Intellectual Properly Rights, Organization, and FirmPerformance�

Ashish Aroray Sharon Belenzonz Luis Riosx

July 12, 2010

Abstract

Using novel data on the organizational structure of 1,491 American publicly-listed corporations, with 3,834 private and public a¢ liates, as well as characteristicsof 595,396 patents that they hold, we analyze intra-�rm patent assignment. Tomeasure decentralization, we distinguish between those patents that are assigned toheadquarters and those that are assigned to a¢ liates. We �nd that decentralizationexplains a substantial portion of the variation in the market value of the �rmsin our sample. Decentralization is more likely for �rms that operate in discretetechnology industries, have a greater focus on incremental R&D, rely more heavilyon acquisitions, and for those that manage a diverse range of technologies. Moreover,decentralized �rms tend to invest less in R&D, generate fewer patents given R&D,and grow faster. We discuss several theories that can explain our �nding. Our resultsprovide the strongest support for the oldest set of theories of decentralization, namelywhere decentralization economizes on communication between a¢ liates and �rms,at the cost of lower coordination.Keywords: decentralization, patent assignment, market value, R&D

JEL Classi�cation: D23 D83 L22

�Acknowledgement: We thank Hadar Gafni, Chen Pu, and Haobo Zheng for excellent research assis-tance. All remaining errors are our own.

yDuke University, Fuqua School of Business (ashish.arora@duke.edu), and NBERzDuke University, Fuqua School of Business (sharon.belenzon@duke.edu)xDuke University, Fuqua School of Business (luis.rios@duke.edu)

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1 Introduction

Our understanding of how organizations function has advanced substantially in the decades

since Coase, Simon andWilliamson began to investigate the determinants of organizational

arrangements. And yet due to the di¢ culty of looking inside the black box of the �rm,

many debates pertaining to the internal organization of �rms (including why �rms cen-

tralize or decentralize, which is our present subject) remain unresolved. In this paper we

advance this exploration by analyzing new data on the assignment of patent rights within

large American �rms, using the decision to assign patents either to headquarters or to

a¢ liates as a proxy for the level of de facto decentralization in a �rm. We investigate the

factors associated with the extent of decentralization, as well as the outcomes that are

associated with decentralization, while deliberately not espousing any particular theory.

Instead, we synthesize the predictions of the major theoretical approaches and assess how

these stand up to testing using our data.

Though a vast management and economics literature deals with the decentralization of

tasks and authority in organizations (see Mookherjee 2004 for a review), the majority of

this work tents to be theoretical, and empirical studies remain scarce. Most of the empirical

studies focus on showing the impact of changes in communication costs or the adoption of

information technology on decentralization. For example, using US data, Rajan and Wulf

(2005) provide empirical evidence that �rms tend to select �atter organizational structures

in more recent years relative to the past. Bresnahan, Brynjolfsson and Hitt (2002) and

Caroli and Van Reenen (2001) �nd that with more adoption of information technology

(and human capital), �rms tend to adopt more decentralized organizational structures.

Acemoglu, Aghion, Lelarge, Van Reenan and Zilibotti (2007) show that for Britsh and

French manufacturing �rms in the 1990s, those closer to the technological frontier, op-

erating in more heterogeneous environments, or younger, are more likely to decentralize.

Colombo and Delmastro (2004) �nd that local information increases decentralization to

plant managers in Italian �rms, as does superior communication technology, but central-

ization increases with the need for coordination. Bloom, Sadun, and Van Reenen (2008)

study decentralization to local plant managers in a large sample and �nd that trust and

social norms increase decentralization as does product market competition (which proxies

for importance of local information). Hubbard (2003) shows that the use of on-board com-

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puters in trucking improved coordination between dispatchers and drivers, and increased

productivity.

These empirical studies are motivated by a variety of theoretical �ndings. A standard

approach to decentralization posits the existence of di¤erences in information between

levels of the �rm (e.g., Radner and Jacob Marschak 1972). In our context, an a¢ liate

(which we use loosely to include divisions and business units) may have more information

than headquarters about which innovations are worth patenting, which patents are worth

maintaining and enforcing, and which licensing deals are worthwhile. If communicating

this information to headquarters in full detail is costly or otherwise not possible, the team

production literature (e.g., Radner and Jacob Marschak, 1972) has argued that splitting

tasks by means of a hierarchy can be useful to minimize delay (Radner 1993; Van Zandt,

1999), facilitate specialization (Bolton and Dewatripont, 1994), or both (Patacconi, 2009).

Many of the empirical studies (e.g., Hubbard, 2003; Bresnahan et al., 2002; Colombo and

Delmastro, 2004) have found evidence consistent with this view. Baker and Hubbard

(2003) showed that the use of on-board computers also improved the monitoring of perfor-

mance, increasing the attractiveness of contracting with for-hire carriers as an alternative

to using their own trucks and drivers �an extreme form of decentralization.

However, as Mookherjee (2004) notes, the bulk of this literature in economics has fo-

cused on aligning the incentives of the principal and the agents under conditions where

there are di¤erences in information. Aghion and Tirole (1997) provide a model in which

a principal delegates authority as a credible way of leaving information rents with the

agent, so as to provide incentives for suitable choice of projects. Of course, as clear in

Baker and Hubbard (2003), incentive alignment and coordination problems both present

and can interact (see also Dessein, Garicano, and Gertner, 2009).1As Mookherjee (2004)

1Strategy scholars emphasize additional incentive-related channels through which decentralization maya¤ect behavior. Most notably decentralization is arguably associated with higher �exibility (Child, 1984,Mintzberg, 1979), independence (Kanter, 1985), initiative (Chandler, 1977), or merely through the mo-tivation arising from the perception of freedom (Gupta and Govindarajan, 2000) or pride of ownership(Estrin et al. 1987), which may stem from being associated with the generation of inventions. Pride ofownership is especially interesting because it may provide a complementary explanation to some of our�ndings: if the a¢ liates in our sample are themselves wholly owned by a parent company, there may notbe any tangible bene�ts to �owning�a patent, but there may be some real increase in the inventor�s psy-chological welfare. This is especially interesting as a counterpoint to the well documented �not inventedhere�(NIH) syndrome , e.g. (Rotemberg and Saloner, 1994, Szulanski 2009; Teece 1996) that has plaguedmany �rms post-merger, where some divisions are reluctant to embrace ideas coming from other parts ofthe organization.

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summarizes, the existence of costly communication or collusion among agents,or incom-

plete contracts implies that decentralization - delegating authority to the a¢ liate �can

be superior to maintaining central control. Decentralization trades o¤ superior use of in-

formation (available to the a¢ liate but not headquarters) against the loss of control, and

as potentially greater information rents to the a¢ liate. Loss of control can be ine¢ cient

if the a¢ liate�s incentives are not perfectly aligned with those of the �rm as a whole (e.g.,

the a¢ liate may forgo patenting innovations that are useful to other units) or it reduces

coordination (e.g., research projects may be duplicated, synergies or spillovers may not be

captured).

Incomplete contracts are also closely related to the property rights literature (e.g., Hart

and Moore, 1990) where assignment of property rights is shown to be a form of credible

commitment. A key di¤erence in property rights theory is the existence of relationship-

speci�c investments which create ex-post hold up and the threat of expropriation. Riordan

(1990) provides a model in which a principal delegates authority to provide incentives for

cost reduction. Similarly, Belenzon, Berkovitz and Bolton (2009) argue that a¢ liates in

business groups have superior incentives to invest in more basic innovation because they

enjoy greater legal protection against the "parent" �rm expropriating their rents from

innovation. But even without formal assignments of property rights, credible delegation of

authority (formal or informal) can create similar incentives (e.g., Aghion and Tirole, 1997;

Baker, Gibbons and Murphy). Credible delegation of authority can be optimal because it

promotes high-powered incentives to managers in the form of contracting on observable

outcomes, or by managing strategic communication of information, which may be costless

but possibly biased (Dessein 2002; Alonso, Dessein and Matoushek, 2008). Indeed, Kastl,

Martimort and Piccolo (2009) provide empirical evidence that delegation is associated

with greater investments in R&D in Italian manufacturing �rms.

To simplify broadly, there are three main theoretical approaches to decentralization.

In the information processing theory, any divergence of interests is ignored. Instead the

key is that it is costly for an a¢ liate to convey all relevant information to headquarters,

for instance because decisions need to made quickly. Here, decentralization implies a

trade-o¤ between superior use of local information against loss of coordination. In the

incentive alignment view, decentralization is also motivated by di¤erences in information

but additionally involves the payment of informational rents to the agents. In the property

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rights view, decentralization is associated with the allocation of property rights in order

to elicit non-contractible e¤ort.

By construction, our data focuses solely on parent and a¢ liate �rms which exist under

a wholly-owned structure. Since the parent company owns the a¢ liate, it maintains com-

plete ultimate ownership of a patent even if the patent is legally assigned to a¢ liates who

may (or may not) manage it day-to-day. If assignment does not a¤ect ultimate ownership,

what can assignment structure capture? We advance two possible interpretations.

A narrow view is that patent assignment to an a¢ liate is a signal of credible delegation

of authority over IP management. IP management in the patent context involves deciding

which innovations to protect with patents (and how extensively), which patents to main-

tain, which patents to license, and which to enforce. It also involves providing appropriate

incentives to engineers and scientists to invent or submit inventions for patenting. There

are several reasons to suspect that patent assignment is associated with a real transfer of

authority over IP assets. First, assignment of patent rights to a¢ liates allows the a¢ liate

to autonomously contract with outside licensees. For example, Genentech, though wholly-

owned by Ho¤man La Roche, directly contracts on licensing the patents in its charge to

outside �rms. This ability to independently write contracts may have incentives implica-

tions not found in �rms where all contracts are centrally underwritten. Second, assignment

of patent rights may be associated with a delegation of informal authority to the extent

patent assignment is perceived as a credible commitment from headquarter that they will

tacitly approve the a¢ liate�s IP related decisions.2 Assignment may also reinforce the

identi�cation and long-term ties between a manager and the patents she manages, so that

opportunistic behavior costly in terms of reputation (Gibbons, et al., 1999; Alonso and

Matoushek, 2007).

It is plausible, however, that such a decentralization of IP management is also corre-

2Importantly, there are similar predictions for the relationship between assignment structure and per-formance whether patent assignment is viewed as having a causal e¤ect on performance, or whether itis seen as a proxy of organizational structure. According to the information processing view "the basicreason for the success (of the multidivisional �rm) was simply that it clearly removed the executives re-sponsible for the destiny of the entire organization from the more routine operational activities, and sogave them time, information, and even psychological commitment for long-term planning and appraisal"(Chandler, 1966). The incentives perspective reaches a similar conclusion that by creating autonomous"pro�t centers," the new M-form organizations could yield more precise information about each divisionhead�s performance because they avoided "moral hazard in teams" problems between product and func-tional divisions. It thus became easier to use monetary incentives or career concerns in order to fostermanager�s initiative (Aghion and Tirole, 1997).

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lated with the e¤ective decentralization of innovation management more broadly. Among

other things, this would imply delegating authority over how much to invest in R&D, and

the choice of R&D projects. For this paper, we focus on the narrower interpretation.3

We develop a dataset that details the organization of patent assignment in a signi�cant

subset of large American �rms. Our paper combines data from several sources: (i) patent

level information from the United States Patent and Trademark O¢ ce (USPTO), (ii)

ownership structure data from Icarus and Amadeus by Bureau Van Dyke (BVD), (iii)

Merger and acquisition data from Thomson Reuters SDC Platinum and Zephyr by Bureau

Van Dyke, and (iv) accounting information from U.S. Compustat. Our sample includes

595,396 patents that are matched to 1,491 American publicly-listed corporations, a total

of 30,834 of their private and public a¢ liates, of which 2,615 as assigned at least one

patent. We matched a total of 595,396 patents to our �rm sample, where 112,428 of these

patents (18.9%) are assigned to independent a¢ liates. We further develop data on patent

reassignments, both to ensure accuracy, but also because reassignment of patents signal

intent ��rms have to incur cost and e¤ort to reassign patents. It is reassuring that the

vast bulk of reassignments are from headquarters to a¢ liates, indicating that at the very

least, our measure of decentralization re�ects intent rather than mere inertia.

We supplement our measure of IP management with our �ling attorney measure. For

each patent in our sample, we identify its �ling attorney as recorded by the USPTO. We

then classify attorneys by their corporate or law �rm a¢ liation, in order to determine

for each patent whether the �ling attorney was an employee of the innovating �rm or a

third party. This allows us to measure the extent to which patent �ling is either centrally

managed by each �rm�s IP legal department or is decentralized (which is our assumption

in cases where most of a �rm�s patents are �led by outside attorneys). Interviews with

in-house and third-party patent attorneys suggest that a central IP legal department

generally has much broader responsibilities in identifying those inventions that are chosen

for �ling than outside counsel.4

3A di¤erent interpretation, which also supports the delegation of authority interpretation, is that a¢ l-iates which may be potentially divested in the future are also likely to enjoy greater autonomy, includingautonomy in managing their intellectual property. For instance, we learned that when Motorola divestedits semiconductors manufacturing business (now called Freescale Semiconductors), there was considerabledelay in sorting out which Motorola patents were going to be assigned to the divested business.

4While similar in nature, there are several important di¤erences between ex-post (a¢ liate vs. head-quarters assignment) and ex-ante (in-house vs. outsourced attorney) delegation of authority. While thekey concern for ex-post decentralization is the lack of coordination of licensing activities that may result

6

Using our novel data on the organization of IP management, we provide empirical evi-

dence on a subject which is rich in theory but has yielded only a few empirical studies. We

follow these empirical papers and consider the main sets of theories concerning decentral-

ization. Unlike many of the studies, which use survey based measures of decentralization,

we use observed behavior, namely the assignment of patents to a¢ liates. Patent data

are widely available and our study opens the possibility for further research using patent

assignments in this manner. As well, unlike much of the prior empirical work, we study

both the factors that condition decentralization as well as the implications for innovation

behavior and outcomes. As a result, we are able to go further in terms to trying empirically

test alternative theories of decentralization.

We �nd a positive and strong relationship between decentralization and market value,

indicating that decentralization of IP management is potentially interesting and impor-

tant. We next investigate the channels through which decentralization a¤ects performance,

by examining whether the relationship between decentralization and the level of R&D and

patenting, and the nature of patents, is consistent with the predictions of the various the-

ories. Our results are broadly consistent with the view that decentralization is a response

to di¤erences in information, especially information processing, but they do not support

the �property rights�view.

The rest of the paper is organized as follows: Sections 2 and 3 describe the data;

Section 4 explores the relationship between decentralization and �rmmarket value; Section

5 develops the empirical implications of the principal theories of decentralization; and

Section 6 summarizes our �ndings and concludes.

2 Data

Our paper combines data from several sources: (i) patent level information from the

United States Patent and Trademark O¢ ce (USPTO), (ii) ownership structure data from

in substantial sales cannibalization, such concern does not exist, to a �rst order, for ex-ante decentraliza-tion. On the bene�t side, while in the case of ex-post decentralization division-level employees are likelyto be better informed about products and markets (Jensen and Meckling, 1992), or about user needs(von Hippel, 1988), ex-ante local knowledge is more likely to relate to the identi�cation of potentiallypatentable inventions. Thus, while the cost to the organization of not acting on ex-post local knowledgeare manifested in lost business opportunities, the ex-ante costs, to a �rst order, are manifested in the lossof IP rights over potentially important inventions.

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Icarus and Amadeus by Bureau Van Dyke (BVD), (iii) Merger and acquisition data from

Thomson Reuters SDC Platinum and Zephyr by Bureau Van Dyke, and (iv) accounting

information from U.S. Compustat.

2.1 Patents

Patent data are from the USPTO for the period 1975-2007. We match all granted patents

to our sample of publicly traded American �rms and their a¢ 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 Icarus and Zephyr. It is important to stress that

we match patents to parent companies only if the parent company is listed as the patent

assignee. Thus we are able to distinguish between centrally assigned patents - patents

that are directly assigned to the parent company, and decentralized patents - patents that

are assigned to a¢ liates. We matched a total of 595,396 patents to our �nal sample of

Compustat �rms. 112,428 of these patents (18.9%) are assigned to independent a¢ liates.

2.2 Parent �rms and a¢ liates

Ownership data consists of two parts: cross-sectional ownership information from Icarus

and Amadeus for 2008, and M&A data from SDC Platinum and Zephyr. The cross-

sectional data informs us on existing active a¢ liates, and the M&A data informs us on

historical a¢ liates that may have dissolved or been fully integrated by the parent company,

as well as those that have been kept independent. A¢ liates are identi�ed in the following

way. For each parent corporation, we include all �rms in which it owns at least 50% of

equity (for public a¢ liates we use a 20% threshold . Our ownership data includes a¢ liates

that are indirectly held in our sample corporation, and European a¢ liates (the appendix

provides details on the ownership algorithm used for a¢ liates classi�cation). Our �nal

sample includes 1,491 American publicly-listed corporations. We supplement and con�rm

our ownership data for our �nal sample of American public corporations by manually

collecting information on all a¢ liates from the U.S. Securities and Exchange Commission�s

EDGAR database , �rms�annual reports, and from detailed on-line searches. We identify

�dormant�a¢ liates - wholly owned subsidiaries with no signi�cant economic activity that

are founded mostly for tax purposes, as well as a¢ liates that are established solely as

holding vehicles for the purpose of IP management. Patents that are assigned to these

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a¢ liates are classi�ed as headquarters. This screening leaves us with a total of 30,834

a¢ liates. Of these, 2,615 are assigned at least one patent during our sample period.

2.2.1 Dealing with M&A

A central issue in our analysis is the post-merger management of acquired �rms. The

decentralization variation in our data comes mostly from two sources: the degree of post-

acquisition integration of a¢ liates (with a lower bound being those kept independent), and

the speed at which patents are generated centrally in relation to existing a¢ liates. For each

acquired �rm we determine whether it remained independent post-acquisition, or whether

it was dissolved. We take several steps in determining whether a �rm is independent. First,

we check whether the �rm appears in Amadeus or Icarus as an independent company.

Second, we manually check each company listed in the �rst step whether it continues to

operate independently from the parent company. We check their corporate websites to

con�rm that their legal disclaimers and investor relations information references a parent

company. Dissolved acquisitions are much more problematic. Because we match patents to

�rms based on the 2008 ownership structure, we lose historical acquisitions that were fully

integrated in the parent company and ceased to exist as separate legal entities. Though we

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 we do

not match are centralized). To mitigate this problem we performed an exhaustive manual

search to identify a signi�cant majority of these absorbed �rms and match them to their

patents. Please see Appendix A.1.3 for a description of this process.

Figures 1 and 2 show how di¤erent the observed patent-assignment behavior of �rms

can be. Abbott Laboratories and Johnson & Johnson are similar in many ways. Both �rms

are heavy patentors, operate in similar industries (pharmaceuticals and medical devices)

and have historically engaged in numerous acquisitions. However, Abbott assigns 67%

of patents centrally, and we see very few patents assigned to Abbott a¢ liates. On the

other hand, Johnson & Johnson shows a scant 8% of centralized patents, and has several

a¢ liates with a higher share of patents than itself.

Insert Figures 1 and 2 here

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2.3 Attorneys

A second measure of IP decentralization is the use of in-house attorneys. Many inno-

vative �rms internalize at least a portion of their legal IP functions by having patent

attorneys on sta¤. On the other hand, some �rms rely solely on third-party intellectual

property law �rms. Arguably, in-house patent attorneys impose a more centralized deci-

sion making process over which inventions to patent, because these attorney are closer to

the headquarters. Interviews with prominent in-house and third-party attorneys in the

�eld, experienced in the full range from start-ups to multinational innovating �rms, sup-

port this assumption. By developing a new dataset with information about the speci�c

attorney used for each patent (we attained a 90% match of attorney to patent) we are able

to accurately measure the ratio of patents �led by in-house attorneys versus third party

law �rms.

This measure of the use of in-house attorneys provides us with a new perspective on

decentralization. While patent assignments can be seen to address the ex-post delegation

of authority with regards to IP management and commercialization decisions (such as

potential licensees, renewal decisions, etc.) the decision to delegate the critical patent

�ling function demonstrates an ex-ante decentralization decision. Thus, we interpret a

higher share of patents �led by in-house attorneys as an indication of a more centralized

decision process with regards to what (and how) inventions get patented. We �nd that

almost 40% of all patents in our sample were �led using a third-party attorney. Though

we do not have a benchmark with which to compare this, the 40% share seems plausible

given the impact of small �rms on this ratio. As one of the attorneys we interviewed put

it: "Because you need a critical mass, docket clerk, legal secretary, etc., small companies

would generally have outside counsel. International companies also would rely on outside

counsel though they might do a lot of the work themselves." Of course, we control for �rm

size (among many other factors) in our estimations (see Appendix A.3 for a description of

the attorney matching process).

But clearly there is more than size involved in the choice to utilize an in-house or

external �ling attorney. For example, we identify only 598 out of DuPont�s 12,115 patents

as having been �led by an external �rm. In contrast, Intel outsourcing 12,144 of its 13,725

patents to outside �rms, with a single �rm �ling 6,414 of these. Also telling is the fact

10

that Intel outsources only about 46% of its overall legal functions, in contrast to 88%

of its patent �ling functions. Despite the marked di¤erence in �ling behavior, there is

no indication that either �rm is constrained by any lack of internal capability or access

to external talent. DuPont boasts of its Six Sigma-level �DuPont Legal Model�which

integrates the functions of its internal department with its primary law �rms. Similarly,

Intel counts on legal team �spread over around 20 countries. . . with. . . approximately 650

people...[of whom]. . . around 230 are attorneys,�according to Intel General Counsel Bruce

Sewell. We do not propose at this point that decentralization is solely (or even primarily)

responsible for such di¤erent �ling behavior.

2.4 Reassignments

Our measures of assignment structure builds on the assignee name that appears on the

patent document when it was granted. Patent assignment can change over time. To test

the robustness of our results to changes in assignment, we develop comprehensive data

on patent reassignment. Reassignment data is taken directly from the USPTO website

(using a specialized �spider�program), and then merged to our �nal patent sample. We

are interested in two reassignment types: (i) assigning a patent that was originally as-

signed to headquarters to an a¢ liates, and (ii) reassigning to headquarters a patent that

was originally assigned to an a¢ liate. For example, we �nd evidence of the �rst type of

reassignment in many patents held by Boston Scienti�c, which were assigned to a¢ liates

such as Advanced Bionics and Sci Med Life Systems years after Boston Scienti�c bought

them. As well, we see patents going from acquired a¢ liate to headquarters, for example

Matrix Semiconductor assigning 157 of its 421 patents to parent company Sandisk.5 To

determine reassignment type we match old and new assignees to our �rm name sample.

This matching is very challenging because many patents undergo multiple reassignments

over their lifetime for reasons that are not germane to our study. For example, patents are

very often reassigned to correct errors in the initial document, or for purposes of collat-

eralization for lenders. This last case usually entails multiple transactions, such as when

the collateral reverts to borrower, or when loans are assigned within �nancial institutions.

Thus, we need to track the patent�s path (including such diversions) over time. Ultimately,

5A third type of reassignment is inter-�rm. Because the current paper deals with intra-�rm allocationof IP rights, we exclude inter-�rm reassignment from our sample.

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41,244 patents in our sample are reassigned. Close to 90% of these reassignments are as-

signing a patent from headquarters to an a¢ liate (36,180 patents), supporting our view of

assignments as being associated with a long-term e¤ective delegation of authority.

2.5 Accounting

Accounting data are from U.S. Compustat. We match our �rms using a string name

process similar to the one we utilize to match patents to our ownership structure data.

Please see Appendix A.2.2 for details on the algorithms. The book value of capital is

the net stock of property, plant and equipment; Employment is the number of employees.

R&D is used to create R&D capital stocks calculated using a perpetual inventory method

with a 15% depreciation rate (Hall, Ja¤e and Trajtenberg, 2005). So the R&D stock,

GRD, in year t is GRDt = Rt + (1 � �)GRDt�1 where Rt is the R&D expenditure in

year t and � = 0:15. Patents stock, GPatt, is calculated in an analogous way. Patents

stock in year t is GPatt = Pt + (1 � �)GPatt�1 where Pt is the citations-weights �owof patents in year t. 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. Firm value is 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. Tobin�s Q (market value over

capital) was winsorized by setting it to 0.1 for values below 0.1 and at 20 for values above

20.

3 Descriptive statistics

Tables 1 and 2 provide summary statistics on our �rm sample. Our sample�s average

�rm is valued at $1.5 billion, has $3.6 billion in sales, $486 million in R&D stock, and

holds a stock of 145 cites-weighted patents. Our main variable of interest is the share of

decentralized patents stock. That is, the share of patents that are assigned to a¢ liates,

and not headquarters. On average, the ratio of decentralized patents across �rms is about

35%. Using the patent as the unit of analysis, about 18.9% of patents are assigned to

a¢ liates, because, as we shall see later, �rms that patent a lot are less likely to assign

12

patents to a¢ liates. We also distinguish between patents that are managed by in-house

attorneys, and patents that are managed by attorneys that are not employed by the focal

�rm. Across �rms, 31% of the patents are managed by in-house attorneys. The average

number of citations received is 8.8 (or 11.3 when restricting the sample to patents that

receive at least one citation).

Table 3 reports the raw correlations between our main variables of interest. Several

interesting patterns emerge. First, the share of decentralized patents is negatively corre-

lated with the share of in-house patent attorneys (-0.07). This is consistent with the view

that both variables measure an underlying decentralization strategy. Second, decentral-

ization is strongly correlated with the mergers and acquisitions (0.38). On the other hand,

there is a negative relationship between decentralization and patents and R&D stock (-

0.10). The share of in-house attorneys is mostly correlated with �rm sales, patents and

R&D stocks (0.14, 0.16 and 0.15, respectively). This suggests a strong link between the

use of in-house attorneys and operation scope, which is what we would expect if patent

management exhibits substantial increasing returns to scale, or cross-patent spillovers.

Our measure of decentralization of IP management is admittedly imperfect. In addition

to the usual data problems, it is possible that patents could be assigned to a¢ liates without

any delegation of authority. However, we take some comfort in the similar patterns for the

use of external attorneys, because if patent assignments were random, they ought not to be

related to whether the �ling was done using in-house or external attorneys. Reassignment

data also indicate that �rms deliberately assign some patents to their a¢ liates, indicating

that assignments to a¢ liates re�ect intent, rather than merely inertia or noise. Moreover,

if assignments to a¢ liates were mostly noise, it would be di¢ cult to explain the persistent

correlation we document between decentralization and variables such as market value,

sales growth, R&D, and patenting. It is also possible that authority could be delegated

to divisions, which nonetheless are not reported as independent a¢ liates. Similarly, an

a¢ liate may employ in-house attorneys to �le patents, while still enjoying considerable

autonomy on managing its intellectual property. If so, this would work against our �nding

a systematic relationship between our measure of decentralization and �rm performance.

Thus, the true relationships may be stronger than what we report here.

Insert Tables 1-3 here

13

4 Decentralization and market value

Given the novelty of our measure of decentralization, we begin by establishing that it is

prima facie interesting. To this end, we estimate a simple version of the value function

approach proposed by Griliches (1981)6. The market value of �rm i at period t, Vit; takes

the following form:

lnV alueit = �1 lnAssetsit�1 + �2 lnGRDit�1 + �3 ln(1 +GPatit�1)

+ �4ShareDecit�1 + �t + �j + �it (1)

V alue denotes �rm market value, Assets, GRD and GPat denote physical, R&D, and

citations-weighted patent stocks, respectively. ShareDec is our main variable of interest,

and is constructed as the share of the �rm patents stock that is assigned to a¢ liates. �t

and �j and are complete sets of year and three-digit industry dummies, and �it is an iid

error term. The reported standard errors are always robust to arbitrary heteroskedastic-

ity and allow for serial correlation within �rms. �4 captures the decentralization-value

relationship.

Table 4 reports the estimation results for the market value equation. Column 1 in-

cludes separately the stocks of centralized and decentralized patents. The coe¢ cient on

centralized patents stock is not signi�cantly di¤erent than zero (a coe¢ cient of 0.023 and

a standard error of 0.014), where the coe¢ cient on decentralized patents stock is large

and is highly signi�cant (a coe¢ cient of 0.106 and a standard error of 0.016). Column 2

controls for ownership structure by adding the total number of a¢ liates controlled by the

�rm, and for scale using �rm sales. The e¤ect of decentralized patents remains robust,

however slightly drops in magnitude (a coe¢ cient of 0.101 and a standard error of 0.020).

The coe¢ cient on number of a¢ liates is positive and is highly signi�cant (0.086 and a

standard error of 0.015). In column 3, instead of separately including the stocks of cen-

tralized and decentralized patents, we include the overall stock of patents, and the share

of patents that are decentralized. The coe¢ cient on the share of decentralized patents is

positive and signi�cant (a coe¢ cient of 0.237 and a standard error of 0.054). The coef-

�cient on overall patents stock is positive and signi�cant as well (0.058 and a standard

error of 0.016).6See also Ja¤e (1986), Hall et al (2005) or Lanjouw and Schankerman (2004).

14

Column 4 adds number of a¢ liates and sales. The coe¢ cient on the share of decentral-

ized patents drops, but remains large and highly signi�cant (0.182 and a standard error

of 0.050). Columns 5 and 6 report the estimation results of sub-samples of �rms with

above and below median patents stock. The coe¢ cient on decentralization is large and

is highly signi�cant in the latter sub-sample. Thus, the relationship between the share

of decentralized patents and market value is driven mostly by di¤erences in assignment

structure for �rms that hold a substantial stock of patents. Column 7 checks the sensitiv-

ity of the results to outliers by excluding the �ve largest patenting �rms from the sample7.

The results remain robust. The e¤ect of share decentralized is not only highly signi�cant,

but also large. Based on the estimated coe¢ cient reported in Table 4, column 3, a one

standard deviation increase in share decentralized is associated with an increase in �rm

value by $120 million (0:182� 0:45� 1462), or 8.2% of the average �rm value.

Table 5 report robustness checks for the market value equation. Columns 1-7 examine

linear Tobin�s Q speci�cations, and columns 8-10 examine similar non-linear speci�ca-

tions8. Similar results hold for the Tobin�s Q estimation. In the whole sample (column 1),

the coe¢ cient on the share of decentralized patents is 0.247 (a standard error of 0.039).

This coe¢ cient is higher than in the respective market value equation (0.182 and a stan-

dard error of 0.050). Columns 2-3 split the sample to �rms with above and below median

overall number of a¢ liates. The e¤ect of decentralization is much more pronounced when

focusing on the sample of �rms that hold many a¢ liates. For this sample, the coe¢ cient

on share decentralized is 0.359 (a standard error of 0.070). On the other hand, for the

sample of �rms with few a¢ liates, this coe¢ cient is 0.049 (a standard error of 0.071).

Column 4 and 5 split the sample to above and below median of annual sales. The coef-

�cient on share decentralized remains stable across these sub-samples Columns 6 and 7

examine the robustness of the results to patents that were acquired through a merger or

and acquisition. A concern is that the e¤ect of decentralization captures cross-�rm dif-

ferences in M&A activity. While excluding M&A patents reduces the coe¢ cient on share

decentralized, it remains large and highly signi�cant (0.193 and a standard error of 0.063).

Column 7 adds the share of M&A patents. The coe¢ cient on share decentralized drops

7These companies are IBM, General Electric, Motorola, Hewlett-Packard, and Eastman Kodak.8In columns 1-7 we approximate ln(1 + �2(GRD=Assets)it�1 + �3(GPat=Assetsit�1)) to

(�2(GRD=Assetsit�1) + �3(GPat=Assetsit�1)): In columns 8-10 we do not make such approximationand estimate the non-linear term using Non-Linear Least Squares.

15

to 0.168, but remains highly signi�cant. The coe¢ cient on share M&A is positive and

signi�cant (0.158 and a standard error of 0.053).

Note well that no causal inference is asserted here. We are not claiming here that a

centralized �rm would do well to decentralize, nor of course the converse. Instead, we are

content to observe for now that our measure of decentralization is systematically related

to market value of the �rm, controlling for size, tangible assets,R&D stock, and industry,

and that this relationship survives a variety of robustness checks described above. Simply

put, there is a prima facie case for further exploring the determinants and correlates of

decentralization.

Insert Tables 4 and 5 here

5 Decentralization: Putting theories to the test

5.1 Determinants of decentralization

Recall that there are three main theories of decentralization: The property rights theory,

which focuses on eliciting e¤ort, and the two theories motivated by di¤erences in infor-

mation: the information processing theory, and the incentive alignment theory. These

theories have implications both for when we ought to see decentralization, and for the

patterns of correlations between decentralization and observable outcomes such as sales

growth, R&D and patenting.

All three theories have similar predictions for how the nature of the technology should

a¤ect decentralization. They all predict that complex technologies - where marketable

innovations consist of numerous patentable elements �should be associated with greater

centralization. For instance, in complex technology industries, �rms frequently amass

portfolios of patents that are used to deter entry or cross-license to other incumbents

(Hall and Ham, 2001). This requires greater central control over what is patented and

how the patents are used. The property rights theory similarly argues for decentraliza-

tion when the outcome can be more closely tied to the a¢ liate�s e¤orts (e.g., Belenzon,

Berkovitz and Bolton, 2009). The greater need for coordination directly shifts the trade-

o¤ towards centralization in the information processing theory. The incentive alignment

models present a more nuanced picture. In Aghion and Tirole (1997), the importance of

16

coordinating the actions of a¢ liates implies that authority is less likely to be delegated.

However, by their nature, the incentive alignment models are sensitive to the speci�cation

and other models may yield di¤erent results. For instance, Arora, Fosfuri and Roende

(2010) develop a model of delegation of licensing authority, in which, for some ranges of

parameter values, authority is more likely to be delegated when licensing creates negative

externalities for other a¢ liates.

A key source of variation across �rms in our data related to decentralization is mergers

and acquisitions. This pattern is consistent with all the major theories. The property

rights theory argues that decentralization involves de-facto delegation of authority. The

delegation of authority provides the a¢ liate with protection against expropriation or hold-

up by the headquarters, and so the a¢ liate is willing to make �rm speci�c investments.

This theory predicts that �rms which frequently acquire other technology producers will

have to provide these target �rms (now a¢ liates) with incentives to make long term invest-

ments. These incentives include delegating authority over innovation and IP management,

because managers of the acquired a¢ liates are unlikely to have long relationships with

headquarters. Unlike managers with longer tenure, acquired managers would identify less

with the new parent, and may even have smaller chances of advancing in the corporate

hierarchy. To invest in �rm speci�c assets, including research, managers of acquired a¢ li-

ates would require credible commitments, in the form of greater autonomy on innovation

and IP. Theories based on di¤erences in information also predict a positive relationship

between propensity for acquisitions and decentralization, because of greater costs of com-

munication. An acquisition creates di¤erences in information between the acquired �rm

or division, and the parent. Without the shared understanding and common language

that would develop in the course of a long relationship, it is costlier for the a¢ liate to

communicate local information to the headquarters (information processing). Lower trust,

also a product of a short relationship, increases the problems of aligning incentives of en-

couraging truthful communication from the a¢ liate (see also Bloom et al, 2009). Thus,

decentralization is a natural response unless the parent is willing to make substantial

investments to lower information processing and reduce the di¤erence in information.

These theories o¤er di¤erent views on whether a �rm oriented towards more basic

research is more or less likely to decentralize. The property rights theory would predict

greater decentralization as the a¢ liates have to be provided incentives to invest in basic

17

research, and more importantly, to invest in complementary investments to commercialize

fundamental innovations. The information processing view predicts greater centralization

in order to improve coordination, since basic research likely involves greater spillovers and

overlap with other parts of the �rms. However, the incentive alignment theories o¤er

a more mixed set of predictions. Aghion and Tirole (1997), for example, would predict

that a basic research orientation ought to result in greater centralization as well, because

the outcomes are likely to be more important to the payo¤ of the �rm (the principal).

On the other hand, a more basic research orientation may also imply a greater level of

importance for the information available to the a¢ liate and, consequently, a greater need

to decentralize.

Another potential source of variation is in how diverse or focused the �rm is. The

property rights view is largely agnostic on this since its focus is on the need to provide

incentives to the a¢ liate to make non-contractible investments. The property rights view

thus has no clear predictions regarding whether diversi�ed �rms ought to be more or

less centralized. The information processing theory, in which decentralization allows the

a¢ liate to use its local information more e¢ ciently but at the cost of a loss in coordination

with other parts of the �rm, predicts that diversi�ed �rms are more likely to decentralize

than focused �rms. The intuition is that a �rm with multiple a¢ liates operating in closely

related technologies will have a greater need to coordinate the actions of the a¢ liates. By

contrast, similarly sized �rms with a¢ liates in di¤erent technologies will have a lower need

for coordination. All else held constant, according to the information processing theory, a

diversi�ed �rm will be more decentralized than a focused �rm. Here, incentive alignment

theories generally predict no e¤ect, because the relationship between headquarters and an

a¢ liate ought to be una¤ected by the existence of an unrelated a¢ liate.9

Table 6 summarizes the main predictions.

9For instance, Aghion and Tirole (1997) would predict that a more diversi�ed �rm is similar to anoverloaded principal, indicating greater decentralization. However, this argument is strongly redolent ofthe information processing view.

18

Property Rights Incentive Alignment Information Processing

Discrete technologies More decentralization More decentralization More decentralization

Focus on Basic R&D More decentralization Less decentralization Less decentralization

High Acquisitions and Divestitures

Diversity high

More decentralization

No effect

More decentralization

No effect

More decentralization

More decentralization

Property Rights Incentive Alignment Information Processing

Discrete technologies More decentralization More decentralization More decentralization

Focus on Basic R&D More decentralization Less decentralization Less decentralization

High Acquisitions and Divestitures

Diversity high

More decentralization

No effect

More decentralization

No effect

More decentralization

More decentralization

TABLE 6. Predictions for Determinants of Decentralization

5.1.1 Decentralization equation

We estimate the following speci�cation for the determinants of decentralization:

Pr(Affiliatei) = �(�1 lnCitesi + �2Basici + �t + �j + �k) (2)

Where Affiliatei is a dummy that receives the value of 1 for patents that are assigned

to a¢ liates (rather than headquarters). Cites is the total number of citations a patent

receives, Basic denotes patent characteristics that we associate with basic inventions (de-

scribed in more detail below), �t denote the patent grant year, �j denotes the patent main

technology area, and �k is the �rm.

Table 7 reports the estimation results (marginal e¤ects of a Probit model). Here the

general pattern of results is consistent with the predictions of all three theories. First,

there is a strong positive relationship between discrete technologies and decentralization.

We classify our sample patents to 7 main technology areas based on their International

Patent Classi�cation code10. Discrete technology areas are pharmaceuticals, biotechnol-

ogy, and chemicals, where complex technologies include telecommunications, electronics,

semiconductors, and information technology. As column 1 shows, there is a clear pattern

of lower decentralization probability for complex technologies. The base category is phar-

maceuticals, and the sample average probability of decentralization is 18.8%. Among the

discrete technologies, there is not much di¤erence in decentralization probability relative

to pharmaceuticals. For example, the marginal e¤ect of the biotechnology dummy is 0.015

(a standard error of 0.006), which indicates the probability of decentralization in biotech-

nology is only higher by 1.5 percentage points than the probability of decentralization

10Patent that are not class�ed to any of the main categories are classi�ed under Other.

19

in pharmaceuticals. However, striking di¤erences emerge when examining the more com-

plex technologies. The marginal e¤ect of telecommunications is -0.098 (a standard error

of 0.004), which means that the probability of decentralization in telecommunications is

about half the probability in pharmaceuticals. For information technologies and semi-

conductors, the results actually indicate that, on average, decentralization is completely

muted. While the cross-industry results are de�nitely interesting and consistent with the

predictions of all three main theories, it is important to emphasize the low regression R2

(0.037). As we would expect, a substantial fraction of the variation in our data is still left

unexplained.

Second, our results suggest that patent basicness is negatively related to decentraliza-

tion, as predicted by the information processing theories (incentives alignment-based and

communication costs-based), but inconsistent with the predictions of the property rights

theories. Our Basic characteristics variables include patent generality and originality11.

Generality is measured as the breadth of the technology areas across which a patent�s ci-

tations are dispersed, and Originality is the equivalent measure for the citations contained

in the patent. We �nd that centrally assigned patents tend to receive substantially more

citations than decentralized ones. Based on the estimates of column 1, a one standard

deviation increase in the number of citations increases the probability that a patent is

assigned to an a¢ liate by 2.7 percentage points, or 14.3 percent of the average a¢ liation

probability (15:9� (�0:009)� 0:189). For generality, the e¤ect is much smaller. Movingfrom the 10th percentile to the 90th percentile raises a¢ liation probability by only half a

percentage point (0:99� (�0:005)). The e¤ect of patent generality is substantially higherwhen excluding acquired a¢ liates, or for within-�rm estimation (7.9 and 5.8 percent of of

average a¢ liation, respectively). Column 2 con�rms these results continue to hold when

11We follow the widely accepted methodology developed by Trajtenberg, Henderson and Ja¤e (1997)and de�ne patent generality as inversely proportional to the concentration of the citations it receivesacross technology areas. Patent i�s generality,Gi, is computed as:

Gi = 1�Xj

�CijCi

�2(3)

Where, j denotes citing three-digit U.S. class (419 classes), Cij is the number of citations received bypatent i from patents in technology �eld j and Ci is the total number of citations received by patent i.

Following Hall (2002) we correct Gi for the number of citations received, as cGi = � CiCi�1

�Gi:

In addition to patent generality, we also include patent originality, which is the equivalent measure forthe concentration across technology �elds of the citations made by the patent.

20

we exclude those patents that receive no citations from the estimation sample.

Third, whether a patent belongs to an acquired a¢ liate has a strong e¤ect on the

probability it is decentralized. Controlling for M&A, the explanatory power of the re-

gression jumps sharply (from 0.037 to 0.180). It is worth remembering that the decision

to keep an a¢ liate as a distinct operating entity is itself a choice that the parent makes,

and is relevant for out three theories under discussion, since this choice that makes it

possible to delegate authority to the a¢ liate. The marginal e¤ect of M&A dummy is

0.272 (a standard error of 0.001), which means that the probability of decentralization is

higher by 27% (or 145% of the mean) for acquired patents that for internally generated

patents. Finding such a strong relationship between decentralization and acquisitions is

indeed consistent with all three theoretical predictions. It is also interesting to note that

controlling for acquisitions substantially mutes the technology-areas e¤ects (for example,

the coe¢ cient on telecommunications drops from -0.098 to -0.058). This pattern suggests

that cross-industry di¤erences in acquisition strategies explains a substantial part of the

observed variation of IP organization.

Forth, column 4 shows a positive e¤ect of technical diversity on decentralization prob-

ability. Controlling for patents stock and sales, �rms that patent in more diverse areas,

as indicated by the value of their Unrelated Diversi�cation, have on average a higher de-

centralization probability (a marginal e¤ect of 0.007). Moving from the 10th to the 90th

percentile of Unrelated Diversi�cation, raises the probability of decentralization by about

11% of the mean (0:007�3:10:189

).

Lastly, column 5 reports OLS results of estimating a within-�rm speci�cation using

a linear probability model. Comparing column 5 with column 3, we see that controlling

for patent characteristics, �rm �xed e¤ects explain a very large fraction of the variation.

Speci�cally, including �rm �xed e¤ects raises the R2 from 0.09 in column 1 to 0.48 in

column 4. This is consistent with the idea that patent decentralization re�ects underly-

ing organization structure, or at the very least, �rms di¤er systematically in the extent

to which they assign patents to a¢ liates. Moreover, the importance of acquisitions to

decentralization remains robust and even rises (a marginal e¤ect of 0.449). Nonetheless,

the technology e¤ects e¤ectively disappear and the estimates for patent characteristics

substantially weaken.

Broadly, all of these results are more consistent with the information-di¤erence based

21

theories we have discussed and less so for the property rights theories. All of the theo-

ries predict a positive relationship between mergers and acquisitions and decentralization,

which is consistent with our �ndings. Similarly, as predicted by all the theories, dis-

crete technologies are associated with decentralization. However, whereas the property

rights theory would have predicted more basic and general patents to be associated with

decentralization, we �nd the opposite: decentralized patents are on average less general

and receive fewer citations. Interestingly, the information processing theories (incentives

alignment-based and communication costs-based) are the only theories that predict the

positive relationship we �nd between diversity and decentralization.

Insert Table 7 here

5.2 Implications of decentralization for outcomes

In the context of the property rights theory, the assignment of patents to a¢ liates can be

a signal of a credible commitment by headquarters to the a¢ liate that the latter will be

allowed to retain the rents it generates through innovation, patenting and licensing. Thus,

decentralization should be associated with greater investment by a¢ liates in R&D, and

conditional on R&D, greater investment in patenting. The idea is that in the property

rights view, decentralization encourages specialized and longer term investments. Insofar

as patents are options on the future, it also implies greater patenting conditional on R&D.

These should also result in greater sales growth for the a¢ liate because of the greater

e¤ort expended by the a¢ liate in its activities. Since interactions with other a¢ liates are

largely ignored in this theory, this should result in higher sales growth for the �rm as a

whole.

Sales growth can also be a¤ected by licensing. The a¢ liate is likely to have superior

information about licensing opportunities. Often, good licensing opportunities become

visible in the course of market transactions with customers or suppliers. On the other

hand, a¢ liates are typically rewarded for sales and market share (which more closely

track the bulk of managerial actions), and would therefore prefer to use the patents in-

ternally instead of licensing (Arora, Fosfuri and Roende, 2010)12. The implication is that

12The greater rewards for production performance (e.g., sales, pro�tability) is a robust �nding in Arora,Fosfuri, and Roende (2010). In their model, an a¢ liate has superior information about licensing possibil-ities, but licensing also reduces pro�ts in the product market by increasing competition. If the a¢ liate is

22

decentralization is likely when licensing opportunities are perceived to be rich because

the information rents that the a¢ liate would need in order to fully capture those possi-

bilities are high. Thus, the incentive alignment predicts that decentralization should be

associated with greater sales growth for the a¢ liate. In the information processing story,

licensing will be decentralized when the gains from using the a¢ liates superior informa-

tion about licensing opportunities outweighs the cannibalization of sales in other a¢ liates

(about which the headquarters will have superior information). Thus, decentralization will

be chosen when the cannibalization of sales from licensing is limited, suggesting a positive

correlation between decentralization and sales growth for the �rm as a whole. In sum, all

theories broadly predict a positive relationship between sales growth and decentralization.

The information processing and the incentive alignment theories have no direct impli-

cations for the level of R&D investment. Decentralization is more likely when an a¢ liate

has superior information about R&D possibilities, it does not follow the possibilities under

decentralization are favorable under decentralization: An a¢ liate may �nd more promis-

ing opportunities for research than those perceived by headquarters or less promising ones

However, there are two indirect, but possibly o¤setting, e¤ects. Under decentralization,

there may be unrealized synergies and spillovers. This may result in duplicative R&D

by di¤erent a¢ liates, resulting in higher overall R&D. On the other hand, if uncaptured

spillovers imply a higher cost of R&D (or less attractive opportunities for R&D), this

would reduce R&D. If there is duplicative R&D, then the patenting yield �patents con-

ditional on R&D investment �is likely to be lower. If there are unrealized synergies, the

patenting yield will also be lower because the R&D will be less predictive. In sum, the

theories based on di¤erences in information weakly predict lower R&D investment and

predict lower patenting conditional on R&D.

There is, however, one major di¤erence in the implications of information processing

on the one hand, and incentive alignment and the property rights theories on the other.

As a �rm operates in more diverse environments, the information di¤erences between

headquarters and a¢ liates grow. In the information processing view, the growing in-

formation di¤erences should increase the value of decentralization, all else held constant.

Changes in information sets are also relevant in the incentive alignment theories but there

delegated the authority to license, the �rm rewards it for licensing to provide incentives for licensing, butthe incentives for licensing are (optimally) weaker than those for production.

23

is no consistent prediction on how this should a¤ect outcomes or the value of decentral-

ization. Similarly, in the property rights theories, the willingness of the a¢ liate to make

relationship-speci�c investments should not be a¤ected as other units in the �rm enter

new technology areas. Therefore, whereas the information processing view implies that

all else equal, decentralization should be more valuable for �rms operating in multiple

technical areas than for more focused �rms, the incentive alignment and property rights

theory predict no di¤erence between the two types of �rms.

Table 8 summarizes the main predictions.

Property Rights Incentive Alignment Information Processing

R&D Increase Decrease (Unclear) Decrease (Unclear)

Patenting Increase Decrease or no effect Decrease or no effect

Quality of patents Higher Lower or same Lower or same

Sales growth Higher Higher (Unclear) Higher (Unclear)

Marginal Value of Decentralization no change withtechnical diversity

No change with technicaldiversity

increase with technicaldiversity

Property Rights Incentive Alignment Information Processing

R&D Increase Decrease (Unclear) Decrease (Unclear)

Patenting Increase Decrease or no effect Decrease or no effect

Quality of patents Higher Lower or same Lower or same

Sales growth Higher Higher (Unclear) Higher (Unclear)

Marginal Value of Decentralization no change withtechnical diversity

No change with technicaldiversity

increase with technicaldiversity

TABLE 8. Predictions for Outcomes

5.2.1 R&D equation

The various theories have contrasting implications for how R&D and patenting is related

to decentralization. We estimate the relationship between decentralization and R&D ex-

penditures and the �rm propensity to patent.

The R&D equation is speci�ed as:

lnR&Dit = �1 lnSalesit�1 + �2 ln(1 +GPatit�1) + �3ShareDecit�1 + �t + �j + �it (4)

The relationship between decentralization and R&D is captured by �3: According to

the property rights theory, delegation of authority encourages innovation e¤orts, hence we

expect we expect b�3 > 0: The information based theories, on the other hand, emphasize theimportance of knowledge location and the costs that are associated with its transmission

within the organization. While the bene�ts of decentralization lie on reducing the need for

24

costly communication, the downside of decentralization is lower coordination of activities.

In the context of R&D investment, lower coordination is likely to be associated with lower

knowledge spillovers from one division to another. If R&D costs, for example, decrease with

knowledge spillovers, decentralization would be associated with lower R&D investment.

This means b�3 < 0: On the other hand, it is worth emphasizing that low coordination

can also result with duplicative R&D, hence over-spending on similar R&D projects by

di¤erent divisions. If true, we expect b�3 > 0:Table 9 reports the estimation results. The general pattern suggests a negative rela-

tionship between decentralization and R&D investment. This �nding is driven mostly by

�rms with above median number of patents, is robust to di¤erences in acquisition strate-

gies, and to an alternative speci�cation of R&D intensity. Based on the estimation results

of column 1, a one standard deviation increase in the share of decentralized patents lowers

R&D investment by $7.7 million, or by 7% of the sample mean (�0:157 � 0:45 � 109).Thus, the R&D result is consistent with the local information theories, but not with the

property rights view which predicts greater R&D e¤ort under delegation of authority and

higher-powered incentives.

Insert Table 9 here

5.2.2 Patent equation

We proceed to investigate the patent equation - namely, the output of the R&D equation.

The patent �ow equation (at the �rm-year level) is speci�ed as13:

ln(1 + Pats)it = 1 lnSalesit�1 + 2 lnGRDit�1 + 3ShareDecit�1 + �t + �j + �it (5)

The relationship between patent propensity and decentralization is captured by 3: The

property rights theory predicts greater patenting, given R&D, due to stronger e¤ort that

is induced by e¤ective delegation of authority (accordingly, R&D productivity is measured

by patents per R&D). That is, b 3 > 0. The information theories, however, yields oppositepredictions, where similar to R&D, lower coordination due to decentralization potentially

leads to weaker capitalization on knowledge spillovers - b 3 < 013All results are robust to alternative speci�cations, such as Negative Binomial for patents count.

25

Table 10 reports the estimation results for patents The results support the information

theories. There is a negative and signi�cant relationship between decentralization and

patent propensity. Based on the estimates of column 1, a one standard deviation is

associated with 2.1 fewer patents per year (close to 10% of the sample mean), given R&D

(�0:183�0:45�25). This pattern is robust to acquisitions, outliers, and weighting patentsby citations.

Insert Table 10 here

5.2.3 Sales growth equation

We estimate the following sales growth equation:

� lnSalesit = �1 lnSalesit�1 + �2 lnAssetst�1 + �3 lnGRDit�1 + �4 ln(1 +GPatit�1) (6)

+ �5ShareDecit�1 + �t + �j + �it

Where � lnSalesit is lnSalesit � lnSalesit�1. �5 captures the relationship betweendecentralization and sales. The property rights and information theories predict a positive

relationship between decentralization and sales growth. While the property rights view

highlights the greater focus by division managers on sales and market share rather than

on licensing, the information theories point to the higher coordination costs, in terms of

negative externalities and sales cannibalization, of licensing relative to sales. Thus, all

predict that when decentralization is observed, it will be associated with greater sales

growth.

Table 11 reports the estimation results. The general pattern of results is consistent

with what we expect. Columns 1 and 2 examine the e¤ect of decentralized and centralized

patent stocks. Consistent with our previous �ndings, centralized patent stock has no

signi�cant e¤ect of �rm growth (a coe¢ cient of 0.005 and a standard error of 0.003). On

the other hand, decentralized patents stock has a positive and a highly signi�cant e¤ect

on sales growth (0.016 and a standard error of 0.003). The results remain robust when

controlling for ownership structure using overall number of a¢ liates (column 2). Columns

3 and 4 include the share of decentralized patents. The coe¢ cient of share decentralized

is positive and is highly signi�cant (0.042 and a standard error of 0.010). Columns 5 and

26

6 split the sample to above and below median patents stock. The coe¢ cient on share

decentralized remains robust for the two sub-samples Lastly, column 7 excludes from the

sample very larger patentors, with no major change in the estimates.

Insert Table 11 here

5.2.4 Incentives alignment vs. information processing

Our results thus far are broadly consistent with the view that decentralization is moti-

vated by di¤erences in information between a¢ liates and headquarters. Several of our

results are inconsistent with the view that decentralization is motivated by a desire to

elicit e¤ort from the managers of a¢ liates, the property rights view. In contrast to the

predictions of property rights theory, and in contrast to the �ndings reported by Kastl et

al. (2009), we do not �nd that decentralization is associated with higher R&D. Nor do we

�nd that decentralization is associated with higher patenting, in contrast with Belenzon et

al. (2009). Further, we do not �nd that decentralized patents are more general or garner

more citations.

In other words, two main theories can rationalize the observed patterns: information

processing and incentives alignment. Information processing builds on the idea that when

local knowledge is valuable and communication costly, it may be more e¢ cient for an

organization to delegate decision making authority to the better informed division man-

agers, than to shift knowledge to headquarters for processing. While faster processing is

a key advantage of decentralization, the increase in coordination costs is signi�cant. The

incentives view argues that delegation of authority to division managers may be especially

advantageous in terms if higher-powered incentives in the form of information rents.

We test between information processing and incentives by investigating how the decentralization-

performance relation varies with patenting diversity. According to information processing,

the bene�ts from decentralization are more prominent as patenting diversity increases.

This is because the bene�ts from specialization increases with complexity (a division of

labor argument), and because the communication of information becomes more costly in

more diverse organizations. Moreover, the cost of decentralization is lower when divi-

sions focus on very di¤erent tasks as the scope for spillovers and rent cannibalization is

reduced. On the other hand, under the incentives view, the decentralization-value relation-

27

ship should not systematically vary with diversity, since incentives can be very important

also in highly specialized organizations. In fact, in highly specialized organizations the cost

of decentralization in the form of reduced coordination may be of paramount importance

because of the large scope of potential cross-unit spillovers and sales cannibalization.

We Follow Palepu (1985) and construct �rm technological diversity as the Entropy

measures, which include Total Diversi�cation (TD), Related Diversi�cation (RD), and

Unrelated Diversi�cation (UD). These measures are constructed as follows. Suppose a

�rm operates in N segments which belong to M main technology areas. We use three-

digit and two-digit U.S. class to de�ne segments and technology areas, respectively. We

denote by P ji the share of the ith segment patents of total �rm patents in technology area j.

Related diversi�cation measures the extent the �rm operates in several business segments

within an industry, and is de�ned as:

RDj =Xi2jP ji ln

�1

P ji

�If a �rm operates in several technology areas, its aggregated related diversi�cation is

the weighted sum of RDj; where the weight, Pj; is the share of technology area j patents

of the �rm�s total patents.

RD =Xj2M

RDjPj (7)

Unrelated diversi�cation measures the �rm patents spread across di¤erent (two-digit)

technology areas, and is de�ned as:

UD =Xj2M

Pj ln

�1

P j

�(8)

Total Diversi�cation (TD) is a weighted average of the �rm�s diversi�cation within and

between sectors and is computed as the sum of RD and UD.

We estimate the following speci�cation:

lnV alueit = �1ShareDecit�1+�2ShareDecit�1�Diversityi+Z 0it�1�5+ �t+ �j + �it (9)

Diversityi is one of the above diversi�cation measures, and Z is a vectors of additional

�rm-level controls. Our main interest is the coe¢ cients �2 and �3: Consistent with in-

28

formation processing, we expect b�2 > 0. That is, the marginal value of decentralizationintensi�es as the corporation becomes more diverse. On the other hand, consistent with

the incentives theory, there is no clear reason to suspect the marginal value of decentral-

ization to rise with diversity (b�2 = 0).The estimation results are reported in table 12. Columns 1 and two estimates the

baseline market value equation separately for specialized and diversi�ed �rms. We classify

�rms as specialized if their value of Unrelated Diversi�cation falls in the lowest sample

quartile, and as diversi�ed if their total diversi�cation value falls at the highest diversi-

�cation quartile. The coe¢ cient om share decentralized is large and signi�cant for the

diversi�ed sub-sample (0.359 and a standard error of 0.086), and is small and insigni�cant

for the specialized �rms sub-sample (0.047 and a standard error of 0.112). Column 3

adds to the baseline speci�cation the interaction between Total Diversi�cation and share

decentralized. Consistent with the information processing view, the coe¢ cient on this

interaction (b�2) is positive and signi�cant (0.011 and a standard error of 0.004). The coef-�cient on share decentralized remains positive and signi�cant (0.117 with a standard error

of 0.057). Next we decompose diversi�cation to within and between technology areas. Col-

umn 4 includes the interaction between share decentralized and Unrelated Diversi�cation.

The relationship is very strong. The interaction coe¢ cient is 0.140 (a standard error of

0.096), where the level e¤ect of share decentralized is no longer signi�cant (a coe¢ cient of

-0.096 and a standard error of 0.108). Columns 5 includes the interaction between Related

Diversi�cation and share decentralized, which yields much smaller estimates of how the

e¤ect of decentralization of market value varies with �rm diversity (a coe¢ cient of 0.011

and a standard error of 0.005).14

Based on the estimates of column 4, evaluated at the sample average, a one standard

deviation increase in share decentralized is associated with a $100 million increase in14An important concern is that the interaction between decentralization and diversity is driven by

patenting scale. By construction, �rms with fewer patents are likely to be less diverse than �rms withmany patents. If decentralization is more important for market value for �rms with few patents than�rms with many, b�2 would be upward biased. To mitigate this concern we check the robustness of theestimates reported in column 4 to adding an interaction between ShareDec and ln(1 + GPatit�1): Thecoe¢ cient on the interaction term between ShareDec and Unrelated Diversi�cation falls to 0.093 (from0.140), but it remains signi�cant (a standard error of 0.044). To further check the variation in our datadoes not stem solely from comparing �rms with few patents to �rms with many patents, we restrict thesample to include �rms with above median patents stock. The coe¢ cient on the interaction term betweenShareDec and Unrelated Diversi�cation is 0.126 (a standard error of 0.041).

29

market value (0:45� (�0:096 + 1:76� 0:140)� 1462): This �gure is lower than the $120million �gure than is based on the estimates from table 5 where we did not control for

diversi�cation. A one standard deviation increase in diversity, almost double the e¤ect of

decentralization to $191 million (0:45� (�0:096 + (1:76 + 1)� 0:140)� 1462):Columns 6 includes an alternative measure of diversity - Her�ndahl-Hirschman Index

(HHI) of patent concentration across three-digit U.S. technology class. The same pattern

of results continue to hold.

Lastly, column 7 investigates the extent to which the marginal value of decentralization

varies with geographical diversity. Similar to the above theoretical arguments, if communi-

cation costs increase when the corporation�s R&D labs are more geographically dispersed,

we would expect the marginal value of decentralization to rise with geographical diversity.

We measure geographical diversity in the following way. For each corporation, we generate

a list of all inventors and their location as indicated on the patent document. We then

construct the Her�ndahl-Hirschman Index (HHI) of patent concentration across Ameri-

can cities (excluding foreign inventors). The results are once again consistent with the

information processing story: the coe¢ cient on the interaction term between geographical

diversity and decentralization is negative and is highly signi�cant (-0.598 and a standard

error of 0.191).

Insert Table 12 here

5.3 Robustness checks

5.3.1 Patent attorneys

We argue that �rms with more robust in-house patent law departments are more likely to

centrally manage the patent-�ling process than those �rms which consistently use outside

attorneys for patent �lings. Also, we would expect these centrally managed legal depart-

ments to have more input and in�uence in the process of deciding which patents get �led.

Thus, while our measures of patent assignment aim to capture the delegation of authority

ex-post after the patent has been granted, our measure of in-house patent attorneys ad-

dress the ex-ante delegation of authority over the decision of what gets patented. Whereas

assignment decisions may re�ect a �rm�s strategy to exploit local knowledge about po-

tential commercialization opportunities, attorney decision may re�ect the need for greater

30

control over IP costs, as well as perhaps greater control over using patents for licensing

and cross-licensing. That is, division manager may be better informed than central patent

attorneys (unless those attorneys are closely related to the division, a possibility we cannot

rule out given our measure) about which inventions are more valuable for the division�s

operations. Central attorneys, on the other hand, may have a better sense of the value of

patents for overall �rm objectives such as cross-licensing to gain access to other markets.

Table 13 reports the estimation results for the relationship between in-house attorneys

and our outcome variables. The general pattern of results suggests a negative relation

between in-house attorneys and market value. Column 1 reports a negative and a highly

signi�cant coe¢ cient on share of in-house attorneys of -0.126 (a standard error of 0.050).

Column 2 adds share of decentralized patents, and column 3 excludes the �ve largest

patenting �rms from the sample. In both cases, the negative e¤ect of the share of in-house

attorneys remains robust.

Column 6 examines the relationship between the share of in-house attorneys and overall

R&D expenditures. The coe¢ cient on in-house coe¢ cient is signi�cantly positive (0.059

and a standard error of 0.031). Column 5 and 6 report the results for patent count,

and citations-weighted patents, respectively. In both cases there are strong evidence of a

positive relationship between the use of in-house attorneys and patent intensity.

Lastly, column 5 reports the estimation results for sales growth. Unlike our observed

impact on market value, there is no signi�cant relationship between the use of in-house

patent attorneys and sales growth (a coe¢ cient of -0.013 and a standard error of 0.010).

Insert Table 13 here

5.3.2 Alternative measures of assignment

Table 14 reports robustness checks for our ley results using alternative measures of a¢ liate

assignment. These measures are cross-sectional and exploit only between-�rm variation.

The new measures include a dummy for whether the �rm has at least one patent that is

assigned to an a¢ liate, concentration of assignment across the di¤erent a¢ liates of the

organization, and a set of dummies for the share of decentralized �rm patents.

Columns 1-3 examine market value. Column 1 includes a decentralization dummy (at

least one patent is assigned to an a¢ liate). The coe¢ cient on this dummy is large and is

31

highly signi�cant (0.175 and a standard error of 0.050). Columns 2 includes concentration

of assignments, which shows a strong and negative relationship between concentration and

�rm value (a coe¢ cient of -0.414 and a standard error of 0.093). Column 3 investigates in

greater detail the assignment-market value relation by including separate dummy variables

for di¤erent shares of decentralized patents. Firms with relatively little decentralization

(share of decentralized patents is positive, but lower than 0.2 of all �rm patents) are not

signi�cantly di¤erent from �rms with no decentralization (the base category). However,

�rms with higher decentralization (a share of decentralized patents between 0.2 and 0.8)

exhibit the largest value premium (a coe¢ cient of 0.256 and a standard error of 0.068).

The value premium falls as we move to highly-decentralized �rms (share of decentralized

patents is greater than 0.8), but it remain highly signi�cant (0.173 and a standard error

of 0.063).

Columns 5-7 investigate the relationship between assignment, R&D investment, patents,

and sales growth. Similar pattern of results hold for these measures as well.

Insert Table 14 here

5.3.3 Reassignment

We determine whether a patent is assigned to an a¢ liates or headquarters by examining

the assignee name that appears on the patent document when it was granted. However,

assignees can change over the patent life-cycle. Reasons for reassigning a patent include a

merger or an acquisition, or a managerial decision within-�rms of how to allocate IP assets

across the organization units. Using data on reassignments, as coded by the USPTO, we

test the robustness of our key results. 41,244 patents in our sample are reassigned. Close

to 90% of these reassignments are assigning a patent from headquarters to an a¢ liate

(36,180 patents). Finding that reassignments from a¢ liates to headquarters is a rare event

supports our view of assignments as being associated with a long-term e¤ective delegation

of authority. There is no big di¤erence in the share of reassigned patents between M&A

and internal patents. For M&A patents, 8% are reassigned (8,410 patents), where for

internal patents, 7% are reassigned (32,834 patents). For M&A -related reassignments,

23% are reassignments from a¢ liates to headquarters, where for internal patents, about

32

91% of reassignments are from headquarters to a¢ liates.15 Table 15 reports the robustness

of our main results for reassignment. For each reassigned patent, we modify its assignee

post-reassignment year. Our �ndings are robust to reassignment.

Insert Table 15 here

6 Conclusions

This paper develops a new way of using patent data in order to explore the impact of

�rm organization on �nancial performance. We analyze the assignment of patent rights

within American �rms by distinguishing between those patents that are assigned to head-

quarters and those that are assigned to a¢ liates. We use novel data on the organizational

structure of 1,491 American publicly-listed corporations, with 3,834 private and public

a¢ liates, as well as characteristics of 595,396 patents that belong to these corporations.

Our key measure is whether a patent is assigned to the parent corporation or to an a¢ li-

ate. The assignment of intellectual property to a wholly owned a¢ liate cannot have legal

signi�cance. However, it likely re�ects a de facto delegation of authority to the a¢ liate in

how the intellectual property is managed. Indeed, we �nd systematic cross-�rm variation

in the extent to which such delegation takes place, suggesting that patent assignment to

a¢ liates is plausibly proxing for greater autonomy of the a¢ liate in managing intellectual

property relevant to its operations, and perhaps even greater autonomy on managing its

innovation activities more generally. Patent reassignments, which are predominantly from

headquarters to a¢ liates, indicate that initial patent assignments to a¢ liates are not mere

happenstance but likely re�ect underlying organizational structure. Ancillary measures,

such as the use of in-house attorneys (providing greater central control and indicating

lower a¢ liate autonomy) lend further support to this interpretation.

We �nd that decentralization explains a substantial portion of the variation in the

market value of the �rms in our sample, suggesting that studying decentralization using

15In terms of citations, reassigned patents receive substantially fewer citations than patents that arenever reassigned. The average reassigned patent receives 4.3 citations, where the average non-reassignmentpatent receives 9.9 citations. This pattern robust for period e¤ects. For example, for patents granted post2000, reassigned patents receive, on average 4 citations, relative to 9.5 citations for patents with noreassignment that are granted in the same period. The pattern holds for patents granted before 2000, andfor alternative time cohorts.

33

patent assignments is interesting because it may be consequential for performance, and

not simply for the light it can shed on various theories of decentralization. We consider

three sets of theories about decentralization in organizations. Each set of theories has

distinct implications for the factors that condition when �rms decentralize as well as how

decentralization is related to innovation outcomes such as R&D, patenting, and sales.

Our results provide the strongest support for the oldest set of theories of decentralization,

namely where decentralization economizes on communication between a¢ liates and �rms,

at the cost of lower coordination. Incentive based theories, especially those where dele-

gation of authority is motivated by a desire to provide incentives for e¤ort or investment

by a¢ liates, receive less support. Over and above these �ndings, this project contributes

by revealing a new way of using patent data to proxy for di¤erences in organizational

structure.

34

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37

A AppendixThis section details the construction of the data platform used in this project. The centraldatasets consist of a patent-level panel and a �rm-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 O¢ 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 StructureAssignee information is available from the USPTO, but many of the patent assignmentsare made to a¢ liate �rms. Furthermore, �rms vary in their choice to utilize a¢ liates fortheir assignments, resulting in noisy (at best) or biased (likely) patent and citation countsat the �rm level. As Hall, et al. (2001) put it:�There is a further reason for this to be a lower bound: the assignee code is not

�consolidated�, that is, the same �rm may appear in di¤erent patent documents undervarious, slightly di¤erent names, one assignee may be a subsidiary of the other, etc. Thus,if for example we were to compute the percentage of self-citations using the CompustatCUSIPs (after the match) rather than the assignee codes, we would surely �nd higher�gures.�Thus, our goal is to trace the chain of ownership for every relevant patent precisely

back to the Compustat CUSIP identi�cation number. Below, we detail the steps taken.

A.1.1 Control chain generator.

The linchpin of this project is the identi�cation of an ultimate owner (�UO�) for a largeportion of the companies reported as patent assignees by the USPTO. Here we follow themethodology employed by Belenzon and Berkowitz (2010). We obtain ownership structuredata from the Icarus and Amadeus databases by Bureau Van Dyke (BVD). The Amadeusownership database includes detailed information of the percentage of ownership betweenshareholders and their subsidiaries. We develop an ownership algorithm that constructsthe internal structure parent and a¢ liate groupings based on their inter-company owner-ship links.The algorithm follows three steps: (i) completes missing ownership links, (ii) generates

lists of all subsidiaries and parents for each company, and (iii) constructs the ownershipchains bottom-up. To illustrate our methodology, it would be useful to consider thefollowing example. Suppose Figure A.1 correctly describes the ownership structure of aconglomerate. The ultimate owner �rm at the apex of the group controls 7 public andprivate �rms. Amadeus provides detailed data on direct ownership links. Thus, our rawdata include the links A! D, B ! F , C ! G, and D ! E. Note that the percentage ofownership for the link C ! G has to be larger than 20 (because �rmG is public), where forthe percentage of ownership for all other links has to be larger than 50 (because the other

38

subsidiaries are private). Because there is no information about indirect ownership links,the link A! E is missing from the raw data. The �rst step of the algorithm is to completemissing links. As we observe the ownership relations A ! D and D ! E, our algorithminfers the ownership relation A ! E . Note that at this stage of the algorithm we stilldo not know whether the ownership relation is direct or indirect (and if it is indirect,how many layers separate �rm E from �rm A). The second step of the algorithm is toconstruct two lists for each �rm: shareholders and subsidiaries. This step saves valuablerunning time, which is especially important when dealing with large scale ownership data.The following table is generated:

Firm Shareholder SubsidiaryA - D, EB - FC - GD A EE A, D -F B -G C -

Note that from step 1, we already know that �rm A is a shareholder of �rm E. Thethird and �nal step of the algorithm is to construct the structure of the group based onthe above ownership relations. Because of the missing links problem, our algorithm doesnot assume that an ownership relation is direct; the only input the algorithm receives isthe existence of the ownership relation. We start with a �rm that has no subsidiariesfrom the list generated in step 2. We illustrate the procedure for �rm E, which is themost interesting in this example. Firm E is placed at the bottom of the ownership chain.Next, we move to the shareholder list of �rm E. It includes �rms A and D. Startingarbitrary with A, place A above E. Proceeding to �rm D, there are three possibilitiesfor its location: (i) D is above E and above A; (ii) D is above E, but below A; (iii) Dis above E, but not below neither above A (di¤erent ownership chain). For (i) to be theright structure, D has to appear in the shareholder list of �rm A. From step 2, we rulethis out. For (ii) to be the right structure, D has to appear on the subsidiary list of �rmA. From step 2, we rule this out. For (ii) to be the right structure, D has to appear on thesubsidiary list of �rm A. From step 2, this holds. Finally, for (iii) to be the right structure,A cannot appear on either the shareholder or subsidiary lists of �rm D. From step 2, thisis ruled out. At the end of this procedure, we have determined for each ownership chainthe highest shareholder �rm - we call this �rm the leading shareholder.

A.1.2 Dealing with M&A

A central issue in our analysis is the post-merger management of acquired �rms. Thedecentralization variation in our data comes mostly from two sources: the degree of post-acquisition integration of a¢ liates (with a lower bound being those kept independent), andthe speed at which patents are generated centrally in relation to existing a¢ liates. For eachacquired �rm we determine whether it remained independent post-acquisition, or whetherit was dissolved. We take several steps in determining whether a �rm is independent. First,

39

we check whether the �rm appears in Amadeus or Icarus as an independent company.Second, we manually check each company listed in the �rst step whether it continues tooperate independently from the parent company. We check their corporate websites tocon�rm that their legal disclaimers and investor relations information references a parentcompany.Dissolved acquisitions are much more problematic. Because we match patents to �rms

based 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 all�rms in SDC Platinum where the acquiring �rm appears in our sample. We then add toour data all patents that belong to acquired �rms that no longer appear in the 2008 data.SDC platinum is likely to miss smaller acquisitions, so we also did an extensive searchof public sources (such as Lexis-Nexis,EDGAR and general web searches) to generate alist of all acquisitions for the top 500 patenting corporations in our sample. As this is aniterative process, the resolution of M&A issues was not completed until the �nal stagesof all our patent and �rm matching (i.e. this last step would have been taken after thecompletion of A.2.1 below). For acquisitions that do not appear in SDC we classify itspatents as follows: if the �rm is active in 2008 (thus, it is matched to one of the �rms inour �rm universe) then we classify it as an a¢ liate of the acquiring corporation. However,in case there is no match between this �rm and our �rm universe, we classify all of itspatents to the acquiring �rm headquarters.Overall, we matched 50,931 patents to SDC and Zephyr. An underlying assumption

of this matching is that an a¢ liate exists in 2008. If the a¢ liate was historically dissolvedit will not appear in our �rm 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 �rms 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 �rms have less than 40 patents over their lifetime, so it is reasonable to assumethat they are not patent-intensive �rms. For each unmatched �rm remaining in our sample,we manually investigate whether it was acquired by any of our sample parent corporations,or by any �rms that themselves were acquired by our parent corporations. These two stepslead us to identify 53,761 patents, which we proceed to classify as centrally assigned. Intotal, we identify 104,692 as being acquired through a merger or an acquisition. Of thesepatents, 55,702 (53%) are assigned to a¢ liates, and the remaining patents are assigned toheadquarters.

A.2 Matching patent dataWe 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 guidance

40

in manual searches, however we concentrate on matches using the a¢ liate company namesand our ultimate owner company names from UO. The algorithm utilizes both automatedrules and manual inputs to reduce most �rm 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 �rms (our focus) thanthose lists developed by the NBER Patent Data Project. Furthermore, one re�nementover 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 signi�cantly, 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 �INTERNATIONAL

BUSINESS 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

that the sub string �HI�above is treated under a di¤erent set of conditions. Consider thename �VERIZON INC/HI�(it is common in Compustat to include state identi�ers):1. Verizon Inc./HI2. 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-

Bred would have resulted in a corruption of the identi�er.One of the tradeo¤s in matching is always between high yield and multiplicity errors.

For example, 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 e¤ort was expendedto resolve ambiguities by performing actual checks of patent images and web searches.Ultimately, we match over 846,000 patents to our UO �le.

41

A.2.1 Matching to Compustat

Having matched patents to �rms 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 e¤ectivelyserves as a �lter 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 in A.2.1, with some modi�cations to account for idiosyncratic Compustat�junk words.�

A.3 ReassignmentsOur measures of assignment structure depend on the assignee name that appears on thepatent document when it was granted, but patent assignment can change over time. To testthe robustness of our results to changes in assignment, we develop comprehensive data onpatent reassignment by using data collected from the USPTO website. Reassignment datais collected using a specialized automated �spider�web-crawler program. This data is thensearched for content, processed and merged to our �nal patent sample. We are interested intwo reassignment types: (i) assigning a patent that was originally assigned to headquartersto an a¢ liates, and (ii) reassigning to headquarters a patent that was originally assignedto an a¢ liate. To determine reassignment type we match old and new assignees to our�rm name sample. This matching is very challenging because many patents undergomultiple reassignments over their lifetime for reasons that are not germane to our study.For example, patents are very often reassigned to correct errors in the initial document,or for purposes of collateralization for lenders. This last case usually entails multipletransactions, such as when the collateral reverts to borrower, or when loans are assignedwithin �nancial institutions. Thus, we need to track the patent�s path (including suchdiversions) over time. Ultimately, 41,244 patents in our sample are reassigned. Close to90% of these reassignments are assigning a patent from headquarters to an a¢ liate (36,180patents). Finding that reassignments from a¢ liates to headquarters are rare supports ourview of assignments as being associated with a long-term e¤ective

A.4 AttorneysA signi�cant challenge in coding and interpreting �ling attorney information is the factthat in 65% of our sample, the patents list only the personal name of an individual at-torney, who may in fact be either a¢ liated with a law �rm or be an in-house attorney.Complicating the problem is the issue of attorney mobility, as many attorneys changeda¢ liations during their carrier, so that it is not uncommon to see prominent in-house at-torneys spin o¤ their own private practices or joining a rival �rm after leaving a companylike IBM or Motorola. This necessitated a lengthy iterative matching process to identifylongitudinal �rm a¢ liation information for most of the attorneys in our sample. To en-sure robustness, we started with the entire database of attorneys from the USPTO, whichprovides information on any attorney currently registered to �le with the USPTO. Thisdataset provides �rm a¢ liation for most observations, however it utilizes string informa-tion for both the attorney and the �rm names, and required extensive manual cleaning in

42

order to create unique identi�ers for attorneys, law �rms, and in house legal departments.Additionally, we employed several commercial services as checks to the quality of ourmatching, and gained access to the entire attorney database from www.freshpatents.com,which was matched to the USPTO to identify potential mismatches. Ultimately, the datashowed that close to 60% of our sample patents are �led by an in-house attorney.

A.4.1 Generate unique alphanumerical identi�ers for all Attorneys reportedby USPTO

The USPTO provides a dataset with the name of the �ling attorney for 3,162,580 patents.There are several challenges in dealing with this data. Only name and patent numbersare provided, so there is no additional information to sort or group names (e.g. �rma¢ liation, geographic area, etc). Though 81,847 �unique�names are in the �le, a cursorylook quickly shows that there are often many variants of the same name. For example,there are 12 variations for the name Lynn L. Augspurger:Augsburger; LynnAugsburger; Lynn L.Augsperger; Lynn L.Augspuger; LynnAugspurger, Esq.; LynnAugspurger, Esq.; Lynn L.Augspurger, Esq; Lynn L.Augspurger; LynnAugspurger; Lynn A.Augspurger; Lynn L.Augspurger; Lynne L.This is an example of how many things can go wrong with a name spelling. Both

vowels and consonants are variously added, omitted and changed. As with other namematching, we begin by removing �junk.�We develop a list of 85 frequent terms suchas �Esq.,� �c/o,� �J.D.,� �Attention,� as well as punctuation marks. However beforeremoving punctuation marks, we exploit a fortunate feature of the data: all personalnames contain the semi-colon character (�;�) as a separator. On the other hand, no�rms contain this character. For example,�Whyte Hirschboeck Dudel�is a �rm, whereas�Wiesner; Leland�is an individual. This is extremely powerful later when we attempt toassociate individual attorneys with �rms. Thus, after cleaning of junk, the names aboveare reduced to:Augspuger LynnAugspurger LynnAugsburger LynnAugspurger Lynn AAugspurger Lynn LAugsperger Lynn LAugspurger Lynne LAugsburger Lynn L

43

But our solution for dealing with misspellings is not simple, and necessitates severaloriginal programing routines that tap STATA�s string function tools. First, we use splitnames into one-word variables and utilize STATA�s soundex_nara function to create cor-responding codes for each word in each name. The command soundex_nara returns theU.S. Census soundex code for a string. The soundex code consists of a letter followedby three numbers: the letter is the �rst letter of the name and the numbers encode theremaining consonants. Similar sounding consonants are encoded by the same number.This turns the strings above into:s1 s2 s3A212 L500A216 L500A216 L500A216 L500 A000A216 L500 L000A216 L500 L000A216 L500 L000A216 L500 L000Through experimentation, we then develop rules about what level of con�dence we need

to consider a name �matched�to the name above or below it in an ordered list. In thiscase, only the �rst observation would have been �agged for manual checks. However, thisis a very extreme case given both the di¢ culty in spelling the attorney�s name, and the factthat, as an in-house attorney with IBM, Lynn L. Augspurger is responsible for thousandsof patents, which increases the odds of misspellings. Once we have a reasonable levelof con�dence in the attorney name cleaning, we assign unique alphanumerical identi�erscoded with an �x�pre�x if they are �rms or �I�if they are individuals. The next steps,which add law-�rm a¢ liation, serves to identify miscoding (e.g. two attorneys with similarsoundex pro�les within the same �rm would be �agged for manual checks).

A.4.2 Identify �rm a¢ liation using USPTO data

We begin with the USPTO registration database, which is available at https://oedci.uspto.gov/OEDCI/.This provides us with the entire roster of active patent attorneys and agents registeredwith the USPTO. A typical entry looks like this:�Adolphson, Kenneth , B , , Ware Fressola Van Der Sluys & Adolphson LLP , , 755

Main St P O Box 224 , 5 Bradford Green Building , Monroe , CT , US , 06468 , 203-261-1234 , 30927 , ATTORNEY ��Konkol, Chris , P , , Eastman Kodak CO , , 343 State St , , Rochester , NY , US ,

14650 , 585-722-0452 , 30721 , AGENT�Rather than clean up the data, as we have done in prior steps, we proceed here by

harvesting as much information as possible from substrings that are usually consideredextraneous (junk). For example, we �ag observations with substrings like �LLP,�or �&�aspotential �rm names. We also isolate the actual attorney name per the more standardizedprocedure, and use this to match to our dataset developed in A.3.1. Finally, we search for�rm names that appear in our UO dataset (e.g. we would identify �EASTMAN KODAK�in the second example above). One set of routines we develop looks for law �rm namesby using identi�ers such as �&.� It collects the word that follows this symbol, as well

44

as any non-numeric strings that precede it. In the �rst case above, this would generate�Ware Fressola Van Der Sluys & Adolphson�as a potential law �rm name (which is infact correct). A �nal check is to look for any of a list of su¢ xes such as �LLP,��LLC,��PC,�on the third substring after �&.�In this case it would �nd �LLP,�and con�rm thatthis is the �rm for Kenneth Adolphson.We develop several similar procedures to identify individual attorneys (e.g. a string

of the form �Last_name, First_name, street_address, City, State, Country, Zip, Phone�would be �agged as having no �rm a¢ liation at all).

45

figure 1: Abbott Laboratories (example of a highly centralized firm)

figure 2: Johnson & Johnson (example of a highly decentralized firm)

Variable # Obs. Mean Std. Dev. 10th 50th 90th

Market Value ($mm) 15,892 1,462 7,144 5 129 3,023

Tobin's Q 15,892 1.12 2.07 0.13 0.58 2.24

Sales Growth 15,819 0.106 0.360 -0.118 0.083 0.357

Sales t-1 ($mm) 14,396 3,637 12,010 34 602 8,255

Assets t-1 ($mm) 15,892 2,106 7,284 11 204 4,725

R&D Expenditures ($mm) 15,892 109 469 0.0 5.4 184

R&D Stock t-1 ($mm) 14,396 486 1,949 0.0 26 858

R&D Stock t-1 / Assets t-1 15,892 1.03 4.46 0.00 0.25 2.19

No. Affiliates 1,491 21 54 2 6 47

TABLE 1. Summary Statistics for AccountingDistribution

Notes: This table provides summary statistics for key accounting variables used in the econometric analysis.Market Value includes common stock, preferred stock and debt, net of current assets. Tobin’s Q is the ratiobetween Market Value and Assets . R&D Stock is computed using the perpetual inventory method with adepreciation rate of 15%. No. Affiliates counts the total (patenting and non-patenting) number of affiliates that areheld by firm.

Variable # Obs. Mean Std. Dev. 10th 50th 90th

Patents Count 15,892 25 100 0 2 48

Patents Cites-Weighted 15,892 20 88 0 0.8 31.0

Patents Stock t-1 15,892 145 601 0.7 13.2 243.3

Dec. Patents Stock t-1 15,892 18 96 0 0.1 24.7

Cen. Patents Stock t-1 15,892 106 506 0 4.9 155.0

Share Decentralized t-1 15,892 0.35 0.45 0 0 1.0

Share In-House Attorneys t-1 15,892 0.31 0.41 0 0.02 0.99

Share M&A t-1 15,892 0.25 0.40 0 0 1.0

Total Diversification 1,491 5.9 15 0.7 2.7 10.8

Unrelated Diversification 1,491 1.76 1 0 1.79 3.09

Related Diversification 1,491 4.1 15 0 0.9 7.9

Technological Concentration 1,491 0.33 0.28 0.1 0.24 0.80

Geographical Concentration 1,491 0.20 0.27 0 0.10 0.54

Generality 404,160 0.57 0.32 0 0.66 0.99

Originality 433,770 0.27 0.28 0 0.22 0.67

Citations per Patent 595,396 8.8 15.9 0 4 22

TABLE 2. Summary Statistics for PatentsDistribution

Notes: This table provides summary statistics for key patent variables used in the econometric analysis. Patents Cites-Weighted normalizes the number of patents a firm receives by the ratio between the number of citations the patentreceives and the average number of citations received by all patents granted in the same year. Patents Stock iscitations-weighted and is computed using the perpetual inventory method with a depreciation rate of 15%.Decentralized (Dec. ) and centralized (Cen. ) patents refer to whether they were assigned to an affiliate orheadquarters, respectively. Share Decentralized divides a firm's total number of affiliate-assigned patents by its totalnumber of patents. Share In-House Attorneys is the share of patents that were filed by in-house attorneys. Theremaining patents were managed by third-party attorneys. Share M&A is the share of patents that are acquired througha merger or an acquisition. Total , Unrelated , and Related Diversification are computed as the Entropy measure usingpatent US technology class codes. Generality is the HHI measure of concentration of the citations a patent receivesacross three-digit U.S. class. Generality and Citations per Patent are at the patent-level. Technological Concentration is the Herfindahl-Hirschman Index (HHI) of patent concentration across three-digit U.S. technology class.Geographical Concentration Herfindahl-Hirschman Index (HHI) of patent concentration across American cities.

Share Decentralized

Share In-House Attorneys Share M&A Sales Patents Stock R&D Stock

Share Decentralized 1.00

Share In-House Attorneys -0.07 1.00

Share M&A 0.39 0.02 1.00

Sales -0.01 0.14 0.05 1.00

Patents Stock -0.10 0.16 -0.03 0.26 1.00

R&D Stock -0.10 0.15 0.04 0.42 0.51 1.00

Notes: This table reports the correlation matrix between our key variables. Share Decentralized divides a firm's total number of affiliate-assigned patents by its total number of patents. Share In-House Attorneys is the share of patents that are managed by in-house attorneys. Theremaining patents are managed by third-party attorneys. Share M&A is the share of patents that are acquired through a merger or anacquisition.

TABLE 3. Correlations

Firms:

ln(Dec. Patents stock )t-1 0.106 *** 0.086 ***(0.016) (0.015)

ln(Cen. Patents stock )t-1 0.023 0.019(0.014) (0.014)

Share Decentralized t-1 0.237 *** 0.182 *** 0.099 * 0.273 *** 0.182 ***(0.054) (0.050) (0.063) (0.071) (0.049)

ln(1 + No. Affiliates ) 0.033 ** 0.034 ** 0.040 * 0.025 0.035 **(0.017) (0.017) (0.024) (0.023) (0.017)

ln(1+Patents stock )t-1 0.058 *** 0.052 *** 0.026 0.082 *** 0.056 ***(0.016) (0.015) (0.033) (0.023) (0.015)

ln(R&D stock )t-1 0.043 ** 0.026 0.041 ** 0.023 0.039 ** 0.028 0.026(0.018) (0.017) (0.018) (0.017) (0.020) (0.031) (0.017)

ln(Assets )t-1 0.824 *** 0.444 *** 0.824 *** 0.441 *** 0.506 *** 0.333 *** 0.442 ***(0.020) (0.030) (0.020) (0.030) (0.038) (0.048) (0.030)

ln(Sales )t-1 0.422 *** 0.425 *** 0.374 *** 0.508 *** 0.423 ***(0.030) (0.030) (0.037) (0.046) (0.030)

Four-digit SIC dummies

Year dummies

R2

Observations

Exc. largest patentors

(6)

14,238

0.828 0.815

15,89214,396

(4)(2)

All

Dependent variable: ln(Market Value)

(3)

Yes

Yes

Yes

Yes

Patents > Median

Yes

Yes

(5)

15,892

(7)

TABLE 4. Decentralization and Market Value

0.816

Yes

Yes

(1)

Yes

0.826

All

7,19814,396

Yes

Yes

0.851

7,198

All

Yes

AllPatents ≤ Median

0.817

Yes

Yes

0.828

Notes: This table reports OLS estimation results of the effect of patent decentralization on firm market value. The level of analysis is firm-year. Centralized and Decentralized patents refer to whether they were assigned to an affiliate or headquarters, respectively. Share Decentralized divides a firm's total number of affiliate-assigned patents by its total number of patents. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

Dependent variable:

Share Decentralized t-1 0.224 *** 0.049 0.359 *** 0.165 *** 0.251 *** 0.193 *** 0.168 *** 0.308 ** 0.265 *** 0.245 ***(0.049) (0.071) (0.070) (0.070) (0.059) (0.063) (0.051) (0.050) (0.051) (0.050)

Share M&A t-1 0.158 *** 0.194 ***(0.053) (0.054)

ln(1 + No. Affiliates ) 0.010 0.101 *** 0.018 0.053 ** -0.003 0.004 0.007 0.008 0.003 0.002 ***(0.016) (0.035) (0.056) (0.028) (0.017) (0.018) (0.016) (0.014) (0.017) (0.011)

Patents stock t-1/Assets t-1 0.004 ** 0.007 ** 0.001 0.004 ** 0.008 *** 0.003 0.004 ** 0.024 ** 0.016 *** 0.023 ***(0.002) (0.003) (0.002) (0.002) (0.002) (0.002) (0.002) (0.005) (0.005) (0.005)

R&D stock t-1/Assets t-1 0.040 *** 0.047 *** 0.107 *** 0.047 *** 0.217 *** 0.063 *** 0.051 *** 0.124 ** 0.119 *** 0.122 ***(0.009) (0.010) (0.030) (0.009) (0.035) (0.014) (0.010) (0.011) (0.012) (0.011)

ln(Sales )t-1 -0.015 0.019 -0.034 0.026 -0.101 *** -0.009 ** -0.015 -0.028 ** -0.027 *** -0.028 ***(0.013) (0.017) (0.023) (0.023) (0.025) (0.014) (0.012) (0.008) (0.007) (0.008)

Four-digit SIC dummies

Year dummies

R2

Observations

Yes

7,217 7,179

ln(Market Value ), OLS

Sales ≤ median

12,109

All

Yes Yes

0.348 0.377

Yes

Yes

Yes

Yes

0.311

7,198

0.214

14,396

Yes

All

14,396

0.213

12,109

All

0.303

TABLE 5. Robustness Checks for Firm Value

(1) (6)(4) (10)(5) (9)

0.216

Yes

(2) (3)

No. Affiliates ≤

median

No. Affiliates >

median

Yes

Yes YesYes

(8)

Exc. M&A patents

14,396

0.302

14,396

ln(Tobin's Q ), NLLS

Yes

Yes

Exc. M&A patents

Yes

0.382

Yes

7,198

(7)

All

Yes

Yes

0.305

Sales > median

Yes

Notes: This table reports robustness checks for the effect of patent decentralization on firm market value. Share M&A is the share of patents that were acquired through a merger or acquisition. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

log(1+Citations) -0.005 *** -0.006 *** -0.007 *** -0.006 *** 0.004 ***-(0.001) (0.001) (0.001) -(0.001) -(0.001)

Generality -0.023 *** -0.023 *** -0.018 *** -0.016 *** -0.009 ***(0.002) (0.002) (0.002) (0.002) (0.001)

Originality -0.009 *** -0.009 *** -0.010 *** -0.012 *** -0.002(0.002) (0.002) (0.002) (0.002) (0.001)

Dummy for M&A 0.272 *** 0.263 *** 0.449 ***(0.001) (0.001) (0.001)

Diversity 0.007 ***(0.002)

Average Patents Stock -0.011 ***(0.001)

Average Sales -0.012 ***(0.001)

Dummy for Biotechnology 0.015 *** 0.012 * -0.012 *** -0.022 *** -0.024 ***(0.006) (0.007) (0.005) (0.005) (0.004)

Dummy for Chemicals -0.018 *** -0.017 *** -0.010 *** -0.004 -0.013 ***(0.004) (0.004) (0.003) (0.003) (0.003)

Dummy for Telecommunications -0.098 *** -0.089 *** -0.058 *** -0.047 *** 0.024 ***(0.004) (0.005) (0.004) (0.004) (0.003)

Dummy for Electronics -0.104 *** -0.096 *** -0.073 *** -0.072 *** -0.008 ***(0.004) (0.004) (0.003) (0.003) (0.003)

Dummy for Semiconductors -0.273 *** -0.291 *** -0.181 *** -0.169 *** -0.008 ***(0.005) (0.006) (0.004) (0.004) (0.003)

Dummy for Information Technology -0.191 *** -0.186 *** -0.132 *** -0.115 *** 0.001(0.004) (0.005) (0.003) (0.003) (0.003)

Dummy for Engineering -0.025 *** -0.017 *** -0.005 -0.013 *** -0.014 ***(0.004) (0.004) (0.003) (0.003) (0.003)

R2

Observations

All

(5)

Within-firms

(4)

AllAt least one

cite

Dependent variable: Dummy for Decentralization

All

TABLE 7. Patent Decentralization

(1) (3)

0.180

595,396

0.191

595,396 595,396

0.577

(2)

595,396 467,672

0.037 0.036

Notes: This table reports the estimation results of a linear probability model that examines the determinantsof decentralization. The base technology area in column 5 is Pharamcueticals . We assign specific codes, and include respective dummy variables (columns 2-5), for patents that receive or make less than two citations. All columns include an unreported Other technology category. Column 5 repors OLS results of a Linear Probability Model. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation. ***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

Dependent variable:

Share Decentralized t-1 -0.157 *** -0.095 -0.163 *** -0.205 *** -0.182 *** -0.218 ***-(0.061) -(0.101) -(0.067) -(0.066) -(0.060) -(0.065)

ln(1 + No. Affiliates ) 0.015 0.079 ** -0.012 0.010 -0.007 -0.011(0.020) -(0.039) (0.022) (0.020) (0.019) (0.019)

ln(Patents stock )t-1 0.244 *** 0.183 *** 0.246 *** 0.245 *** 0.221 *** 0.222 ***-(0.018) -(0.050) -(0.025) -(0.018) -(0.017) (0.017)

ln(Sales )t-1 0.673 *** 0.638 *** 0.679 *** 0.672 *** -0.272 *** -0.273 ***(0.024) (0.034) (0.029) (0.024) (0.023) (0.022)

Share M&A t-1 0.124 * 0.092(0.068) (0.066)

Four-digit SIC dummies

Year dummies

R2

Observations

0.861

10,987 4,126 6,861

0.745

Yes

(5)

All

(3)

AllPatents ≤

medianPatents >

median

(1)

0.836

10,987

Yes

0.835

Yes

Yes

ln(R&D )

All

Yes

YesYes Yes

Yes

Yes

0.681

Yes

0.681

TABLE 9. Research and Development

All

(6)

ln(R&D /Sales )

(2) (4)

10,987

Yes

10,987

Notes: This table reports the estimation results of the relation between R&D expenditure, patenting activity, and decentralization. Columns 4-6 include a dummy variable for observations where patent count (cites-weighted) equals zero. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

Patents:

Firms:

Share Decentralizedt-1 -0.183 *** -0.151 ** -0.150 ** -0.214 *** -0.177 *** -0.177 ***(0.058) (0.066) (0.066) -(0.056) -(0.061) -(0.060)

ln(1 + No. Affiliates) 0.087 *** 0.091 *** 0.090 *** 0.097 *** 0.101 *** 0.100 ***(0.025) (0.025) (0.025) (0.024) (0.024) (0.024)

ln(R&D Stock)t-1 0.325 *** 0.325 *** 0.315 *** 0.272 *** 0.272 *** 0.260 ***(0.024) (0.024) (0.024) (0.022) (0.022) (0.021)

ln(Sales)t-1 0.181 *** 0.182 *** 0.179 *** 0.156 *** 0.157 *** 0.154 ***(0.021) (0.021) (0.021) (0.020) (0.020) (0.020)

Share M&A t-1 -0.091 -0.075 -0.102 ** -0.083(0.077) (0.076) (0.073) (0.073)

Four-digit SIC dummies

Year dummies

R2

Observations

0.625

All

Yes

Yes

Yes

0.566

Yes

14,396

Yes

Yes

Yes

Yes

(1)

All

(4)

All

Yes

(2)

Yes

Yes

TABLE 10. Patenting

(6)

All

(3)

Dependent variable: ln(1+Patents)

Count Cites-weighted

(5)

Exc. largest

patentors

Exc. largest

patentors

14,396

0.625

14,238

Yes

0.608

14,23814,396

0.586

14,396

0.586

Notes: This table reports the estimation results of the relation between R&D expenditure, patenting activity, and decentralization. Columns 4-6 include a dummy variable for observations where patent count (cites-weighted) equals zero. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

Firms:

ln(Decn. Patents stock )t-1 0.016 *** 0.013 ***(0.003) (0.003)

ln(Cen. Patents stock )t-1 0.005 0.003(0.003) (0.003)

Share Decentralized t-1 0.048 *** 0.042 *** 0.053 *** 0.029 ** 0.042 ***(0.010) (0.010) (0.015) (0.014) (0.010)

ln(1 + No. Affiliates ) 0.021 *** 0.021 *** 0.027 0.022 *** 0.021 ***(0.004) (0.004) (0.005) (0.005) (0.004)

ln(R&D Stock )t-1 0.011 *** 0.011 *** 0.012 *** 0.012 *** 0.013 *** 0.017 * 0.011 ***(0.005) (0.005) (0.005) (0.005) (0.005) (0.011) (0.005)

ln(1+Patents stock )t-1 0.009 *** 0.007 ** -0.004 *** 0.015 *** 0.007 **(0.003) (0.003) (0.009) (0.005) (0.003)

ln(Sales )t-1 -0.095 *** -0.099 *** -0.095 *** -0.099 *** -0.136 *** -0.062 ** -0.099 ***(0.018) (0.018) (0.018) (0.018) (0.026) (0.027) (0.018)

Four-digit SIC dummies

Year dummies

R2

Observations

Yes

0.144

All All

Yes Yes

Patents ≤ Median

Yes

TABLE 11. Sales Growth

(1) (2) (7)(5)(3) (4)

All All

Yes

7,198

Yes Yes

0.134

Yes

0.107 0.111

Dependent variable: Δln(Sales )t-1

0.106 0.111

Exc. largest patentors

Yes

Yes Yes

(6)Patents > Median

Yes

14,396 14,396

0.111

Yes

Yes

7,198 14,23814,396 14,396

Notes: This table reports the OLS estimation results of the effect of patent decentralization on firm sales growth. Centralized and Decentralized patents refer to whether they were assigned to an affiliate or headquarters, respectively. Share Decentralized divides a firm's total number of affiliate-assigned patents by its total number of patents. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

Firms:

Share Decentralized t-1 ×         

Total Diversification 0.011 ***(0.004)

Share Decentralized t-1 × 

Unrelated Diversification 0.140 ***(0.047)

Share Decentralized t-1 × Related Diversification 0.011 ***

(0.005)

Share Decentralized t-1 × 

Technical Concentration Index 0.011 *** -0.245 ***(0.005) -(0.102)

Share Decentralized t-1 × 

Geography Concentration Index 0.011 *** -0.598 ***(0.005) -(0.191)

Share Decentralized t-1 0.047 0.359 *** 0.117 ** -0.096 0.139 *** 0.251 *** 0.277 ***(0.112) (0.086) (0.057) (0.108) (0.053) -(0.069) (0.057)

Total Diversification -0.003 **(0.001)

Unrelated Diversification -0.081 *(0.043)

Related Diversification -0.003 **(0.001)

Technology Concentration Index -0.003 ** 0.213(0.001) -(0.140)

Geography Concentration Index -0.003 ** 0.204(0.001) -(0.129)

R2

Observations

Specia-lized

Diver-sified All All

0.869

3,933

0.828

14,396

0.8280.806

3,957

TABLE 12. Diversity

Dependent variable: ln(Market Value ), OLS

(1) (2) (3) (4) (5) (7)

14,396

0.828

14,396

0.828

14,396

(6)

All

0.828

14,396

AllAll

Notes: This table examines how the effect of decentralization on firm value varies with firms' technological diversity. Total, Unrelated, and Related Diversification are computed as the Entropy measure using patent IPC codes. Firms are classified as specialized (column 1) or diversified (column 2) based on the 1st and 4th quartile of Total Diversification , respectively. All regressions include the following additional controls: ln(R&D Stock ), ln(Assets ), ln(Sales ) and ln(1+ No. Affiliates ). All regressions include complete sets of three-digit US SIC code and year dummies. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

Dependent variable:

Firms:

Share In-House Attornys t-1 -0.133 *** -0.126 *** -0.128 *** 0.059 ** 0.289 *** 0.253 *** -0.013(0.050) (0.050) (0.050) (0.031) (0.055) -(0.063) (0.010)

Share Decentralized t-1 0.182 *** 0.182 *** -0.119 ** -0.171 *** -0.213 *** 0.041 ***(0.050) (0.050) (0.060) (0.047) (0.056) (0.010)

ln(1 + No. Affiliates ) 0.033 ** 0.034 ** 0.015 0.072 *** 0.098 *** 0.021 ***(0.017) (0.017) (0.019) (0.021) (0.024) (0.003)

ln(Patents stock )t-1 0.052 *** 0.054 *** 0.057 *** 0.190 *** 0.007 **(0.015) (0.015) (0.015) (0.017) (0.004)

ln(R&D stock )t-1 0.025 0.028 0.031 * 0.245 *** 0.264 *** 0.012 ***(0.018) (0.018) (0.018) -(0.021) -(0.022) (0.005)

ln(Assets )t-1 0.435 *** 0.436 *** 0.438 *** 0.401 *** 0.052 ***(0.031) (0.031) (0.031) (0.039) (0.017)

ln(Sales )t-1 0.443 *** 0.429 *** 0.427 *** 0.317 *** 0.143 *** 0.153 *** -0.099 ***(0.031) (0.030) (0.031) (0.042) (0.017) (0.020) (0.018)

Four-digit SIC dummies

Year dummies

R2 0.828

Observations

0.115

14,396

(6)

Patents, Cites-

weighted

All

Yes

Yes

0.590

14,396

Sales Growth

(4) (5)

Yes

All

Yes

Yes

All

Patents, Count

Yes

0.850 0.742

ln(R&D )

14,39610,987

0.827

14,238

All

Yes

Yes

TABLE 13. Attorneys

Exc. largest patentors

(2) (7)

Yes Yes

Yes

ln(Market Value ), OLS

(1) (3)

All

14,396

Yes

14,396

0.827

All

Yes

Yes

Note: This table examines the relationship between the use of in-house patent attorneys and firm market value.Share In-House Attorneys is the share of patents that were filed by in-house attorneys. The remaining patents werefiled by third-party attorney. The dependent variable in columns 7-8 is ln(1+patents) . We assign a code to yearswhere no patents are granted, and include a dummy variable that equals one for these observations. Standard errors(in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms.***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

Dependent variable: ln(R&D)

Dummy for Positive Assignment 0.175 ***(0.050)

Concentration of Assignment -0.414 ***(0.093)

Dummy 0<Share Assigned≤0.2 0.110 * 0.118 0.618 *** 0.573 *** 0.030 ***(0.064) (0.079) (0.084) (0.080) (0.012)

Dummy 0.2<Share Assigned≤0.8 0.256 *** -0.097 0.323 *** 0.224 *** 0.053 ***(0.068) (0.088) (0.083) (0.080) (0.012)

Dummy Share Assigned>0.8 0.173 *** -0.197 ** 0.062 0.048 0.044 ***(0.063) (0.084) (0.074) (0.068) (0.013)

ln(1 + No. Affiliates ) 0.031 * 0.019 0.028 * 0.015 0.057 ** 0.071 *** 0.019 ***(0.017) (0.018) (0.018) (0.020) (0.024) (0.023) (0.003)

ln(Patents stock )t-1 0.040 *** 0.039 *** 0.042 *** 0.237 *** 0.005(0.015) (0.015) (0.015) (0.018) (0.003)

ln(R&D stock )t-1 0.018 0.024 0.022 0.302 *** 0.251 *** 0.011 ***(0.017) (0.017) (0.017) (0.023) (0.021) (0.004)

ln(Assets )t-1 0.444 *** 0.456 *** 0.448 *** 0.057 ***(0.030) (0.030) (0.030) (0.018)

ln(Sales )t-1 0.422 *** 0.406 *** 0.417 *** 0.670 *** 0.172 *** 0.149 *** -0.106 ***(0.031) (0.031) (0.031) (0.024) (0.020) (0.019) (0.021)

R2

Observations 14,39614,396 14,396

Δln(Sales )t-1

0.5970.638 0.111

ln(Market Value )

14,396 14,396

0.828

14,396

0.828 0.836

10,987

0.828

(1) (3) (7)Patents, Count

TABLE 14: Alternative Measures of Assignments

(2) (5)(7)Patents, Cites-

Dependent variable: ln(Market Value), OLS

(5)

Note: This table examines the robustness of our key results for alternative measures of patent assignment. Dummy forPositive Assignment receives the value of one for firms that assign at least one of their patents to an affiliate (and zero forfirms that assign all their patents to headquarters). Concentration of Assignment is the HHI measure of concentration of patentassignment across the organization affiliates and headquarters. We also include dummies for low, medium, and high ShareAssigned (less than 20%, 20% to 80%, and over 80%, respectively). All regressions include complete sets of three-digit U.S.industry code and year dummies. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serialcorrelation through clustering by firms. ***, **, * denote significance levels of 1, 5 and 10 percent, respectively.

Dependent variable:

Firms:

ln(Dec. Patents stock )t-1 0.078 ***(0.014)

ln(Cen. Patents stock )t-1 0.013(0.013)

Share Decentralized t-1 0.181 *** 0.183 *** -0.111 ** -0.185 *** -0.210 *** 0.038 ***(0.048) (0.048) (0.058) (0.057) (0.056) (0.010)

ln(1 + No. Affiliates ) 0.029 * 0.029 * 0.030 * 0.009 0.085 *** 0.091 *** 0.020 ***(0.017) (0.017) (0.017) (0.019) (0.025) (0.025) (0.003)

ln(Patents stock )t-1 0.052 *** 0.055 *** 0.193 *** 0.008 **(0.015) (0.015) (0.017) (0.004)

ln(R&D stock )t-1 0.025 0.022 0.025 0.340 *** 0.274 *** 0.010 ***(0.017) (0.017) (0.017) (0.025) (0.012) (0.004)

ln(Assets )t-1 0.457 *** 0.452 *** 0.454 *** 0.399 *** 0.059 ***(0.030) (0.030) (0.031) (0.038) (0.018)

ln(Sales )t-1 0.414 *** 0.416 *** 0.414 *** 0.321 *** 0.184 *** 0.157 *** -0.106 ***(0.032) (0.031) (0.031) (0.042) (0.024) (0.024) (0.021)

Four-digit SIC dummies

Year dummies

R2 0.828

Observations

Yes

0.625

14,396

(5)

Patents, Count

All

Yes

14,396

Sales Growth

0.110

14,396

Yes

Yes

Patents, Cites-

weighted

All

Yes

(7)

AllAll

ln(Market Value ), OLS ln(R&D )

(4)(1) (6)

Yes

All

14,396

Yes

Exc. largest patentors

Yes

0.826

14,238

0.586

TABLE 15: Reassignment

Yes

Yes

(2)

Yes

Yes

(3)

10,987

Yes

0.849

Yes

14,396

0.828

All

Notes: This table examines the robustness of our key results to post-grant change in assignment. For each patent inour sample we track its assignment history. We flag patents that have been reassigned, and determine whether the newassignment reflects decentralization (a reassignment from headquarters to an affiliate), or centralization (areassignment from an affiliate to headquarters). Standard errors (in brackets) are robust to arbitrary heteroskedasticityand allow for serial correlation through clustering by firms. ***, **, * denote significance levels of 1, 5 and 10percent, respectively.