***R&D Trends in the U.S. Economy:***

***Strategies and Policy Implications***

**The National Institute of**

**Standards and Technology**

**March 1999**

### 

***R&D Trends in the U.S. Economy:***

***Strategies and Policy Implications***

Gregory Tassey\*

Senior Economist

National Institute of Standards and Technology

March 1999

gtassey@nist.gov

301-975-2663

\*The author is indebted to Paul Doremus for

many useful comments on previous drafts

**Contents**

**Executive Summary** ES-[1](#executive-summary)

Key Findings ES-[1](#key-findings)

Needed: A Technology-Based Economic Policy Model
ES-[2](#needed-a-technology-based-economic-policy-model)

Major Policy Implications ES-[4](#major-policy-implications)

**1.0. Introduction** [1](#introduction)

**2.0. The Economic Dimensions of Technology Policy**
[2](#the-economic-dimensions-of-technology-policy)

2.1. Economic Trends [3](#economic-trends)

2.2. Productivity and Technology [6](#productivity-and-technology)

2.3. R&D Intensity [8](#rd-intensity)

2.4. Competitiveness of Supply Chains
[10](#competitiveness-of-supply-chains)

2.5. Economic Policy is Technology Policy, and Vice Versa
[12](#economic-policy-is-technology-policy-and-vice-versa)

**3.0. Technology Development and Life Cycle Models**
[14](#technology-development-and-life-cycle-models)

3.1. The Technology Life Cycle [15](#the-technology-life-cycle)

3.2. The Dual Value of an R&D Capability
[18](#the-dual-value-of-an-rd-capability)

3.3. Transition to a Life-Cycle Based Economic Model
[19](#transition-to-a-life-cycle-based-economic-model)

3.4. Elements of an Economic Policy Model of R&D
[22](#elements-of-an-economic-policy-model-of-rd)

3.5. Market Failure and Underinvestment over the Technology Life Cycle
[26](#market-failure-and-underinvestment-over-the-technology-life-cycle)

**4.0. R&D Trends and Market Failures**
[29](#rd-trends-and-market-failures)

4.1. The Tenuous Nature of Competitive Advantage
[29](#the-tenuous-nature-of-competitive-advantage)

4.2. Underinvestment in Generic Technology Research
[31](#underinvestment-in-generic-technology-research)

4.3. Underinvestment in Infratechnology Research
[32](#underinvestment-in-infratechnology-research)

4.4. The Changing Nature of R&D and Innovation
[33](#the-changing-nature-of-rd-and-innovation)

4.5. Innovation vs. Technology Use [36](#innovation-vs.-technology-use)

**5.0. Policy Implications** [38](#policy-implications)

5.1. The Relative Size and Impacts of Government-Funded Research
[38](#the-relative-size-and-impacts-of-government-funded-research)

5.2. Patterns of Risk, Market Failure, and the Government Response
[39](#patterns-of-risk-market-failure-and-the-government-response)

5.3. Tax incentives vs. Direct Funding
[43](#tax-incentives-vs.-direct-funding)

5.4. The Federal Budget [44](#the-federal-budget)

**6.0. Summary** [46](#summary)

**References** [49](#references)

**Figures**

Figure 1 Long-Term Trends in Productivity and Income, 1948--1997 7

Figure 2 R&D Intensity Trends in U.S. Manufacturing, 1983-1996 9

Figure 3 Trade Balances in Computers, Peripherals, & Major Components,
1989--1997 10

Figure 4 Transition Between Two Technology Life Cycles 16

Figure 5 Risk Reduction over a Technology Life Cycle 17

Figure 6 Economic Model of Technology-Based Industry 23

Figure 7 Profit Differentials from Major and Incremental Innovations 36

Figure 8 Volatility in Employment Growth, 1994--1995 37

Figure 9 Relative Expenditures by Phase of R&D over Technology Life
Cycle 38

Figure 10 Risk Reduction over an R&D Life Cycle 42

Figure 11 Industry and Federal Shares of U.S. R&D Funding, 1960--1998 44

Figure 12 Federal Non-Defense R&D Funds, FY1959--1998 45

**Tables**

Table 1 Total Factor Productivity Growth (TFP) in Post-War Economic
Cycles 7

Table 2 Phases in the Biotechnology Life Cycle 24

Table 3 Uses of Infratechnologies by Stage of Economic Activity 25

Table 4 IRI Annual R&D Trends Forecast for Industry-Funded

Directed Basic Research, 1991--1999 32

Table 5 Thirty Most Valuable Product Exports: Distribution by Complexity
34

## Executive Summary

"Technology is essential for long-term economic growth". This statement
generates little dissent, except occasionally during and shortly after
recessions when some associate technology with employment turbulence and
job loss. Such myopic views fade quickly as the subsequent economic
expansion creates jobs, higher incomes and improved profits.

In spite of this 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. 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.

##### Key Findings

The dominance of the United States as a source of technology for other
economies is declining, with reduced shares in practically every foreign
market. Moreover, technology acquired by U.S. domestic companies through
imports has more than tripled over the last two decades. This trend is
symptomatic of the relentless globalization of R&D capability.

In this context, the most frequently discussed issue is the *amount* of
R&D conducted in the U.S. economy. The United States still funds and
conducts more R&D than any other country. However, many other economies
are rapidly expanding their R&D investment. One indicator is the fact
that the other six G--7 nations now invest more collectively in R&D than
does the United States. Many newly industrialized nations are ramping up
domestic R&D programs, as well.

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.

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 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.

U.S. industry is attempting to meet foreign competition by becoming more
efficient in conducting applied R&D. This effort currently manifests
itself in shorter R&D cycle times. According to one study, industry cut
the average R&D project time by 45 percent (18 months to 10 months) in a
five-year period (1993-98). However, this study also found that
shortening the R&D cycle time was in large part accomplished by reducing
R&D project objectives (that is, targeting incremental improvements that
can be achieved in less time). Several studies demonstrate that such
improvements are not as beneficial to company performance over time as
are more radical and risky innovation efforts.

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

The "S" portion of S&T is relatively easy to deal with. Basic science is
widely recognized as largely what economists call 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. As evidence of this
muddled policy arena, one need only cite a few examples:

-   Pronounced shifts in the composition of government R&D funding away
    from defense to "civilian," without accompanying policy rationales
    for the redistribution.

-   Continued efforts to rewrite S&T/R&D policy in a "post-Vannevar Bush
    era," with little success.

-   Perpetuation of debates over public versus private sector roles
    based on simplistic taxonomies of the R&D process.

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 combination of institutions---public and private---should
receive support.

This report provides a conceptual basis for describing the economic
roles of technology, determining the categories of private investment
failures, and identifying the types of economic data needed to conduct
analyses for effective policy decisions. A broad range of data are used
to apply the framework to current policy issues and to identify the
types of data that need to be collected on a regular basis. The focus is
on R&D, as opposed to the broader set of S&T policy issues.

The modern R&D investment process is driven by a complex set of
technical, economic, and institutional factors. This complexity belies
the very simple conceptual models that have influenced R&D policy. As
Section 3 of this report shows, 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 their supporting infrastructures on R&D
investment. They include:

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

-   The effect on risk assessment of the capital intensity of many
    research processes (i.e., of the cost of these processes).

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

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

-   The unavailability at key points in a technology's life cycle of
    technical infrastructure (such as standards).

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

Ultimately, this report argues that the Federal government's role in
supporting industrial R&D is poorly understood and therefore
inefficiently funded and managed due to the absence of the following
three elements of R&D policy analysis:

(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. 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.

##### Major Policy Implications

As the rest of the world becomes technologically competitive, the United
States must consider at least three major policy implications:

(1) Acceptable rates of growth cannot be sustained in the future through
    a small high-tech sector. These industries are under constant
    competitive pressure. Whereas industries making up U.S. high-tech
    supply chains previously have been competitive from top to bottom,
    foreign competition is continuing a process of hollowing out these
    supply chains, thereby reducing the value added (contribution to
    GDP) that occurs in the domestic economy. One example is the
    computer industry, which is simultaneously experiencing a trade
    surplus in finished computers and trade deficits for all major
    components. Another example is optoelectronics, which arguably is as
    important as semiconductors in terms of its "enabling" function for
    downstream information technology (IT) applications.

(2) To remain competitive, the remainder of the economy will purchase
    large shares of needed technology from other industries. However, as
    R&D investment intensity increases around the world, these other
    industries will increasingly be foreign---unless the domestic
    high-tech sector is greatly expanded.

(3) Domestic R&D must be treated for what it really comprises---networks
    of industry, university, and government inputs. These inputs will
    vary over the typical technology life cycle based on the nature of
    the particular technology relative to existing R&D capabilities and
    technology trajectories.

Economists thought at the beginning of the 1990s that the maximum annual
real growth rate for the U.S. economy was in the range of 2.0--2.5
percent. An unusual confluence of positive factors raised the actual
growth rate to about 3 percent in the 1997-98 period. However, this
performance has not allowed the two major components of GDP---profits
and real incomes---to grow simultaneously. Corporate profits grew
rapidly for most of the 1990s, while real incomes stagnated. In the last
two years, real incomes have grown while the growth rate in corporate
profits has declined significantly.

Another indicator of overall competitiveness and, therefore, economic
growth potential is the merchandise trade balance. This balance has been
negative for the past 22 years. Subsets of this broad category of trade
are revealing. The more technology-intensive the subset of trade codes
(product fields) used to calculate a trade balance, the more favorable
the balance. However, the positive balance still realized for trade in
advanced technology products also represents only a small fraction of
traded merchandise (about 500 of some 22,000 commodity classification
codes used in reporting merchandise trade). The limited impact on
overall trade and the value added contributed to U.S. GDP reinforces the
point that technology-intensive products account for too small a
fraction of overall U.S. trade.

This pattern of economic performance is not assessed correctly due in
part to misunderstood economic statistics. For instance, labor
productivity (which is what gets reported in the media) has grown faster
than has total factor productivity (the more accurate measure).
Examining total factor productivity trends provides a more accurate and
sobering view of actual economic performance and future potential
growth, especially in the dominant service sector. Given the major role
of technology in productivity growth, the patterns of underinvestment in
R&D analyzed in this report should get immediate attention from policy
makers.

The sources of economic growth are highly concentrated. Only a few
companies or even individual establishments within their industries are
going to be successful innovators and users of technology and best
practice. A diversified economic infrastructure is necessary to develop
enough of these engines of growth to drive national economic prosperity.

Who these successful companies are also will change drastically over
time. The 1990s are understood correctly as a period of steady job and
business profit expansion. However, these net gains mask considerable
churning in various sectors, where many jobs are created and also
destroyed. Data from Cognetics, Inc. show that for one yearly change,
1994-1995, almost 700,000 business establishments were added to the
economy while slightly less than 600,000 ceased operating. New firms
plus expanding ones created 16.2 million jobs, but firms that went out
of business and those contracting caused 12.7 million jobs to disappear.
All together, 30 percent all jobs in the U.S. economy were in situations
of flux---i.e., they were in appearing, disappearing, expanding, or
contracting firms.

Even though most net new jobs (84% in the 1992-96 period) come from
small firms (\<100 employees), growth in employment is highly skewed
among these companies. Most small businesses do not create large numbers
of jobs. Rather, a small number of fast growing small companies
(**"**gazelles**"**) account for the majority of net new jobs. These
gazelles are responsible for creating about 70 percent of the net new
jobs in the U.S. economy between 1992 and 1996, yet they account for
only about 3 percent of roughly 9 million enterprises (corporations,
partnerships, proprietorships). This creative destruction represents a
healthy U.S. economy, but it also demonstrates the tenuous character of
competitive position and hence the need to provide the technical and
other infrastructure that enables rapid economic change.

In a Department of Commerce study, rapidly growing companies or
individual plants were found to have yielded net employment gains four
times the rate of manufacturing plants in general. Such plants were
above average users of advanced technology and "best practice" and
appear to have benefited from investing in new technology early in that
technology's life cycle.

The limited number of highly successful companies and their distribution
across the economy combined with evidence that technology is a major
factor in their economic success argue for a much broader technology
basis for the U.S. economy in order to maximize the number of such
companies over time.

The end point of this analysis is simply this: The fact that less than
10 percent of the U.S. economy is truly high-tech means that the
remaining 90 percent is substantially similar to competing industries in
other countries. Although many in this latter group do some R&D and
display varying degrees of capability to absorb technology from external
sources, they are now basically members of a growing global crowd. Their
increasing dependence on foreign supply chains raises the probability
that their ability to contribute to future economic growth will diminish
over time. Moreover, even the more R&D-intensive industries are
experiencing increasing difficulties in rationalizing critical types of
technology research that are essential to long-term competitiveness.

### 

***R&D Trends in the U.S. Economy:***

***Strategies and Policy Implications***

**Gregory Tassey**

#### 

#### 1.0. Introduction

## 

Over the post-war period, policy makers have slowly realized that
technology is critical to growth in the output of goods and services and
thereby to growth in economic welfare. Unfortunately, this consensus has
not translated into a common view of how technology actually gets
developed and utilized. As a result, a seemingly endless debate has
ensued over the nature of technology and the implications for private
and public sector roles.

If technology is acknowledged as the major driver of long-term growth in
an advanced economy, then from the macroeconomic (economy-wide)
perspective the *amount* of technology investment is a key factor in
determining national growth rates. This issue tends to be the focus of
R&D policy debates. However, the *composition* of technology also is
critical, especially over more than one technology life cycle. That is,
the *types* of technology that an economy either develops or acquires
are a significant determinant of long-term growth rates.

In addition, past economic studies have shown that efficiency in the
conduct of research and development (R&D) is essential, not only to
develop new technologies but also to acquire technical knowledge from
external sources of that knowledge. Thus, the *productivity* of the R&D
process is increasingly important, not only to take advantage of
technology development opportunities but also to acquire technology in
an timely and efficient manner. If either of these dual
processes---technology development or technology acquisition---fail to
function, inadequate flows of technology result and rates of innovation
and productivity growth decline.

Current trends in U.S. technology investment create major concerns with
respect to both determinants of economic growth: the amount and type of
technology produced or acquired. In this regard, economic and technology
policy makers have begun to realize that the private sector is
underinvesting in certain types of R&D. However, there is little
agreement with respect to the types and extent of underinvestment. No
one has developed a coherent strategic response.

One contributing factor is that relatively few economists have focused
their research on technological change and, of those that have done so,
most have failed to target their research toward the needs of policy
makers. Moreover, the science and technology (S&T) policy community has
largely ignored the economic dimensions of R&D policy.

This report addresses three major problems currently inhibiting
effective R&D policy:

(1) A consensus economic model that accurately represents the complex
    role of technology in economic growth has not been achieved. As a
    result, data identification and collection are not driven by a
    coherent set of analytical requirements. Poor policy analysis and
    protracted debate over basic issues are the result.

(2) Even if such a model were available and accepted, current economic
    data in general and R&D investment data in particular are highly
    inadequate for effective R&D policy analysis. Most policy documents
    refer in passing to only aggregate R&D trends, and then assert
    market failures and equally vague policy responses. As a result,
    policy recommendations are poorly constructed and have a weak track
    record of successful implementation.

(3) To the limited extent that R&D policy analysis is undertaken, it is
    poorly integrated into the broader economic growth policy framework.
    The needed synergies among R&D policy, S&T policy, and economic
    growth policy are simply not in place.

#### 2.0. The Economic Dimensions of Technology Policy

The need for a more analytically-based and data-driven R&D policy
derives from four factors:

(1) R&D's multiple roles and growing importance to the U.S. economy.

(2) The private sector's scope and depth of efficiency in developing and
    commercializing new technologies.

(3) A conflict between individual corporate R&D strategy, bound by time
    and cost constraints, and the collective need to invest in
    next-generation technologies to enhance long-term competitiveness.

(4) Sustaining economic performance over the long-term requires an
    upward shift in total factor productivity (TFP), yet TFP and GDP
    growth rates have been modest in recent decades.

##### 2.1. Economic Trends

**2.1.1. The Macroeconomic Environment.** In the 1990s, the U.S. economy
performed better than the economies of most other countries. This
performance has led many to conclude that technology combined with
restructured corporate strategy, organization, and management have
permanently restored the United States to the pinnacle of the global
competitive ladder.

However, recent economic events are beginning to cast doubt on some of
these beliefs or, at a minimum, dampen overly enthusiastic assessments.
Specifically, corporate profit growth has been fueled to a significant
extent in this decade by downsizing and reorganizations, declining
interest rates, corporate buybacks of shares of common stock, lower
health care costs, and, in too many cases, manipulation of income
statements in the form of repeated "non-recurring" charges.

None of these factors are likely to continue to exert anywhere near
their previous levels of influence. Moreover, the two major components
of GDP are payments to owners of capital (profits) and payments to
owners of labor (wages and salaries). Most of GDP growth in the 1990s
has been the result of higher profits. Real incomes hardly budged for
most of this decade. In fact, the median household income in constant
dollars is unchanged and, moreover, has not grown in real terms since
1980. To increase consumption, consumers have increased household debt
as a percent of disposable personal income from 70 percent in 1980 to 94
percent in 1997.

The excessive expansion of credit has been worldwide and has led to
equally excessive global consumption. This trend has, in turn,
stimulated huge amounts of investment. While investment is in general a
good thing, the level of global investment in the 1990s has generated
excess production capacity in a wide range of industries. This excess
capacity will take some time to eliminate and, while this adjustment is
occurring, only the most efficient domestic industries around the world
will perform at even acceptable levels.

Because technology is the major long-term driver of economic growth,
only those industries that have either developed or assimilated superior
technology will prosper. Even under generally favorable economic
conditions, advanced economies have found sustaining growth in low and
moderate technology-based industries to be increasingly difficult. These
economies have recognized the growing challenges in technology-based
markets arising from competition with each other and increasingly with
emerging industrialized nations. However, corporate strategy and
government policy changes have not adapted to these challenges, which
may undermine the economy's ability to maintain, let alone increase,
market shares.

The following analysis argues that technology development and technology
utilization are complementary investments. Unfortunately, the current
economic structure of the U.S. economy consists of a small "high-tech"
sector that develops new technology, a larger sector that attempts to
compete largely by absorbing technology, and the remainder that does
little of either. This situation will be increasingly inadequate to
sustain desirable rates of growth. In fact, the high-tech sector
contributes only a small fraction of GDP. Thus, the U.S. economic system
is in need of substantial upgrading not only to take advantage of the
tremendous opportunities presented by an array of emerging technologies,
but, in the longer term, to even maintain current rates of economic
growth as the rest of the world becomes increasingly technologically
competent.

**2.1.2. Impacts of Technology on Economic Growth.** Few argue anymore
against the critical importance of technology for the long-term growth
of an advanced economy. For most of the post-war period, technological
change was confined to a few areas of the manufacturing sector. These
short supply chains of industries developed or absorbed technology at a
relatively slow pace, with many years typically going by before
technical advances in non-market areas such as defense spun-off into
mainly producer goods which only indirectly affected consumer
products.[^1] End users of both products and services had at most only a
vague understanding of how technology affected their lives.

Today, the technology economy has become incredibly broad and diverse.
The number of producer goods industries that are technology-based is
expanding so that, in some cases, entire supply chains are driven by the
pace of technological change. Consumers, who were once passive users of
technology, now understand and critically evaluate technology to a far
greater degree than was the case just a few decades ago. In fact, in
many markets consumers have become a driver not just for highly visible
products like PCs, but also for many forms of technology-based services
ranging from communications to health care. For example, instead of
passively waiting for an inefficient trial-and-error development process
to occasionally yield new drugs, consumers comb the Internet for
information on the exploding number of biotechnology-based
pharmaceuticals currently in research and clinical trials and put
pressure on companies and government to further accelerate the process.
"Chat rooms" exchange knowledge, experience and names of providers
relating to a wide range of technology-based products and services.

Consequently, the economic role of technology is getting increasing
attention from companies and their governments all over the world. The
alteration of old paradigms for what determines competitive success
presents enormous opportunity but also immense challenges and risks. The
staggering demand for technology across all sectors of the world's
economy argues for new and rapid strategic responses. These responses
are evolving in advanced and emerging economies alike. In all cases,
some combination of industry and government roles comprise the national
economic growth strategy.

**2.1.3. R&D Efficiency.** As the technological content of domestic
production and international trade continues to rise, the importance of
a broad, deep, and productive R&D establishment increases. What is
referred to in this report as the "high-tech" sector contributed
approximately 7 percent of U.S. GDP in 1996.[^2] Other industries also
conduct varying amounts of R&D, but their R&D intensities (R&D-to-sales
ratios) are modest to low. These latter industry groups must import
substantial amounts of technology from external sources.

However, technology diffuses from its source industries to many other
parts of the economy. Economists estimate that technology, through this
diffusion process, accounts for from one-third to one-half of GDP growth
and at least two-thirds of productivity growth.[^3] Thus, the leverage
of the high-tech sector on the rest of an advanced economy is large and
therefore extremely important.

When the U.S. economy was unrivaled in its ability to both innovate and
utilize new technologies, the time paths of production and diffusion of
technology were not particularly important. Today, however,
globalization of technology investment and utilization has significantly
shortened the average technology life cycle, increasing demands for more
efficient R&D and technology assimilation processes. At the same time,
the nature of many advanced technologies has become increasingly
complex, in terms of depending on multiple science bases as well as
being used in higher-level systems technologies. In fact, many
industries, including the huge service sector, have had difficulty
absorbing and productively utilizing new technologies, as evidenced by
sluggish growth in TFP.

Thus, long-term productivity growth and the consequent increases in a
nation's standard of living depend on both the efficient development and
effective use of technology. And, while much technology moves across
national borders, economic studies show that a broad and deep domestic
R&D capability is required for two critical economic purposes: (1) to
develop new technologies ahead of foreign competitors and thereby gain a
"first mover" advantage, and (2) to effectively absorb and utilize
technologies that originate elsewhere.

##### 2.2. Productivity and Technology

Investment in and the effective use of technology are the most important
determinants of long-term economic growth. A number of macroeconomic
studies over several decades have shown that technical knowledge
accounts for one-third to one-half or more of the output of goods and
services. These estimates vary because of differences in time periods
covered, alternative measures of economic output used, and the scope of
the definition of technical knowledge. With respect to the last factor,
the degree to which quality improvements contribute to output growth has
a significant effect on the estimate of technology's overall
contribution to GDP. All of these studies point to the same general
conclusion: in the post-war period, technology has been an essential
ingredient in the economic growth of industrialized nations.

The U.S. economy has performed well in the 1990s compared to most of the
rest of the world. However, the euphoria over this "economic resurgence"
needs to be tempered by at least three major trends:

(1) the ultimate measure of economic growth, GDP, has not grown all that
    impressively;

(2) the major driver of long-term growth---the *rate of increase* in
    productivity---has been low in recent decades;

(3) the U.S. merchandise trade balance has been negative for the past 22
    years.

The trend implied by Table 1 contrasts with the more positive picture of
productivity growth, where average labor productivity (ALP) is used
instead of the more comprehensive and hence theoretically accurate total
factor productivity (TFP).[^4] For a good part of the post-war period,
these two measures have tracked each other reasonably well. However, as
Figure 1 shows, the two have diverged in the 1990s. The reason for this
divergence appears to be the result of an underestimate by the Bureau of
Labor Statistics of the number of hours worked. This underestimate has
the effect of raising estimated ALP relative to TFP.[^5] The fact that
real income growth tracks TFP supports this explanation. The implication
for economic growth policy is that neither enough technology nor its
effective absorption by the economy as a whole is taking place.

The controversy over rates of productivity growth has been especially
pronounced in the service sector. A NIST-sponsored study of technology
investment and utilization in the service sector estimated a rate of
return (RoR) on investment in IT capital of 196 percent, compared with
an RoR of 11 percent on non-IT capital investment.[^6] Such a

+-------------------------+-----------------------+--------------------+
| #                       |                       |                    |
|                         |                       |                    |
| Table 1                 |                       |                    |
|                         |                       |                    |
| ***Total Factor         |                       |                    |
| Productivity Growth     |                       |                    |
| (TFP) in Post-War       |                       |                    |
| Economic Cycles***      |                       |                    |
+-------------------------+-----------------------+--------------------+
| **Time Interval**       | **Ave. Annual Percent | #                  |
|                         | Change in TFP**       |                    |
|                         |                       | **Ave. Annual      |
|                         |                       | Percent Change in  |
|                         |                       | Real GDP**         |
+-------------------------+-----------------------+--------------------+
| 1948-58 (war to         | 1.79%                 | 3.8%               |
| Sputnik)                |                       |                    |
+-------------------------+-----------------------+--------------------+
| 1958-73 (Sputnik to oil | 2.32                  | 6.0                |
| shock)                  |                       |                    |
+-------------------------+-----------------------+--------------------+
| 1973-82 (post-OPEC      | −0.43                 | 2.0                |
| inflation)              |                       |                    |
+-------------------------+-----------------------+--------------------+
| 1982-91 (economic       | 0.55                  | 3.5                |
| restructuring)          |                       |                    |
+-------------------------+-----------------------+--------------------+
| 1991-97 (economic       | 0.75                  | 3.0                |
| recovery)               |                       |                    |
+-------------------------+-----------------------+--------------------+
| Source: Bureau of Labor |                       |                    |
| Statistics, Bureau of   |                       |                    |
| Economic Analysis       |                       |                    |
+-------------------------+-----------------------+--------------------+

![](media/image1.wmf)result might seem paradoxical in light of the
argument by Stephen Roach and other economists that productivity growth
in this sector has been sluggish at best. However, the 196 percent RoR
is for capital investment alone. The NIST report points out that "the
cost of IT implementation can be four to five times the cost of the
investment in hardware and software".[^7] Thus, the rate of return on
capital investment is leveraged by huge implementation spending. This
second investment is going to result in a much lower RoR on *total*
investment, which is reflected in the low total factor productivity
estimates that Roach points to.[^8]

Table 1 also shows a strong correlation between TFP growth and GDP
growth. Even with the addition of the relatively good GDP growth rates
achieved in 1997 and 1998, the average growth rate for the 1990s will
fall well below that achieved in the early post-war decades. Recent
growth rates do not appear sufficient to allow simultaneous advances in
*both* corporate profits and also personal incomes---the two components
of GDP.

Beneath these national trends are pronounced crosscurrents. Many
industries and even entire supply chains are rapidly losing competitive
position in global markets, while other industries with significant
growth potential are emerging. The current shifts in investment to
knowledge-based technologies, upheavals in employment patterns, and slow
rates of economic growth among industrialized nations are clear
manifestations of a long-term cyclical model of economic growth.

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 a given life cycle, the new technologies
that will drive the next life cycle already exist, but with small market
shares.[^9] Thus, opportunities for adaptation are available, but
attempts to take advantage of them usually do not occur until economic
conditions have become significantly distressed.

##### 2.3. R&D Intensity 

Because technology is the primary driver of long-term growth in
productivity (and ultimately output and real incomes), the economy's
investment in new technology should be a critical policy concern. Both
the *amount* and *composition* of investment in R&D are key indicators
of prospective technological innovation and economic growth.

The U.S. economy has a surprisingly concentrated high-tech sector (as
defined by R&D intensity). Technology can obviously be acquired from
sources outside an industry, implying that the amount of internal R&D
may not be a critical variable. However, economists and business
analysts both agree that endogenous R&D is essential, not only to
realize first mover advantages from innovative new products, services,
and production processes, but also to efficiently assimilate technology
from external sources when that is the appropriate strategy.[^10]

The most common definition of R&D intensity is the ratio of R&D spending
to sales. Because R&D investment is the main source of technological
innovation, this indicator is a key predictor of an industry's long-term
ability to compete globally. Two alternative R&D investment intensity
measures are provided by NSF; one uses company financed R&D, and the
other uses total R&D funds available to the industry (company plus
federal and other sources). In 1997, the company-funded R&D intensity
for the U.S. economy as a whole was 2.9 percent and the total R&D
intensity was 3.4 percent. For the manufacturing sector, the
corresponding ratios were 3.3 percent and 3.9 percent, respectively.

Several industry groups within U.S. manufacturing maintain high
R&D-to-sales ratios, but others invest little or nothing in R&D. In
fact, R&D intensity varies from more than 10 percent in high-tech
industries to less than 1 percent in others. As indicated in Figure 2,
the company-funded R&D-to-sales ratio for all manufacturing industries
increased from 2.6 percent in the early 1980s to 3.3 percent in 1997.

![](media/image2.wmf)Service industries exhibit a similarly wide
divergence in R&D intensity. The service sector as a whole had a
company-funded R&D-to-sales ratio of 2.2 percent in 1996. However,
individual industries vary enormously. Services such as transportation,
utilities and FIRE (finance, insurance, and real estate) show ratios of
1.0 percent or less. Health services (at 5.9 percent) and engineering
and management services (at 6.1 percent) are in the middle of the pack,
while the leaders are 9.4 percent for R&D services and 12.4 percent for
computer and data processing services. Surprisingly, service industries
that are classified in the same industry group vary substantially with
respect to R&D intensity. For example, computer and data processing is
classified as one of a number of "business services," but other
industries in this group have an average R&D-to-sales ratio of 1.1
percent.

The U.S. economy often is referred to as technology-based, and
indigenous R&D capability is essential not only for innovation but also
for efficiency in absorbing technology from external sources. High
ratios of R&D to sales imply high rates of technological innovation and
technology-based competition. High-tech industries also employ
considerable technology from other industries and deliver a large
portion of the technology used by the rest of the economy. Thus, they
are critical to the overall economy along several dimensions.

Even with its significant leverage on the rest of the economy, the
high-tech sector is too small to sustain desirable rates of economic
growth given the continuing rapid increase in competitive positions of
other national economies in moderate technology-based markets. Moreover,
the high-tech sector not only is a relatively small portion of the U.S.
economy, but also is concentrated geographically within six states
(which account for half the Nation's R&D).

##### 2.4. Competitiveness of Supply Chains 

Even the obviously high-tech industries with large R&D investments, such
as computers, are not automatic winners in the global marketplace. From
an R&D policy perspective, analysis of the factors determining success
or failure must carefully define what is meant by an industry.

![](media/image3.wmf)Computers often are cited as a highly competitive
technology-based industry, and the phrase "the computer revolution" is
used to convey the notion that computers are driving growth in many
other industries as well. While these statements are true in general
terms, they are too vague to be a useful guide to policy. In fact, they
can be misleading. The term "computer industry" usually is applied to
the companies that assemble and sell the actual computer. However, a
lengthy supply chain consisting of industries providing silicon,
semiconductors made from the silicon and other advanced materials,
integrated circuit subsystems composed of an array of semiconductor
devices is necessary to produce a computer (which is an assembly of
subsystems). For that matter, computers are often sold to become part of
larger manufacturing systems and services.

This supply chain contributes far more to GDP than the computer industry
alone. A policy focus on the computer industry alone easily can ignore
problems in other levels of the supply chain. In fact, Figure 3 shows
quite clearly that the major components of the computer are being
produced outside the United States economy to an increasing degree. This
means that substantial value added by the computer supply chain is
occurring elsewhere. The obvious loss of domestic employment and profit
generation in a high-tech supply chain should be a cause for concern.
Finally, there is no lock on market share at any level in a high-tech
supply chain. For example, Taiwanese companies are responsible for both
the design and manufacture of over 50 percent of the worldwide supply of
notebook computers, regardless of whose label is on the product.[^11]

Another supply chain example is optoelectronics, which arguably is now
as important an enabling technology as is semiconductors for a myriad of
IT applications. The United States did much of the original generic
technology research in the 1960s and 1970s. However, Japan, through ever
increasing R&D expenditures, now has taken over the optoelectronic
component market. In just one area, optical storage technology, Japanese
companies now have about \$40 billion in annual sales compared with \$6
billion for U.S. companies.[^12] Yet, farther down the supply chain,
U.S. companies are large users of optoelectronic components and are
therefore dependent on Japan (and increasingly on other Asian nations,
especially Taiwan). Once the competitive position of the domestic supply
chain deteriorates, resurrecting it can be extremely difficult.

This general problem has become more severe in recent years, as most
high-tech supply chains have experienced vertical disintegration. U.S.
industries were once characterized by vertically integrated companies
with sufficient market power and time to operate integrated R&D
programs. Today these companies are dependent on external technological
advances by suppliers. This dependency is forcing them not only to help
develop their supplier technologies but also to transfer this
technology, often to foreign suppliers. The ability of supplier
industries to independently conduct the necessary R&D is particularly
constrained when those industries are populated by small firms.

One example is an emerging technology for manufacturing semiconductor
components called extreme ultraviolet (EUV) lithography, which is likely
to replace current processes for manufacturing integrated circuits. The
generic technology was demonstrated at two Department of Energy
laboratories (Lawrence Livermore and Sandia). In 1997, DoE licensed the
technology to a consortium of U.S. semiconductor manufacturers (EUV
Limited Liability Corp.), which is supposed to commercialize the
technology. However, the semiconductor equipment manufacturing industry
in the U.S. supply chain had deteriorated badly in the 1980s. European
and Japanese firms took over market leadership.

Even with considerable assistance from Sematech, this industry has
struggled to regain a competitive position. Several U.S. firms
(Ultratech Stepper and Silicon Valley Group Lithography) have the
potential to compete. However, members of the EUV consortium, such as
Intel, want to transfer the new technology to the much larger and better
established foreign competitors in order to maximize the potential that
the technology will be commercialized. The lesson here is that, once
behind, a domestic industry has to run faster than its competition to
catch up.

Sometimes, the loss of competitiveness at one level in a supply chain is
rationalized by labeling the technology and its applications at that
level as having matured to "commodity" status. That is, the degree of
product standardization typical of the latter phases of a technology
life cycle reaches the point at which additional profit potential from
product differentiation has declined substantially. Profits are now
determined more by process technology and simply by overall low-cost
production capability. The latter factor tends to favor industries in
emerging economies. However, allowing domestic supply chains to be
hollowed out for what are often short-term conditions can come back to
haunt the domestic economy, as new technology development opportunities
and innovation frequently appear as the result of foreign R&D. A number
of electronic components, such as LEDs, are providing examples of this
life cycle transition effect (see text box).[^13]

##### 2.5. Economic Policy is Technology Policy, and Vice Versa

The importance of using the supply chain as the unit of policy analysis
cannot be understated. A few R&D-intensive industries cannot support a
large, diversified economy. Relying on 7 percent of GDP to drive the
remainder of the economy will not work in the long run. The service
sector is the dominant user of information technology (IT) but is only
just learning how to efficiently develop its unique type of technology.
In recent years this sector has averaged \$200 billion in annual IT
equipment purchases, but they have had to spend around four times that
amount to assimilate and use this equipment.[^14]

The macroeconomic effects of a narrow technology-based industrial
structure is evident in U.S. trade balances. The merchandise trade
balance provides an overall indicator of the average competitiveness of
manufacturing. It has been negative for two decades and recently is
becoming more so (the merchandise trade deficit reached a record \$219
billion in 1997 and was even larger in 1998).

Economic studies show that export opportunities for advanced economies
such as the United States depend on the use of increasingly
sophisticated technology. This fact is reflected in subsets of
merchandise trade that are technology-based. A group of industries
designated by the Department of Commerce as high-tech include
significant numbers of high-tech products but also include less
technology-intensive products (called the DoC--3 industry basis for
technology-based trade). This industry group maintained a positive trade
balance until 1994 but has been negative since that time. A more narrow
definition of high-tech trade that only includes selected advanced
technology trade codes across industries still yields a positive trade
balance.

In other words, the more technology-intensive the subset of trade codes
(or product fields) used to calculate a trade balance, the more
favorable the balance. However, the positive balance realized for trade
in advanced technology products also represents only a small fraction of
traded merchandise (about 500 of some 22,000 commodity classification
codes used in reporting merchandise trade). The limited impact and hence
the value added contributed to U.S. GDP reinforces the point that
technology-intensive products comprise too small a fraction of overall
U.S. trade.

Entire supply chains obviously can contribute more to GDP than one or
two levels in these chains. The decline in the less technology-intensive
trade balance (DoC--3) implies a hollowing out of actual and potential
value added from so-called high-tech industry groups. The only viable
long-term policy response is to broaden the economy's technology base.

The conventional wisdom seems to be that mature industrialized nations
are constrained to grow more slowly (2 percent to 3 percent per year)
than newly industrialized nations (7 percent to 10 percent). Some
economists have pointed to a convergence of economic growth rates over
time, at least within major technology cycles. They argue that nations
progress technologically not only through domestic innovation but also
through technology they acquire from the perhaps 10 to 20 nations that
are the technological leaders. Because the laggards have a good deal
more to learn from the leaders than the reverse, the balance of trade in
ideas inherently favors the laggards. This relationship appears to have
led to convergence in national technology capability in the 19th and
20th centuries, during an era of rapid technological advancement.[^15]

However, no economic theory proves that the growth rates of advanced
economies inevitably have to slow down to only 2−3 percent per year. The
reasons for current slow rates of growth are linked to past amounts and
types of investment. These investment patterns can be analyzed and
changed through an effective economic growth policy, with R&D policy as
one element.

One commonly expressed belief is that globalization has increased
convergence to the point that sustainable competitivee advantage is no
longer possible. This is definitely not the case. Certain economic
assets, including technology-based ones, are less mobile than others.
This means that, once acquired, these assets are not easily exported or
imitated.[^16] Each nation has a unique combination of public and
private economic assets. Beyond political, cultural, and general
business practices and organizational modes, competitive positions in
today's technology-based markets are determined by a complex set of
factors. These include synergies from supply chain effects, increasing
returns from network externalities, installed-base and lock-in effects,
extramural R&D strategies, and the availability of supporting technical
infrastructures.[^17]

As the following sections will argue, supporting such a complex national
innovation system is a daunting policy objective. Developing effective
technology-based growth policies is achievable only with a combination
of the right conceptual framework and the appropriate technical and
economic data.

Unfortunately, in the face of wrenching change, U.S. economic growth
policy has struggled for several decades to make a convincing case for a
greater national investment in research and development and, even more
so, for an appropriate role for government in support of this
investment. This effort has been extremely weak for several reasons:

(1) The R&D policy community has failed to adequately articulate the
    economic case for technology and the process (R&D) that produces it.

(2) What passes for policy analysis has failed on several counts,
    including trend identification and market failure analysis.

(3) Matching appropriate policy responses with specific market failures
    is hardly attempted. The vast majority of policy reports simply
    assert the importance of technology, skip the market failure
    analysis, and therefore fail to match market failures with
    appropriate policy responses.

Congressman George Brown (D--CA) summed up the problem for R&D policy
particularly well:

"\[We have\] a clumsy and unsophisticated set of tools for evaluating
the best of human innovation and thinking. We also need to be conducting
outcomes assessments for our science and engineering activities and we
need to collect the data needed to make these assessments"

#### 3.0. Technology Development and Life Cycle Models

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.

A technology-oriented economic growth policy is needed because private
markets do not function completely efficiently by themselves.
Technology-based industries experience varied and often severe market
failures, especially at certain points in their life cycles. Some
aspects of R&D-related market failures are easily understood, such as
the fact that science is largely a pure public good and receives little
private financing. Research in basic science requires very long time
horizons, the results are highly unpredictable, and the knowledge
produced diffuses rapidly and is almost impossible to fully own. Basic
research clearly requires government funding.

From an investment incentive perspective, technology research is much
more complex and hence difficult to understand. For most of the post-war
period, the process of technology-based growth has been grossly
oversimplified, resulting in policy gridlock. To remedy this problem,
efficient and effective R&D policy in support of broader economic growth
objectives must derive from an accurate conceptual model of how
technology evolves over a series of life cycles.

##### 3.1. The Technology Life Cycle

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
such a 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".[^18]

![](media/image1.wmf)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. For example,
certain classes of semiconductors, computers, and many types of software
are excellent 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 performance curve, such as
curve 2 in Figure 4, that initially will be below the existing or
"defender" technology (represented by curve 1). The risk of low
performance technically, 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 technology life cycle the amount and speed of advance
    achieved by domestic industry over a technology's economic life 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 (for example, Intel) 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.

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.

![](media/image1.wmf)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 5 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.

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.

-   The time between invention, innovation (first commercial use), and
    major economic impact (widespread market penetration) can be long,
    spanning several decades. However, the market transition to a new
    technology life cycle requires preparation and can take place in
    other parts of the domestic or world economy before firms using the
    old technology realize that change has occurred.

-   A major reason that such cycle transitions are difficult is the fact
    that firms need complementary economic assets (skilled labor,
    capital, and infrastructure) to successfully develop and market new
    technologies. These assets may not be available and vary
    significantly from one life cycle to the next.

The current transition to a knowledge-based service economy is based on
continuing major changes in digital electronics and the advent of
complex knowledge systems based in part on that technology. These
changes are requiring major adaptation by the economies of
industrialized nations. The typical pattern is, once again, proving
itself---many of the economic leaders of the previous technology life
cycle are being left behind as the new cycle emerges. The major reason
for this pattern is the tendency for existing companies to remain locked
into investments in the economic assets that work in the current
(defender) technology life cycle. Finally, even when entry positions in
new technologies are achieved, the dynamics of a technology-based
economy demand continual adaptation over the entire life cycle.

##### 3.2. 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.[^19]

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 the
world's leading innovator in test and measuring equipment, but one of
HP's most successful products in recent years is the ink-jet printer.
This product 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.

HP, which licensed Canon's patents, achieved a major advance when it
succeeded in moving all the electronics except one resistor off the
print head and incorporating the internal baffling into the plastic mold
of the print-head case itself. The result was a product of great
simplicity, leading to the lowest cost print head in the industry
(inexpensive enough to be disposable). In 1984, HP introduced the
ThinkJet, the first ink-jet printer for the desktop market. Initially,
the ThinkJet was inferior to the dot-matrix printer, and leading
Japanese dot-matrix companies like Epson viewed this technology
virtually with contempt.

In summary, HP was not the inventor of inkjet printer technology but
could be called an innovator at the component level. The company spent a
significant sum on developing an improved imitation of the original
Canon technology. In 1997, HP generated over \$5 billion in sales from
ink-jet printers. Moreover, the success of the ink-jet gave HP skills in
high-volume manufacturing, helped it establish relationships with
computer retailers, and improved the strength of the company's brand
name.[^20]

This example clearly demonstrates the twin benefits of conducting
R&D---technology absorption and internal technology development.
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.

##### 3.3. 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 value added to the gross domestic product of the
economy. 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.

R&D policy has for decades suffered from a failure to incorporate the
above factors and thereby improve upon the early post-war Vannevar Bush
model. In such a model, science is recognized as a national resource but
supports growth in GDP through a mysterious entrepreneurial process.
This process transforms science into commercially viable technology
through a series of steps that must be understood in order to improve
the functioning of the economic system in a much more competitive world
economy.

A more 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 other dimensions of R&D investment
patterns. Each of these concepts enables a characterization of factors
in the evolution of technology through 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, and refers 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 are simultaneously changing.

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.[^21] 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".[^22]

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.[^23]

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 clearly 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.[^24] The knowledge gained not only feeds back into the
existing life cycle but it also eventually contributes to the subsequent
technology life cycle.

Criticisms of the linear model have merit. However, the debate over
whether to use such a 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 represents a higher level of
aggregation in the hierarchy of knowledge, namely broader areas of
technology, than does the chain link framework that focuses on single
technologies.

##### 3.4. 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 6. 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.[^25]

1.  ![](media/image1.wmf)**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.[^26] 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.

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 2, multiple areas of science have had to
advance before a larger set of generic product and process technologies
could evolve. These generic technologies have advanced over the past 20
years and are just now beginning to yield significant numbers of
proprietary market applications.

+--------------+---------------+---------------------+----------------+
| Table 2      |               |                     |                |
|              |               |                     |                |
| ######### Ph |               |                     |                |
| ases in the  |               |                     |                |
| Biotechnolog |               |                     |                |
| y Life Cycle |               |                     |                |
+--------------+---------------+---------------------+----------------+
| ####### Ba   | #######       |                     | **Specific     |
| sic Research |               |                     | Products       |
|              | ##            |                     | Developed**    |
|              | ##### Generic |                     |                |
|              |  Technologies |                     |                |
+--------------+---------------+---------------------+----------------+
|              | ##            | #######             |                |
|              | ##### Product |                     |                |
|              |               | ####### Process     |                |
+--------------+---------------+---------------------+----------------+
| -            | -   gene      | -   combinatorial   | -   protease   |
|    molecular |     testing   |     chemistry       |     inhibitors |
|     and      |               |                     |                |
|     cellular | -   gene      | -   recombinant     | -   hormone    |
|     biology  |     therapy   |     DNA/genetic     |                |
|              |               |     engineering     |   restorations |
| -   m        | -   gene      |                     |                |
| icrobiology/ |     delivery  | -   nucleic acid    | -   DNA probes |
|     virology |     systems   |     amplification   |                |
|              |               |     and probes      | -              |
| -            | -   gene      |                     |    neuroactive |
|   immunology |               | -   gene transfer   |     steroids   |
|              |    expression |                     |                |
| -            |     systems   | -   transgenic      | -   ne         |
| neuroscience |               |     animals         | urotransmitter |
|              | -   antisense |                     |     inhibitors |
| -            |               | -   cell culture    |                |
|   physiology | -   apoptosis |                     | -   vaccines   |
|              |               | -   immunoassays    |                |
| -            | -   ant       |                     | -              |
| pharmacology | iangiogenesis | -   monoclonal      |    coagulation |
|              |               |     antibodies      |     inhibitors |
| -   genomics |               |                     |                |
|              |               | -   cell            | -              |
|              |               |     encapsulation   |   inflammation |
|              |               |                     |     inhibitors |
|              |               | -   implantable     |                |
|              |               |     delivery        |                |
|              |               |     systems         |                |
+--------------+---------------+---------------------+----------------+

**3.4.2. Infratechnology.** The other category of industrial technology
with significant public good content is infratechnology.
Infratechnologies are a varied set of technical tools and constructs
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.[^27]

As Table 3 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.[^28]

+-----------------------+----------------------+-----------------------+
| Table 3               |                      |                       |
|                       |                      |                       |
| **Uses of             |                      |                       |
| Infratechnologies by  |                      |                       |
| Stage of Economic     |                      |                       |
| Activity**            |                      |                       |
+-----------------------+----------------------+-----------------------+
| **R&D**               | **PRODUCTION**       | **MARKET              |
|                       |                      | DEVELOPMENT**         |
+-----------------------+----------------------+-----------------------+
| **[Timing &           | **[Process & Quality | **[Transaction Cost   |
| Effi                  | Co                   | Red                   |
| ciency]{.underline}** | ntrol]{.underline}** | uction]{.underline}** |
|                       |                      |                       |
| -   Materials         | -   Process Modeling | -   Acceptance Test   |
|     Characterization  |                      |     Methods           |
|                       | -   Measurement and  |                       |
| -   Measurement       |     Test Methods     | -   Interface         |
|     Methods           |                      |     Standards         |
|                       | -   Process and      |                       |
| -   Techniques        |     Quality Control  | -   Comp              |
|     (design for       |     Techniques       | atibility/Conformance |
|     manufacturing,    |                      |     Test Facilities   |
|     rapid             |                      |                       |
|     prototyping)      |                      |                       |
+-----------------------+----------------------+-----------------------+
| Source: Tassey        |                      |                       |
| \[1997, p. 158\]      |                      |                       |
+-----------------------+----------------------+-----------------------+

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 about
15 percent per year, reaching approximately \$5.5 billion in 2001.[^29]
Thus, the cost of not having needed infratechnologies and associated
standards in place can be substantial. Another NIST study of
interoperability in the U.S. automobile industry found that lack of
standard formats for the transfer of electronic product design data
between suppliers and automobile manufacturers is costing that industry
approximately \$1 billion per year.[^30]

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.

##### 3.5. Market Failure and Underinvestment over the Technology Life Cycle

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 tend to appear at various points in the typical life
cycle. Hence, this concept helps to 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)[^31].

Because technology 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.[^32]

**3.5.1. 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 risky.*** 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. Under certain competitive structures, a
    sufficient rate of return cannot be projected.

(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.

(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.*** 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. 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.[^33]

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.

**3.5.2. 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 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.

#### 4.0. R&D Trends and Market Failures 

##### 4.1. 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 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. 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 production and commercial 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,
numerous data 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.

##### 4.2. Underinvestment in Generic Technology Research 

Universities are the primary source of the 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.

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.

Case studies have shown that long-term, high-risk corporate research is
declining.[^34] In Table 4, the Industrial Research Institute survey
data for "directed basic research" spending plans by industry during the
1990s supports the case studies.[^35] 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 Section 3. In eight of the
11 case studies, the researchers found that government was a major
source of funds

+--------+------------------------------+-----------------------------+
| Table  |                              |                             |
| 4      |                              |                             |
|        |                              |                             |
| ***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 |                              |                             |
+--------+------------------------------+-----------------------------+

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.[^36]

##### 4.3. 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.[^37]

##### 4.4. The Changing Nature of R&D and Innovation 

As previously described, the various elements of the typical industrial
technology are developed and combined according to the capabilities of
the R&D network. Depending on the amount, composition, and overall
efficiency of this process, varying amounts and types of technology will
be produced in different time frames.

**4.4.1. Measures of Technical Output.** The output of R&D investment is
technical knowledge, of which a large portion is codified in the form of
patents. Recognizing that patents vary greatly in terms of their
economic importance, a number of indicators have been developed recently
that adjust for the importance of a patent (citation frequency) and
depreciation rates (age of older patents cited), as well as the number
of patents in a particular area of technology. This methodology was
applied in a Department of Commerce/Office of Technology Policy study of
patenting in five diverse industries/areas of technology---information,
advanced materials, health (pharmaceuticals and biotechnology),
automotive, and express package transportation and logistics.[^38]

The results showed that, for patents granted during 1982-96, the United
States led in four of the five areas of technology studied---advanced
materials being the exception. A similar analysis for all patents
worldwide over this time period showed that shorter life cycles and
greater dependency on underlying science are broad trends. The
implications of this study are:

-   U.S. "technological strength," as measured by the number and quality
    of patents, is pervasive across diverse areas of technology;

-   Technology in virtually all areas is increasingly science based;

-   Technology life cycles are compressing.

Another recent analysis of patent data shows that aggregate R&D
intensity does not correlate with the quality of patents, but the early
research phase of R&D correlates strongly. In fact, research intensity
leads to higher shares of granted patents and higher citation
ratios.[^39]

**4.4.2. Technological Complexity.** Complex R&D results in complex
products and processes, and both are increasing over time. In one study,
complexity was defined as "a technological process or product \[that\]
cannot be understood in full detail by an individual expert sufficiently
to communicate all the details of the process or product across time and
distance to other experts".[^40] Using this definition, Table 5 shows
that in 1970 and 1995 the 30 most valuable product exports accounted for
48 percent and 46 percent of total global trade, respectively. However,
the *portion* of these 30 products, simple or complex, produced by
complex processes almost doubled from 43 percent to 82 percent over this
time period.

One implication of these data is that process technology is at least as
important as product technology. In the 1980s, U.S. industry was
appropriately criticized for not investing enough in process technology.
This area of underinvestment led to losses in

+------------------------------------+---------------------------------+
| ###### Table 5                     |                                 |
|                                    |                                 |
| ###### Thi                         |                                 |
| rty Most Valuable Product Exports: |                                 |
|                                    |                                 |
| **Distribution by Complexity in    |                                 |
| 1970 and 1995 (1995 \$billions)**  |                                 |
+------------------------------------+---------------------------------+
| #####                              | #####                           |
|                                    |                                 |
| ##                                 | ######                          |
| #### Simple Process/Simple Product |  Simple Process/Complex Product |
+------------------------------------+---------------------------------+
| 1970 = 58%, \$85                   | 1970 = 0%                       |
|                                    |                                 |
| 1995 = 20%, \$380                  | 1995 = 0%                       |
+------------------------------------+---------------------------------+
| 1970 = 12%, \$18                   | 1970 = 31%, \$46                |
|                                    |                                 |
| 1995 = 23%, \$435                  | 1995 = 59%, \$1128              |
+------------------------------------+---------------------------------+
| ###                                | ######                          |
| ### Complex Process/Simple Product | Complex Process/Complex Product |
+------------------------------------+---------------------------------+
| Source: Kash and Rycroft \[1998\]  |                                 |
+------------------------------------+---------------------------------+

market share in a number of critical industries, including
semiconductors, optoelectronics, and automobiles. The imbalance has been
redressed to a significant extent in the 1990s, but major weaknesses
still exist in critical areas such as producer electronic products,
including computer parts. In such areas, production technology in other
nations is sufficiently advanced that, when combined with low-cost
labor, the value added from these levels of the U.S.-based electronics
supply chain ultimately moves abroad. More alarming are newer
technologies, such as digital displays, which have been devastated by
inadequate production technology.

**4.4.3. Technological Complexity and Market Dynamics.** Technical
complexity generates several of the market failures cited earlier and
has led to inadequate private sector R&D investment. In addition, the
impact of these barriers often is increased by interactions with equally
complex product structures and their markets. These additional barriers
result in large part from the difficulties in integrating complex
technologies that come out of the R&D process with other categories of
corporate investment as well as with external market forces.

One of the most important causes of this interaction problem is the fact
that key technologies are *systems* and therefore require multiple
integrated component technologies and complex organizations to implement
and manage these systems (examples include factory automation and
communications networks).

Unfortunately, corporate R&D is shifting away from more radical
objectives, which typically require both complex and multidisciplinary
R&D. Such R&D is distinctly different from the dominant (in terms of
funding) applied R&D. This difference means that the conventional
corporate investment decision process does not work well. Such
dysfunction is critical because more radical technology research is the
type of R&D that yields the highest rates of return in the long run.

**4.5.3. The Time Factor.** The above market failures arising from
technological complexity and its effects on corporate decision making
are made worse by shifts in corporate strategy and industry structure in
response to growing global competition. As more nations adopt
technology-based strategies which entail both external technology
acquisition and internal technology development, more competitors for
U.S. industries are appearing. This increased competition is good for
consumers because it provides a wider range of new products and
services. Also, with more companies worldwide investing in technological
innovation, the products and services appear sooner.

These competitive pressures are requiring reductions in *product* cycle
time---defined as the time from receipt of customer order to delivery.
In a survey by NIST's National Quality Award Program, 79 percent of
responding CEOs cited cost and cycle time reduction as a "major trend"
affecting their company.[^41] U.S. companies have managed to reduce
product cycle times substantially. A National Planning Association study
of 189 companies found that in 1990 new products or major product
improvements took 35.5 months to complete (from concept to production),
but in 1995 this time had shrunk to 23 months.[^42] In another study,
sponsored by the Manufacturing Institute, approximately three fourths of
U.S. manufacturing companies reported shorter total cycle times.[^43]

Technology has been a major investment vehicle for achieving both cycle
time and cost reductions. In the Manufacturing Institute study, these
two objectives were cited by three fourths of responding manufacturing
firms as the major benefits of investment in technology. However, total
cycle time compression also has forced a shortening of R&D cycle time.
An annual survey by Carey Curtis in conjunction with the Industrial
Research Institute has found that the average R&D cycle time has shrunk
from 18 months to 10 months in a five-year period (1993-1998).

This reduction in cycle time has been achieved not only through
increased efficiency. Much of the reduction is due to increased emphasis
on incremental research objectives in order to meet shorter
time-to-market requirements. Survey data indicate that, in some
industries, shorter product R&D cycles have led to quality and
reliability problems and lower financial performance.[^44]

![](media/image1.wmf)In contrast, 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 the Curtis
data and a study by Kim and Mauborgne (see Figure 7) clearly indicate,
longer term R&D projects are more profitable.

##### 4.5. Innovation vs. Technology Use

Even within an R&D-intensive industry, growth in value added typically
is driven by a relatively few innovative firms. Technological innovation
is concentrated even though new technologies give rise to entirely new
industries with substantial employment. The software industry, for
example, barely existed 15 years ago but today employs over 500,000
workers. Studies of employment growth by Cognetics, Inc. have shown that
even though most net new jobs (84% in the 1992-96 period) come from
small firms (\<100 employees), growth in employment is highly skewed
among these companies.[^45] In other words, most small businesses do not
create large numbers of jobs. Rather, a small number of fast growing
small companies (**"**gazelles**"**) account for the majority of net new
jobs.

Gazelles are defined as companies whose sales have grown at a compound
annual rate of 20 percent or more for four years in a row (which means
they have doubled in size). These gazelles are responsible for creating
about 70 percent of the net new jobs in the U.S. economy between 1992
and 1996, yet they account for about 3 percent of the 9 million
enterprises in the Cognetics database (corporations, partnerships, and
proprietorships). Gazelles are distributed evenly across all sectors of
the economy.

Another study by the Department of Commerce of employment trends in
individual manufacturing plants found that only a minority exhibited
strong and persistent employment growth over extended periods of
time.[^46] These "heroic plants" yielded net employment gains four times
the rate of manufacturing plants in general. Such plants were above
average users of advanced technology and best practice, and appear to
have benefited from investing in new technology early in that
technology's life cycle.

![](media/image2.wmf)Together, these studies indicate that only a
relatively few companies or even individual establishments are going to
be successful innovators and users of technology and best practice, and
therefore a diversified economic structure is necessary to develop
enough of these "engines of growth" to drive national economic
prosperity.[^47]

Who these companies are will change drastically over time. The 1990s
have seen steady job expansion in the U.S. economy. However, these net
gains mask considerable churning in various sectors; many jobs have been
created and also destroyed. Figure 8 shows this pattern quite clearly.
For one yearly change, 1994-1995, almost 700,000 business establishments
were added to the economy while slightly less than 600,000 ceased
operating. New firms plus expanding ones created 16.2 million jobs, but
firms that went out of business and those contracting caused 12.7
million jobs to disappear. All together, 30 percent all jobs in the U.S.
economy were in situations of flux---i.e., they were in appearing,
disappearing, expanding, or contracting firms.

The limited number of highly successful companies and their distribution
across the economy, combined with evidence that technology is a major
factor in their economic success, argue for a much broader technology
basis for the U.S. economy in order to maximize the number of such
companies over time.

#### 5.0. 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.

![](media/image1.wmf)The first of these is a well-defined policy issue
and fortunately is now getting focused attention by policy makers.
However, as described in this report, the second and third elements are
complex and not particularly well understood. Thus, effective policy
development remains constrained.

##### 5.1. The Relative Size and Impacts of Government-Funded Research 

Figure 9 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
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.[^48]

##### 5.2. Patterns of Risk, Market Failure, and the Government Response

**5.2.1. Matching Market Failure and Policy Response.** Section 3
identified four major categories of market failure. 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 conducted.

(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 large potential 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.[^49] 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 for a particular technology that do not require government
    support.[^50] Instead, the efficient policy response would combine
    direct funding of generic technology research with appropriate joint
    strategic planning among government, industry, and
    universities.[^51]

> 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.[^52] These
    labs also can provide neutral third party facilitation of the
    standards process.

    2.  **Responding to technical and Market Risk Barriers.** As
        described earlier in this report, 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
        imperfections and resulting underinvestment in R&D and/or slower
        market penetration by the new technology also must vary. Figure
        10 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 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, as discussed in Section 3, 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.[^53] 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 10.
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.[^54] 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 ![](media/image1.wmf)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 10, 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 10 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.[^55] 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".[^56] Government programs such as ATP attempt to help fill
this gap.

##### 5.3. Tax incentives vs. Direct Funding 

Risk aversion by the private sector with respect to aggregate investment
in proprietary R&D occurs frequently and can persist for some time. Such
broad-based underinvestment in R&D can be treated by tax incentives
aimed at leveraging existing private-sector R&D investment strategies.
The R&E tax credit has been the primary policy response for the past 17
years, and is now estimated to cost about \$2 billion annually.[^57]

The credit was designed to stimulate a targeted portion of overall
R&D---the early ("experimental") phases that entail the higher levels of
risk.[^58] However, in practice, a large portion of R&D conducted by
industry ends up eligible. Whether this result is good or bad is a
different issue from the feasibility of using tax incentives to foster
specific types of R&D. What it does show is the difficulty in targeting
a tax incentive to achieve specific R&D policy goals.

In fact, specific tax incentives cannot be efficiently tailored to each
technology's unique set of development and utilization barriers. Nor can
they be easily altered over time to meet the funding requirements of
individual technological life cycles. Thus, this policy instrument will
generally not be effective as a response to market failures associated
with the need to prove generic concepts underlying emerging technologies
or the need to develop a range of infratechnologies to form the bases
for industry standards. Tax incentives provide a general R&D investment
incentive and thereby tend to increase the same kind of R&D that
industry already is doing.[^59]

For underinvestment in generic technology and infratechnology research,
direct government funding and performance of R&D in partnership with
industry and universities allows funds to be allocated to different
technologies at appropriate points in these technologies' life
cycles---and to these technologies only at those points. Tax incentives
cannot be started or stopped based on the evolutionary pattern of a
particular emerging technology. Attempts to focus a tax credit or
deduction on emerging technology research will leak, as does the current
R&E tax credit, into conventional applied R&D that needs no incentive.

One frequently proposed remedy is to target a tax credit at an
institutional mechanism that is used for the desired type of research.
Because consortia or joint ventures are commonly employed to conduct
early-phase research, focusing incentives on that general mechanism
arguably is one way to reduce leakage of the incentive to other types of
R&D. However, the more targeted the tax incentive the more difficult it
is to implement effectively. The Internal Revenue Service spent many
person months in the 1980s trying to write tax rules implementing the
original credit, which was reasonably broad-based in scope. Defining
what is actually the coverage of a tax incentive and then enforcing it
always will be a problem.[^60]

Direct government funding can more efficiently leverage private-sector
investment in research focused on the early phases of a technology's
development. A more optimal amount and type of research can be supported
and, once this research reduces technical uncertainty to a level
sufficient to allow industry to begin to apply conventional R&D
investment criteria, the support can be terminated.

![](media/image2.wmf)The perceived weakness of direct funding is that
government must select candidate technologiesthe so-called picking
winners and losers problem. However, not only does the complexity of
market failures found in technology-based industries argue for the use
of this mechanism, but institutional mechanisms are readily available to
objectively and judiciously select research areas and determine the
timing of financial support. The main mechanism for achieving this
result is cooperative strategic planning between industry and
government, combined with ongoing review of a project's progress and
post-project impact assessment. All industrialized nations have direct
funding programs and most emphasize cooperative research mechanisms.

##### 5.4. The Federal Budget 

As Figure 11 indicates, federal funds have accounted for a progressively
smaller fraction of the Nation's overall R&D spending. Part of the
decline in the government's share is due to decreasing defense spending
on R&D. Another major factor is the overall decline in government
spending resulting from more conservative fiscal policies. A third and
most troubling factor is the lack of a consensus as to the legitimate
role of government funding for different types of research.

**5.4.1. The Federal Non-defense R&D Budget**. At the national level,
the portion of the federal R&D budget directed toward economic goals is
the most relevant for R&D policy analysis. As Figure 12 indicates,
federal non-defense R&D spending in constant dollars has increased
steadily from a low point in FY1983 of \$18.6 billion to a projected
\$28.8 billion in FY1999. However, even with this growth, federal
non-defense R&D has not reached the peak year's constant-dollar level of
\$34.4 billion in FY1966.[^61]

![](media/image3.wmf)Furthermore, the non-defense budget is highly
skewed toward health care in both amount and growth rates. In
particular, a significant imbalance has evolved between health care and
other areas of science lumped together as "General Science," relative to
their respective contributions to the economy. From 1990 to 1998, health
R&D grew by 26 percent (in constant 1992 dollars), while all other
non-defense areas grew by just 3 percent.[^62] In FY1998, health care
research received 40 percent of the Federal non-defense R&D budget and
the health care sector (pharmaceuticals plus health services) accounted
for 6.3 percent of GDP. In contrast, general science research (which
covers most other high-tech industries) received only 9 percent of the
non-defense R&D budget while accounting for about the same share of GDP.

Federal R&D policy has not responded to contemporary corporate R&D
trends and practices. In particular, R&D policy has not adapted to the
decline in corporate generic technology research. The economics of
generic technology research implies an increased role for government
funding and the need for federal laboratories to focus more resources in
this area. In this regard, support for government funding of generic
technology research has been tepid and has lagged behind competitor
nations. Comparing NIST's Advanced Technology Program (ATP) with Europe
and Japan shows that competitors of the United States have much larger
national programs (relative to the size of their economies) for funding
this early phase research in support of economic growth.[^63]

The previous example of optoelectronics is a good illustration of
declining government support for critical technologies. The United
States is still competitive in the applications end of this supply
chain. However, not only has much of the front end of this supply chain
been lost to foreign competition, but government's share of overall R&D
funding has declined from 23 percent in 1991 to 10 percent in 1996.[^64]
Without the type of microeconomic analysis described earlier in this
report, one cannot identify what the right amount should be, but
ignoring the warnings signs will not likely result in more value added
by U.S. firms.

#### 6.0. Summary

Two broad technological and economic trends dominate all others and
therefore have significant policy implications:

(1) The factors that have leveraged corporate profits in the 1990s have
    diminished in terms of their potential future impact, and therefore
    future growth depends on substantial changes in economic growth
    strategies; and

(2) Technological opportunity and its dominant effect on global economic
    growth will continue to expand.

In the context of these trends, the two important units of
technology-based economic policy analysis are (1) the industry supply
chain and (2) the technology life cycle. An economy is made up of a
large number of supply chains, but only a relatively few can be labeled
high-tech. High-tech industries account for about 7 percent of GDP.
Although a number of other industries have moderate R&D intensities, the
rest of the economy for the most part relies on external sources of
technology, in particular, high-tech industries---both domestic and
foreign. Moreover, domestic R&D is not only concentrated on an industry
basis but geographically as well.

R&D displays a life cycle pattern and investing in it therefore presents
different sets of investment barriers at different points in time. The
life cycle concept is important, even though many feedback loops within
technology trajectories occur and much technology development depends on
cross-linking among several areas of science and technology.

The barriers to adequate investment across supply chains and over
technology life cycles can be collected into two major problem areas.
First, industries adopt technologies with varying degrees of
completeness and efficiency. They are therefore susceptible to market
share loss by increasingly technology-intensive foreign competition. In
such situations, the contribution to the economy of entire supply chains
can be greatly reduced or even eliminated over time. Second, the
high-tech sector is too small to drive acceptable rates of economic
growth by itself, and major elements of this sector are demonstrating a
shortening of R&D investment time horizons. Even more worrisome for the
long run is a pervasive problem for R&D firms in transitioning
investment strategies to new technologies. Together these market
failures are resulting in loss of competitive position at specific
levels (industries) in technology-based supply chains. Only the more
narrowly defined version of high-tech trade still shows a trade surplus.
Two decades of deficits in the much broader merchandise trade balance
provide ample evidence of the aggregate decline in overall
competitiveness.

Long-term economic growth strategy must be based on a much more
pervasive technology-intensive sector. Achieving this objective will not
be easy because of continually declining corporate investment in generic
technology and inadequate investment in appropriate technology
infrastructures. Remedying these problems will require a much more
enlightened R&D policy framework.

Specifically, effective R&D policy development requires a more detailed
specification of the range of market failures that occur in
technology-based industries. Appropriability problems, for example,
occur to some degree through a variety of mechanisms in the life cycles
of all technologies. The manifestation of appropriability problems is
the leaking or spilling over of technical knowledge to parties who do
not pay the creators of this knowledge its full value. Such spillovers
lower the expected rates of return from investment in technology and
thus act as a disincentive to private-sector R&D spending.

What complicates R&D policy analysis is the fact that spillovers occur
in several ways and at several phases of the R&D life cycle. Moreover,
other types of barriers to acceptable private rates of return, such as
market structure and transaction cost barriers, frequently occur. Thus,
a broader market failure taxonomy is needed, one that includes the
following categories:

-   **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. Based on the range
of data gathered for this report, 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.

The major technology trends identified in this report 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 usually afforded by the timely development of generic
technology is essential.

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 bottom line is that R&D policy and long-term economic growth policy
must become much more of a co-evolutionary system in order to achieve
the synergies needed for a competitive, technology-based economy.

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[^1]: 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.

[^2]: 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.

[^3]: Recent assessments of the literature on this topic can be found in
    Jarboe \[1998\] and Jasinowski \[1998\].

[^4]: ALP relates output of goods and services to a measure of labor
    input, while TFP relates output to a weighted combination of capital
    and labor inputs.

[^5]: See Roach \[1996, 1998a, 1998b\].

[^6]: TASC, Inc. \[1998, pp. 52--53\] and Link and Scott \[1998a, pp.
    188--189\].

[^7]: TASC, Inc. \[1998, p. ES--10\].

[^8]: It is very important to note that this point does not in any way
    imply that IT is a bad investment. Productivity issues do not deal
    with the fact that new technologies create new products and services
    and failure to invest in them will likely mean eventual expulsion
    from relevant markets. The productivity "problems" are simply an
    example of the transition costs between major technology life
    cycles.

[^9]: See Freeman \[1979\].

[^10]: See Cohen and Levinthal \[1989\].

[^11]: McElroy \[1998\].

[^12]: Bureau of Export Administration \[1998, p. II-4\].

[^13]: Asian Technology Information Program \[1998\].

[^14]: Lenzner and Gordon \[1999\] provide an example: \"Companies have
    spent billions in recent years on \"enterprise software\" from SAP,
    Oracle, PeopleSoft and the like. And for every dollar clients have
    put up, Andersen bills another \$4 to \$10 for customizing it,
    installing it, lashing it to other systems and training people to
    use it. If software were simple, Andersen would be out of work.\"

[^15]: Baumol \[1986\].

[^16]: Doremus et al \[1998\] point to enormous differences among
    multinational companies, which they trace to the unique political
    and economic characteristics of their home countries. See also Kogut
    \[1998\].

[^17]: Tassey \[1992, Chap. 12; 1997, pp. 225-226\].

[^18]: 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 \[1997, Chap. 4\].

[^19]: See Cohen and Levinthal \[1989\].

[^20]: Elias \[1995, p. 12\].

[^21]: See, for example, Klein and Rosenberg \[1986\].

[^22]: National Science Board \[1996, p. 4-10\].

[^23]: Richter \[1998\].

[^24]: 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.

[^25]: Economists would distinguish among these elements by applying 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.
    Critical for R&D policy is the fact that technology infrastructure
    and other generic technical knowledge fall 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.

[^26]: See for example, Nelson \[1987, 1992\].

[^27]: Tassey \[1997, Chap. 8\].

[^28]: For summaries of microeconomic studies of infratechnologies and
    associated methodologies, see Link and Scott \[1998\] and Tassey
    \[1997, 1999\].

[^29]: Finan \[1998\]. The estimate does not include the labor and
    overhead required to implement this measurement infrastructure.

[^30]: Research Triangle Institute \[1999\].

[^31]: These four categories of market failure and the appropriate
    policy responses are discussed in detail in Tassey \[1997, Chaps.
    6--10\].

[^32]: Tassey \[1997, Chap. 5\].

[^33]: This phenomenon is the general economic basis for the Justice
    Department's antitrust suit against Microsoft.

[^34]: See, for example, Corcoran \[1994\], Duga \[1994\], and Geppert
    \[1994\].

[^35]: This is comparable to generic technology research.

[^36]: Rice, Peters, and Morone \[1998, pp. 57--58\]. Also, see Eidt and
    Cohen \[1997\].

[^37]: Tassey \[1997, Chap. 8\].

[^38]: Albert *et a*l \[1998, pp. 29-41\].

[^39]: Ernst \[1998, p. 8\]. "Research" is defined in this study as
    basic plus applied research (following the OECD definition).

[^40]: Kash and Rycroft \[1998\].

[^41]: This report and other impact assessments of the National Quality
    Program are available at http://www.quality.nist.gov/crit2.htm.

[^42]: Cappelli \[1997\].

[^43]: Swanidass \[1998, p. ix\].

[^44]: Ellis and Curtis \[1995\].

[^45]: Birch, Haggerty, and Parsons \[1997\].

[^46]: Musick \[1998\].

[^47]: Unfortunately, these studies do not identify the source of the
    technology associated with "gazelle" or "heroic plant" performance.
    That is, to what extent do these plants obtain technology from a
    parent firm as opposed to external sources in their supply chain?

[^48]: National Science Board \[1998, Appendix Tables A 4-7 and A
    4-11\].

[^49]: 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.

[^50]: Tassey \[1997, pp. 107-111\].

[^51]: Tassey \[1997, Chap. 7\].

[^52]: Tassey \[1997, Chap. 8\].

[^53]: For an overview of these new R&D decision techniques, see
    Luehrman \[1998\].

[^54]: Vonortas \[1997\] provides a comprehensive overview of
    cooperative R&D.

[^55]: Mowery \[1998, p. 39\].

[^56]: *Unlocking Our Future: Toward a New National Science Policy*. A
    report to\
    Congress by the House Committee on Science, September 24, 1998.

[^57]: By comparison with this tax expenditure, the SBIR Program
    allocates over \$1 billion in R&D funds. The Advanced Technology
    Program has a much smaller budget of approximately \$200 million.
    However, ATP receives considerably more scrutiny in terms of its
    appropriate roles and effectiveness than do the other two.

[^58]: For a good analysis of the R&E tax credit's structure and
    alternative structures, see Whang \[1998--1999\].

[^59]: In fact, the 1996 Industrial Research Institute annual survey
    found that 55 percent of responding companies indicated that the
    credit was "not at all" influential in establishing the *level* of
    their companies' R&D investment.

[^60]: Tassey \[1995; 1996; 1997, Chap. 6\]. The current credit does not
    exclude collaborative R&D. However, the magnitude of the difference
    between existing provisions and current proposals is not
    sufficiently large to provide significant new incentives.

[^61]: The Federal Budget for fiscal year 1999, Historical Table 9.8. By
    a different taxonomy, the National Academy of Sciences (NAS) arrives
    at a "Federal Science and Technology (FS&T) budget of \$33.0 billion
    for 1997.

[^62]: National Science Board \[1998, p. 4--45\].

[^63]: Tassey \[1997, pp. 119−129\].

[^64]: Bureau of Export Administration \[1998, p. IV-2\].