Analysis of International Technology Development and 

Commercialization Policy Instruments and Infrastructures

Gregory Tassey

Economic Policy Research Center

University of Washington

July 2018

The United States emerged from World War II as the dominant technology-driven economy in 

the world. The development of new technologies benefited from substantial investment in basic 

science.1 For decades, this science base was drawn upon to the extent that virtually every major 

technology with global reach was developed and initially commercialized within the U.S.

economy. This was accomplished by an unprecedented combination of government and industry 

investment in research and development (R&D) coupled with subsequent investment in 

technology-implementing hardware and software, skilled labor, and a world leading technologybased infrastructure, including top-rated universities and government laboratories.

As a consequence, for three decades after WWII the United States was the dominant leader in 

 

1 The U.S. still has 79 of the world’s top 100 universities in 2018, as ranked by uniRank (http://www.4icu.org/topuniversities-world/).
2

science and technology development and subsequent innovation. Although most of this 

technology was initially developed for specific missions such as national defense and later for

space exploration, energy independence, and national health, the resulting “generic 

technologies” or “technology platforms” eventually were applied to commercial markets. 

Without significant competition from a global economy still recovering from WWII, the 

inefficiency of this indirect and drawn out process of depending on spinoffs from missionoriented R&D was not burdensome. U.S. jobs, personal incomes, and overall wealth rose steadily, 

as a range of U.S. developed technologies spurred significant productivity growth. 

But, while mission technologies had substantial R&D budgets and large government 

bureaucracies to manage their development, the Federal Government had and continues to have 

very little institutionalized technology-based economic development (TBED) policy capability.

The lack of a substantive and comprehensive national innovation policy derives from the U.S.

tradition of a laissez-faire approach to developing commercial applications. It was asserted that

market opportunity was sufficient to entice risk taking and thus lead to the commercialization of 

innovative products and services. 

This rather simplistic market sufficiency philosophy was not a problem in the absence of 

significant foreign competition nor was the fact that government R&D and associated 

infrastructure support were strongly oriented toward social objectives. The U.S. economy has 

had a strong tradition of entrepreneurial activity, which encouraged risk taking. Coupled with 

plentiful venture capital, the U.S. economy experienced substantial innovation and subsequent 

technology-based growth. The rate of growth was further supported by a U.S. educational system 

that produced a consistently strong pool of skilled workers, including large numbers of immigrants 

from around the world who contributed significantly to the U.S. advantage in both skilled workers 

and entrepreneurs.

1. The TBED Policy Deficit 

The cost in terms of restrained economic growth of an inadequate TBED policy is due to the 

fact that today’s global technology platforms are increasingly developed elsewhere in the world, 

creating increasingly severe pressure on domestic industries and supporting infrastructures. 

Indicative of restrained U.S. growth is the fact that domestic fixed private investment (FPI) in 

physical assets such as machinery, land, buildings, installations, vehicles, and technology is too 

low, and survey after survey of industry managers show that our supply of skilled labor is 

inadequate. U.S. Government research institutions and R&D budgets are still oriented largely 

toward a set of social objectives such as defense and public health that only indirectly leverage 

economic growth. At the same time, other economies have focused largely on optimizing their 

R&D investment to maximize the growth of their domestic economies. 

The negative economic impact has been pronounced. For the first 30 years after World War II 

(1948-1978), when the United States was the dominant technology-driven and thus the highest 
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productivity economy, the average annual real growth rate of Gross Domestic Product (GDP) was 

3.9%. During the next 30 years, the growth rate dropped to 3.0%, as the effects of globalization 

began to be felt. Since the 2008 recession, real economic growth has averaged 2.1%, and the 

Federal Reserve forecasts the growth rate to remain at around 2% for the foreseeable future. 

Thus, the U.S. economy is expanding at half its post-WWII pace. 

One component of GDP that deserves special attention is household income, as it is the single 

largest source of demand. In 2016, U.S. real median household income ($59,039) finally exceeded 

the level reached nine years earlier in 2007, just before the Great Recession. Many analysts have 

characterized this event as encouraging, but the reality is that in addition to taking too long to 

occur as a cyclical rebound, this important income measure has barely nudged above the 1999 

peak of $58,665. In other words, in the past 17 years, real household income has been flat. 

By far the most important policy issue for long-term domestic economic growth is the fact that 

China, South Korea, and other Asian nations have raised their productivities steadily for several 

decades. This makes their exports increasingly competitive on their own merits. Thus, even if all 

trade barriers are removed, an economy still has to develop competitive products and services 

to grow.

As described in detail in this paper, a major global shift is occurring away from the U.S.

economic growth model of laissez-faire economic growth policy toward a “public-private 

investment” model that more efficiently supports technology-based economic development

(TBED). The key attribute of this growth model is cooperative and coordinated investment by the 

public and private sectors. 

Fig. 1 Trade Balances in “Advanced Technology Products” and All Manufacturing: 1988-2016

-1,000

-900

-800

-700

-600

-500

-400

-300

-200

-100

0

100

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Drain on GDP over 28-year period: 

$10.6 trillion (in 2009 dollars)

All Manufacturing

Advanced Technology Products

$billions

Source: Census Bureau
4

Further, this model is being implemented to an increasing extent on a regional basis to 

promote competitive differentiation and to capture co-location synergies. However, the 

aggregate impact of the collective advancement of competitor TBED policies in Europe and Asia 

is seen in the long-term decline in U.S. trade competitiveness, as demonstrated in Fig.1.

As described in Tassey (2018b), TBED policies are a complex set of public and private 

investments. This stands in contrast to traditional U.S. technology-based economic growth policy

in which the only frequently mentioned metric driving “innovation policy” was R&D intensity. 

R&D intensity is certainly correlated with per capita GDP, but this summary statistic ignores a 

wide range of financial and institutional policies that determine the efficiency of R&D spending 

and thereby the rate of productivity growth and ultimately GDP growth.

Even so, it remains a an important summary indicator of an economies investment in 

technology as the means of competing in the global economy. As Table 1 shows, the U.S.

economy has not kept up with its major competitors. Thus, R&D efficiency issues aside, the 

American economy is not responding to globalization.

Table 1 Comparative Growth Rates in National R&D Intensity

% Change 

1991-2015

United States France Germany U.K. China Japan South Korea

5.0% -4.3% 18.6% 16.3% 183.6% 13.8% 135.0%

 Source: OECD, Main Science and Technology Indicators/Country R&D Intensities, 2015

Moreover, even with a high R&D intensity, many steps and a complex set of investments and 

institutional resources are required to compete in the increasingly technology driven global 

economy where competing economies are investing aggressively in the range of public and 

private assets that create a modern, competitive innovation ecosystem.

Having a leading university system that produces advances in basic science is a prerequisite for 

technology-based competitiveness. However, the complexity of successfully developing and 

commercializing new technologies requires a complex set of institutional and private sector 

investments. Each stage of the evolution of a technology’s life cycle is dependent on unique sets 

of public and private assets. 

For example, Parilla et al (2015) argue that “U.S. manufacturers are often unable to bridge the 

‘valley of death’ between the basic research phase and industrial production due to lack of capital 

and other key resources.” The valley-of-death label results from the fact that the increasing 

complexity of modern science does not allow a direct transition to the applied R&D, which 

produces the technologies that drive innovation. 

The result of this critical “transition phase” of R&D between science and the development of 
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proprietary technologies are the quasi-public “technology platforms” that prove technological 

concepts and hence reduce technical and market risk sufficiently to allow positive corporate 

decision making with respect to undertaking the substantial applied R&D that produces the 

market ready “proprietary technologies”. The valley of death will widen without effective support

for pre-competitive technology research that produces the technology platforms enabling the 

subsequent and much larger proprietary research performed in corporate laboratories.

2. The Technology Element Model as a Basis for TBED Policies 

Recognizing the existence of this evolutionary process, industrialized nations have increased 

investment in domestic ecosystems based on the a “technology element model (TEM)” depicted 

in Fig. 2. This model provides the necessary framework for TBED policies aimed at specific phases 

of the evolutionary development and commercialization of new technologies. Therefore, a brief

description is presented here to provide the framework for discussion of specific TBED policies.

The TEM indicates the development and commercialization sequence (arrows) of the typical 

modern industrial technology and also characterizes the various elements in terms of their

public-good content (colors) and hence the appropriate role for government in support of each 

element. 

Value

Added Production Strategic

Planning

Market

Development

Entrepreneurial

Activity

Risk

Reduction

Proprietary

Technologies

Technology 

Platforms

Science Base

Joint Industry-State 

Government Planning

Market Targeting 

Assistance and 

Procurement Incentives

Product Test Facilities 

& Scale-Up Assistance, 

Interface Standards

Interface 

standards

Tech Transfer/Diffusion from 

Universities (Bayh-Dole Act)

Incubators

Joint Funding by Industry & 

State Government (MII’s) 

R&D Tax Incentives

Entrepreneurial Training

Fig. 2 TBED Policy Tools Across the Technology Life Cycle 

Scale-Up/Capital 

Formation 

Incentives

System 

Integration

Industry Funding

Gov’t Funding

Mixed Funding

Based on Tassey (2007, 2010, 2014)

Innovation Districts

Access to capital 

Accelerators

Measurement & Test Methods 

Applied R&D/Fraunhofer
6

Governments around the world are expanding investment across this range of technology 

elements, as described in the following sections. The policy objective is to capture maximum 

value added for their economies by developing a domestic science base and integrating the 

university sourcing of this science with subsequent industry technology platform development. 

Once a new platform is developed, a second set of TBED policies are implemented to support the 

applied R&D that leads directly to commercialization and market penetration, and, in so doing,

achieve maximum valued added for their domestic economies. 

The cross-cutting policy principle in the face of rapidly expanding and increasingly sophisticated 

TBED policy structures across the global economy is a focus on increasing R&D efficiency (and, 

more broadly, innovation efficiency) by integrating public and private research, technology 

development, and commercialization assets in an integrated innovation ecosystem. 

That is, the policy imperative is to implement comprehensive “public-private asset” economic 

growth policies, which means focusing on investment in the range of public and private economic 

assets that produce sustained high rates of productivity growth. 

Such investment is how the U.S. rose to become the world’s leading economy. Unfortunately, 

Americans are poor students of their economic history. The last trade surplus for the U.S. in goods 

was in 1975, so it is hard to blame the Chinese or the South Koreans in the last two decades or 

the Japanese and Germans in the past four decades, as well as other nations who have increased 

their productivity and hence competitiveness. Our denial of the need to invest in sufficient 

productivity growth to successfully compete in global markets over time has resulted in 

inadequate export growth in higher value-added products and increased susceptibility to import 

competition with the result of virtually stagnant real incomes. 

“For the country to seize on manufacturing’s recent momentum, however, it 

must rebuild what Harvard Business School researchers Gary Pisano and Willy Shih 

(2012) call the ‘industrial commons’—the concentrations of research institutions, 

skilled workers, and suppliers that form the backbone of America’s most 

competitive industrial hubs. Manufacturers thrive when they draw on the 

collective knowledge and spillovers from clusters of similar firms and deep pools 

of labor, which in turn are anchored by supportive institutions such as universities, 

research institutes, community colleges, and industry consortiums.5 These 

networks, which concentrate in regional economies, are together responsible for 

the key driver of industrial competitiveness: a highly trained workforce that can 

use technology to translate basic and applied research and development (R&D) to 

large-scale commercial innovations” (Parilla et al, 2015).

In responding to these policy imperatives, it is also essential to incorporate fundamental trends 

in the nature of technology. An OECD (2015a) report succinctly states the general trend driving 

current innovation policy:

“Today in many OECD countries, firms invest as much in the knowledge-based 

capital (KBC) that drives innovation, such as software, databases, research and 
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development, firm-specific skills and organizational capital, as they do in physical 

assets, such as machinery, equipment or buildings.”

Policy analysts may respond that of course that is the case. However, when TBED policies 

around the world are examined, one finds that implementation strategies vary significantly in 

terms of both size and scope. The following sections describe the different TBED policy structures 

economies have been chosen to achieve successful innovation and subsequent global market 

penetration. The technology element model will be used to indicate specific policy targets and 

the consequent stimulation of knowledge production and subsequent knowledge flows 

downstream economic activity.

3. Applying the Technology Element Model to Global TBED Policies 

The complex and dynamic character of modern technologies require each TBED policy

instrument to be implemented at the appropriate stage of the technology’s life cycle and done 

so in an integrative mode with adjacent phases of the target technology’s life cycle. The specific

policy content and timing are determined by the nature of the target technology and hence its

evolutionary development path. That is, the technology elements and their associated 

investment requirements, as depicted in Fig. 2, will evolve along a specific expansion path to 

produce economic benefits. 

All modern technologies originate from advances in science, produced largely by universities. 

In the past, such knowledge slowly and inefficiently diffused into industry where technological 

applications were developed. Realizing the barrier to technology development and 

commercialization that this inefficient process imparts, countries are providing stronger 

incentives to universities to promote and even participate in technology development in 

cooperation with domestic firms. As discussed in the next section, most economies have 

responded by implementing a range of university IP management policies and, equally important, 

support for actual technology development. 

However, results have been mixed, which has stimulated governments around the world to 

address both the complexity and the evolutionary nature of TBED. The development and 

commercialization of new technologies requires cooperation/partnering among a range of 

suppliers of specific technology elements. Some countries, such as China, are investing in 

mammoth “innovation hubs” in an attempt to capture economies of scale and especially 

economies of scope. Others, especially European economies, are promoting a relatively larger 

number of smaller clusters. The U.S., with its traditional laissez-faire approach to innovation 

policy, has a diverse range of clusters, which have resulted from independent state initiatives. 

As long as this “experimental” approach by the U.S. is monitored and analyzed, and lessons 

learned applied across all regional economies, it could be the best overall strategy. However, as 

the discussion in this paper shows, not enough is known about scale and scope effects, as well as 

timing of application. Some older and now large innovation clusters (alternatively, “hubs,”

“districts,” or “orchards,” as such agglomerations of technology assets are now called) have 
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demonstrated better economic performance than others. So, policymakers need to know what 

factors determine success.

TBED investment policy assessments are further complicated by the fact that in addition to the 

categories of economic assets underpinning regional TBED “clusters” that have significant 

private-good content, the complex technical infrastructure that supports high-tech supply chains 

and has considerable public-good content must be planned for and supplied beginning early in 

the implementation of a region’s growth strategy. Such infrastructure not only provides technical 

support to individual private companies but also provides integration infrastructure assistance to 

enable migration of technical knowledge among these companies.

2

More generally, the complexity of today’s technologies and their changing nature as 

development proceeds, especially their public-good content, requires a number of different 

policy instruments—each one tailored to the nature of the investment activity at a particular 

phase in the technology’s development.

The correct policy structure is determined by the nature of industrial technologies and hence 

their evolutionary development path. In this regard, Fig. 2 depicts the elements of an industrial 

technology and the order in which they typically evolve to produce commercial success. For each 

element, the public-good content is indicated by the blue shading, while red shading represents 

private-good content.

The subsequent process of transforming science into technology and eventually innovation is 

characterized in Fig. 2 by the arrows, which show the flow of progressively more applied technical 

knowledge (upward in the diagram) until commercialization occurs. 

Thus, science is first used to create “technology platforms,” which provide the proof of concept 

necessary to allow industry to rationalize the substantial investment in applied R&D that 

eventually creates innovative products and services. This proof-of-concept research, by virtue of 

occurring early in the R&D cycle and having a range of potential commercial applications well 

beyond the set of target markets of even large R&D-intensive companies demands a publicprivate cooperative approach to conducting such research. 

The end point of crossing of the “valley of death” is a “technology concept model” or 

“technology platform,” as it is labeled in Fig. 2 (Tassey, 2007, 2014). This “proof of concept,” as 

the R&D management literature calls it, is extremely important in facilitating the much larger 

later-phase R&D investment (largely undertaken with private funding). Because of the collective 

(industry-wide) use of technology platforms, direct participation of industry in the research 

 

2 The term “technical infrastructure” encompasses a wide range of materials properties data, measurement and 

testing techniques, process and quality control methods, and market transaction (product acceptance testing) 

protocols. Many of these “infratechnologies” are provided by NIST and end up as industry standards. The 

semiconductor industry, for example, has over 1,000 standards, without which it could not function. See Tassey 

(2014, 2017b).
9

process facilitates early and more efficient in applied R&D that eventually produces innovations. 

Even after applied R&D is initiated within individual companies, the complexity of today’s R&D 

requires access to external (to the firm) resources, such as the research consortium that 

developed the technology platform, including the university participant. Such interactions are 

more efficient when the parties involved are co-located (i.e., within an innovation cluster) due to 

the tacit nature of early-phase technical knowledge.

A critical policy consequence of such systematic government funding of early-phase technology 

development is that the multi-phase evolution of a modern technology’s development explains 

why the innovation cluster concept has become so important. It brings public and private 

resources together to allow “risk pooling” and the assimilation of complementary public and 

private research assets that can only be provided by a combination of private companies and

public research assets typically found in universities and government laboratories. Silicon Valley 

has prospered because it evolved somewhat serendipitously from having both categories of 

assets in the same geographic region.

Another important factor in TBED cluster formation is the fact that the early phases of a 

technology’s development produce complex knowledge that has a tacit character, as opposed to 

the more codified character of applied technology produced in the later phases of the R&D cycle. 

Such knowledge passes more readily through person-to-person contact (Tassey, 2007). 

Thus, the roles of industry and government and the needed policy support change over a 

technology’s life cycle. This fact means the TBED planners must not only construct effective policy 

mechanisms but also impose a dynamic management overlay to adjust the use each policy 

mechanism over a technology’s life cycle.

4. Protection and Allocation of University Intellectual Property 

The Bayh-Doyle Act of 1980 was a major policy change for the relationship between U.S.

government-funded research at universities and subsequent use of this research by domestic 

firms in innovation efforts. The Act requires universities to file for patents when appropriate from 

such research, as a first step in created IP that could be efficiently transferred to industry. The

result has been a significant impact on the rate and direction of innovative activity. The efficiency 

potential of such a knowledge diffusion mechanism has not been lost on other governments.

However, as described below, different approaches have been taken with different degrees of 

impact.

University Research and Intellectual Property 

All modern technologies originate from advances in science, produced largely by universities. 

In the past, such knowledge slowly and inefficiently diffused into industry where technological 

applications were developed. Realizing the barrier to technology development and 

commercialization that this inefficient process imparted, Congress passed the Bayh-Dole Act in 

1980 to provide stronger incentives to universities to promote follow-on technology 
10

development investment. As discussed in the next section, this action has had mixed results.

Increasingly intense global competition and the growing complexity of science and the new 

technologies based on it have forced national governments to improve their overall innovation 

infrastructure to provide more efficient mechanisms for drawing upon new scientific discoveries 

to develop new technology platforms with large economic growth potential. 

While the processes by which knowledge is transferred through patents are affected by 

numerous factors, a dominant one is the ownership of the intellectual property rights (IPR) from 

research results. Who owns a patent has a “gate keeper” effect on the potential for eventual 

commercialization, as different types of owners will have different incentives to attempt or 

facilitate the investment required to turn the IP into technology with commercial value.

Over the past three decades, most European countries have moved away from inventor 

ownership of patent rights towards different systems of institutional ownership (Geuna and 

Rossi, 2011). This trend is a reaction to the earlier passage in the United States of the Bayh-Dole 

Act. In fact, a good portion of Europe’s major economies now allow universities to retain IPR on 

inventions resulting from government-funded research.3

In all of these economies, the emphasis on universities owning the intellectual property (IP) 

from government-funded research has created a “huge opportunity” for local innovation derived 

from a regional cluster’s university IP. The policy metric used is increases in the number of 

technological innovations developed as a result of licenses issued by a university to local small 

firms, many of which were spinoffs from the university stimulated by IP ownership. Germanspeaking and Scandinavian countries were slower to convert to university ownership of IP (Geuna 

and Rossi, 2011).4

However, although most western industrialized economies’ IPR policies now specify 

institutional ownership, these policies are implemented in a number of forms characterized by 

differences such as

(1) Specific national laws (public research acts or similar) or by default (i.e. through general 

laws on IPR ownership). In some cases, non-binding national codes of practice have been 

formulated to provide guidance to universities.

(2) How the rights are vested in the university. For example, under the “pre-emption rights” 

principle, the researcher is the first owner of the invention unless the university “claims” 

the invention within a specified period of time. In the event that the invention is not claimed 

 

3 Only two countries, Italy and Sweden, continue to maintain systems primarily centered on assigning IPR 

ownership to the inventor.

4 OECD’s “The Innovation Policy Platform” has a comprehensive treatment of IPR and various systems for 

managing them (https://www.innovationpolicyplatform.org/content/processes-and-contributions-ip-systemsinnovation?topic-filters=12164).
11

within that period, the IPR remain with the inventor.

(3) The institutional form of the research, which can affect the ownership. Such forms include 

(a) “contract research”, under which IPR are automatically are assigned to the institution, 

(b) “open research” (wholly university funded, where there is an agreement with external 

sponsors to consider the research “open” in that rights belong to the inventor and the 

institution can acquire them only if the inventor does not intend to use or publish them),

(c) “service inventions” (from an employee’s activity) or “free inventions” (rights are 

assigned to the inventor and the institution who can commercialize them under a nonexclusive license).5

Rigby and Ramlogan (2012, pp. 21-22) summarize interesting data compiled by Australia’s 

National Survey of Research Commercialization showing the returns on investment in technology 

licensing income, including income from licenses, options, and assignments as a percentage of 

research spending in Australia, North America and Europe from 2000 to 2009. 

The data show a substantial disparity between the licensing incomes of U.S. universities 

relative to research funding and the same ratio in other countries. The U.S. average ratio for all 

U.S. universities is 4.7 %. Australia’s returns were quite variable over the years due a small sample 

(for example, its 2009 high was determined by one patent that generated the majority of the 

country’s 2009 income. The Canadian results show a gradual decline from a peak of 2.3% in 2001 

to 1.1% in 2008, while the U.K. has shown a gradually increase from a low base of 0.6% in 2000 

to 2.1 % in 2008. 

Rigby and Ramlogan state that the contrasting performance between Europe and the U.S. has 

given rise to considerable policy discussion at the European Commission level about the so-called 

“European paradox,” referring to the perceived failure of EU policies to effectively translate 

scientific advances into commercially viable technologies. 

However, they also point to research, which shows that when distinguishing between 

university owned and university invented patents, the perceived gap between the U.S. and 

Europe university patent production is much smaller. Specifically, Crespi et al (2007) find that,

on average for six countries studied, two-thirds of the patents with at least one university 

inventor are not owned by universities. Lissoni et al.(2008) show a similar result from a study of

France, Italy, and Sweden. The conclusion is that European universities may actually contribute 

as much technical knowledge as their American counterparts; it is just that they are less likely to 

claim ownership of the IP. 

Finally, Rigby and Ramlogan cite a study by Conti & Gaule (2011) that uses licensing data to 

assess whether U.S. technology transfer offices (TTOs) have outperformed their European 

equivalents in negotiating license agreements or earning license revenue. Using explanatory 

 

5 Adapted from Geuna and Rossi (2011). See pp. 11-12 for more detail.
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variables such as quality of academic institutions, their research orientation, number of 

publications, demand for technology, TTO staffing levels and experience, the authors determined

that, in terms of licensing revenue, European TTOs performed as well as their U.S. counterparts 

with respect to number of licenses but earned significantly less revenue. 

The study determined that the reason for the licensing revenue differential is that “U.S. TTOs 

were more focused on generating revenue relative to other objectives such as local development 

and faculty service.” Moreover, they employed more staff with industry experience that likely 

meant greater experience at negotiating the financial clauses of licensing contracts.

Clearly, IPR management is a complicated policy tool and needs constant review and 

adjustment to maximize TBED. And, this complexity is accentuated by the fact that across 

European countries, legislative frameworks regulating academic patenting are often weak, 

leaving, for example, most European universities to define their own internal IPR management

policies. 

Nevertheless, Geuna and Rossi (2011) cite data from the Eurostat Science and Technology 

database, which show that most countries achieved a remarkable increase in university patenting 

in response to the shift to university IP ownership, with patent output in the EU-27 doubling 

between the late 1990s and the mid-2000s. 

They also point out that patenting was just one of a number of expansions/upgrades to 

national economic growth strategies. In fact, all European countries have instituted broader sets 

of TBED policies. For example, in the U.K. support for entrepreneurial activities in universities 

began to increase in the mid-1980s, which was a natural complementary policy focus to the 

establishment of technology transfer offices (Meyer and Tang, 2007). In fact, by the mid-1990s, 

government financial support for entrepreneurial activity had become widespread across 

Europe. For example, in Germany, the switch to institutional ownership of academic IPR was 

complemented by substantial federal subsidies for regional patent exploitation institutions

(Geuna and Rossi, 2011). 

Similarly, in Norway the switch to institutional ownership of IP was accompanied by the setting 

up of a technology transfer infrastructure composed of university TTO’s, with actual utilization of 

new technologies supported by seed capital funding, mostly provided by government (Iversen, 

Gulbrandsen and Klitkou, 2007). The Danish government also provided substantial funding for 

the creation of a technology transfer infrastructure following the introduction of institutional 

ownership (Lissoni et al, 2009). 

In Sweden, numerous organizations to support technology transfer were established in the 

1990s. Specifically, a series of technology bridging foundations were founded to help universities 

build links with industry and other stakeholders; science parks were established with public 

funding; national competence centers were financed jointly by industry, universities and 

government; and, universities set up their own TTO’s (The Economist, 2018). 

The policy observation from this discussion is the broad adoption across industrialized nations 

of IPR management tools and, even more important, specific institutionalized functions to 
13

promote economic use of the IP. However, such efficiency improvements in IP management are 

only the beginning of the public-private asset model’s policy tools to enhance the successive 

development and eventual commercialization of new technologies. Collectively, these tools 

constitute the innovation ecosystem that determines the ultimate efficiency of an economy’s 

TBED strategy. The next section discusses the major policy instruments currently being used by 

regional economies within the context of a national innovation strategy.

5. Global TBED Strategies 

The relentless advance of the global technology-based economy is being accompanied by the 

evolution of a public-private asset policy model, which is driving the selection and use of TBED 

policy instruments. Individual policy tools target specific underinvestment phenomena, but the 

collective economic impact will only be substantial over time if these tools are integrated into 

larger ecosystems that can achieve economies of scope by improving the productivity of all 

embedded private and public institutions. To this end, traditional and emerging TBED policy 

instruments are summarized below and their different implementations across national 

economies are contrasted.

Clusters, Innovation Districts and Tech Hubs 

The previous section emphasized direct transfer of knowledge from universities to private 

firms who attempt to commercialize this knowledge. However, while improving the efficiency of 

this process is extremely important, long-term innovative economic output requires a much 

broader ecosystem of private and public institutions to provide a wide array of market 

applications of new technologies, including eventually high-tech services.

That is, achieving large economic impact requires capturing economies of scope—many market 

applications from single technology platforms. To this end, new infrastructures such innovation

”hubs”/“clusters” and the broader concept of “innovation districts” are integrating university 

research into broader R&D efforts with IP issues specified early in the R&D cycle by the collective 

management of the cluster. Such “institutional innovations” are essential to efficiently cross the 

valley of death and provide the new technology platforms that drive the varied applied R&D 

efforts and the a subsequent variety of innovations that create profits and worker income growth

at a level that creates substantial regional economic impact.

6

Katz and Wagner (2014) point out that 

“innovation districts are emerging in dozens of cities and metropolitan areas in 

the United States and abroad and already reflect distinctive typologies and levels 

of formal planning. Globally, Barcelona, Berlin, London, Medellin, Montreal, Seoul, 

 

6

See Tassey (2018b) for extended discussion of these policy tools in the U.S. economy.
14

Stockholm and Toronto all provide examples of evolving districts. 

In the United States, districts are emerging near anchor institutions in the 

downtowns and midtowns of cities like Atlanta, Baltimore, Buffalo, Cambridge, 

Cleveland, Detroit, Houston, Philadelphia, Pittsburgh, St. Louis, and San Diego. 

They are also developing in Boston, Brooklyn, Chicago, Portland, Providence, San 

Francisco and Seattle where underutilized areas (particularly older industrial 

areas) are being re-imagined and remade. Still others are taking shape in the 

transformation of traditional exurban science parks like Research Triangle Park in 

Raleigh-Durham, which are scrambling to meet demand for more urbanized, 

vibrant work and living environments.”

Tech hubs are similar to Innovation Districts in that they are associated with urban 

environments. CBINSIGHTS counts seven American cities among the top 25 Tech Hubs.

Significantly, whereas CBINSIGHTS (2008) agrees that Silicon Valley is still the dominant tech hub, 

they project that “Beijing and Shanghai are poised to be the tech hubs of the future. They lead 

among high-growth hubs for unicorns, mega-rounds, and large exits. Company creation is 

accelerating”.

The TBED strategies of European and Asian tech-based economies are discussed in detail later 

in this paper. The main point here is that the rest of the world is rapidly expanding investment in 

“innovation districts,” “innovation orchards,” “tech hubs,” or whatever variant of the generic 

innovation cluster model one wants to adopt. 

Startup Accelerators and Access to Global Markets 

A startup accelerator is a policy mechanism for assisting entrepreneurs in the early 

development of new technologies. As described by Clark (2014), a startup accelerator is an 

institutional mechanism that provides a young company both technical and management

guidance and financial resources to leverage grow in its size and value in order to “accelerate” 

the initial round of conventional venture financing. Hathaway (2016) characterizes this

mechanism as supporting “early-stage, growth-driven companies through education, 

mentorship, and financing in a fixed-period, cohort-based setting.” The typical length of 

application is 3-6 months.

Cohen (2013) notes that accelerators “usually provide a small amount of seed capital, plus 

working space. They also offer a plethora of networking opportunities, with both peer ventures 

and mentors, who might be successful entrepreneurs, program graduates, venture capitalists, 

angel investors, or even corporate executives.” 

The role of an accelerator seems to be evolving in many countries. According to The Economist

(2018), “a perennial complaint about young tech firms in France is that—despite their gifted 

engineers and smart ideas—few know how to scale up fast enough to interest big investors.” This 

seems to be due to a widespread fear of going global too soon, not only in terms of marketing 

strategies, but for early-stage financing. 
15

However, a few French startups have engaged with one of the large accelerators, Y 

Combinator. As an example, Y Combinator helped a young company, Angolia, to take on more 

risk by looking outside France for business opportunities and to raise capital. As a result, Angolia 

was able to raise $74 million from a group of investors led by Accel, a London-based venture 

capital firm.

According to the Global Accelerator Report, more than $206M was invested in 11,305 startups 

across the world in 2016.

7 The United States continues to be the leading country both in terms 

of startups accelerators and in dollars invested via this mechanism. The U.S.-Canada share was

3269 startups funded at $107,264,392 (Izquierdo et al, 2016)

In the previous year, The Global Accelerator Report 2015 observed that a majority of 

accelerators across the global economy still intended to follow the traditional "cash-for-equity" 

model, which involves investing a small amount of seed money in a startup in exchange for an 

equity share. The typical investment was about $25,000 in exchange for a 5-10 percent equity

share (Izquierdo et al, 2015).

However, a significant change seems to be occurring in the way accelerators operate. The 

Global Accelerator Report 2016 points out that only 33 percent of accelerators now indicate that 

they will generate revenue from exits in the future, a significant shift from 2015. This shift in the 

accelerator business model is resulting from the fact that the small number of exits (178 reported 

in 2016) has proven insufficient to refinance the operations of accelerators. Moreover, exits 

usually do not occur earlier than three to five years into a startup's lifecycle, thereby delaying 

realization of cash flows from an accelerator’s investments for several years. 

Thus, the Report notes, nearly all (90.4 percent) of accelerators globally are exploring new 

models of revenue generation, such as charging for mentorship, subletting office space, hosting 

events and working with corporations who contribute resources. Revenue from corporations has 

seen the largest increase among alternative sources. More than half (52.1 percent) of 

accelerators are at least partially funded by a corporation, and 67.2 percent aim to generate 

future revenue from services sold to corporations.

6. Specific Country TBED Strategies 

The global economy is increasingly driven by investment in technology and the embodiment 

of the resulting technologies in efficient production systems. Even large economies like the 

United States must continually upgrade and refresh their investments in technology and the 

efficiency with which new technologies are deployed. 

However, the relentless growth of global technology-based competition from other parts of 

the global economy is making the required management of continual change within these 

 

7

See Global Accelerator Report 2016, produced by Gust (gust.com/accelerator reports/2016/global).
16

economies more difficult. As summarized in this Section, for example, Northern Europe is an 

advanced region of the global economy with respect to comprehensive TBED policies and levels 

of implementation, and its economies have relatively long histories of comprehensive innovation 

strategy development. Yet, these nations are having to expand efforts to upgrade and expand 

TBED policy instruments to meet the growing competition.

Significant differences exist among nations in the nature and intensity of their TBED 

investments, which indicates the lack of an understanding of the importance of technology as a 

critical economic input, but, more important, the political will to reallocate resources toward 

technology-based economic growth.

Nevertheless, the world’s economy is on average moving relentlessly toward increasing 

reliance on technology. The following provides some examples of the varying scope and depth of 

national strategies to this end.

Germany 

Germany is famous for its technical and engineering prowess. It has invested over decades in 

a range of high-tech assets, which make its economy particularly competitive within its strategic 

focus. 

As is the case with most economies, Germany has focused on a particular set of industries and 

on a particular phase of the technology life cycle. Germany’s focus has been advanced 

manufacturing with emphasis on developing a continually competitive portfolio of products 

based on existing technology platforms As a result, its economy has performed consistently well

in established manufacturing industries where itssteady incremental change in both product and 

process technologies has resulted in high rates of productivity growth. 

Unlike the United States, which has a decentralized and incomplete national TBED policy 

structure (many policy initiatives are undertaken by individual states), the German Government 

plays a central and substantive role in close coordination with German industry to devise and 

implement TBED policies.

For example, in 2010 the Germans created “High-Tech Strategy 2020” to promote digitalization 

of manufacturing. It consisted of ten initiatives, including an Industry 4.0 program (similar to IIOT

programs in other countries). The goal is not only to maintain German leadership in

manufacturing, but also to ensure it is a major supplier of advanced manufacturing technologies

going forward. The government has invested approximately $250 million in government and 

academic research and it facilitates collaborations among government, business, and trade 

unions to create and manage manufacturing innovation strategies (Dobbs et al, 2015).8

 

8 Dobbs further point out that the benefits of Industry 4.0 for an industrialized economy depend on ubiquitous 

connectivity, not only of machinery and systems within the four walls of the factory, but also across the entire 
17

To accomplish its engineering-driven superiority, its educational system turns out highly 

qualified scientists and engineers as well as middle-skill technical workers trained in 

apprenticeship programs. The above average skills of German workers allows the economy’s 

companies to pay above average compensation. Another characteristic of the German economy 

that helps drive its engineering-based innovation is the presence of strong small and mid-sized 

(mittelstand) companies, many of which are family-owned and known for making long-term 

investments (Dobbs et al, 2015).

Coupled with an efficient industry structure and a strong education system is a well-conceived

government policy infrastructure, which supports the domestic industry through a range of TBED 

institutions. A comprehensive Brookings analysis of Germany’s manufacturing sector (Parilla et 

al, 2015) identifies the critical policy tool as the use of public-private collaborations to conduct

applied research that accelerate and efficiently achieve high and sustained rates of innovation.

9

This “cluster” strategy is part of a “dual model” with the other part being an entrenched

vocational education system to ensure a continual supply of highly trained workers.

10 As a result, 

Germany with its comprehensive set of public and private institutions that support advanced 

manufacturing is maintaining consistent trade surpluses, while the U.S. continues to suffer trade 

deficits.

11

The Brookings analysis shows that superior performance, including compensation, is reflected 

in the fact that medium- and high-technology industries account for a larger share of the sector’s

total output in Germany (58 percent) than in the United States (42 percent). Yet a cross-national

comparison reveals that Germany invests only marginally more in public R&D as a share of its

economy. The implication is that their more distributed and institutionally diverse support for 

manufacturing has yielded a more effective TBED policy structure.

More specifically, a distinguishing difference between the German and U.S. TBED strategies is 

the German emphasis on government support for applied research. The Fraunhofer Institutes 

are widely recognized as a dominant policy instrument at this phase of the R&D cycle. Their 

 

manufacturing value chain. Within the plant, connected machinery and sensors to monitor the movement of

parts and activities of labor can raise efficiency, including through real-time supply-chain optimization, real-time 

yield management, and predictive maintenance. This system relies on a continuous flow of data about 

equipment performance to avoid breakdowns and to schedule maintenance only when needed. Some of the 

greatest benefits could arise from (1) allowing retailers and even consumers to connect directly to 

manufacturers and (2) matching excess manufacturing capacity to demand.

9 Much of the remainder of this section draws upon this excellent analysis.

10 German vocational training consists of a widely used combination of formal (classroom) education and on-thejob training. See https://www.make-it-in-germany.com/en/for-qualified-professionals/traininglearning/training/vocational-training-in-germany-how-does-it-work.

11 For example, in 2016, the U.S. had a trade deficit of $869 billion, compared to Germany’s trade surplus of $322 

billion. For the manufacturing sector, the U.S. trade deficit was $667 billion compared to a surplus of $425 

billion for Germany (Brookings analysis of United Nations Conference on Trade and Development data).
18

purpose is to improve the efficiency of drawing upon existing technology platforms to produce 

applied technologies. To these ends, Germany has established 67 Institutes with 23,000 

employees. Most are in Germany, but a few additional institutes are located in other countries, 

including the United States.

Fraunhofer institutes offer contract R&D services that cover a range of technical-based 

activities, including market and patent surveys, and feasibility studies to support R&D portfolio 

decisions. The focus is on applied R&D that produces commercial prototypes, production scaleup assistance—including the design, construction and testing of pilot plants, and even 

production-stage analysis and testing services.12

Each institute has a distinct specialization (applied polymer research, nanoelectronics, etc.) 

which falls under broader industry sector targets, such as microelectronics, materials and 

components, biopharmaceuticals, information technology, etc. Approximately 70 percent of 

Fraunhofer’s funding is generated by contracts with industry and public institutions, with state 

and federal governments contributing the remaining 30 percent. Thus, the actual price to clients 

includes a subsidy, as the Fraunhofer institutes only charge companies for the direct costs of a 

project.

Fraunhofer institutes undertake between 6,000 and 8,000 industry projects per year. The size 

of these projects varies significantly, ranging from less than 1,000 to several million dollars.

Smaller client firms, who do not have sufficient in-house capabilities to address a particular 

technical challenge, will contract with a Fraunhofer institute to perform pre-production research 

or develop a prototype. At other times, Fraunhofer institutes will simply allow private-sector 

researchers access to specialized machinery in order to test prototypes and conduct advanced 

research. Often, Fraunhofer and industrial scientists work hand-in-hand on a project, promoting 

the mobility of scientists between universities and industry.

Parilla et al (2015) conclude that, through these myriad support functions, Fraunhofer 

institutes have been the most prominent actor in Germany’s promotion of firm-level R&D. In 

addition to supporting individual firms, Fraunhofer institutes help regional clusters develop a 

unique comparative advantage by establishing industry or technological specializations within 

individual institutes. Together, these networks form the backbone of a regional innovation 

ecosystem. However, the Fraunhofer network has also facilitated “long distance” collaborations, 

which occur when the closest Fraunhofer facility does not have expertise in the technology of 

interest for a particular firm.

Additionally, the Brookings analysis points out that regional funding facilitates collaboration

among universities, other research institutions, and private companies. In Bavaria, the Ministry 

 

12 See https://www.igb.fraunhofer.de/en/companies.html and

https://www.igb.fraunhofer.de/en/companies/what-does-fraunhofer-do.html. Some early phase proof-ofconcept research is also undertaken.
19

of Economic Affairs, Infrastructure, Transport and Technology provides funds for joint research 

through programs such as the New Materials Development Program, Microsystem Technologies 

Program, and Information and Communication Technologies Program.

State governments also provide innovation vouchers to SMEs to support their R&D activities.

Such vouchers allow firms to conduct additional research themselves or to redeem the voucher 

at a research institution of their choosing. The effect of the innovation vouchers is twofold. First, 

they promote the participation of SMEs in R&D activities that, due to high financial cost and 

uncertainty on the return of the investment, would not have occurred otherwise. Second, the 

vouchers also provide additional funding for research institutions, particularly for applied 

research organizations, to conduct risky research at a lower cost. The state of Bavaria offers 

innovation vouchers through its Program for the Introduction of Technologies (BayTP), while also 

offering loans and capital for recently created start-ups in high-tech industries.

Germany has achieved this superior performance through a comprehensive TBED policy 

structure, as indicated in Table 2. The Leibniz Association and Max Planck Society are world class 

Table 2 German Public & Private Research Institutions by 

Research Focus/Funding Source

(pub =public funded; prv = private funded)

Basic Research Applied Research

 Academies (pub) Companies/Industrial Research (prv)

 Federal Institutions (pub) Fraunhofer-Gesellschaft (prv/pub) 

 Helmholtz13 (pub) Helmholtz (pub)

 Leibniz Association (prv/pub) Industrial Research Associations (prv)

 Max Planck Society (pub) Lander Institutions (pub)

 Research Infrastructures (pub) Networks & Clusters (prv)

 Universities (pub) Universities (pub)

 Source: Parilla et al (2015)

basic research organizations. Importantly, several of these institutions are oriented toward 

supporting economic growth, rather than social objectives, in particular, the Fraunhofer Society. 

Drawing upon the country’s superb research infrastructure, its manufacturing sector squeezes 

maximum productivity out of each existing technology platform.

Further, their highly developed public-private infrastructure facilities joint planning for future 

technology life cycles. Germany’s joint planning for the advent of the Industrial Internet of Things 

 

13 The equivalent of the U.S. Department of Energy’s National Laboratories.
20

(IIOT) is a case in point.14

OECD data cited by Parilla et al (2015, p.7) indicate modest advantages for Germany over the 

U.S. in total researchers per 1000 workers(8.2 for Germany vs. 8.8 for the U.S.) and R&D intensity 

(2.98 percent for Germany vs. 2.79 percent for the U.S.). However, the greater R&D intensity of 

the German economy and the emphasis on applied R&D drive a higher productivity 

manufacturing economy. 

The higher productivity of the German economy is evident in roughly a 30 percent gap in 

worker compensation between U.S. and German manufacturing workers.15 The German 

educational system is definitely a major factor in the German productivity advantage. It strongly 

focused on producing highly skilled workers, with significant emphasis on education curricula and 

strong apprentice programs. The well-developed set of public and private research institutions

focusing on applied R&D, especially in manufacturing, has led to a substantial advantage in 

patents per 1,000 researchers of 53 to 39).

The German apprentice system is particularly noteworthy because of its widespread use. 

German manufacturers do not recoup the cost of training apprentices during the actual 

apprenticeship. Rather, such a “human capital investment” will pay off as the resulting highly

skilled full-time workers yield higher average productivity. Apprenticeships also allow German

manufacturers to evaluate young workers before hiring them full-time, thereby reducing the 

considerable long-term commitment risk inherent in German labor policy (Parilla et al, 2015).

In summary, the German education and training system is effective because (1) it first provides 

young workers with exceptional basic technical training for high-tech industries, which provides

a foundation for learning specialized on-the-job skills; (2) the specifications for specific 

occupational skills are made in the context of best practices in human capital development, with 

a strong emphasis on active learning and adaptability, and (3) the emphasis on internal company 

training (through apprentice programs) facilitates adaptation of worker skills to the specialized 

needs of regional clusters, as well as facilitating adaptation of generic production processes to 

the needs of an innovation clusters particular technology focus (Parilla et al, 2015).

In contrast, U.S. regional innovation clusters rely more on external public educational 

infrastructure, specifically universities and community colleges to tailor curricula to company 

needs. However, this approach does not allow the progressive tailoring of worker skills until after 

full-employment commitment.

 

14 The industrial internet of things (IIoT) is a network of connectivity among machines in multiple industries in a 

supply chain to enable enhanced management of entire product technology life cycles. This is accomplished by 

sensors within products installed during production that collect performance data and transmit it over the 

Internet to external parties, such as the producer of the equipment who use the data to determine maintenance

and upgrade requirements.

15 See https://www.bls.gov/fls/ichccindustry.htm#C. 
21

Overall, Germany’s public-private collaborations in technology development and its dual 

model for worker training are often considered global best practices. Taken together, German 

TBED policies are implemented by a set of public and private institutions, which work 

collaboratively to support manufacturing through investment, implementation, and dynamic 

management of change. Collaboration has become a dominant characteristic of TBED strategies. 

Especially, in emerging technologies, most national economies have emphasized collaborative 

research for creating new technology platforms from which patentable technologies can be 

derived. For example, Fornahl et al (2011) found that R&D subsidies focusing on single German 

biotech firms do not enhance the performance, while collaborative research subsidies (i.e., 

subsidies that are granted to joint R&D projects with two or more partners), have positive impact. 

The United States has at least one major advantage in establishing regional innovation clusters 

in that it has 39 of the top universities in the Leiden Impact Rankings vs. zero for Germany. This 

fact, combined with Germany’s focus on orienting its highly skilled labor force, applied research 

infrastructure, and highly stable industry supply chains, may collectively be a barrier to 

developing and assimilating radically new manufacturing technologies. 

Nevertheless, in a 2012 study of 36 OECD economies, Germany ranked third in proportion of 

graduates in STEM fields, compared to 33rd position for the United States. More than 800,000 

students participated in apprentice programs in the manufacturing sector.16 This complement to 

traditional education expands and diversifies the availability of the needed skilled workforce and 

at least gives Germany many of the tools needed to transition between technology life cycles.

If U.S. TBED policies are systematically broadened to provide adequate investment in the 

comprehensive set of support mechanisms described in this paper, the German expertise in 

continual product development and improvement can be matched and also combined with a 

world-class research infrastructure for developing radically new technologies with superior longterm growth potential. This combination will achieve a high level of competitiveness over

successive technology life cycles.

In this regard, the often heard assertion that Manufacturing U.S.A is a direct imitation of

Fraunhofer institutes is incorrect. The MII’s focus on an earlier phase of R&D (proof-of-concept 

or technology platform development for emerging technologies), as opposed to the Fraunhofer 

focus on more applied R&D and hence competitiveness within a single technology life cycle. 

Consequently, regional U.S. TBED strategies based on MII’s or simply technology platformfocused clusters should have greater technology diversification and long-term growth potential.

Perhaps the most important policy point is that both economies are expanding the scope of 

their TBED strategies—just from opposite directions. U.S.strategies are expanding forward in the 

R&D cycle to augment the economy’s traditional advantage in radical technology innovation. 

 

16 Brookings analysis of OECD data. See Parilla et al (2015).
22

Doing so emphasizes process technologies and better supply-chain integration in order to make 

continual product and process technology improvement over the entire technology life cycle the 

norm (Tassey, 2014a). 

Meanwhile, the Germans, whose strengths are in the mid- and late-life cycle requirements for

continuous improvement and process and quality control, and who have benefited from stable 

industry structures, are now emphasizing more radical innovation and diversity of innovation 

sources by promoting startups and organizing national efforts to transition to Industry 4.0.

In fact, the breadth and depth of startups in Germany have expanded to the point that Silicon 

Valley Bank, the dominant U.S. funder of domestic startups, has opened an office in Germany.

One of its first clients was Lilium Aviation, which is developing electric flying taxis, requiring “a 

difficult combination of drone-like vertical take-off and forward jet propulsion”.17 Moreover, 

Germany has an accelerator program, German Accelerator Tech, that supports efforts by highpotential German tech startups to successfully enter the U.S. market.

18

The important policy point is that German startups increasingly have a common emphasis on 

science-based innovation, in contrast to the traditional engineering-based focus. As The 

Economist points out, the portfolio of technologies targeted is also expanding to include 

heretofore largely ignored areas such as healthcare, e-commerce, and logistics services, as well 

as a continuing focus on advanced manufacturing. To support this expansion of targeted

technologies, entrepreneurship programs are proliferating at German universities.

Finland 

As globalization of the technology-based economy has expanded, the resulting growing 

number of economies (largely in Asia) that have acquired some degree of technological capability 

and combined it with lower wages has grown significantly. Most western economies have failed 

to respond adequately.

However, a few are making substantial investments in productivity-enhancing technologies 

and management techniques. Finland is one of the economies that have committed to a truly 

high-tech growth strategy. At 3.2 percent, their economy has one of the highest R&D intensities 

in the world and considerably above the EU average of 2.0 percent.19 Prime Minister Matti 

Vanhanen stated that "Because we cannot compete with Asian companies with low wages, our 

only possibility has been to stay a few steps forward."20

What this means in policy terms is a focus on steady growth in innovation and productivity 

through a comprehensive TBED strategy. However, a small economy such as Finland is in effect 

 

17 “Taking Off: Entrepreneurship in Germany,” The Economist, June 16th, 2018: 56-57.

18 https://germanaccelerator.tech/. 

19 https://en.wikipedia.org/wiki/List_of_countries_by_research_and_development_spending. Data are for 2014.

20 https://www.cnet.com/news/why-is-finland-europes-technology-leader-the-prime-minister-explains/.
23

the equivalent of a single regional economy in the United States. Thus, some significant degree 

of specialization is required and the TBED model must be applied rigorously and expertly. The 

largest companies have to operate on a global scale, requiring effective public-private growth 

strategies to achieve the scale and scope economies required for success (Kankanen et al, 2013).

Notably, the increasing scope of ICT and its effects on the variety and flexibility of 

communication and information applications is helping smaller economies rapidly upgrade their 

overall information infrastructure. In Finland’s case, it ranks highest among OECD economies with 

respect to percent of enterprises using cloud computing services (OECD, 2015b).

Sweden 

Sweden has a high-tech economy and one of the more systematic and well thought out 

national TBED strategies. Because of its small size, it faces the same imperative as Finland to 

achieve maximum efficiency from a focused technology portfolio. 

The Swedish Government states that “innovation is vital to long-term productivity

development, and thereby to growth and future prosperity”. Further, the Government 

recognizes that the economic benefits from policies supporting innovation will be achieved only

if a portfolio maximization strategy is used to identify and then support specific technologies 

targeted at identified economic and social goals.

This characterization of a modern technology-based economy has driven a comprehensive 

strategic planning and policy process in Sweden, which has resulted in set of targeted industries, 

markets, and social objectives. The implied hierarchy of goals making up the country’s TBED

policies is reflected in the The Swedish Innovation Strategy (2015):

“An innovation can take the form of new products or technological solutions. It 

may also be new ways of planning and developing urban or rural areas and built 

environments. It can be combinations of goods, systems and services for the

global telecommunications market or smart transport solutions. Innovation can 

also take the form of new ways of designing or organizing healthcare services for 

the elderly, new ways of submitting tax returns, new methods of involving 

customers or users in developing services or goods and new ways of taking 

advantage of and distributing art and artistic achievements. Innovation can also 

be new ways of using old, naturally occurring conditions, e.g., cooling energyintensive data servers through localization in cold climates or new ways of using 

land, ecosystem services, raw materials from nature and biologically/ecologically 

based technologies and methods.”

However, Sweden with a population of nine million people is equivalent to one of the many 
24

regional innovation hubs in the United States.

21 While the Swedish Government wants to 

promote diversification, it also recognizes the imperative to maintain sufficient scale and scope 

of its current investment targets in specific technologies. It therefore pursues active involvement 

in the broader EU economy.

In many ways this philosophy mirrors the evolution of innovation ecosystems in American state 

TBED policies. State economies benefit from the location of TBED investment, but the companies 

collocated in the innovation cluster must be able to access external sources of ideas, expertise, 

and eventually markets to be successful.

As is the case for U.S. innovation clusters, Sweden’s TBED strategy emphasizes the selection 

and development of a unique set of assets to be used for the development and commercialization 

of unique technology platformsthat can spew out applied technologies to be used by its domestic 

businesses to produce and offer goods and services in global markets. 

In order to develop an efficient domestic innovation climate that can compete in targeted niche 

global markets, domestic businesses must maintain active relationships with customers and

suppliers in global value chains and knowledge networks. The fact that exports represent half of 

Sweden’s GDP seems traumatic due to the potential for inadequate access to foreign markets. In 

fact, Sweden’s exports to Asian nations has declined in recent years, as these customers continue 

to invest in and become competitive in the same areas of technology.

Exports will obviously continue to be of crucial importance to Sweden’s growth potential, so 

market segment targeting and subsequent appropriate investment is essential. In this regard, the 

concept of open innovation—access to a wider variety of sources of input into companies’ 

innovation efforts—has become a major element of Swedish TBED policy design, as must be the 

case for U.S. regional economies.

The Swedish Innovation Strategy (2015) describes the open innovation imperative as

“Open innovation is about businesses combining knowledge and ideas 

developed internally and externally. Open innovation implies that businesses 

actively involve external actors in their own innovation work. Phenomena such as 

”crowd sourcing”, which is facilitated by new digital and social tools, enable 

involvement of expertise from a number of different sources, not always involving 

monetary rewards. 

Many businesses, from global business groups to smaller enterprises, are reorganizing their research and innovation activities towards open innovation. The

level of investment in innovation can be retained, but the work is carried out by a 

small number of in-house personnel in collaboration with external actors in 

 

21 For example, the greater San Francisco area, which includes oldest U.S. innovation cluster, Silicon Valley, has 

population of approximately nine million people.
25

universities, research institutes and other businesses, as well as with individual 

users and experts. The networks and relation of people, businesses and other 

organizations are therefore core factors in open innovation processes. The 

development of networks is facilitated by the mobility of people and efficient 

digital tools for social interaction.”

The Swedish Innovation Strategy states that Sweden “will be a global leader in the utilization 

of the possibilities offered by digitalization.” In effect, Sweden wants to be a leader in applying 

information and computer technologies (ICT) to promote the efficient and productive use of the 

internet and other digital services, as well as develop new e-services within both the private and 

public sectors. In addition, another goal is to ensure that digital information and digital tools will 

be utilized “in the best way possible within research and innovation” processes to create new 

products and services. 

Such digitalization strategies are common among industrialized nations. What is perplexing is 

the almost total lack of attention in advanced economies’ innovation strategies to the broader 

range of technical infrastructures that are essential to R&D productivity and advanced 

manufacturing. Such infrastructure exists, but it is extremely complex and is quality varies across 

technologies, as well as national economies. For example, any supply chain dependent on 

advanced materials must have access to computational modelling capability and highly accurate 

materials characteristics databases.22

In Sweden, there is still only a small proportion of small and medium-sized enterprises that 

collaborate through networks. Even more surprising, surveys from the Swedish Agency for 

Economic and Regional Growth indicate that the intensity of collaboration seems to be 

decreasing. Publicly-financed resources at universities and institutes for R&D, scale-up, and

specialized equipment or test and demonstration facilities, are important resources in the 

innovation process conducted by small and medium-sized enterprises, who typically have

inadequate resources for the entire TBED cycle. 

Yet, The Swedish Innovation Strategy (2015) recognizes that “In the global knowledge 

economy, the importance of relationships among different actors in innovation processes is 

increasing”. Thus, even though digital communication networks are improving the capacity for

more formal interaction and knowledge exchange over long distances, the tacit character of 

early-phase technical knowledge development means that such knowledge does not transfer 

efficiently among researchers without direct personal contact. Thus, geographic proximity has 

become a major driver of innovation cluster formation.

Swedish policy development also recognizes the importance of a cluster’s access to unique 

research facilities. This requirement is influencing the character of cluster formation all over the 

world. Examples in Europe are the European Spallation Source, MAX IV, and the SciLife Lab, along 

 

22 See Scott et al, 2018.
26

with numerous unique test and demonstration facilities for emerging technologies such as 

nanotechnology and biotechnology.

Switzerland 

The economy of Switzerland ranks first in the world in the 2015 Global Innovation Index.

23

Switzerland is an example of a small economy that has invested intensively in a comprehensive

TBED ecosystem supporting just a few targeted industries. The Swiss success underscores the 

fact that a regional focus in large economies, such as the United States, is appropriate in terms 

of scale and scope and, in fact, is actually desirable as doing so offers diversification for a large 

national economy.

OECD provides some interesting indicators of Switzerland’s TBED strategy.

24 Starting with 

science, Switzerland has the highest number of Nobel Prizes per capita among European 

economies (25 Nobel Laureates with Swiss citizenship), which has been achieved in part through 

access to the nation’s investment in world class research infrastructure, such as the European 

Organization for Nuclear Research (CERN). Another factor is the aggressive solicitation of top 

researchers from other countries. Switzerland is the country with the world highest proportion of 

foreign researchers. The result is that Swiss researchers produce 3.2 research papers per 1,000 

inhabitants, ranking it tops in the world. 

With respect to industry R&D and innovation, a large part of the business sector engages in 

R&D, although Switzerland has one of the smallest shares of BERD financed by government 

among the OECD countries. In fact, Switzerland traditionally refrains from granting R&D subsidies 

to business firms. 

Yet, to turn science into economy-driving technologies, Switzerland, led by private-sector 

investment,still allocates 3% of its GDP to R&D, which ranks among the top tier of R&D intensities

in the world. Importantly, unlike the United States, the Swiss federal government produces a

strategic planning document every four years, the ERI Message, which provides an integrated

framework for education, research and innovation policies that guide and support the national 

innovation ecosystem’s support of its technology-based economy.

The Commission for Technology and Innovation (CTI), the main Swiss innovation promotion 

agency, supports market-oriented R&D projects, development of start-up companies and 

knowledge and technology transfer. As indicated above, the Swiss innovation system benefits 

 

23 https://www.globalinnovationindex.org/userfiles/file/reportpdf/gii-full-report-2015-v6.pdf. For an overview of

the Swiss economy, see https://en.wikipedia.org/wiki/Economy_of_Switzerland. The Swiss have a number of 

world-class companies in a select set of industries: food processing (Nestlé), chemical for industrial and 

construction use (Sika AG), pharmaceutical (Novartis and Roche) and roof coating chemicals (LafargeHolcim is 

the largest construction materials group in the world).

24 http://www.oecd.org/switzerland/sti-outlook-2012-switzerland.pdf.
27

from modern, high quality research infrastructures. 

As an expansion of the technology investment priorities stated in the ERI Message, the State 

Secretariat for Research and Education in 2011 issued a roadmap for research infrastructures. 

Called the CH-Roadmap, it specifies planned investments in strategic areas and includes 

proposals for participation in international research infrastructures. 

In support of the overall innovation ecosystem, the CTI focuses on encouraging higher 

education and public research institutes (PRI’s) to collaborate with business. Most of its programs 

provide technical support rather than funding. Such support includes CTI Start-Up (mentoring 

and networking services for young entrepreneurs), CTI Invest (a venture capital platform), and 

Venturelab (entrepreneurship training).

Swiss multinationals are closely linked to global research and innovation hubs—the main way 

for a small economy (or regional hub in a large economy) to access economies of scope in major 

new areas of research. A federal strategy for the internationalization of education, research and 

innovation was adopted in 2010. 

In addition to bilateral research agreements or cross-border cooperation (e.g. the Lead-Agency 

process with Germany, Austria and Luxembourg to co-ordinate funding decisions), the Swiss 

firms participate in EU programs (e.g. Research Framework Program FP, COST, EUREKA and the 

Lifelong Learning Program). 

A major issue for the Swiss education system is the compartmentalization of its different 

education institutions, which affects internal mobility and access to higher education. The 2011 

Law for the Support and Co-ordination of Universities aims to improve co-ordination between 

the Confederation and cantons, which are responsible for the quality of the higher education 

system. 

United Kingdom 

After decades of subpar performance by the U.K. economy, the Labor government came to 

power in 1997 and initiated a major effort to improve Britain’s innovation policy. In addition to a 

national lag in R&D expenditures, the U.K. was an historic underperformer with respect to 

commercializing basic research. For decades the U.K. was known for producing a significant 

number of Nobel laureates and hence new science, but poor application efforts with respect to 

developing commercially viable technologies from that science. 

In November 2000, the U.K. Government published Productivity in the U.K.: the evidence and

the Government’s approach. It concluded that “the U.K.’s productivity gap can be accounted for 

by its deficit in physical and human capital and its lower rate of innovation compared to other 

major economies.”25

 

25 HM Treasury, Productivity in the U.K.: the evidence and the Government’s approach (2000), p. 17.
28

Richard Lambert, director-general of the Confederation of British Industry, issued a second 

report in 2003 that stated “Universities will have to get better at identifying their areas of 

competitive strength in research. Government will have to do more to support businessuniversity collaboration. Business will have to learn how to exploit the innovative ideas that are 

being developed in the university sector”.

26

Stimulated by the Bayh-Dole Act, increases in U.K. public expenditure to fund science and 

knowledge transfers and hence to bridge the “valley of death” resulted in important progress.

Significant increases in funding for university tech transfer offices were implemented in this 

decade (2001-2010). For example, Straw (2009) points out that Cambridge Enterprise Ltd., the 

university’s commercialization office, experienced an increase in staff to 37, up from five people 

in 1999, and this was typical for major universities.

Of particular note, the number and size of regional innovation clusters and the value of 

companies within them have both increased (Straw, 2009). Given the importance of innovation 

clusters, it is noteworthy that the U.K. viewed the United States as encountering difficulty with 

attempts to create regional clusters around second-tier universities. While agreeing that the 

Massachusetts Institute of Technology and Stanford University stand out as the core of the two 

most successful clusters in the world, the view was also expressed that the U.K. may have 

nevertheless succeeded in outperforming the United States in terms of the commercialization 

initiative efficiency (success in creating technology development ventures). For example, when 

the University of Wisconsin at Madison and the University of Washington, Seattle, were ranked 

against U.K. universities generally thought to have inferior research capability, the U.K. schools 

actually performed better (Library House metric, 2008; Lord Sainsbury, 2007).

China 

In analyzing foreign TBED policies, China, along with Germany, are the two most important 

case studies. However, they are important for very different reasons. As previously described, 

Germany, as a long-standing industrialized economy, has perfected its TBED policy structure in 

support of its target strategy of maximizing competitiveness in applied manufacturing

technology. However, except for its intensive effort to be a leader in the emerging Industrial 

Internet of Things (IIOT), its innovation capacity does not seem to be above the average for 

industrialized economies.

China, on the other hand, while currently struggling to evolve from the “engineering 

innovation” phase of its evolutionary path to a mature industrialized economy, is also making 

huge investments in the TBED assets required to become a major player in “science-based”

technology platforms, which produce the majority of radically new technologies and subsequent 

innovations. To this end, it is making significant strides in expanding and effectively 

 

26 Richard Lambert, Lambert Review of Business-University Collaboration: Final Report (2003), p. 2 (cited in Straw, 

2009).
29

implementing the requisite TBED policies. Because of the scope and magnitude of the

government’s efforts in a large and growing economy, China is rapidly becoming a major player 

in global technology-based markets.

Much of the policy discussion surrounding China’s economic growth policies centers on its

mercantilist actions to artificially expand its trade surpluses through acquired technologies. The 

country has been relentlessly charged with directly stealing intellectual property from the U.S.

and other advanced economies and indirectly forcing technology transfers through mandatory 

joint ventures with domestic firms.

The McKinsey Global Institute (MGI) in assessing China’s future growth potential states that 

the Chinese economy will have to evolve from “an ‘innovation sponge,’ absorbing and adapting 

existing technology and knowledge from around the world, into a global innovation leader” 

(Dobbs et al, 2015).27 This strategy was implemented (in other emerging Asian economies, as 

well) by focusing on labor force and capital expansion. Technology was largely acquired from 

external sources.

However, as wage rates have risen with a consequent decline in the efficacy of this strategy, 

the Chinese have seen clearly that their economy will have to invest in the full range of TBED 

assets that have appeared over the last two decades in varying degrees and in various forms in 

western economies. Asian economies, being younger, are therefore in the process of acquiring 

the required set of assets.

The MGI study argues that while China has become a strong innovator in areas such as 

consumer electronics and construction equipment, it has made little progress in advanced

technologies such as biopharmaceuticals, automobile engines, and nanoelectronics. The study 

points out that this situation exists in spite of the fact that the country spends more than $200 

billion on research (second only to the United States), turns out close to 30,000 PhDs in science 

and engineering each year, and leads the world in patent applications (more than 820,000 in 

2013).

The study further argues that the Chinese have only reached competitiveness largely in 

“efficiency-driven” innovations; that is, they are competitive in relatively mature technologies

where being competitive require (1) an emphasis on product improvements and efficient 

manufacturing and (2) a focus on customer- and efficiency-driven driven markets.

In contrast, becoming competitive in higher valued-added markets driven by radically new 

technologies will require China to a focus on new technology platform development derived from 

advances in science, which spawn whole new areas of product innovation opportunity (as 

indicated in the Technology Element Model depicted in Fig. 2).

 

27 The MGI study is a long and excellent assessment of the range of TBED policies and implementation experiences 

of the Chinese Government. Substantial use is therefore made of its content in this section.
30

With respect to the Chinese Economy’s current state of evolution, the MGI study points out 

that it has an extensive ecosystem, which has provided an “unmatched environment for 

efficiency-driven innovation”. The country has achieved the world’s “largest and most highly 

concentrated supplier base, a massive manufacturing workforce, and a modern logistics 

infrastructure,” which collectively give “Chinese manufacturers a lead in some important 

knowledge-based manufacturing categories, such as electrical equipment, construction 

equipment, and solar panels”.

The Chinese manufacturing sector is progressing toward a higher technical level at a time when 

being competitive in a global manufacturing environment means moving toward a highly flexible

manufacturing structure that includes increasing digitization not just within companies but in the 

broader technical infrastructure that supports advanced R&D. Moreover, global investment in 

such infrastructure is expanding across entire supply chains through the application of emerging

IIOT infrastructure. The MGI study points to Guangdong province as the center of the Chinese 

competitive response, where manufacturers have set up joint platforms to share the benefits of 

R&D and operations among companies in the same clusters. The aggressive use of robots will 

significantly upgrade moderately skilled workers’ productivity.

Still, as the MGI report points out, much of this effort emphasizes “accelerated learning,” which 

is a global characteristic of “engineering-based” innovation (incremental advancement of existing 

technologies). Such efforts combined with purchasing by government-owned enterprises

(market pull effect) achieve competitiveness in relatively mature technology-based industries 

such as communications equipment, wind power, and high-speed rail. 

To elevate the Chinese economy to the top tier of technology-based economies, China’s 

government will have to increase emphasis on TBED policies that focus on “science-driven”

technology development. Here, the overall policy imperative is to become competitive in the 

early part of the technology life cycle by implementing higher degrees of cooperative research 

aimed at the development of new technology platforms derived directly from scientific advances 

(Fig. 2), which, in turn, can enable entirely new sets of engineering-driven market applications.

Moreover, long-term and broad success for a science-based TBED strategy also requires the 

creation and growth of startupsto take on niche markets within the broader spectrum of markets 

emanating from new technology platforms that are the core growth engine for innovation 

clusters. Small firms also are critical because some of them will grow into large firms and give the 

domestic industry market presence in more applications of the core technology platforms. 

In China, a new wave of entrepreneurs is emerging and starting to drive customer-focused 

innovation. Increasingly, Chinese youth aspire to launch businesses: in 2013, 12 percent of Peking 

University graduates said they had launched a company or were self-employed, compared with 

4 percent in 2005. In a 2015 survey of college students on Renren, a social networking service, 

56 percent said they would be willing to become entrepreneurs while in college.

Another source of entrepreneurial talent is returning students who have studied overseas. 

More and more students are returning to China, and in the Zhongguancun technology hub near 
31

Beijing, an estimated 3,400 startups have been founded by some 8,000 returnees (Dobbs et al, 

2015).

Access to early-round funding has improved dramatically. Funding by angel investors and earlystage venture funds grew by a factor of 14 between 2009 and 2014, rising to an annual rate of 

about $6 billion. Almost 70 percent of early-stage investment was in Internet and IT-related 

businesses. Alibaba, Baidu, and Tencent have invested more than $11 billion in approximately 

100 ventures, looking for both investment returns and to fill the strategic needs of their 

businesses (Dobbs et al, 2015).

The rapid evolution of China’s TBED ecosystem is evidenced dramatically by the country’s rapid 

rise in the provision of venture capital. According to Global Insights, since 2012, Silicon Valleybased tech companies received a total of 12,000 venture capital financings, followed by New 

York-based companies with 5,000. With respect to funding, Silicon Valley companies brought in 

a total of $140B.

However, China (specifically, the emerging technology hubs in Beijing and Shanghai) are rapidly 

catching up to the leaders with respect to startups and venture financing. In fact, Beijing and 

Shanghai seem poised to be the tech hubs of the future. They lead among high-growth hubs for 

unicorns, mega-rounds, and large exits. 

Most important, new company creation is accelerating. Half of the so-called "high growth" 

cities are located in Asia, where “high growth” is defined relatively high-dollar venture

investments. Chinese cities are the dominant leaders.

Most cities with stable growth of startups and a higher concentration of later-stage rounds of 

financing are still in the United States, according to a survey conducted by CB Insights. However, 

about 80% of the Asian startups that hit the $1 billion milestone in capitalization between 2012 

and 2017 (so-called “unicorns”) are in China (mainly Shanghai which created 29 and Beijing 

creating 17). By comparison, Silicon Valley created 57 in the same period of time, but New York 

only created 13 (Bhardwa and Cheng, 2018).

Such investment is just one part of a broad and aggressive effort by the Chinese Government 

to push continual increases in both private and public R&D. However, the creation and use of 

TBED assets such as innovative small firms and their involvement in innovation clusters continues 

to lag. Further, intellectual property rights management is a continuing problem.

Overall, the Chinese TBED strategy is a major force in the ongoing process of “global 

convergence” beginning with Japanese ascendency to the status of a “technology-based 

economy” in the 1980s.28 This process is ongoing today, largely in the form of Asian economies’ 

 

28 The process of convergence is a phenomenon going back centuries in which emerging economies catch up with 

(growth faster than) the global leaders by acquiring skills and assets from the leaders and combining these 

assets with lower-cost labor. The process is abetted by the fact that the leaders tend to rely on accumulated 

stocks of capital, which experience diminishing returns to a greater extent than in emerging economies where 
32

acquisition of existing technologies, which are not only embedded in capital but “engineered” to 

further enhance newly attained comparative advantages. 

The process of convergence is abetted by the ability to “engineer” existing technology (as 

pointed out by Dobbs et al (2015), which accelerates convergence and even leadership in markets 

based on moderately mature technologies. Still, this strategy only works for a while. As success 

breeds higher productivity and hence higher labor incomes, these newer economies must 

convert to a broader and integrated TBED strategy to induce more domestic innovation in newer, 

emerging technologies.

In this regard, China has been accused of using the lure of a huge domestic market to force 

foreign firms to transfer technology to Chinese companies as the price for access to this lucrative 

market. While true, the emphasis on this tactic distracts policymakers elsewhere from the much 

broader set of TBED strategies summarized here, which the Chinese Government continues to 

expand and fine-tune. In fact, China enters the contest for leadership in the next era of 

manufacturing with strong advantages, including its extensive manufacturing ecosystem, large 

labor force, and modern infrastructure. 

In summary, Chinese manufacturers can adopt advanced technology and build on China’s 

advantages within an increasingly digital ecosystem that will link all participants in the

manufacturing ecosystem, refine hybrid automation models, and facilitate open design and 

manufacturing platforms (Industry 4.0). As is the case for Germany discussed earlier, aggressive 

policy actions can yield for China the flexibility, variety, and mass customization that global 

markets are increasingly demanding (Dobbs et al, 2015).

The implications of the above characterization of the emerging innovation capacity of what will 

soon be the world’s largest economy are summarized by

“China can become a platform for accelerated innovation, not just for Chinese

companies, but also for foreign multinationals that want to take advantage of 

Chinese cost and speed to produce innovations for China, emerging markets, and 

the world. Moreover, the Chinese model for rapid, low-cost, and nimble 

innovation can be adapted for use around the world. The overall effect could be 

accelerated innovation globally, challenges to market leaders from new 

innovators, and new, lower-cost products and services that fill unmet needs of 

emerging-market consumers and keep up with the shifting demands of consumers 

 

newly acquired capital can be combined with lower-cost capital and labor. Thus, the argument by economists 

has been that all economies should eventually converge in terms of per capital income 

(https://en.wikipedia.org/wiki/Convergence_(economics)).
33

in advanced economies.” (Dobbs et al, p. 12).

There is another critical element of Chinese TBED policy structure that is essential to long-term 

growth in an increasingly competitive global economy. As in other major industrialized 

economies, manufacturing in China is evolving from a capital-intensive sector, which in the past 

has led to industry structures dominated by a relatively few large firms, which, by acquiring

substantial IP and attaining some combination of economies of scale and network externalities

that favors giant corporations with massive scale, have been able to dominate both domestic and 

global markets.

Today, however, market drivers such as mass customization, open innovation, and digital 

infrastructure are drastically lowering minimum efficient scale for competitive operation, 

especially within regional clusters where tacit knowledge and interactive relationships can 

flourish. This has led to an explosion of small firms, leveraged by regional technology-based 

infrastructure.

In summary, this “democratization” of manufacturing is effected by a “maker” movement in 

which individuals and startups can translate good ideas using online design tools to develop new

products using a network of engineers and designers within the cluster and then contract for 

their development and manufacture with production facilities using advanced techniques such 

as 3D printing. China’s manufacturing ecosystem provides an ideal environment for an explosion 

of innovation. 

For example, Between 2003 and 2014, China accounted for 35 percent of global growth in wind 

power capacity. To effect needed technology transfer, the government imposed localization rules

under the Wind Power Concession Project—requiring 50 percent local content starting in 2003 

and 70 percent starting in 2006, As a result, foreign manufacturers and suppliers established 

plants or joint ventures in China, and in so doing, created regional clusters where domestic 

players could access global talent and knowledge networks.

In summary, this forced technology transfer policy had significant effects on the evolution of 

knowledge in the wind-power industry, where technology transfer, local-content regulations, 

and investments in domestic capacity deepened Chinese engineering capabilities. The 2003 Wind 

Power Concession Project helped China’s wind-power industry advance quickly along the 

learning curve (Dobbs et al, 2015).

However, just as the design and implementation of TBED policies across the 50 U.S. states has 

been uneven, Chinese companies have not performed particularly well in other industries that 

have also been the targets of regional TBED strategies, such as in the automotive sector where 

Chinese companies have less than 10 percent of global revenue. In this sector, the innovative 

capacity of Chinese automakers has been limited by a weak supply chain, resulting in limited 

opportunities to benefit from collaboration with suppliers on new products (Dobbs et al, 2015).

In emerging technologies, the Chinese Government has become increasingly aggressive. For 

example, to promote the development of electric vehicles, the Government has provided 

substantial R&D subsidies, as have other industrialized nations. However, the Chinese have also 
34

implemented demand-pull policies in the form of significant purchase subsidies to increase 

consumer demand, but also designed to pull specific product technology attributes such aslonger 

life batteries. The government has set a target market of three million units per year by 2025.

The scale of the discount to buyers is set at Yuan 34,000 (roughly $5,300) for cars with range of 

250-300 km, Yuan 45,000 for 300-400km and Yuan 50,000 for cars 400 km and above.29

Perhaps most important for long-run success, the Chinese understand the importance of a

complete TBED strategy, as is found in large, mature innovation clusters such as Silicon Valley. 

For example, the MGI report points out that electric vehicles require the integration of three 

evolving technologies: batteries, electric motors, and control systems. 

The policy implication is that being successful in complex emerging technologies, such as 

electric vehicles, requires a comprehensive multiple supply-chain strategy. To this end, not only 

has the Chinese government invested $6 billion across the several technology categories, it has 

also driven the formation of consortia of “car manufacturers, suppliers, and infrastructure 

players” (Dobbs et al, 2016).

In addition, like Europe, the Chinese government is investing in critical infrastructure to 

accelerate market penetration by electric vehicles through subsidization of electric charging 

stations. Such acceleration of market penetration will feed back into the evolution of the 

innovation cluster by stimulating more investment in scale-up and production capabilities. These 

investments are being driven by a 15-year roadmap prepared by the Society of Automotive 

Engineers of China for the Ministry of Industry and Information Technology (Fusheng, 2016).

These TBED policy elements emphasize the critical importance of the innovation cluster model

for China’s transition to a science-based innovation economy. In this regard, China has three 

major innovation cluster cities—Beijing, Shanghai, and Shenzhen—and a group of smaller but 

rapidly growing innovation clusters in other cities.

And, the Chinese Government is applying similar comprehensive TBED strategies in other 

important emerging technologies. It has initiated major research programs in autonomous 

vehicles, cyber systems, and artificial intelligence. China’s long-term strategy calls for "partiallyautonomous" cars to account for 50 percent of sales by 2020. "Highly-automated" cars (not quite 

fully autonomous) will take 15 percent of sales in 2025. By 2030, fully autonomous vehicles are 

expected to account for 10 percent of sales, which in the huge Chinese market means sales of 4 

million complete robocars annually (Dunne, 2016).

As (Dobbs et al, 2015) describe in some detail, Beijing stands out as center of technology 

innovation. 26 of 112 national universities are located in the Beijing area. Foreign high-tech 

companies have research centers in the Beijing area, including IBM and Microsoft. Leading 

Chinese tech companies, including Lenovo, Xiaomi, and Baidu, are headquartered in the 

 

29 Source of subsidy estimates: Citi (https://www.platts.com/latest-news/metals/london/citi-bullish-chinas-newsubsidy-program-for-long-26974717). Referenced in Dobbs et al (2015).
35

Zhongguancun district of northeastern Beijing.

The second of the three major Chinese innovation clusters is Shanghai, which is becoming the 

hub for life sciences, and includes contract research organizations that serve both Chinese and

global companies with clinical trials and other outsourced services.

The third major innovation cluster, Shenzhen, was designated as a special economic zone in 

the 1990s and has become the center of tech and Internet startups, including Tencent and 

Huawei. More patents have been awarded to Shenzhen companies than to companies in Beijing 

and Shanghai combined. Proximity to electronics manufacturing operations in Guangdong

Province has made Shenzhen a center of tech hardware innovation. 

At the core of any economy’s TBED strategy is a robust technical infrastructure that leverages 

the productivity of all technology foci (Tassey, 2014). Information infrastructure is obviously a 

critical component. As part of its determination to move beyond engineering-based innovation 

(characteristic of “middle income” economies), China has made huge investments in information 

technology. Tanzi (2018) points out that “90 percent of U.S. advanced technology imports from 

China are information and communications goods that include computer central processing 

units, peripherals and telephone switching components”. Census data show that this IT trade 

with China resulted in a deficit of $151 billion in 2017.

30

As China’s regional TBED ecosystems continue to evolve, more foreign high-tech companies 

will locate within these innovation clusters. The implication for U.S. TBED policies is that 

individual efforts by state governments will not evolve sufficiently to match the coordinated and 

high priority support from national governments in other economies, such as China. The 

combined national and regional government efforts will help innovation clusters first reach 

minimum critical mass and then grow to an optimal size at which rates of innovation and 

economic growth will be maximized.

However, as mentioned briefly above, this characterization of China’s TBED strategy ignores 

the other side of Chinese growth strategy, which is an aggressive mercantilist approach to 

obtaining foreign technology and subverting open competition from foreign companies trying to 

compete in China’s large and lucrative domestic market.

As described by Ed Gerwin in a Progressive Policy Institute (PPI) report, “the linchpin of China’s 

future-oriented mercantilism is an extensive array of plans, policies, rules, and practices to enable 

the transfer and assimilation of foreign technology and intellectual property for China’s benefit. 

To achieve these goals, China is employing many unfair or illegal measures, including using 

foreign ownership restrictions and licensing approvals to compel American and other nations’ 

companies who wish to do business in China to transfer their technology. Further, the Chinese 

Government directs and funds a highly coordinated effort by Chinese state-owned and private 

 

30 See https://www.census.gov/foreign-trade/statistics/product/atp/2017/12/atpctry/atpg04.html.
36

firms to acquire foreign tech firms. China’s conduct poses a threat to the United States where IPintensive industries alone support more than 45 million jobs and represent 39 percent of U.S. 

GDP (Gerwin, 2018).

31

Equally important for the future, after several decades of currency manipulation to create 

unfair comparative advantage in support of its engineering-based innovation strategy, the new 

focus of Chinese state industrial policy is on higher value-added industries based on sciencebased innovation strategies, as described above. Executing this strategy demands quick and 

massive absorption of IP to enable the targeted transition and to provide Chinese firms with the 

experience and capability to become science-based innovators.

As Nager (2016) points out, keeping the Chinese currency (RMB) artificially low is less

important than it once was. China is instead ramping up aggressive technology acquisition 

mechanisms as a precursor to the goal of achieving sufficiency in internal innovation-based 

growth strategies. Thus, he argues that China continues to be the culprit in persistent U.S. trade 

deficits, just through a different path.

Moreover, China has not become guiltless with respect to currency manipulation. The 

traditional approach to manipulating one’s currency is to directly intervene in currency markets. 

China has done this for years by buying U.S. debt, which creates a demand for dollars and thereby 

raises the value of the dollar relative to the yen. 

Today, China still buys our debt but has not used this tool in an aggressive manner to directly

manipulate the bilateral exchange rate. This “backing off” from direct currency manipulation, 

however, does not mean China has forsaken this technique altogether. By subsidizing exports

and thereby lowering their prices, the Chinese Government is effectively depreciating its 

currency with respect to the competitive positions of competing economies for these export

markets.

Another argument is that the loss of U.S. jobs in the heavily traded manufacturing sector is due 

more to productivity increases, which lowers labor content per unit of output, than to currency 

manipulation. Such an assertion is important for addressing the central issue of this paper: the 

relationship between TBED policies and the resulting increases in productivity, and the long-term 

growth in jobs and also in workers’ incomes.

The fact is, while increasing productivity does lower labor content per unit of output, it also 

makes exports more competitive in global markets, thereby raising output and global market 

shares. This the best of all possible scenarios because the increase in sales stimulated by higher 

productivity (greater performance and/or lower cost) raises output, which requires more labor

 

31 Also see United States Trade Representative, “2017 Report to Congress on China’s WTO Compliance,” pp. 2-12. 
37

is required even with lower unit labor content. And, because companies pay for productivity, 

workers receive higher incomes, as well. If this were not the case, how could Germany with one 

of the highest labor compensation rates in the world manage to achieve persistent trade 

surpluses (Tassey, 2018a).

The bottom line for TBED policy is that developing and using technology as the tool for 

achieving long-term high rates of growth in productivity is the only way to raise workers’ real 

incomes over time. Government growth policies must support the public-good components of

each competitive asset category.

South Korea 

Beginning in the mid-1980s, the Korean Government ramped up programs to encourage hightechnology industries such as semiconductors, where engineering-driven innovation could be 

applied to improve on science-based innovation elsewhere, in particular the United States. As 

has been the case with China, the Korean TBED policy focus began to shift in the 1990s to more 

knowledge-intensive industries in which science was increasingly directly drawn upon to create 

new technologies. This shift, in turn, required increasing domestic investment in a broader array 

of policy instruments, which drove regional innovation cluster development (Enterprise Society, 

2012).

A National R&D Program was initiated to coordinate and fund a range of policy instruments to 

raise the economy’s R&D intensity and, in particular, to increase industrial firms’ R&D spending. 

The latter was achieved by generous R&D tax incentives. By 2007, 80% of Korea’s R&D 

expenditure was occurring within the private sector. South Korea’s national R&D intensity is now 

4.23 percent, compared to 2.74 for the United States.

32

Most important, like China, South Korea has recognized the need to elevate its existing 

“industrial complexes,” in which only modest co-location synergies were being achieved. In 

particular, while production agglomeration was realized in support of engineering-based 

innovation strategies, there was not sufficient R&D capability to achieve the desired “innovation 

complexes” (Ministry of Knowledge Economy, 2010).

As a result, South Korea’s Government has focused intensely on developing a set of institutions 

and industry structures to effect the desired shift to a science-driven technology development 

and innovation strategy. Recognizing the growing complexity of emerging technologies and 

therefore the imperative to co-locate large and small firms within a regional supporting 

ecosystem, the Korean Government developed and implemented four major policy thrusts

(Enterprise Society, 2012):

 

32 Country data are for 2015. Source: NSF, Science and Engineering Indicators, 2018 

(https://www.nsf.gov/statistics/2018/nsb20181/assets/1038/tables/tt04-05.pdf).
38

(1) Fostering of region-specific industrial clusters. Supported by national and local

government. Regional innovation councils were set up to oversee the 

development of the clusters.

(2) Creation of environments conducive to the fostering of entrepreneurship and 

innovation. Industry structure incentives, such as support for startups and small

firms generally, access to financing, business support services, targeted education 

and training, and university-driven R&D.

(3) Enhancement of a collective learning processes within innovation networks. 

Includes strategies such as the removal of legal and regulatory obstacles to interfirm cooperation. Also included were incentives for collaborative research 

between industry and universities, plus access to professional services and the 

fostering of social networks.

(4) Regional social capital. Support from community and other non-government 

organizations, who provided networking forums such as conferences, workshops 

and seminars both within South Korea and with neighboring economies, such as 

Japan, Singapore and China, to promote cross-border collaboration for 

education, research and company partnering.

A major institutional element of this overall strategy is a set of Government Research Institutes 

(GRIs) to support the development of new platform technologies, which can spawn new 

industries. They are therefore comparable to the U.S. Manufacturing Innovation Institutes

(MII’s).

Korea has a long history of promoting co-location synergies. Until the last decade, however, 

most clustering was just production agglomeration; i.e., such clustering did not include sufficient 

R&D capability. 

Recognizing that this situation was not a sufficient infrastructure for the industry-universityinstitute collaboration on R&D required to achieved science-based innovation status, a major 

effort was initiated to elevate the economy’s TBED infrastructure and overall innovation strategy.

This effort, ongoing for the past two decades, consists of the implementation of an Industrial 

Complex Cluster Program (ICCP) at seven model complexes in multiple economic regions of the 

Korean economy. 

The overall purpose of ICCP is to upgrade each region to true global innovation clusters 

centered on designated technologies and associated markets. These efforts have focused on 

integrating complementary research functions for each technological focus under an industryuniversity-government institute framework. The long-term goal is to upgrade the productivity of 

R&D capabilities through enhanced inter-cluster exchange and collaboration (i.e., achieve risk 

pooling, economies of scope in early-phase technology research.

In terms of actual organizational strategy, this policy thrust has been implemented through the 

establishment of “mini clusters”. Mini Clusters refer to relatively small alliances of a range of
39

“companies, universities, research institutes, supporting organizations, and local governments, 

grouped by the types of strategic business or technological characteristics. For example, the 

Changwon cluster, which has adopted machinery as its strategic business, has organized and

operated mini clusters that specialize in machine tools, molds, transportation equipment, 

mechatronics, and metal materials” (Ministry of Knowledge Economy, 2010).

So far, about 60 mini clusters have been initiated by the Korean Government. The Government 

directly manages the structures, objectives and working methods of all 60 mini clusters, which 

leads to a uniformity of structure and overall management.

33

7. TBED Policy Instruments and Overall Policy Structure 

An OECD (2015a) report emphasizes that most of the key technologies in use today, such as 

the Internet and genomics, “have their roots in public research, illustrating how essential 

public investments are”. Thus, as underscored in OECD (2017b), the design, implementation, 

and management of national innovation strategies are critical to economic success. 

Such a statement can be labeled as obvious, but implementing it effectively and consistently is 

not trivial. The process of developing a national strategy requires involvement of all stakeholders

across the entire technology life cycle. As discussed here and in Tassey (2018a), individual policy 

instruments are typically applied at specific phases of a technology’s development and 

commercialization. This reality, coupled with the fact that the actors involved at each phase are 

to varying degrees different, imposes real management and coordination requirements on the 

overall ecosystem.

To be efficient, however, TBED policy instruments must be designed for maximum impact in 

overcoming specific patterns of underinvestment. For example, R&D is supported by both 

government funding and targeted government tax incentives (Tassey, 1996). The two policy tools 

target different market failures. So, policy makers must choose wisely.

In implementing a comprehensive TBED strategy, the total amount of resources committed is 

also a critical indicator, as it indicates the level of commitment to technology as the key driver of 

long-term growth in output (GDP) and income. In this regard, the OECD (2015a) report points out 

that the U.S. economy ranks just below the median in terms of spending by government and 

higher education institutions on R&D in 2007 as a percentage of resulting GDP in 2013.

34

Although the U.S. economy was the clear technological leader for several decades after World 

War Ii, the needed evolution of its innovation policy compared to other nations has lagged. For 

 

33 In contrast, two basic forms of clusters exist in Germany. One is “bottom-up” clusters, which are industry driven 

and largely managed by the private sector. The other, “top-down” clusters, are initiated and controlled by 

government. Source: TCI (http://www.tci-network.org/news/81CI).

34 The implication is an assumed six-year lag between the R&D spending and the impact on GDP.
40

example, this country was the first to implement an R&D tax credit. However, other economies, 

seeing the value of this incentive, imitated the U.S. initiative and subsequently expanded its size. 

In the late 1980s, the U.S. credit was the most generous among OECD members. But, the 

Information and Technology Foundation (ITIF) has calculated that the United States has steadily 

fallen in the relative generosity of this policy tool for stimulating private-sector R&D. By 2004, 

the U.S. credit had fallen to 17th. By 2014, the United States ranked 19th among OECD countries 

and 27th among all countries measured, and its ranking has continued to slip. In 2016, the U.S.

ranked a low 25th among just the OECD nations and 26th for small and medium-sized enterprises. 

(Kennedy and Atkinson, 2017).

More broadly, and hence even more serious, is the fact that the U.S. Federal Government has 

no comprehensive set of TBED policies that are organized into an ecosystem to serve as a model 

for individual states, as well as to provide state governments with direct financial and technical 

support. More generally, the U.S. has no substantive and comprehensive innovation policy

function at the federal level.

As OECD has stated, “The development and implementation of innovation policies also require 

strong capabilities within the public sector, including in building trust in government action and 

ensuring the support of stakeholders for policy actions, assuring co-ordination between 

individual economic agents, as well as fostering greater experimentation in the economy” (OCED, 

2017).

As a general principle, establishing a goal-based national strategy for innovation is one thing; 

its implementation is often another matter. Most industrialized nations seem to realize that the 

development of new technology platforms should be at the top of the national economic 

investment agenda and have invested in building the institutions and capabilities needed for 

discovery and invention, as described in this paper. 

While progress is being made toward this holistic (ecosystem) objective, investments in most 

countries have not yet reached the size and scope to attain targeted TBED efficency. China, for 

example, is not a top global competitor in major science and innovation driven industries. For 

example, it has less than a 1 percent share of global revenue in branded pharmaceuticals, 3 

percent in biotech, and 3 percent in semiconductor design. In these markets, Chinese firms still 

tend to focus on lower value-added products, such as generic pharmaceuticals. Its struggles

reflect both how long it takes to build science-based innovation capacity despite its large 

investments in R&D (Dobbs et al, 2015).

The policy message is that the development of TBED policies must recognize that each

economy operates in a complex, dynamic and uncertain environment, where government policy 

initiatives will initially, at least, be inadequate. Thus, a commitment to monitoring and evaluation 

of policies, and then learning from experience and adjusting policies over time, is the only way 

to ensure that government action is efficient and will reach its objectives.

This imperative for a competent and well-resourced national policy capability creates 

considerable concern for the U.S. TBED policy structure. While individual states are ramping up 
41

both the scale and scope of specific policy instruments, they are doing so without anywhere near 

the scale and scope of support needed from the Federal Government. In contrast, leading 

technology-based economies such as Germany and China have a much more committed central 

government role.

A additional point can be made that may appear to be a contradiction to the repeated emphasis 

on regional specialization and sufficient investment to achieve both scale and scope economies 

at the regional level. That is, companies within a TBED cluster will likely benefit not only from 

involvement in other regions, but also from participation in other countries’ regional hubs.

In this regard, as Dobbs et al (2015) point out, this could mean locating more R&D activity in 

emerging technology-driven economies such as China, where, for example, Microsoft has funded

3,000 scientists and engineers in its Asia-Pacific Research and Development Group in Beijing. 

Similarly, Chinese companies can strengthen innovation capabilities by adding R&D facilities in 

other markets or using overseas R&D joint ventures. Lenovo executives credit having dual 

headquarters in Beijing and North Carolina with helping the company achieve global leadership 

in PC sales.

Most important, policy learning rests on a well-developed institutional framework, strong 

capabilities for evaluation and monitoring progress, applying identified good practices, and an 

efficient government bureaucracy. Incorporating policy monitoring and evaluation will support 

evidence-based decision making and accountability and enable policy learning over time, as can 

formal experimentation with policies at a small scale Tassey (2014b). Better measurement of 

innovation outcomes and impacts is essential in this context.

The major policy message is that the lack of a substantive U.S. Federal Government strategy 

implemented through an institutionalized TBED policy infrastructure is placing too great a burden 

on individual state programs. Learning, scale and scope effects, and simply increased investment 

need national government support, shown by the discussions of other economies’ TBED 

strategies. In today’s increasingly competitive technology-driven global economy, governments 

compete against each other as much as do private companies.

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