Policy Issues for R&D Investment in a

Knowledge-Based Economy Gregory Tassey

ABSTRACT. The ‘‘Internet Revolution’’ induced an

unbalanced perspective on future economic growth strategies.

Because information technology (IT) largely constitutes an

infrastructure upon which other economic activity is based, its

economic role is to facilitate the productivity of investment in a

wide range of products and services that meet final demand.

Other economies around the world can and are investing in the

same infrastructure, so the efficiency advantages now being

realized by the U.S. economy will be fleeting unless U.S. R&D

efforts produce a new and broad range of innovative products

and services that take advantage of this infrastructure.

A deep and diverse technology-based manufacturing sector

must be a core objective of a national R&D strategy. United

States’ manufacturing contributes $1.5 trillion to GDP,

employs 20 million workers, accounts for more than 70% of

industrial R&D, and constitutes the main source of technology

for the larger service sector. While knowledge-based services are

the largest source of economic growth for the U.S. economy,

their long-term performance is highly dependent on synergies

with a domestic manufacturing sector. These synergies will be

even more important in the future because services are

increasingly exposed to foreign competition.

Knowledge-based services can be supplied from anywhere in

the world—as long as these foreign sources can rapidly access

and assimilate the necessary technology components. This

caveat is the critical point for economic growth policy.

Considerable research supports the argument that hardware

and software components are most efficiently supplied to

services by a manufacturing sector that is geographically close

and institutionally integrated with the service applications.

Policy debates have raged for decades over the nature and

magnitude of underinvestment in manufacturing R&D. The

need to resolve the relevant policy issues has increased, as

industry is funding less of the long-term, high-risk research that

creates the technology platforms supporting new industries and

future economic growth.

Unfortunately, only about a third of U.S. manufacturing is

high-tech by conventional definitions. Some of the remaining

industries develop technologies internally, but most purchase a

large proportion of their technology from the high-tech sector.

Because a technology acquisition strategy can be more easily

imitated by foreign competitors, traditional industries are much

more susceptible to exchange rate variations, global economic

cycles, and secular shifts in foreign competition. Thus, with

global technological capabilities relentlessly increasing, the

long-term prospects for the moderate and low R&D-intensive

portions of U.S. manufacturing are not good.

This paper presents a conceptual framework and available

data as inputs for the analysis of Federal R&D investment

strategies. Such strategies must recognize the full range of

public and private technology assets constituting a national

innovation system. A developed and efficient innovation system

has characteristics making imitation by foreign competitors

difficult and thereby enables sustained competitive advantage.

JEL Classification: 038, 033

1. Introduction

The attention given by financial markets, policy

makers, and the public to investment in information-based services and the underlying infrastructure, particularly the Internet, has accentuated a

trend over the past two decades to relegate

manufacturing to a secondary role in economic

growth strategies. After all, private services

account for 64% of gross domestic product

(GDP) and government services another 13%.

However, manufacturing is critical to an

advanced economy’s long-term growth and will

continue to be so into the foreseeable future. The

United States’ manufacturing sector now accounts

for only 15% of GDP, but this percentage still

translates into a $1.5 trillion contribution to GDP

and 17 million jobs.

Especially important is the fact that the service

sector acquires most of its technology from

manufacturing firms, as indicated in Figure 1 by

the much higher share of R&D performed by the

manufacturing sector. This fact emphasizes

the substantial dependency of services on

Senior Economist

National Institute of Standards and Technology

Stop 1060

Gaithersburg, MD 20899

E-mail: tassey@nist.gov

Journal of Technology Transfer, 29, 153–185, 2004

# 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.
manufacturing firms for technology and thus the

critical role of the myriad communications and

market transactions between the two sectors.

Recent studies show that the efficiency of such

interactions still declines with geographical distance, in spite of the globalization of markets.1

This phenomenon offers an advantage for large,

diversified economies because they have greater

potential for achieving synergies from vertical and

horizontal integration (real or virtual) among

technologies and industries. Moreover, the argument that services are a better long-term economic

growth strategy because they are immune to

import competition is also suspect. Services are

increasingly provided by foreign sources, most

recently including developing nations.

Perhaps most important, the so-called New

Economy is largely an infrastructure-driven economic transformation. The underlying infrastructure technologies facilitate new economic growth

trajectories based on innovations in product,

process, and service technologies. However, these

three categories of innovation do not automatically happen just because new infrastructure

appears. The Internet is greatly increasing the

efficiency of intra-company operations, alliances,

and marketplace transactions, but the virtual

marketplace does not guarantee a flow of new

domestically produced products and services.

Economic growth policy therefore needs to be

equally concerned with investment in new products and production processes, which can benefit

from the IT revolution. After the initial assimilation of IT to increase operation and transaction

efficiency, economic gains will be realized more

slowly and they will be distributed on a global

basis. Economic growth policy must therefore

reemphasize technological innovation in hardware

and software as major building blocks of future

economic growth.

This paper presents a conceptual framework for

identifying policy issues affecting economic growth

in a technology-based economy with a focus on the

manufacturing sector. This framework is then

supported by an analysis of trends in R&D—the

single most important category of investment for

an industrialized nation.

2. A different view of the new economy

Analyses of technology’s long-term economic

impacts indicate a strong cyclical pattern in which

waves of innovation occur. These technology

waves have appeared a number of times in the

past century. Typically, as the economic boom

that follows a burst of innovation proceeds,

complacency progressively sets in. The resulting

technological obsolescence eventually causes a

series of economic crises until a new set of

technology trajectories appears. The current IT

transformation is a perfect example. Several

decades of slow productivity advances with

sluggish growth in output resulted in inflation

and little or no growth in real incomes. This

segment of the long-term growth cycle appeared to

come to an end in the late 1990s with huge

investments in IT and other technologies (particularly biotechnology).

However, as with past technology waves, the

global distribution of the economic rewards is

uncertain. Given that all industrialized nations

have relatively small high-tech sectors (including

the United States), each of these nations is

vulnerable to economic decline during the reshuffling that occurs as the new technologies emerge.2

A major reason is the turbulence that accompanies

the initial impacts of the technology. In particular,

overestimates of the technology’s short-term benefits, the requirements for assimilation, and the

new sources of continued innovation all combine

to realign competitive positions and put a domestic economy’s growth trajectory at risk.

To understand how to sustain and broaden the

initial gains in innovation and productivity, four

Figure 1. Major industry sector shares of GDP and R&D

performance, 2000.

154 Tassey
policy questions need to be answered about the

future potential of what has been labeled the ‘‘New

Economy’’:

1. Is the U.S. economy truly high-tech?

2. Is information technology the only important

growth trajectory?

3. Are current R&D investment patterns adequate

for sustained growth?

4. What are the sources of R&D investment and

how can they be expanded and sustained?

The trends leading to these questions imply

broad and sweeping economic change. However,

the effects of IT are primarily in the areas of

market transaction efficiencies and corporate

processes and operations. The major impacts are

the creation of some new industries, restructuring

of many established industries, and shifts in types

of jobs and work location.

Such structural impacts are obviously critically

important. These same categories of impacts

resulted from the Industrial Revolution in the

late 1800s and early 1900s, when major investments in new infrastructure integrated largely

isolated regional markets into national ones.

New communications technologies greatly

increased the flow and timing of information and

standardization facilitated the emergence of factories. For the first time, geographically dispersed

factories produced components, which were integrated into more complex products at yet another

location.

This restructuring of the economic system

caused major changes in job content and location.

The period was characterized by a distinct lack

of pricing power in the key technology-driven

markets, which spawned a survival-of-the-fittest

corporate environment.3 The same process is

underway today, as technological competence

is spreading globally. The significant difference is

that the price wars of today’s global markets are

the result of new national economies whose

acquisition and development of technology is

eroding market shares of established industrial

economies, including the United States. The

persistent trade deficit proves this point.

Like the Industrial Revolution, the advent of

new product technologies in today’s economy is

proceeding largely on a separate track from the

evolution of the underlying infrastructure. Independent advances in science give rise to new

technologies, but do so decades later. The message

for policy makers today is that the current

economic transformation, like the one a century

earlier, cannot be solely infrastructure-based. The

new IT infrastructure is pervasive and profound in

its potential impact, but it does not guarantee a

flow of new innovative products and services.

Realizing benefits from any growth strategy is

neither easy nor quick. The ‘‘productivity paradox’’ referred to the fact that investments by U.S.

industry in information technology in the 1980s

and early 1990s initially had minimal impact on

domestic productivity growth. Three reasons for

this pattern have become evident:

1. IT investments, while growing rapidly in the

1990s, started from such a small level that only

in the late 1990s did the installed base reached a

size sufficient to measurably affect national

productivity growth.4

2. A transition from an R&D strategy focusing on

the development and use of stand alone

computers to an increasing emphasis on R&D

at the systems level, particularly based on

linking computers through communication networks, has greatly increased the productivity

impacts of IT.5

3. Radical technological changes are not easily

absorbed and utilized. Major changes in the

organization of work, industry structure, and

supporting infrastructure have had to occur.

These changes took place slowly and thus only

by the late 1990s did growth in both productivity and output begin to accelerate at rates not

seen for several decades.

Moreover, while much of the economic growth

in the past decade has been due to efficiency gains

from investments in IT infrastructure, a significant

portion has resulted from an increase in private

debt to record levels (150% of GDP in 2001). The

expected stimulation of inflation from this accumulation of debt has been delayed in part by

cyclical effects, one-time productivity increases,

and a strong dollar (the latter being due in part to

the stock market bubble and the dollar’s role as a

reserve currency). These conditions are unstable

and cannot be expected to maintain their current

Policy Issues for R&D Investment 155
levels of subdued impact. The policy message is

that economic growth strategies need to be

reexamined.

This paper argues that higher growth rates

cannot be sustained by just riding the information highway. The evolving IT infrastructure

has the capacity to leverage a diverse range of

final consumption services, as well as many

types of manufactured goods. However, industrial products (hardware and software) are

essential components of IT-based infrastructure

and IT-based services in general. The implied

synergies are real. Therefore, technologically

advanced economies are limiting their longterm growth potential if policies allow investment to be channeled into just a few sectors.

Diversification not only has the advantage of

stabilizing long-term growth, but its synergistic

effect raises the rates of growth in all sectors.

A better label for the current economic transformation is the Knowledge Economy. This term

encompasses the trends in both IT infrastructure

and emerging product and service technologies,

which leads policy analysis to the needed broader

view of technology-based growth. Clearly, companies in all sectors reflect the transformation to

knowledge-based economic activity.6 However,

the locations around the global economy where

knowledge is developed and applied to domestic

economic advantage are only beginning to be

determined.

What is a manufacturing firm?

What constitutes a manufacturing firm is becoming increasingly blurred. Cisco Systems is one of

the largest ‘‘manufacturing’’ firms in terms of

market capitalization, but it does little manufacturing internally and the majority of its products

arrive at the customer without ever having been

seen or touched by a Cisco employee (Ansley,

2000). Yet, this company has great influence over

the R&D network in the supply chain of which it is

a part. Other large high-tech manufacturing firms

are no longer classified as such in government

databases because a majority of their revenues

comes from services. IBM is a prominent example.

For the purposes of this paper, manufacturing

is viewed broadly to include hardware and software systems. The main rationale is to distinguish

R&D policies for hardware and software and the

systems they comprise from services, especially

information infrastructure services like the Internet. Within today’s technologies, hardware and

software are increasingly integrated at both the

component and system levels. The resulting

systems are then used to provide various services.

The new North American Industrial Classification

System (NAICS) partially recognizes this reality

by distinguishing to a degree between software

products and software services.

3. The changing environment for high-tech

manufacturing

Many analysts of future economic growth trends

argue that the U.S. economy can and will become

almost totally reliant on services (implying an

equal reliance on imports for manufactured

goods). However, the synergies between manufacturing and services are significant and defeat this

argument. Much of the output of manufacturing

industries is consumed as stand alone products,

but increasingly such output is integrated into

systems of products that provide services as the

final form of consumption (communications,

operations management, financial management,

wholesale and retail trade, etc.). The integration of

hardware and software into service systems

requires synergies among multiple levels in the

relevant chain of supplier industries. Such supply

chain integration still occurs more efficiently

within a single economy because of access to

specialized labor pools, technical infrastructures,

and markets.7 However, a number of economic

factors must be understood and managed to

realize these efficiency gains.

The IT-manufacturing interface

First, IT in general and the internet in particular

have been touted as the locus of future growth

strategies. While massive in its potential impact,

the internet does not appear to be more important

156 Tassey
than previous revolutionary technologies that have

also led to widespread changes in patterns of living

and working. For example, the mass-produced

and hence affordable automobile led to the

suburbs, shopping malls, etc. The telephone

greatly affected business behavior and location

and, combined with the radio, had an enormous

effect on information production and distribution

(exactly what is being attributed to the internet).

While extremely important, none of these technologies were close to sufficient to drive economic

growth by themselves. Specifically, each of the

above, including the internet, constitute infrastructure. Their economic role is to facilitate

private sector investments in a wide range of other

technologies, which collectively make sustained

economic growth possible.

Moreover, the focus on the internet ignores

the immense impact of IT on manufacturing and

hence the potential of advanced manufacturing

to make major sustained contributions to economic growth. In particular, significant increases

in manufacturing productivity are being driven

by IT-based integration of internal corporate

activity and business interactions among companies in a supply chain. IT also enables substantial

increases in the productivity of manufacturing

processes. These processes are capital intensive,

which implies considerable investment per

worker. In fact, manufacturing appears to be

spending more intensively on IT than high-tech

services.8

Such capital-intensive industry structures characterized by massive investment in IT-based

technologies offer the potential for substantial

increases in labor productivity. Moreover, higher

capital-to-labor ratios are resulting in a declining

portion of total costs for labor in most areas of

advanced manufacturing.9 Providing labor has the

requisite skills to perform the demanding operations in complex manufacturing environments, the

resulting high levels of labor productivity result in

relatively high pay.10

Although these trends are frequently noted,

how they happen and why they happen to a

greater extent in some economies are not so well

understood. Private investment in IT and other

technologies is essential, but this investment does

not take place in a vacuum. In particular, an

elaborate economic infrastructure is required.

In competitive economies, economic infrastructures facilitate (1) the financing of investments

in advanced manufacturing technologies, (2) the

conduct of advanced manufacturing R&D, (3)

the integration of the results of R&D into

production systems, and (4) the provision of

skilled labor to effectively use both the equipment and associated software. Such infrastructures are difficult to construct and maintain.

Economies with efficient R&D networks, venture capital markets, integrated supply chains

(virtual or actual), and education and training

facilities have competitive advantages that are

not easily established—or imitated.

The technology life cycle

Technologies appear, mature, and become obsolete in a series of evolutionary phases, which

greatly affect R&D decisions. Thus, a second

economic factor is the timing of R&D investments

relative to the evolution of an industrial technology. This timing issue has two dimensions:

investment decisions directed at attaining market

share within a technology’s life cycle and those

focused on making the transition between life

cycles.

As the market for a product technology

expands and this technology is integrated into

larger systems, successive improvements in both

design and process technologies increase total

market value and a subset of firms that have

participated in this market come to dominate.

Eventually, opportunities to apply the underlying

or generic technology decline, design volatility

decreases and the product’s structure takes on a

commodity character (the personal computer is an

example). Competition shifts to efficiency in

production processes and hence to price and

service as increasingly important determinants of

market performance.

Over such a life cycle, this evolutionary pattern

of technology-based competition increasingly

favors less advanced and low-cost economies.

They can acquire the maturing product technology

and combine it with cheap labor and incremental

improvements in process technology. Even within

industries viewed as high-tech, this pattern occurs.

Certain classes of semiconductors, computers, and

many types of software are excellent examples of

Policy Issues for R&D Investment 157
maturing phases of life cycle patterns and the

resulting competitive convergence. Thus, constant

but changing R&D investment is required to

maintain ‘‘first mover’’ advantage.

In addition, sustained economic growth not

only requires constant attention to competitive

factors over a life cycle, it demands advance

planning for access to the next generation technology. This transition between two generic technology life cycles presents a different set of

competitive threats.11 The greater the differences

between two generations of a technology, the

greater the risk to individual companies and even

entire industries.

Most traumatic is the situation in which a

radically new technology appears that performs

the marketplace function of the existing or

defender technology more efficiently. Such transitions typically demand a different set of research

skills for participation in the new technology life

cycle, which existing firms do not fully possess.

Hence, they assign higher technical and market

risk values to the prospective research program,

with the result that necessary investments are

postponed.

A company evaluating the risk of investing in a

new technology faces a projected potential performance pattern, such as curve 2 in Figure 2.

Initially, the performance of the new technology

(especially relative to cost and hence the price

charged) will be below that of the defender

technology represented by curve 1.12 The risk of

lower technical or economic performance, possibly

for some time, adds to the risk associated with the

dynamics of the marketplace. And, these dynamics

further compound the innovator’s risk because the

defender technology seldom gives up without a

fight.13

Two key policy concerns based on the technology life cycle concept follow. First, within a life

cycle, the amount and speed of technological

advance achieved by a domestic industry over a

technology’s economic life is critical, because such

gains in performance determine the realized

economic return. Innovating industries with high

R&D-sales ratios will usually do well, especially

over the first part of the life cycle. However, slow

adaptation of R&D capabilities over latter portions of the cycle (which can cover extended

periods of time) often allow foreign competitors

to take significant market share and thereby

establish the ability to be innovators in the next

generation of the technology.14

Second, transitioning between technology life

cycles is an even more difficult issue for the policy

process to address. A number of high-tech

companies manage transitions among successive

product life cycles quite effectively. However, the

transition between two generic technology life

cycles, especially to a radically new generic

technology, is seldom achieved by the majority, if

any, of firms applying the defender technology.

Most of these companies lose out to new

industries—either domestic or foreign.15 This

process of ‘‘creative destruction’’ is not a problem,

as long as the new industry resides within the

domestic economy. Otherwise, value added (jobs

and profits) is lost.

In addition to these basic patterns, changes in

competitive dynamics are altering the reward/risk

ratio for R&D investments within and between

technology life cycles. As life cycles compress,

R&D at the company level no longer can exist in

isolation of a supporting network. To increase

R&D efficiency, corporations increasingly require

access to R&D conducted by other firms in their

supply chains and to the broader technology

infrastructure provided by a national innovation

system. If domestic R&D resources are not

available, U.S. companies do not hesitate to

form research partnerships with foreign companies, outsource R&D overseas, or directly invest in

Figure 2. Transition between two technology life cycles. foreign research facilities. These research relation158 Tassey
ships often lead to follow-on foreign manufacturing relationships. Thus, the maintenance of an

effective domestic R&D network is essential for

attracting domestic and foreign R&D funds and

subsequent manufacturing, which increases

domestic value added and hence economic

growth.16

Major R&D policy issues therefore include the

role of government in facilitating efficiency

within life cycles and the critical transitions

between cycles. In an oversimplified model,

applied technology research is automatically

initiated by the private sector once an adequate

science base is provided and a new technology

life cycle is thereby begun. In reality, however, a

major concern for R&D policy arises at this

transition from basic research to technology

research. Here, for the first time, technical risk

associated with meeting demand and market

risk associated with uncertain demand conditions must be added to estimates of pure

technical risk. Combining this additional technical and market risk complicates corporate

R&D policy decisions way beyond what is

involved in allocating government funds for

basic research.

This situation is depicted in Figure 3, which

indicates that technology research, with its ultimate objective of market applications, encounters

an initial major increase in technical risk, RR0

.

Such a jump occurs because the scientific principles presented must now be proven capable of

conversion into specific technological forms with

specific performance attributes that meet specific

market needs. The additional risk (RR0

) occurring

in the early phases of technology research can and

does act as a substantial barrier to private

investment in R&D.

However, this large discontinuity in the total

risk reduction process continues to be debated. If

it did not occur, the gradual slope of the curve in

Figure 3 would support proponents of no government support for R&D beyond basic science.

Understanding the evolution of and the interaction between technical and market risk and the

consequent impacts on private-sector investment

must be a key element of R&D policy analysis. In

fact, the substantial jump in total risk caused by

the divergence between market-derived technical

requirements on the one hand and research

requirements, time discounting, and corporate

strategy mismatches on the other can and does

lead to substantial underinvestment.

Moreover, consideration of the discontinuity

aside, the slope of the risk reduction curve varies

depending on a number of R&D efficiency factors.

An important one is the availability to domestic

firms of a range of infratechnologies, which

provide research tools, scientific and engineering

data, and the basis for numerous standards that

collectively constitute the technical infrastructure

of a high-tech industry. Such technical infrastructure has a substantial impact on productivity, but

it also has a strong public good character resulting

in underinvestment by industry.17

In summary, technology life cycles have a

number of distinct characteristics that have

implications for R&D policy:

. Major scientific breakthroughs, followed by

clusters of key technological innovations, set

off major long-term economic expansions.

. However, the time between invention, innovation (first commercial use), and major economic impact (widespread market penetration)

can be long, spanning several decades. One

reason is that the market transition to a new

technology life cycle requires contributions by

multiple elements of a national innovation

system, which typically are not all in place

Figure 3. Risk reduction over a technology life cycle. when needed. Thus, this process can take place

Policy Issues for R&D Investment 159
in other parts of the world economy before

domestic firms using the old technology realize

that change has occurred.

. More specifically, such cycle transitions are

difficult because firms need complementary

economic assets (skilled labor, capital, and

technical infrastructure) to successfully develop

and market new technologies. These assets may

not be available and vary significantly from

one life cycle to the next, which makes their

acquisition and assimilation difficult.

Supply chain integration

A third major economic factor is the changing

structure of U.S. manufacturing industries and the

implications for both R&D and technology

utilization. Faced with rapid technological change,

growing global competition, and accelerating

quality improvement expectations, corporations

have found managing diverse product and service

lines of business increasingly difficult and subject

to sub-par performance. Moreover, with external

communications and data transfer costs decreasing, much of the rationale for vertical integration

has eroded.18 To concentrate on core competencies, companies are spinning off lines of business.

Many of these businesses become separate domestic entities or move to foreign locations.

Each level in a supply chain is therefore now in

play in that the combination of more focused

corporate strategies and greater foreign competition increases the volatility of shifts in global

market shares. Loss of market share in some

industries is inevitable and, in fact, not a national

concern—if these losses occur in lower value added

industries. However, if hollowing out of supply

chains occurs in higher valued added industries,

the constraints on economic growth are more

serious.

One response to the potential for excessive

hollowing out is the huge investment in IT-based

infrastructure, which seeks to optimize performance for an entire supply chain based on a

management system that coordinates the initiation

and revision of plans and schedules across

institutionally separate supply chain functions.

The presumption is that supply chains can be

made significantly more efficient through domestic

market integration, thereby retaining the higher

value added industries within the domestic economy. However, an argument also can be made that

such virtual integration supported by a modern IT

infrastructure can be easily extended over the

entire global economy.

In reality, such integration has turned out to be

difficult to accomplish, even domestically. Focusing on a core competence and the implied

increased dependency on a larger number of

external suppliers and customers is not only

requiring new forms of organization for the

modern corporation but also supporting infrastructures that leverage modern information/production technologies. The technical infrastructure

required to efficiently achieve supply chain integration is as complex as the economic activity it

seeks to support.

Companies have tended to try proprietary

solutions to integration first, often as extensions

of market strategies but just as frequently because

an adequate infrastructure for industry-wide integration is not available. The automotive supply

chain is a good example. Multiple proprietary

systems exist for the transfer of both product

design and operations data among firms in the

several levels of this supply chain. In spite of

industry efforts to standardize on a single infrastructure, little progress has been made. A study of

the movement of product design data among levels

in the automotive supply chain estimated the costs

from inadequate interoperability to be at least $1

billion per year.19 Because other manufacturing

supply chains (such as aerospace) have similar

massive product data transfer requirements, the

cost to the entire manufacturing sector from the

lack of interoperability standards clearly is much

larger. Similar problems exist for the transfer of

data relating to overall business transactions

between companies. One industry group estimated

that $82.5 billion was spent on integration tasks of

all types in 1998, so the leverage from increased

efficiency would seem to be great.20

Supply chains differ across sectors, with the

impact of integration depending on the competitive structures of the markets involved and the

types of relationships among companies, including

the availability of key infrastructures. Many of the

crosscutting infratechnologies that become the

basis for standardized elements of the supply

chain infrastructure have a public good character

160 Tassey
and will therefore receive inadequate private

investment. Examples of critical infrastructure

include:

. Standards for reliable and secure communication of sensitive information across companies.

. Standardization of interfaces (middleware)

among proprietary information systems.

. Standardization of data formats to enable

transmission and interpretation among firms.

. Efficient methods for updating standards to

accommodate the introduction of new technologies.

In summary, the importance of supply chain

integration derives from the trend towards

corporate strategies based on core competencies

and hence an increased need for greater

infrastructure support for efficient marketplace

exchanges and effective integration of overall

business activity. The public good character of

the R&D that produces the infratechnologies

serving as the bases for a wide range of interface

standards results in substantial underinvestment by industry. Thus, national economies

that supply such infrastructure increase domestic efficiency and attract investment at several

levels in a supply chain. The economic impact is

substantial because multiple levels in a supply

chain contribute more to GDP growth than

investments leveraging a single industry.

A second major issue with respect to supply

chain integration is the effect of vertical disintegration on R&D investment. The trend in

corporate specialization is causing a segmentation

of private sector R&D. Large, previously integrated companies now expect firms at upstream

levels in their supply chain to conduct more

R&D.21 This segmentation is occurring at individual levels in supply chains, as well. Companies

are increasingly specializing in one of the major

categories of manufacturing activity: product

design, component manufacturing, or system

integration (assembly).22 The trend promotes

even more focused R&D. The consequence is a

significant reduction in private funding of earlyphase generic technology research, where the

higher risks and longer time horizons require the

capture of economies of scale and economies of

scope (multiple market applications must be

realized to ensure an adequate return on investment).

As partial responses to the underinvestment in

generic technology research, users and suppliers

license technology from each other and participate

in joint R&D ventures, partnerships, and research

consortia. Manufacturing firms are also increasingly subcontracting R&D to universities and

other firms. Access to external sources of R&D

reduces cycle times and improves the overall

productivity of company R&D investment.

Many R&D collaborations involve manufacturing and service companies, as well as manufacturing firms from adjacent industries in a supply

chain. The frequency of collaborative research is

higher within a single economy due to geographic

proximity, cultural similarities, and the availability

of a single government research infrastructure

(national labs, funding, intellectual property

rights, etc.) available to all participants.23 However, such synergies can be difficult to achieve.

Coordination, joint management, and assignment

of intellectual property rights all inhibit efficient

collaborations. More important, the core competency motivation can restrain investment in R&D

portfolios that pursue radically new generic

technology platforms with broad market potential.

The increasing complexity of manufacturing R&D

policy

The final economic factor is simply the difficulty in

understanding the several distinct elements of the

typical industrial technology, how they are developed, and how they eventually integrate into

systems that meet final consumer demand. The

development, market transaction, and transfer/

assimilation stages all can suffer breakdowns due

to complexity of the technology and/or the market

and infrastructure systems that attempt to deliver

it.

Using biotechnology as an example of technological complexity, Table I lists the multiple areas

of science (column 1) that have had to advance

before a larger set of generic product and process

technologies could evolve. These generic technologies (columns 3 and 4) have been created over the

past 25 years and are just now beginning to yield

Policy Issues for R&D Investment 161
significant numbers of proprietary market applications (column 5). As described earlier, generic

technologies have characteristics of public goods.

Industry therefore frequently underinvests in the

early phases of a technology’s life cycle where

proof of concept and laboratory prototypes are

essential outcomes with substantial pre-competitive characteristics. Although examples can be

found of technological innovations that have

occurred before substantial basic and generic

technology research have been conducted, the

increasing complexity of technology and the

competitive imperative to develop it efficiently

means that industry generally will not commit the

much larger amounts of funds required for

proprietary applied research and development

until this generic technology base is in place.24

Table I also shows the other category of

industrial technology with significant public good

content—infratechnology (column 2). Infratechnologies are a varied set of technical tools

that perform a wide range of characterization,

measurement, integration, and other infrastructure

functions.

Examples of these functions include:

. Measurement and test methods.

. Artifacts such as standard reference materials

that allow these methods to be used efficiently.

. Scientific and engineering databases.

. Process models.

. The technical basis for both physical and

functional interfaces between components of

systems technologies (such as factory automation and communications systems).

Such technical tools are ubiquitous in the technology-based economic growth process. They affect

the efficiency of R&D, production, and marketing.

Because individual infratechnologies typically

have a focused application and hence impact

(e.g. measurement and test methods are applied

to specific steps in a production process), their

economic importance has been overlooked. HowTable I

Interdependency of public–private technology assets: Biotechnology

Generic technologies

Science base Infratechnologies Product Process Commercial products

. genomics

. immunology

. microbiology/

virology

. molecular and

cellular biology

. nanoscience

. neuroscience

. pharmacology

. physiology

. proteomics

. bioinformatics

. biospectroscopy

. combinatorial

chemistry

. DNA chemistry,

sequencing, and

profiling

. electrophoresis

. fluorescence

. gene expression

analysis

. magnetic resonance

spectrometry

. mass spectrometry

. nucleic acid

diagnostics

. protein structure

modeling/analysis

techniques

. antiangiogenesis

. antisense

. apoptosis

. bioelectronics

. biomaterials

. biosensors

. functional

genomics

. gene delivery

systems

. gene testing

. gene therapy

. gene expression

systems

. monoclonal

antibodies

. pharmacogenomics

. stem-cell

. tissue engineering

. recombinant

antibody

engineering

. cell encapsulation

. cell culture

. DNA arrays/chips

. fermentation

. gene transfer

. Immunoassays

. implantable

delivery systems

. nucleic acid

amplification

. recombinant

DNA/genetic

engineering

. separation

technologies

. transgenic animals

. coagulation

inhibitors

. DNA probes

. inflammation

inhibitors

. hormone

restorations

. nanodevices

. neuroactive

steroids

. neuro-transmitter

inhibitors

. protease inhibitors

. vaccines

Public Public–Private / Private–Public ? Private

162 Tassey
ever, the complexity of technology-based economic activity and the demands by users of

technology for greater accuracy and higher quality

have reached levels that require a large number of

diverse research-intensive infratechnologies within

single industries. The resulting aggregate economic

impact of these infrastructure technologies is

substantial.25

The pervasive and substantial aggregate impact

of measurement infratechnologies in high-tech

industries is indicated by a study of the semiconductor industry’s investment in measurement

equipment. This industry invested about $2.5

billion in measurement equipment in 1996, triple

the amount spent in 1990. This expenditure was

projected to continue growing at least 15% per

year.26 Thus, the cost of not having the required

infratechnologies and associated standards in

place to support this investment is substantial.

The range and technical sophistication of

infratechnologies support a varied and complex

standards infrastructure. Infratechnologies are a

necessary basis for standardization at all levels in

the modern manufacturing process: individual

equipment, the process systems level, and the

customer/supplier interface. In service industries,

infratechnologies help define output, interoperability, security protocols, and intellectual property.27

Infratechnologies also include the various techniques, methods, and procedures that are necessary to implement the firm’s product and process

strategies. Methods such as quality management

can be differentiated upon implementation within

a firm. However, they must be traceable to a set of

generic underlying principles if customers are to

accept claims of product quality. Hence, they have

an infrastructure or public good character in their

development and also in their dissemination,

especially for smaller firms.

4. Competitive trends in manufacturing

The above trends imply that an important focus

for R&D policy analysis is the supply chains of

related industries that make up a technology-based

economy. Vertical disintegration (specialization) is

distributing R&D across companies and linked

industries. The emergence of R&D networks as

part of a national innovation system is becoming

increasingly evident within the U.S. manufacturing

sector. This sector has significantly improved its

performance over the past 15 years. Advances in

productivity have been accomplished through

massive investments in automation and information technology, reorganization of workflow, and

restructuring of relationships with vendors and

customers. However, the sector faces increasing

competition in global markets, as more economies

acquire R&D capabilities.

Many economic analyses have been undertaken

over this period to explain the relationships

between R&D investment, the resulting technology and subsequent innovation and productivity

growth, and finally output or GDP growth. While

virtually all analysts now agree that technology is

the single most important driver of long-term

productivity and output growth at the national

level, much less agreement exists with respect to

how technology affects competitive position and

hence growth at the industry level over time. In

particular, major strategic issues are in dispute

such as the optimal proportions of technology

developed within an industry and acquired from

external sources. As discussed shortly, U.S.

manufacturing industries exhibit a wide dispersion

of these proportions.

All manufacturing industries face increased

global competition, but only a minority of

industries can be said to be capable of competing

long-term in global markets. Federal Reserve

Board data provide one indicator of this difference

in competitiveness. Figure 4 shows the distinctly

different growth rates in high-tech and non-hightech manufacturing.28 The much higher growth

rates of technology-based industries in turn pull

investment in further growth.

The high cash flow generated by this growth

enables investment in more capacity. Figure 5

shows the same substantial difference in rates of

capacity growth, as Figure 4 does for output

growth. However, these huge differentials in rates

of growth in output and investment in recent years

must be qualified by the fact that the manufacturing industries included in the Fed’s definition of

high-tech accounted for only 8.1% of industrial

production in 1998. Moreover, the period of time

covered by these data was one of extraordinary

investment in IT-related industries, which

Policy Issues for R&D Investment 163
accounts for a portion of the performance

difference.

However, R&D-intensive manufacturing industries have produced trade surpluses, while the rest

of the manufacturing sector has experienced

negative trade balances for much longer periods

of time. This significant difference in competitiveness is indicated in Figure 6. The Census Bureau’s

Advanced Technology Products trade balance was

positive until recently.29 From 1988 through 1998,

the surplus averaged $28.4 billion, which was a

significant percentage of total trade in this class of

products. However, this surplus shrunk in the next

two years to $5.3 billion in 2000 and turned

negative for the first time in 2002. Moreover, these

surpluses have been overwhelmed by the huge

deficits in trade for all goods, which reached $484

billion in 2002.30

Some of the reasons for the persistent trade

deficits are well known by now. Other economies

are increasingly able to absorb and improve

product technologies. As a technology’s life cycle

evolves (see Figure 2), product designs standardize

and competition shifts to price. The impact of

process technology on cost then becomes an

increasingly important competitive factor. In

recent decades, foreign industries have developed

process technologies to the point of being equal or

superior to those in U.S. industries. These

competitors are now able to produce comparable

or even higher quality products at lower cost.

Increased R&D in other nations includes

investment in technical infrastructure to support

private domestic investment. This evolution of

national innovation systems has provided additional incentives to shift U.S. R&D to foreign

locations. Whereas traditional direct foreign

investment strategies in production and marketing

shift lower value added economic portions of

supply chains overseas, shifting R&D investment

accentuates the trend toward higher value added

economic activity abroad.

While low and moderate technology-based

domestic industries lose market shares to foreign

competitors, technologically advanced industries

continue to maintain a trade surplus. Unfortunately, as Figure 6 indicates, this high-tech trade

and the resulting surplus that existed until recently

are way too small. The relatively small and

recently shrinking high-tech trade surplus highFigure 4. Rates of growth in output for high-tech and other

manufacturing industries: Annual percent change, 1995–1999.

Figure 5. Rates of growth in capacity for high-tech and other

manufacturing industries, annual percent change, 1995–1999.

Figure 6. U.S. trade balances for high-tech products and all

goods 1988–2002 (in $billions).

164 Tassey
lights the need to better understand the relationships among R&D, innovation, and productivity

growth over the entire technology life cycle.

5. Productivity growth and R&D policy analysis

Economic research has consistently shown that

technological change accounts for the majority of

long-term productivity growth. For example, a

recent study by Oliner and Sichel (2000) estimates

that for the period 1995–1999, the combination of

innovation and capital deepening (acquisition of

technology through capital investment) has

accounted for two-thirds of productivity growth.

However, these two sources of productivity

growth—technological change and capital deepening—have significantly different roles in determining long-run economic growth.

Both sources are evident in the IT-driven

resurgence in productivity during the late 1990s.

However, much of the acceleration in IT investment in the 15 years from 1980 to 1995 suffered

from the productivity paradox. This term refers to

the fact that the expected productivity gains from

individual IT investments such as computers,

software, and telecommunications equipment did

not show up in national productivity statistics.

These potential gains were thwarted by the

substantial difficulties encountered in integrating

individual components into efficient and hence

productive systems.

System-level productivity has only recently

begun to improve enough to drive aggregate

productivity at a faster rate. After increasing

1.5% per year in the period 1991 to 1995, labor

productivity (output per hour) increased at an

annual rate of 2.7% in the period 1996 to 2000.

This acceleration in labor productivity growth

elicited glowing commentary on the U.S. economy

from many economists and policy analysts.31

However, several cautions are in order. First,

most economic studies have indicated that the

sources of productivity growth are limited to the

IT-producing industries and, to varying degrees,

the most intensive users of IT. For example,

Gordon (2000) concludes that, once changes in

statistical adjustments for inflation and business

cycle effects are accounted for, all productivity

growth is derived from the 12% of the economy

involved in the production of durable goods,

notably computers. The implication is that the

remaining 88% of the nonfarm private business

economy has had no significant structural acceleration in productivity growth, including the

remainder of manufacturing.32

Similarly, a Federal Reserve Board study

estimated that the one percentage point increase

in the productivity growth rate in the last half of

the 1990s resulted about equally from increased

investment in IT (computers, software, and communications equipment) and innovation in the

actual design and production of computers

(including components such as semiconductors).33

Other studies have concluded that, while the IT

manufacturing sector’s rapid productivity growth

has been a significant contributor to the economy’s

overall growth rate, IT has added value primarily

through capital deepening in the non-IT portion of

manufacturing. That is, these studies concluded

that IT products, rather than IT producers, are

driving productivity increases.34

The most important point is that in all these

studies the IT portion of the manufacturing sector

is the original source of the technology that

ultimately drives productivity gains elsewhere.

This concentration of the source of productivity

growth is due to the fact that the IT sector is where

most technological advances have occurred. Moreover, Jorgenson (2001) calculates that the ITproducing industries’ contribution is proportionately far greater than IT’s 4.26% share of GDP.

The effect of technological change is evidenced by

pronounced declines in the prices of IT products

and services, especially computers.

Many economists project this trend to persist

and therefore expect continued decent rates of

productivity growth. However, dependency on

such a few industries for sustained productivity

growth is risky at best. Jorgenson (2001, p. 49), for

example, estimates a sharp reduction in TFP if the

semiconductor industry’s product cycle returns to

three years from the more recent two-year pattern.

The dependence of future productivity growth on

a product life cycle reduction in a single industry

that accounts for 0.8% of GDP is truly a ‘‘razor’s

edge’’ economic growth path. Even if one attributes long-term productivity growth more evenly

across all of what is commonly called the ‘‘hightech’’ sector (IT-related industries plus pharmaPolicy Issues for R&D Investment 165
ceuticals), the U.S. economy’s growth is largely

dependent on industries that account for only 7–

10% of GDP.

Second, virtually the entire debate over the

sources of productivity growth has been based

on analyzes of trends in labor productivity.

However, labor constitutes just one input to

economic activity. Consequently, the relationship

of labor to overall productivity growth is

affected by the magnitude and nature of investments in other inputs. Specifically, the amount of

investment in capital and the amount and type

of technology embodied in this capital (plus socalled disembodied technological change) are

critical determinants of ‘‘measured’’ labor productivity. Thus, a more comprehensive and

accurate measure of productivity is needed,

which relates output to at least the two major

inputs, capital and labor.

Figure 7 shows a 50-year trend in a more

comprehensive measure, total factor productivity

(TFP), along with trends in average labor productivity (ALP) and real compensation.35 Real

hourly compensation has tracked the comprehensive and hence more accurate measure of productivity, TFP, over the past 25 years. Companies

obviously have to pay for all inputs, not just labor,

and it is the relationship of output to the weighted

average of these inputs that determines true

productivity and therefore ultimately profits.

The fact that TFP has grown at a decidedly

slower rate than ALP raises questions about the

magnitude of the New Economy’s impact, especially as the post-1995 resurgence in TFP has not

fully reestablished the annual growth rate in the

1948–1973 period.36 Policy analysis should also

question the sufficiency of an economic growth

pattern where virtually all productivity growth is

due to a few small industries.

In summary, productivity is an efficiency

measure and thereby provides vital information

on the relationship between investments and the

resulting portfolio of products and services.

However, no matter how efficient an economy

becomes, at some point this portfolio becomes

obsolete and must be replaced with new, innovative products and services. This fact of long-term

growth has significant implications for R&D

policy. Specifically, an IT infrastructure is providing broad-based efficiency improvements and

opportunities to develop many innovative products and services, but these innovations will have

to emanate from a much broader R&D investment

portfolio than that producing just information

technology.

6. Implications of R&D investment trends for policy

analysis

The implications of the preceding discussion are

that technology is critical for productivity and

economic growth, and, because most technology

results from R&D spending by the private sector,

both the amount and composition of this investment are important policy variables. Policy

debates have raged for decades over the nature

and magnitude of alleged underinvestment, with

no consensus emerging. The importance of this

debate has increased, as the national R&D

enterprise is changing in significant ways. Four

major changes are:

1. The increasing emphasis by companies on

short-term payoffs from investments of all

types, including R&D.

2. The wider distribution of the sources of R&D

across the typical industrial supply chain.

3. A relentless increase in the complexity of

industrial R&D, particularly its greater multidisciplinary character and the growing importance of the technology integration phase of

R&D.

4. A relative decline in Federal funding of civilian

R&D, especially non-health research, which

Figure 7. Long-term trends in productivity and income: 1965–

2001.

166 Tassey
raises the importance of both the amount and

composition of industry R&D funding.

These changes argue for an expansion of R&D

policy analysis to include an emphasis on corporate investment strategy within and across technology life cycles and in the context of various

positions in supply chains. In particular, a distinction needs to be made between the factors affecting

the amount of R&D investment and the composition of this investment.

Amount of R&D

The most frequently discussed technology policy

issue is the amount of R&D conducted in the U.S.

economy. A commonly used indicator of this

policy variable is R&D intensity (R&D-to-sales

ratio). This indicator is important because current

sales are driven by past R&D and therefore

current R&D spending is a predictor of future

sales growth. By comparing current R&D to

current sales, one gets a rough assessment of the

adequacy of new investments in technology for a

company’s or industry’s competitive position in

the current and possibly the next technology life

cycle.

Figure 8 indicates that most of U.S. manufacturing is not R&D-intensive. In fact, only a few

industries have R&D intensity ratios that predict

long-term global competitiveness. That is, relatively few industries have the high ratios (in the 8–

12% range) that seem to be required for sustained

world class innovation. Most important, these

industries together account only for 7–10% of

GDP.37

The policy implication is that the remaining

90%þ of the economy, including those industries

that are moderately R&D intensive, is vulnerable

to varying degrees to the increasing R&D intensity

from a broad array of industries in other countries.

Industries with moderate to low R&D intensities

must rely on technology supplied by other

industries—domestic or foreign. In a closed

economy (no foreign trade), suppliers of technology would obviously be domestic and would not

fear cheaper and higher quality imports. However,

the increasing ability of foreign industries to

acquire or develop modest amounts of technology

allows them to compete very effectively with their

U.S. counterparts.38

Due to downsizing in the face of growing

import competition, especially in less technology

intensive areas, U.S. manufacturers’ sales grew

very slowly in the 1980s. The resulting increased

proportion of this sector’s sales from R&Dintensive industries raised the overall R&D-tosales ratio for the entire manufacturing sector in

the first half of the 1980s from approximately 2.5%

to 3.0%, as indicated in Figure 8. However,

manufacturing R&D grew more slowly over the

next decade and the R&D-to-sales ratio remained

flat from 1985–1995. In the 1996–2000 period,

both R&D spending and sales accelerated with

R&D spending growing slightly faster, resulting in

the ratio increasing to 3.3%.

Thus, downsizing and restructuring by many

manufacturing firms and even entire industries

have resulted in a manufacturing sector that is

smaller relative to the overall economy but on

average more competitive. However, a 3.3%

R&D-to-sales ratio is not adequate to attain

long-term competitiveness for manufacturing in

a global economy that is rapidly expanding its

R&D capability. The wide disparity in R&D

intensities among manufacturing industries

(Figure 8) implies that not all manufacturing

firms or even entire industries have adapted to

the demands of global, technology-based competition. Future improvement in real growth

rates for this sector will have to come from

Figure 8. R&D-to-sales trends in manufacturing: 1983–1998

company and other (except federal) R&D funds as percentage

of net sales.

Policy Issues for R&D Investment 167
sustained real growth in R&D sufficient to

further increase R&D intensity.39

Research has shown that a minimum amount of

R&D must be conducted by companies just to

maintain the capability to absorb technology

effectively from external sources.40 However,

considerably more R&D than this minimum is

required for sustained competitiveness in advanced

economies. In fact, larger, more advanced economies emphasize the innovation objective of R&D

(as opposed to technology absorption objective) as

the primary strategy for maintaining high rates of

economic growth.41

To some degree, decreased R&D investment in

one industry can be made up by increased

expenditures in other industries in a supply chain,

as long as these industries are sufficiently integrated in all phases of economic activity. In

particular, research has shown that significant

portions of knowledge spillovers from R&D are

localized and that technology-intensive industries

need to be more clustered geographically than

other industries, thereby making attainment of an

integrated R&D capability difficult.42

These limitations on R&D spillovers are accentuated by the fact that the performance of R&D is,

in fact, concentrated geographically. The six states

with the highest levels of R&D expenditures—

California, Michigan, New York, New Jersey,

Massachusetts, and Texas (in decreasing order of

magnitude)—account for approximately one-half

of the entire national expenditure. The top ten

states—adding, in descending order, Pennsylvania,

Illinois, Washington, and Maryland—account for

nearly two-thirds of the national effort.43 The

implication is that much of the U.S. economy on a

geographical basis has weak R&D networks,

which means the advantages of regional clustering

within supply chains supported by a robust

technical infrastructure are not being realized.

The difficulties in achieving acceptable R&D

intensities and establishing effective R&D networks are increasing as R&D becomes increasingly dispersed across levels (industries) in the

supply chains making up the U.S. economy.

The greater complexity of modern R&D and its

increased dispersion is creating a need for a

more diverse technical infrastructure to support

the necessary private R&D investment. To this

end, analyzing R&D expenditures at just one

level (an industry) in a supply chain will be

increasingly inadequate for assessing economic

growth potential, as will a singular focus on

private or public R&D. Instead, a policy

objective must be efficient R&D networks

supported by public and private investment to

maintain competitive positions in multiple

linked industries.

However, the ever advancing complexity of

technology and the greater risks associated with

long lead times means that almost any distribution

of private sector R&D over a supply chain is likely

to be inadequate with respect to essential investment in next generation and especially next wave

technologies. The latter are the basis for new

industries and major international shifts in competitive position. Inadequate investment therefore

also reflects a problem with the composition of

R&D.

Composition of R&D

As the National Science Board points out, ‘‘ . . .

any discussion of the nation’s R&D must always

be careful to distinguish between where the money

comes from originally and where the R&D is

actually performed’’.44 However, the source of

funds, as opposed to the performer, controls the

composition of the R&D. In this regard, ‘‘most of

the nation’s R&D is paid for by private industry,

which provided 65.9% ($149.7 billion) of total

R&D funding in 1998. Nearly all of these funds

(98%) were used by industry itself in the performance of its own R&D and most (70%) were for

the development of products and services rather

than for research’’.45

The focus of industry-funded R&D on specific

market objectives is accentuated by considerable

anecdotal evidence that points to a shift in

composition toward short-term development projects. R&D Magazine summarized its survey

(conducted jointly with Battelle) of industry’s

projected R&D spending plans for 2000 by stating

‘‘Gone . . . is industrial support of basic industry

R&D, replaced mostly with support of high-tech

development’’.46

168 Tassey
To some extent, U.S. industry has recognized

the implications of this trend and has at least

partially compensated for its reduction in internal

long-term research by increasing external funding

in universities. However, such shifts in location of

performance do not change the overwhelming

corporate strategic mandate to make R&D pay off

in shorter periods of time. Overall, the composition of U.S. private-sector R&D is shifting toward

shorter-term objectives, at the expense of nextgeneration research.

Case studies conducted in the mid-1990s indicate that long-term, high-risk corporate research

has been declining for some time.47 More recent

data on trends in U.S. corporate R&D spending

are available through surveys by the center for

innovation management studies (CIMS) at North

Carolina State University in conjunction with the

Industrial Research Institute (IRI).48 The IRI/

CIMS surveys provide a unique breakdown of

company R&D spending between corporate (central research) and segment (line-of-business R&D)

spending. In a sample of 77 firms, an average of

25.8% was funded by central corporate research

(acknowledged as longer-term and higher-risk

generic technology research) and 67.8% was

funded by segments/divisions (shorter-term, commercialization-oriented research).49 More significant, the IRI/CIMS database provides trend data

for a smaller sample of 23 firms.50 For the 1993–

1998 period, the average amount spent on corporate or central research declined from 21.2% to

17.1% of total R&D expenditures. Table II provides similar breakdowns (last column) for some

individual companies, which show a lower average

allocation to central research of 9.4%.

The decline in funding of long-term, high-risk

technology research is not limited to industry.

Federal shares of funding for all three major

components of R&D tracked by NSF (basic

research, applied research, and development)

have declined steadily over the past 30 years.

During the 1980s, Federal support for applied

research was intentionally de-emphasized in favor

of increased funding of basic research. Even with

somewhat of a renewed willingness to fund

generic/pre-competitive applied research in the

1990s, Federal funding in 1998 for applied

research was only 70.8% of that for basic research,

as reported to NSF by research performers.51

Figure 9 shows the overall national trends, at

least to the extent possible with public data.

Applied research, which contains the critical

generic/pre-competitive technology research

expenditures, has grown the slowest of the three

major phases of R&D during the 1990s by a

substantial margin. This research represents the

transition between scientific research, which has no

market objective, and technology development,

which is market focused. In the early portion of

applied research, market risk enters the calculation

for the first time and technical risk takes on new

meaning for corporate R&D managers.52 At this

point in the R&D life cycle, time-to-market and

technical risk are so high that the investment

criteria typically applied to the majority of

corporate R&D are not used.

The term generic/pre-competitive applied

research exemplifies the huge problem faced by

policy analysis due to an inadequate taxonomy for

Table II

Fraction of corporate R&D in central research laboratories

selected companies, 1998

Company-funded R&D as percent of sales

Company

Total

company

Central

lab Ratio

Nokia 12.2% 1.2% 10.0%

Rockwell 5.0 0.5 10.0

General Electric 3.2 0.4 13.0

Hughes 2.0 0.3 14.0

United Technologies 5.1 0.3 6.5

Raytheon 3.0 0.1 2.8

Source: HRL Laboratories and company data (GE’s sales do

not include its GECS affiliate).

Figure 9. Trends in U.S. R&D by major phase of R&D, 1991–

2000.

Policy Issues for R&D Investment 169
the R&D process. The term applied research is too

broad to provide needed insights into funding

trends for the truly breakthrough technologies that

drive much of economic growth. Companies divide

their R&D between a central research facility and

their lines of business. Under current NSF

definitions, both units will often conduct what is

classified as applied research. However, central or

corporate research is typically more long-term,

more exploratory, and more discontinuous relative

to existing corporate market strategies. Hence,

such generic or fundamental research is overall

much more risky. Companies do not even use the

same project selection and evaluation criteria for

the two areas of research. The policy significance is

large, as investment behavior is quite different in

the two cases.53

As discussed in the following sections, the

Federal role in funding the generic or fundamental

phase of technology research (with the exception

of biomedical research) has declined in the past

two decades. Such research ends with proof of

concept, frequently embodied in a conceptual

model or laboratory prototype, and typically

brings new technologies to the levels of technical

and market risk addressable by conventional

corporate R&D investment criteria. This milestone

is still a long way from the generally agreed

endpoint for applied research, which is a commercial prototype.

Making a distinction between generic technology research and applied technology research is

critical for both corporate strategy and government R&D policy. By the time applied research is

initiated, broad technology and market strategies

have been determined to a significant extent by

earlier exploratory or pioneering research. As

described here and earlier, research aimed at new

technology platforms is subject to substantial

underinvestment by industry.54

An important characteristic of generic technology research for policy purposes is its discontinuous character (radically different from the

existing corporate R&D portfolio). This characteristic makes the potential market applications

highly uncertain for corporate managers. From 11

case studies of radical innovation efforts within

major corporations, a team from Rensselaer

Polytechnic Institute concluded that ‘‘the life cycle

of a discontinuous innovation project is profoundly different from a continuous improvement

project’’. The 11 projects studied exhibited several

of the categories of market failure described in the

next section. In eight of the 11 case studies, the

researchers found that government was a major

source of funds.55

The previous discussion of R&D composition

focuses on R&D objectives. Another set of issues

with policy implications arises with respect to the

R&D process. In particular, the multidisciplinary

character of industrial R&D and technology

integration are both steadily increasing in importance. Multidisciplinary R&D makes maintenance

of minimum threshold levels of science and

engineering skills increasingly difficult for individual firms, resulting in increased efforts to partner

with private and public institutions or simply not

conducting the research.

The systems nature of many emerging technologies has made technology integration a critical

part of the R&D process. Studies have shown that

differences in the technology integration process

are more important than disparities in project

management methods, leadership qualities, and

organizational structure in explaining variations in

R&D productivity. Data indicate that a company’s ability to choose technology components

wisely and effectively and integrate them has

explained variations in time to market and product

quality by a factor of two or three over other

variables.56

The types of compositional R&D market failures identified above significantly reduce the longterm competitive prospects for U.S. industries. In

particular, studies have shown that longer-term,

more radical (discontinuous) R&D and subsequent innovations have a disproportionately

greater impact on economic growth. Based on a

survey of high-tech companies in several countries,

Figure 10 shows the differential impacts of radical

and incremental innovations. Incremental innovations (extensions of existing generic product

technologies) accounted for a large majority

(86%) of all product launches, but just 62% of

sales and only 39% of profits. In contrast, major or

discontinuous innovations (radical in the sense

they were based on new-to-the-firm generic technologies) were few in number as might be expected

(14% of all product launches), but accounted for

38% of sales and 61% of profits.

170 Tassey
These critical attributes of different types of

industrial R&D have been obscured because the

traditional policy model treats technology as a

homogeneous entity or ‘‘black box’’. Under such a

model, basic science is largely a pure public good

and therefore funded by government to a significant extent, while the derived technology and

its applications (the black boxes) are the province

of the private sector with the implication of no role

for government.

This simplistic model is contradicted by the fact

that the R&D process eventually producing the

black boxes consists of a series of phases

creating progressively more applied knowledge.

The black box model persists in spite of many

case studies of emerging technologies (some

discussed below) showing a critical role for

government based on characteristics of the

earlier phases of technology research that lead

to private sector underinvestment. Moreover,

the development of black boxes (hardware and

software) and their integration into larger

technology systems requires an array of infratechnologies, which also have large public good

content and therefore suffer from private sector

underinvestment.

7. Policy rationales for government R&D support

Trend data have been provided indicating that

U.S. industry can underinvest in the type of

research that, although long-term and high-risk,

ultimately provides the highest rates of return and,

in fact, is necessary for long-term economic

growth at the national level. Barriers or market

failures occur that change the private sector’s

expected rate-of-return calculations causing systematic underinvestment. Economists have

explained underinvestment in R&D largely by

the concept of spillovers, which refers to the

tendency of knowledge either to directly leak or

spillover from the originating source or to be

incompletely compensated for in marketplace

transactions.

While this phenomenon is an important

characteristic of technology-based markets, in

reality, such economic activity is much more

complicated and suffers from additional barriers

not so commonly identified or understood.

Specifically, the following six sources of market

failure (underinvestment) occur across technologies and at specific points in a technology’s life

cycle:57

1. Technical complexity: The multidisciplinary

nature of R&D, driven by intrinsic complexity

and the systems nature of many emerging

technologies is raising risk calculations.

2. Time: Increased global competition in R&D is

shortening technology life cycles and raising

discount rates (particularly for long-term, highrisk technology research).

3. Capital intensity: Estimates of risk climb

dramatically as the projected cost of a research

project increases relative to a firm’s total R&D

portfolio.

4. Economies of scope: Market and hence R&D

strategies often are more focused than the

potential scope of markets enabled by an

emerging technology, thereby reducing investment incentives derived from rate-of-return

calculations.

5. Spillovers: Leakage or spillover of technical

knowledge to companies that did not contribute to a research project is typically greater the

earlier in the R&D cycle an investment is

undertaken.

6. Infratechnologies and standards: Technical

tools, methods and techniques, science and

engineering data bases, and the technical basis

for standards have a public good character and

low visibility; such technical infrastructure is

Figure 10. Profit differentials for major and incremental

innovations.

Policy Issues for R&D Investment 171
therefore subject to underinvestment over most

of the technology life cycle.

Any one of these six barriers can have serious

negative impacts on private-sector R&D investment. Moreover, the severity of their impacts can

vary over technology life cycles and among levels

in supply chains.

More generally, R&D policy must recognize

that for long-term, high-risk, technology research,

which is discontinuous or radical relative to

conventional R&D, industry uses

. Different investment criteria compared to what

is used for the majority of industrial R&D

investment.

. Different institutional mechanisms for making

these investment decisions.

. Different institutional mechanisms for conducting this type of technology research.

8. Federal funding of industrial R&D

Corporate strategists, economists, and policy

analysts unanimously agree that technology is

the main driver of long-term economic growth.

Yet, as pointed out in earlier sections, the hightech portion of the U.S. and other economies is

quite small. The implication is that considerable

vulnerability exists as more and more foreign

industries attain the capacity to compete in

moderate and low R&D-intensive products and

services. Because the manufacturing sector conducts over 70% of industrial R&D, it is affected

disproportionately by changes in Federal policies

toward R&D funding.58

R&D funding trends

U.S. R&D spending has increased at an average

annual rate of 4.7% in real terms over the past 25

years. However, this growth has succeeded in

raising only a small fraction of U.S. industries to

the high R&D intensities that seem necessary to

sustain competitive positions in expanding technology-based global markets.

Most, but not all, agree that some degree and

type of underinvestment in technology research

occurs. However, techniques are poorly developed

for identifying underinvestment phenomena and

then selecting among alternative policy response

mechanisms. Moreover, public subsidies for R&D

(tax incentives, direct funding, or government

laboratory research) have been based on rationales

that are often non-economic, leaving the economic

growth impacts as an ineffective trickle-down

effect.

In spite of these inadequacies, the Federal

Government was the main provider of the

Nation’s R&D funds until the last two decades—

accounting for 54% in 1953 and as much as 67% at

its peak share in 1964. The Federal share first fell

below 50% in 1979 and then stabilized in the 40–

45% range in the 1980s. The rapid increase in

industry funding of R&D during the 1990s

coupled with the general restrictions on Federal

spending progressively reduced the Federal share

to an all-time low of 29.5% in 1998, as shown in

Figure 11.

A frequent observation is that the relative

decline in Federal R&D funding is largely due to

cutbacks in defense R&D, which is asserted to not

directly affect economic growth. This assertion is

definitely not true for a portion of DoD R&D

expenditures, which over several decades has been

largely responsible for the emergence of a number

of major new technologies (as described below).

Moreover, the non-defense portion of the Federal

R&D budget, which arguably is more market

focused than the defense portion, has remained

relatively constant in real dollar terms for 35 years

(Figure 12). A declining defense R&D budget and

a static non-defense budget should be closely

examined with respect to its adequacy for providFigure 11. Federal and industry R&D expenditures, 1953–

1999 percent of total R&D spending.

172 Tassey
ing industry with a range of technology platforms

to launch new industries. The models of technology-based economic growth summarized in this

paper and the examples of major economic impact

from past government R&D funding described in

the following sections emphasize the importance of

such analysis.

Two critical policy issues should be the point of

departure for this analysis: (1) How will the

substitution of private for government funding

affect the amount of R&D funding over time, and

(2) how will this shift affect the composition of

R&D?

In the five years from 1994 to 1998, industry

funding of R&D grew in real terms by 8.9% per

year, allowing many to conclude that no policy

issues exist. However, in the previous ten years

(1985 to 1994), industry R&D funding grew at an

annual real rate of 2.8%.

59 One factor explaining

the difference in growth rates in these two periods

is the business cycle. Companies fund most of their

R&D with internal funds, so R&D tends to go up

when cash flow is high and decline when it is low.

Given the long-term nature of much R&D and the

need to develop new technologies within increasingly short time frames, the amount of R&D

funding and its stability over time should be a

long-term policy concern.

However, the composition of R&D is probably

the more significant dimension of the declining

share of Federal R&D funding. Some of the

elements of an industrial technology are shared

among companies—voluntarily or involuntarily

for reasons discussed earlier. The two major

shared elements are the technology base of an

industry (the generic or fundamental technology)

and infratechnologies (techniques, methods, databases, etc., many of which become industry

standards). Because these elements are shared,

they take on the character of public goods, which

imply underinvestment by private sources of

funding.

The more radical and generic the technology,

the more difficult it is for companies to (1) hold

on to the intellectual property (spillovers

occur), (2) diversify into the entire range of

new markets created by the technology (economies of scope are not captured), (3) access the

required multidisciplinary research capabilities

(modern technologies are complex), and (4)

rationalize undertaking the required long-term

research (high corporate discount rates lower

the present value of projected future earnings).

Public-private investment: Generic technology

Criticisms of linear models of innovation (basic

science, generic technology, innovations—in that

order) are justified. Feedback loops are regular

occurrences in which marketplace experiences feed

back into product or process design. Moreover,

important innovations do occur and then the

underlying science is developed to explain how the

technology works. For example, Pasteur invented

the vaccine and in the process discovered some

new principles of microbiology. More recently,

packet switching—the basis for computer networks including the internet—evolved to a significant degree ahead of theory.60

However, it is hard to imagine apoptosis,

antisense, monoclonal antibodies, or other generic

biotechnologies being developed through experimentation rather than derived from previous

advances in microbiology. In fact, the greatest

difference between traditional pharmaceutical

research and biotechnology research is that the

former was largely trial and error, whereas the

latter is based on fundamental science and a set of

generic technologies that evolved from this science.

The former may prove the existence of a nonlinear

model of innovation, but it is far less efficient than

the more linear evolution of biotechnology

research.61

The increasing dependency of innovation on

basic science and derived generic technologies is

Figure 12. Federal non-defense R&D funds FY1959–1998

(1992 $billions).

Policy Issues for R&D Investment 173
seen in the changing relationship between patents

and research. From 1987/88 to 1993/94, the

linkage between industrial technology (represented

by patents) and science (represented by the citation

of scientific papers in patents) tripled and has more

than doubled again since then. These studies also

reveal that U.S. patents preferentially cite the

highest quality research (indicated by research

papers with the highest overall citation frequency).

Finally, the institutional origins of the papers cited

in the patents were dominated by public sector

organizations. The analysis showed that 73% of

the papers cited in U.S. patents were authored in

public sector institutions, such as universities and

government laboratories.62 Thus, for major new

technologies, the science base increasingly must be

in place before significant and sustained rates of

applications can take place. That is, an evolutionary pattern (i.e., a linearity) exists in major

technology life cycles.63

Part of the difficulty in attaining a consensus

model of innovation arises from the increasing

dominance of systems technologies. Advances in

component technologies within systems create

demand for advances in the remaining components to allow the system technology to advance.

Moreover, initial advances in some components

cross-fertilize advances in other components.

These phenomena have given rise to as a ‘‘chain

link’’ models of innovation.64 Such models not

only embody interactive relationships among

stages in the development and commercialization

of technology, but also include complementary

roles for several distinctly different technologies.

Here, the pattern of technological progress is

ascribed more to a mating of complementary

technology assets, independent of any evolutionary process.

Unfortunately, such derived demand for

advances in component technologies within a

broader system technology has been confused

with the relationship between the generic technology base underlying each component. For example, a National Research Council paper states that

‘‘ . . . development of magnetic core memory for

computers did not flow directly from advances in

materials research (although it certainly drew

upon such research), but from the need to develop

a memory system with short enough access times

and high enough reliability to support real-time

computing’’.65 Such a statement reflects confusion

between the derived demand for a technology

(needs at the system level) and the science and

generic technology base (the results of materials

research) that enables a specific technology’s

development (magnetic core memory) in response

to that demand.

Equally important for R&D policy is the fact

that the advancement of basic science sufficient to

allow technology development to begin does not

guarantee immediate or even eventual commitment of private sector funds (see Figure 3). 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.66 And, 20 years

after the first biotechnology company went public,

NIH still provides research funding to dozens of

the more than 300 biotechnology companies.

These companies now have ‘‘140 products

approved and on the market . . . [and] a pipeline

heading for the FDA that could double that

number in the next 18 months’’.67

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 in the United States and not

in a competing economy because the Federal

Government funded both the science base and

the subsequent early phases of technology

research, allowing U.S. industry and U.S. capital

markets to reach positive investment decisions

ahead of the rest of the world.68

The NIH example is a case study in government

response to the entire set of R&D market failures

that beset the development of any radically new

technology. U.S. R&D policy has condoned

government funding of generic technology or

more radical infratechnology research when some

non-market objective (such as health care) is

available as a driver. This philosophy is apparent

in the current distribution of Federal R&D

funding shown in Figure 13. Health has received

continually larger shares of the non-defense R&D

budget and the result has been U.S. leadership in

biotechnology.

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

national defense) allowed Federal funding of

major new technologies at threshold levels, which

subsequently drove economic growth for decades.

The fields of computing and communications

provide a number of compelling examples of how

government funding played 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. These

amounts have represented a major fraction of all

research funding in the field of computing.69

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 the underlying 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, the early versions of these technologies were simply proofs of concept.70 Such

preliminary technology platforms were necessary

to provide a generic knowledge base to which

industry could apply their conventional investment

criteria and determine whether to commit funds

for applied R&D and eventual commercialization.

During the 1980s, networks were widely

deployed, initially to support scientific research.

The National Science Foundation (NSF) was the

major supporter of networking, 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 requirement presented

a negative investment incentive to firms with

substantial commitments to proprietary networks.

Moreover, telephone telecommunications companies, whose lines carried the packet-switched

information, resisted computer networks, including the internet, because the nature of voice

communications networks is strikingly different

from the evolving computer networks.71

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

Figure 13. Federal R&D for health and general science, 1980–

2000 (1992 $Millions).

Policy Issues for R&D Investment 175
IBM, RISC was not commercialized until DARPA

funded additional research at UC-Berkeley and

Stanford University as part of its very large scale

integrated circuit (VSLI) program in the late 1970s

and early 1980s.72

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. Continued

Federal investments advanced the generic technologies over a decade or more until conventional

industry R&D criteria could rationalize investments in applied R&D. Now, private investment is

driving the commercialization of many AI technologies.

When defense R&D dominated Federal funding, DARPA determined the Federal portfolio of

generic technology research. In the late 1980s, the

growing importance of a broad range of technologies for domestic economic growth led Congress

to establish a civilian counterpart to DARPA—the

Advanced Technology Program (ATP) at the

National Institute of Standards and Technology

(NIST). ATP’s mission is to fund the gaps in

private sector funding of generic technology

research. Due in part to a lack of consensus over

Federal roles in supporting technology research,

ATP’s funding has been uncertain over the past

decade.

Public-private investment: Infratechnology

Infratechnologies are a ubiquitous set of technical

tools, which are increasingly important to the

productivity of technology-based economic activity. As is the case with generic technology,

infratechnology is a quasi-public good, so both

industry and government invest in R&D to

develop this category of technical infrastructure.

As evidenced earlier in the example of biotechnology (Table I), these infratechnologies—either

directly or through incorporation in industry

standards—are pervasive in terms of their scope

and hence their economic impacts. They leverage

the productivity of R&D, enhance quality and

process control, and facilitate efficient marketplace

transactions for complex, technology-based products and services.73

Economies of scope are more pronounced in

infratechnology research than is the case for much

generic technology research. This fact justifies

government conduct of a significant portion of

infratechnology research, whereas most generic

technology research can be co-funded by government and conducted by industry (as in the ATP

model). NIST’s laboratories provide a wide range

of infratechnologies to industry to leverage the

productivity of industry’s R&D investment. This

technology infrastructure ultimately affects all

three stages of economic activity: R&D, production, and market development.

Microeconomic studies undertaken over the

past decade have documented high social rates of

return (SRR) for government investments in

infratechnologies in support of manufacturing

R&D. A majority of the 25 projects studied yielded

SRR estimates that exceed even a high hurdle rate

of 25% (by substantial amounts in many cases).

Some recent assessments of NIST infratechnology

research programs are summarized in Table III. As

indicated by the wide range of net present value

(NPV) estimates, some infratechnologies are localized in their economic impact, yielding NPVs that

are relatively small. However, many such infratechnologies and associated standards are needed

by a single industry. Thus, the aggregate NPV

from all technical infrastructure supporting a

single industry is quite high. Yet, because of their

frequent localized effect and the systems nature

and general complexity of most technology,

individual elements of technical infrastructure are

relatively invisible to policymakers.

The outcome metrics selected for these studies

are identical with those used by companies in

selecting and evaluating the impacts of their R&D

portfolios. This allows some degree of comparison

between investments in proprietary technologies

by industry and investments in the supporting

technical infrastructure by government or by

industry-government partnerships. However, the

primary use is for resource allocation within the

two types of investment. One of the many

challenges for R&D policy is the identification of

technical infrastructure needs followed by a

prioritization of those needs and selection of

development and delivery mechanisms.

176 Tassey
In the case of infratechnology research, gaps

have developed between needs and availability to

industry. NIST’s laboratories provide industry

with a wide range of infratechnologies. However,

Figure 14 reflects the fact that the budget for NIST

infratechnology research has grown at a significantly slower rate over the past 25 years (1977–02),

compared with industry-funded R&D. The markedly different growth rates have resulted in the

NIST laboratory research budget declining by a

factor of two relative to industry R&D spending

during this period.74

9. Funding the gaps: principles of R&D policy

The above examples of Federal R&D funding

reinforce the point that the critical policy issues are

the amount, timing, and type of government

funding for public good elements of technology

research. These elements leverage the development

of new technologies as a complement to industry’s

capacity to undertake the required research over a

technology’s life cycle.

As long as Federally funded research is viewed

as a set of options on further research and the

results of each option are reviewed by all

stakeholders before deciding to continue, industry’s conventional R&D decision making process should kick in at the appropriate point in

the R&D process and government’s role should

diminish rapidly. That is, industry will either

reject the projected technology trajectory or

take over an increasingly large portion of total

research funding. Case studies cited here have

shown this pattern to be the case many times

over.

As government funding is extended forward in

the R&D life cycle, that is, to include applied

research and especially development, the market

mechanism is increasingly compromised. In conTable III

Economic impacts of federal infratechnology investments: NIST research projects

Industry/project Output Outcomes Measure*

Semiconductors: Software for design

automation (Gallaher and Martin,

1999)

. Software model . Increase R&D efficiency

. Increase productivity

SRR: 76%

BCR: 23

NPV: $17m

InformationTechnology: Models and

standards for computer security

(Gallaher et al., 2002)

. Models

. Conformance testing

. Increase R&D efficiency

. Enable new markets

SRR: 62%

BCR: 109

NPV: $292m

Semiconductors and fiberoptics: Laser and

fiberoptic power and energy calibration

(Marx et al., 2000)

. Calibrations . Increase productivity

. Reduce transaction costs

SRR: 43%–136%

BCR: 3–11

NPV: $48m

Chemicals: Standard reference materials

(SRMs) for sulfur in fossil fuels (Martin

et al., 2000)

. Measurement method

. Standard reference materials

. Increase R&D efficiency

. Increase productivity

. Reduce transaction costs

SRR: 1,056%

BCR: 113

NPV: $409m

SRR 1⁄4 social rate of return, BCR 1⁄4 benefit-cost ratio and NPV 1⁄4 net present value. All NPVs stated in 1998 dollars for comparison

purposes.

Figure 14. Ratio of NIST laboratory funding to industryfunded R&D: 1977–2002.

Policy Issues for R&D Investment 177
trast, early-phase generic technology research

funding serves the role of providing industry

with technology platform options and thereby

increases the likelihood of a more optimal

technology trajectory being selected and/or

selected sooner. In essence, this approach implies

a portfolio approach to government R&D funding

in that the early phases of a number of technology

trajectories are funded with the realization that not

all elements of the portfolio will eventually become

industrial technology platforms.

In addition to advancing the elements of an

industrial technology with a public good character, Federal research funds also help create a

broader and deeper research capacity in industry

and the supporting technical infrastructure. NIH

funding virtually created a biotechnology

research infrastructure in universities and industry, while being a major technical resource itself.

ARPA and NSF funded academic research in all

areas of computer network technology. ARPA

managers worked closely with the researchers

they supported and convened many meetings for

information sharing and planning.75 The resulting broader and deeper R&D establishment

permitted more diverse and rapid market applications, once significant private investment

kicked in.

A critical policy lesson is that much of NIH’s

support of biotechnology and DARPA’s support of computer-related technologies and

communications networking technology preceded private investment. Additional support

then augmented the evolution of the technology

base where private investment was too narrow

in scope or too resistant to the more radical

versions of the underlying technology. The

result was world technological leadership, the

creation of new industries, and substantial

economic growth.76

A second policy lesson is that funding for

generic technology and infratechnology research

is small relative to the subsequent investments by

industry in applied R&D, as indicated in Figure 15.

At the same time, the public good character of this

research creates formidable barriers to private

sector investment. Fortunately, the relatively small

cost allows government to fund a broad portfolio

of research in emerging technologies and infratechnology research. The key requirements for

successful government research programs are the

targeting of the right technology elements at the

appropriate phases in the respective technology life

cycles.77

Of course, even with ample Federal funding,

the complete model of industrial innovation

reveals numerous pitfalls along the path to global

competitiveness. The United States has become

the world leader in biotechnology not only

because of sustained Federal research funding

but also because of a risk-taking culture supported by an extensive venture capital infrastructure. Yet, over its 25-year history, the

biotechnology industry has experienced a number

of peaks and valleys of investor enthusiasm. These

violent capital market cycles have caused R&D

cycles to lengthen and many biotechnology

companies to license their technology at unfavorable terms or even sell out to large domestic or

foreign pharmaceutical firms at unattractive terms

to investors.

An example is monoclonal antibodies (MABs).

This technology platform with many potential

therapeutic applications was first developed in the

mid-1980s and was heralded as the magic bullet for

treating cancer and possibly other diseases. However, first generation MABs were not particularly

effective for several reasons and investors quickly

lost interest. Many companies in this area were

acquired or went out of business. However,

sustained funding by NIH and a trickle of venture

capital allowed research to continue. A decade

Figure 15. Typical relative expenditures by phase of R&D

over technology life cycle.

178 Tassey
later, second generation MABs and even newer

hybrid technologies that use MABs as targeting

devices for other therapeutic agents have

appeared. As a result, investor enthusiasm has

returned.

Another area of biotechnology with a similar

pattern is antisense technology, which blocks the

formation within cells of unwanted proteins. In the

early 1990s, antisense was a hot technology, but

degradation and toxicity problems caused difficulties for the first generation drug candidates. By the

mid-1990s, hardly anyone was interested. Now,

many of the earlier problems appear to have been

solved and the technology is coming back into

favor again.

The R&D policy message is that when the time

to commercialization is relatively short, perhaps a

year or two, risk is relatively easily estimated and

incorporated into R&D decision making. For the

longer early phases of the R&D cycle, estimated

risk increases and inconsistent intra-company and

capital market support results. Federal research

funding for basic science and the generic technologies based on this science is critical to sustain the

innovation patterns that appear frequently in

emerging technologies. In addition, the timely

availability of infratechnologies is also extremely

important. Such technical infrastructure can significantly improve the efficiency of the R&D

process and thereby attain critical reductions in

R&D cycle times and cost.

Federal R&D funding alone is obviously not a

sufficient long-term economic growth strategy.

In fact, this paper has emphasized the critical

concept of a national innovation capacity,

which includes education and financial infrastructures and the dominance in dollar terms of

private R&D funding. However, Federal R&D

funding is an essential element of a national

innovation system by helping provide a future

technology base sufficient to ensure a significant long-term contribution to economic

growth by a range of technologies. To this

end, a portfolio of emerging technologies

should be supported, which includes a sufficiently diversified manufacturing technology

platform to capture the synergies of supply

chain integration with the rapidly expanding

IT-based service sector.

As the Council on Competitiveness recently

stated, ‘‘Given the rising bar for competitiveness,

the U.S. needs to be in the lead or among the

leaders in every major field of research to sustain

its innovation capabilities’’.78

10. Policy analysis and the development of policy

options

Once the need for analysis in a particular area of

policy has been determined and specific market

failure mechanisms identified and analyzed, policy

options must be developed that respond effectively

to each type of market barrier. A comprehensive

discussion of such a process is beyond the scope of

this paper, but its importance for effective R&D

policy development should not be overlooked

(Tassey, 2003). Unfortunately adequate policy

analysis capability does not exist. Neither consensus models of industrial innovation nor appropriate data to modify and utilize such models are

available. The two decades of debate over the still

temporary research and experimentation (R&E)

tax credit, the lack of consensus over the roles of

government R&D funding for economic growth

purposes, and the continuing inability to agree

when each of these two distinctly different policy

instruments (tax incentives and direct funding)

should be used are clear evidence of the overall

problem.79

One of the most critical unresolved issues in

the R&D policy arena is the nature of interactions between public and private funding of

R&D. Economists have researched this question

for several decades without resolution. An

analysis of why some research shows a positive

(complementary) relationship and some finds a

negative (substitution) one for this interaction is

complex.80 Findings that government R&D

funding either complements or substitutes for

private sector R&D spending depend strongly on

factors such as the unit of analysis (the firm, the

industry or the national economy) or the phase

of the R&D cycle funded (basic, generic, applied,

or development). Such differences in the estimated impact of public sector R&D should not

be surprising, given the inconsistencies among

studies with respect to underlying models and

data.

Policy Issues for R&D Investment 179
This paper has provided analysis targeted at

improving R&D policy analysis. To this end, the

literature and the case studies cited support the

proposition that several critical elements of the

typical industrial technology have an infrastructure character, which creates substantial underinvestment by the private sector and therefore

demands a public sector response. Other elements

of industrial technology—the majority from an

R&D spending perspective—require little or no

direct government support.

Thus, the characteristics of market failure need

to be embodied in a comprehensive model of R&D

investment behavior and resulting innovation

patterns in order to accurately estimate the

optimal amount and composition of R&D and

the existing gaps in investment. Only in this

context can the various underinvestment phenomena be accurately characterized, which, in turn,

will allow appropriate policy responses to be

developed. Until the distinctions among the

proprietary and public elements of industrial

technology are fully understood and used in

R&D policy analysis, inadequate policy options

will result.

11. Conclusion

A number of changes in the global economic

environment have emerged over the past two

decades, which demand attention by all industrialized nations. These changes have particularly

strong implications for the United States, which

once had a virtual monopoly on technology-based

economic growth. In particular, the globalization

of R&D has greatly magnified the importance for

R&D policy of four long-term trends:

1. A skewed distribution of R&D funding in the

U.S. economy, with only a fraction of manufacturing and service industries warranting the

label of ‘‘R&D intensive’’.

2. A concentration of R&D performance in 10

states, with the remaining states subject to slow

growth and persistent job losses.

3. A shift in the composition of industry-funded

R&D toward more specific product and process

objectives.

4. Skewed Federal funding of long-term, high-risk

technology research at the expense of the

provision of a diversified portfolio of technology platforms and trajectories as the bases for

new industries.

Sustained economic growth in a large and

diversified economy such as the United States

will require not only more national R&D spending

but also the provision of considerably more longterm funding for a range of emerging technologies.

In spite of the rapid growth of the service sector,

many of these technologies are and will be for the

foreseeable future classified in the manufacturing

area. Within the U.S. R&D portfolio, substantial

Federal funding is currently available for only one

emerging technology—biotechnology.

Given the wide range of public and private

economic assets that drive long-term growth, a

responsive national investment strategy should

emphasize those incentives that entice investment

in the most productive and immobile determinants

of economic growth. Technology generally meets

these requirements, even though technical knowledge spills over and thus is relatively easily

acquired by users other than the originator.

However, within the technology investment

arena, more specific policy targets stand out. One

critical objective is R&D capability. States regularly bid against one another to attract production activity, but R&D capability is seldom

similarly in play. This is because private R&D

assets are closely tied to public research and other

infrastructure, whose efficiency requirements may

be national in scope. These assets are relatively

immobile domestically and even more so internationally, which means that once created they are

not easily imitated.

In other words, technology—and the public and

private institutions that support its development

and use—are interdependent components of a

national innovation system. This system is not

easily replicated due to the complexity of the

actors, institutions, and ultimately market applications that produce the economic benefits. One

dimension of this complexity is diversification of

an economy’s technology base and its myriad

applications. The absolute size of a nation’s R&D

investment is increasingly critical. However, diversification of technology development and utiliza180 Tassey
tion across manufacturing and service industries

yields three critical additional advantages: more

growth opportunities, more stable growth trajectories, and greater synergies among economic

sectors. The U.S. economy has made substantial

investments in a modern IT-based infrastructure.

It must now make the long-term investments in

innovative product and service technologies that

take advantage of this infrastructure and thereby

drive future economic growth.

Acknowledgments

Michael Casassa, Paul Doremus, Mathew Heyman, Jen McAndrew, and Chad Snyder provided

valuable comments on various drafts of this paper.

Notes

1. For example, a recent National Bureau of Economic

Research study found that ‘‘technological knowledge is to a

substantial degree local, not global, as the benefits from foreign

(R&D) spillover are declining with distance’’. See Keller (2000).

2. Many different definitions exist for the ‘‘high-tech’’ sector

of an economy, which is unfortunate for policy analysis

purposes. The definition of ‘‘the high-tech sector’’ used here

includes both manufacturing and service industries grouped

into four major categories: high-tech manufacturing (IT-related

plus industrial electronics), communication services, software

and computer-related services, and pharmaceuticals. See Tassey

(1999b, p. 5) for more detail.

3. In fact, both transformations were similar at the broad

macroeconomic level, being characterized by productivity

gains, strong economy, low-inflation environment, and stable

interest rates.

4. Oliner and Sichel (2000).

5. McAfee (2000, p. 11).

6. Blair and Kochan (2000, pp. 1–2) point out that in 1978

the book value of physical assets (property, plant, and

equipment) owned by publicly traded non-financial corporations was 83% of a company’s capitalization (the value of

outstanding bonds and common stock). In 1998, 69% of the

average company’s value was in the form of intangible

(knowledge) assets (so-called ‘‘goodwill’’), as opposed to 17%

20 years earlier.

7. The term ‘‘supply chain’’ refers to the vertically

integrated set of industries that adds value beginning with

raw materials and eventually produces a final product or

service. Each level (industry) in a supply chain adds value

until final demand is met and the sum of the value added by

each level is the supply chain’s contribution to GDP. An

example of a first level in a supply chain would be silicon and

other semiconductor materials. These materials are used to

manufacture semiconductor devices, which are combined to

form electronic components and equipment such as computers. The latter are further combined to form ‘‘systems,’’ such

as an automated factory that manufactures a product

(computer) or a telecommunications network that provides a

service.

8. For example, the Conference Board estimates that the

manufacturing sector’s expenditures on IT are growing at 10–

15% per year. Also, the International Data Corporation

estimated that the manufacturing sector’s spending on web

development infrastructure reached $24 billion by 2002,

while spending by comparably sized financial services was

$17 billion.

9. For example, the amount of equipment required per

worker in a liquid crystal display plant is about $1 million. In a

software company, the equipment required can be as little as

$10,000 per worker (Fingleton, 1999). Higher capital-labor

ratios are labor saving, which is particularly attractive in tight

labor markets.

10. Manufacturing sector salaries and wages are currently

higher than those in the service sector. However, as discussed

later in this report, while capital deepening raises measured

labor productivity, this ‘‘increase’’ is not due to an intrinsic

improvement in labor skills. Corporate managers recognize this

and do not raise wages and salaries proportionately. Long-term

growth in payments to labor depends on increases in skill levels

and the consequent contribution to multifactor productivity.

11. A generic or fundamental technology is the basis for

specific applications targeted at particular markets. A generic

technology could be represented by a conceptual model or a

laboratory prototype. Considerable applied R&D is required to

create market applications from the generic technology, but

without subsequent advances in the generic technology,

innovations will be accidental and limited. See Tassey (1997,

Chap. 7).

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

Technical problems that typically occur in newly commercialized technologies; (2) lack of economies of scale due to small

initial markets; (3) higher cost due to reliance on a manufacturing process that has not been optimized for the new technology

(small initial market shares do not provide sufficient incentive

to invest in the necessary process R&D, or, if the innovator is a

small company, the initial cash flow may not be adequate to

fund that R&D); or, (4) interfaces (standards) between the new

technology and other components in the broader technology

system are typically not defined early in the technology’s life

cycle, thereby raising system integration costs.

13. For example, in the face of a challenge from flat-panel

displays, manufacturers of cathode-ray tubes continue to reduce

costs and improve picture quality, thereby constantly raising the

hurdle for the invading technology (curve 1 shifts upward).

14. 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 modern commercial wireless communication and detection system incorporates, was discovered in the United States, but Japanese

industry today clearly dominates commercial markets. See

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

Policy Issues for R&D Investment 181
15. The transitions from vacuum tubes to semiconductors and

from cathode-ray tubes to solid-state (flat-panel) displays are

two examples.

16. According to NSF data, the two industry groups with the

largest absolute shares of R&D funds from foreign sources are

chemicals (due largely to pharmaceuticals/biotechnology) and

computers—two areas of technology with arguably superior

domestic R&D networks. Both groups also have twice as much

inflows of R&D funds as outflows to foreign locations.

17. Tassey (1997, Chap. 8).

18. Besanko et al. (1996).

19. Brunnermeier and Martin (1999).

20. Enterprise Integration Council (2000).

21. More generally, vertical integration has declined as a

framework for corporate strategy for three reasons: (1) Such a

structure forges artificial customer relationships reducing price

competition, (2) buying components from affiliated divisions of

the same company precludes optimization by the customer of

multi-component systems, and (3) in a global marketplace of

intense competition, corporate managers are having increasing

difficulty managing multiple technologies and associated

markets.

22. For example, 90% of the firms responding to the Bear

Stearns’ 3rd Annual Electronics and Supply-Chain Survey (mid

2000) said they planned to increase the use of electronics

manufacturing services over the next 12 months.

23. The Industrial Research Institute annual survey indicates

that ‘‘R&D is becoming more externally collaborative’’.

Industrial Research Institute (2000, pp. 11–13).

24. See Tassey (1997, pp. 63–67) for a discussion of the

linearity of the innovation process.

25. For summaries of microeconomic studies of infratechnologies and associated methodologies, see Link and Scott (1998)

and Tassey (1997, 1999).

26. Finan (1998). The estimate does not include the labor and

overhead required to implement this measurement infrastructure.

27. Tassey (1997, Chap. 9).

28. Federal Reserve Board (http://www.federalreserve.gov/

releases/G17/Revisions/19991130/g17.pdf). The Federal

Reserve definition of ‘‘high-tech’’ includes computers, communications equipment, and semiconductors.

29. Census’ definition of ‘‘high-tech’’ includes about 500 of

some 22,000 commodity classification codes used in reporting

merchandise trade. Note that this definition is based on

‘‘product fields’’ classified as technology-based, as opposed to

‘‘industries’’ which is used in most other definitions.

30. Data from the International Trade Administration, U.S.

Foreign Trade Highlights, Table III. See http://www.ita.doc.

gov/td/industry/otea/usfth/aggregate/H99t03.txt

31. Source: Bureau of Labor Statistics.

32. In another paper, Gordon (1999) concludes that the noncomputer portion of manufacturing has actually experienced

slower productivity growth in the late 1990s compared to the

generally acknowledged slow-growth period of 1972–1995.

33. Oliner and Sichel (2000).

34. McAfee (2000, p. 4–5).

35. The main reason ALP is used so frequently is its availability.

It is released on a quarterly basis a short time after the end of a

quarter, whereas TFP is made available annually approximately

18 months after the fact. Note that TFP is sometimes referred to

as multifactor productivity (MFP).

36. During 1995–1999, characterized as the take-off period for

the New Economy, ALP accelerated to an average annual

growth rate of 2.1%, while TFP grew at less than half that rate

(1.0%).

37. Both industry and government definitions of the ‘‘hightech’’ sector result in a share of GDP within this range.

38. Economic studies have consistently shown a strong

relationship between R&D investment and both productivity

and output growth (see OECD, 2000; Boskin and Lau, 2000;

Oliner and Sichel, 2000, and Cameron, 1998). The work of

Griliches (1988) suggests an elasticity of output from R&D of

between 0.05 and 0.1 with a social rate of return to R&D of

between 20 and 50%. Recent research (Cameron, 1999) has

indicated significant variation across industries in the effect of

R&D on multifactor productivity growth. However, no

evidence has been produced to indicate diminishing returns

from increased R&D across the range of R&D intensities found

in manufacturing industries. Thus, no support exists for the

argument that some industries need less R&D than others. In

fact, available data such as that presented in Figures 4, 5, and 6

support the proposition that low and moderate R&D-intensive

industries (technology adsorption strategy) do not have a bright

future.

39. Based on social rate of return calculations, Jones and

Williams (2000, 1998) estimate that ‘‘optimal R&D investment

is at least four times larger than actual investment’’.

40. See Cohen and Levinthal (1989).

41. Griffith et al. (1998).

42. Keller (2000), Porter (2000), Audretsch and Feldman

(1996), Acs et al. (1994), Feldman (1994), Jaffe et al. (1993), and

Rosenberg (1982).

43. Bennof and Payson (2000). California’s R&D effort

exceeded, by more than a factor of three, the next-highest

state, Michigan, with $14.7 billion in R&D expenditures for

1997 (the last year for which data are available). After

Michigan, R&D levels for the top ten declined relatively

smoothly to $7.4 billion for Maryland. The 20 highest-ranking

states in R&D expenditure accounted for about 86% of the U.S.

total, while the lowest 20 states accounted for only 4%.

44. National Science Board (2000, p. 2–7).

45. National Science Board (2000, p. 2–9).

46. Studt and Duga (2000).

47. See, for example, Corcoran (1994), Duga (1994), and

Geppert (1994).

48. Bean et al. (2000).

49. The remaining R&D funds came from the Federal

Government (5.2%) and outside contract work (1.1%).

50. However, Bean et al. (2000) found this sample to be

representative of the entire IRI membership (136 firms), at least

with respect to the trend in R&D intensity.

51. National Science Board (2000, pp. 2–31). Note that each

phase of national R&D requires more funds than the previous

one. For example, in 1998 the United States spent $37.9 billion

on basic research, $51.2 billion on applied research, and $138.1

billion on development.

52. See Figure 3 and associated discussion.

182 Tassey
53. See Tassey (1997, 1999).

54. Tassey (1999b).

55. Rice et al. (1998).

56. Iansiti and West (1997).

57. See Tassey (1997, 1999b) for detailed discussions of these

market failure categories.

58. In addition to funding, increasing the amount of R&D

conducted is constrained by an inadequate supply of scientists

and engineers. The percentage of engineers in the labor force

has remained constant over the last 25 years for which data are

available (Romer 2000). In 1997, the number of scientists and

engineers engaged in R&D constituted 0.8% of the labor force,

which is about the same as the previous peak in the late 1960s

(National Science Board, Appendix Table 3–25 and Romer,

2000).

59. National Science Board (2000, pp. 2–21).

60. In particular, packet switching for routing messages

through the ARPANET advanced empirically beyond theory.

See National Research Council (1999, p. 8). Parallel processing

is another example of an innovation that did not follow the

simple linear model. Demand in the 1980s for increased

computing power and the widespread availability of microprocessors led to commercialization, which preceded a good

theoretical understanding of how multiple processors can work

efficiently together, and spurred advances in that theory. See

Office of Technology Assessment (1995, p. 24).

61. By some analysts’ estimates, the pharmaceutical industry

today develops drugs for 500–600 ‘‘drug targets’’. However,

within just a few years the ability of biotechnology to isolate

and focus on specific intercellular and intracellular mechanisms

will expand the number of drug targets by an order of

magnitude to 8,000 to 10,000.

62. Narin et al. (1997) and Hicks et al. (2000).

63. See Tassey (1997, pp. 63–67, 1999b, p. 21).

64. Klein and Rosenberg (1986).

65. National Research Council (1999, p. 146).

66. McMillan et al. (2000).

67. Alan Carr, Chase Hambrecht, and Quist, quoted in

‘‘What Next for Biotechs?’’ Barron’s, March 20, 2000.

68. Approximately $1 billion of NIH research funding goes

directly to industry each year.

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

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

71. 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. National

Research Council (1999, p. 172).

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

73. Tassey (1997, Chap. 8).

74. As an additional perspective, one corporate R&D establishment, General Electric, employs 9,100 people. NIST—

charged with providing technology infrastructure support to the

entire U.S. economy—employs 3,200.

75. National Research Council (1999, p. 171).

76. Obviously, private funding increasingly takes over from

government funding and becomes dominant as the R&D life

cycle progresses. Without such a pattern, little commercial

technology would be developed. The appropriate combinations

in the right time frames are the key policy variable. For

example, Lerner (1996) has shown that SBIR awards are more

effective in regions with substantial venture capital availability.

However, 40% of these awards have gone to two states

(California and Massachusetts), once again emphasizing the

skewing of R&D in the U.S. economy in response to a

geographically limited innovation infrastructure.

77. California seems to have bought into this policy lesson. In

December 2000, the state announced that it has committed $300

million over four years and companies in that state have

committed more than twice that amount to develop new generic

technologies that will allow replication of the Silicon Valley

model (clustering of high-tech firms driven by a new technology, associated infrastructure, and other synergies).

78. Porter and van Opstal (2001).

79. See Tassey (1995, 1996, 1997 (Chap. 6), 1999b) and

Whang (1998, 1999) for discussions of tax incentives vs. direct

funding as alternative R&D policy mechanisms.

80. See David et al. (2000) for a comprehensive assessment of

this literature.

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