Make, Buy, Organize: The interplay between R&D,external knowledge, and firm structure.∗
Ashish Arora† Sharon Belenzon‡ Luis A. Rios§
February 9, 2012
Abstract
We explore the interplay between three key components of innovation strategy:R&D structure, the nature of R&D performed, and external knowledge acquisition.Our novel patent-based measure of decentralization permits their concurrent studyon a very large scale. We match 576,052 patents to 1,014 corporations and their 2,768affi liates to document systematic and persistent heterogeneity in the organizationand management of firms’R&D, which is not driven by size or industry. Centralizedfirms invest more in research, patent more per dollar, and derive proportionallymore value from internal R&D than from external knowledge, and the opposite istrue for decentralized firms. Firms of both kind acquire external technology, butcentralized firms do so less frequently and favor firms with fewer patents, which areoften absorbed, whereas firms acquired by decentralized firms tend to remain distinctaffi liates of the parent firm. Our findings suggest that despite potentially complexinteractions between our focal factors, firm organization plays an important role indeveloping internally coherent but markedly different strategies to grow either viainternal development or external acquisition of knowledge.Keywords: decentralization, acquisitions, patent assignment, market value, R&D
JEL Classification: D23 D83 L22
∗Acknowledgement: We thank seminar participants at the NBER Summer institute, IFN StockholmConference, HBS TOM Seminar, Technion Israel Strategy Conference, Sumantra Ghoshal Conference,Searle Center Conference on Entrepreneurship and Innovation, Georgia Tech REER, and Kenan-FlaglerBusiness School. Also Nick Bloom, Tom Hubbard, Will Mitchell, Raffaella Sadun, Nathan Letts and RayGilmartin for helpful comments. We thank Hadar Gafni for excellent research assistance. All remainingerrors are our own.†Duke University, Fuqua School of Business ([email protected]), and NBER‡Duke University, Fuqua School of Business ([email protected])§Duke University, Fuqua School of Business ([email protected])
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1 Introduction
In this paper we empirically explore the interplay between the organizational structure of
R&D, research focus, and acquisition of external knowledge in large, multi-divisional firms.
Tying together different strands of strategy research we argue that these three dimensions
of innovation strategy are closely interrelated, and that together they condition a firm’s
ability to create value and grow.
First, the structure of a firm, particularly in terms of whether R&D is decentralized
among business units, is often related to the nature of R&D performed. Firms that invest
heavily in internal research tend to centralize R&D, whereas decentralized R&D is more
applied and incremental (Argyres & Silverman, 2004). This may be because R&D intensive
firms are more likely to invest in basic research, which has economies of scale and scope
(Kay, 1998), whereas business unit managers are more likely to focus on research closely
tied to existing products than on large research projects with uncertain payoffs (Lerner &
Wulf, 2007).
Second, the nature of the R&D performed can significantly affect how a firm accesses
new knowledge from the environment. Once again, a variety of factors condition the
relationship. For instance, internal R&D may substitute for external knowledge (Hitt, et
al., 1993), or complement it by enhancing absorptive capacity (Cohen & Levinthal, 1990)
and the ability to monitor technological opportunities (Rosenberg, 1990). Decentralized
firms, with their more modular R&D organization may find it easier to acquire other firms
and grow their knowledge base, whereas large centralized R&D departments may suffer
from the Not-Invented-Here syndrome.
Third, a firm’s knowledge sourcing process may in turn shape its organizational struc-
ture over time– for example, a reliance on internally generated knowledge may reinforce
and perpetuate competencies (Levinthal & March, 1993). Conversely, new knowledge is
often accessed via mergers and acquisitions (Barney, 1991; Ahuja & Katila, 2001), which
may themselves lead to resource reconfiguration and structural change as acquired firms
are integrated into the acquiring parent (Karim & Mitchell, 2000).
The phenomenon is complex. The choices that a firm makes along one dimension are
likely co-determined with their choices along other dimensions, and establishing causal-
ity is a chimera. The firm’s structure and strategy may co-evolve with its environment
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(Lewin & Volberda, 1999), implying inter-dependencies between the choices of different
firms. Furthermore, the firm’s strategy and structure may reflect idiosyncratic founding
conditions and temporary shocks. Faced with such complexity, it is no wonder that much
of the literature "trades off rich contextual descriptions of the interdependencies among
decisions for analytical precision and theoretical rigor" (Zollo & Singh, 2004, pp. 1235).
In this paper, we shift the tradeoff in order to capture some of this richness, while
being cognizant of the limits that this approach imposes on our ability to draw inference.
We look for empirical evidence that there are mutually coherent patterns in what firms
choose to do along these three dimensions: the extent and mode of acquisition of external
knowledge, the extent and type of R&D investments, and the manner in which internal
R&D efforts are organized. Our triadic set of relationships are likely codetermined as
elements of an overall firm strategy, which itself is unobserved. Thus, we do not ascribe
causal interpretations to our empirical observations. Nonetheless, we go beyond merely
a rich description, since theory suggests testable patterns for firms’ choices along our
three dimensions. By systematically exposing these relationships, we seek to advance the
development of a theory of innovation and firm growth, whereby firms choose how to
develop innovations as well as their internal organization.
To this end, we develop a new measure of the organizational structure of R&D. Of the
three factors we address, internal organization is the most overlooked dimension. This
fundamentally "within the black box" firm characteristic is quite different from a firm’s
research focus or external knowledge sourcing tendencies, which have been tracked using
patents, R&D spending and alliances (Arora, et al., 2001; Henderson & Cockburn, 1994;
Mowery, et al., 1996). The few significant empirical studies that have looked at R&D
organization have often relied on survey instruments or required painstaking manual data
construction (Argyres & Silverman, 2004; Kastl, Martimort, & Piccolo, 2009; Lerner &
Wulf, 2007), and yielded small samples. On the other hand, our measure relies on published
data and is readily scalable, therefore less likely to suffer from small sample variation or
response bias.
We match 595,396 patents to 1,024 publicly traded American firms and 3,004 patenting
affi liates, and use the decision to assign patents to an affi liate (as opposed to the corporate
parent) as a proxy for the decentralization of R&D.We combine this with measures of basic
research as well as R&D spending, patenting, M&A activity and post-M&A integration
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Our second contribution is to identify and highlight striking patterns in the organiza-
tion of R&D, the nature of R&D, and the acquisition of external knowledge among a set
which includes nearly all the patenting firms in America. In the process, we find confir-
matory evidence of the practical importance of this subject by relating market value to
different measures of the knowledge strategy of the firm.
Our results highlight much systematic heterogeneity. In particular, firms in our sample
strongly cluster at the tails of the centralized/decentralized continuum, and these differ-
ences are persistent even after controlling for size and industry. Confirming and building
on earlier findings (e.g. Argyres & Silverman, 2004), we find that decentralized research
is less basic and less likely to draw upon scientific research. We also find that firms with
centralized R&D invest more in R&D, and conditional on the R&D investment, patent
more.
Centralization is also related to orientation towards external knowledge. Both cen-
tralized and decentralized firms acquire external technology via acquisitions. However,
centralized firms do so less frequently, and tend to acquire firms with fewer patents. Fur-
ther, the target firms are quickly integrated and absorbed by centralized firms, whereas
firms acquired by decentralized firms tend to remain distinct within the parent firm. These
results are robust to a variety of controls, and to alternative empirical measures. Impor-
tantly, we find confirmatory evidence that these complex interactions result in measurable
differences in the composition of firms’market value. Whereas centralized firms draw
more value from internal R&D stocks, decentralized firms draw relatively more value from
externally acquired patents.
The rest of the paper is organized as follows. Section 2 relates our work to prior lit-
erature and develops hypotheses about what patters we should observe under a number
of different conditions. Section 3 describes the data and our principal measures. Sec-
tion 4 presents our empirical findings. Section 5 concludes by summarizing our findings
and discussing the implications for theory and practice as well as suggestions for future
research.
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3: Centralized firms create more value from internal research, whereasdecentralized firms create more value from acquired patents.
Structure(centralized ordecentralized)
ExternalKnowledge
InternalR&D
InnovationStrategy
Figure 1: Relationships between our hypotheses
2 Theory and Hypotheses
For practical reasons, we organize our empirical investigations around the degree to which
R&D is centralized. Firms in our sample tend to be widely dispersed in terms of their
external orientation and R&D intensity, without clear break-points. By contrast, the
distribution along the centralization/decentralization continuum has “fat tails,”so decen-
tralized firms are generally quite decentralized, and vice-versa. Also, because our measure
of R&D centralization is itself novel, it makes sense to report how centralized and decen-
tralized firms differ. This is largely a matter of exposition rather than substance.
To bound the scope of the paper, we do not discuss how the environment (e.g. demand,
strategic interactions) may condition underlying firm strategy (Siggelkow & Levinthal,
2003) or condition choices along the dimensions we study. In the empirical analysis, we
nonetheless include controls for both industry and geography. As well, we explore the
robustness of our results for discrete and complex technology industries (Cohen, et al.,
2000).
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Following Ahuja and Katila (2001), we state as hypotheses our expected findings,
couched as patterns of association we expect in the data, rather than as causal rela-
tionships. Importantly, we are agnostic to the direction of causality in formulating our
hypotheses.
2.1 R&D centralization and investment
Theoretical perspectives do not provide clear guidance on how centralization should con-
dition investment in R&D. Some have argued that effective decentralization may provide
incentives for divisional managers to invest in R&D. For example, in Aghion and Tirole’s
(1997) model a principal delegates authority to an agent as a credible way of providing
incentives for suitable choice of projects. Similarly, Belenzon, et al.’s (2009) argue that
units in business groups have superior incentives to invest in more basic innovation be-
cause they enjoy greater legal protection against the parent firm expropriating their rents
from innovation. Empirically, Kastl et al. (2009) find that decentralization in small Italian
manufacturing firms is associated with greater investments in R&D.
However, R&D investments tend to be long lived, lumpy, with uncertain payoffs, which
may spillover to other divisions of a firm (Cockburn, et al., 1999). Divisional managers,
who are mobile and accountable for short-term performance, will tend to skimp on R&D
(Podolny & Baron, 1997). By contrast, corporate managers, who can take a longer term
view, may be charged with finding new growth opportunities (Galunic & Eisenhardt,
2001), leading to centralization of R&D.
From a different perspective, firms that make substantial R&D investments are those
that seek growth through innovations, which can create new lines of business. Such firms
will tend to centralize R&D because managers of existing lines of business are not likely to
invest in exploring opportunities that will not directly benefit their own division. This is
illustrated by the history of R&D at DuPont. After experimenting with decentralization
in the 1920, DuPont had to centralize R&D because existing business units could not
be relied upon to invest in promising new lines of research (Hounshell and Smith, 1988).
This centralization also coincided with an increase in the scale of R&D and breakthroughs
such as Nylon and acrylic fiber, which were produced by central R&D labs. Therefore, we
propose that:
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Hypothesis 1a: Greater centralization of R&D is associated with greater investments inR&D.
The centralization of R&D at Du Pont also resulted in significantly higher investments
in basic research, which is consistent with a number of theories. Basic research has higher
potential economies of scope (Kay, 1998), which would favor centralization of efforts.
Further, individual business units are less likely to invest in basic R&D, projects which
would provide benefits to other units in the firm (Hitt & Hoskisson, 1990; Feinberg &
Gupta, 2004; Nobel & Birkinshaw, 1998). Basic R&D also tends to have longer time
horizons, unappealing to divisional managers with shorter-term objectives. Conversely,
divisional managers have may have superior access to local knowledge, such as information
about customer needs or about production problems (Henderson & Clark, 1990). This
implies that R&D managed by divisional managers is likely to be more tightly focused on
improving existing products and lowering costs.
In a paper that is closely related to ours, Argyres & Silverman (2004) study the orga-
nization of R&D in a sample of 71 large US corporations. They find that decentralized
R&D results in lower impact research outcomes, as well as research that is narrower in
technical and organizational scope. These findings have been supported by later work
(e.g. Leiponen & Helfat, 2010; Lerner & Wulff, 2007). Given the significant heterogeneity
in settings and methods for this prior work, it is important for our study to document
whether these patterns are also observable in our data, which includes nearly the universe
of patenting firms in the US over a 10 year period.
Hypothesis 1b: Firms with centralized R&D are more likely to invest in basic research.
The difference in the types of research projects undertaken also has implications for
patenting behavior. Insofar as decentralized R&D projects are focused on improving
existing products, their results may also be intrinsically less patentable. Similarly, insofar
as decentralized R&D is focused on improving existing production processes, this too will
imply fewer patents per R&D dollar because process innovations are more likely to be
protected through secrecy or tacit knowledge, rather than patents (Cohen, et al., 2000).
Conversely, insofar as centralized R&D projects are broader in scope and more scientific
in orientation, the outcomes should be more patentable (Arora & Gambardella, 1994).
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Further, central R&D labs may have greater incentives to patent as a way of signaling their
productivity and justifying their budgets. By contrast, R&D in units is more likely to be
measured by how it contributes to the performance of the unit, rather than by measures
such as patenting and publication. For all these reasons, greater decentralization should
be associated with fewer patents, conditional on R&D intensity. Therefore, we propose
that:
Hypothesis 1c: Greater centralization of R&D should be associated with greater patentingpropensity.
2.2 R&D structure and the acquisition of external knowledge
There is an extensive literature on how and why firms acquire external knowledge through
acquisitions (Kogut & Zander, 1993; Fleming, 2001; Kim & Kogut, 1996). A central
question in this literature is whether external knowledge is a substitute or complement for
internal knowledge, and the factors that condition this relationship (Hitt, et al., 1993).
We add to this stream by exploring how the external orientation of firms relates to their
organizational structure and to internal R&D. As Karim and Mitchel (2004) put it: "The
issue is not whether internal development or acquisitions are the most appropriate means
of obtaining resources, but how each of the two approaches provides distinct contributions
that [create value]."
Insofar as decentralization is associated with a modular organizational structure (Helfat
& Eisenhardt, 2004; Karim, 2006) decentralized firms should find it easier to deal with
larger acquisitions. This is because the target may be more likely to be left alone, with
a degree of autonomy, and managed as other business units are managed. Whether and
when the acquired firm is integrated or recombined would depend on the potential synergies
with more existing units (Karim &Mitchell, 2004). By contrast centralized firms will often
have to rapidly integrate a target or allow it to function autonomously. The first would
be costly, while the second implies an increase in decentralization.
In other words, decentralization can enable firms to be more outward oriented in their
acquisition of external knowledge, and conversely, acquisitions of R&D intensive firms can
push the firm towards decentralization unless the acquisition is integrated, which is costly
for centralized firms.
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More subtle organizational issues may also be at work. It is possible that large central-
ized labs with a strong research orientation suffer more from a not-invented-here syndrome
(“NIH”) as compared to smaller, more customer oriented labs (Katz & Allen, 1982; Ches-
brough, 2006). This might be a different mechanism driving a substitution effect between
internal development and external sourcing (Laursen & Salter, 2006). By contrast, decen-
tralized firms are less likely to have a scientific elite with organizational power (Hounshell
and Smith, 1988). In sum, decentralized firms should on average face lower costs of man-
aging a technology intensive acquisition, and therefore be more likely to grow through the
acquisition of external knowledge. Thus, we propose:
Hypothesis 2a: Centralized firms will be less reliant on acquisition of external technology.
It is noteworthy that this hypothesis is perfectly consistent with the seemingly con-
tradictory finding that centralized firms are more likely to build on external technology
(Argyres & Silverman, 2004). This is because we discuss here the level of acquisitions,
rather than the extent to which, conditional on acquisitions, they are internalized and
built upon. This brings attention to the fact that external technology is far from homoge-
nous, so it is important to consider not just how much, but also what kind of external
technology is accessed by different firms.
Innovation economics and the knowledge based view provide useful insights. If cen-
tralized and decentralized firms systematically differ in the type of R&D they engage in,
these firms should have different absorptive capacities (Cohen & Levinthal, 1990). Thus,
a firm with strong basic research focus should acquire different types of complementary
knowledge (Cassiman & Veugelers, 2006). For example, firms with a basic research focus
should be more likely to acquire uncertain technology that needs to be built upon (Argyres
and Silverman, 2004), and whose future value is hard to asses. By contrast, firms without
a strong research base will lack the ability to acquire unproven or immature technology
and thus should be more likely to acquire technology that is proven in the market.
And if centralized firms acquire different types of targets, they will also differ in how
they deal with them. Specifically, they may tend to leverage the acquired knowledge of
the target, rather than investing on its future potential. Consistent with this, Puranam
and Srikanth (2007) show that when small technology-based firms were integrated into the
firm that acquired them, their technology was more likely to be built upon, but the small
9
firms were less likely to innovate in the future.1
The foregoing arguments lead us to testable implications. First, they suggest that firms
with decentralized R&D are more likely to engage in "bigger" acquisitions, that is, larger
pools of patents which represent refined and developed technology. Conversely, central-
ized firms would favor acquiring technologies that would compliment their internal R&D,
perhaps filling important holes in existing capabilities, but not substituting in internal
research. This is more likely to be in the form of "small" acquisitions.
Second, if one views integration as a way of building upon the existing knowledge
brought by the acquisition, then we would expect that centralized firms are more likely to
integrate the acquisition by absorbing the target. By contrast, decentralized firms should
be more likely to preserve the autonomy of the acquired firm. We note that insofar as
larger targets are more diffi cult to integrate and absorb, these two empirical predictions
are mutually consistent. This leads to the following hypotheses:
Hypothesis 2b: Conditional on acquisition, centralized firms are more likely to integrate(absorb) their acquisitions. Conversely, decentralized firms will be less likely to absorbtargets.
Hypothesis 2c: Centralized firms gain a greater proportion of their externally acquiredknowledge through small acquisitions than decentralized firms.
2.3 Organization of R&D and outcomes
We turn here to the relationship between organization and performance outcomes. Neither
theory nor historical experience suggest that any specific form of organization is superior
to the other, since myriad trade-offs are involved. For example, decentralized research
might neglect spillovers and underinvest in longer-term research, resulting in unrealized
opportunities for value creation (Nobel & Birkinshaw, 1998). But just as well, central
R&D labs might be less knowledgeable about, and less responsive to, the needs of cus-
tomers (Furman, 2003; Jensen & Meckling, 1992; Von Hippel, 1998), or they may be
1Additionally, a significant literature on the role of integration in managing acquisitions (Haspeslagh &Jemison, 1991) highlights the many contingencies involved in understanding acquisitions, such asorganiza-tional or technological complementarities between target and acquirer (e.g. Thompson, 1967), experienceof acquirer (Zollo & Reuer, 2005), and the trade-off between coordination and autonomy (Zollo & Singh,2004; Puranam, et al., 2006).
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more susceptible to wasteful expenditures on scientifically interesting "pet" projects with
limited value for the firm (Seru, 2010).
Knowledge sourcing frameworks do not give clear predictions either. Even if firms with
decentralized R&D rely on acquisitions to grow, there is no consensus as to whether this
will help or hurt (Hitt, et al., 1990; Karim & Mitchell, 2004). Similarly, although a large
proportion of the academic work on R&D conceptualizes incremental research as being
less valuable, there is ample evidence that minor, incremental improvements are also a
significant source of profits and productivity advance (Hollander, 1963; Rosenberg, 1988).
As our foregoing discussion argues, firms may choose their organizational form, exter-
nal orientation and R&D focus based on their idiosyncratic and complex combination of
history and environment. Thus, comparing the performance of one set of choices with
another cannot be informative about what a firm ought to do. That said, we posit two
testable general patterns.
First, if indeed centralization of R&D goes hand in hand with a firm’s underlying
strategy of growth and value creation through internally generated innovations, we would
expect that internally generated knowledge is the key source of value for these firms. Sec-
ond, whereas decentralization reflects a strategy of creating value through acquiring and
assimilating external knowledge, firms with decentralized R&D should derive proportion-
ally more value from externally acquired technology. Therefore:
Hypothesis 3: Centralized firms create more value from internal research, whereas decen-tralized firms create more value from acquired patents.
3 Methods
3.1 Sample and data
Our paper combines data from several sources: (i) patent level information from the United
States Patent and Trademark Offi ce (USPTO), (ii) Ownership structure data from Icarus
by Bureau van Djik (BVD), (iii) Merger and acquisition data from Thomson Reuters SDC
Platinum and Zephyr by Bureau Van Djik, (iv) data on R&D labs from the Directory
of American Research and Technology, and (v) accounting information from Compustat.
The Appendix details the procedures used to construct the various datasets that we use.
11
Patent data are from the USPTO for the period 1975-2007. We match all granted
patents to all publicly traded American firms and their affi liates. The matching is based
on comparing the assignee name and address as it appears on the patent document to the
name and address of companies in Bureau van Djik’s Icarus database. Because patents are
often assigned to wholly-owned affi liates of other firms, we are able to distinguish between
"centrally" assigned patents assigned to the parent company, and "decentralized" patents
assigned to affi liates.
We matched a total of 576,052 patents to our final sample of 1,014 Compustat firms
(the "parent" or "headquarter" firms). These firms in turn have 2,768 wholly-owned and
distinct legal entities (the "affi liates"). In total, 100,951 (17.5%) of our sample patents
are "decentralized" by our measure. To illustrate, Johnson & Johnson, Inc., is one of our
parent firms, and its wholly-owned affi liate Ethicon holds 1,121 out of J&J’s total stock
of 5,565 patents. J&J itself holds only 336 patents in its own name (the rest are with the
affi liates).
Ownership data consists of two parts: cross-sectional ownership information from
Icarus for 2008, and M&A data from SDC Platinum and Zephyr. The cross-sectional
data informs us on active affi liates as of 2008, while the M&A data helps us reconstruct
ownership links to affi liates that have dissolved. This is especially important since we
exploit the substantial variation in post-merger absorption to shed light on different ac-
quisition strategies. We identify ‘dormant’affi liates - wholly owned subsidiaries with no
significant economic activity that are founded, for example, for tax purposes, as well as
affi liates that are solely holding vehicles for IP management. Patents assigned to these
are considered centralized.
To determine whether an acquired firm is "absorbed" or maintained as a wholly owned
affi liate, we identify all firms that have patents but are no longer active. We then match
these firms to the SDC M&A database to see whether any of these firms have been directly
acquired by a sample firm or by one of the affi liates of a sample firm. Thus, for example,
we identify 121 patents in the USPTO assigned to WebTV Networks. WebTV did not
exist as a separate company as of 2008. By matching to SDC, we find that this firm was
bought in 1997 by Microsoft, and quickly absorbed into Microsoft’s MSN Networks.
Accounting and financial data are from U.S. Compustat. We match our firms using
a string name process similar to the one we utilize to match patents to our ownership
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structure data.
We identify the research labs for a large subset of our sample by utilizing the Directory
of American Research and Technology, which lists such facilities for most publicly-traded
companies in America. We updated and supplemented the information through extensive
manual web searches.
We develop data on firm publications to proxy for science-based inventive activity
using Thomson’s ISI Web of Knowledge, which includes publication records on thousands
of international scientific journals (such as chemistry or bioengineering). Each publication
has an address field which contains the authors’affi liation, which we use to match the firms
in our sample. 289 corporations in our sample published at least one scientific paper. Top
publishing firms include IBM (27,879 publications), Merck (14,585 publications), Pfizer
(7,595 publications), Eli Lilly (7,574 publications), HP (6,874 publications), and Lockheed
Martin (5,482 publications).
3.2 Patent Assignment as a Proxy for Decentralization
A major empirical contribution of our paper is the development of a new patent-based
measure of decentralization. We classify patents as centralized or decentralized based on
whether they are assigned to the parent firm or to the affi liate. Aggregating up to the
parent firm level provides a measure of how centralized or decentralized a firm’s patent
portfolio is. This measure has the advantage of being based on observed behavior, useful
for large samples, and replicable.
Given the ownership structure of our firms, headquarters fully own their affi liates and
maintain complete ownership of all patents, even if these are assigned to affi liates who
manage it day-to-day. Thus, patent assignments in our sample have no legal ownership
implication. However, we interpret patent assignment as a proxy for the delegation of
authority or autonomy over R&D management. There are several reasons to suspect this.
Assignment of patent rights to affi liates allows the affi liate to directly contract with outside
licensees. Assignment may also reinforce the identification and long-term ties between a
manager and the patents she manages, so that opportunistic behavior becomes costly in
terms of reputation (Gibbons, et al., 1999), or it may increase division worker’s intrinsic
sense of autonomy (Puranam, et al., 2006). Similarly, assignment of patent rights may be
associated with a credible delegation of informal authority to the extent patent assignment
13
is perceived by managers as a credible commitment from headquarter that they will tacitly
approve manager’s licensing decisions. We are agnostic as to what mechanism would be
more likely in any given context. However, there is little in the literature to suggest that
such assignments would in any way reduce a division’s autonomy or authority, and much
to suggest that they ought to increase it.
It is possible that a business or division inside a firm have de facto authority over its
R&D and innovation, but not have patents assigned to it. In other words, assignment of
patents to affi liates may be a suffi cient, but not necessary condition for decentralization
of R&D. Patent assignment may also be driven by a desire to have patents assigned in
the name of the relevant business to help assert patents, obtain injunctions, and receive
adequate damages, or even for reputational reasons (Agarwal, et al., 2009). Another po-
tential concern, which is less salient in our context, is that a unit would receive patent
assignments without enjoying the hypothesized autonomy. These motives are arguably or-
thogonal to autonomy, so we likely measure decentralization of R&D with error. Although
this is not classical measurement error, any bias would tend towards attenuation on our
estimated coeffi cients.
Patent assignments are also sometimes driven by income tax strategies. Indeed, within
our sample, some firms were found to assign many patents to subsidiaries with no visible
economic activity, in states with favorable tax conditions, such as Delaware. To mitigate
this, we conservatively classify all such assignments as if the patents were assigned to the
parent firm.
To better understand the implications of our measure, we conducted several interviews
with IP managers, attorneys, and high-level executive at firms across industries within our
sample. Although these were not formally structured or systematic, our discussions rein-
forced the interpretation that assignment is strongly associated with effective delegation of
authority in the R&D process. In fact, not one person interviewed found this association
surprising. For example, a Vice-President and Chief Patent Counsel for a global medical
devices firm opined that patent assignment to affi liates "reflects the underlying structure
of the firm," and that it indicates with high certainty, that "affi liates enjoy autonomy
regarding IP, choice of R&D projects, and perhaps also in the overall R&D investment by
the division."2
2Because of confidentiality, we are unable to disclose the name of the companies whose employees were
14
More systematically, we validated our measure by comparing how closely it matched
the classifications employed by Argyres & Silverman’s 2004 study ("AS"). As part of their
study, they classified 71 prominent firms as centralized, decentralized, or hybrid. In order
to infer organizational structure, AS had to sift through 10-K filings, annual reports, and
historical information. This information was matched to the IRI survey, which contained
self-reported organizational structure, but which redacted the names of the firms.
Considering that 12 years separate the data in our respective studies, and that AS
included a number of non-US firms, it is encouraging that we nonetheless have 56 of
their 71 firms in our sample. We utilize this overlap as a test of the robustness of our
decentralization measure. We find that our patent-based measure perfectly matches 38
out of 56 firms as centralized, decentralized, or hybrid, which is 68% of our overlapping
sample. More importantly, 18 of the firms where our categorizations do not agree involved
hybrids, rather than outright opposite classifications. There were only three firms where
our respective classifications were diametrically opposed. For confidentiality reasons we
are unable to disclose the names of these firms, but upon study, we discovered that all three
had exhibited organizational changes within the time frame that separates AS’s data from
ours. Thus we are confident that our methodology is capturing decentralization, though
it suggests some sensitivity to the thresholds for classifying firms with intermediate levels
of decentralization. We address this in our empirical section by creating categories for
high and medium levels of decentralization.
3.3 Variable definitions and measures
3.3.1 Internal R&D focus
Our principal measure of the extent to which a firm relies on internally generated innova-
tion is R&D intensity, as captured by discounted stock of R&D, divided by lagged sales.
Because a firm’s R&D spending as a fraction of sales tends to be very stable over time, our
results are not sensitive to alternative ways of measuring R&D intensity. To measure the
degree to which a firm’s R&D is basic or science-oriented we use Publications, which is the
number of scientific publications, divided by the stock of R&D. Scientific publications are
a commonly accepted measure of a firm’s basic science orientation (Gambardella, 1995;
Stern, 2004. See also Salter & Martin, 2001 for a review).
interviwed for this paper.
15
A subsidiary measure of the basicness of research is whether the firm has an R&D lab,
captured by Lab propensity, a measure of the probabilty of having at least one lab. In
addition, we also use Patent propensity, the number of patents divided by the stock of R&D
as a measure of the nature of research. We expect firms with a higher share of product vs.
process innovations to file for more patents from a given R&D investment, when compared
to firms doing relatively more incremental and short-term research. Note that this measure
may also reflect a firm’s strategy for appropriating rents from R&D (Cohen, et al., 2000),
as well as the incentives for patenting it provides its internal researchers.
3.3.2 External orientation
We use the share of acquired patents within the total stock of the firm’s patents to
measure Share acquired, that is, the degree to which a firm relies on externally acquired
technology. In addition, we utilize Acquisition propensity, a measure of the probabilty of
acquisition.
The other measures relate to what the firm acquires and what it does with the acquisi-
tion. We classify an acquisition as "big" if at least 32 patents were held by the target (32
marks the top tertile in terms of patents acquired per transaction). Similarly, we classify
acquisitions as "small" if fewer than 5 patents belonged to the target (5 marks the lowest
tertile). Dividing the number of small acquisitions by a firm’s total acquisitions gives us
our Share of small acquisitions variable. To explore post-merger integration, we divide a
firm’s count of patent-weighted absorbed acquisitions by the total number of targets, to
obtain Share absorbed.
3.3.3 Structure
We classify firms according to tertiles of decentralization, in terms of the share of patents
assigned to affi liates, and operationalize using categorical variables for High Decentraliza-
tion, Medium Decentralization, and Low Decentralization. The last category is equivalent
to "centralized," and we use it as our baseline in all regressions.
3.3.4 Market Value
To explore correlations between our variables of interest and outcomes, we use a version
of the value function approach proposed by Griliches (1981). Market value is defined as
16
the sum of the values of common stock, preferred stock and total debt net of current
assets. The book value of capital includes net plant, property and equipment, inventories,
investments in unconsolidated subsidiaries and intangibles other than R&D. In addition,
we use log-sales to control for size, four digit NAICS codes to control for industry, and year
dummies to control for time. In robustness checks, we also control for the geographical
location of the firm’s patenting activities.
3.4 Descriptive statistics
Table 1 provides summary statistics for our sample. The average firm generates $3.4 billion
in annual sales (with a median of $600 million), invests $129 million in R&D (median $10
million), has 174 patents in stock (a median of 19), and a market value of $1.4 billion
(median $135 million). On average, 33% of patents are assigned to affi liates, and 27% of
patents are acquired via M&A.
Table 2 breaks down the sample by level of decentralization, showing a number of inter-
esting facts. First, there is no clear relationship between firm size (as measured by sales)
and degree of decentralization. Centralized firms generate on average $1.9 billion in sales,
compared to $3.6 billion by firms with medium decentralization. However, decentralized
firms are smaller, with only $2.4 billion in annual sales. This non-monotonic relation-
ship between level of decentralization and sales is mitigates the concern that structure is
a mere consequence of size. Future research could explore whether there is a U-shaped
relationship here, though we find no theory that would predict this.
Second, highly decentralized firms hold the fewest patents. Even though highly de-
centralized firms are larger in terms of sales than centralized firms ($2.4 billion in annual
sales versus $1.9 billion), their number of patents is substantially smaller (53 versus 99).
Third, there is a strongly positive relationship between decentralization and external
orientation. Splitting the sample along tertiles of Share acquired we find that centralized
firms are typically internally oriented: 80 percent of our centralized firms have also the
lowest share of acquired patents, and only 11 percent are classified as highly externally
oriented. In sharp contrast, 57 percent of the highly decentralized firms are also highly
external, and only 36 percent are classified as internally oriented.
Fourth, centralized firms are likely to be more R&D intensive than decentralized firms.
We classify firms to low, medium and high R&D intensity (using sample tertile as cutoff
17
values). For centralized firms, 43 percent are highly R&D intensive, and 30 percent have
low R&D intensity. For highly decentralized firm the opposite pattern emerges. Only 25
percent of these firms have high R&D intensity, as compared to 40 percent which have low
R&D intensity.
We also explore the correlation between external oriented and publishing firms (not
reported in Table 2). We find a negative relationship between external orientation and
R&D intensity. his relationship is weaker than the relationship between decentralization
and R&D intensity, and in the next section we show that conditional on decentralization,
the relationship dissipates.
4 Estimation results
4.1 R&D investment and nature of research
We begin by exploring the relationship between structure, external orientation and the
nature of research. Table 3 presents the estimation results. In all specifications we include
measures of lagged firm sales, which control for firm size, as well as SIC dummies to rule
out the impact of industry heterogeneity. The omitted category is Low Decentralization
(which is equivalent to Centralization). We include a dummy variable that receives the
value of unity for observations with missing R&D values. The results of our first three
specifications strongly support Hypothesis 1a, which predicts a negative relationship be-
tween decentralization and R&D intensity. Column 1 shows a strong negative relationship
between high decentralization and R&D intensit (a coeffi cient estimate of -0.41 with a
standard error of 0.10), using only dummies for degree of centralization and controls for
R&D stock, sales, industry, and year. The estimate implies that firms which have a highly
decentralized structure have on average 41% lower R&D intensity. Column 2 shows no
significant relationship between R&D intensity and external orientation. Column 3 in-
cludes both decentralization dummies and Share acquired. Interestingly, the coeffi cient for
high decentralization is very similar to the coeffi cient estimate in Column 1, indicating
that external orientation is not a relevant mechanism driving the relationship between
structure and internal R&D.
Hypothesis 1b posits that more basic research would be more likely to take place in
centralized and internal firms. Our findings support this view. Columns 4 to 6 present
18
estimation results for the publication propensity equation (publication flow over R&D
stock) and columns 10-13 test whether the firm has an R&D lab or not. Both can be
thought of as measuring whether the firm invests in basic, scientific research. Whereas
Lab propensity directly measures the presence of research (as opposed to development)
activity, Publications measures the extent to which firms employ scientists and allow them
to participate in the broader scientific community (Gambardella, 1995; Stern, 2004).
Column 4 shows a strong negative relationship between being highly decentralized and
publishing in science journals: highly decentralized firms have on average a publication
to R&D ratio that is close to 27 percent lower than the respective ratio for centralized
firms. We find a similar pattern for Share acquired (Column 5), which is negatively
correlated with publications. However, when controlling for both decentralization and
orientation (Column 6), the coeffi cient on Share acquired is smaller than in Column 5 (but
still negative) and not statistically significant. On the other hand, the negative estimate
for High decentralization continues to hold. In other words, centralized firms are also more
likely to engage in basic scientific research, even after controlling for external orientation.
Columns 7 to 10 estimate the relationship between the likelihood of having a registered
research lab with decentralization and external orientation. In general, the results suggest
a negative relationship between Lab propensity and both decentralization and external
orientation. But unlike our findings for publications, we do not find such a neat pattern
here, where higher centralization is progressively associated with higher probability of a
lab. Instead, the probability of having a lab is consistently highest for firms with medium
decentralization. This may reflect the peculiarities of this measure. Having a registered
research lab is likely to be driven both by the scale of the firm and by the scale of its
research effort, and in our sample the largest firms in terms of both size and patenting
have intermediate levels of centralization. Column 10 confirms what we would expect:
R&D intenstity is positively related to Lab propensity.
Columns 11 to 13 present estimation results for the patent propensity equation (patent
flow over R&D stock). Column 11 presents the estimation results of including just de-
centralization with controls for R&D stock, sales, industry, and year. Similar to what
we found for R&D intensity, there is a negative relationship between patent propensity
and decentralization, with highly decentralized firms having on average a patent-to-R&D
ratio about 20 percent lower than that of centralized firms. There is no difference however
19
between firms with a medium level of decentralization and our baseline centralized firms.
Column 12 includes only Share acquired. Unlike in the R&D intensity equation (Col-
umn 2), here we find a strong and significant negative relationship between external ori-
entation and patenting propensity (-0.26). This relationship continues to hold when we
control for centralization in Column 6. Interestingly, once we include measures of both
centralization and external orientation, we see that external orientation appears to be
more important for patenting, since the coeffi cient on High Decentralized, while remaining
negative (-0.15), becomes statistically insignificant. Indeed, as we show in the next sec-
tion, and consistent with hypothesis 2a, external orientation is very strongly related to our
measure of decentralization. Therefore, we caution against over-interpreting this result.
4.2 External orientation
Table 3 reports the relationship between decentralization and external orientation, condi-
tional on R&D intensity. It is important to note that we control for size in all specifications
by using the log of sales by the acquiring firm. Thus, for example, we mitigate the like-
lihood that bigger firms simply buy bigger targets. As well, we use a set of 248 4-digit
industry dummies to mitigate concerns such as the size of an acquired pool of patents
being related to the nature of the industry. For example, in pharmaceuticals we would
expect firms to derive more value from buying individual patents than in industries that
rely on complex technologies such as electronics.
Column 1 shows a very large and significant coeffi cient estimate on the dummy for
high decentralization (0.75 with a standard error of 0.03), and a much lower estimate on
the coeffi cient on the dummy for medium decentralization (0.17 with a standard error of
0.03). This strongly supports our Hypothesis 2a, which predicts that centralized firms will
be less reliant on external technology.
In Column 2 we include R&D intensity, while excluding the decentralization dummies.
The coeffi cient on R&D intensity is negative and highly significant (-0.03 with a standard
error of 0.01). There is no clear argument (or hypothesis) which would suggest either
a negative or positive relationship between R&D intensity and external orientation. For
example, Hitt, et al. (1993) argue that acquisition intensity diverts managers’resources
away from R&D, leading to a vicious cycle that diminishes firm’s ability to innovate. On
the other hand, a strong R&D programmay increase a firm’s absorptive capacity (Cohen &
20
Levinthal, 1990), which should lower the cost of acquiring external knowledge bases. It may
well be that the relationship is contingent, so that internal R&D complements external
knowledge acquisition only when firms invest in basic R&D (Cassiman and Veugelers,
2006). Nonetheless, this is an interesting finding in and of itself, which calls for more
study.
In Column 3 we control for both decentralization dummies and R&D intensity. Inter-
estingly, there is no significant difference in the estimates for the decentralization dummies.
However, the relationship between external orientation and R&D intensity completely dis-
appears when controlling for decentralization. Finding that conditional on structure there
is no relationship between external orientation and decentralization can help us rule out
mechanisms which can potential drive the negative relationship between R&D intensity
and external orientation. For instance, a seemingly plausible argument where R&D in-
tensive firms are more likely to rely on internal development cannot explain the whole
story, because if this were the case we would expect this relationship to hold regardless of
firm structure. Thus finding that structure strongly conditions the relationship between
R&D intensity and firms’reliance on external knowledge suggest a complex relationship
between the intensity of R&D and external orientation, perhaps mediated by structure.
Columns 4 to 6 proceed to explore three facets of firms’acquisition strategy: the likeli-
hood of acquisition, the share of acquisitions that are small, and the share of targets that
were dissolved post-acquisition, rather than kept independent. In Column 4 the dependent
variable is our dummy for Acquisition propensity. The results show that more decentral-
ized and highly R&D intensive firms are more likely to acquire, with a coeffi cient for high
Decentralization that is almost twice as large as that for the medium decentralization
(0.088 versus 0.046). This lends further support to our Hypothesis 2a, which predicts that
centralized firms should rely less on externally acquired technology.
As discussed in our hypotheses section, there are reasons to think that larger acqui-
sitions (as measured by the number of patents held by target) would serve a different
purpose than smaller ones. Column 5 explores this issue by focusing on the share of small
firms acquired. Consistent with our Hypothesis 2b, our results show that the share of
small acquisitions strongly decreases with the level of decentralization. In other words,
not only are decentralized firms more likely to acquire, they are also more likely to acquire
larger targets.
21
Column 6 examines post-acquisition integration. The dependent variable is now Share
absorbed. The results show that structure is strongly related to post-acquisition absorp-
tion: decentralized firms are less likely to absorb their acquisitions, relative to centralized
firms. As well, this relationship is considerably stronger for highly decentralized firms.
This finding is consistent with Hypothesis 2c, which predicts a positive relationship be-
tween centralization and integration. Together, our results related to Hypotheses 2a-2c are
consistent with the notion that centralized firms may acquire more nascent external tech-
nology to integrate into their existing research, whereas decentralized firms may acquire
more developed technology that is already commercialized.
4.2.1 Robustness checks
We perform several robustness checks for the relationships documented above, by removing
outliers (very small and very large firms), as well as controlling for geography dispersion,
technology breakdown, number of patents, and number of subsidiaries.
Outliers. An important concern in our analysis is whether the relationships we docu-
ment are driven by comparing very large to very small firms. For instance, very small firms
are likely to have a more central structure by construction, since they will simply have
fewer people, layers and divisions to decentralize. Conversely, they will be more likely to
rely on internal development (since there will be a smaller set of potential targets). Large
firms on the other hand are likely to support more complex decentralized structures, as
well as have a stronger external orientation by virtue of having a larger population of
potential targets to acquire (since firms seldom acquire targets larger than themselves).
To check the sensitivity of our results to firm size, we excluded very small and very large
firms from the sample (lowest and highest sales deciles). As reported in Columns 1 to 5
in Table 5, the main results continue to hold.
Geographic dispersion. Though there is a growing literature on the geographical
location and management of R&D activities (e.g., Leiponen & Helfat, 2010; Singh, 2008;
Lahiri, 2010), the question of geography is logically distinct from the question of internal
organization. As Singh (2008) puts it, a firm could have a decentralized formal organization
even with relatively small number of R&D locations, while another firm might have a much
more centralized organization despite having a much greater number of R&D locations.
Thus, although the location of activities may have implications for how they should be
22
managed, other considerations such as access to users, talented researchers, or knowledge
spillovers are arguably more important (Kogut, 1991; Alcácer, 2006; Jaffe, et al., 1993). By
contrast, the salient trade-off in the internal organization of R&D involves the allocation
of decision making within the organization about which R&D projects to fund and how
to manage them. Nonetheless, in order to mitigate any contamination from the impact of
geography on our sample, we include a robustness check employing a vector of 197 location
dummies. This controls for the share of patents that each firm generates within a given
Core-Based Statistical Area (CBSA), absorbing location specific effects. Columns 6 to 10
in Table 5 confirm that our results hold after including geography controls.
Number of affi liates. Our measures of decentralization and external orientation rely
heavily on subsidiary data. Firms with no subsidiaries would be classified as centralized by
construction. To test whether our set of relationship is driven by the distinction between
corporations with and without affi liates, we estimate the main specifications for a sample
that includes only corporations with at least one affi liate, regardless of whether it patents.
As shown in Columns 1 to 5 in Table 6, all relationships continue to hold in the new
sample.
Number of patents. Decentralization and external orientation are constructed by di-
viding the number of assigned or acquired patents by the total number of patents the
firm has. It could be that our measures of decentralization may be less accurate for firms
which have only a small number of patents. To test for this potential measurement error,
we estimate the main specifications for a sample that excludes firms with fewer than 15
patents in total (the 25th value of the number of patents distribution). Columns 6 to 10
present the estimation results. The same pattern of results continues to hold. As expected,
excluding small firms improves the precision of the estimates. For instance, in the patent-
ing propensity equation (column 8), the coeffi cient estimates for High decentralization and
Share acquired are both highly significant (-0.13 with a standard error of 0.11, and -0.26
and a standard error of 0.10). The statistical significance of these respective estimates is
much lower when including the smaller patenting firms.
Complex vs. discrete industries. It has been shown that the value of patenting varies
according to the degree to which firms operate in complex of discrete industries (Cohen,
et al., 2000), for reasons such as the differential value of patents as negotiating currency
across industries. Though we include 284 industry dummies (4 digit SIC), we split we
23
split the sample between discrete and complex industries (Levin, et al, 1987) to explore
whether the relationships we have postulated differ systematically between discrete and
complex industries.
The estimation results are presented in Table 7. In general there are no major differ-
ences between complex and discrete industries. The relationship between decentralization
and Share acquired (Columns 1 and 6) is very strong in both industry types. However,
though the role of Share acquired in conditioning the relationship between nature of
research and decentralization is very strong in complex industries (Columns 3 to 5), is
estimated less accurately for the set of discrete industries (Columns 8 to 10). One rea-
son for this may be the smaller number of observations in discrete industries. Though
we cannot rule out structural differences between complex and discrete industries in how
innovation strategy differentially affects choices they make between decentralization and
R&D orientation, the evidence does not point to any such clear difference. Future research
could make progress along this line.
4.3 Firm market value
Finally, we investigate the performance implications of structure, external orientation and
basic research, for the market value of firms. To this end, we estimate a version of the
value function approach proposed by Griliches (1981). The interpretation of a market
value regression is not straightforward. The one we follow here is that this is the value
placed upon the stock of the various assets of the firm. We do not exploit within-firm
variation over time because the organization of R&D within a firm varies very little over
time.
To control for patent quality we weight each patent by the ratio between the number
of citations it receives and one plus the average number of citations received by all patents
that were granted in the same year (one is added to both numerator and denominator to
avoid zero weights).
Table 8 presents the estimation results. Column 1 estimates the baseline value spec-
ification for the complete sample of firms. When we look at all firms together, we find
no effect of R&D stock (a coeffi cient estimate of 0.02 with a standard error of 0.02), but
we do find a large and significant estimate of the coeffi cient on patents stock (0.04 with
a standard error of 0.02). Column 2 proceeds by distinguishing between patents that are
24
generated internally and patents that are acquired via M&A transactions, which we term
"external." The results show a very strong positive correlation between the share of ac-
quired patents and market value, in sharp contrast to the coeffi cient on patents generated
by internal R&D, which is not significantly different from zero.
Columns 3 to 5 break up the sample by the firms’relative degree of decentralization
using as cutoffs the tertile values for the share of affi liate-assigned patents. Our first strik-
ing finding is that for centralized firms, R&D stock has a very large and highly significant
positive correlation with market value (a coeffi cient estimate of 0.12), but patent stock
(internal and external) is not significantly correlated with value. The pattern is opposite
for more decentralized firms (Columns 4 and 5) where we find a very large positive cor-
relation between external patents and value, which increases with decentralization, from
0.099 for Moderately Decentralized to 0.132 for Highly Decentralized. However, we find
no significant correlation between R&D stock and value. As with centralized firms, inter-
nal patents are not correlated with value for decentralized firms. These results strongly
support our Hypothesis 3, which predicts that centralized firms should be more likely to
rely on internal research to create value, whereas decentralized firm should be more likely
to derive value from acquired patents.
Because patent valuations tend to vary by industry, especially between complex and
discrete technologies, we check the sensitivity of our main results by separately estimating
the value equation for complex and discrete industries. As Columns 6 through 11 show,
the main results seem to hold especially for firms operating in complex industries, where
the findings are generally stronger (Columns 6-8). On the other hand, the statistical
significance fades in discrete industries, though the general direction of the coeffi cients is
in line with the observed patterns. Because the sample of firms within discrete industries
is roughly half that found within complex industries, it is diffi cult to infer the meaning of
the lack of statistical significance here.
5 Discussion and Conclusion
In this paper we exploit rich new data on over a thousand American firms over ten years,
to explore the interplay among three important dimensions of a firm’s innovation strategy:
R&D organization, knowledge sourcing and research focus.
25
We find evidence of systematic heterogeneity which is persistent even after control-
ling for size and industry. In terms of whether R&D is centralized or not, firms strongly
cluster at the tails of the distribution. Interestingly, this facet of their organization seems
to be highly correlated with other aspects of innovation strategy. Centralized firms in-
vest more in research, which is more basic and more rooted in science. They also patent
more per dollar. Centralization is also related to orientation towards external knowledge.
Both centralized and decentralized firms access external technology via acquisitions, how-
ever, centralized firms do so less frequently, and tend to acquire firms with fewer patents.
Further, the target firms are often integrated and absorbed by centralized firms, whereas
firms acquired by decentralized firms tend to remain distinct within the parent firm. These
results are robust to a variety of controls, and to alternative empirical measures.
Correlation is not causation. Indeed, our claim is that the choices firms make along
the three dimensions are mutually supportive and coherent, and reflect an underlying
firm strategy for growth through innovation. We speculate that firms that choose to seek
innovations primarily by developing new knowledge internally cannot rely simply on incre-
mental research that merely improves on existing goods and services. Instead, such firms
must invest in more basic, long-term research. Typically, such research is easiest to man-
age in central labs, because existing business units are unlikely to support it adequately.
It is not that these firms do not seek external knowledge. They do, but principally to com-
plement their internal knowledge. By contrast, other firms may be unwilling or unable to
make the same large investments in internal research to fuel innovation and growth. Their
internal R&D is likely to be focused on improving existing products and processes, which
is best managed by the business units that produce those products and services. Such
firms are more likely to look outside for new technologies.
The point of the paper is not whether one or the other type of strategy is better.
Rather, firms likely choose one or the other based on their initial founding conditions and
capabilities, their environment, and how their capabilities and environments evolve. We
leave to future work the investigation of these mechanisms. What may matter more than
the particular strategy is how well it fits the firm’s capabilities, and how well the firm
executes on it. The upshot is that both types of strategies can create value, albeit in
different ways.
Consistent with this, we find that whereas centralized firms derive value from internal
26
R&D, decentralized firms derive value from externally acquired patents. In plain words, it
would seem that forward-looking investors value the internal knowledge that centralized
firms accumulate, whereas they value the external knowledge that centralized firms ac-
quire.This suggests that despite potentially complex interactions between our focal factors,
firm organization plays an important role in developing internally coherent but markedly
different strategies to grow either via internal development or external acquisition of knowl-
edge.
We acknowledge that we have merely scratched the surface. Nonetheless, our empirical
findings should inform future theory development. In particular, they point to the poten-
tial importance of organizational structure as the bedrock on which strategy is founded..
From a normative perspective, our paper should alert managers to the perils of pre-
scriptions which do at least account for the three main facets of R&D strategy. In other
words, given the role of structure in conditioning the relationship between internal devel-
opment and external knowledge integration, it is unlikely that innovation strategy can be
charted by way of simple "make/buy" logic that glosses over the firm’s structure. Con-
versely, knowledge-intensive firms contemplating radical change in terms of increasing or
decreasing their centralization should take into account the way in which structure may
shape the course of their innovation.
27
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A Appendix
This section details the construction of the data platform used in this project. The centraldatasets consist of a patent-level panel and a firm-level panel, which are linked via theunique patent id numbers. Each of these panels is built up iteratively, by incorporatingdata from the following sources: (i) patent level information from the United States Patentand Trademark Offi ce (USPTO), (ii) ownership structure data from Icarus and Amadeusby Bureau Van Dyke (BVD), (iii) Merger and acquisition data from Thomson ReutersSDC Platinum and Zephyr by Bureau Van Dyke, (iv) accounting information from U.S.Compustat, and (v) extensive manual searches of on-line resources, such as corporate andgovernments websites, and search engines.
A.1 Ownership Structure
Assignee information is available from the USPTO. Our main decentralization variabledepends on identifying patent assignments are that are made to affi liate firms rather thanto parent firms. Thus, our goal is to trace the chain of ownership for every relevantpatent precisely back to the Compustat CUSIP identification number. The linchpin ofthis project is the identification of an ultimate owner (“UO”) for a large portion of thecompanies reported as patent assignees by the USPTO. Here we follow the methodologyemployed by Belenzon and Berkowitz (2010). We obtain ownership structure data fromthe Icarus databases by Bureau Van Dyke (BVD). We develop an ownership algorithmthat constructs the internal structure parent and affi liate groupings based on their inter-company ownership links. Please refer to the appendix Belenzon and Berkowitz for furtherdetails.
31
A.2 Matching patent data
We standardize a name cleaning algorithm that is run both on the UO dataset and the 2007NBER Patent and Citations Dataset in order to match observations by company name.We utilize the assignee codes contained in NBPATS only as quality checks, or for guidancein manual searches, however we concentrate on matches using the affi liate company namesand our ultimate owner company names from UO. The algorithm utilizes both automatedrules and manual inputs to reduce most firm names to a one or two word string variable.Extensive testing was performed to yield the highest rates of matching, while minimizingmultiplicity errors (which occur when two distinct names are rendered equal by deletingdistinguishing words). Like previous work in name matching, we capitalize all letters, andremove extraneous characters and strings such as “&,”“THE,”“ASSOCIATES,”etc. Wecompile a list of 175 most common such “junk” words (i.e. non-essential for uniquelyidentifying companies). Our list is more targeted to American firms (our focus) thanthose lists developed by the NBER Patent Data Project. Furthermore, one refinementover previous such name matching projects is our use of a process whereby junk wordsare truncated in a right-to-left fashion. This increases the match yield significantly, aswe are able to remove, for example, the word “INTERNATIONAL”from “PIONEER HI-BRED INTERNATIONAL, INC,”(because it occurs on the right side) while allowing it toremain in “INTERNATIONAL BUSINESS MACHINES CORPORATION.”To illustrate,the truncation would proceed as follows:
1. Pioneer Hi-Bred International, Inc.
2. PIONEER HI-BRED INTERNATIONAL, INC. (capitalize)
3. PIONEER HI BRED INTERNATIONAL INC (remove punctuation)
4. PIONEER HI BRED INTERNATIONAL (remove last word if “junk”)
5. PIONEER HI BRED (remove last word if “junk.”Stop)
Here, the algorithm stops when it reaches a “non-junk”word. For “INTERNATIONALBUSINESS MACHINES CORPORATION,” it would have stopped after truncating theword “CORPORATION.”
We can further see the power of this “right-to-left”approach by looking at the way thatthe sub string “HI”above is treated under a different set of conditions. Consider the name“VERIZON INC/HI”(it is common in Compustat to include state identifiers):
1. Verizon Inc./HI
2. VERIZON INC./HI (capitalize)
3. VERIZON INC HI (remove punctuation)
4. VERIZON INC (remove last word if “junk.”)
5. VERIZON (remove last word if “junk.”Stop)
Here, the sub string “HI”is properly removed, whereas removing it from Pioneer Hi-Bredwould have resulted in a corruption of the identifier.
32
One of the trade-offs in matching is always between high yield and multiplicity errors. Forexample, one can see how too aggressive an algorithm can render “American Express,”“American Airlines,”and “American Standard” into “AMERICAN.”Our choice was toerr on the side of higher multiplicity, but to rely on manual checks to correct any mis-coded companies. By always keeping track of the original, uncleaned names, we addedextra steps to check any duplicates (i.e. cases where the same cleaned name correspondedto more than one original name). At this stage, extensive manual effort was expendedto resolve ambiguities by performing actual checks of patent images and web searches.Ultimately, we match over 846,000 patents to our UO file.
A.2.1 Matching to Compustat
Having matched patents to firms to ultimate owners, we proceed to match as many ulti-mate owners as possible to a CUSIP (in order to tap into Compustat accounting informa-tion). Because only publicly traded companies are listed by Compustat, this effectivelyserves as a filter to eliminate government and institutional entities that may have mis-takenly made it into our sample by this point. We utilize the standardized matchingalgorithm used for the patents, with some modifications to account for idiosyncratic Com-pustat “junk words.”
A.2.2 Corporate R&D labs.
An important measure in our analysis is whether a patent is generated at a corporate lab ornot. For our purposes, this is a binary outcome, as we are not interested in distance (oncea patent is not lab-generated, we do not care how far from headquarters it came from, justas we do not care how far away the lab was from headquarters). We identify the researchfacilities for the majority of our sample by utilizing the Directory of American Researchand Technology, which lists such facilities for all publicly traded companies in Americathat are considered research-oriented. This gives us the city and zip code information foreach firm’s R&D lab facility. Because the directory does not capture every firm in oursample, we compliment this with a manual search that spanned 987 firms. Using publiclyavailable sources such as corporate websties and financial filings, we identify the locationfor these firm’s labs.
Next, we obtain inventor information for all patents from the USPTO database, whichis given in string format that provides city or town name and state, for example "Joliet,IL." The first step is to match inventor location to a database of zip codes by utilizing acommercial zip code database obtained from www.zip-codes.com. This entailed significantautomated and manual matching due to very different naming conventions utilized by thetwo data sources. For example, the USPTO city name field contains numerous noiseterms such as "Late of" or "Both of," as well as variations of names.
Once we had zip codes for every inventor for every patent, and every corporate lab forevery firm, we proceeded to match them by CBSA code. One limitation faced by manystudies that utilize location as a measure concerns the multiple towns, cities, and zip codes
33
are often within the same metropolitan region. Thus, relying solely on Zip code or cityname, one would miss that Boston and Cambridge facilities may in fact be within thesame R&D complex. This is even more problematic when we match to inventors, sinceinventors’addresses are more prone to variation within the area around an R&D lab (asthey live in suburbs, etc.). To counter this, we matched our inventor and lab data tothe US Offi ce of Management and Budget’s Core-Based Statistical Areas (CBSA areas),which is accessible at www.census.gov/population/www/metroareas. This database givesus the CBSA code associated with each Zip Codes.
After identifying the CBSA code for each inventor and each lab, we identify every patentin our sample where at least one inventor is located in the same CBSA as a lab (at the firmlevel). Thus, this patent-level indicator variable lab_match takes on the value of one forpatents where we assume that the inventor was affi liated with one of the firm’s R&D labs.Our assumption is that if the patent came from an inventor located in close proximity to acorporate lab, is very likely that she would have been involved with the lab in generatingthat invention.
A.2.3 Dealing with M&A
A central issue in our analysis is the post-merger management of acquired firms. Thedecentralization variation in our data comes mostly from two sources: the degree of post-acquisition integration of affi liates (with a lower bound being those kept independent), andthe speed at which patents are generated centrally in relation to existing affi liates. For eachacquired firm we determine whether it remained independent post-acquisition, or whetherit was dissolved. We take several steps in determining whether a firm is independent.First, we check whether the firm appears in Icarus as an independent company. Second,we manually check each company listed in the first step whether it continues to operateindependently from the parent company. We check their corporate websites to confirmthat their legal disclaimers and investor relations information references a parent company.
Dissolved acquisitions are much more problematic. Because we match patents to firmsbased on the 2008 ownership structure, we lose historical acquisitions that were fullyintegrated in the parent company and ceased to exist as separate legal entities. Thoughwe do capture post-acquisition patents as those are likely to be assigned to headquarters,we may nonetheless over measure decentralization (because all historical patents that wedo not match are centralized). To mitigate this problem we take two steps. We match allfirms in SDC Platinum where the acquiring firm appears in our sample. We then add toour data all patents that belong to acquired firms that no longer appear in the 2008 data.As this is an iterative process, the resolution of M&A issues was not completed until thefinal stages of all our patent and firm matching. For acquisitions that do not appear inSDC we classify its patents as follows: if the firm is active in 2008 (thus, it is matchedto one of the firms in our firm universe) then we classify it as an affi liate of the acquiringcorporation. However, in case there is no match between this firm and our firm universe,we classify all of its patents to the acquiring firm headquarters.
Overall, we matched 169,432 patents to SDC and Zephyr. An underlying assumption of
34
this matching is that an affi liate exists in 2008. If the affi liate was historically dissolvedit will not appear in our firm universe, hence, its patents will not be included in oursample. In order to overcome this problem, we take two steps. First, for the largest 500patenting corporations in our sample we manually collect data, from public sources, ontheir historical acquisitions. This list allows us to identify those firms that were acquiredand fully dissolved. Second, we generate a list of the top 1,000 American assignees (asindicated by the address of the assignee) that were not matched to our data. The remainingunmatched firms have less than 40 patents over their lifetime, so it is reasonable to assumethat they are not patent-intensive firms. For each unmatched firm remaining in our sample,we manually investigate whether it was acquired by any of our sample parent corporations,or by any firms that themselves were acquired by our parent corporations.
35
Variable # Obs. # Firms Mean Std. Dev. 10th
50th
90th
Share assigned 11,304 1,014 0.33 0.42 0 0.03 1
Share acquired 11,304 1,014 0.27 0.41 0 0 1
R&D intensity 11,304 1,014 0.11 0.36 0 0.02 0.20
R&D expenditures ($mm) 11,304 1,014 129 498 0 10 237
R&D stock t-1 ($mm) 11,304 1,014 489 1,820 0 34 945
Patents stock 11,304 1,014 174 664 2 19 314
Patents flow 11,304 1,014 26 101 0 2 46
Publications flow 11,304 1,014 10 58 0 0 8
Dummy for a research lab 11,304 1,014 0.75 0.43 0 1 1
Market value ($mm) 11,304 1,014 1,433 7,083 6 135 3,067
Tobin's Q 10,905 10,905 0.95 1.61 0.13 0.53 1.95
Sales t-1 ($mm) 11,304 1,014 3,410 9,805 35 600 8,205
Assets t-1 ($mm) 11,304 1,014 2,117 7,157 12 206 4,878
Notes: Values are at the firm-year level. Share assigned divides the sum of a firm's affiliate-assigned patents by its total number of patents. Share
acquired is a similar measure for a firm's acquired patents. R&D intensity is discounted stock of R&D, divided by lagged sales. R&D Stock is computed
using the perpetual inventory method with a depreciation rate of 15%. Patents stock and Patents flow are citations-weighted and is computed using the
perpetual inventory method with a depreciation rate of 15%. Dummy for a research lab takes the value of one for firms that have at least one R&D
facility. Market Value includes common stock, preferred stock and debt, net of current assets.
Table 1. Summary Statistics for Main Variables
Distribution
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Number
of firms
Average
Sales
Average
Patents
Stock
Low Medium High Low Medium High
Level of decentralization
Low 338 1,859 99 271 31 36 102 92 144
% of total Low 80.2% 9.2% 10.7% 30.2% 27.2% 42.6%
Medium 338 3,641 209 120 109 109 101 127 110
% of total High 35.5% 32.2% 32.2% 29.9% 37.6% 32.5%
High 338 2,364 53 120 25 193 135 119 84
% of total High 35.5% 7.4% 57.1% 39.9% 35.2% 24.9%
All firms 1,014 2,621 121 511 165 338 338 338 338
External orientation
Table 2. Decentralization, External orientation, and Basic Research
Notes: This table divides the sample by tertiles of decentralization. The unit of analysis is a firm. External orientation is the share of
external patents. R&D intensity the ratio between R&D and sales Classifications for both categories are are based on the sample tertiles,
suging the sample average. In Column 3, patents are weighed by the number of citations they receive.
R&D intensity
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Dependent variable:
High decentralization -0.41** -0.44** -0.27** -0.21* -0.10** -0.08* -0.06 -0.23** -0.15
(0.10) (0.10) (0.10) (0.10) (0.04) (0.04) (0.04) (0.09) (0.10)
Medium decentralization -0.02 -0.03 0.08 0.10 0.08* 0.09** 0.08** -0.00 0.03
(0.09) (0.09) (0.11) (0.11) (0.04) (0.04) (0.03) (0.10) (0.10)
Low decentralization: base
Share acquired -0.12 0.06 -0.22** -0.13 -0.09** -0.06 -0.06 -0.26** -0.20*
(0.07) (0.07) (0.08) (0.08) (0.03) (0.03) (0.03) (0.08) (0.09)
R&D Intensity 0.04**
(0.01)
ln(R&D Stock )t-1 -0.83** -0.82** -0.83** -0.72** -0.71** -0.72**(0.02) (0.03) (0.02) (0.03) (0.03) (0.03)
ln(Sales )t-1 -0.17** -0.16** -0.17** 0.19** 0.19** 0.20** 0.07** 0.07** 0.07** 0.07** 0.22** 0.22** 0.22**(0.02) (0.02) (0.02) (0.03) (0.03) (0.03) (0.01) (0.01) (0.01) (0.01) (0.03) (0.03) (0.03)
Four-digit SIC dummies (248) Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year dummies (26) Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
R2
0.68 0.67 0.68 0.86 0.86 0.86 0.30 0.28 0.30 0.32 0.80 0.80 0.80
Observations 8,680 8,680 8,680 11,304 11,304 11,304 11,304 11,304 11,304 11,304 11,304 11,304 11,304
Table 3. The Relationship between Decentralization and Innovation
Notes: R&D intensity is discounted stock of R&D, divided by lagged sales. Publication propensity is the number of scientific publications divided by R&D stock. Lab propensity reports
marginal effects of Probit estimations of the probability of having at least one lab, evaluated at the sample mean. Patent propensity is the number of patents divided by R&D stock. The base
category in all regression is low-decentralization. Columns 4 to 6 includes a dummy variable that takes the value of one for observations where publications flow is zero, and zero for all other
observations. Columns 7 to 9 include the respective dummy variable for patents. Standard errors are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by
firms. **, * denote significance levels of 1 and 5 percent, respectively.
R&D Intensity Patent propensityPublications Lab propensity
(1) (2) (3) (4) (5) (6)
Dependent variable:
Acquisition
propensity
Share of
small
aqcuisitions
Share
absorbed
High decentralization 0.75** 0.75** 0.09** -0.27** -0.48**
(0.03) (0.03) (0.01) (0.05) (0.06)
Medium decentralization 0.17** 0.17** 0.05** -0.19** -0.23**
(0.03) (0.03) (0.01) (0.05) (0.05)
R&D Intensity -0.03** 0.00 0.01* -0.02 -0.01(0.01) (0.01) (0.00) (0.02) (0.02)
ln(Sales )t-1 0.01 0.02 0.01* 0.03* -0.01 -0.00(0.00) (0.01) (0.00) (0.00) (0.01) (0.01)
Four-digit SIC dummies Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes
R2
0.60 0.30 0.60 0.14 0.24 0.32
Observations 11,304 11,304 11,304 11,304 1,250 1,250
Table 4. The Relationship between Decentralization and External Orientation
Notes: The unit of observation is a firm-year. Share acquired divides the sum of a firm's acquired patents by its total number of
patents. Acquisition propensity reports marginal effects of a Probit estimation of the probability of acquiring a firm, evaluated at
the sample mean. Share of small acquisitions divides the number of small acquisitions by a firm's total number of acquisitions. It
classifies acquisitions as small if the number of patents owned by the acquired patent is less or equal to 5 (1st quartile of patents
held by affiliates). Share absorbed is a similar ratio for absorbed firms. It classifies an acquisition as absorbed if it ceases to
operate as a separate legal entity after the acquisition year. Standard errors are robust to arbitrary heteroskedasticity and allow
for serial correlation through clustering by firms. **, * denote significance levels of 1 and 5 percent, respectively.
Share acquired
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Dependent variable:
Share
acquired
R&D
intensity
Patent
propensity
Publication
propensity
Lab
propensity
Share
acquired
R&D
intensity
Patent
propensity
Publication
propensity
Lab
propensity
High decentralization 0.75** -0.36** -0.22** -0.23** -0.06 0.70** -0.28** -0.03 -0.15 -0.12**
(0.03) (0.10) (0.10) (0.10) (0.04) (0.03) (0.09) (0.10) (0.11) (0.04)
Medium decentralization 0.19** -0.09 -0.02 0.00 0.05 0.16** 0.06 0.08 0.12 0.05
(0.03) (0.08) (0.09) (0.10) (0.04) (0.03) (0.09) (0.09) (0.11) (0.03)
Share acquired -0.04 -0.07 -0.12 -0.08** -0.02 -0.28** -0.18* 0.06
(0.08) (0.09) (0.07) (0.03) (0.07) (0.09) (0.08) (0.03)
R&D intensity 0.00 0.00 0.04**(0.01) (0.01) (0.01)
ln(R&D stock )t-1 -0.77** -0.88** -0.70** -0.83**(0.04) (0.02) (0.03) (0.02)
ln(Sales )t-1 0.02* -0.10** 0.34** 0.24** 0.09** 0.01 -0.20** 0.24** 0.19** 0.03**(0.01) (0.02) (0.04) (0.03) (0.01) (0.01) (0.02) (0.03) (0.03) (0.01)
Four-digit SIC dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Geographic controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
R2
0.61 0.72 0.81 0.88 0.32 0.66 0.75 0.82 0.88 0.35
Observations 9,043 6,905 9,043 9,043 9,043 11,298 8,674 11,298 11,298 9,873
Table 5. Robustness Checks: Size and Geographical Dispersion
Removing lowest and highest sale deciles Adding geography controls
Notes: The base category in all regression is low decentralization. Share acquired divides the sum of a firm's acquired patents by its total number of patents. R&D intensity is
discounted stock of R&D, divided by lagged sales. Patent propensity i s the number of patents divided by R&D stock. Publication propensity is the number of scientific
publications divided by R&D stock. Lab propensity reports marginal effects of Probit estimations of the probability of having at least one lab, evaluated at the sample mean.
Geographic controls is a set of share variables of a firm's patents distribution across 197 CBSA regions. Columns 6 to 10 refer to firms with subsidiaries, incluidng non-patent
subsidiaries. Standard errors are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. **, * denote significance levels of 1 and 5
percent, respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Dependent variable:
Share
acquired
R&D
intensity
Patent
propensity
Publication
propensity
Lab
propensity
Share
acquired
R&D
intensity
Patent
propensity
Publication
propensity
Lab
propensity
High decentralization 0.70** -0.28** -0.03 -0.15 -0.12** 0.72** -0.46** -0.12** -0.24* -0.06
(0.03) (0.09) (0.10) (0.11) (0.04) (0.03) (0.10) (0.11) (0.12) (0.04)
Medium decentralization 0.16** 0.06 0.08 0.12 0.05 0.16** -0.07 0.00 0.07 0.07*
(0.03) (0.09) (0.09) (0.11) (0.03) (0.03) (0.09) (0.10) (0.12) (0.04)
Share acquired -0.02 -0.28** -0.18* 0.06 0.03 -0.26** -0.16 -0.07*
(0.07) (0.10) (0.08) (0.03) (0.08) (0.09) (0.09) (0.03)
R&D intensity 0.00 0.04**
(0.01) (0.01)
ln(R&D stock )t-1 -0.70** -0.83** -0.73** -0.84**
(0.03) (0.02) (0.03) (0.03)
ln(Sales )t-1 0.01 -0.20** 0.24** 0.19** 0.03** 0.01 -0.19** 0.24** 0.22** 0.06**
(0.01) (0.02) (0.03) (0.03) (0.01) (0.01) (0.02) (0.03) (0.03) (0.01)
Four-digit SIC dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
R2
0.66 0.75 0.82 0.88 0.35 0.58 0.70 0.80 0.85 0.30
Observations 11,298 8,674 11,298 11,298 9,873 10,324 8,070 10,324 10,324 10,324
Table 6. Robustness Checks: Affiliates, and Number of Patents
Only firms with affiliates Only firms in top three quartiles in total number of patents
Notes: The base category in all regression is low decentralization. Top three quartiles in patents are firms wtih more than 15 patents. Share acquired divides the sum of a firm's
acquired patents by its total number of patents. R&D intensity is discounted stock of R&D, divided by lagged sales. Patent propensity is the number of patents divided by R&D
stock. Publication propensity is the number of scientific publications divided by R&D stock. Lab propensity reports marginal effects of Probit estimations of the probability of
having at least one lab, evaluated at the sample mean.. Columns 6 to 10 refer to firms with subsidiaries, incluidng non-patent subsidiaries. Standard errors are robust to arbitrary
heteroskedasticity and allow for serial correlation through clustering by firms. **, * denote significance levels of 1 and 5 percent, respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Dependent variable:
Share
acquired
R&D
intensity
Patent
propensity
Publication
propensity
Lab
propensity
Share
acquired
R&D
intensity
Patent
propensity
Publication
propensity
Lab
propensity
High decentralization 0.74** -0.38** -0.09 -0.20 -0.07 0.76** -0.44* -0.20 -0.14 -0.030
(0.03) (0.12) (0.13) (0.11) (0.05) (0.05) (0.20) (0.18) (0.22) (0.06)
Medium decentralization 0.18** -0.10 -0.02 0.15 0.02 0.13** -0.04 0.01 0.10 0.12
(0.04) (0.09) (0.12) (0.14) (0.04) (0.05) (0.17) (0.18) (0.23) (0.06)
Share acquired -0.06 -0.22* -0.13 -0.08* 0.25 -0.11 -0.21 -0.05
(0.09) (0.10) (0.09) (0.04) (0.14) (0.18) (0.18) (0.06)
R&D intensity -0.01 0.04** 0.03* 0.04**(0.01) (0.01) (0.01) (0.02)
ln(R&D stock )t-1 -0.75** -0.84** -0.57** -0.78**(0.04) (0.03) (0.04) (0.05)
ln(Sales )t-1 0.01 -0.11** 0.26** 0.16** 0.08** 0.02* -0.22** 0.10** 0.25** 0.06**(0.01) (0.02) (0.04) (0.04) (0.01) (0.01) (0.04) (0.04) (0.04) (0.01)
Four-digit SIC dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
R2
0.61 0.71 0.81 0.88 0.32 0.67 0.71 0.81 0.83 0.33
Observations 7,383 5,501 7,383 7,383 7,383 3,921 3,179 3,921 3,921 3,921
Table 7. Robustness Checks: Complex vs. Discrete Industries
Complex industries Discrete industries
Notes: The base category in all regression is low decentralization. Share acquired divides the sum of a firm's acquired patents by its total number of patents. R&D intensity is discounted stock of
R&D, divided by lagged sales. Patent propensity is the number of patents divided by R&D stock. Publication propensity is the number of scientific publications divided by R&D stock. Lab
propensity reports marginal effects of Probit estimations of the probability of having at least one lab, evaluated at the sample mean. Columns 6 to 10 refer to firms with subsidiaries, incluidng non-
patent subsidiaries. Standard errors are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. **, * denote significance levels of 1 and 5 percent,
respectively.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Firms: All All Low Medium High Low Medium High Low Medium High
ln(R&D Stock )t -1 0.02 0.02 0.12** 0.00 0.04 0.14** -0.01 0.06 0.10 0.07 0.08
(0.02) (0.02) (0.04) (0.02) (0.05) (0.05) (0.02) (0.05) (0.07) (0.036) (0.09)
ln(Patents Stock )t-1 0.04**
(0.02)
ln(External Patents Stock )t-1 0.11** 0.03 0.10** 0.13** 0.00 0.12** 0.13** 0.15 0.05 0.10
(0.02) (0.05) (0.02) (0.04) (0.06) (0.03) (0.05) (0.17) (0.03) (0.06)
ln(Internal Patents Stock )t-1 0.01 -0.01 0.03 0.00 -0.02 0.08** -0.02 0.07 -0.04 -0.03
(0.01) (0.03) (0.02) (0.03) (0.04) (0.03) (0.04) (0.08) (0.03) (0.06)
ln(Assets )t-1 0.86** 0.85** 0.79** 0.88** 0.85** 0.74** 0.83** 0.83** 0.80** 0.89** 0.80**
(0.02) (0.02) (0.04) (0.03) (0.05) (0.06) (0.04) (0.07) (0.08) (0.04) (0.08)
Sales Growth 0.57** 0.56** 0.43** 0.60** 0.64** 0.36** 0.55** 0.67** 0.50** 0.48** 0.47**
(0.06) (0.05) (0.06) (0.09) (0.09) (0.09) (0.14) (0.11) (0.06) (0.12) (0.14)
Four-digit SIC dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
R2
0.83 0.83 0.84 0.84 0.85 0.85 0.82 0.86 0.85 0.88 0.86
Observations 11,304 11,304 3,034 4,583 3,687 2,007 2,903 2,470 1,027 1,680 1,212
Table 8. Firm Market Value and the Organization of R&D
Dependent variable: ln(Market Value )
Notes: This table reports OLS estimation results for the relationship between market value, patents, and R&D. External patents are those obtained through acquisitions. Internal patents that are
generated by internal divisions. The level of decentralization is based on tertiles of share of assigned patents. The unit of observation is firm-year. Standard errors are robust to arbitrary
heteroskedasticity and allow for serial correlation through clustering by firms. **, * denote significance levels of 1 and 5 percent, respectively.
Level of decentralization Level of decentralization
All industries Discrete industriesComplex industries
Level of decentralization