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 (ashish.arora@duke.edu), and NBER‡Duke University, Fuqua School of Business (sharon.belenzon@duke.edu)§Duke University, Fuqua School of Business (luis.rios@duke.edu)

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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

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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.

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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

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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