Research Policy 34 (2005) 287–303

The disaggregated technology production function: A new

model of university and corporate research

Gregory Tassey ∗

National Institute of Standards and Technology, 100 Bureau Drive Stop 1060, Gaithersburg, MD 20899-1060, USA

Received 4 August 2004; received in revised form 21 November 2004; accepted 6 January 2005

Available online 23 March 2005

Abstract

Technology and the process that produces it, research and development (R&D), are typically characterized as homogeneous

entities. In reality, the typical industrial technology is composed of three elements: a generic technology base, supporting

infratechnologies, and proprietary market applications (innovations). The first two have public good characteristics, and therefore,

explicitly modeling them is essential for public policy purposes. The fundamental relationships among these elements require

a technology production function that captures the supporting roles of the public good elements in creating proprietary applied

technology. These critical quasi-public technology goods are supplied to a significant extent by exogenous (external) sources:

central corporate research labs, government labs, and increasingly, universities. The expanding university role beyond basic

research complicates the structure and functioning of the national R&D establishment and increases the need for a more accurate

model of technological change to better inform R&D policy.

Moreover, in assessing the resulting applied technology’s impact on economic growth, both the general and partial equilibrium

literatures enter the technology variable into a production function with the common “production” assets (physical capital and

labor). Such models obscure an important distinction between technology and these production assets—namely, the fact that

technology is primarily a “demand-shifting” asset. As such, its role is correctly specified only when combined with the other

major demand-shifting asset, marketing. Allocations to these two assets vary across competing firms implying a spatial model of

competition, while still providing traceability to the exogenous sources of public good technology elements, such as universities.

Published by Elsevier B.V.

Keywords: Public good technologies; Research and development (R&D); Investment risk; Market failure; Knowledge production function

Both macroeconomic and microeconomic growth

models have made technology an endogenous explanatory variable. However, the vast majority of this literature has treated technology and the process that creates

∗ Tel.: +1 301 975 2663.

E-mail address: tassey@nist.gov.

it, research and development (R&D), as homogeneous

entities. Only a few efforts have attempted even a partial disaggregation, which have consisted of separating

scientific research from technology research.

In reality, the typical industrial technology consists

of several private and quasi-public elements. The failure to disaggregate the technology variable based on

0048-7333/$ – see front matter. Published by Elsevier B.V.

doi:10.1016/j.respol.2005.01.012
288 G. Tassey / Research Policy 34 (2005) 287–303

the distinctly different character of each element and

its associated unique investment incentives has limited economists’ ability to explain R&D investment

behavior and the subsequent relationships with economic growth. These limitations significantly reduce

the effectiveness of R&D policy.

The policy analysis problem has become more demanding in recent years for several reasons: (1) corporate laboratories have reduced their share of national

spending on the quasi-public elements, in particular,

early-phase research on new, radical technologies; (2)

government spending on such research has been erratic and highly skewed toward a few technologies tied

to specific social objectives; (3) universities have assumed a larger role in such early-phase technology research, with implications for intellectual property (IP)

and research portfolio management.

University conduct of early-phase technology research has become increasingly important, as it has

gown substantially over the past several decades in response to a declining corporate role and the 1980 BayhDole Act. This legislation enabled the assignment of IP

rights to universities who conduct federally funded research, and such ownership has created large revenue

opportunities. However, it has also led to disputes with

industry over control of and access to the IP and relationships with spin-offs/start-up firms.1

At the same time, the shortening of technology life

cycles due to rapidly expanding research capabilities

and R&D investment in Asia and Europe have created

an imperative to better understand the R&D investment

process, so that public policy can better match incentives and research capabilities for the participants in

each phase of the R&D cycle. The ascending role of

the university in the early phases of technology research complicates the R&D establishment structure

and hence the management of government funding

policies.

To better understand the R&D policy requirements

for dealing with such complexity, industrial technology

and the degree to which existing R&D growth models

are inadequate are assessed in the next three sections. A

model is then developed in Section 4 for representing

both public and private elements of an industrial technology in the same technology output function. Section

1 See, for example, Lerner (2005) and Mowery and Sampat (2005).

5 presents a framework for using such a model to describe the risk profile of the typical R&D cycle and

thereby focus policy analysis. Finally, Section 6 develops a performance function in which the economic

impact of technology output can be assessed in a spatial

model of competition.

1. The complexity of industrial technology

Technology is far from a homogeneous “black

box”, as implicitly characterized in much of the economics literature. In contrast, each industrial technology should be described in terms of three major elements: a fundamental or “generic” technology base,

proprietary technologies (market applications) derived

from the generic technology, and a set of “infratechnologies” that facilitate the development and utilization

of the other two elements.

1.1. Public good technology elements

The enabling role of generic technologies for the

development of market applications (innovations) has

been discussed qualitatively (Link and Tassey, 1987;

Tassey, 1991, 1997, 2005; Nelson, 1992).2 Similarly,

Dosi (1982, 1988) defines a “technology paradigm”,

which is portrayed as a “pattern” of solutions of selected technoeconomic problems based on highly selected principles derived from the natural sciences.

Such “highly selected principles” form a generic technology base from which market applications are drawn.

A generic technology provides in essence a “proof-ofconcept”, which reduces risk sufficiently to enable applied R&D investments to be rationalized.3

2 A generic technology is not the same thing as a “general purpose

technology” (Bresnahan and Trajtenberg, 1995). The latter refers to

a (homogeneous) technology with multiple market applications (i.e.,

economies of scope), a distinctly different concept from the generic

base upon which a particular technology is developed.

3 The classic example of a generic product technology is Bell labs’

proof in the late 1940s and early 1950s of the concept that the principles of solid state physics could be used to construct a semiconductor

switch or amplifier, resulting in the creation of the transistor (Nelson,

1962). The best known example of a generic systems technology is

the Internet. As a system (the communications network), technological advances were first required in its major underlying network

technologies, such as queuing theory, packet switching, and rout-
G. Tassey / Research Policy 34 (2005) 287–303 289

Infratechnologies are the other quasi-public technology good. They have a strong infrastructure and

hence public good character. Infratechnologies include

research tools (measurement and test methods), scientific and engineering data, the technical bases for interface standards, quality control techniques, etc. Collectively, they constitute the technical infrastructure of an

industry and are ubiquitous across technology-based

economic activity. Infratechnologies often are implemented as industry standards (Tassey, 1982, 1997).4

Both generic technology and infratechnology elements are drawn upon by competing firms. However,

although attainment of partial property rights is possible, spillovers and other sources of market failure

are prominent. The resulting underinvestment varies

across technologies and over each technology’s life cycle, which complicates public policy responses. Nevertheless, every industrialized nation provides funds to

leverage generic technology and infratechnology research and assimilation, thereby underscoring recognition of the public good content, even though identifying

and measuring this content remains difficult (Tassey,

2005).5

Several decades of economic studies have attempted

to provide the needed explanatory models. At the national economy level, theories of endogenous technological change (Romer, 1990) have acknowledged

the existence of excludable and non-excludable (public good) elements of the typical stock of technology.

ing. Demonstration of such in the 1960s led to prototype networks

in the 1970s (ARPANET) and 1980s (NSFNET), which eventually

led to the Internet. See National Research Council (1999, p. 169).

Occasionally, a generic technology can take the form of a “method

of inventing”. Examples are methods for manufacturing hybrid corn

seeds and research methods for developing nanotechnologies (Darby

and Zucker, 2003). 4 Note that infratechnologies are part of an industry’s technology

base in contrast to what are referred to as “infrastructure technologies”. The latter are produced by industries (electricity, transportation, and communications) whose primary role is to provide services

to other industries.

5 The National Science Foundation disaggregates R&D data into

“basic research”, “applied research”, and “development”. Unfortunately, this taxonomy only partially captures the quasi-public good

character of the several targeted elements of R&D. The reason seems

to be that the U.S. and other national classification systems somewhat

arbitrarily define the phases of R&D, instead of first defining the intrinsic targeted elements of industrial technology based on unique

investment incentives and then defining the process phases that produce these targeted elements.

Microeconomic models have partially revealed the heterogeneous nature of the typical industrial technology

and related them to various output measures (Griliches,

1995). However, the few efforts at disaggregation have

focused exclusively on the distinction between scientific research and applied R&D.

1.2. Technology trajectories and life cycles

In addition to assessments of static representations

of industrial technology, the analyst must also be concerned with the dynamics of technology-based growth

(i.e., the process of creative destruction). Here, the literature has characterized technological change by “trajectories” or directions of market applications (Nelson

and Winter, 1982; Dosi, 1982, 1988; Achilladelis and

Antonakis, 2001). The implication is that the nature of

the underlying generic technology and the subsequent

evolution of a supporting infrastructure (infratechnologies) combine to determine the direction of subsequent

market applications, which is often assumed to be linear for some time into the future.

However, portraying these trajectories as linear,

steady-state expansion paths of a homogeneous technology is incomplete. Rather than linear, such trajectories display cyclical patterns, which are reflected in

shifts in the composition of R&D over time in response

to the evolving generic technology and its eventual obsolescence. Technological opportunity therefore also

changes. Disruptive technologies, based on radically

new generic technologies, spawn cascades of more incremental advances as firms enter the nascent industries

to apply the generic technology and achieve economies

of scale and scope. The early part of a technology’s life

cycle is correctly portrayed as being characterized by

high risk but also increasing returns (at least at the industry level), as the proof-of-concept effect (i.e., the

generic technology) unleashes market applications.6

6 The technology life cycle based on a generic technology, which

is the focus here, is typically one cycle within a longer “technology

wave” driven by periodic major advances in science (Kondratiev,

1925; Kuhn, 1962; Freeman, 1973; Graham and Senge, 1980). For

example, advances in solid state physics led to a wave of generic

digital electronic technologies. In the other direction, a technology

trajectory or life cycle of a generic technology is characterized by

successive product cycles (Utterback, 1979). For example, a PC is a

product cycle within a generic digital computing trajectory.
290 G. Tassey / Research Policy 34 (2005) 287–303

Within the generic technology’s life cycle, major

technological opportunities decline over time as

the set of potential applications of the underlying

knowledge base is exhausted. Competition then shifts

to incremental product improvements tied to shorter

times to commercialization and to process innovations

that focus on reducing cost as the basis of competition.

However, while such an evolutionary pattern might imply decreasing returns to the original stock of generic

technology after the initial market penetration, a successful technology achieves substantial efficiencies,

such as economies of scale in production and distribution, economies of scope in markets penetrated, evolution of efficiency-enhancing infratechnologies and

associated standards, and general learning economies.

These factors leverage returns to incremental improvements and thereby delay the onset of decreasing

returns.

1.3. Implications for a new model

Several concepts relevant for model development

emerge from this discussion: (1) the typical technology consists of several discrete elements that respond

to distinctly different investment incentives; (2) the elements interact over a technology’s life cycle to influence individual firms’ and entire industries’ growth

paths; (3) the technology elements exhibit different degrees of public good content, which has significance for

investment behavior and hence for the sources of funding and conduct of various types of R&D. Therefore, to

better understand how technology drives growth within

this life cycle context and the complementary roles of

industry, universities, and government, a more disaggregated view of technology and its evolutionary impact on economic growth is necessary. The next section

assesses the characterization of technology in several

alternative technology-based growth models.

2. Defining the technology variable

At the macroeconomic level, Solow’s (1956) model

defined a steady-state growth path for an economy in

terms of an optimal capital–labor ratio determined by

an exogenous stock of technology. For more than three

decades, this model governed economic growth analysis, until important articles by Romer (1990) and Jones

(1995, 2002) turned the role of technology completely

around by making the stock of technology endogenous

to an economy’s production function.

If the supply of technology is completely exogenous

to an economic system, as in Solow-type growth models, its source is presumably government, with the implication that technology is a pure (and homogeneous)

public good. Such models show how an equilibrium

capital–labor ratio is attained and how a steady-state

growth path based on an implied level of technology

is determined. The introduction of additional technology (embodied in capital) will increase the equilibrium

capital–labor ratio over time.

However, the source of the technology and the process by which it increases productivity are not specified. Hence, R&D investment incentives cannot be considered. Conversely, endogenous growth theory treats

technology as completely determined within the private

economy, implying a purely endogenous research and

development decision process. While the latter theory

allows for spillovers among private economic agents,

no interaction among donors and recipients of technology capital is specified.

Reality is a combination of the two models. That

is, in the modern “mixed” economy, investments are

made by both the public and private sectors, resulting

in a stock of technology that has a “quasi-public” good

character. The problem is to separate the private and

quasi-public technology elements according to unique

sets of investment incentives and define their economic

functions.

The investment incentives dimension is becoming

increasingly important for public policy. Companies

obviously invest in applied technology from which

they can sufficiently exclude others by using patents,

secrecy, and other appropriation mechanisms. Many

firms and increasingly universities invest in partially

excludable generic technology, which they expect will

diffuse or spillover to some degree and at some rate

but yet still allow some degree of property rights to be

maintained.7

7 A rationale for this strategy is the fact that the rate of spillover

is neither instantaneous nor costless. For example, Mansfield et al.

(1981) estimate from survey data that imitating an innovation by

another firm could cost as much as 50–75% of the cost of that innovation. Nelson (1992) observes, based on empirical studies of strategies for appropriating the results of R&D in Levin et al. (1987),
G. Tassey / Research Policy 34 (2005) 287–303 291

However, such next generation and especially

radical technology research has become increasingly

difficult for individual companies to rationalize. The

main impact of spillovers is a lower expected return

on investment (ROI), including reduced potential for

the increasing returns benefit for first movers put forth

by Arrow (1962) and others. In companies, generic

technology research usually takes place in a central corporate research lab, which provides proof-of-concepts

(generic technologies) to the firm’s line-of-business

units, which then conduct applied R&D leading to

market innovations. Central research budgets have

been reduced relative to line-of-business budgets in

recent years. Even this trend probably understates the

reduction in private sector investment in generic technologies, as many firms now use researchers in their

central labs to assimilate generic technologies from external sources; i.e., they create inward spillovers from

other company, government, or university sources,

as opposed to actually conducting breakthrough

research.

As a partial substitute, universities are increasingly

conducting generic technology research as they receive more funding from both industry and government sources. This funding is designed to compensate

for the relatively high risk of early-phase technology

research and the substantial spillovers that characterize

the research results. As universities can now own the

IP from government-funded research, patents from new

generic technologies can provide a significant source of

income. Moreover, many state universities have been

pushed in the direction of generic technology research

by state governments as part of economic development

strategies.

The microeconomics literature has partially recognized the need for a disaggregated technology framework to address these phenomena but has not progressed beyond a dichotomous model in which technology is separated into scientific and technological

stocks of knowledge. In such models, scientific information is appropriately characterized as a pure public

good (Nelson, 1959) with exogenous sources of supply acknowledged. However, technological knowledge

that letting generic technology spillover does not provide a disincentive to conduct the research because “first mover” strategies allow

market applications to gain market share and thereby provide an

above-hurdle-rate return.

is assumed to be a purely private good, even taking

spillovers into account.8

3. Alternative technology production functions

3.1. Endogenous growth models

In all endogenous growth models, private investments in R&D are undertaken to create technology and

thereby improve or maintain competitive position. In

the macroeconomic arena, Romer (1990) distinguishes

between “rival” and “non-rival” elements of technology in his general equilibrium growth model. The rival

(excludable) component of technology is that portion

embodied in human production capital HQ. The nonrival component K (presumably disembodied with respect to any specific factor of economic activity) is

viewed as partially excludable.9

In such a model, the traditional output function,

based on the interaction between physical capital CQ

and human production capital HQ, is modified to reflect

the fact that the stock of rival technical knowledge embodied in HQ “interacts” with the stock of non-rival

technology K and then with CQ to determine an output

Q, as shown in Eq. (1):

Q = C1−α Q (KHQ)

α (1)

This specification requires a technology output function:

K ̇ =  ̄δHK (2)

8 A number of studies have attempted to empirically test this general specification by separately including basic research and applied

R&D variables in a modified production framework (see Mansfield,

1980, 1991; Link, 1981; Griliches, 1986; Jaffe, 1989; Leyden and

Link, 1991; Toole, 1999). 9 Romer uses the public finance literature’s two main attributes

of any economic good: the degree to which it is rivalrous and the

degree to which it is excludable (Cornes and Sandler, 1986). The

rivalrous nature of a good is the degree to which use by one economic

agent (individual or firm) limits its use by another. Excludability is

the degree to which one agent can prevent another from using a

good. Microeconomists characterize the inability to exclude use of

technology by others as resulting in “spillovers” (Griliches, 1991;

Jaffe, 1998).
292 G. Tassey / Research Policy 34 (2005) 287–303

where HK is the human research capital and  ̄

δ is the

research productivity.10 The technology creation process is characterized as the application of rival human

research capital to create non-rival, partially excludable

technology goods (new “designs”).

Jones (1995) modified Romer’s functional form to

allow variation in scale effects due to differences in the

productivity of the existing stock of technical knowledge and R&D. This is accomplished by first representing the productivity parameter as

 ̄

δ = δKφ (3)

where φ < 0 is diminishing returns, φ > 0 is increasing

returns, and φ = 0 is the constant returns case, and then

substituting in Eq. (2) to get

K ̇ = δHγ

KKφ (4)

where γ allows for inefficiencies in the use of human

research capital, namely duplication of effort, and has

values in the range 0 < γ ≤ 1. For γ = 1 and φ = 0, this

equation reduces to the Romer growth model (fixed

efficiency of human research capital and constant returns to the scale for the stock of available technical

knowledge).11

Although such models catch up to several decades

of microeconomic research in which technology is endogenous to company or industry growth models, they

also specify a separate technology production function

with several distinct inputs and at least imply a relationship among these inputs. However, while these models

also characterize elements of technical knowledge as

rival and non-rival and, more importantly, as excludable

and non-excludable (or, partially excludable), they fail

to relate these characteristics to the inputs in the technology production function.

Specifically, such models do not address the longstanding need to disaggregate technical knowledge

into public and private elements according to the

10 Of course, additions to the stock of knowledge result from investments in both human research capital, HK, and physical research

capital, CK. Including both major factors of knowledge production

would change Eq. (2) to K ̇ =  ̄δ(CKHK). In microeconomic models,

technology is assumed to be produced by an R&D investment involving both research labor and research capital in fixed proportion.

11 Note that in the constant returns-to-scale case, the growth rate

of knowledge is independent of the stock of knowledge, depending

solely on the growth rate of the R&D labor force.

existence of different investment incentives. Thus,

while the output of the technology production function

is characterized by Romer as “new designs”, implying

proprietary (excludable) technology, this output is also

characterized as “partially excludable”, implying that

generic technical knowledge is simultaneously produced and is a byproduct of the technology production

process.

This approach contradicts the well-documented ordering of the R&D process in which early-phase

“proof-of-concept” research, exhibiting low excludability, nevertheless precedes the development of proprietary “designs” (i.e., innovations). The latter are the

output of later-phase applied R&D and can legitimately

be thought of as entering directly into a production

function.

Thus, the distinct phases of R&D, which respond

to different investment incentives and thereby require

different sources of funding (government, industry)

and different performers (government, industry,

universities), are not addressed by such models. That

is, although scale effects imply the possibility of a

life cycle character to knowledge creation and partial

excludability implies the possibility of public and

private good elements, neither characteristic can be

represented without some further disaggregation of

the technology production function and a realignment

of the interactions among technology elements.

3.2. Microeconomic approaches

Research on the economics of technological change

has been conducted by microeconomists for decades,

but technical knowledge production has remained characterized for the most part as resulting simply from a

set of lagged R&D expenditures (Griliches (1986):

Kt = 

i

wiRt−i (5)

Obviously, the specification of the desired function

becomes complex when several elements with different degrees of excludability are taken into account.

Multiple categories of knowledge need to be identified

and interrelationships (directions of knowledge flows)

specified. Within an industry, spillovers occur from one

firm to another, among a consortium of firms acting collectively, or to all firms from a source exogenous to the

industry (government or universities). Non-excludable
G. Tassey / Research Policy 34 (2005) 287–303 293

technology thus moves into and out of firms and into

the industry as a whole from external sources.

A few examples can be found of partial disaggregation of the technology variable. Mansfield (1980)

and Griliches (1986) hint at a distinction between endogenous and exogenous sources of knowledge by including a variable eλ in their production functions,

which Griliches calls “disembodied external technical

change”:

Qt = AeλtKα

t Cβ

t L1−β t (6)

Griliches (1979) also briefly mentions a generalized

production function of the form F(KN, KE, X), where

excludable technology, KE, is accumulated intentionally and non-excludable technology, KN, is created as

“a side effect” of producing KE.

12

3.3. Specifying the public and private good

technology elements

Given this limited treatment of the public good content of industrial technology and associated investment

incentives and the incorrect characterization of KN as

a side effect of producing KE, existing definitions of

excludable and non-excludable technology and the relationships with their respective exogenous or endogenous sources need to be specified based on four themes:

(1) Market applications (innovations) are derived from

a largely excludable stock of knowledge KE. This

stock, in turn, derives from a generic technology

base or “technology platform” KN, which is only

partially excludable.

(2) Firms use both KN and KE to compete. However,

whereas KE is endogenous to the firm, KN is at

least partly exogenous to the firm and frequently

the entire industry, often originating in universities or government laboratories. Thus, if anything,

inward spillovers of KN may dominate, a phenomenon not considered in the microeconomics

literature, but one that is increasingly important in

global technology-based markets and underscores

the increasing role of universities.

(3) While the non-excludable portion of technology

has been regarded by economists as a spillover

12 Nelson (1992) mentions the same relationship.

from investment in KE, KN is generally a necessary precursor for the majority of investments in

KE.

(4) Technology-based growth cannot take place

rapidly or over sustained periods of time without

investment in an elaborate technical infrastructure,

η, composed of a set of infratechnologies that leverage the productivity of both developing and using

KE and KN.

3.4. Investment incentives and risks

The above themes imply a model in which the interactions among KE, KN, and η are not simply one

of complementarity among endogenous variables. Further, the dependency of KE on KN implies linearity with respect to the R&D process, a concept that

may appear to contradict a portion of the innovation literature.13 Specifically, in contrast to Griliches’

characterization of KN as a side effect of producing

KE, companies need access to KN early in a technology’s life cycle as a means of entry or life cycle

transition.

The more free riding that takes place, the less incentive individual firms have to invest in the quasipublic good technology elements (generic technologies

and infratechnologies). At some level of spillovers, all

or most private investment in KN is suppressed. Expanding global competition in R&D-intensive industries tends to increase spillovers, simply because the

existence of more scientists and engineers and global

R&D funding increases the aggregate capacity to absorb new technical knowledge. Complex partnerships

of public and private institutions are evolving to emphasize inward spillovers (i.e., absorption of somebody

else’s KN). This fact supports the proposition that KN

is not a “side effect” of firm-level R&D, but a critical

13 For example, Klein and Rosenberg (1986) argue that the innovation process is not a simple linear phenomenon. Rather, it involves

syntheses across technologies (“chain-link” model) and feedback

loops during a typical technology life cycle. Moreover, examples

can be found of important technologies that evolved ahead of the

discovery of the fundamental or generic technology base. However,

the vast majority of modern technologies display a linear evolutionary life cycle, albeit as a series of repeated or “nested” cycles (Tassey,

2004, 2005). Similarly, Freeman et al. (1982) provide case studies,

which show that successive waves of product innovations depend on

prior development of an underlying science base.
294 G. Tassey / Research Policy 34 (2005) 287–303

technology asset that must be available to enable the

remainder of the R&D cycle.

Furthermore, the shortening of technology life cycles in recent decades raises the risk adjustment firms

make to ROI estimates for R&D, especially long-term,

high-risk research. The higher time and risk discount

factors lead to greater underinvestment at the corporate

level in generic technology research and increases the

need for external sources of KN.

14

In summary, investments in R&D take place at the

firm level for KE and at several levels (firm, industry,

and government) for KN. This is the case because of the

quasi-public good nature of KN. That is, it is partially

excludable and hence subject to free riding. Moreover,

the generic character of KN implies scope economies as

well as non-excludability, both of which are attributes

of public goods. This fact, in turn, implies the need for

exogenous sources such as universities in an efficient

national innovation structure. Correctly modeling the

process of technological change therefore requires the

inclusion of both private and public good elements in

the technology production function.

4. A disaggregated technology production

function

4.1. The technology production function

The preceding conceptual framework argues that

much of the investment in KN occurs prior to investment in KE. Specifically, the results of investment in

KN accumulate as a generic technology or technology

platform, which drives applied R&D for specific market applications. A significant portion of KN must be

available in the early part of a technology’s life cycle

to provide the basis for the applied R&D, which creates KE. The more robust is KN and the more easily it

is accessed, the greater is the productivity of a firm’s

applied R&D in producing KE.

15

14 This leads to risk pooling strategies, such as participation in research consortia with universities and government research institutes

who subsidize and/or perform this type of research. All industrialized nations support research consortia, implicitly recognizing the

quasi-public good character of technology development.

15 Although not explicitly addressed in this model, feedback loops

exist. In concert with the learning-by-doing literature, advancing KE

Another important attribute of the disaggregated

model is the fact that a firm’s R&D productivity is leveraged by the availability of an infrastructure consisting

of a set of technical tools, databases, and specifications,

which frequently become industry standards. These infratechnologies often do not arise from KN and may

have applicability across a number of related industries.

The R&D, process, and marketing productivities of the

typical technology-based industry depend on hundreds

of infratechnologies and associated standards.

Thus, an investment-based model of innovation and

its impact on economic growth must represent the productivity of private sector applied R&D in terms of both

private and public sector expenditures that precede it

and also the contributions of the supporting innovation

infrastructure. The generalized form of such a model is

Q = SNF(KN, KE, X) (7)

where SN is the science base from which generic

technology is derived and is assumed to be fixed,

based on the concept of long-term technology life cycles or “waves” spawned by periodic bursts of scientific discovery. X is a set of factors that affect output/performance in addition to the non-proprietary and

proprietary technology elements.16

At any point in time, technical output is equivalent

to the growth in the stock of proprietary (excludable)

technology, KE, represented by

K ̇ E = δRλ

E (8)

where RE is applied R&D expenditures targeted at developing excludable technology (innovations) and δ is

an R&D productivity factor.17 In production functions,

scale parameters are assumed constant. Here, the scale

parameter λ is affected by the internal organization and

management of the firm and by the overall productivity of an economy’s education and technical infrastructures. These factors change very slowly, so assuming λ

to be constant is reasonable.

and subsequent production and marketing experiences feed back to

stimulate directions in applied R&D that produces additional KE. 16 The contents of X are largely ignored by the economics of technological change literature. However, the fact that technology affects

both the composition and the rate of the output of an industry in a

cyclical manner suggests that other factors are important in managing

the technology life cycle (see Section 6). 17 Technical output is assumed to be identical to or at least perfectly

correlated with innovative output.
G. Tassey / Research Policy 34 (2005) 287–303 295

δ is a key distinguishing element of this model. In

technology production functions, where K is treated

as a homogeneous entity, the rate of growth in K is a

function of the current stock of knowledge (Eq. (4)). In

the disaggregated model, however, KE is a function of

available KN and this relationship is expressed through

the productivity parameter.18 For an individual technology, δ is determined by the available stock of KN

relative to a target rate of investment in innovation enabled by KN:

δ = ηe−KN/RE (9)

where η represents the set of infratechnologies that

leverage the efficiency of R&D. Change in this infrastructure occurs more slowly than does KE and such

change tends to occur early in the innovation process.19

Thus, a reasonable approximation is to assume η is a

process constant over the R&D cycle.20

Substituting Eq. (9) into (8) gives the technology

production function:

K ̇ E = ηe−KN/RERλ

E (10)

KN is assumed to be available to all firms in an industry, although in reality it will not be accessible in equal

amounts across firms. KN thus serves as a measure of

innovation opportunity for individual firms, which they

draw upon in producing stocks of proprietary (excludable) technology, KE. The negative sign on KN may be

counterintuitive, but a generic or platform technology

is of value to the firm only as a facilitator of the applied R&D that produces KE. KN is essential to reduce

technical and market risk to levels that allow conventional corporate R&D investment criteria to be applied.

18 Thus, the rate of growth of KE is not directly dependent on the

existing stock of excludable technology, as in some other knowledge

production functions (for example, Jones, 1995). This is because KE

is more directly a function of KN. 19 Critical measurement methods, interface specifications, etc., are

typically required to be in place before substantial R&D can be rationalized, but once adopted as standards they tend to remain unchanged

for extended periods of time.

20 This representation has an analog in physical chemistry. Combining reactants to produce a new chemical entity requires an “activation energy” to initiate the reaction process (in this model, the

activation energy is the enabling generic technology research expenditures, KN). The chemical reaction itself proceeds at a specific

(target) thermal energy (applied R&D expenditures, RE). See, for

example, Atkins (1994).

The less developed is KN, the more inefficient are attempts at market applications through applied R&D

(i.e., the productivity of applied R&D is reduced and

the risk associated with a given R&D expenditure is

increased).21

Empirical testing of Eq. (10) will require confronting the typically difficult problem of estimating

stocks. Quantifying KN for purposes of empirical

analysis is a particularly difficult challenge due to the

substantial spillovers. However, approximations are

possible. For a larger firm with an annual budget for

its central research laboratory, as well as for applied

R&D conducted by its lines of business, the ratio of the

two budgets can be a surrogate for that firm’s overall

technology strategy. Eq. (9) can then be thought

of as an average productivity for the firm’s overall

R&D investment.22 This approach implies a constant

adjustment to KN from generic technology research,

RN, for the firm’s portfolio of research projects.23

Under these assumptions, (9) can be modified to

give

δ = ηe−RN/RE (11)

Not only will this assumption facilitate empirical work,

but it may be better than attempting to correct flows

by some arbitrary percentage asserted to represent

obsolescence of technology in an economic sense.

21 This proposition is complicated by the fact that some (mostly

large) firms conduct generic technology research in “central” or “corporate” research labs. The results of this research are transferred to

the applied R&D operations in the company’s lines of business. The

research objectives and the R&D project selection criteria are distinctly different for the central lab and the line-of-business labs, and

management of the two types of research are organizationally separate (Buderi, 2000). Thus, the generic technology produced by the

central lab is in effect exogenous to the operations of the lines of

business. Of course, such generic technology leaks more slowly to

other firms than if it were produced by a source outside a single

firm (industry consortium, university, government laboratory, etc.).

Hence, the excludability of specific additions to an industry’s KN will

vary over time and across firms. In any case, the spillover rate will

be higher than for KE. 22 This assumption of continuous RN is even more justified for interindustry analyses.

23 A number of large companies set the budgets of their central

research labs as a percentage of total R&D expenditures. However,

the content of “corporate research” varies from one firm to another,

thereby weakening comparisons across firms. See Buderi (2000).
296 G. Tassey / Research Policy 34 (2005) 287–303

Fig. 1. Risk reduction in the R&D process.

5. Risk reduction over the R&D cycle

5.1. Types of risk

For corporate R&D decision making, the amount of

generic technology available directly affects the risk associated with R&D project selection; i.e., the targeted

rate of change in KE. The relationship between KN and

KE from an investment incentive perspective is depicted

in Fig. 1. At an early point in the R&D cycle, the decision making process faces a pure “technical” risk,

R0, that summarizes estimates of the probability that a

technology can be developed from the body of scientific

knowledge (i.e., can be shown to work in some generic

sense). In addition, the characteristic that distinguishes

technology research from scientific research is the fact

that the ultimate intent is commercialization. Thus, an

additional amount of technical risk must be estimated

and added to R0 because the scientific principles presented now have to be proven capable of conversion

into specific technological forms with specific performance attributes that meet specific market needs.

A “market” risk also must be estimated to allow

for the significant probability that, even with technical

success, demand for the new technology will be overestimated or that market penetration will be slower than

projected due to a number of possible barriers (Tassey,

2005). Either of these potential occurrences will lower

estimated ROI, so a market risk estimate must be added

to the projected technical risk.

5.2. The “risk spike”

The hump in the investment risk curve therefore represents the fact that technology research, with its ultimate objective of market applications, encounters an

initial major increase in risk associated with the target ROI from commercialization. This additional risk,

RS, occurs in the early phases of technology research

and acts as a substantial barrier to private investment

in later-phase applied R&D.

Fig. 1 shows this pattern of risk over the R&D cycle

for two hypothetical technology risk profiles, A and B.

The combined increases in technical risk and market

risk are represented by RSA or RSB, respectively. Such

“risk spikes” might be thought of as the “public (or

social) component” of total risk because they occur in

the early generic technology research phase, which has

the public good dimension described earlier. Technology A is the more radical innovation and thus presents

a greater initial risk spike, RSA. Eliminating the risk

spike requires KN.
G. Tassey / Research Policy 34 (2005) 287–303 297

Without the risk spike, firms would be faced only

with a reduction in the “private risk” component, RP.

In this situation, conventional R&D investment criteria

would targetKE and deal with RP because it falls within

acceptable reward–risk ratios. Thus, if at the level of

pure technical risk, R0, application of conventional corporate R&D criteria would result in private investment

based on risk-adjusted ROI estimates for KE, the policy

problem is to overcome the risk spike so that corporate

investment criteria can be applied at R0.

The importance of overcoming the risk spike for the

more radical Technology A is underscored by the fact

that overall risk can actually decline to a lower level

than that for Technology B. This occurs because, if

Technology A is successfully developed, the resulting

set of market applications will likely have a larger collective value than B (reduces market risk to a greater

degree). This is depicted in Fig. 1 by a greater decline

in private risk for Technology A, RPA.

Understanding the evolution of and the interaction

between technical and market risk and the consequent

impacts on private sector investment must be a key element of R&D policy analysis. However, neither economic models nor current R&D policy completely recognizes the large discontinuity in the total risk reduction process. If it did not occur, the gradual slope of

the curve in Fig. 1 would support proponents of no

government support for R&D beyond basic science.24

Consideration of the risk discontinuity aside, the

slope of the risk reduction curve varies depending on a

number of R&D efficiency factors. An important one

is the availability of a range of infratechnologies. Such

technical infrastructure has a strong public good character resulting in underinvestment by industry. Infratechnologies and associated standards are ubiquitous

across technology-based economic activity. This characteristic decreases their visibility and increases difficulties in impact assessment, leading to substantial

underinvestment.

24 The fact that all industrialized nations have government programs

that fund generic technology research in industry and universities

indicates a general recognition of the existence of the risk spike.

However, the lack of a consensus model of R&D investment and

subsequent empirical analysis to support decision making leads to

uncertainty on the part of policy makers and thus underfunding of

generic technology research, as exemplified by the history of NIST’s

Advanced Technology Program.

5.3. Policy implications

Analysis of investment in KN is complicated by this

technology element’s quasi-public good content and

the high technical and market risk associated with more

radical and longer term technology research. Because

this research has an ultimate market objective (in contrast to basic research), several risk factors must be

added to the investment criteria. The higher the risk

spike associated with the initiation of technology research, the lower is the projected R&D productivity δ

and the subsequent rate of growth in technology KE.

Thus, the unavailability of KN is a barrier to the conduct of proprietary (private) research and should be

thought of in terms of the risk its absence imposes on

subsequent applied R&D decision making.

From a public policy perspective, a decline in KN

investment increases the risk of failure to be competitive in the next generation or radically new technology.

Thus, one major policy concern is technological opportunity, as represented by an industry’s stock of generic

technology. As indicated by the negative sign on KN in

Eq. (9), the technology platform upon which an industry’s existence is based represents a threshold level of

generic technical knowledge that must be available and

accessed to enable positive investment decisions with

respect to applied R&D.

The second policy concern is the adequacy of the

supporting technology infrastructure, η, which drives

the efficiency of the overall R&D process. Infratechnologies are individually small but ubiquitous in most

high-tech industries. Thus, their aggregate economic

impact is substantial.25 However, their small individual size, public good character (most are eventually

adopted as standards), and the fact that they derive from

different generic technologies than do the core industry

technologies combine to create substantial underinvestment.

A third concern is the shifting nature of the policy

response mechanisms. Ideological conflicts and inadequate R&D investment models based on market failure

analysis contribute to poor research portfolio management by both government and industry (Tassey, 2005).

25 The semiconductor industry, for example, spends billions of dollars per year on measurement. Individual measurement infratechnologies are used in the R&D, production, and market transaction

stages of economic activity. See, for example, Finan (1998).
298 G. Tassey / Research Policy 34 (2005) 287–303

The university role has been shifting for several decades

as a partial response to these problems. Two traditional

roles of the university have been to produce scientific

knowledge and human research capital (HK). The portion of this human research capital that stays within

the university sector of the R&D establishment is increasingly focusing on the conduct of RN. This shift

in research composition is evidenced by the explosion in university patents since Bayh-Dole and has led

to conflicts with industry over access to this generic

technology.26

6. A technology-based performance function

6.1. Production versus performance functions

A critical issue for the economics of technological

change is the selection of an appropriate performance

model in which the impact of the output of the technology production function can be analyzed. The correct

performance function should both accurately represent

the strategic role of technology; in particular, distinguish between the proprietary and public good technology elements.

However, the vast majority of the innovation literature treats technology as an independent and largely

self-sufficient demand-shifting asset. That is, an implied assumption is that the life cycle resulting from

the emergence of a new technology paradigm and the

subsequent succession of market applications (innovations and subsequent product life cycles) is accurately

represented by a single-strategy framework based on

overcoming technical risk (i.e., through the conduct of

R&D). This assumption is implicit in the many models

of technology-based growth that combine technology

with the major production assets, capital, and labor.27

Within this framework, endogenous growth theory,

both macroeconomic and microeconomic, attempts to

26 The large increase in university patents since 1980 demonstrates

that the results of RN are sufficiently excludable to convey considerable IP to universities and that government funding of much of this

research compensates for the loss of the remaining IP from spillovers.

See Mowery and Sampat (2005). 27 Economists have identified different innovation strategies among

firms in the same industry: innovator, fast follower, “wait and see”

(Dosi, 1988).

explain technology-based growth by modifying the traditional production function. Both a “state of technology” represented by a shift parameter28 and an explicit

technology variable can be added to capital and labor

inputs in the traditional production function to represent the impact of technological change.

However, a production function, which explains output based on complementary technical relationships

among physical, human, and technological inputs, can

at best only partially reflects the strategic role of technology investment. In fact, a fundamental way, reliance

on such a vague set of complementary relationships

confuses analysis of this role.

Resolution of these issues, especially to emphasize

technology’s creative destruction role may benefit from

focusing more on technology’s ultimate economic role,

which is to shift demand through either (1) the introduction of new products and services—a product attribute

strategy or (2) cost-reducing process innovations—a

price reduction strategy.

Therefore, the output of the technology production

function should become an input into a performance

function, which emphasizes the fact that a firm’s strategy is to shift demand in its favor through enhanced

products and services or through process improvements

that reduce cost and hence price. That is, if assessing

the impact of technology in determining relative performance among firms in a given industry (or among

industries) is the objective, then the performance function should emphasize the roles of demand-shifting investments.

6.2. The demand-shifting roles of technology and

marketing

A major shortcoming of the production function approach is the fact that technology cannot shift demand

by itself.29 In his review of the microeconomics of innovation, Dosi (1988, p. 1152) observes that “after allowing for the effect of firm size, one still generally

observes a substantial unexplained interfirm, intrasectoral variance, in terms of both R&D investments and,

even more so, innovative output”. This observation results from the fact that, unlike the business literature,

28 See, for example, Grossman and Helpman (1994). 29 That is, the saying “build a better mousetrap and the world will

beat a path to your door” is inaccurate.
G. Tassey / Research Policy 34 (2005) 287–303 299

only a small number of economic studies have recognized that the cyclical pattern of creative destruction

depends on marketing strategies as well as technology

strategies.

Freeman (1973) conducted an exhaustive study of

numerous elements affecting the success and failure

of industrial innovations. His results emphasized the

role of marketing (including market research) as well

as a firm’s technical capabilities. Levin et al. (1987,

p. 33) found that for most industries lead times and

technological progress (learning curve effects) combined with marketing appear to be the “principle mechanisms of appropriating returns for product innovations”. Particularly relevant for the model developed

here is a longitudinal study of 12 R&D firms by

O’Conner and Ayers (2005) that demonstrates the integration of technology and market planning by hightech firms in the early phases of attempts at radical

innovations.

The shortcomings of models that ignore complementary roles of technology and marketing are evidenced at all phases of the technology life cycle, but

especially for small, innovative firms, including spinoffs from university research. The latter are frequently

technology rich and marketing poor, resulting in the

need to give up equity or even the entire company to

acquire marketing expertise.

6.3. A demand-shifting model of economic

performance

The process of creative destruction (i.e., the initiation of the technology life cycle) begins with initial

endowments of generic technology and marketing assets. Each firm entering the young industry undertakes

a process of asset accumulation and learning in response to its initial endowments plus industry dynamics, relative prices, and external institutional changes.

Using the technology production function developed

in Section 4, initial technology asset accumulation,

KN, can be linked to external sources such as universities and government laboratories. Firms then attempt

to shift demand in their favor by investing in unique

combinations of the demand-shifting assets, applied

technologies (KE) and marketing capabilities. Each

firm attempts to maintain its unique strategic innovation/imitation and marketing approach within competition space.

This process is seen clearly in young technologybased industries such as biotechnology, where firms

pursue strategies such as accessing KN from a university and then attempt to apply it through arrangements

with, say, university professors. At the same time, these

firms evolve a marketing strategy commensurate with

the perceived relative strength and unique features of

the targeted technology.30

Even when a shift in strategy seems warranted, the

ability of a firm to make adjustments is limited because

mobility barriers exit. For example, Mansfield et al.

(1971) found that the maximum adjustment to a firm’s

stock of technology capital in any year is 10–15%.

Similarly, adjustments to marketing require changes

in sales personnel, advertising programs, and organizational structures.

Tassey (1983)showed that, because both technology

and marketing assets take time and resources to accumulate and because organizational structures evolve to

maximize returns on these capabilities, strategies based

on particular combinations of the two assets tend to

remain stable over time and are distinguishable from

other strategies by competing firms within the same

industry.

The stability over time of asset-based demandshifting strategies implies a spatial model of competition. In such as model, competitive strategies are

based on unique combinations of demand-shifting assets that determine firm-specific expansion paths. Each

expansion path represents a strategy in demand-shifting

space. Thus, assuming equal access by competing firms

to stocks of capital and labor, relative performance can

be explained using a spatial model of competition based

on firm-specific allocations to R&D and marketing.

Such a relationship is best expressed by a homothetic function of the general form:

P = F[f (R, M ̄ )] (12)

where P is performance (sales or profits). R ̄ and M are

the two demand-shifting assets possessed by a firm.31

30 Darby and Zucker (2003) use empirical studies of technology

diffusion in biotechnology to conclude that companies can retard

spillovers of generic technologies by hiring and keeping star scientists whose tacit knowledge (a significant part of generic technologies) can only diffuse slowly, largely through person-to-person

contact.

31 See Tassey (1983) for the derivation of the specific functional

form.
300 G. Tassey / Research Policy 34 (2005) 287–303

A homothetic function is hypothesized because the

elasticity of substitution must be constant only at the

points of intersection between isoquants and a particular ray from the origin, which represents the expansion

path or “strategic trajectory” for a particular firm. In

contrast to conventional production theory, allowing

the elasticity of substitution to vary along an isoquant

captures differences among competing firms with respect to demand-shifting strategies.

For empirical work, the above general form can be

converted to

log Pi = φ log xi + φ(a0 + a1xi + a2x2

i

+ a3x3

i +···) + vi (13)

where x = M/R ̄ and the coefficient φ is a returns-toscale parameter. R ̄ could be aggregate R&D spending

or, preferably, the ratio RE/RN, and M is the marketing

expenditures.

Using Compustat data for marketing (advertising) and aggregate R&D spending, Tassey (1983)

calculated F-ratios for six technology-based industry groups (chemicals, drugs, machinery, computers,

electronic components, and instruments). These ratios

strongly supported the hypothesis of distinctly different and constant proportions of marketing and technology assets across firms. That is, the variation in

annual ratios of marketing (advertising) to R&D expenditures for firms in an industry group was significantly less than the variation in the mean values of the firms’ respective time series. Thus, a spatial model of competition in demand-shifting space is

supported.

The performance function was tested using OLS regressions to select the polynomial (M/R) that best fitted the data. The model had high explanatory power

for all industry groups using sales and net income as

dependent variables. The net income model was particularly impressive, as the constant term was not a

major explanatory factor. A consistent negative sign

on the square of the demand-shifting strategy variable indicates a limit on the range of feasible strategies, as represented by investments in marketing relative to R&D. The sales model showed varying degrees of decreasing returns to scale for investment in a

firm’s demand-shifting strategy, with the more R&Dintensive industries approaching the constant returns

case. The net income model showed the same distribution of returns-to-scale values across industry groups,

but with all values closer to or actually at constant

returns.32

Two important policy implications are inherent in a

homothetic performance function. First, assuming that

the ratios of RE/RN and M together represent unique

product and process technology strategies across firms,

then the conclusion is that significant competition exists in technology-based industries derived from a singular underlying generic technology base. This fact

counters the long-held view of many U.S. R&D policymakers that government funding of early-phase technology research subverts the market mechanism by

“picking winners and losers” (i.e., determining which

innovations are developed and reach the marketplace

and which do not).

The second implication is that a broader and deeper

generic technology base will support more strategies

and hence firms, thereby expanding aggregate output

or at least the diversity of output for a particular

industry. At any size, however, only a limited number

of strategies can be supported in technology-marketing

space. Thus, countries that invest in national innovative capacity (industrial structures, universities,

and government research institutes) will likely not

only develop the generic technology first, but will

“fill” the technology-marketing strategic space ahead

of competition in other countries and thereby gain

a large share of the benefits from the particular

technology.

7. Conclusion

A number of factors determine the rate and direction of technological advance: technological opportunity (the inherent performance capacity of the generic

technology), the public and private good characteristics of the technology, industry structure and behavior,

and demand conditions. Such technological progress

32 In the decreasing returns case, the selected demand-shifting investment policy will determine the long-run equilibrium values of

R&D and marketing inputs, so that an optimal size for demandshifting assets exists. For the constant returns case, the rate of growth

would be independent of the sizes of the R&D and marketing assets

and the optimal investment policy would simply be determined by

the ratio of demand-shifting assets commensurate with the firm’s

industry strategy and a rate of growth.
G. Tassey / Research Policy 34 (2005) 287–303 301

is also affected by the existence of relatively high

risk, which is regularly used as the rationale for government support of R&D. However, the general approach taken oversimplifies the realities of technologybased competition and has therefore contributed significantly to the lack of consensus with respect to government roles in support of technology-based economic

growth.

A disaggregated technology production function is

needed to reflect the fact that private sector investment

decisions are affected by the existence of and interaction among the three major elements of an industrial

technology: generic technology, infratechnology, and

proprietary technology. Each element exhibits a distinctly different risk profile and therefore responds differently to investment incentives.

These elements of the typical industrial technology

have varying degrees of public good content and therefore must be funded from different combinations of

public and private funds. Specifically, industry’s problems with respect to acquiring and using technology

are twofold. First, an adequate science base must be

available and accessible at any point in time. Industry

itself contributes relatively little to the evolution of this

science base because of the almost pure “public good”

character of scientific research. Thus, government provides the vast majority of the funds for this research,

largely through universities. Second, industry’s investments in the three technology elements suffer to different degrees from a number of partial market failures,

and government’s role is therefore more difficult to define and implement.

Once available, individual firms use applied technology in a logical effort to achieve competitive advantage. In developing this applied technology, these

competing firms largely draw upon an industry-unique

generic technology base and a set of infratechnologies that define that industry’s infrastructure. The

quasi-public good character of these shared technology elements implies the need for exogenous

sources, such as universities and government laboratories.

Simply stating that R&D is risky or that industry’s R&D cycle times are too long does not automatically lead to the need for an R&D support by

government. The application of the models developed here allows a more disaggregated and hence

presumably more efficient algorithm for market failure identification and characterization. However, while

this framework should lead to more accurate policy analysis and development, it also underscores the

potential for error due to the need for a more complicated analytical framework and corresponding data

set.

Finally, the economics literature has largely focused

on technology as a single demand-shifting asset. Yet,

it cannot be delivered to the market without other assets, specifically marketing, that provide information

to consumers about the attributes of the technology.

A spatial model of technology-based competition captures the diversified strategies of individual firms with

respect to investment in multiple demand-shifting assets. Equally important, in the modern global economy,

the initiation of the respective competitive trajectories

in demand-shifting space increasingly depends on the

supply of public good technology elements from exogenous sources, namely universities and government.

Acknowledgment

The author is indebted to Albert Link, John Nail,

Chad Snyder, and Lorel Wisniewski for helpful suggestions on earlier drafts.

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