the function for asset accumulation from
Equation (2).
Yit = 1 − Kt−1 + It−1 Kt−1 × Cit Lit St expit (4)
The challenges then, are (1) determining the number of periods over which to accumulate intangible
assets, and (2) expanding the accumulation expression
accordingly. We build models for one through ten years
of accumulation, then compare solutions to determine
best fit.
The expansion consists of expressing all values of K
only in terms of prior investment, I, (since the main goal
of the paper is trying to characterize K in such a fashion). We accumulate according to Equation (2). In the
simplest case (which we call T1), we assume that investments accumulate only over a single year; i.e., all prior
investments are irrelevant. There is no stock to depreciate and no asset mass to accelerate current investment.
Accordingly, we define the asset accumulation after one
year of investment to be:
K1 = I0 (5)
If we expand the accumulation function to a two-year
model (T2), the relevant form of Equation (2) is
K2 = 1 − K1 + I1 K1 (6)
We substitute for K1 everywhere using Equation (5), and
obtain K2 in terms of investments in years 0 and 1:
K2 = 1 − I0 + I1 I0 (7)
Organization Science/Vol. 14, No. 2, March–April 2003
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
Finally, to make the lag structure (and nesting) even
more vivid, we show the T3 version of Equation (2)
K3 = 1 − K2 + I2 K2 (8)
Again, we substitute for K2 everywhere using Equation (7), to show the full expansion for three-year accumulation expressed exclusively in terms of investment
(in Years 0, 1, and 2).
K3 = 1− 1−I0 +I1 I0 (9)
+I2 1−I0 +I1 I0 We continue in this fashion to build 10 models (T1 to
T10), corresponding to increasing years over which we
accumulate investment. These equations characterize the
asset stock, Kt , and the accumulation function in isolation (apart from final goods production).
For each expression of Kt , there is a corresponding
output equation that embeds the accumulation function
within the firm’s final goods-production function. Equation (10) restates Equation (4) as a two-year model, substituting Equation (7) for Kit .1 Thus, output in Year 2 is
a function of the current knowledge stock, capital, labor,
and spillovers. The knowledge stock in turn comprises
the eroded investments from two years ago (Year 0), plus
the investments in the prior year (Year 1), modified by
the asset stock existing at that time (investments from
Year 0).
Yi2 = 1 − Ii0 + Ii1 Ii0 × Ci2 Li2 S2 expit (10)
Note that expanding in this manner imposes a restriction that intangible assets are fully eroded beyond the
allowed years of accumulation. Thus, if we test a sixyear model, assets created seven or more years ago have
no residual value because we don’t include R&D spending from seven or more years ago. We test 10 versions
of the model (T1 to T10) to see if and when this restriction (allowed years of accumulation = estimated years
until complete erosion) is binding.
Given both multiplicative and additive elements in
Equation (10), we utilize nonlinear regression for estimation of these models. One concern with nonlinear
regression is that solutions may represent local maxima
rather than global maxima. To minimize that possibility,
we ran several different search techniques, and multiple
seed values for each coefficient. Further, we took advantage of the fact that we had essentially 10 versions of
the same basic model (varying only by years of accumulation), to check stability of the solutions.2
Organization Science/Vol. 14, No. 2, March–April 2003
If rents accrue from asset stocks rather than asset
flows (H1), then production functions with intangible
asset stocks should be econometrically superior to ones
with only intangible asset flows (single-year investments). Further, if Dierickx and Cool’s (1989) formulation of the accumulation process is valid, then the
coefficients for erosion, , time compression, , and
asset mass efficiency, , should be positive and significant. If the accumulation process inhibits rival mobility (H2), then consistent investment should yield asset
stocks whose value increases in perpetuity—preventing
firms from catching up. Note, however, that we expect
some of these conditions to fail because this study is
motivated by counterfactual evidence that entrants can
displace incumbents.
4.
Prior Tests of Asset Accumulation
Two related bodies of empirical literature have implicitly tested the two hypotheses. The R&D productivity
literature addresses the first hypothesis—that accumulated intangible asset stocks rather that asset flows are an
important factor in the firm’s production function. The
industrial organization literature on mobility deterrence
addresses the second hypothesis—that asset accumulation deters rival mobility. We briefly review these literatures to anticipate our results.
The Empirical Literature on R&D Productivity
The R&D productivity literature examines the role of
R&D as a source of economic progress at firm, industry,
and economy levels. The principal focus of the literature is the performance effect of R&D investment, and
the principal vehicle is the firm production function—
either examining final goods output as a function of
R&D investments or patents, or examining patent output
as a function of R&D investments. Studies have consistently found R&D stock to be an important factor in the
firm’s production function. The output elasticity of R&D
stock typically takes on values between 0.05 and 0.10
(Griliches 1980, Griliches and Mairesse 1984, Adams
and Jaffe 1996). Efforts to refine the specification to consider lag effects and asset erosion rates have found that
models with no lags and zero asset erosion tend to be
superior (Griliches and Lichtenberg 1984).
Adams and Jaffe (1996) have directly examined the
hypothesis that asset stocks are superior to asset flows
in determining firm output. They found that the two
approaches have comparable explanatory power, which
we would only expect if firms maintain consistent R&D
spending patterns and if there are no asset mass efficiencies. While these results tend to refute the resource
195
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
accumulation hypothesis, Adams and Jaffe (1996) built
asset stocks assuming an asset erosion rate of 15%. Thus
it is possible that the actual productivity of assets stocks
is obscured by the computational manner in which they
were accumulated. An important contribution of this
paper is a technique to dynamically build the intangible
asset stock.
The Empirical Literature on Mobility Deterrence
The mobility deterrence literature examines the ability
of capital accumulation to deter rival mobility. Thus, it
is the physical asset equivalent of resource accumulation
theory. While the literature treats physical capital rather
than intangible assets, the hypotheses are quite similar to those proposed by resource accumulation theory.
Both theories examine the conditions under which asset
accumulation will preserve the shares of firms within an
industry. Theoretical studies of mobility deterrence have
generally concluded that accumulated physical assets
deter rival mobility in the absence of discounting and
uncertainty (see Gilbert 1989 for a review). While there
haven’t been extensive empirical tests of mobility deterrence, those tests that have been conducted tend to support the analytical conclusions.
Lieberman (1987a) conducts an empirical test of
mobility deterrence in the chemical industry. He examines whether firms’ investments in capacity appear to
reflect strategic response to entry (rather than nonstrategic expansion to accommodate market growth, or strategic expansion to deter entry). Lieberman finds that under
high concentration and rapid growth, firms do in fact
invest strategically to deter rival mobility.
The differences between physical assets and intangible assets play competing roles in determining whether
mobility deterrence results will carry over to intangible assets. Asset mass efficiency and time compression diseconomy (the two features unique to intangible
stocks) accelerate the asset accumulation of leaders relative to followers. This would tend to make deterrence
more likely with intangible assets than physical assets.
This acceleration is offset, however, by spillovers arising
from the nonexcludability of intangible assets. In fact,
Lieberman (1987b) found that in the chemical industry, spillovers of knowledge gained from manufacturing
experience were so substantial that essentially no proprietary benefit from experience remained. To the extent
that the intangible assets are tacit or causally ambiguous, these spillovers may be minimized. Thus, the net
deterrence capability of intangible asset accumulation is
an empirical question.
196
5.
Method
We want to test resource accumulation theory in a
restricted setting—looking at a single intangible asset
in a single industry. We want to restrict attention to
one industry, because the productivity of factor inputs
typically varies across industries. Given that factor productivity varies across industries, it is almost certainly
true that accumulation coefficients vary across industries. Further, we want to choose an industry that is dominated by a single intangible asset so that we can avoid
accumulating multiple assets simultaneously.
R&D Assets
There are two intangible assets that lend themselves
to empirical examination: reputation (through advertising investments) and technical knowledge (through R&D
investments). We chose R&D assets as the focus of our
study because the SEC requires that R&D investments
be reported as a separate line item. Thus, R&D data
is readily available. In contrast, advertising expenditures
are reported at the discretion of firms. In our sample,
only 34% of firms report advertising expenditures.
Industry
We chose the pharmaceutical industry as our setting for
two reasons. First, earlier studies of R&D productivity have indicated that it has one of the highest R&D
elasticities (e.g., Adams and Jaffe 1996)—meaning that
the productivity of R&D investments is higher in pharmaceuticals than in other industries. Accordingly, pharmaceuticals also have the highest R&D intensity of all
industries—higher R&D spending per dollar of sales.
For those reasons, R&D decisions in the pharmaceutical
industry should receive a good deal of managerial attention, and therefore should be more rational than in other
settings.
Second, advertising expenditures in the industry are
small relative to R&D expenditures. On average, advertising intensity is one-half R&D intensity.3 Further,
advertising intensity is only weakly correlated with R&D
intensity (correlation coefficient = −031). Accordingly,
advertising is unlikely to bias the estimates for R&D
accumulation coefficients.4
Industry Trends
While R&D intensity varies across pharmaceutical firms
and over time, average industry R&D intensity over the
observation period grew from 11.7% of sales in 1980 to
19% in 1996. A number of factors in the industry contribute to the growth in R&D intensity. The four main
elements are technological change (both in the drugs
Organization Science/Vol. 14, No. 2, March–April 2003
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
themselves and the process for their discovery and development), the growth of managed care, the growth of
generic drugs, and changes in FDA regulation. We discuss each of these factors briefly.
Of these factors, technological change has the most
direct effect on R&D. Technological change occurred
first in the process for drug discovery. Historically,
drug discovery involved trial-and-error processes of
finding compounds and testing them against a series
of ailments—working forward from compound to disease. Accordingly, the dominant scientific discipline in
pharmaceutical research was chemistry. However, over
the past few decades advances in molecular biology
have allowed firms to utilize a process of “rational
drug design.” In rational design, scientists work backward from the disease to the compound. They develop
hypotheses about the therapeutic modality most likely to
be effective against the disease, and then test compounds
with that therapeutic modality. Accordingly, biology is
becoming the dominant scientific discipline in pharmaceutical research.
The changes in the underlying process of discovery have been complemented by computer technology that expedites the entire process, from identifying
genetic underpinnings of ailments to high-speed screening of compounds for their therapeutic effect. Related
shifts have occurred in the underlying technology of
the drugs themselves. Biotechnology has introduced the
use of recombinant DNA, monoclonal antibodies, and
genomics.
These technological changes, both in the drugs and the
discovery process, appear to have increased the productivity of R&D investments, since firms have dramatically
increased their rate of R&D investment over the period.
Other factors affect R&D indirectly in that they create a competitive stimulus. The first such factor is the
increased penetration of generic drugs—nonbranded versions of pharmaceuticals that have come off patent.
Generic share of the market has grown from 22% in
1985 to 43% in 1995 to 67% in 2000 (Saftlas 1996,
Saftlas and Worrell 2001). The impact of generics is to
decrease the effective life of a branded drug. Generics
are now able to reach the market within two years of
patent expiration, and immediately capture a substantial
share of product sales.
The second factor indirectly affecting R&D is managed care. Managed care’s share of drug purchasing
grew from 25% of the market in 1985 to 52.5% in 1995
to 70.4% in 2000 (Saftlas 1996, Saftlas and Worrell
2001). Managed care affects the market for prescription drugs through formulary restrictions for member
physicians, through copay incentives for patients, and
Organization Science/Vol. 14, No. 2, March–April 2003
through purchasing clout. The net effect is lower prices
on patented drugs and a more immediate shift to generics when drugs come off patent.
Pharmaceutical firms have responded to the generics by marketing directly to consumers, and by creating over-the-counter (OTC) versions of drugs coming off
patent. They have responded to managed care by acquiring pharmaceutical benefit management (PBM) firms.
While generics and managed care have tended to work
against pharmaceutical firms, trends in FDA regulation
have tended to operate in their favor. The three most
notable FDA actions were faster drug approvals, adoption of GATT, and relaxation of rules governing directto-consumer (DTC) advertising. Faster drug approvals
have implicitly increased the effective patent life of
new compounds; The General Agreement on Tariffs and
Trade (GATT) has explicitly extended the patent life
from 17 years to 20 years for most drugs, and DTC
advertising has increased sales over the patent life.
Since all these industry factors are interacting and
evolving slowly over time, we make no effort to isolate each effect. Rather, we control for their joint effects
through year dummies.
Data
To conduct the empirical analysis, we gather data from
two primary sources. Industry data on R&D investments
(for spillovers) is obtained from the Research and Development in Industry report published by the National
Science Foundation. This data is reported by three-digit
SIC and broken down into both federal spending on
R&D and company (and other) spending on R&D.5 In
order to generate spillover data we make the assumption
that spillovers are a function of industry R&D. Thus,
we gather company (and other) R&D data for SIC 283,
“Drugs and Medicines,” in each year from 1979 though
1998.6
Data on sales, capital, and labor (for constructing firm
production functions) are taken from the COMPUSTAT
industrial annual file which contains annual operating
data on the largest companies listed on the New York,
American, and NASDAQ Stock exchanges, along with
companies listed on other major and regional exchanges.
This database is divided into both “active” and “inactive”
files, with the active file containing all firms with active
operations in the most recent year for which data was
available (1999). In addition, a single SIC code identifies
the primary business of each firm. We begin by selecting the sample from the active file of all firms with SIC
2834 “Pharmaceutical Preparations” designated as their
primary business.7 Having selected the 20-year period
197
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
between 1979–1998, we eliminate all firm-year observations prior to 1979 and those in 1999. This selection
process results in a list of 190 pharmaceutical, generic,
and biotechnology firms. Firms on this preliminary list
include both research and startup firms as well as fully
integrated pharmaceutical manufacturers.
An examination of the sample raises one major concern. A number of firms in the sample had spent heavily on R&D for several years but had produced only
insignificant revenue. These firms are clearly in startup
or research mode with no viable commercial products,
implying a basic difference in technology compared
to larger, more integrated firms. To improve the technological homogeneity of the sample, and to include
only firms with meaningful sales experience (crucial to
our production function methodology), we eliminate the
smallest firms in the sample collectively constituting
1% of total industry sales in 1998. This eliminates 141
of the 190 firms. Of the remaining 49 firms, an additional 9 firms are eliminated due to incomplete data.
This results in a final data set that includes 40 firms
(23 pharma; 3 biotech; 14 generic) and 488 usable firmyear observations.
For each firm in each year, we extract the variables in
Equation (9). Output (Y ) is measured as total firm sales
in the observation year; investment (I) is measured as
current year spending on R&D; capital (C) is measured
as the book value of net property, plant and equipment;
labor (L) is measured as full-time equivalent employees; spillovers (S) is measured as current year industry spending on R&D. These data are summarized in
Table 1.
We construct 10 separate models, beginning with a
one-year model and progressively adding years of accumulation until the final model in which we accumulate
knowledge over 10 years (T10). In order to estimate
the models, we use the nonlinear regression function
found in SAS v8 (PROC NLIN) and the standard GaussNewton methodology with both the time compression diseconomy and asset mass efficiency coefficients
bounded between zero and one (as per our theoretical
Table 1
development). Finally, our objective is to evaluate each
model and select the knowledge accumulation model
that offers the best fit. As our basic measure of fit, we
calculate R-squared for each model.8
Robustness Checks
Because we use nonlinear regression and have concerns
that models might settle on local rather than global
solutions, we conduct numerous robustness checks. The
first such check is sensitivity to changes in the sample. We test four definitions of industry. The first three
definitions, which gradually exclude firms based on
size (sales), are selected from the Compustat “Active”
database: (1) Active firms accounting for 100% of 1998
industry sales; (2) Active firms accounting for 99% of
1998 industry sales (which we use as our primary sample); (3) Active firms accounting for 95% of industry
sales in 1998; and finally, (4) an industry definition that
consists of all firms in both the Compustat “Active” and
“Inactive” files.9 We ran robustness checks across these
industry definitions and found that results are insensitive to the sample. This is largely because the results
in all samples are dominated by large fully integrated
pharmaceutical firms (FIPCOs).
In addition to the tests of industry sample, we also test
sensitivity to search algorithms, seed values for coefficients, alternative functional forms for spillovers as well
as fixed effects. The main results we are about to discuss
are reliable across these tests.10
6.
Results
The results for empirical test of the resource accumulation model over the 10 accumulation years are given
in Table 2. The first model in Table 2 presents the
flow model (T1)—the impact of single year investments
in R&D on output. The remaining models accumulate
R&D investments according to Equation (4) for 2 (T2)
to 10 (T10) years.
A number of things in Table 2 are worth noting.
First, coefficients for factor inputs other than knowledge
are significant across all models. While we will discuss
Baseline Data Summary: Active Firms Comprising 99% of Industry Output
Sales ($Million)
R&D in year t ($Million)
Employees (1000 FTE)
Spillover ($Million)
Capital ($Million)
Mean
Std. Dev.
Sales
R&D
Employ
Spill
Capital
31474
3273
191
60980
11248
47803
5569
233
33820
17392
100
092
087
029
094
100
074
035
096
100
002
080
100
032
100
Notes. Correlation between R&D in year t and year t − 1 = 093.
198
Organization Science/Vol. 14, No. 2, March–April 2003
Organization Science/Vol. 14, No. 2, March–April 2003
08823∗∗∗
00368
00950∗∗
00302
00000
—
03091∗∗∗
00464
03705∗∗∗
00245
06627∗∗∗
00252
08753∗∗∗
00448
00939∗∗
00302
00000
—
03079∗∗∗
00464
03729∗∗∗
00242
06630∗∗∗
00252
08819∗∗∗
00356
00952∗∗
00301
00000
—
03094∗∗∗
00464
03699∗∗∗
00246
06626∗∗∗
00252
T4
08815∗∗∗
00355
00953∗∗
00301
00000
—
03095∗∗∗
00464
03697∗∗∗
00246
06626∗∗∗
00252
T5
08813∗∗∗
00354
00953∗∗
00301
00000
—
03095∗∗∗
00464
03697∗∗∗
00246
06626∗∗∗
00252
T6
08813∗∗∗
00354
00954∗∗
00301
00000
—
03095∗∗∗
00464
03697∗∗∗
00246
06626∗∗∗
00252
T7
08813∗∗∗
00354
00954∗∗
00301
00000
—
03095∗∗∗
00464
03697∗∗∗
00246
06626∗∗∗
00252
T8
08813∗∗∗
00354
00954∗∗
00301
00000
—
03095∗∗∗
00464
03697∗∗∗
00246
06626∗∗∗
00252
T9
08813∗∗∗
00354
00954∗∗
00301
00000
—
03095∗∗∗
00464
03697∗∗∗
00246
06626∗∗∗
00252
T10
50648
49676
49629
49618
49616
49615
49615
49615
49615
49615
1126000
1126000
1126000
1126000
1126000
1126000
1126000
1126000
1126000
1126000
09550
09559
09559
09559
09559
09559
09559
09559
09559
09559
38405600∗∗∗ 26154600∗∗∗ 26180400∗∗∗ 26186400∗∗∗ 26187700∗∗∗ 26188100∗∗∗ 26188200∗∗∗ 26188200∗∗∗ 26188200∗∗∗ 26188200∗∗∗
00756∗
00294
—
—
03110∗∗∗
00464
03958∗∗∗
00229
06637∗∗∗
00255
T3
T2
Note. Values under coefficients are standard errors.
∗
p < 005.
∗∗
p < 001.
∗∗∗
p < 0001.
Yi2 = 1 − Ii0 + Ii1 Ii0 Ci2
Li2 S2 expit .
SSE (×10E8)
CSS (×10E8)
RSQ
F -Stat
Labor ()
Spillovers ()
Capital ()
Asset Mass Efficiency ()
Time Compression ()
—
T1
Regression Results Comparing All 10 Accumulation Models
Erosion ()
Table 2
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
199
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
these other inputs after we discuss accumulation, the
main thing to note is that these coefficients take on typical values and are stable across the models. The sum
of the coefficients on capital and labor is 0.97, implying
diminishing returns to scale in the absence of R&D. The
conclusion we reach at this point is that embedding asset
stock accumulation inside a production function seems
to be a reasonable methodology. Thus, we have some
confidence going forward with this approach.
We turn next to hypothesis testing. We begin by
comparing the flow model (T1) with the accumulation
models (T2–T10), and find first that the accumulation
models converge on a single solution (identical coefficients and explained variance), and second that the convergent solution has only marginally better fit than the
flow model (R2 = 0956 for the stock model versus 0.955
for the flow model). This result supports the first hypothesis (H1), that intangible asset stocks are an important
factor in the firm production function, but also suggests that flow models are probably adequate. All these
results are consistent with prior work: The coefficients
on R&D flows fall within normal ranges (Griliches 1980,
Griliches and Mairesse 1984, Adams and Jaffe 1996);
both stocks and flows are significant in the firm production function (Adams and Jaffe 1996); and the two
approaches (stocks and flows) have comparable explanatory power (Adams and Jaffe 1996).
While the test of Hypothesis 1 is important, it has
been supported elsewhere; thus, we are relatively more
interested in the intermediate good-production function
and its implications for deterring rivals (H2). The first
thing to note is that the coefficient on asset mass is zero
across all 10 models. This is true not only in Table 2,
but also in all models with fixed effects (Tables 3 and 4).
This is fairly compelling evidence that there are no asset
mass efficiencies.
The second thing to note is that the coefficients for
asset erosion and time compression diseconomy are consistent across the models. Both coefficients are significant and take on values within the expected range. The
coefficient on asset erosion is 0.88. This is a very high
level of asset erosion, corresponding to complete (99%)
write-off of new investments within three years. This
suggests that firms must make substantial investments
each year merely to preserve the value of the existing asset stock. Methodologically, this is an indication
that the restriction on allowed years of accumulation is
nonbinding beyond the T3 model. The coefficient on
investments (time compression) is 0.095. This is above
the coefficient in the flow model, but within the normal
ranges for R&D elasticity (Griliches 1980, Griliches and
Mairesse 1984, Adams and Jaffe 1996).
200
Thus, our conclusion regarding resource accumulation
theory is that the Dierickx and Cool (1989) formulation
is partially correct. There is evidence of asset erosion
and time compression diseconomy, but there appear to
be no asset mass efficiencies.
It is difficult to interpret what accumulation looks like
by examining the coefficients themselves, but we need to
understand accumulation to draw conclusions regarding
the ability of asset accumulation to deter rivals. To aid
our intuition, we generated a set of fictitious asset stocks,
wherein a firm makes a constant annual investment of
$1,000 MM.11 We then applied the coefficients in each
relevant year of the model to that investment pattern. For
example, we made a $1,000 MM investment in the first
year, then depreciated it and added a new $1,000 MM
investment (to which we applied time compression and
asset mass efficiency) in the second year. We continued
in this fashion for 20 years, then repeated the process
for all nine models to produce the implicit accumulation
paths shown in Figure 1.
The most important implication of the figure is that
asset stocks reach steady state within three years. Up
until Year 3, the $1,000 MM annual investment helps
to increase the knowledge stock. Thereafter, the same
investment is required merely to maintain the existing
asset stock. Moreover, we achieve 94% of steady state
in just the first year.
Thus, we conclude that the intangible asset accumulation process itself does not confer sustainable advantage, refuting the second hypothesis. Rivals can achieve
a leader’s level of intangible asset stock within three
years by merely matching the leader’s investment in each
of those three years. This is not to say that matching
the level of the leader’s intangible asset stock makes
a rival the leader. It merely says that if the accumulation process itself were the source of the leader’s advantage, as resource accumulation theory implies, then a
rival could indeed achieve leadership within three years.
Thus, resource accumulation in and of itself is not a
source of sustainable advantage (at least in this industry).
To show this more vividly, we compute the asset
stocks for each firm in our database over its respective
observation years. We do this in the same fashion as
was used to generate Figure 1. Here, however, we use
actual R&D expenditures of firms over time, whereas
in Figure 1 we assumed a constant annual investment
for a fictitious firm of $1,000 MM. The results are given
in Figure 2. The figure shows a number of interesting
phenomena. First, while firms reach steady state quickly,
they appear to grow their stocks at a common rate. This
likely corresponds to the industry growth rate. Second,
late entrants appear to catch up to their peer group within
Organization Science/Vol. 14, No. 2, March–April 2003
Organization Science/Vol. 14, No. 2, March–April 2003
01488∗∗∗
00253
—
—
02992∗∗∗
00548
03456∗∗∗
00373
06452∗∗∗
00503
Sig∗∗∗
SSE (×10E8)
17384
CSS (×10E8)
1126000
RSQ
09846
∗
Yi2 = 1 − Ii0 + Ii1 Ii0 e i firm
Firm Fixed Effects
Labor ()
Spillovers ()
Capital ()
Asset Mass Efficiency ()
Time Compression ()
—
T1
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T3
17310
17311
1126000
1126000
09846
09846
dummies
Ci2
Li2 S2 expit .
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T2
Regression Results with Firm Fixed Effects
Erosion ()
Table 3
17311
1126000
09846
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T4
17311
1126000
09846
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T5
17311
1126000
09846
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T6
17311
1126000
09846
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T7
17311
1126000
09846
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T8
17311
1126000
09846
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T9
17311
1126000
09846
10000
—
01489∗∗∗
00252
00012
00009
02930∗∗∗
00549
03454∗∗∗
00372
06583∗∗∗
00512
Sig∗∗∗
T10
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
201
202
48701
1126000
09567
00664∗
00320
—
—
03133∗∗∗
00482
04030∗∗∗
00266
06596∗∗∗
00273
∗∗∗
47814
1126000
09575
t
08772
00463
00858∗∗
00331
00000
—
03113∗∗∗
00483
03771∗∗∗
00282
06611∗∗∗
00270
T2
Yi2 = 1 − Ii0 + Ii1 Ii0 Ci2
Li2 S2 expit +
SSE (×10E8)
CSS (×10E8)
RSQ
Labor ()
Spillovers ()
Capital ()
Asset Mass Efficiency ()
Time Compression ()
—
T1
Regression with Year Effects
Erosion ()
Table 4
∗∗∗
∗∗∗
08815
00369
00878∗∗
00331
00000
—
03131∗∗∗
00484
03730∗∗∗
00287
06610∗∗∗
00270
T4
47747
1126000
09576
t ∗ yeardummies.
47760
1126000
09576
08825
00381
00873∗∗
00331
00000
—
03128∗∗∗
00484
03738∗∗∗
00286
06610∗∗∗
00270
T3
∗∗∗
47744
1126000
09576
08810
00367
00879∗∗
00331
00000
—
03132∗∗∗
00484
03728∗∗∗
00287
06610∗∗∗
00270
T5
∗∗∗
47743
1126000
09576
08808
00367
00879∗∗
00331
00000
—
03132∗∗∗
00484
03727∗∗∗
00287
06610∗∗∗
00270
T6
∗∗∗
47743
1126000
09576
08807
00367
00879∗∗
00331
00000
—
03132∗∗∗
00484
03727∗∗∗
00287
06610∗∗∗
00270
T7
∗∗∗
47743
1126000
09576
08807
00367
00879∗∗
00331
00000
—
03132∗∗∗
00484
03727∗∗∗
00287
06610∗∗∗
00270
T8
∗∗∗
47743
1126000
09576
08807
00367
00879∗∗
00331
00000
—
03132∗∗∗
00484
03727∗∗∗
00287
06610∗∗∗
00270
T9
47743
1126000
09576
08807∗∗∗
00367
00879∗∗
00331
00000
—
03132∗∗∗
00484
03727∗∗∗
00287
06610∗∗∗
00270
T10
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
Organization Science/Vol. 14, No. 2, March–April 2003
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
Figure 1
Implied Asset Stock for Constant Annual Investment of $1,000 MM
Contribution of asset stock
2.50
T2
2.00
T3
1.50
T5
T4
T6
T7
1.00
T8
T9
0.50
T10
0.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Years
two years. This indicates they are matching their peer
leader’s investments. This adds external validity to our
results—firms behave as though our model predictions
about the viability of catching the leader are correct.
Third, there appear to be strategic groups. The FIPCOS
have large asset stocks with steady-state contributions of
$2.25 MM. In contrast, the generics tend to have asset
stocks with contributions of only $1.5 MM.
Robustness Checks. Robustness checks for tests of
industry size, the sampling technique, and the nonlinear
search algorithm provide confidence in our approach, but
are not particularly interesting. They are available from
the authors upon request. The tests that do provide some
insight are models that introduce firm effects and year
effects.
In building a firm-effects model, we wanted to preserve the integrity of the accumulation function. We
believed that the true source of firm heterogeneity lies
somewhere within the R&D function, possibly in different accumulation capability. Accordingly, the firm
fixed effects were embedded in the intermediate goodproduction function rather than appended to the final
goods-production function
∗
Yi2 = 1 − Ii0 + Ii1 Ii0 ei firm dummies
×Ci2 Li2 S2 expit (11)
Results for the model with firm effects are given
in Table 3. The T1–T10 model all yield identical
results: Erosion is 1.0, asset mass efficiency is 0.0,
and time compression diseconomy is 0.149. This model
has higher R2 than does the baseline without firm
effects (as is the norm for fixed effects models). Taken
together, the results indicate that the appropriate means
to characterize the contribution of R&D is through a
Organization Science/Vol. 14, No. 2, March–April 2003
firm-specific flow model. When we adequately control
for differences across firms, there is no benefit to accumulation.
Year effects are intended to capture single-year
changes in industrywide conditions such as demand or
supply shocks. Accordingly, they are not part of the
accumulation function and can be appended to the final
goods-production function.
Yi2 = 1−Ii0 +Ii1 Ii0 ×Ci2 Li2 S2 expit + t ∗ yeardummies (12)
t
Results for tests of year effects are given in Table 4.
The basic results are also preserved as we introduce
these effects: Asset mass is always 0, and all models at
T3 and above converge on a single solution with significant asset erosion and time compression diseconomy.
While the coefficient on asset erosion is the same here
as in the baseline, the other coefficients differ slightly
(but only in the third decimal place).
Because there was much going on over the observation period, it is also interesting to look at the coefficients on the year dummies themselves (Figure 3). The
major pattern is one of declining productivity from the
beginning of the observation period until 1994, then
increasing productivity thereafter. It appears that the
penetration of generics and managed care were taking a
toll on the pharmaceutical firms, and their responses of
increased R&D investment and advertising did not take
hold until 1995.
Observations Regarding Other Factor Inputs. We
mentioned earlier that the other factor inputs (capital and
labor) are significant across all models in Table 2, and
the combination of their coefficients indicates that there
203
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
Figure 2
Computed Asset Stocks for Firms in Database
Knowledge Stock Accumulation T3
ABBOTT LABORATORIES
ALLERGAN INC
ALPHARMA INC -CL A
ALZA CORP
3.00
AMERICAN HOME PRODUCTS CORP
ASTRAZENECA PLC -SPON ADR
BARR LABORATORIES INC
2.75
BAUSCH & LOMB INC
BRISTOL MYERS SQUIBB
2.50
CHIRON CORP
ELAN CORP PLC -ADR
FOREST LABORATORIES -CL A
2.25
GENENTECH INC
Knowledge Stock Contibution
GLAXO WELLCOME PLC -SP ADR
ICN PHARMACEUTICALS INC
2.00
ICOS CORPORATION
IVAX CORP
JOHNSON & JOHNSON
1.75
K V PHARMACEUTICAL -CL A
KING PHARMACEUTICALS INC
1.50
LILLY (ELI) & CO
MANNATECH INC
MERCK & CO
1.25
MYLAN LABORATORIES
NATURES SUNSHINE PRODS INC
NOVARTIS AG -SPON ADR
1.00
NOVO-NORDISK A/S -ADR
PERRIGO COMPANY
0.75
PFIZER INC
ROCHE HOLDINGS LTD -SP ADR
SCHEIN PHARMACEUTICAL INC
0.50
SCHERING-PLOUGH
SHIRE PHARMACETCLS GRP -ADR
SICOR INC
0.25
SMITHKLINE BEECHAM PLC -ADR
TEVA PHARM INDS -ADR
TWINLAB CORP
0.00
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
USANA INC
WATSON PHARMACEUTICALS INC
Year
Figure 3
WEIDER NUTRITION INTL -CL A
are diminishing returns to scale in the absence of R&D.
More interesting perhaps are spillovers. Spillovers are
the public good arising from other firms’ investments in
R&D. Not only is the coefficient on spillovers a large
number, 0.37, but its economic impact is tremendous.
The base on which the coefficient is applied (industry
R&D spending) is an order of magnitude larger than that
for the average firm (as shown in Table 1).
Trend in Coefficients on Year
500
300
200
100
-200
-300
-400
-500
-600
204
y9
8
y9
6
y9
4
y9
2
y9
0
y8
8
y8
6
y8
4
-100
y8
2
0
y8
0
Coefficient on year (T3)
400
7.
Discussion
In summary, our goal was to reconcile resource accumulation theory with the counterfactual evidence that
entrants can outperform incumbents. Toward that end,
we investigated three questions: First, is the Dierickx and
Cool (1989) formulation of asset accumulation correct?
Second, are asset stocks an important factor in the firm’s
Organization Science/Vol. 14, No. 2, March–April 2003
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
production function (H1)? Third, does the accumulation
process deter rival mobility (H2)?
We found resource accumulation theory to be only
partially correct. With respect to the Dierickx and Cool
(1989) formulation, we found only two of the three factors we tested to be significant.12 Asset erosion and time
compression diseconomies take on values expected by
Dierickx and Cool (1989). However, there appears to be
no evidence of asset mass efficiencies.
With respect to Hypothesis 1, we found that R&D
assets do accumulate, and the resulting asset stocks are
an important factor in the firm’s production function.
However, we also found that their explanatory power is
comparable to R&D flows. This occurs because asset
stocks reach steady state very quickly. Thus, there is
1:1 correspondence between asset stocks and the flows
required to maintain those asset stocks in steady state.
Finally, with respect to Hypothesis 2, we found that
the accumulation process is not inimitable, and therefore does not deter mobility. For asset stocks to deter
rival mobility, we expect them to grow in perpetuity with
constant investment. This is not the case, at least in this
industry. Firms’ asset stocks reach steady state within
three years. While consistent investments up until steady
state add to a firm’s asset stock, after steady state the
same consistent investment is required merely to maintain the asset stock. Accordingly, rivals can achieve the
asset stocks of leaders by merely matching their investments for three years.
In reconciling resource accumulation theory with
the counterfactual evidence that entrants sometimes
outperform incumbents, we find that intangible assets do
accumulate, and that the accumulated asset stocks make
significant contributions to firm performance. However
we also find that these asset stocks are unable to deter
rivals because they reach steady state rather than growing in perpetuity. Thus, entrants can catch up to, and
potentially exceed, incumbents’ asset stocks.
The research holds implications for the literatures that
guided our empiricism. First, for the mobility deterrence
literature, our results for intangible asset accumulation
differ from the prior results for capital accumulation.
The prior work found that, absent uncertainty and discounting, accumulated physical assets were able to deter
rival mobility. The findings here indicate those results
are unlikely to hold for intangible assets.
Second, for the R&D productivity literature, we introduce methodology to statistically estimate the accumulation and erosion coefficients for R&D assets. Prior
studies have made assumptions about the erosion rate
and have manually constructed assets stocks. Using asset
erosion rates near 15%, those studies have tended to find
Organization Science/Vol. 14, No. 2, March–April 2003
that the asset stocks contributed to the firm production
function. These results may go away when the higher
asset erosion rates are introduced.
There are limitations to this study. First, we examine
only one industry—pharmaceuticals, and only one intangible asset—knowledge stock (accumulated R&D). We
assumed that because R&D was critical to the pharmaceutical industry, the most pronounced effects of R&D
stocks would occur there. It may be true, however, that
R&D asset stocks are more important in industries where
the need for R&D is less apparent (of course, the story
then is no longer one of accumulation advantage, but of
perceptual advantage—knowing to invest in R&D when
its benefits are less obvious). Nevertheless, we recommend duplicating this study in other industries.
Second, we aggregate all knowledge in the pharmaceutical industry. Our level of aggregation was chosen both because investment and performance data are
unavailable by therapeutic class, and because even if
they were available, it would pose problems with degrees
of freedom. There are caveats in this level of aggregation. It is likely that knowledge within a particular therapeutic class does not retain 100% of its value when
applied to another therapeutic class. However, any level
of aggregation is problematic—knowledge from one
drug within a therapeutic class does not retain 100% of
its value when applied to another drug, and knowledge
from one plant site does not retain 100% of its value
when transferred to another site (Adams and Jaffe 1996).
Nevertheless, we recommend replicating this study at
other levels of analysis.
Third, the results for other intangible assets may differ from those of R&D assets. Of particular interest are
reputation assets arising from advertising expenditures.
We recommend repeating this test in consumer goods
industries using accumulated advertising expenditures in
lieu of accumulated R&D expenditures. Just as there is
economics literature examining the cumulative effects of
R&D expenditures on patent production and firm output,
there is marketing literature examining the cumulative
impact of advertising levels, frequency, and aging on
product sales (see for example, Leone 1995, Broadbent
1997). Both literatures tend to find diminishing returns
to current investment (time compression diseconomy)
as well as erosion effects (referred to as “depreciation”
for R&D and “decay” for advertising). The attention to
diminishing returns and erosion suggests that asset mass
efficiencies are likely to be trivial in both settings. The
one distinction between R&D and advertising that might
affect results is that there isn’t an advertising equivalent
205
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
to spillovers. Accordingly, differential asset accumulation across firms may be more feasible for reputation
(advertising) than for knowledge (R&D).
Finally, there is an issue of matching years in which
investment occurs to years in which output is examined.
Following convention in R&D productivity studies, we
compare output and asset stocks from the same year
(see Griliches 1984). An alternative approach is to lag
R&D. This seems particularly attractive in an industry
characterized by long lags between research and commercial success. However, our accumulation method has
an inherent lag structure—we count investment from
several years back and the model solves for the appropriate contribution from each “lagged” year. Our steadystate results suggest that further lags are unnecessary.
The fact that stocks reach steady state implies that lags
are irrelevant—the asset stock in the output year is the
same size as the asset stock four years earlier (apart
from industry growth). This would explain Griliches and
Mairesse’s (1984) failure to find lag effects of R&D
capital on firm output.
Given our results, it appears that the real merit of
knowledge stock accumulation may arise in its role as a
complement to manufacturing scale. This is the interconnectedness component of Dierickx and Cool (1989)—
that complementary assets lead to increasing returns.
Complementarities between R&D and manufacturing
exist because large-scale manufacturing provides greater
incentive to invest in R&D due to the large base over
which to exploit its outputs and amortize its costs (Nelson 1989). Similarly more R&D leads to new avenues
for expanding manufacturing output even further.
This interaction is explicitly modeled in Nelson and
Winter (1982) as well as Klepper (1999). The interpretation of both of those papers is that being early in a
market is important when there are increasing returns
from the knowledge accumulation and manufacturing
scale complementarities.
This paper examined whether there are increasing
returns from knowledge accumulation alone. The answer
appears to be no.
Acknowledgments
The authors are grateful for the financial support of the Huntsman Center
for Global Competition and Innovation and for the helpful comments of
Phil Anderson, Dan Levinthal, Marvin Lieberman, Hans Pennings, Huggy
Rao, two anonymous reviewers, and participants in the Reginald Jones
Center Brown Bag at Wharton.
Endnotes
1
Equations (3) and (9) both show an exponent, , on the knowledge
stock. This is to preserve the Cobb-Douglas form. In our empirical
model, we can’t econometrically distinguish between the coefficients
206
generating K and the contribution of K to output (there would be infinite pairs of accumulation vectors and values for output elasticity, ).
Accordingly, our values for the accumulation vector will actually represent the true accumulation vector inclusive of output elasticity.
2
We felt there was no need to go beyond 10 years because the effective patent life in the pharmaceutical industry is approximately 10
years (Saftlas 1996). This 10-year effective life is derived from a 20year patent life (the new pharmaceutical patent life under GATT),
minus the average development period of 10 years. Our results will
indicate whether this assumption is valid.
3
For the subsample of firms reporting advertising expenditures.
4
While we lack advertising expenditures for all firms, we attempt to
control for their effects through firm dummies.
5
We would have preferred to gather industry R&D data at the fourdigit level. However, four-digit data was not available in this NSF
report. While our spillover measure is coarse, the fact (demonstrated
later in this paper) that the coefficient on spillovers is highly significant in our analysis indicates that data at the three-digit level is
satisfactory.
6
At the time of our work, preliminary 1999 figures were available.
However, the NSF shifted from the use of SIC codes to NIACS codes
in 1999 and had only made the data backwards compatible to 1997.
As such, we have chosen to only collect data for the 20-year period
ending in 1998. In addition, company (and other) data was not available for 1979 and as such, total federal, company, and other data were
used in this year only.
7
The use of the active file allowed us to created a consistent panel in
which the same firm-year observations were used across all accumulation models.
8
Note that in the case of the nonlinear model, r-squared [which is
calculated as: 1-variance (full model)/variance (mean model)] is not
bounded by zero and one since the general nonlinear model does not
nest the mean model. See Kvalseth (1985).
9
Note that the sample of active and inactive firms does not allow for
the use of a consistent panel as the estimation of each model uses a
different set of firm-year observations.
10
Results from the robustness checks (aside from tests of fixed effects,
which we include in the next section) are not particularly interesting
in that they largely confirm the main results. They are available from
the authors.
11
This is about one standard deviation above the mean for firms in
the sample—falling between the FIPCOs and the other firm types.
12
Dierickx and Cool (1989) actually outline five factors. As mentioned earlier, we ignored two of these, interconnectedness and causal
ambiguity, because we already know they produce heterogeneity in
isolation, and we wanted to examine accumulation in isolation.
References
Adams, J., A. Jaffe. 1996. Bounding the effects of R&D: An investigation using matched establishment-firm data. Rand J. Econom.
27(4) 700–721.
Broadbent, S. 1997. Building better TV schedules: New light from
the single source. J. Advertising Res. 37(4) 27–31.
Cohen, W. R. Levin. 1989. Empiricial studies of innovation and market structure. R. Schmalensee, R. Willig, eds. Handbook of Industrial Organization. Elsevier Science Publishers, Amsterdam, The
Netherlands, 1059–1107.
Organization Science/Vol. 14, No. 2, March–April 2003
ANNE MARIE KNOTT, DAVID J. BRYCE, AND HART E. POSEN Strategic Accumulation of Assets
Dierickx, I., K. Cool. 1989. Asset stock accumulation and sustainability of competitive advantage. Management Sci. 35(12)
1504–1511.
Ghemawat, P. 1991. Commitment: The Dynamic of Strategy. The Free
Press, New York.
Gilbert, R. 1989. Mobility barriers and the value of incumbency.
R. Schmalensee, R. Willig, eds. Handbook of Industrial
Organization. Elsevier Science Publishers, Amsterdam, The
Netherlands.
Griliches, Z. 1980. R&D and the productivity slowdown. Amer.
Econom. Rev. 70(2) 343–348.
, ed. 1984. R&D, Patents, and Productivity. University of
Chicago Press, Chicago, IL.
, F. Lichtenberg. 1984. R&D and productivity growth at the
industry level: Is there still a relationship? Z. Griliches, ed.
R&D, Patents, and Productivity. University of Chicago Press,
Chicago, IL.
, J. Mairesse. 1984. Productivity and R&D at the firm level.
Z. Griliches, ed. R&D, Patents, and Productivity. University of
Chicago Press, Chicago, IL.
Itami, H. 1987. Mobilizing Invisible Assets. Harvard University Press,
Cambridge, MA.
Klepper, S. 1999. Firm Survival and the Evolution of Oligopoly.
Working paper, Carnegie-Mellon, Pittsburgh, PA.
Knott, A. M. 2001. Exploration and exploitation as complements.
N. Bontis, C. W. Choo, eds. The Strategic Management of Intellectual Capital and Organizational Knowledge: A Collection of
Readings. Oxford University Press, New York.
Kvalseth, T. O. 1985. Cautionary note about R-squared. Amer. Statistician 39(4) 279–285.
Leone, R. 1995. Generalizing what is known about temporal aggregation and advertising carryover. Marketing Sci. 3(2) G141–G151.
Levinthal, D. 1997. Adaptation on rugged landscapes. Management
Sci. 43(7) 934–950.
, M. Warglien. 1999. Landscape design: Designing for local
action in complex worlds. Organ. Sci. 10(3) 342–357.
Lieberman, M. 1987a. Postentry investment and market structure
in the chemical processing industries. Rand J. Econom. 18(4)
533–549.
. 1987b. The learning curve, diffusion and competitive strategy.
Strategic Management J. 8(5) 441–452.
Organization Science/Vol. 14, No. 2, March–April 2003
, D. Montgomery. 1988. First-mover advantages. Strategic Management J. 9(S) 41–58.
Lippman, S., R. Rumelt. 1982. Uncertain imitability: An analysis
of interfirm differences in efficiency under competition. Bell J.
Econom. 13 418–438.
Makadok, R. 1998. Can first-mover and early-mover advantages be
sustained in an industry with low barriers to entry/imitation?
Strategic Management J. 19(7) 683.
Mansfield, E. 1968. The Economics of Technological Change. W.W.
Norton & Company, New York.
National Science Foundation (NSF). Several years. Research and
Development in Industry.
Nelson, R. 1989. Capitalism as an engine of progress. Res. Policy. 19
193–214.
, S. Winter. 1982. An Evolutionary Theory of Economic Change.
Harvard University Press, Cambridge, MA.
Rivkin, Jan W. 2000. Imitation of complex strategies. Management
Sci. 46(6) 824–844.
Rumelt, R. 1984. Towards a strategic theory of the firm. R. Lamb,
ed. Competitive Strategic Management. Prentice Hall, Englewood
Cliffs, NJ.
. 1987. Theory, strategy and entrepreneurship. D. Teece, ed. The
Competitive Challenge. Harper & Row, New York.
Saftlas, H. 1996. Standard and Poor’s industry surveys, Healthcare:
Pharmaceuticals. August 29.
, B. Worrell. 2001. Standard and Poor’s industry surveys, Healthcare: Pharmaceuticals. June 28.
Scherer, R. 1967. Research and development resource allocation under
rivalry. Quart. J. Econom. 81 367–391.
Teece, D. 1987. Profiting from technological innovation: Implications for integration, collaboration, licensing, and public policy. D. Teece, ed. The Competitive Challenge. Harper & Row,
New York.
, G. Pisano, A. Shuen. 1997. Dynamic capabilities and strategic
management. Strategic Management J. 18 (7) 535–556.
U.S. Department of Commerce (DOC). Several years. Annual Survey
of Manufacturers.
Winter, S. 1987. Knowledge and competence as strategic assets.
D. Teece, ed. The Competitive Challenge. Harper & Row,
New York.
207