# R&D Policy Models 

# and Data Needs

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## **Gregory Tassey**

### **National Institute of Standards and Technology**

#### **Strategic Planning and Economic Analysis Group**

**APPAM 1999 Research Conference**

###### November 4, 1999

[**tassey@nist.gov**](mailto:Tassey@nist.gov)

[**www.nist.gov/director/planning/strategicplanning.htm**](http://www-i.nist.gov/director/planning/strategicplanning.htm)

**R&D Policy Models and Data Needs**

##### Gregory Tassey

S&T policy is a subset of economic growth policy; it therefore needs to
be grounded in sound economic rationales, if it is to be appropriately
addressed in the broader policy arena. In spite of the general
recognition of science and technology's (S&T) critical role in economic
and social well being, debilitating disagreement continues over the
mechanisms by which technology drives economic growth and the respective
roles of industry and government in supporting technology investment.

In this context, the most frequently discussed issue is the *amount* of
R&D conducted in the U.S. economy. A commonly used indicator of the
amount of R&D undertaken by a high-tech industry is its R&D intensity
(R&D-to-sales ratio). In the United States, relatively few industries
have the high R&D-to-sales ratios (in the 8--12 percent range) that will
allow continued world class innovation. These industries together only
account for about 7 percent of GDP.[^1]

At the same time, the *composition* of U.S. private-sector R&D is
shifting toward shorter-term objectives, at the expense of
next-generation research. Annual surveys by the Industrial Research
Institute show continued declines in private-sector intentions to fund
long-term, high-risk research. Such compositional market failures
significantly reduce the long-term competitive prospects for U.S.
industries.

When global technology-based competition was less severe, the average
technology life cycle was sufficiently long to allow U.S. industry to
apply lower discount rates to future returns from R&D investment and
thereby undertake more next-generation technology research. Neither
private-sector nor government research establishments had to be
particularly efficient.

These conditions have largely disappeared. Today, central corporate
research labs have been de-emphasized, redirected or eliminated
entirely. Funding of government research for economic objectives has yet
to regain its peak level in real terms reached in the mid-1960s. New
programs providing decades of research support similar to those for
molecular biology (which created a world-leading biotechnology industry)
or for information networks (which led to the Internet) cannot be found
 at least not at levels that project first mover advantages for U.S.
industries.

##### 1.0. Needed: A Technology-Based Economic Policy Model

From a policy perspective, The "S" portion of S&T is relatively easy to
deal with. Basic science is widely recognized as close to a pure public
good, which means that massive underinvestment occurs without government
support. This premise has been understood and incorporated into policy
since the end of World War II.

The "T" portion is another matter. Unlike basic science, technology is a
"mixed" good, containing both private and public elements. Because of
the complexity of market failure mechanisms and resulting
underinvestment patterns, the roles of industry, universities, and
especially government are poorly defined, leaving endless debates over
different classes of possible policy responses.

Most S&T analyses gloss over the economics of R&D investment and the
associated market failure mechanisms, jumping to a set of poorly defined
and supported policy recommendations. For example, support for some form
of "collaboration," "cooperation," or "partnering" appears in most
policy documents. Yet, few specifics are available regarding what
technologies, what point in their life cycles (e.g., what phase of R&D),
and what number and combination of institutions---public and
private---should receive support.

As discussed later, a varied set of factors can and do thwart private
sector investment in certain types of essential R&D. These sources of
market failure derive from the influence of today's emerging
technologies and supporting infrastructures on R&D investment. The
number and complexity of these market failures has caused the Federal
government's role in supporting industrial R&D to be poorly understood
and therefore inefficiently funded and managed. More specifically,
inadequate R&D policy analysis is due to the absence of the following
three elements:

(1) The R&D policy process needs a conceptual model for assessing
    government roles in support of technology development, based on the
    idea of a national innovation system. This includes the concept of
    networks as the basic structure of a modern, effective R&D
    establishment. That is, the typical industrial technology consists
    of a set of distinctly different technology elements requiring the
    combined efforts of both private and public entities. That is what
    distinguishes this model from 50 years of simplistic concepts where
    science is a pure public good and technology is viewed as a
    homogeneous, purely private good. Because pure public goods are
    provided by government and pure private goods are provided by
    industry, government roles in supporting *technology* development
    have either been undeservedly rejected or poorly conceived and
    implemented.

(2) Such a model must be grounded in an economic context that provides
    clear descriptions of the mechanisms by which technology drives
    productivity and economic growth. Policy studies typically make the
    general statement that "technology is essential for economic growth"
    and move on. While this characterization engenders little dissent,
    it also is woefully inadequate for defining issues relating to
    technology development and diffusion. In particular, the competitive
    dynamics of technology-based economic activity needs to be
    represented in a way that allows incisive analyses of the factors
    that determine underinvestment at critical stages in a technology's
    evolution.

(3) More data from a variety of sources need to be collected and
    analyzed to fully identify, characterize, and assess government
    roles in supporting industry's R&D investment. Most S&T policy
    studies not only fail to provide a robust conceptual model and its
    economic underpinnings, but they offer little or no data either to
    elucidate the relevant policy issues (in particular, the market
    failure mechanisms that lead to the need for government support) or
    to identify and construct policy responses that efficiently remove
    the market failure.

##### 2.0. Life Cycle Models of Technology Development

As increasing numbers and types of technology permeate the global
economy and as the number of technology-based competitors grows, the
volatility of global markets will increase. Technology (and hence R&D)
life cycles will continue to shorten, industry structures will evolve
more rapidly, and employment opportunities will be more volatile. To
deal effectively with this complexity, decision makers need a realistic
and policy-relevant economic framework of investment and performance
over the technology life cycle and, equally important, the transition
between life cycles.

Peter Drucker in his classic 1985 *Harvard Business Review* article
simply but accurately summed up the nature of technology-based progress:

"Knowledge-based innovations differ from all others in the time they
take, in their casualty rates, and in their predictability, as well as
in the challenges they pose to entrepreneurs...They have, for instance,
the longest lead times of all innovations. There is a protracted span
between the emergence of new knowledge and its distillation into usable
technology. Then there is another long period before this new technology
appears in the marketplace in products, processes, or services...To
become effective, innovation of this sort demands not one kind of
knowledge but many".

Drucker argues that major technology life cycles have persistently taken
about 50 years from the initiation of significant basic research to the
emergence of market applications. Simple numbers---the increasing
resources around the world devoted to scientific and technology
research---argue for a reduction in this time frame in the future.

Within these long-term cycles, national economies will undoubtedly vary
in how they absorb scientific advances and demonstrate economic
potential through technological proof of concept. This fact raises a
critical issue from a national economic growth perspective: How can the
domestic industry gain early access to both the basic science and the
generic technology based on that science?

The technology assessment literature identifies three distinct cycles.
The shortest and least controversial is the "product life cycle," which
is simply the time from concept or initiation of product development
through market penetration and eventual decline. A number of product
life cycles are typically derived from the same underlying generic or
fundamental technology, which collectively forms a "generic technology
life cycle". The generic technology is not static but evolves during its
life cycle. However, this evolution is not major compared with
differences from the previous generic technology that drove product
development for the same market functions before being replaced by the
current technology. Finally, several generations of generic technology
life cycles typically evolve from the same underlying set of basic
scientific principles. Eventually, a major new science base appears,
allowing a transition to a new long-term "major technology life cycle"
or "wave".[^2]

Over the typical generic technology life cycle, product technologies
become progressively more stable. As opportunities to apply the
underlying or generic technology decline, design volatility decreases
and an industry's product structure takes on a "commodity" character
(electric power transmission 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 now maturing 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 examples of
maturing phases of this life cycle pattern and the resulting competitive
convergence.

The transition between two generic technology life cycles presents a
different set of competitive threats. The more radical the transition
between two cycles, the greater the risk to individual companies and
even entire industries. Such a transition typically demands
multidisciplinary skills needed to conduct the research for the new
technology life cycle, skills that existing firms do not fully possess.
Hence, they assign higher technical and market risk values to the
prospective research program. A company considering undertaking the risk
of investing in the new technology faces a value (performance per
dollar) curve, such as curve 2 in Figure 1, that initially will be below
the existing or "defender" technology (represented by curve 1). The risk
of high-cost performance, possibly for some time, adds to the market
risk associated with the dynamics of the marketplace.

Therefore, two key policy issues based on this technology life cycle
concept are

(1) *Within* a generic technology life cycle the amount and speed of
    advance achieved by domestic industry is critical because these
    performance variables determine the economic return realized on the
    total investment over successive product life cycles.

(2) Transitioning *across* 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.

![](media/image1.wmf)Under a Vannevar Bush-type of model, government can
rationalize funding years, even decades, of basic research. At some
point, enough knowledge is accumulated to allow judgements of risk
associated with potential market applications of a new technology based
on the underlying science. In an oversimplified model, applied
technology research should be more or less automatically initiated and a
new technology life cycle started.

However, a major problem for R&D policy arises at this transition from
basic research to technology research. Here, *for* the first time,
market risk assessments must be added to estimates of technical risk.
Combining technical and market risk complicates corporate R&D decisions
way beyond what is involved in allocating government funds for basic
research.

Figure 2 indicates that technology research, with its ultimate objective
of market applications, encounters an initial major increase in
technical risk 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. This
additional risk, RR\', occurring in the early phases of the R&D life
cycle, can and does act as a substantial barrier to private investment
in technology research. Understanding the evolution of and the
interaction between technical and market risk and the consequent impacts
on private-sector investment are the key elements for effective R&D
policy analysis.

![](media/image1.wmf)The last phase of a major technology life cycle is
characterized by increasingly intense competition, shrinking profit
margins, and structural unemployment and/or, underemployment---all of
which have been observed in major industry groups within industrialized
nations during the 1990s. Certainly, economic growth policy must help
ease transition costs, but it also must facilitate adaptation to the
next technology life cycles by identifying the relevant market failures
and developing appropriate responses. During the last phases of a given
life cycle, the new technologies that will drive the next life cycle
already exist, but with small market shares.[^3] Thus, opportunities for
adaptation are available, but attempts to take advantage of them usually
do not occur until economic conditions have become significantly
distressed.

##### 3.0. The Dual Value of an R&D Capability 

Conventional wisdom actually understates the importance of R&D in
technology-based industries by indicating that capital spending exceeds
R&D spending. Such a relative ratio of investment has been true until
recently. However, R&D has two strategically critical roles in the
"knowledge-based economy": (1) implementation of internal innovation
strategies, and (2) to provide the capability to absorb technology from
external sources.[^4]

Given the growing importance of these functions, many companies are
increasingly spending more on R&D than on plant and equipment. More than
one-half of Hewlett-Packard's sales now come from products the company
developed within the previous two years. In 1997 and 1998, HP spent more
on R&D than on plant and equipment (\$3.1B versus \$2.3B and \$3.4B
versus \$2.0B, respectively). To some extent, such relative increases in
R&D spending are due to the fact that high-tech firms such as HP are
contracting out more of their manufacturing requirements than in the
past, which reduces capital expenditures. However, such strategies only
accentuate the evolution of these companies toward a focus on
knowledge-based competitive advantage.

HP exemplifies the dual R&D strategy. The company is recognized as one
of the world's leading innovators, but one of HP's most successful
products in recent years is the ink-jet printer, which was the result of
successful imitation and improvement upon the innovating firm, Canon.
Canon had the patents on early ink-jet designs but made a strategic
error by choosing to attempt a complex implementation that ultimately
set it years behind.

However, R&D at the company level cannot exist in a vacuum. 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. The overall health of the entire R&D
network, in turn, determines the breadth and depth of national
competitiveness.[^5]

##### 4.0. Transition to a Life-Cycle Based Economic Model

The essence of technology-based economic growth is that a body of
scientific knowledge is drawn upon for the development of technologies
which, to varying degrees and in various forms, eventually result in
markets that contribute to the gross domestic product of the economy.
This value added is therefore the end point for economic growth policy,
and the factors determining its growth over time constitute the critical
elements of policy analysis.

An effective economic model of technology-based growth will incorporate
four basic elements:

(1) The early phases of technology development encounter considerable
    technical and market risks that are often beyond the capabilities
    and hence the investment criteria of individual firms or even groups
    of firms.

(2) Efficient development of technology by industry requires a complex
    set of supporting technical infrastructures to evolve along with the
    technology itself.

(3) Transactions in technology-based markets involve sophisticated
    products and services, which require equally sophisticated
    infrastructure support.

(4) R&D investment patterns and the consequent success or failure of
    domestic industries in global markets is time dependent; that is,
    the *technology life cycle* is the dominant framework for the
    critical analysis of how different R&D investments are
    made---including the early investments that initiate the cycle
    across supply chains.

Corporate managers, business analysts and economists have argued over
how to best represent all four elements in a conceptual economic model.
So-called linear models capture the time dimension to a degree but have
been appropriately rejected as too narrow in scope and overly
simplistic. However, no consensus on a more comprehensive
policy-relevant replacement has emerged. Other concepts, such as
feedback loops and chain-link or cross-fertilization among several areas
of science and technology, represent different dimensions of R&D
investment patterns. Each of these concepts enables a characterization
of factors in the evolution of technology at various phases in the R&D
life cycle.

While each concept has its limitations, they are not mutually
contradictory. In fact, they embody complementary elements of the needed
economic policy model. The feedback loop seems to be an attractive
concept for R&D managers, referring to the integration of the three
phases of economic activity---R&D, production, and marketing. Market
experiences feed back to R&D and production, resulting in adjustments to
product design and process technologies. Certain feedback loops are
consciously compressed by companiesin particular, the interactions
between product and process R&D that reduce manufacturing problems. This
phenomenon is most prominent in shorter product life cycles. The
existence of information feedback creates a concept of technological
change that is more dynamic and even circular, in which the phases of
R&D and subsequent market use change almost simultaneously.

Academic researchers, who are interested in a broad representation of
the innovation process itself, have proposed more complicated frameworks
such as the chain link model.[^6] Such models not only embody
interactive relationships among stages in the development and
commercialization of technology, but include complementary roles of
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. In fact,
some proponents purport to show how "technological breakthroughs are
just as likely to precede, as to stem from, basic research".[^7]

The chain link effect occurs most prominently in the mid-length or
generic technology life cycles. That is, when new generic technologies
are being developed and applied for the first time, cross fertilization
often occurs among heretofore separate areas of technology.

For example, HIV protease inhibitors were synthesized by chemists in the
pharmaceutical industry based on understanding the structure of HIV
protease as determined by biologists using physicists\' x‑ray
diffraction techniques. Two drug companies finalized their formulations
using the ultra‑powerful x‑ray beams from synchrotron radiation sources
normally used for nuclear physics research. Today, about 35% of the
running time on the Department of Energy\'s synchrotron radiation
sources is used for this kind of structural biology. Conversely, the
development of neural network computing algorithms to efficiently sort
complex multi‑dimensional data sets has it origins in neurobiologists'
attempts to develop an understanding of the brain's structure.[^8]

The example of biotechnology (text box) does not mean that technology
evolves in a purely linear process. Obviously, areas of science such as
molecular biology continue to advance and thereby make possible new
technological applications. Moreover, the basic research producing these
advancements is often influenced by the success or failure of past
technological applications. As the HIV protease example shows,
chain-link phenomena are real and important. Feedback loops have been
accentuated in recent years by corporate strategies such as concurrent
engineering, rapid prototyping, and, more recently, quality function
deployment approaches to managing technology development and
commercialization.

In general, neither increases in scientific knowledge nor technological
progress are particularly steady. Major advancements occur and feed a
host of applications for a period of time. The process of developing
these applications for the marketplace itself creates knowledge, which
feeds back to stimulate a more orderly evolution of the initial
breakthrough. Demand pressures and technological opportunity promote
linking across heretofore separate areas of technologies.

However, the example of molecular biology and other important areas of
science, such as solid-state physics, demonstrate that a linear
progression of knowledge still takes place as the technology life cycle
progresses.[^9] The knowledge gained not only feeds back into the
existing life cycle but it also eventually contributes to the subsequent
technology life cycle.

Thus, the debate over whether to use a linear model or to adopt one of
the more sophisticated variants is somewhat artificial. In some cases,
those who use a linear model typically do so for simplicity's sake. The
policy analysis to be undertaken does not require a more complex
representation, only acceptance of a net progression of knowledge to
successively more applied levels. Most users of such linear models
acknowledge that reality is more complicated. Moreover, another
distinction is that the two conceptual approaches address two different
steps in long-term technological change. The linear model typically
represents a higher level of aggregation of technology than does the
chain link framework, which focuses on single technologies.

##### 5.0. Elements of an Economic Policy Model of R&D

Any sound policy approach to understanding technology-based growth must
satisfy two basic requirements.

(1) The first requirement is the need to define a set of elements that
    comprise the typical industrial technology. These elements are
    distinguished primarily by differences in private sector incentives
    to invest in them.

(2) A second requirement is that the relevant investment behavior be
    depicted over time because of the existence of multiple cycles that
    characterize private sector investment in technology research.

With these two requirements satisfied, a more accurate and
policy-relevant analysis of specific market failure mechanisms is
possible. Equally important, appropriate government policy responses can
be constructed and matched to these market failures, thereby greatly
increasing the efficiency of R&D policy.

The general elements of industrial technology that are relevant for R&D
policy are depicted in Figure 3. The relevance of these elements for
policy analysis derives from the fact that each needs a unique set of
R&D skills, facilities, and supporting infrastructures. Each one also
requires a different research time frame (especially relative to each
other in the R&D cycle). Finally, each element displays a markedly
different degree or type of public good content. Thus, the sources of
and processes for the development of each technology element are
significantly different.[^10]

**5.1. Generic Technology.** A particular science base evolves over
several decades and eventually reaches a critical mass, at which point
industry begins to extract commercially relevant technology from this
science base. Typically, before large amounts of private-sector funds
can be committed to developing market applications of the technology,
the generic technical concept must be demonstrated. This necessary
*generic technology research* demonstrates that the general technology
may work in specific market applications. For many technologies, the
"proof of concept" can be a laboratory prototype. Each generation of
digital electronic technology (transistor, integrated circuit,
multifunction chip) was first demonstrated in the laboratory and then
was further developed to yield market applications over many product
life cycles.

The existence of generic technology as the basis for huge amounts of
follow-on private sector investment was clearly identified by Richard
Nelson, but policy models have only vaguely recognized its critical
importance.[^11] In essence, generic technology provides general
inferences about how the technology works, identifies the attributes
that determine the performance of the technology, demonstrates how these
attributes combine to realize overall performance, and indicates initial
ranges of variation for each attribute.

![](media/image1.wmf)The broader, deeper, and more accurate the generic
technology is, the greater the amount of R&D stimulated and the larger
the number of market applications eventually produced. Equally important
for R&D policy is the fact that generic technology diffuses to a
relatively greater extent than do more applied versions of the
technology. Such spillovers mean that generic technology is
simultaneously drawn upon by competing companies in developing market
applications. It therefore has some of the characteristics of
infrastructure.

Biotechnology provides an excellent example of how generic technologies
arise out of the science base, but ahead of specific market
applications. The biotechnology industry is, in fact, typical of the
existence of multiple technology elements and the complex relationships
among them. As shown in Table 1, multiple areas of science have had to
advance before a larger set of generic product and process technologies
could evolve. Supported by a number of infratechnologies, these generic
technologies have advanced over the past 20 years and are just now
beginning to yield significant numbers of proprietary market
applications.

+-------------+-------------+-------------+--------------+-----------+
| Table 1     |             |             |              |           |
|             |             |             |              |           |
| ####        |             |             |              |           |
| ##### Phase |             |             |              |           |
| s in the Bi |             |             |              |           |
| otechnology |             |             |              |           |
|  Life Cycle |             |             |              |           |
+-------------+-------------+-------------+--------------+-----------+
| #######     | #######     |             | #######      | #######   |
|             |             |             |              |  Products |
| ####### Bas | ######      |             | #            |           |
| ic Research | # Generic T |             | ###### Infra |           |
|             | echnologies |             | technologies |           |
+-------------+-------------+-------------+--------------+-----------+
|             | ####        | ####        |              |           |
|             | ### Product | ### Process |              |           |
+-------------+-------------+-------------+--------------+-----------+
| -           | -   gene    | -   co      | -   bio      | -         |
|   molecular |     testing | mbinatorial | spectroscopy |  protease |
|     and     |             |             |              |     i     |
|             | -   gene    |   chemistry | -   m        | nhibitors |
|    cellular |     therapy |             | itochondrial |           |
|     biology |             | -           |     DNA      | -         |
|             | -   gene    | recombinant |              |   hormone |
| -   mi      |             |             |    standards |     res   |
| crobiology/ |    delivery | DNA/genetic |              | torations |
|             |     systems |             | -   DNA      |           |
|    virology |             | engineering |              | -   DNA   |
|             | -   gene    |             |    profiling |           |
| -           |             | -   nucleic |              |    probes |
|  immunology |  expression |     acid    |    standards |           |
|             |     systems |     am      |              | -   ne    |
| -   n       |             | plification | -   nucleic  | uroactive |
| euroscience | -           |     &       |     acid     |           |
|             |   antisense |     probes  |              |  steroids |
| -           |             |             |  diagnostics |           |
|  physiology | -           | -   gene    |              | -         |
|             |   apoptosis |             | -   th       |  neuro-tr |
| -   p       |             |    transfer | ermodynamics | ansmitter |
| harmacology | -   antia   |             |     of       |     i     |
|             | ngiogenesis | -           |     pro      | nhibitors |
| -           |             |  transgenic | tein-nucleic |           |
|    genomics |             |     animals |     acid     | -         |
|             |             |             |              |  vaccines |
|             |             | -   cell    | interactions |           |
|             |             |     culture |              | -   co    |
|             |             |             | -   bi       | agulation |
|             |             | -   i       | oinformatics |     i     |
|             |             | mmunoassays |     database | nhibitors |
|             |             |             |              |           |
|             |             | -           |              | -   inf   |
|             |             |  monoclonal |              | lammation |
|             |             |             |              |     i     |
|             |             |  antibodies |              | nhibitors |
|             |             |             |              |           |
|             |             | -   cell    |              |           |
|             |             |     en      |              |           |
|             |             | capsulation |              |           |
|             |             |             |              |           |
|             |             | -           |              |           |
|             |             | implantable |              |           |
|             |             |             |              |           |
|             |             |    delivery |              |           |
|             |             |     systems |              |           |
+-------------+-------------+-------------+--------------+-----------+

**5.2. Infratechnology.** The other category of industrial technology
with significant public good content is infratechnology.
Infratechnologies are a varied set of technical tools that perform a
wide range of measurement, integration, and other infrastructure
functions. 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.[^12]

As Table 2 indicates, these 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. However, the complexity
of technology-based economic activity and the demands by users of
technology for accuracy and high levels of quality have reached levels
that a large number of diverse research-intensive infratechnologies are
requiredeven within single industries. The resulting aggregate economic
impact of these infrastructural technologies is substantial.[^13]
Examples of infratechnologies supporting a specific industry
(biotechnology) is shown in Table 2.

One indication of the pervasive and substantial impact of measurement
infratechnologies has been provided by a NIST 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 is expected to continue growing at least
15 percent per year, reaching between \$3.5 billion

+----------------------+----------------------+------------------------+
| Table 2              |                      |                        |
|                      |                      |                        |
| **Uses of            |                      |                        |
| Infratechnologies by |                      |                        |
| Stage of Economic    |                      |                        |
| Activity**           |                      |                        |
+----------------------+----------------------+------------------------+
| **R&D**              | **PRODUCTION**       | **MARKET DEVELOPMENT** |
+----------------------+----------------------+------------------------+
| **[Timing &          | **[Process & Quality | **[Transaction Cost    |
| Effic                | Co                   | Re                     |
| iency]{.underline}** | ntrol]{.underline}** | duction]{.underline}** |
|                      |                      |                        |
| -   Materials        | -   Process Modeling | -   Acceptance Test    |
|     Characterization |                      |     Methods            |
|                      | -   Measurement and  |                        |
| -   Measurement      |     Test Methods     | -   Interface          |
|     Methods          |                      |     Standards          |
|                      | -   Process and      |                        |
| -   Techniques       |     Quality Control  | -   Com                |
|     (design for      |     Techniques       | patibility/Conformance |
|     manufacturing,   |                      |     Test Facilities    |
|     rapid            |                      |                        |
|     prototyping)     |                      |                        |
+----------------------+----------------------+------------------------+
| Source: Tassey       |                      |                        |
| \[1997, p. 158\]     |                      |                        |
+----------------------+----------------------+------------------------+

and \$5.5 billion in 2001.[^14] Thus, the cost of not having needed
infratechnologies and associated standards in place to support this
investment can be substantial. Another NIST study of interoperability in
the U.S. automotive supply chain found that the lack of standard formats
for transferring electronic product design data between suppliers and
automobile manufacturers is costing that industry approximately \$1
billion per year.[^15]

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 even at the customer/vendor interface. In service industries,
infratechnologies help define output, interoperability, security
protocols, and intellectual property.

Infratechnologies also could include the various techniques, methods,
and procedures that are necessary to implement the firm\'s product and
process strategies. Methods such as total 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 infrastructural or
public good character.

##### 6.0. Market Failure and Underinvestment 

The concept of technology life cycles is particularly important for R&D
policy because it implies a time order or evolutionary
character---including both a beginning and an end---to various market
failures that appear at different points in the typical life cycle.
Hence, this concept helps determine of appropriate policy responses
within life cycles as well as the critical transitions between cycles.

In general, four basic categories of underinvestment can and do occur:

(1) aggregate underinvestment by an industry (e.g., insufficient total
    R&D);

(2) underinvestment in applied R&D in new firms (e.g., insufficient
    venture capital);

(3) underinvestment in new generations of existing technology or in
    radically new technology (e.g., insufficient generic technology
    research)

(4) underinvestment in supporting technology infrastructures (e.g.,
    insufficient infratechnology R&D)[^16].

Because the process of technology development evolves cyclically, market
failures that lead to underinvestment tend to repeat themselves.
Moreover, distinctly different types of market failure exist and
therefore require different government or industry/government response
modes. In each case of market failure, the particular barrier increases
the probability that the project's risk-adjusted and time-discounted
expected rate of return will not exceed a company's hurdle rate. In such
cases, where the social or aggregate economic rate of return is high
(above society's hurdle rate), underinvestment occurs and economic
growth is reduced below its potential.[^17]

**6.1. Important Characteristics of Market Failure.** The following are
the major types of market failure that can and frequently do occur over
the typical technology life cycle.

(1) ***Technology is inherently complex.*** A certain amount of risk is
    reasonable given the considerable expected rewards from technology
    investment. However, the more radical or complex the attempted
    technical advance (technical risk), or the longer the time period
    needed to conduct the R&D during which demand can shift or
    competitors can commercialize the technology first (market risk),
    the greater the probability of an inadequate rate of return being
    realized.

(2) ***The benefits from technology tend to diffuse or leak to firms
    beyond the originator (innovator).*** Such spillovers are of two
    major types: price and knowledge.

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(a) *Price spillovers* occur when the market price does not fully
    capture the additional benefits from the new technology. That is,
    the user (buyer) in effect receives some of the benefits for free.
    Up to a point, the existence of such spillovers is desirable
    because, if the new technology were fully priced, the buyer would be
    indifferent between the old and the new technologies. Market forces
    determine the actual distribution of benefits. The supplier
    (innovator) must simply capture enough of the benefits to meet or
    exceed the investment hurdle rate applied to the original R&D
    investment decision. For most technologies and competitive
    structures, a sufficient rate of return cannot be projected when
    significant price spillovers ocurr.

(b) *Knowledge spillovers* refer to leakage of the innovator's new
    technical knowledge horizontally to competing suppliers. This type
    of spillover is good for the economy as a whole, but it decreases
    the expected returns for potential innovators. To the extent that
    rates of return fall below the private hurdle rate, investment by
    potential innovators will not occur.

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(3) ***Market Structure can reduce expected rewards from technology
    investment.*** An increasing number of technologies are systems.
    System components must interface seamlessly with other components to
    work effectively. That is, they must interact in a fully functional
    manner with minimal effort by the user (system integrator).
    Otherwise, the cost and flexibility advantages from having choices
    among suppliers of individual components (as opposed to buying
    turnkey systems from a single supplier) are eroded.

    Interfaces between complex components often are far from seamless,
    frequently being just as complex as the components themselves. In
    such cases, achieving compatibility or interoperability results in
    significant additional costs, thereby raising total system costs and
    lowering the expected rate of return to both the suppliers and users
    of the components. These costs are particularly high (and, in fact,
    interoperability is usually not achieved) when competing private
    interests attempt to provide this type of infrastructure
    independently. The higher cost of non-interoperable systems, coupled
    with reduced functionality, lowers the technology's rate of market
    penetration.

    This segmentation of markets for systems technologies can last for
    long periods of time and therefore result in significant economic
    loss.[^18]

(4) ***Corporate strategies often are narrower in scope than a new
    technology's market potential.*** Some new technologies (advanced
    ceramics, for example) have applications in a number of markets
    previously served by very different technologies and hence
    industries. Companies in the existing industries typically do not
    have the strategic profile or the production and marketing knowledge
    to target all the potential applications (i.e., to capture economies
    of scope). Thus, the expected rate of return to each company is
    lowered relative to the risk of developing the generic technology,
    which applies to all potential applications.

(5) ***Increasingly dominant "systems" technologies require complex
    infrastructure.*** Sophisticated customers demand customized
    versions of the technology, which require highly complex and
    flexible productions systems. Automated production systems and
    technology-based services such as finance, communications, and
    entertainment all require sophisticated information infrastructures.
    Significant private investment in applications only occurs after the
    infrastructure is developed and implemented (for example, several
    decades of government support for the Internet were required before
    private investment in applications using it took off).

In addition to price and knowledge spillovers, network externalities are
frequently labeled as a third type of spillover.[^19] However, this
phenomenon does not cause a redistribution of benefits from the
innovating firm to other firms. Rather, it usually results in increased
benefits for all market participants. The expanding market for the
network increases the value for individual network participants and
therefore most likely for the original supplier (innovator). The market
failure risk here is associated with the potential for network
externalities to generate increasing returns to scale for the innovator.
If increasing returns are strong enough, the innovator could become a
monopoly and bar entry by other firms and possibly restrain innovation
for some period of time.[^20]

In summary, the scope of market failure mechanisms affecting
technology-based investment is broad. Spillovers are the most frequently
(and sometimes are the only) reason cited for private-sector
underinvestment in R&D. However, as the above analysis indicates,
spillovers are only one characteristic of technology-based competition
that can lead to inadequate investment. These characteristics directly
affect either the benefit or the cost side of the investment
calculation.

**6.2. Transitions between Technology Life Cycles.** One of the main
factors causing shifts in market share leadership across companies,
industries and countries is the phenomenon of transitions between
successive technology life cycles that serve the same market function
(e.g., computing or communications). New technologies often have very
different characteristics from the old or defender technologies that
they seek to replace. Hence, strategies, organizational characteristics,
and R&D capabilities that work well in one life cycle are not effective
in the next cycle.

An example is computing. The first life cycle's generic technology was
manifested in the mainframe computer. Processing power was a scarce
resource and hence everything was optimized to maximize the productivity
of the processor. A single, huge machine was housed in an
air-conditioned room, operated by technicians who strictly allocated
users' access. In the second life cycle, the personal computer reversed
the control of the system. The user now owned the highly dispersed
processing power. Now, a third technology life cycle is emerging in
which the Internet offers the prospect of a reversion to a centralized
computing system. In each of these three periods, the nature of the
technology, the organizations supplying it, and the interface with the
user vary significantly.

Within each life cycle, technology evolves according to conventional
market dynamics. Efficiency can be achieved *within* short-term product
life cycles largely by the private sector, with modest infratechnology
support from government. However, major market share shifts occur across
national economies, if the original generic technology is not improved
over its life cycle. Even greater shifts in competitive position occur
*between* major life cycles. Failure to prepare domestic industries for
the transition by advancing new generic technologies typically causes
major losses in sales, profits, and jobs to foreign competition.

##### 7.0. The Tenuous Nature of Competitive Advantage

Even when industry has access to a rich generic technology base for
applied R&D, market success can be fleeting or never achieved. The past
several decades are littered with U.S. firms and entire industries that
failed to conduct enough R&D or the right type of R&D to create and
sustain competitive advantage. Too many instances exist of important
technologies whose development was initiated in the United States but
were taken over by industries in other nations to the substantial
benefit of those other nations' economies.

**7.1. Examples of technology-based markets lost to foreign
competition.** The following are just a few examples of technology-based
economic growth opportunities that have been lost by U.S. industry for a
variety of reasons:

-   **Semiconductor production equipment: Steppers.** The stepper was
    invented in the United States, but market share is now almost
    totally Japanese. The loss of competitive position in semiconductor
    manufacturing equipment not only reduces the contribution to
    domestic economic growth in the electronics supply chain, but it
    also may reduce the productivity of other levels in that supply
    chain (particularly, U.S. semiconductor manufacturers). These
    manufacturers can find themselves at a competitive disadvantage
    relative to their competitors in Japan, who are more closely linked
    to the Japanese equipment suppliers. (Solving this vertical
    integration problem has been the primary objective of SEMATECH.)

-   **Flat panel displays.** RCA invented the liquid crystal display,
    but had too narrow a view of its applications and essentially gave
    the technology to the Japanese. The Japanese now own 95 percent of a
    world market that is growing rapidly and driving downstream
    equipment/product markets.

-   **Advanced Ceramics.** The current revolution in wireless
    communications would not have been possible without the discovery
    and development of oxide ceramics. The ceramic requirements of
    microwave applications (e.g. filters, oscillators, resonators) are
    critical and these components collectively represent a majority of
    end-use devices. Every modern commercial wireless communication and
    detection system in actual use or in advanced development
    incorporates oxide ceramics. This important compound was discovered
    and its processing phase diagrams determined in the early 1970s at
    Bell Labs and the National Bureau of Standards, respectively.
    Despite these early and fundamental U.S. advances, Japanese industry
    today clearly dominates the markets for these ceramics (estimated at
    \$700 million for 1997). For example, only two U.S. companies
    (Motorola, which serves a captive market, and Trans‑Tech, a small
    company with annual sales of \$35 million) produce ceramic
    components, as compared with six major companies in Japan.
    Furthermore, control of this technology has stimulated additional
    Japanese R&D and led to most of the new materials that are enabling
    dramatic advances in miniaturization and performance of wireless
    communications equipment.

-   **Robotics.** Today\'s industrial robotics market (worth about \$6
    billion) is dominated by Japanese companies such as Fanuc, Yaskawa
    Electric, Kawasaki Robotics, Mitsubishi Electric, and Motoman. U.S.
    companies---Westinghouse, General Electric, General Motors, and
    Unimation---first established the industrial robotics market in the
    1960s but, by the mid‑1980s, all had sold their robotics interests
    to Japanese companies. Today, the largest U.S. robotics manufacturer
    (Adept Technology, San Jose) employs just 375 people. The only
    non‑Japanese robotics manufacturer of any size is the Swiss‑Swedish
    company ABB Flexible Automation.

-   **Video cassette recorder.** Ampex and RCA developed the video tape
    recorder (VTR) in the 1950s. By the 1980s, its descendent, the
    videocassette recorder (VCR), was in one-half of all American homes,
    with virtually all of these VCRs made by Japanese firms.

-   **Semiconductor memory devices.** The United States invented the
    transistor and the integrated circuit. Yet between 1979 and 1986
    U.S.-based firms saw their world-market share decline from 75
    percent to 27 percent. After that, U.S.-based firms recovered some
    of this loss. However, the persistently weak U.S. position is
    indicated by the fact that only two of the top ten firms worldwide
    in the major memory circuit markets (DRAM and SRAM) are based in the
    United States.

-   **Digital watches.** The first electronic digital watch was
    introduced in 1971 by a U.S. company, Time Computer, which held all
    the patents. The semiconductor chip that ran the watch was purchased
    from RCA. Today, the Japanese dominate both the component and watch
    markets. Even though a number of U.S. electronics companies (such as
    Motorola, National Semiconductor, and Texas Instruments) made
    significant improvements and cost reductions early in the technology
    life cycle, the Japanese relentlessly improved the technology and
    reduced its cost, thereby taking over this market.

-   **Interactive electronic games.** In 1972 Magnavox developed the
    first interactive game designed to be played on the screen of a TV.
    Two years later, Atari, Inc., was formed and led the market for a
    number of years. Now, that market is controlled by Japanese firms.

Many argue that competitive failures such as the above examples are the
fault of poor industry strategy and that the inherent efficiency of the
market place demands no government interference. This view has
considerable merit with respect to market applications within a single
technology life cycle that result largely from applied R&D. However, if
applied R&D needed no stimulus at all from government, various tax
incentives such as the R&E tax credit would not be needed. Moreover,
data cited below suggest that industry is increasingly under investing
in two critical areas:

-   Next generation technology (the technology base for the next
    technology life cycle) and

-   The extremely varied sets of technical tools that are used to
    conduct R&D, control production processes, and execute the sale of
    complex technology-based goods and services.

**7.2. Underinvestment in Generic Technology Research.** Universities
are the primary source of a nation's basic science. Scientific knowledge
diffuses in part through contact with industry and certain types of
partnering, but mainly through new graduates in science and engineering.
The next step---proving the generic technological concept, so that
corporate R&D managers can make the technical and market risk
assessments necessary for follow-on applied R&D investments---is much
more complex. This early-phase generic technology research does not
absorb a large portion of total R&D spending in most areas of
technology, but its critical position in the R&D cycle means it has the
potential to leverage much greater amounts of follow-on applied R&D.

As the next section describes, various combinations of industry,
government, and universities fund or conduct generic technology research
through a variety of organizational arrangements. These partnerships are
attempts to deal with the quasi-public good nature of generic
technologies and the consequent set of market failures that result from
the mismatches between their characteristics and private-sector risk
tolerances, R&D capabilities, and market strategies.

+--------+------------------------------+-----------------------------+
| Table  |                              |                             |
| 3      |                              |                             |
|        |                              |                             |
| ***IRI |                              |                             |
| Annual |                              |                             |
| R&D    |                              |                             |
| Trends |                              |                             |
| Fo     |                              |                             |
| recast |                              |                             |
| for*** |                              |                             |
|        |                              |                             |
| ***Ind |                              |                             |
| ustry- |                              |                             |
| Funded |                              |                             |
| Di     |                              |                             |
| rected |                              |                             |
| Basic  |                              |                             |
| Res    |                              |                             |
| earch, |                              |                             |
| 1      |                              |                             |
| 991--1 |                              |                             |
| 999*** |                              |                             |
+--------+------------------------------+-----------------------------+
| ####   | **Percent of Respondents     | **Percent of Respondents    |
| #####  | Increasing Directed Basic    | Decreasing Directed Basic   |
| Survey | Research**                   | Research**                  |
+--------+------------------------------+-----------------------------+
| *      | 8--14 %                      | 23--38 %                    |
| *1991- |                              |                             |
| 1995** |                              |                             |
+--------+------------------------------+-----------------------------+
| **     | 17 %                         | 5 %                         |
| 1996** |                              |                             |
+--------+------------------------------+-----------------------------+
| **     | 15 %                         | 13 %                        |
| 1997** |                              |                             |
+--------+------------------------------+-----------------------------+
| **     | 14 %                         | 28 %                        |
| 1998** |                              |                             |
+--------+------------------------------+-----------------------------+
| **     | 14 %                         | 37 %                        |
| 1999** |                              |                             |
+--------+------------------------------+-----------------------------+
| S      |                              |                             |
| ource: |                              |                             |
| Indu   |                              |                             |
| strial |                              |                             |
| Re     |                              |                             |
| search |                              |                             |
| Ins    |                              |                             |
| titute |                              |                             |
+--------+------------------------------+-----------------------------+

![](media/image1.wmf)Case studies have shown that long-term, high-risk
corporate research is declining.[^21] In Table 3, the Industrial
Research Institute survey data for "directed basic research" spending
plans by industry during the 1990s supports the case studies.[^22]
Moreover, this research has a discontinuous character, thereby
stretching out the R&D life cycle and making the eventual market
applications highly uncertain. 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 exhibited many of the types of market failure
described earlier. In eight of the 11 case studies, the researchers
found that government was a major source of funds after the project was
formalized. For the most part, these funds were used to extend, expand,
or accelerate projects. Partnering with other firms (large and small),
universities, and government laboratories was a common approach.[^23]

The critical point for R&D policy is that major technological advances
take more time and entail more risk than do incremental change and
improvements. More radical R&D often is contracted out or accomplished
through partnering; both options lengthen R&D cycle time. Yet, as a
study by Kim and Mauborgne (see Figure 4) clearly indicates, longer-term
and more radical R&D projects are more profitable.

**7.3. Underinvestment in Infratechnology Research.** Support for
infratechnology research also has been generally inadequate. For
example, the budget for the NIST laboratories has grown only 2.0 percent
per year in real terms in the last 24 years, 1973-97, compared with an
average annual real growth rate of 8.9 percent for industry-funded
R&D---the productivity of which NIST's measurement-related
infratechnology research is charged with supporting. The markedly
different growth rates have resulted in the NIST laboratory research
budget declining by one half relative to industry R&D spending during
this period.

The NIST labs provide a wide range of measurement-related
infratechnologies to industry. As described earlier, these
infratechnologies---either directly or through incorporation in industry
standards---are pervasive in terms of 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.[^24] Because of the large number
of infratechnologies required by a single technologically advanced
industry, the collective economic impact is large. However, their
infrastructure and hard-to-visualize character and the diffuse nature of
their impacts create difficulties for policy makers.

##### 8.0. Policy Implications

Corporate R&D investment decisions do not take place in a vacuum. They
are driven by "the invisible hand"perceived technological and market
opportunitiesas well as by "the invisible foot"the threat of
competition. In addition to these factors, investment decisions are also
strongly influenced by the breadth and depth of a supporting
technological infrastructure. Major elements of this infrastructure are:

-   the availability of skilled workers;

-   access to generic technical knowledge and external scientific and
    engineering research capabilities;

-   the existence of a set of technically complex infrastructures
    including science and engineering data bases, measurement and test
    methods, interface standards, and quality assurance procedures.

The first of these is a well-defined policy issue and fortunately is now
getting focused attention by policy makers. However, the second and
third elements are complex and not particularly well understood. Thus,
effective policy development remains constrained.

**8.1. The Relative Size and Impacts of Government-Funded Research.**
Figure 5 indicates that amounts spent on generic technology research and
infratechnology research between the 10^th^ and 5^th^ years before
commercialization are relatively small. However, significant risk
reduction in terms of advancing the generic technology to at least proof
of concept or laboratory prototype often is essential to stimulate the
much larger applied R&D investment by individual companies that
eventually brings products, processes, and services to market. The
availability of appropriate infratechnologies makes this process more
efficient and, in some cases, is necessary for it to occur at all. The
subsequent acceleration of industry R&D spending over the average R&D
life cycle is pronounced. Industry spends 4.4 times as much on applied
research as on basic research and 2.7 times as much on development as on
applied research.[^25]

**8.2. Matching Market Failure and Policy Response.** Four major
categories of market failure were identified above. Each requires a very
different policy response:

(1) ***Aggregate underinvestment by an industry---insufficient total
    R&D.*** This problem is the result of either excessive risk
    avoidance due to adverse macroeconomic conditions or inadequate R&D
    capability (which raises entry costs and hence risk). Tax incentives
    can be effective as long as the R&D being targeted is comparable to
    the type already pursued by industrythat is, as long as the normal
    corporate R&D investment decision criteria can be applied. In other
    words, a tax incentive can stimulate more of the same type of R&D
    already ![](media/image1.wmf)conducted. [^26]

(2) ***Underinvestment in the formation of new firms---insufficient
    venture capital.*** Venture capital is plentiful in the United
    States and available for most areas of technology, once the generic
    technology is sufficiently advanced to allow the private sector
    sources of venture funding to make assessments of both technical and
    market risk. The policy issue is how to get to that point. A
    government role in advancing generic technology research has been
    accepted in the United States only in a few situations where an
    industry deemed necessary to achieve a major social objective is
    either inadequately structured or not formed at all. That is, if a
    new technology has potentially large social as well as economic
    benefits, is not capital intensive (it can be supplied by small
    firms), and is radical enough to inhibit investment by large firms
    focused on the existing technology, then R&D policy can consider
    subsidizing the creation of a new industry structure as a policy
    objective.

> Biotechnology is an example of a radically new technology that meets
> these criteria. The National Institutes of Health (NIH) therefore
> funds some applied research in small biotech firms. The Small Business
> Innovation Research (SBIR) Program was created to fund research in new
> technologies that are both socially and economically important. In
> such cases, funding R&D by young, development-stage companies can
> expand both the number and variety of such firms.[^27] However, from
> an economic growth perspective, such policies for one or two
> industries will be highly inadequate in the evolving world economy.

(3) ***Underinvestment in new generations of existing technology or in
    radically new technology---insufficient generic technology
    research.*** Because of the highly microeconomic character of
    technology development, and hence the uniqueness of the sets of
    market failures that affect individual technologies, broad R&D
    incentives, such as a tax credit, are generally not effective. Tax
    expenditures tend to leak to both technologies and phases in the R&D
    process that do not require government support. Instead, the
    efficient policy response would combine direct funding of generic
    technology research with appropriate joint strategic planning among
    government, industry, and universities.[^28]

> Early-phase, generic technology research often is conducted through a
> variety of partnership mechanisms, because this approach allows a more
> effective combination of research skills and facilities, pools risk,
> and increases the rate of technology diffusion. All nations with
> technology-based growth strategies have industry-government programs
> to cooperatively advance the early phases of a variety of technologies
> with considerable economic potential.

(4) ***Underinvestment in supporting technology
    infrastructures---insufficient infratechnology R&D.***
    Infratechnologies not only have common use characteristics
    (including their use as standards), but they often derive from a
    different science and generic technology base than does the core
    technology being applied by industry through its internally funded
    R&D. The latter fact argues for a strong role by government
    laboratories in the conduct and diffusion of infratechnology
    research. Government labs can realize economies of scale and scope
    from unique research skills and facilities that can be applied to
    meet the infratechnology needs of a number of industries.[^29] These
    labs also can provide neutral third party facilitation of the
    standards process.

**8.3. Responding to technical and Market Risk Barriers.** As described
earlier, the intrinsic technical and market risk faced by R&D firms
varies over a technology's life cycle. Therefore, both industry and
government responses to market imperfections and resulting
underinvestment in R&D and/or slower market penetration by the new
technology also must vary. Figure 6 indicates how a set of existing
policy responses can be used to mitigate market failures over the
different phases of an R&D life cycle.

For example, after basic research has gone on for some time, the
accumulated level of knowledge begins to spark interest by industry in
the possibility of developing technologies based on this science.
Diffusion of the basic science and subsequent initial evaluation of its
economic potential can be facilitated through graduates of universities
and entities such as the National Science Foundation's Science and
Technology Centers ![](media/image1.wmf)and Engineering Research
Centers, where industry and university researchers can interact. For
specific areas of technology, industry-operated entities such as the
Semiconductor Research Corporation (SRC) fund research under a similar
format. More recently, the Focus Center Research Program (FCRP) was
established as a network, including the U.S. semiconductor industry, the
U.S. Government, and 14 universities.

Diffusion of basic scientific knowledge through mechanisms such as the
NSF centers and the FCRP can provide industry with an indication of an
emerging technology's potential. However, the decision process for
investing in the early phases of technology research is a difficult one
for individual companies to manage effectively. There are several
reasons for this difficulty:

-   competitive pressures of fighting for market share in the current
    technology life cycle consume corporate energies;

-   corporate management applies different decision criteria to
    long-term projects compared with the bulk of corporate R&D;

-   recognition of the extremely high technical and market risks
    associated with a potential technology whose concept has not been
    proven even in the laboratory;

-   R&D capabilities needed to launch a significant research program in
    a new technology, especially involving multiple research disciplines
    and laboratory facilities, are frequently incomplete within
    individual firms.

Even with new R&D management approaches such as options pricing
techniques, these factors can combine to raise overall risk to
prohibitive levels.[^30] The lack of appropriate R&D capability relative
to the skills and facilities required for pursuing development of
emerging technologies increases risk substantially beyond that
associated with an immature state of development. This latter factor is
especially important when new technological opportunities require
combining several heretofore separate areas of scientific or technical
knowledge.

The result is the large jump in overall risk from R to R\' in Figure 6.
This combined technical and market risk must be reduced to levels that
fit within conventional R&D decision criteria---that is, those criteria
applied by companies to the majority of candidate R&D projects.

In response, risk pooling is a common strategy for conducting
early-phase technology research. Consortia are widely used for this
purpose in advanced economies.[^31] A number of cooperative
organizational forms can be used to share costs and complementary R&D
capabilities. National laboratories and research institutes frequently
participate in research consortia because they have the time horizons,
the multiple disciplinary skills, and the unique research facilities to
undertake generic technology research.

Cost-shared research consortia with various combinations of industry,
government, and universities as partners are the efficient approach to
conducting much early-phase generic technology research in the United
States. NIST's Advanced Technology Program (ATP) stimulates investment
in generic technology through cost-shared industry/government
partnerships. Similar but larger programs include the European Framework
Program and Japan's Industrial Science and Tech­nology Frontier Program
and its System to Support Development of Creative Technology for New
Industries. As portrayed in Figure 6, such programs focus on generic
technology research, i.e., early-phase research where economies of
scope, long time to expected commercialization, R&D capability
mismatches, and related factors inhibit corporate R&D investment
decision making.

Once beyond this early-phase, industry can usually apply conventional
R&D decision criteria and take on most of the applied research and
development required to take the generic technology to
commercialization. Figure 6 indicates that firms also will use consortia
for applied research. However, such industry-led consortia seldom have a
time frame of more than five years, with three to five years being the
typical range.[^32] Scientific research programs are typically at least
10 years long and often span several decades. Proof of concept through
generic technology research often begins 5 to 10 years before
commercialization. In other words, this period in the R&D life cycle can
constitute a funding "gap" between basic research---largely a public
good---and applied R&D---largely a private good. This "valley of death"
is described by Congress in the widely discussed "Ehlers' report" as a
"widening gap between federally-funded basic research and
industry-funded applied research and development".[^33] Government
programs such as ATP attempt to help fill this gap.

##### 9.0. Summary

A number of categories of technology embody significant public good
elements:

-   **emerging** technologies that entail high risk and long gestation
    periods but create new markets with significant value added;

-   **systems** technologies that provide infrastructure to many product
    and service technologies and thereby drive growth in major economic
    sectors;

-   **enabling** or multi-use technologies which benefit multiple
    segments of an industry or group of industries, but encounter
    economies of scope and diffusion investment barriers;

-   **infratechnologies** which leverage investment in both development
    and use of proprietary technologies, but which require distinct
    competencies to develop and common ownership (such as standards) to
    effectively use.

For these classes of technology, the evolutionary patterns of technical
and market risk over the relevant life cycle can and do result in
inadequate investment a key points in the life cycle. This
underinvestment results from a much wider set of economic conditions
(market failures) than commonly believed. Specifically,

-   ***Technical complexity:*** The need for multiple disciplines to be
    combined within one organizational structure to conduct R&D.

-   ***Time:*** The negative effect on investment decision making of
    excessive discounting

-   ***Capital intensity:*** The effect on risk assessment of the
    capital intensity of many research processes (i.e., of the cost of
    these processes, especially as a percentage of a firm's R&D
    portfolio).

-   ***Economies of scope:*** A broad and uncertain scope of potential
    market applications for many of the most important emerging
    technologies.

-   ***Spillovers:*** A tendency for excessive "leakages" or
    "spillovers" of the technical knowledge produced by individual
    companies to others that did not contribute to the research.

-   ***Infratechnologies and standards:*** Public good character and low
    visibility cause inadequate investment.

-   ***Market segmentation:*** The emergence of sophisticated users who
    demand sets of performance attributes that cannot be provided by
    existing industrial R&D capabilities.

Three particularly important negative impacts of these market failures
are:

(1) ***Corporate investment decision dysfunction with respect to
    longer-term, complex, and multidisciplinary technology research.***
    Underinvestment is particularly pronounced in the early phases of
    the R&D life cycle, which most strongly exhibit the investment
    barriers resulting from the intrinsic *technica*l risk of the
    technology and its mismatches with existing corporate strategies and
    competences.

(2) ***Excessive compression of R&D life cycles with resulting
    disincentives to undertake long-term, high payoff research.***
    Global competition is forcing shorter total product life cycles
    which, in turn, are forcing corporate R&D portfolios to
    overemphasize product-line extensions and incremental process
    improvements. In general, less *market* risk is assumed by the
    private sector.

(3) ***Failure to project access to the markets for increasingly
    system-based technologies.*** Many of today's most important
    technologies have complex system structures, which require equally
    complex interfaces to enable market entry by small and medium
    suppliers and system optimization by users. Without the needed
    infrastructure, inefficient industry structures evolve.

Given the nature of technology market failures and the significance of
rectifying them, the two major R&D policy issues facing the U.S. economy
today are

(1) ***Understanding and providing appropriate policy responses for the
    early phases of technology research.*** Efficiently bridging the
    valley of death---the widening gap between federally funded basic
    research and industry-funded applied research and development---does
    not require large amounts of federal funding. However, this funding
    for generic technology research is essential to lowering the
    substantial technical and market risks typical of early phases in a
    technology's life cycle and must be available when the window of
    opportunity is open. All industrialized nations have or participate
    in industry-government partnerships of various forms to provide this
    essential category of technology infrastructure.

(2) ***Identifying and providing technical infrastructures needed by
    technology-based industries.*** The needs for these infrastructures
    vary over the typical technology life cycle and have strong public
    good content, thereby requiring effective government support. As
    with generic technology, research support for the needed range of
    infratechnologies requires relatively modest amounts of funding.
    However, this funding must not only be adequate, but it needs to be
    directed to unique research facilities that can achieve the large
    economies of scale and scope that characterize this type of
    technology infrastructure and that can also efficiently diffuse it
    to industry, standards organizations, and other users.

The major technology trends identified here obviously have implications
for the amount and type of R&D needed to achieve steady, high rates of
economic growth. Equally important, technology trends interact with
corporate strategy, industry structure, and government policy (in
particular, policies that provide technical infrastructure at the
various phases in a technology's life cycle). Technology trends or
trajectories, once established, can have dramatic effects on a number of
industries or even sectors of the economy in terms of both rate and
directions of growth. Hence, early evaluation of the multiple
trajectories afforded by the timely development of generic technology
and supporting infratechnologies is essential.

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[^1]: The high-tech sector is defined here as consisting of four major
    categories: high-tech manufacturing (IT-related plus industrial
    electronics), communication services, software and computer-related
    services, and pharmaceuticals). For alternative definitions of
    IT-related high-tech industries, see American Electronics
    Association \[1997, p. 128\] and Department of Commerce \[1998,
    Appendix p. A1--2\]. The AEA definition results in a 6.1 GDP
    estimate for 1996 and the Commerce definition yields about 8 percent
    for 1998. To either of these definitions should be added
    pharmaceuticals, which brings the AEA-defined high-tech sector's GDP
    contribution to 7 percent. Obviously, no precise definition can be
    constructed. What is included is a matter of drawing the line at
    some level of dependency on internally-funded R&D. All of the
    industries included in the AEA definition plus pharmaceuticals have
    company-funded R&D intensities of 8 percent or more. The next most
    R&D intensive industry group is health services with an R&D
    intensity of about 6 percent. Including this large industry group
    would add approximately 5.5 percentage points to the high-tech
    industry GDP contribution. Even with this expanded definition,
    "high-tech" is still a small fraction of the U.S. economy.

[^2]: Schumpeter \[1939, 1950\] and Caracostas and Muldur \[1995, p.
    77\]. Several generations of microprocessors are an example of
    product life cycles "nested" under a generic technology life cycle
    (integrated circuits). These generic technology cycles are, in turn,
    nested under a major technology life cycle (solid-state digital
    electronics). See Tassey \[1997, Chap. 4\].

[^3]: See Freeman \[1979\].

[^4]: See Cohen and Levinthal \[1989\].

[^5]: The term "supply chain" as used in this report refers to the
    vertical structure of industries that begin with raw materials and
    eventually serve a final demand. 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. 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.

[^6]: See, for example, Klein and Rosenberg \[1986\].

[^7]: National Science Board \[1996, p. 4-10\].

[^8]: Richter \[1998\].

[^9]: In the important area of digital electronics, the transistor could
    never have been invented without prior knowledge of the basic
    science of solid-state physics, which provided the theoretical basis
    for thinking that a semiconducting material could be used to
    construct an electronic switch or amplifier. Once invented, the
    importance of the transistor stimulated an avalanche of further
    basic research making possible further cycles in digital electronics
    technology.

[^10]: Economists distinguish among these elements with the concepts of
    "rivalous" and "non-rivalous" goods. The former cannot be consumed
    by one individual without preventing consumption by others (e.g.
    toothbrush), while the latter can be collectively consumed
    (software). Non-rivalous goods present investment incentive problems
    because "free riders" consume the good but do not pay for it.
    However, a second condition---that of "excludability"---is necessary
    to determine private sector investment in non-rivalous goods.
    Patents, for example, are a mechanism for excluding free riders from
    consuming technical knowledge produced by others. Technology
    infrastructure falls into the non-rivalous category, but excluding
    additional consumers of this knowledge is not in the public
    interest. Thus, these types of public technology goods are funded at
    least partially by government to compensate for spillovers.

[^11]: See for example, Nelson \[1987, 1992\].

[^12]: Tassey \[1997, Chap. 8\].

[^13]: For summaries of microeconomic studies of infratechnologies and
    associated methodologies, see Link and Scott \[1998\] and Tassey
    \[1997, 1999\].

[^14]: Finan \[1998\]. The estimate does not include the labor and
    overhead required to implement this measurement infrastructure.

[^15]: Research Triangle Institute \[1999\].

[^16]: These four categories of market failure and the appropriate
    policy responses are discussed in detail in Tassey \[1997, Chaps.
    6--10\].

[^17]: Tassey \[1997, Chap. 5\].

[^18]: The role of standards is extremely important here. See Tassey
    \[2000\].

[^19]: See Arthur \[1996\].

[^20]: This phenomenon is the general economic basis for the Justice
    Department's antitrust suit against Microsoft.

[^21]: See, for example, Corcoran \[1994\], Duga \[1994\], and Geppert
    \[1994\].

[^22]: This is comparable to generic technology research.

[^23]: Rice, Peters, and Morone \[1998, pp. 57--58\]. Also, see Eidt and
    Cohen \[1997\].

[^24]: Tassey \[1997, Chap. 8\].

[^25]: National Science Board \[1998, Appendix Tables A 4-7 and A
    4-11\].

[^26]: Tassey \[1997, pp. 107-111\].

[^27]: Small firms now get a lot of NIH funding, both through the SBIR
    Program and otherwise; but, prior to SBIR they did not. In fact,
    Congressional hearings in 1978 documented the fact that NIH had no
    research contracts with small business at that time.

[^28]: Tassey \[1997, Chap. 7\].

[^29]: Tassey \[1997, Chap. 8\].

[^30]: For an overview of these new R&D decision techniques, see
    Luehrman \[1998\].

[^31]: Vonortas \[1997\] provides a comprehensive overview of
    cooperative R&D.

[^32]: Mowery \[1998, p. 39\].

[^33]: *Unlocking Our Future: Toward a New National Science Policy*. A
    report to\
    Congress by the House Committee on Science, September 24, 1998.