Underinvestment in Public

Good Technologies Gregory Tassey

ABSTRACT. Although underinvestment phenomena are the

rationale for government subsidization of research and development (R&D), the concept is poorly defined and its impact is

seldom quantified. Conceptually, underinvestment in industrial

R&D can take the form of either a wrong amount or a suboptimal composition of R&D investment. In both cases, R&D

policy has not adequately modeled the relevant economic phenomena and thus is unable to characterize, explain, and measure the underinvestment. Four factors can cause systematic

underinvestment in R&D-intensive industries: complexity,

timing, existence of economies of scale and scope, and spillovers. The impacts of these factors vary in intensity over the

typical technology life cycle, so government policy responses

must be managed dynamically. In addition to understanding

the causes of underinvestment in R&D, the magnitude of the

deficiency relative to some ‘‘optimum’’ must be estimated to

enable a ranking of technology areas with respect to expected

net economic benefits from a government subsidy. Project

selection criteria must therefore be based on quantitative and

qualitative indicators that represent the nature and the magnitude of identified market failures. The major requirement for

management of R&D policy therefore is a methodology that

regularly assesses long-term expected benefits and risks from

current and proposed R&D portfolios. To this end, a threestage process is proposed to effectively carry out R&D policy

analysis. The three stages are (1) identify and explain the

causes of the underinvestment, (2) characterize and assess the

investment trends and their impacts, and (3) estimate the magnitude of the underinvestment relative to a perceived optimum

in terms of its cost to the economy. Only after all three stages

of analysis have been completed can the underinvestment pattern be matched with the appropriate policy response.

Key words: R&D innovation, underinvestment, policy

JEL Classification: O3, O2

1. Introduction

Understanding the role of technology in economic

growth requires the development and application

of microeconomic theory. This is because each

technology has unique characteristics, which

interact iteratively over the technology’s life cycle

with unique industry structures and technical

infrastructures. The microeconomic character of

such an evolutionary model is further enhanced

by the fact that the majority of technology investment decisions are made by individual companies

and external financing is often supplied by individuals and small venture capital firms, with

much of the financing for particular technologies

limited to specific geographic regions.

Economists have explored the numerous elements of the microeconomics of technologybased growth, including the determinants of

research and development (R&D) spending, project selection, the tradeoffs between R&D costs

and time within a portfolio management context,

the interactions of this activity with firm size and

market structure, and the responses of R&D

investment decisions to different financial and

regulatory incentives. In addition, the results of

such investments have been estimated using several analytical frameworks.

However, no one has surpassed Mansfield

et al. (1968, 1971, 1982) in the number of these

elements analyzed with respect to both investment and results, the level of disaggregation

explored, and the collection and use of industry

data to enlighten the proposed frameworks.

Mansfield et al. (1977) have had a particularly

pronounced effect on the analysis of the results

of innovation. Their work in the late 1970s in

which social and private rates of return were estimated for a range of R&D investments in manufacturing technologies followed Griliches’ (1958)

path breaking work in agriculture and collectively drew attention to the ‘‘gap’’ between social

and private rates of return. This gap has

spawned several decades of debate over

Stop 1060

National Institute of Standards and Technology

Gaithersburg, MD 20899

E-mail: tassey@nist.gov

Journal of Technology Transfer, 30 1/2, 89–113, 2005

 2005 Springer Science+Business Media, Inc. Manufactured in The Netherlands.
government roles in supporting various amounts

and types of R&D [including Mansfield et al.,

(1982)]. Economists [including Mansfield, (1980,

1991a, b)] subsequently disaggregated R&D into

basic research and applied R&D to examine the

differences in rates of return between the ‘‘pure

public good’’ (basic science) and the target of

applied R&D (‘‘proprietary technology’’).

Mansfield’s broad and highly microeconomic

analysis has provided an analytical platform for

needed progress in assessing the implications of

the ‘‘gap’’ for public policy. This paper focuses

on barriers to R&D investment that result in

suboptimal distributions of R&D investments

across different types of R&D and thereby reduce

potential long-term economic growth. The point

of departure is the proposition that the gap

between the social and private rates of return to

R&D investments is not automatically an indicator of underinvestment and, at least as important, it is not an indicator only of potential

underinvestment in the amount of R&D. Barriers

arise that affect the composition of R&D and

this class of market failures can have significant

long-term negative effects on economic growth.

The resulting added complexity to R&D policy

analysis requires a more microeconomic

approach in the Mansfield tradition.

2. R&D market failure analysis

Firms conduct R&D for two reasons: (1) to

develop new products, services, or processes, and

(2) to maintain a capability to identify and

assimilate technologies from external sources

(Cohen and Levinthal, 1989). Governments of

industrialized nations obviously consider these

activities to be essential for economic growth

because they maintain various sets of R&D support policies. Yet, the theoretical and operational

frameworks for managing such policies are

incomplete.

Economists have consistently estimated the

rates of return from R&D to be considerably

above those obtainable from other assets. However, for R&D in aggregate, significant rate of

return (RoR) differentials for social (industry)

versus private (innovator) R&D investments have

been found.1 Moreover, among types of R&D,

basic research and long-term, high-risk technology research have yielded particularly high rates

of return (Griliches, 1995). The implication of

these persistent rate-of-return differentials is the

existence of systemic ‘‘market failure.’’ However,

the interpretation and policy implications of

these RoR differentials are poorly defined

because the majority of the economics literature

has misspecified the knowledge production and

innovation processes, resulting in inaccurate

R&D policy prescriptions for dealing with the

implied underinvestment.

Assessments of private sector underinvestment

have been based largely on the neoclassical concept of externalities (Pigou, 1932) and defined as

differences between actual investment patterns

and an optimum rate of investment. However,

this concept of market failure has not been made

operational in the sense that specific roles for

government can be deduced (Ruffin, 1996). Coase

(1960, 1992) made a huge contribution toward

resolving the problem by emphasizing that overcoming the difficulties in estimating the amount

and nature of underinvestment mechanisms

resulting from externalities requires the definition

and then the assignment of property rights and

associated transaction costs. The institutional

(government) role therefore becomes extremely

important and also case specific. Consequently, a

microeconomic analysis of each specific case is

required.

For R&D, analysis of market failure mechanisms and the consequent need for government

intervention require accurate models of the

unique set of factors determining this category of

investment. If the needed models were straightforward, government R&D strategies would be

relatively simple to design and manage. However,

private investment incentives respond negatively

to the public good content of technology and this

content is distributed among elements of the typical industrial technology in more complex ways

than generally realized. In particular, most industrial technologies (or, more accurately, elements

of them) have a quasi-public good character,

which complicates and thus inhibits addressing

property rights issues, thereby substantially raising transaction costs, as originally identified by

Coase.

Moreover, other factors besides property

rights are important in determining R&D

90 Tassey
investment patterns, such as the relationship

between the nature of a technology and the

industry and market structures that deliver it. In

addition, defining an optimum rate of investment

is a particularly challenging problem for R&D

policy, but this is a necessary analytical step

independent of identifying the factors determining private sector investment. Assessment of the

time-cost trade-off is one of the many contributions by Mansfield to understanding the technology life cycle.

In the Coase tradition, R&D policy requires a

set of analytical tools that can efficiently identify

and assess the characteristics of an industrial technology that affect investment patterns over time.

Thus, this paper seeks to provide more structure

and rigor to the analysis of underinvestment in

R&D. Specifically, the framework developed

emphasizes underinvestment in the major elements

of the typical industrial technology and examines

how investment patterns change over a technology’s life cycle relative to an optimum. The focus

is on underinvestment relative to the optimal composition of R&D, as opposed to underinvestment

in the aggregate amount of R&D.2

Beginning with Arrow (1962a), economists

developed explanations of underinvestment in

R&D based on the indivisibility and inappropriability of information and the excessive risk

involved in its creation. These factors were developed largely as if technical information were a

homogeneous entity and therefore so would be

the process (R&D) that produces it. Some

research has identified two or more distinct elements of technical information.3 Tassey (1997)

characterizes these elements in terms of uniquely

different investment incentives, responding to differing public good content. It remains to develop

a framework based on the composition of R&D

that identifies specific underinvestment phenomena associated with each element, thereby

enabling accurate policy analysis.

That is, to identify, characterize, and understand the sources and impacts of market failures with respect to the composition of R&D

investment and eventually match underinvestment phenomena with efficient policy response

mechanisms, an analytical framework is needed

that emphasizes analysis at the technology-element level, where the elements are distinguished

by unique investment incentives and patterns. A

‘‘technology-element’’ framework is different

from the more traditional ‘‘R&D-phase’’

approach used by Mansfield et al. (1971) and a

few others, even though the proposed elements

are to a degree the outputs of the conventional

phases of R&D. The technology element

approach is preferred because it is more closely

related to corporate R&D investment decision

making, a fact which facilitates investment

analysis.

To provide this framework, the analysis takes

a three-stage approach: (1) use of conceptual

models that allow specification of the causes and

effects of underinvestment by major technology

element; (2) selection of indicators of suboptimal

investment patterns and resultant impact trends;

and (3) construction of practical quantitative

approaches for defining and estimating the economic impacts of the underinvestment. Collectively, these three steps provide an empirically

based ‘‘cause-and-effect analysis,’’ which becomes

the basis for ranking targets for government

funding and matching underinvestment mechanisms with the appropriate policy responses. The

following sections discuss these three stages that

lead up to the policy response decision.

3. Underinvestment in generic technologies

For policy analysis purposes, a conceptual model

must be selected that portrays the typical industrial technology in terms of its public and private

good elements and thereby focuses policy analysis on the process creating systematic underinvestment. Such a model is also essential for

interpreting the results of this analysis for policy

makers, and ultimately helping to select the

appropriate policy response.

A quasi-public good model of a technology-based

industry

A static representation of a typical technologybased industry is shown in Figure 1. This conceptual approach is significantly different from the

‘‘black box’’ model, which has influenced R&D

policy for the past 50 years.4 The black box

model regards technology as a homogenous

entity and thereby prohibits consideration of the

Underinvestment in Public Good Technologies 91
existence of distinct elements within the typical

industrial technology, each of which responds to

distinctly different investment incentives.

In a more realistic model, the typical industrial

technology is separated into a set of private and

quasi-public elements to reflect the different

investment incentives and R&D management

practices associated with the provision of each of

these elements (Tassey, 1991, 1997). R&D policy

is complicated not only by their existence, but

also by the fact that these elements vary with

respect to their public good content.5 The specified elements are not arbitrary; rather, they represent distinctly different investment incentives

faced by industry and therefore different underinvestment phenomena appear for each element.6

This last point is critical because success of a

public role in supporting industrial R&D

depends on demonstrating both theoretically and

empirically that public R&D is a complement

rather than a substitute for private R&D. However, disaggregating technology into publicly

funded and privately funded components can

imply a dichotomous model consisting of a pure

public good and a pure private good.7 Some of

the required interaction issues (in particular,

complementarity) can be assessed in such a

model, but the quasi-public good nature of several common technology elements is obscured

and therefore an assessment of investment incentives and subsequent underinvestment behavior is

compromised.

In Figure 1, the darker shading indicates the

degree of public good content for a technology

element. In both the black box and technology

element models, funding of basic science is a relatively straightforward policy issue because scientific knowledge is generally regarded as a strong

public good. Thus, basic research is largely the

responsibility of government to fund. This characterization of science simplifies policy management, with the only issues being the amount of

funding and the distribution of this funding

across fields of science.8

The first step in applying scientific knowledge

(i.e., conducting technology research) is to prove

the concept of the new technology. The end point

of such an effort is frequently a laboratory prototype or some other form of ‘‘proof of concept.’’9

If the resulting fundamental or ‘‘generic’’ technology is deemed to have sufficient potential

commercial value, companies assign the substantial follow-on R&D to one of the company’s

line-of-business units to turn the laboratory prototype into a commercial product. Investment

decisions in this phase of the R&D cycle use the

more quantitative portfolio management tools of

corporate finance, such as net present value and

internal RoR metrics. The outcome of applied

R&D is represented by the proprietary technology box in Figure 1.

The importance for industrial competitiveness

of investment in generic technologies is that this

first phase of technology research provides the

basis for ‘‘next generation’’ or radically new

(‘‘disruptive’’) products, processes, and services.10

Developing new technology platforms (generic

technologies) and market applications derived

from these platforms (proprietary technologies)

are two very different investments and are managed quite differently within large R&D-based

companies.

In addition, a third element is represented by

the ‘‘infratechnologies’’ box in Figure 1. Infratechnologies are a diverse set of technical tools

that are necessary to efficiently conduct all phases

of R&D, to control production processes, and to

execute marketplace transactions for complex

technology-based goods.11 These tools are called

infratechnologies because they provide a complex

but essential technical infrastructure.

Many infratechnologies are adopted as industry standards, emphasizing their public good content (Tassey, 2000). Without the availability of

Strategic

Planning

Strategic

Planning Production Production Market

Development

Market

Development Value

Added

Value

Added

Entrepreneurial

Activity

Entrepreneurial

Activity Risk

Reduction

Risk

Reduction

Infratechnologies

Proprietary

Technologies

Proprietary

Technologies

Generic

Technologies

Generic

Technologies

Science Base Science Base

Figure 1. Economic model of a technology-based industry.

92 Tassey
this technical infrastructure, especially codified as

standards, transaction costs for all three major

stages of economic activity—R&D, production,

and marketing—would be much higher, thereby

significantly slowing the evolution of technology

life cycles.12 The multiple economic roles of infratechnologies in the typical technology-based

industry are indicated in Figure 1 by the several

arrows pointing from the infratechnology box to

the target areas of impact.

Looking at a technology-based economy more

broadly, most small companies do not attempt

much generic technology research, relying on

assimilating the required technical knowledge

from external sources. Large companies may

undertake more of this type of research, but a

number of factors related to its public good content result in underinvestment.13 Thus, the proposed taxonomy is essential for R&D policy

analysis because it has implications for the

sources of radically new technology platforms,

access to these generic technologies (property

rights issues), process efficiencies and the transaction costs associated with market access.

Dynamics of R&D and underinvestment

The above conceptual model identifies the major

technology elements relevant for policy analysis

as a static framework. However, beginning with

Mansfield et al. (1971), economists have shown

that complex relationships exit between R&D

investment over time and industry structure, risk

preferences, strategic and project management

capabilities, and the availability of a supporting

technical infrastructure. A static framework overlooks the dynamic process by which new technology is created and utilized and thus how

underinvestment can occur over the technology

life cycle.

Corporate and government R&D investment

decisions are made at the microeconomic level.

Many attempts have been made to incorporate

cause-and-effect relationships for the impact on

output of an aggregate (firm- or industry-level)

technology variable through the use of production functions, including the acquisition and use

of a stock of knowledge. However, most of this

literature assumes a continuous, steady-state

stream of ‘‘opportunities’’ that, in turn, drive an

optimal R&D intensity. The result of such a

model is that the ‘‘stock of knowledge’’ grows

steadily over time in response to a homogenous

R&D process.

In fact, the dynamics of technological change

are far from a continuous flow. Rather, ‘‘lumpy’’

advances in generic technical knowledge drive

applied R&D for periods of time, after which

diminishing returns set in. This requires refurbishment of the generic technology base or the

optimal R&D intensity declines (Klevorick et al.,

1995).

Such a pattern implies a life cycle model of

technological change. The richness of the underlying science base, the pattern of refurbishment

of the generic technologies derived from this science base, and the efficiency of the process of

creating market applications (innovations) combine to determine the optimum R&D intensity

within and across life cycles.14 Therefore, because

the required analytical framework must be based

on a technology life cycle concept, it requires a

dynamic component.

Collectively, investments in the several phases

of R&D determine the technology life cycle’s trajectory and longevity and appropriate analysis

can occur at several levels of aggregation.15 The

majority of research with a life-cycle dimension

has characterized innovation as endogenously

generated by competing firms that draw upon a

knowledge base where the knowledge base is largely fixed. In such a framework, firms have limited windows of opportunity to apply this

knowledge and earn a profit before new technologies mysteriously appear and a subsequent new

wave of innovations takes their market shares

away.

Schmookler (1966) partially solved this problem by introducing the concept of an accumulating knowledge base over time that innovators

utilize to execute the process of creative destruction. Critically important is the proposition that

accessing this knowledge base is affected by its

public good character, which implies spillovers

and increasing returns to subsequent innovators

later in the life cycle (Caballero and Jaffe, 1993).

However, although R&D or the resulting stock

of knowledge has been incorporated into partial

and general equilibrium growth models, the

mechanisms by which this knowledge base

Underinvestment in Public Good Technologies 93
evolves and is drawn upon have not been specified.16

Klevorick et al. (1995) point out that R&D

investment (measured as an intensity ratio to

indicate reliance on R&D as a competitive strategy) has been explained by either search models

(technical knowledge is drawn from a fixed generic knowledge base) or capital models (technical

knowledge is characterized as an evolving stock,

augmented by flows of R&D investment). The

former focuses on the efficiency of search mechanisms and the richness of the underlying generic

technology base to explain R&D intensity. The

latter uses production functions to first relate

R&D investment to increases in the stock of

applied knowledge and then indicate how this

knowledge is used to produce new products/services or increase the efficiency of producing existing products/services.

Search models assume a fixed knowledge base

and hence a fixed set of technological opportunities. Thus, diminishing returns are implied. The

capital model also embodies diminishing returns

through fixed relationships with other factors of

production. These fixed relationships imply a constant set of technological opportunities, that is, a

fixed generic technology base from which firms

can develop market applications (innovations).

In reality, the generic technology base of an

industry does not remain constant. The set of

technological opportunities available to a firm or

an industry changes over time. The rate of

change, that is, the refurbishment of the opportunity set, determines the range of expected rates of

return from investment in innovations and therefore the R&D intensity of the industry that

draws upon this technology base. A central challenge for R&D investment theory is to explain

the difference in incentives to invest in the opportunity set versus applications of this set (actual

innovations).

Unfortunately analysis of the dynamics of

investment in generic technologies (the opportunity set) has been limited. To understand potential constraints on private investment in the

generic technology base requires a disaggregated

technology model (Figure 1) coupled with a lifecycle framework to show how the major technology elements evolve over time and interact with

each other. Investment in the major elements are

affected by the functional linkages among these

elements (indicated by the arrows in Figure 1),

which evolve over the technology’s life cycle.17

The nature of an element or its linkages to other

elements can cause underinvestment, as described

in the following section.

Causes of underinvestment

Beginning with Nelson (1959) and Arrow (1962a),

economists have attempted to explain the causes

of underinvestment in R&D. Nelson focused on

basic science and its pure public good content.

Arrow identified ‘‘technical’’ risk and appropriability (the existence of spillovers) as major causes

of underinvestment in R&D generally. The inability of investors to manage technical risk and the

inability or unwillingness of property rights systems to prevent spillovers (‘‘market’’ risk) can

combine to create underinvestment.18 While virtually all investments entail some degree of aggregate risk, the high levels and interactions of

technical and market risk intrinsic to individual

R&D investments impinge upon the ability to

manage or insure against R&D risk.19

The sources of technical and market risk need

to be specified to eventually provide quantitative

cause-and-effect assessments over a technology’s

life cycle. These sources are more diverse than

commonly believed. Specifically, R&D risk arises

from

(1) Technical Complexity: Complexity and thus

technical risk increase with the magnitude of

the targeted advance and the complexity of

the technology’s interfaces with other technologies within a broader technology system;

(2) Timing: R&D investment time horizons, based

on a combination of acceptable technical and

market risk, are often shorter than those

required to successfully develop radically new

technologies;

(3) Economies of Scale and Scope: Some R&D

is capital-intensive, which unbalances or

limits diversification in R&D portfolios. This

accentuates the tendency of R&D strategies

to focus on achieving scale efficiencies to

reduce both technical and market risk associated with specific applications, at the expense

of potential economies of scope enabled by

94 Tassey
an emerging technology; the result is to

reduce incentives to invest in generic technologies with multi-market potential;

(4) Spillovers: Leakage or spillover of technical

knowledge to companies that did not contribute to the research project creating the

knowledge is typically greater the earlier in

the R&D cycle an investment is undertaken,

thereby increasing market risk.

Any one of these four sources of R&D risk

can have serious negative impacts on the composition of private-sector R&D investment. Moreover, the severity of their impacts varies over

technology life cycles and across technologies.

Their impacts are particularly pronounced in the

early phases of R&D aimed at next generation or

radically new technologies and at supporting

technology elements with a strong infrastructure

character (infratechnologies). The four categories

are discussed in more detail below.

Complexity. Complexity affects risk in a number

of ways. The greater the targeted advance or the

more multidisciplinary the R&D process, the

greater the technical risk. The result can be market

failure of the Arrow risk management type. Complexity is an especially vexing problem early in the

technology life cycle because such research typically requires multidisciplinary research teams and

unique research facilities that do not exist. Making

such investments requires a positive assessment of

a distant and uncertain potential market. Thus,

firms react to research efficiency barriers by not

undertaking more radical research projects, even

though the potential RoR is high. As the technology life cycle evolves, companies often are caught

between inefficient R&D processes and the intense

competitive dynamics of high-tech markets, with

the result that products are rushed to market with

multiple performance defects.20

Moreover, most technologies driving advanced

economies are complex systems in that a number

of different technologies come together to eventually meet final demand.21 The response has been

specialization of private sector R&D on specific

components of the overall system technology.

Specialization creates inefficient R&D investment

at the component level due to inadequate systemlevel performance specifications and system integration requirements. These economic impacts

result from an increase in the public good content of both generic technologies and infratechnologies at the systems level.

Biotechnology is a dramatic example of the

impacts of complexity leading to market failure.

Using Figure 1 as a taxonomy, Table I lists multiple areas of bioscience (column 1) that have

had to advance before a larger set of generic

product and process technologies (columns 3 and

4) could be developed. These generic technologies

have evolved over the past 25 years and slowly

attracted private R&D funds, which are just now

beginning to yield significant numbers of proprietary market applications (column 5).

However, lack of focus on and research tools

for advancing complex generic technologies

before attempting the development of specific

innovations (new drugs) led to attempts to leap

from advances in basic science to product development.22 The result has been a very low success

rate for biotechnology firms. In fact, 25 years

after Genentech became the first public biotech

company, only 12 of the 50 largest companies

are profitable and the industry as a whole is still

losing money.

Table I also shows the other category of

industrial technology with significant public good

content—infratechnologies (column 2). The

demand for infratechnologies is derived from the

demand for the generic technologies and its

applications. However, the public good character

of these technical tools results in significant

underinvestment, thereby further reducing the

productivity of R&D.23

In summary, dealing with complexity is made

more difficult by the lumpy nature of technological advance (periodic quantum advances in the

generic technology) and the increasing systems

nature of modern technologies. These factors

are pushing companies toward more focused

R&D and market strategies within technology

life cycles. Efficient technology systems require

multiple components to be developed simultaneously and optimized so that maximum system

performance is achieved.

Timing. Because technologies evolve in cyclical

patterns, timing of R&D is critical. Underinvestment mechanisms can affect the rate of market

Underinvestment in Public Good Technologies 95
penetration within technology life cycles (‘‘life

cycle evolution’’) or the transition between two

distinctly different technologies and their respective life cycles (‘‘life cycle transition’’). Life cycle

evolution is determined both by macroeconomic

factors (general capital market risk, which primarily affects the amount of R&D investment)

and by the efficiency of R&D processes.

Life cycle transition is an R&D efficiency issue

is affected by the public good characteristics of

the generic technology and associated infratechnologies. At life cycle initiation, corporate decision making targets investments in new generic

technologies in order to prove concepts before

deciding to commit much larger funding to

actual innovation efforts. Generic technology

research not only involves greater technical risk

but also requires longer R&D investment time

horizons. Hence, excessive discounting for both

risk and time results in underinvestment during

this early phase of R&D.

More specifically, a company evaluating the

risk of investing in a new technology faces a set

of projected performance/cost ratios, represented

by curve 2 in Figure 2. Initially the performance/

price ratio for the new technology (point B) will

be below the current ratio for the defender technology (point A on curve 1).24 The consequent

risk of lower technical and cost performance,

possibly for some time, pushes estimates of

positive rates of return into the future, which in

turn lowers the estimated net present value of the

R&D investment.

Moreover, the innovator’s risk assessments are

compounded by the fact that the defender technology seldom gives up without a fight. For

example, in the face of a challenge from flatpanel displays, manufacturers of cathode-ray

tubes continued to reduce costs and improve picture quality, thereby raising the hurdle for the

invading technology (by continuing to move up

Curve 1, even as the curve flattens). Thus,

although diminishing returns for the defender

technology have typically set in when the new

technology appears, potential advances contribTable I

Interdependency of public—private technology assets: biotechnology

Generic Technologies

Science Base Science Base Product Process Commercial Products

• cellular biology • bioinformatics • antiangiogenesis • cell encapsulation • coagulation inhibitors

• genomics • biospectroscopy • antisense • cell culture • DNA probes

• immunology • combinatorial chemistry • apoptosis • DNA arrays/chips • drug delivery

• microbiology/ • DNA sequencing, • bioelectronics • fermentation • inflammation

virology profiling • biomaterials • gene transfer inhibitors

• molecular biology • electrophoresis • biosensors • immunoassays • hormone restorations

• nanoscience • fluorescence • functional genomics • implantable delivery • mRNA inhibitors

• neuroscience

• pharmacology

• gene expression

analysis

• gene delivery systems

• gene testing

systems

• nucleic acid amplification

• nanodevices

• neuroactive steroids

• physiology • bioinformatics • gene therapy • recombinant DNA • neuro-transmitter

• proteomics • magnetic resonance

spectrometry

• gene expression

systems

• separation technologies

• transgenic animals

inhibitors

• protease inhibitors

• mass spectrometry • monoclonal antibodies • vaccines

• nucleic acid diagnostics

• protein structure

• pharmacogenomics

• stem-cell

modeling/analysis • tissue engineering

•

•

A

B

Old Technology

New Technology

Potential or Actual

Performance/Price

Time

1

2

Figure 2. Translation between two technology life cycles.

96 Tassey
ute to the risk of investing in the new technology

at an early point in its life cycle.25

Transitioning across technology life cycles is a

particularly difficult issue for R&D policy process

to address. A number of high-tech companies

manage transitions among successive product life

cycles quite effectively. However, the transition

to a radically new technology life cycle is seldom

achieved by the majority, if any, of the firms

applying the defender technology. Many of these

companies lose out to small, innovative firms

that are willing to take on the high risk posed by

the initial performance/price gap. In cases of radically new technologies, the established companies may be replaced by new industries—either

domestic or foreign.

This aspect of creative destruction occurs in

part because current suppliers are focused on

enhancements of the defender technology in an

attempt to maximize short-term cash flows. They

consequently wait too long to undertake research

targeting the new technology. This pattern also

occurs in part because the established industry is

organized to develop and market the defender

technology and does not have all of the required

R&D skills and facilities needed to migrate to

the new technology.26 In summary, the composition of R&D investment is inadequate from a

long-run (multi-cycle) perspective.27

Companies with long investment time horizons

and high risk tolerances (and hence relatively low

discount rates) might be able to make sufficient

investments in the new technology (and associated

R&D capabilities) before the old technology

matures. This would allow innovation to take

place higher on performance/price curve 2 than

point B. Such a strategy seems logical because market penetration can occur more rapidly. However,

with the typical R&D life cycle about 10 years

(Branscomb and Auerswald, 2002; Cannon, 2002)

and increasing competitive pressures raising discount rates, even large, R&D-intensive companies

with substantial central research labs are decreasing investment in the longer-term research that

produces most new technologies.

The problems in moving up the performance/

price curve from point B are evident in the biotechnology example. The evolving structure of

this industry (small firms focusing on individual

drug candidates) accentuates an inefficient

tendency exhibited in all high-tech industries

to varying degrees toward a forced approach

to product development; that is, attempting to

develop proprietary products with an inadequate

underlying generic technology and supporting infratechnologies. This raises product failure rates

due both to inadequate proof of concept (generic

technology) and to inefficient research methods

that increase R&D costs leave excessive uncertainty with respect to research outcomes. These

higher failure rates discourage risk capital needed

for follow-on investments.

Economies of scale and scope. Arrow (1962) first

identified the problem of indivisibility of technical

information and its consequent effect on R&D

investment. All other factors aside, the typical firm

tries to fully utilize its resource base; that is, it

seeks to capture the benefits from the entire scope

of potential market applications that arise from a

generic technology and supporting infratechnologies. Time and considerable resources are needed to

acquire a unique set of technology assets, so maximum efficiency in utilizing these assets is essential.

First mover and sustained leadership strategies

lead firms to seek to reduce risk by concentrating

on the development of a relatively limited set of

technology assets and related market applications

(so-called ‘‘core competence’’ strategies).

In contrast, portfolio theory preaches diversification into multiple product fields to diversify

market risk. However, firms have found that this

strategy can entail substantial technical risk due

to the increased level of resources required to

achieve and maintain competency in multiple

technologies, each requiring somewhat different

sets of research skills and facilities. Moreover,

diversified portfolios also can require multiple

sets of production and marketing assets. Such

risk presents a disincentive to diversify horizontally or vertically. Increased global competition

in technology-based markets has accentuated this

risk. Thus, firms seek to reduce risk by concentrating on the development of technology assets

for a limited set of market applications (so-called

‘‘core competence’’ strategies).

A narrower technology focus often includes

downsizing central corporate research laboratories and shifting the composition of the research

to more applied topics that directly support the

Underinvestment in Public Good Technologies 97
remaining lines of business. Less generic technology research is conducted because a narrower

market focus implies less opportunity for economies of scope. Many firms, especially small and

medium ones, do little or no generic technology

research, instead relying on external sources for

new technology platforms.

In summary, such strategic shifts can be a

two-edged sword. On the one hand, concentrating R&D resources on fewer technologies and

their market applications can increase economies

of scale. Such a strategy reduces technical risk

(core competence is emphasized) and possibly

market risk (due to specialization). On the other

hand, market risk may actually be increased

because product/service diversification has been

reduced; that is, the firm’s portfolio of technologies and subsequent market applications is more

narrowly focused and therefore more dependent

on a smaller number of markets.

Evolving industry structures offer a partial

adjustment mechanism. In the last two decades,

forms of collaboration for cost sharing and risk

pooling have proliferated, including multi-company consortia and partnerships with universities

and government. However, these complex entities

have contractual, organizational, and management problems that can cause inefficiencies in the

R&D undertaken and thus can lead to suboptimal results (Miotti and Sachwald, 2003).

Thus, while specialization may enhance shortterm performance, it accentuates ‘‘path dependence’’ and thereby reduces firms’ and entire

industries’ ability to adapt to new technology life

cycles by reducing generic technology research

capabilities. Such propositions are reflected in

evolving ‘‘evolutionary theories’’ of technologybased growth (Nelson, 1995) in which acquired

assets and supporting infrastructure tend to

maintain technological pathways as a conscious

attempt to fend off the Schumpeterian process of

creative destruction. However, the life cycle nature of technological change eventually leads to

the very destruction that such strategies seek to

avoid. The incumbents and their defender technology inevitably lose out to the emerging technology.

Spillovers. In the economics literature, market

failure in the technology-based sector of the

economy is most often related to the existence of

externalities called ‘‘spillovers,’’ defined as technical knowledge and thus presumably economic

benefits that are not captured by the firm creating the knowledge. That is, portions of these benefits accrue (spill over) to other firms, so that

these firms ‘‘free ride’’ on the R&D of the innovating firm.

Unfortunately, economists have not reached a

consensus on a taxonomy for characterizing and

analyzing R&D spillovers. The literature identifies several types of R&D spillovers that are not

necessarily distinct from one another. Most

directly associated with R&D investment are

‘‘price’’ or ‘‘market’’ spillovers and ‘‘knowledge’’

spillovers (Griliches, 1979; Mohnen, 1996). In

both cases, technical knowledge passes from the

originator to other economic agents without full

compensation.

Price spillovers occur when the increased value

of new or improved products or services is not

fully reflected in the price differential between the

old and the new versions of the product or service. The portion of the benefits captured, but

not paid for, by the user diminishes the reward

to the supplier (innovator). In both cases, the

return on investment for the innovator is

reduced, which, if sufficiently pronounced, can

constrain incentives for further R&D investments.

Knowledge spillovers occur when technical

knowledge itself leaks or spills over from the

innovator to competing firms without compensation to the innovating firm. The competing firms

use the knowledge to imitate the innovation and

thereby potentially reduce the innovating firm’s

return on investment. To the extent that the firms

imitating the technical advance do not compensate the innovating firm (such as through licensing

arrangements), a ‘‘reward-capture’’ problem can

arise which inhibits private investment in R&D.28

Finally, this literature also identifies ‘‘production’’ spillovers in which knowledge diffuses into

an industry in purchased capital goods embodying new technology developed in the capital

goods industry. Such spillovers result from synergistic interactions within the purchasing industry

(such as learning by doing) and are argued to be

independent of any knowledge spillovers in the

capital goods industry or price spillovers in the

98 Tassey
custody exchange due to market dynamics.

Empirical studies have not shown that production spillovers present a significant disincentive

or occur with any regularity.29

Spillovers create several problems for R&D

policy analysis. On the one hand, they are viewed

negatively because, when excessive, the expected

RoR to the prospective innovator is lowered to

the point that the contemplated investment in

R&D is not undertaken. On the other hand, it is

this unique characteristic of technology—the fact

that it diffuses widely—that enables large and

diffuse economic impacts to be realized.30 Moreover, spillovers do not have to mean one-for-one

subtraction of benefits from the innovator. For

example, network externalities can result from

imitation by other firms coupled with the establishment of an efficiency-enhancing infrastructure

that enable increasing returns for many firms

from serving a larger user population.

The form of technical knowledge influences

the spillover pattern. Technical knowledge has

been classified as either ‘‘embodied’’ or ‘‘disembodied’’. Production spillovers are a market

transfer of embodied technology. Price spillovers

usually refer to this type of technical knowledge,

as well. In contrast, technical knowledge developed within or somehow acquired by an industry

can be disembodied in that the knowledge is

known and used independently of any physical

structure. The industry’s own generic product

and process technologies fall into this category,

as do many types of nonproprietary technology

infrastructure (infratechnologies). Such disembodied knowledge tends to ‘‘leak’’ or spillover,

causing the frequently referenced appropriability

problems.

Knowledge has also been classified as tacit or

codified.31 These terms refer to the form of technical knowledge, that is, whether or not the

knowledge has been formalized as a commodity

in the sense defined by Arrow. Much tacit technical knowledge is embodied in human capital.

Romer (1990) refers to such tacit knowledge as

‘‘rival technical knowledge’’ in that its use in one

application (assigning human research capital to

a particular R&D project) prevents its use in

alternative applications. Such knowledge only

diffuses slowly from one person to another usually and requires close contact between parties

executing the transfer. Thus, spillovers occur

slowly. Generic technology is largely of this type

(Darby and Zucker, 2003).

The previously discussed concept of ‘‘technological opportunity’’ is a means of characterizing

the potential inflows (inward spillovers) of technical knowledge. For example, Klevorick et al.

(1995) used the Yale Survey of R&D managers

to analyze inter-industry differences in technological opportunity. They concluded that these

sources lie primarily outside the industry and

that levels of opportunity result from intrinsic

differences in knowledge spillovers.

Finally, adoption of the disaggregated technology model summarized in Figure 1 allows

the observation that the infratechnology element is a case where spillovers are largely

desirable. The strong infrastructure character of

infratechnologies, frequently manifested in the

form of standards, means that significant economic benefits are realized only when all participants in a market (both demand and supply

sides) have access to the same infrastructure

(Tassey, 2000).

For example, using the same acceptance test

method to consummate a market transaction for

a technologically complex product reduces transaction costs and thereby increases the rate of

market penetration of the new technology. Thus,

policy makers must ensure the availability of an

efficient standards infrastructure to achieve utilization of these infratechnologies. However, the

optimal achievement of such is not easy, as

timing and content of standardization (and hence

the derived demand for the underlying infratechnologies) are difficult investment objectives to

define and hence to manage. ‘‘Free riding’’ with

respect to the standard is actually encouraged to

increase economic efficiency, but this objective

guarantees underinvestment in the underlying infratechnology.

In summary, the evolving literature on spillovers implies a model in which technical

information diffuses in specific channels and at

non-negligible cost. Rate and direction depend on

the degree to which the information is generic or

applied and on its ultimate use (proprietary product/process development or infrastructure). The

implied disaggregated model leads to a differentiated public policy perspective. That is, spillovers

Underinvestment in Public Good Technologies 99
of generic technologies and infratechnologies are

desirable because of their public good content,

whereas proprietary technologies require assignment of property rights to provide incentives for

market applications. This critical distinction

allows a significant improvement in R&D policy

over that implied by the Arrow-type conclusion

in which property rights result in underutilization

of this information.

Defining underinvestment for government policy

development

Having identified the causes of underinvestment

in R&D, a method for estimating underinvestment can now be discussed.

A major analytical challenge for R&D policy

analysis is to assess the extent to which the

innovator (private) rate of return (PRR) falls

below the hurdle rate (HR) for corporate R&D

investment. The analysis also must determine

the reason for the sub-threshold PRR. A low

projected PRR could simply be due to inadequate technical and/or market appeal, which

means the technology has a relatively low

potential social rate of return (SRR). Alternatively, intrinsic or externally imposed market

failures of the type discussed above could be

substantially suppressing the PRR below a relatively high SRR.

While recognizing the existence of a rate-ofreturn gap and noting that the magnitude of

underinvestment can be related to this gap, a

more accurate representation of underinvestment

phenomena is possible. In Figure 3, four R&D

projects are shown.32 The vertical arrows represent the possible SRR and the horizontal arrows

indicate the possible PRR. Hence, the lengths of

the arrows represent the ranges of potential outcomes from a particular R&D investment. Risk

is the probability of an outcome (RoR) being

below the HR.33

These two required elements of policy analysis are incorporated in Figure 3. The first three

projects (A, B, and C) have ranges of expected

PRR below the corporate HR and therefore

would likely not receive significant or sustained

private-sector funding. Project A is an earlyphase (generic) technology research project,

which has significant economic potential. However, at this point in that technology’s development, the actual (or uncertainty about the

actual) levels of technical and market risk are

beyond the ranges acceptable to corporate R&D

managers. That is, the risk-adjusted PRR estimate is lower than individual firms’ HRs, and

thus a corporate commitment to funding this

project in response to conventional decision criteria will be limited.

Such a project is a candidate for government

financial assistance, usually in the form of costshared funding for the conduct of generic technology research. Cost sharing reduces risk

enough to stimulate initial private investment in

applied R&D (in Figure 3, Project A shifts to the

right toward the innovator’s HR). If successful,

this early-phase technology research will reduce

uncertainty about the probability distribution of

technical and market outcomes and shift the distribution above the innovator’s HR. Conversely,

this research could result in an insufficient

increase in the expected PRR and might also

reduce the expected SRR below its HR, causing

termination of R&D in the particular technology

by both industry and government.

Project B also has a very low expected PRR,

while at the same time exhibiting a high SRR. A

policy analyst might conclude that too large an

investment of government funds would be

required to reduce both uncertainty and risk and

thereby raise the expected PRR above the private-sector HR. However, such projects are typical of elements of an industrial technology that

B

A

D

C

Social

Rate of

Return

Private Rate of 

Return

Private Hurdle

Rate

Social

Hurdle

Rate

450

Figure 3. Government R&D project selection: rate-of-return

criteria.

100 Tassey
have a strong infrastructure character and exhibit

a high SRR. For example, the network externalities resulting from standardization greatly

increase the SRR for this type of infrastructure,

but simultaneously prevent ownership of the

embedded intellectual property by any one firm.

In such cases, the PRR may never be permanently raised above the HR. Thus, the R&D may

be funded largely by government to capture the

high SRR from eventual adoption by industry of

the resulting infratechnology as an industry standard.

Projects C and D are quite different from

either A or B. Project D is a typical R&D project

found in a business unit of an R&D-intensive

company. Its range of expected PRR is more

than high enough to receive adequate private

R&D funding. Project C is a marginal project

under existing corporate R&D investment criteria; that is, its expected RoR does not quite

exceed the private-sector HR, so this project is

not funded.

However, projects such as C can have sufficient

SRR (above the social HR) to warrant government investment incentives. In such cases, because

C falls within the general category of applied

R&D projects that a firm’s business unit would

typically consider, the R&D policy response

should be directed at conventional corporate R&D

investment criteria (as opposed to the distinctly

different criteria used to evaluate long-term,

exploratory technology research). For situations

such as project C, a tax credit rather than direct

funding is the correct policy response.34

R&D cycle analysis

The above discussion focuses on relationships

between the nature of the R&D project and its

risk-adjusted expected RoR at different points in

the technology life cycle. Such comparative static

analysis is important for identifying the factors

that determine underinvestment.

In addition, the analyst must also analyze the

dynamic element of underinvestment; that is,

how the pattern of underinvestment affects the

evolution of the technology. In essence, this analysis is an assessment of both technical and market risk management by industry over the

technology life cycle.

As previously defined, generic technologies

(proof of concept) provide the basis (technology

platforms) for an array of market-specific applications (collectively, the technology trajectory).

Although amounts invested in generic technologies are small relative to those for applied R&D,

these investments are the first step in technology

research. The distinction between scientific

research and technology research is extremely

important for market failure analysis. Scientific

research seeks to advance knowledge. The concept of risk is relevant only in the sense that

funding decisions are based on the expectation

that scientific knowledge will be advanced.

In contrast, risk assessments are continually

made by industry over the remainder of the technology life cycle based on estimates of the probability distributions of future market returns.

These estimates drive R&D investment decisions.

The nature of this risk is twofold: the probability

of achieving the technical objectives perceived to

be required for the target markets and the probability that when commercialization occurs, the

market will be receptive.35 As long as reward-risk

ratios exceed private and social HRs, R&D funds

will continue to be allocated to the technology’s

development. Patterns of R&D by which technology becomes progressively more applied is evident

in the technology life cycles underpinning the

major technology drivers of the last 60 years.14

However, the conventional characterization of

a technology’s evolution as a steady increase in

its applied character masks a fundamental discontinuity in the pattern of risk reduction that

strongly affects investment behavior. As discussed

above, the technical complexity of the proof-ofconcept objective of generic technology research,

its frequent mismatches with existing corporate

market strategies and internal research capabilities, and its distance in time from potential commercialization combine to cause a spike in both

technical and market risk. By occurring early in

the technology research cycle, this ‘‘risk spike’’

can block substantial private R&D investment.

Such a discontinuity in risk reduction is portrayed in Figure 4 for two hypothetical corporate

R&D projects, A and B. In both cases, the

assumption is that the underlying science base

has been advanced over time to a point at which

corporate managers can make initial assessments

Underinvestment in Public Good Technologies 101
of the technical and market risk associated with

the development of technologies derived from on

this science.

Based on the body of scientific knowledge

acquired through basic research, the decision

making process might be said to face a purely

‘‘technical’’ risk, R0, that summarizes estimates

of the probability that a technology could be

developed that performs some generalized function. However, 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.

Several of the factors cited in Section ‘‘Dynamics

of R&D and underinvestment’’ can raise this

technical risk. Moreover, total risk is now reassessed in view of the need to meet production

cost objectives.

A ‘‘market’’ risk also must be estimated to

allow for the significant probability that demand

for the new technology will be overestimated or

that market penetration will be slower than projected due to the factors cited in Section

‘‘Dynamics of R&D and underinvestment’’ such

as improvements in the defender technology.

Slower market penetration could mean that production costs will be high relative to the defender technology for a longer period of time due

to failure to realize economies of scale or scope.

This market risk must be added to technical

risk.

In Figure 4, the risk spikes RSA and RSB represent the combined increase in technical and

market risk for projects A and B, respectively.

Such risk spikes might be thought of as the

‘‘public (or social) risk’’ component because they

occur in the early generic technology research

phase, which has the public good dimension

described earlier. Project A is the more radical

innovation and so it presents a greater initial risk

spike, RSA.

Without the risk spike, firms would be faced

only with a reduction in the ‘‘private risk’’ component, RP, associated with applied R&D. Project A also requires greater risk reduction during

applied R&D, RPA.

In the absence of the risk spike, conventional

R&D investment criteria would deal with RP

because it falls within acceptable reward-risk

ratios. Thus, if at the level of pure technical risk,

R0, conventional corporate R&D criteria result

in private investment based on risk-adjusted rateof-return estimates, the policy problem is to overcome the risk spike so that these criteria can be

applied. The importance of overcoming the risk

spike for the more radical technology, A, is

increased by the fact that the overall risk associated with project A will actually decline to a

lower level than that for Project B. This occurs

because, if Project A is successful technically, the

resulting set of market applications will likely

have a larger collective value than B, hence

increasing the probability of a RoR above the

firm’s HR.36 This is depicted in Figure 1 by a

greater decline in private risk for Project A, indicated by RPA.

Current R&D policy tools do not fully recognize the large discontinuity in the risk reduction

process occurring at the transition between scientific and technology research. If this risk spike

did not occur, the risk curves in Figure 4 would

have steadily declining slopes and would support

proponents of little government R&D funding

beyond basic science. However, the substantial

jump in total risk caused by the potential divergence between technical and market requirements

on the one hand and research capabilities, time

discounting, and corporate strategy mismatches

RSA

RSB

R&D

Cycle

Commercial Products

RPA

Risk

Basic

Research

Generic Technology

Research

Applied Research

and Development

10 years Commercialization 6 years

Commercial Products

RPB

R 0 Science Base

A

B

Figure 4. Risk reduction in the R&D process.

102 Tassey
on the other can and do lead to substantial

underinvestment.37

In summary, the advancement of basic science

sufficient to allow technology development to

begin does not guarantee immediate or even

eventual commitment of adequate private sector

funds. Reducing risk spikes through early-phase

generic technology research is necessary to enable

optimal private sector R&D spending. However,

the fact that specific elements of an industrial

technology are quasi-public goods means that

their efficient development over the entire life

cycle requires a mixture of public and private

funding, distributed according to the magnitude

and duration of various market barriers.38

4. Defining and measuring underinvestment

Having identified the critical quasi-public elements embedded in the typical industrial technology, the policy analyst ideally should then

estimate an optimal level of investment for specific technology elements against which to assess

actual investment behavior. For identified underinvestment mechanisms (the ‘‘cause’’), such analysis requires the selection of impact metrics and

measures that enable quantification of the economic impact (the ‘‘effect’’) of identified barriers.

The resulting quantitative estimates are combined

with qualitative analyses of the identified barriers

to enable appropriate policy responses to be

selected and then to allocate resources for those

responses across technologies. Ranking for policy

purposes will be a function of the estimated SRR

and the difference between the PRR and the private HR.

However, economic research has made only

partial progress in developing and applying the

required analytical tools. An overall optimal

R&D intensity has been correlated with assessments of technological opportunities (Klevorick

et al., 1995) and estimated at the national level

by Jones and Williams (1998, 2000).39 At the microeconomic (technology) level, the opportunity

set determines the distribution of possible rates

of return from applied R&D (innovation investments) and thus implies an optimal R&D intensity for that technology-based industry. Based on

initial rankings of technological opportunity, the

policy analyst ideally would estimate SRRs and

PRRs for each technology and compare them

with appropriate HRs. However, arriving at an

optimal R&D intensity to use as a benchmark

for underinvestment analysis, estimating the

appropriate investment ‘‘gap,’’ and then explaining this gap in terms of a market failure mechanism are extremely difficult analytical and

empirical steps.

Rate-of-return analysis

To some extent, the use of return-on-investment

techniques (borrowed from corporate finance)

has proved useful for demonstrating retrospectively the range of economic impacts from specific technology investments.40 However, this

approach is burdened with several conceptual

and empirical issues, especially when applied to

prospective (strategic planning) exercises.

One problem with prospective analyses for

R&D policy decisions is the fact that several

significant and uncertain phases of R&D must

take place followed by investments in production and marketing capabilities before commercialization can occur. This problem is

particularly severe for basic and early-phase

(generic) technology research. In the latter case,

the analysis is complicated by the fact that the

additional R&D following investment in the

generic technology is typically much larger, but

its amount, composition, and timing are driven

by the direction and timing of generic technology research.

Moreover, the time to commercialization

from generic technology research is quite long.

Rate-of-return analysis is sensitive to the

assumed time series of R&D investment and

the subsequent commercialization and market

penetration patterns. Moreover, use of rate-ofreturn techniques or discounted cash flow/net

present value measures produce a negative bias

for prospective R&D investments through their

treatment of risk and reward. Specifically, these

techniques do not ascribe value to the intellectual capital created by the R&D cycle as it

reduces technical risk (Boer, 1998, 2000). That

is, at best only an incomplete market exists for

technology capital.

Nevertheless, even for prospective analyses, the

conventional rate-of-return method can be useful

Underinvestment in Public Good Technologies 103
for the development and management of government R&D support roles.41 To this end, rate-ofreturn analysis can be applied in two ways. One

way is to compare rates of return between major

categories of investment. If the SRR from a category of investment (say, R&D) is higher than that

from another category, the suspicion is that some

barrier is preventing efficient allocation of

resources in the private sector. That is, underinvestment is occurring in the category with the

higher SRR due to a suppressed PRR. Otherwise,

funds would flow into the investment lowering the

SRR until the marginal RoR was reduced to the

opportunity cost of capital.

The relationship between the rate at which

the expected PRR declines with increased R&D

investment and the HR (the opportunity cost of

capital for private sector investors) is shown in

Figure 5. The two negatively sloped lines, P and

P¢, are marginal efficiency of investment (MEI)

curves. Firms rationally invest in the R&D projects with the highest expected PRR first and

then select projects with the next highest PRRs

and so on until the opportunity cost of capital

(HR) is reached. The actual slope of an MEI

curve can be steep or shallow depending on

technological and market opportunity, efficiency

of R&D, etc. The entire curve can shift up or

down depending on intrinsic characteristics of

the technology and its associated infrastructure.

Figure 5 also shows two alternative HRs, HR

and HR¢.

For an MEI curve P and a hurdle rate HR,

this industry will invest an amount Q in R&D.

If one or more of the types of market failure

discussed in Section ‘‘Causes of underinvestment’’ intensifies, the entire MEI curve could

shift downward to P¢ causing a reduction in

R&D investment to Q¢. Similarly, if market

conditions change so that either the PRR on

alternative investments or general risk averseness increases, the HR could shift upward to

HR¢ causing an equivalent reduction in R&D

investment to Q¢.

A second application of rate-of-return analysis

is to represent the amount of underinvestment in

R&D by the ‘‘gap’’ between the SRR and PRR

for the same category of investment. The implication is that if industry can only earn the PRR,

then less R&D investment will be forthcoming.

This conceptual approach would be represented

in Figure 5 if innovating firms are assumed to

capture all the returns generated by their R&D;

that is, the curve P is also the SRR. If some barrier (typically assumed to be spillovers) causes a

downward shift in the PRR curve to P¢, then a

‘‘gap’’ appears leading to a reduction in R&D of

QQ¢ (for a HR).

However, this characterization of underinvestment is simplistic along several dimensions, especially with respect to the implied assumption of

a fixed gap and the implications of a gap for

policy:

(1) The ‘‘gap’’ is important only to the extent

that it pushes the PRR below the HR. Obviously, the closer the PRR is to the HR, the

fewer additional projects will be undertaken

before the marginal PRR declines to the HR.

The slope of the MEI curve is clearly important in this regard.

(2) From an economic growth perspective, ‘‘outward’’ spillovers can actually have a beneficial effect because they create opportunities

for new firms to enter the market for the

technology, which thereby expands competition and hence the range and amount of

economic benefits delivered. That is, the

reverse concept of ‘‘inward’’ spillovers to

other firms in the industry needs to be taken

into account. Moreover, to the extent that

inward price or production spillovers are

Percent

P

R&D

Investment

HR

Q' Q

HR'

P'

Figure 5. Marginal efficiency of R&D investment (expected

private rate of return).

104 Tassey
realized from supplying industries, the innovating industry benefits in the form of

higher PRR, thereby leading to more R&D

in that industry.42 The implication is that

the size of the gap and hence the PRR relative to the HR will likely vary over the technology life cycle.

(3) A larger market resulting form spillovers can

also allow network externalities to be realized

or a general risk reduction to occur that

opens that market to more risk-adverse

groups of buyers. The result may be an

increasing PRR for the initial innovators, as

well as for imitators. Moreover, less steep

MEI curves resulting from additional and larger markets derived from the generic technology lead to more applied R&D. Such effects

usually are significant in the middle and later

portions of the technology life cycle, where

applications become ‘‘commoditized’’ and

competition is based more on cost (personal

computers are an example). Thus, the dynamics of technology-based markets can keep the

PRR above the HR and maintain reasonable

levels of private R&D investment for a longer

period of time (extending the technology life

cycle).

(4) Whereas the SRR is a single aggregate return

on an industry’s R&D investment, the PRR

varies among firms in that industry, including

the primary innovator, which makes the estimate of a PRR for the purpose of ‘‘gap analysis’’ difficult. In the end, what counts for

policy analysis is the number of firms for

which the PRR exceeds the HR at each phase

in the R&D cycle.

Estimating underinvestment

Ideally, estimates of the PRR associated with

each technology element would be obtained at

different points in time as part of the underinvestment analysis. Such estimates would be compared with both the industry’s average HR and

the estimated SRR to determine if a government

policy response might be warranted and, if so,

over what period of time.

However, conventional applications of rate-ofreturn analysis, especially in the early phases of

the R&D cycle, are difficult to execute. For

example, because generic technology research is

the first phase of technology R&D and is aimed

at a next generation or an emerging technology,

significant markets typically do not yet exist.

Point estimates for dates in the future are highly

uncertain and, if relied upon as the sole basis for

initiating an R&D project, can lead to expensive

long-term commitments with frequent negative

outcomes.

Modified techniques are therefore required.

One alternative approach involves regular assessments of progress toward technical goals and

preliminary estimates of market opportunities.

Repeated revisions of assessments of the extent

to which technical and market risks are being

reduced in effect segment the R&D funding processes with zero-based decisions occurring at each

check point. Such ‘‘real options’’ tools can continue to be used until individual companies are

able to apply conventional investment criteria to

estimate PRRs.43 As these PRRs rise above HRs,

government subsidies can be phased out.

The relative funding contributions of government and industry will be determined by the subsidies required to elevate the PRR above the HR

(or to reach a termination decision). Real options

models exist that are highly quantitative,

although application of this approach to earlyphase generic technology research will likely

entail some compromises. The major management requirement is periodic review of technical

risk reduction and observance of industry’s

behavior with respect to its internal R&D strategies (evidence of reductions in both technical and

market risk).

Still, the more quantitative the selected

approach, the better. For infratechnologies, a

quantitative approach can frequently be used.

Here, markets are often established for a hightech product or service, but inadequate technical

infrastructure of some type is creating significant

inefficiencies and thereby retarding market penetration.44 Thus, direct estimates of the cost of the

inadequate infrastructure are obtainable from

industry surveys. Removing or at least reducing

the costs constitute an economic benefit and such

estimates can be related to projected research

program cost estimates to calculate an expected

RoR.45

Underinvestment in Public Good Technologies 105
With respect to identifying, characterizing, and

estimating the magnitude of underinvestment in

infratechnologies, NIST has conducted several

prospective economic analyses of the adequacy

of existing infratechnologies in order to provide

inputs for its strategic planning function.

Table II summarizes five studies of the economic

costs of underinvestment in infratechnologies.46

The choice of topics for such studies is based on

a somewhat unstructured process of background

analyses, consultations with industry, reviews of

other studies (if they exist), and higher order policy directives.

The scope of such studies can vary significantly, making their use in ranking underinvestment phenomena difficult. For example, the first

study in Table II examined the costs of inadequate standards for the exchange of electronic

product design data between automobile companies and their parts and subsystems suppliers.

The estimated annual cost is only for that supply

chain, even though other manufacturing supply

chains (such as aerospace) are known to have

similar interoperability problems. In contrast, the

software testing study conducted two larger case

studies—one in manufacturing and the other in

services—which provided a sufficiently broad

base (along with some other data) to rationalize

extrapolation to the national economy level.

Hence, this study not only produced cost estimates for the two case studies, but also a much

larger (national) estimate of the costs of underinvestment in testing infratechnologies.

Examples of government responses to the ‘‘risk

spike’’

Several decades of large-scale funding of molecular biology research by NIH were required before

private investment kicked in and spawned a biotechnology industry. A recent analysis of U.S.

patent citations in biotechnology found that

more than 70% of them were to papers originating solely at public research institutions

(McMillan et al., 2000). And, 20 years after the

first biotechnology company went public, NIH

still provides research funding to dozens of the

more than 300 biotechnology companies.

The tremendous growth in health care productivity being made possible by a radically new

technology also is creating a new industry with

substantial economic growth potential. This phenomenon is occurring to a greater extent in the

United States in large part because the U.S. Government funded both the science base and portions of the subsequent early phases of

technology research (i.e., the quasi-public good

element). This funding coupled with other factors

such as clustering and a robust venture capital

market has allowed U.S. industry and U.S. capital markets to reach positive investment decisions

ahead of the rest of the world.47

Table II

Economic studies of costs due to inadequate infratechnology investment

Focus of study Infrastructure studied Industries covered

Estimated

annual costs

$billions

interoperability

costs

product design data exchange automotive supply chain 1.0

deregulation metering, systems

monitoring/control

electric utilities 3.1–6.5

software testing all stages of the testing cycle transportation equipment 1.8

financial services 3.3

extrapolation to U.S. 59.5

interoperability

costs

business data exchange:

demand, production, inventory,

procurement, & distribution

automotive supply chain 5.0

electronics supply chain 3.9

medical testing quality of measurement

assurance (calcium)

medical testing laboratories 0.06–0.199

106 Tassey
In the past, non-market motivations (primarily

national defense) allowed Federal funding of

major new technologies at above-threshold levels,

which subsequently drove economic growth for

decades. The fields of computing and communications provide compelling examples of how government funding plays a critical role in advancing

generic technologies and achieving minimum

thresholds of R&D capability necessary to stimulate takeoff in private sector investment. Federal

funding for electrical engineering in areas such as

semiconductors and communications technologies

(major components of computing technologies)

has fluctuated between $800 million and $1 billion

since the 1970s. Funding for computer science

increased from $10 million in 1960 to approximately $1 billion in 1995. Over these time periods,

such amounts have represented a major fraction of

all early-phase technology research funding in

information technology.48

The majority of this funding went to industry

and university researchers. Not only did the government-sponsored research advance key areas of

the underlying science and technology, but it also

fostered a broad and deep R&D capability that

leveraged follow-on private investment by industry. An extremely important aspect of this support is the extension of Federal funding beyond

basic scientific research to generic technology and

even experimental deployment. For example,

before 1970, the Federal government sponsored

individual researchers who developed generic network technologies, such as queuing theory,

packet switching, and routing. During the 1970s,

experimental networks, notably the ARPANET,

were constructed. These networks were primarily

research tools, not service providers. Most were

federally funded because, with a few exceptions,

industry had not realized the potential of the

technology.49

During the 1980s, networks were widely

deployed, initially to support scientific research.

The National Science Foundation (NSF) was the

major supporter of networking during this period,

primarily through the NSFNET, which evolved

into the Internet. At this point in networking technology’s evolution, industry began to see the

enormous economic potential. Companies such as

IBM, Digital Equipment Corp., and Compuserve

established proprietary networks. These networks

were rapidly utilized worldwide for email, file

transfers, and electronic funds transfers.

However, as often happens in the evolution of

a major new technology, companies with a large

share of the initial proprietary applications displayed little interest in the even greater potential

of the generic technology. To be broadly successful and thereby have large economic impact, systems technologies such as the Internet have to be

based on open architectures. This type of technical infrastructure greatly expands markets for

system technologies by facilitating network externalities. Unfortunately, such a requirement presents a negative investment incentive to firms

with substantial commitments to proprietary networks. Moreover, telephone telecommunications

companies resisted computer networks, including

the Internet, because the nature of voice communications networks is strikingly different from

data networks.50

Similarly, IBM pioneered the concept of relational databases but did not pursue commercialization of the technology because of its potential

to compete with established IBM products. NSFsponsored research at UC-Berkeley allowed continued exploration of this concept and brought

the technology to the point that it could be commercialized by several start-up companies and

then by more established suppliers, including

IBM. This pattern was also evident in the development of reduced instruction set computing

(RISC). Though the concept was originally developed at IBM, RISC was not commercialized

until DARPA funded additional research at UCBerkeley and Stanford as part of its Very Large

Scale Integrated Circuit (VSLI) Program in the

late 1970s and early 1980s.51

Other examples of critical government funding

of generic technology research include expert systems, speech recognition, and image processing.

Industry began to invest in these and other areas

of artificial intelligence (AI) in the 1960s but scaled

back when the long time periods required for commercialization became apparent. Federal investments continued in these generic technologies for

a decade or more until conventional industry

R&D criteria could rationalize investments in

applied R&D (i.e., projected PRRs exceeded

HRs). Now, private investment is driving the commercialization of many AI technologies.

Underinvestment in Public Good Technologies 107
Thus, government R&D is frequently the

mechanism to reduce uncertainty through support of basic research and then technical and

market risk (the risk spike) in the early (generic)

phases of a technology’s development. However,

this policy response does not guarantee commercialization in a fixed period of time, if ever. Technical and market risk may be reduced more

slowly than anticipated, or market risk may actually be increased by unforeseen developments in

competing technologies. In the case of gallium

arsenide, the Department of Defense spent $570

million on research between 1987 and 1995, but

the overall market had not reached $1 billion in

annual sales by 1996.52 Two reasons for this pattern were slower-than-anticipated progress in

R&D and repeated extensions of the siliconbased semiconductor life cycle.

5. Summary

The analytical framework presented provides an

approach to explaining and estimating R&D

underinvestment trends in a cause-and-effect

framework. Emphasis is on underinvestment in

the early phases of technology R&D (generic

technology research) and in technology infrastructure (infratechnologies); that is, the focus is

on R&D composition distortions. The final

stage of the policy process, not addressed here,

is the selection, design, and implementation of

policy responses—primarily the forms and management of government-funded research. The

complexity of this last stage requires separate

treatment. Also not analyzed are market failures

associated with the aggregate amount of

industrial R&D, which means primarily applied

R&D. The cause-and-effect phenomena are significantly different from those associated with

composition failures and therefore so are the

appropriate policy responses.

Economic theory needs advancement in the

area of R&D underinvestment phenomena. The

underinvestment mechanisms are not well defined,

with the literature focusing largely on spillovers.

Even spillovers are not sufficiently treated, as several types exist and the relationships to private

sector R&D investment behavior are not adequately specified. Moreover, the economic impact

of spillovers has positive as well as negative

dimensions. This complexity, coupled with the

quasi-public good nature of technology research,

has limited progress in developing models and

useful tools for R&D policy analysis.

The approach taken in this paper involves

three analytical steps. First, the identification and

analysis of the causes of underinvestment is

undertaken to enable ultimate selection of a policy response mechanism. Second, indicators of

underinvestment in R&D are constructed to

allow a scan of relevant private R&D investment

patterns and supporting infrastructure trends and

thereby identify broad investment categories for

more intensive analysis. These indicators are then

compared to data on economic performance in

the relevant industries to prioritize and focus

subsequent analysis. Third, based on these analyses, technology-specific impact metrics and measures are selected to allow quantification across

technologies of the net benefits of past policy

implementations and the expected net benefits

from candidate policy initiatives.

If these three stages of analysis are successfully

carried out, the results can significantly enhance

the selection of policy response mechanisms and

the levels of resources required for candidate initiatives; that is, a match can be achieved between

different types and modes of underinvestment

and the most efficient policy responses.

Acknowledgement

The author is indebted to David Leech and

Andrew Wang for comments on previous drafts.

Notes

1. Early studies at the microeconomic level (individual innovations) are Griliches (1958) and Mansfield et al. (1977). This

literature has been reviewed by Griliches (1995) and Hall

(1996).

2. Because of this dichotomy in underinvestment phenomena, government policies fall into two major categories—tax

incentives and direct funding. Aggregate underinvestment in

R&D is usually the result of broad capital market failure

characterized by excessive aversion to risk and/or the lack of

appropriate institutional mechanisms for funding high-risk

investments (Hall and van Reenen, 2000; Hall, 2002). The

appropriate policy response is a tax incentive and is distinctly

different from the direct funding response that addresses

108 Tassey
underinvestment phenomena in the form of suboptimal composition of R&D. However, tax incentives and direct funding

mechanisms often are incorrectly considered interchangeable

(Tassey, 1997).

3. Link and Tassey, (1987), Tassey, (1982, 1991, 1997),

Nelson, (1992, 1993), Nadiri, (1993), Teubal et al. (1996).

4. The ‘‘black box’’ model has been discarded when a social

objective (national defense, energy independence, health care)

is the ultimate objective. However, when economic growth is

the target, its philosophical hold has been much stronger.

5. Public good content gives an element an infrastructure

character. As with any type of infrastructure, technological

infrastructure can be defined in terms of institutional capabilities rather than the type of technical knowledge. The former

approach has been taken by Justman and Teubal (1995).

Here, the emphasis is on the characteristics of the specific

infrastructure element, which, in turn, implies a set of optimal

institutional capabilities.

6. An alternative four-phase R&D cycle has been proposed

by Branscomb and Auerswald (2002). Their model

(pp. 30–34) uses different labels for the four phases because

their focus is on small R&D firms and the supply of venture

capital. However, the end points of the four phases in their

model are similar, so their framework is functionally identical

to the one used here for corporate R&D generally.

7. See David et al. (2000) for a comprehensive review of this

funding-based approach.

8. However, the distribution of government funds across

areas of science can be a hotly debated issue, as has been

the case with the skewed distribution of U.S. Government

funding toward life sciences. For example, in fiscal year 2001,

Federal funding for life sciences was about $13 billion greater

than funding for all other areas of science combined.

9. Generic technology research is typically done in central

corporate laboratories using techniques based on real options

models to manage the research portfolio. Data on corporate

investment in generic technologies is poor. What data are

available indicate that companies conducting such research

allocate anywhere from 1 to 12% of total R&D spending for

this phase of R&D.

10. Kim and Morbougne (1997) provide evidence of the economic importance of investment in such technologies. R&Dintensive companies in the United States and Europe were

surveyed to obtain sales and profits data on investments in

incremental improvements in the current generation of technology (product line extensions) and in radically new products

based on next generation (i.e., new generic) technologies. For

the average firm in the survey, product line extensions dominate both in terms of number and sales. This result is hardly

surprising, as companies focus most of their resources on

extracting value from their current technology portfolios.

However, a majority of profits were found to be attributable

to the relatively few ‘‘discontinuous’’ (new generic technologybased) innovations.

11. Examples are measurement and test methods, process

and quality control techniques, evaluated scientific and engineering data, and the technical basis for product interfaces

(Tassey, 1997). As quasi-public technology goods, they are

co-supplied by industry and government. In the United States,

the major government source is the National Institute of

Standards and Technology (NIST).

12. The typical high-tech industry depends on a large number of infratechnologies whose collective economic impact is

substantial. For example, a NIST study (Finan, 1998) estimated that the U.S. semiconductor industry would spend $5.5

billion in 2001 on measurement (an important type of infratechnology, much of which ends up as industry standards).

13. In other words, firms are largely ‘‘takers’’ with respect

to the longer-term and higher-impact ‘‘natural trajectories’’

(Nelson and Winter, 1977) that sustain high R&D intensities

and these trajectories usually derive to a significant extent

from external sources (Klevorick et al., 1995). This literature

implies the model proposed here. Once a technology life cycle

is initiated (i.e., proof of concept is established), subsequent

applied R&D investments are driven by the generic technology. Even though the generic technology can itself evolve to a

degree over successive sub-cycles, it provides the platform that

determines the trajectory of applications.

14. Case studies of the major technologies driving modern

economies show this pattern quite clearly; for example, digital

computers (Flamm, 1988), network communications

(National Research Council, 1999), and biotechnology

(Henderson, et al., 1999).

15. At one extreme, Mansfield et al. (1971), Abernathy and

Utterback (1978) and Utterback (1979) provide a detailed

framework and analysis of the impact of the evolution of

individual technologies on specific product life cycles. At the

other, proponents of broad-based ‘‘long waves’’, such as

Mensch (1979) and Graham and Senge [1980], have documented the bunching of related technologies that appear after

a period of advances in the underlying science and when existing capital stocks (and their associated technologies) have significantly depreciated.

16. As pointed out earlier, the conventional approach to microeconomic analysis of analyzing R&D investment by phase

(basic, applied, development) is inadequate because it ignores

the output of each phase and the implications for investment

in subsequent or complementary phases.

17. For simplicity, Figure 1 omits feedback loops, which are

critical sources of information relevant for investment decision making over a technology’s life cycle.

18. Mansfield et al. (1971) made a major contribution to the

understanding of R&D investment patterns by disaggregating

risk into three types: technical, commercialization (innovation),

and market. The conceptual framework developed in this paper

considers commercialization and market risk to be the same.

19. A distinction should be made between ‘‘uncertainty’’ and

‘‘risk.’’ The two terms are frequently used interchangeably,

but they have distinctly different meaning. Risk is the probability of not achieving a technical or market goal; it implies a

known probability distribution (for each objective). Uncertainty refers to the lack of knowledge of the probability distribution. In the R&D cycle, technical uncertainty is

progressively reduced as knowledge is gained about the probability distribution associated with the technical objectives

being pursued. Continued progress in defining this probability

distribution and a similar distribution for market success will

eventually permit the conventional decision tools of corporate

Underinvestment in Public Good Technologies 109
finance to be applied and a risk-adjusted expected rate of

return estimated (based on the product of technical and market risk estimates).

20. Software is a prime example. A NIST study by RTI

International (2002) estimated that limited and ineffective testing of software during the R&D stage costs the U.S. economy

at least $60 billion per year, with the users of software

absorbing over 60% of these costs. This estimate is for direct

costs only (the costs of detecting and fixing errors) and does

not include estimates of the ultimate losses in revenues and

value added.

21. Kash and Rycroft (1998) define complexity as the degree

to which one individual cannot have a total grasp of all elements of a technology. The implications are that communications (including risk assessments) among specialists is

important and research coordination is hard to achieve.

Locating and accessing technical infrastructures also becomes

more difficult.

22. For example, a company may develop a drug candidate

that is effective in terms of the intended impact, say, blocking

a specific RNA pathway that influences unwanted protein

production by a tumor, only to discover after multiple phases

of expensive clinical trials that the specific pathway (one of

many within the tumor cell) is not solely or even particularly

instrumental in the growth of the tumor. The market failure

results from the fact that the generic technology research on

the relationships between different pathways and tumor

growth (‘‘antisense’’ technology) is too broad and complex

for individual companies to undertake and involves significant

spillovers. Moreover, the associated infratechnologies are frequently not adequate, reducing the efficiency (increasing the

time and expense) of conducting this research. Companies,

therefore, often guess at the underlying generic technology

(the overall pathway structure and the interacting roles of all

proteins), as well as the infratechnologies for mapping these

relationships. The result that the efficiency of R&D at the

industry level is reduced. The lack of an explicit disaggregated

R&D model such as the one presented here appears to have

led to underinvestment in generic technologies and subsequent

wasted efforts by biotechnology firms in applications (drug)

development.

23. Using biotechnology as an example is particularly revealing because it is heavily supported by government funding,

but the complexity of the technology still appears to result in

mismatches in the rates of progress among the three major

technology elements identified in Figure 1.

24. This phenomenon can result from several factors: (1)

technical problems that typically are present in newly commercialized technologies; (2) higher cost due to a manufacturing process that has not been optimized for the new

technology (small initial markets do not provide sufficient

incentive or cash flow to invest in the necessary process

R&D); and (3) interfaces between the new technology and

other components in the broader technology system are typically not defined (i.e., as standards) early enough in the technology’s life cycle, thereby raising system integration (and, in

effect, product) costs.

25. For example, reaching the physical limits of silicon-based

semiconductor technology has been predicted for some time,

but advances in the underlying generic technology keep pushing that occurrence farther into the future (new sub-cycles keep

appearing), thereby raising the risk of investing in radically

new semiconductor technologies such as gallium arsenide.

26. From an economic growth policy perspective, one set of

firms replacing another is not a negative result under ‘‘creative destruction’’ models of technology-based competition.

However, if the new set of firms resides in another economy,

a loss of domestic value added occurs in the first economy

and it experiences a reduction in its growth rate.

27. Of course, having the generic technology in place does

not guarantee market success for the innovating firm or even

the entire domestic industry. The video cassette recorder

(VCR) is one of the best known examples, but there are many

others. A major type of semiconductor manufacturing equipment called a stepper was invented in the United States, but

market share is now almost totally Japanese. Oxide ceramics,

which every commercial wireless communication system incorporates, was discovered in the United States but Japanese

industry today dominates commercial markets. See Tassey

(1999, pp. 29–31) for additional examples.

28. One distinction between these two types is that price

spillovers occur through the market mechanism, while knowledge spillovers do not (Jaffe, 1996).

29. For example, Jorgenson and Stiroh (2000) and Stiroh

(2001) find little evidence of production spillovers from purchases of IT capital goods.

30. In particular, some price spillovers are desirable if users

are to benefit substantially from innovations. Thus, market

dynamics are relied upon to effect some reasonable distribution of benefits.

31. See Dasgupta and David (1994).

32. Adapted from Tassey (1997). The figure assumes that

the minimum expected SRR for an R&D project is at least

equal to the PRR (the SRR is above the 45line). This

assumption follows Jaffe (1996, 1998), who used a similar diagram to analyze the relationships between spillovers and rates

of return on government funding decisions. See also Link and

Scott (2001, pp. 776–777). This assumption holds over the

technology life cycle, even though new technologies often

impart negative externalities to the technologies they are

replacing during the transition period (see Figure 2 and associated discussion). Note that the range of expected SRR (vertical arrows) straddles the social HR. This simply reflects the

fact that government R&D investment entails risk, just as private investment does.

33. Conventional underinvestment analysis simply makes a

judgement that the PRR has been suppressed relative to the

SRR. This is an inaccurate approach for two reasons. First,

the role of the private HR is not accounted for and, second,

the dynamics of the technology life cycle is ignored, one

aspect of which is that the PRR changes over this cycle. See

the previous discussion on timing (pp. 96–97) and the next

section.

34. Unlike direct funding of R&D, a tax credit is not

applied to particular projects such as C, but rather provides

an incentive for all such projects by nudging their expected

RoR above the corporate HR. A tax incentive therefore has

the advantage of being technology neutral. However, this

110 Tassey
mechanism has the disadvantage of significant leakage (excess

tax expenditures) because all projects (not only like C but also

like D) can potentially benefit from the tax incentive—whether or not these projects require the incentive. The

conditions under which each of these two policy mechanisms

(direct funding and tax incentives) should be used are discussed in more detail in Tassey (1997).

35. The relationship between technical and market risk was

defined and examined empirically by Mansfield et al. (1971,

pp. 50–54). The probability of commercialization of an R&D

investment is determined by the product of the probability of

technical completion and the probability of commercialization

(given technical completion).

36. For example, Mansfield et al. (1971, p. 53) found that

the probability of commercialization was higher for ‘‘large or

medium’’ technical advances than for ‘‘small’’ ones.

37. The position and slope of the risk reduction curve vary

depending on a number of R&D efficiency factors, in particular, the availability of a range of infratechnologies.

38. This phenomenon is recognized to varying degrees by

virtually every industrialized nation, as evidenced by the existence of technology research support programs. Examples are

the Framework Program in Europe and NIST’s Advanced

Technology Program in the United States.

39. Such estimates are averages across all technologies and

are useful as general targets. However, a macroeconomic target does not take into account varying levels of technological

opportunity among individual technologies.

40. Griliches (1988, 1995), Mansfield (1991b), Nadiri (1993),

Hall (1996), and Cameron (1998) have assessed the literature

on the rates of return from R&D investment and find both

the private rate of return (PRR) and social rate of return

(SRR) to be high relative to other types of investment.

41. For further discussion of these tools and applications to

analyses of specific government R&D programs, see Tassey

(2003).

42. The SRR for the generic technology is also likely to be

higher when the contributions of supplying industries are

taken into account. See Schankerman (1981), Hall and Mairesse (1995), and Jones and Williams (1998). This relationship increases the importance of the supply chain (as

opposed to a single industry) as the unit of analysis for

R&D policy.

43. See Angelis (2000) and Boer (2000).

44. Biotechnology is a prominent example of such a situation, as inefficiencies in technical infrastructure supporting

both R&D and manufacturing abound and collectively are

significantly restraining the evolution of this industry. See

Lagace (2003).

45. Once a research program has been completed, retrospective impact assessments use just this approach. See Tassey

(2003) for detailed descriptions of impact assessment methodologies and examples of both retrospective and prospective

impact studies of NIST research programs.

46. These studies can be accessed at http://www.nist.gov/

public_affairs/budget.htm.

47. Approximately $1 billion of NIH research funding goes

directly to industry each year.

48. National Research Council (1999, p. 2).

49. National Research Council (1999, p. 169).

50. For example, voice traffic is handled by a continuous

connection (a circuit) for the duration of the transmission,

while computers communicate in bursts. Unless a number of

these bursts or ‘‘calls’’ can be combined on a single transmission path (seldom the case in complex, high-capacity transmission systems), line and switching capacity is wasted.

Telecommunications engineers were primarily interested in

improving the voice network and were skeptical of alternative

technologies. Thus, although telephone companies provided

point-to-point communications in the ARPANET, the industry switching technology was not used. National Research

Council (1999, p. 172).

51. National Research Council (1999, p. 9).

52. Elias and Hinzmann (1996, pp. 2, 5).

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