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Foreword
Foreword
This report has been produced in as an accompaniment and background research piece to
reports delivered to the UAE Office of Advanced Sciences.
It’s purpose is to provide a broad background on the state of play in R&D policy, providing a
open frame through which various policy ideas can be organised.
This report does not represent the opinion of UCL STEaPP or the UAE Office of Advanced
Sciences. It merely reflects a broad spectrum of thinking from across academic and
practitioner communities.
Citation
Please cite as:
Bevan, L. D. (2019). Research & Development Policy: An Overview of Key Thinking and
Frameworks. London, UK: UCL Department of Science, Technology, Engineering & Public
Policy (STEaPP).
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Table of Contents
Foreword .............................................................................................................................. 2
Citation................................................................................................................................. 2
List of Tables......................................................................................................................... 5
Executive Summary .............................................................................................................. 6
0 Introduction: What is R&D Policy?................................................................................ 8
1 Major Framings in R&D Policy .................................................................................... 11
1.1 ‘The Endless frontier of Innovation’ ...............................................................................12
Origins of the Narrative......................................................................................................................... 12
View on the Innovation System............................................................................................................. 13
The role of Policy in this view................................................................................................................ 13
Challenges to this view.......................................................................................................................... 14
1.2 ‘The National Ecosystem’ ...............................................................................................15
Origins of the Narrative......................................................................................................................... 15
View on the Innovation System............................................................................................................. 16
The role of Policy in this view................................................................................................................ 16
Challenges to this view.......................................................................................................................... 17
1.3 ‘The Great Challenge’.....................................................................................................18
Origins of the Narrative......................................................................................................................... 18
View on the Innovation System............................................................................................................. 19
The role of Policy in this view................................................................................................................ 19
Challenges to this view.......................................................................................................................... 20
1.4 Comparison of the Frames .............................................................................................20
1.5 Additional Influential Frames and Models .....................................................................22
New Public Management...................................................................................................................... 22
Open Innovation................................................................................................................................... 22
Responsible Research and Innovation ................................................................................................... 23
The Innovation Policy Dancefloor.......................................................................................................... 23
The Entrepreneurial State ..................................................................................................................... 24
Experimental Policymaking in Science Policy ......................................................................................... 24
2 Policy Instruments for R&D Policy .............................................................................. 26
2.1 The Policy Cycle.............................................................................................................. 26
2.2 Mapping out instruments............................................................................................... 27
2.3 Review of instruments ................................................................................................... 28
2.3.1 Government Laboratories ....................................................................................................... 31
2.3.2 HE Funding Allocation Policies................................................................................................. 31
2.3.3 Fiscal Incentives ...................................................................................................................... 35
2.3.4 Network-based Policies ........................................................................................................... 38
2.3.5 Foresight Exercises.................................................................................................................. 40
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2.3.6 Public Engagement.................................................................................................................. 41
2.4 Policy Mixes in R&D Policy .............................................................................................41
3 Key Policy Considerations and Policy Values .............................................................. 45
3.1 General Policy Selection Criteria .................................................................................... 45
3.2 STI-Specific Policy Considerations ..................................................................................46
3.2.1 Linear and Systemic Thinking................................................................................................... 46
3.2.2 Area of Governance ................................................................................................................ 46
3.2.3 The Level of Governance ......................................................................................................... 46
3.2.4 The inventive unit: Researchers, Teams, Firms and Networks .................................................. 47
4 Implementation, Institutional and Governmental Change ......................................... 48
4.1 Challenges in Priority Setting ......................................................................................... 48
4.2 Challenges in Implementation........................................................................................ 48
4.3 Challenges in Institutional Reform and cooridnation ..................................................... 48
5 Measurement and Monitoring of Outcomes .............................................................. 50
5.1 Why Monitor?................................................................................................................50
5.2 How is R&D, Innovation monitored?..............................................................................50
5.2.1 Input Metrics .......................................................................................................................... 50
5.2.2 Intermediate Metrics .............................................................................................................. 50
5.2.3 Output Metrics........................................................................................................................ 50
5.2.4 Frameworks ............................................................................................................................ 50
6 National Models of R&D policy and Innovation.......................................................... 52
6.1 Policy Importation/Replication as a practice..................................................................52
6.2 The Importation of National policy models....................................................................52
6.2.1 R&D Policy Importation/Replication ........................................................................................ 52
6.2.2 The Intractability of National context ...................................................................................... 53
6.2.3 Replicating STI policy: some relevant notes for the UAE context .............................................. 54
6.3 Illustrative Case Studies ................................................................................................. 55
6.3.1 National Snapshot: Estonia...................................................................................................... 55
6.3.2 National Snapshot: South Korea .............................................................................................. 56
7 Summary .................................................................................................................... 58
Key Terms and Acronyms ................................................................................................... 59
References.......................................................................................................................... 61
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List of Tables
List of Figures
Figure 1: A simplified linear model of the roles of R&D policy, Innovation Policy and STI policy
in the path from fundamental research to the marketplace for technologies........................ 8
Figure 2: How the framing of this primer fits in within the policy cycle.................................. 9
Figure 3: The policy cycle. Adapted from Howlett (2011) .................................................... 26
Figure 4: Adapted from (Howlett and Fraser, 2009, fig. 1), with own additions and
modifications ...................................................................................................................... 27
Figure 5: Map of different policy instruments relevant to R&D (and also partially Innovation
Policy)................................................................................................................................. 30
Figure 6: Examples of Input and Output criteria for funding allocation decisions ................ 33
Figure 7: Layers of complexity in STI policy. Taken from Figure 1 in (Magro, Navarro and
Zabala-Iturriagagoitia, 2014) ............................................................................................... 49
List of Tables
Table 1: Comparison of three key framings for R&D Policy.................................................. 21
Table 2: Some typical interactions between different actors on the Innovation Dancefloor.
Adapted from Table 1.2 in (Kuhlmann, Smits and Shapira, 2013) ........................................ 24
Table 3: Overview of R&D Policy Instruments, organised according to NATO and governance
style. Inspired by Table 9.1 in (Howlett, 2011, p. 129) ......................................................... 28
Table 4: Summary of different state steering models and how they come to play in HE funding
policy .................................................................................................................................. 32
Table 5: Summary of typical performance indicators adapter from de Boer et al (2015) ..... 34
Table 6: Overview of different policy instruments relevant to R&D..................................... 43
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Executive Summary
Executive Summary
This document provides a broad overview of contemporary thinking about R&D policy, with
a particular focus on funding priorities and ideas about national innovation models. It
contextualises many of the issues that policymakers face within the broader cycle of
policymaking, considering the framings that surround agenda setting, the broad spectrum of
policy instruments available, the key considerations that are made in selecting between
these, challenges found in implementing R&D policy and how outcomes can be assessed for
policy monitoring.
It begins with reviewing the key framings that shape thinking about what R&D policy should
be and how these shape rationales. This considers and sets the roles of actors in the
innovation system such as the state, private industry, academia and consumers as well as
providing the justifications for policy action. Over the past century, three particular framings
each come with their own rationales for policy action. These have been extremely important
for the way that R&D Policy has been shaped and it is important to be cognisant of these of
in the agenda-setting process.
1. ‘The Endless Frontier’: Government funding of science leads to an inevitable march of
progress
2. ‘National Innovation’: Innovation is path dependent and is a core aspect of national
competitiveness
3. ‘The Great Challenge’: Innovation involves a wide range of stakeholders and must help
us face up to other contemporary policymaking challenges such as sustainable
development
With this background, the various policy instruments available to policymakers for the
promotion of R&D are mapped out and discussed in the context of these big ideas about what
R&D policy should be or do. For example, a number of governments (such as the UK) have
used market-based models to guide funding decisions. This stands in contrast to traditional
ideas about academia being self-driving and curiosity driven. Instruments can be targeted at
both the private and public sector:
In the public sector, the key instruments used to promote R&D are direct funding of either
universities or research institutes. The typical mechanisms of funding has undergone a
number of changes in the last couple of decades. Of particular importance is the influence of
New Public Management (NPM) which has seen a shift to marketisation of university research
and funding based on performance metrics. More recently, ideas about ‘open innovation’ and
challenge-based research have permeated the policy debate.
In the private sector, R&D policy has focussed historically on a series of financial incentives,
such as direct grants and tax incentives to encourage. This kind of government support is
generally seen to be effective, up to a point at which there are diminishing returns from more
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Executive Summary
generous programmes. However, less is known about the interactions of large portfolios of
policies (policy mixes)- how to these polices act in combination?
A number of challenges for implementing R&D policy are identified, including pushing
through the institutional reforms necessary and convincing a wide variety of stakeholders to
have trust in the reforms being suggested.
Innovation and the products of research and development can be tracked in a number of
ways, primarily but looking at input indicators (such as levels of R&D spend, number of
researchers active), intermediate indicators (such as number of patents registered) and
output indicators (such as numbers of new products brought to market). There has been a
propensity in the past to focus on input indicators as an intuitive target. However, this may
confuse means and ends as expenditure is not always necessarily effective in producing good
quality research. Policymakers may wish to think about the outcomes they wish to see form
the research system they are intervening in to decide the relevant indicators to track. This will
also inform what sort of institutions are necessary for the management of that research
system.
Putting all of these layers of the policy process together, an image of a national system can
be developed. A number of countries seeking to emulate the research prowess of developed
nations such as the USA, the UK and Germany have attempted to replicate some or all of their
innovation policy. Such an approach is intuitive, but the experience of these countries urges
caution for a number of reasons:
• Such approaches are not sympathetic to the contextual nature of research and
innovation
• These approaches can lead to the abandonment of areas of indigenous speciality and
overall strategic weakening
• Successful policy interventions consider the long-term development of local
capabilities in locally sympathetic way
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Introduction: What is R&D Policy?
0 Introduction: What is R&D Policy?
This may seem a simple question, but as a matter of fact one that requires a little clarity. R&D
policy overlaps with a number of other key areas of policy, including innovation policy so it is
useful to see the relationships between them.
In accordance with Martin (2016), we define R&D policy as that which includes science policy
and research policy, both for the public and private sector. ‘Innovation policy’ is normally said
to be broader and also includes commercialisation policy. Overarching both of these is
Science Technology and Innovation Policy (STI) policy. In this space of research there is
relatively little consistency over terminology, and hence this report with focus on R&D policy
yet acknowledges when ideas form overlapping domains are discussed.
Figure 1: A simplified linear model of the roles of R&D policy, Innovation Policy and STI policy in the path from fundamental
research to the marketplace for technologies
A substantial body of research exists which considers R&D policies from a purely economic
perspective. Such research will often consider some narrow set of indicators relating to the
performance of some policy programme. There is less research that considers from a policy
perspective, looking at the overall impact policies and policy mixes have on a number of
policymaking priorities (Sá and Litwin, 2011; Martin, 2016).
It is also informative to define a number of key terms. Taking after Popp (2010, p. 277), we
define:
Invention: The creation of a new idea
Innovation: The development of new ideas into products, goods or services
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Introduction: What is R&D Policy?
Diffusion: The spreading of the use of the innovation
A full list of definitions, key terminology is available in the Glossary, towards the end of this
document.
Doern and Stoney (2009, p. 16) provide an analytical framework for understanding R&D
policy. They utilise four main components, which we will also roughly employ in our analysis
and structure the following sections.
1. High Level Policy and Conceptual Discourse- what are the key narratives and framings
used to understand R&D policy?
2. Policy Instruments- what are the policy instruments that can be used?
3. Core policy values and ideas – what are the key values and considerations that are in
play when designing R&D policy?
4. Institutional and governance change- what does it take to implement these policies?
What are the pragmatics of implementing R&D policy in the real world?
We have also aligned these considerations with where they are most relevant in the ‘policy
cycle’
1
to provide a framework for this analysis. Figure 2 shows how the various sections align
with different parts of the policy cycle. In this way this document is intended to act as a guide
to the process as a whole.
Figure 2: How the framing of this primer fits in within the policy cycle
1
A tool used to understand the different phases of policy creation and implementation.
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Introduction: What is R&D Policy?
The first section describes a number of key framings that provide a rationale for policy action.
We examine the key discourses that surround R&D policy in order explain how these shape
rationales for policy action and policy preferences.
Section 2 describes the key policy instruments available for R&D policymaking. It examines
some of the ways that they can be classified, in order to build a map of the policies available
to policymakers. It then reviews a number of the key policy instruments, such as those
concerned with R&D funding.
Section 3 examines how this multitude of policies can be sifted through: what are the criteria
for selection between policies? What are the key considerations and trade-offs that need to
be made?
Section 4 then considers how these policies are implemented. What institutional and
governmental change comes alongside this? What practical considerations might there be in
implementation?
Section 5 then looks at how these policies can be evaluated. What makes a policy successful
and how can monitoring bets be put into practice? How can the success of R&D policy be
measured?
Section 6 then looks at how these ideas operate in practice and how nations use models to
structure their policy programmes. It asks if polices can be effectively transplanted between
national contexts? What are some nascent lessons for those formulating policy and
specifically for the UAE context?
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Major Framings in R&D Policy
1 Major Framings in R&D Policy
In this section we examine the high-level discourse, framings and ‘grand narratives’ that
surround R&D policy, and STI policy as a whole. Put simply- what are the big stories and ideas
about what R&D policy should be and when it is most effective at achieving goals? These high-
level discourses influence the way that policy makers conceive of R&D policy and STI policy.
They also provide what are known as rationales: the beliefs about the nature of the system
that either implicitly or explicitly justify and shape policy interventions (Laranja, Uyarra and
Flanagan, 2008, p. 823).
This section utilises the framework of Schot & Steinmeuller (2018), who enumerate three key
frames that have shapes thinking about R&D and Innovation over the last century and in the
present day. In describing each of these narratives, we will first furnish these with a short and
approximate vignette of the framing, before detailing:
• The history of this frame- why do people think this way?
• The key considerations and roles of different actors within this frame – what is the
role of government in this view?
• The policy implications of this frame- what types of policy intervention are favoured
by this frame?
• Challenges to this frame- what are the drawbacks or issues identified?
It should be noted that the frames presented are somewhat of a simplification, and of course,
very few sign up to the ideas contained within any one narrative fully. In fact, a number of
different frames can be identified from actors in these systems (Khan et al., 2016). These
visions are important to understand as the collective visions of the future that people hold
2
shape their policy preferences and provide justification for government as a shepherd of the
innovation process (Laranja, Uyarra and Flanagan, 2008, p. 824).
2
These care called ‘sociotechnical imaginaries’ in the academic literature
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Major Framings in R&D Policy
1.1 ‘The Endless frontier of Innovation’
Science has always made substantive contributions to the development
of industry. The ceaseless rolling back of the frontier of what we don’t
know by science has an enormous potential to promote growth and
human flourishing. Negative consequences that emerge from innovation
are largely the result of incomplete scientific knowledge, and when they
do emerge are generally dealt with through more innovation. The role of
the public sector is to generously fund science and the job of the scientists
is to knuckle-down and produce transparent scientific reserach. The job
of the private sector is to take these scientific discoveries and turn them
into commercial innovations- fuel for long term economic growth.
Origins of the Narrative
This framing is highly influenced by post-war modernism and incorporates a great deal of
optimism about the promise of technology to further society. After the industrial advances
during the second world war, a consensus emerged that the state can and should support
scientific research to contribute to the modernisation of industry (Schot and Steinmueller,
2018, p. 1554). A key metaphor to understand this is the idea of the ‘endless frontier’,
popularised by Vannevar Bush
3
(1945) in a report
4
describing a vision for a post-war scientific
research system for the United States. This is the idea that the role of science is to roll back
the frontier of what we do not know. Through this ceaseless rolling back of the frontier,
society benefits and there is an inevitable march of progress, with all of society benefiting
from the fruits of knowledge.
Concurrent with the post war renegotiation of the role of the state, economists noticed that
growth in productivity outstripped growth in capital and labour contributions. Hence it was
discerned that technological advances were responsible for this productivity growth. These
advances had their origins in scientific research- thus confirming the value of fundamental
R&D to society (Schot and Steinmueller, 2018, p. 1556). Such an economic approach is broadly
neoclassical and sees technology as an endogenous factor in growth (Laranja, Uyarra and
Flanagan, 2008, p. 825).
3
A celebrated American science administrator who was head of the U.S. Office of Science Research and
Development (OSRD), during the second world war
4
This report and later proposals by Bush lead to the creation of the National Science Foundation (NSF). Notably
Bush’s proposal beat out other proposals for the NSF which wanted the organisation to be administrated in a
populist way, overseen by non-scientists such as consumers and business leaders (Kleinman, 1995)
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Major Framings in R&D Policy
View on the Innovation System
The model of innovation described by this narrative is simple and linear. Firstly, central
government provides fiscal support to science, giving it autonomy over what and how it
decides to conduct its research. Science then produces goods for society and business can
capitalise on the result. In this model expanding consumption goes hand-in-hand with the
technological expansion of civilisation.
The division of responsibilities in such a system is clear. The public sector provides a large
supply side push by generously supporting scientific research. Scientists then conduct
research, driven by curiosity or otherwise by a contribution to some loosely defined societal
mission and with little mind for the eventual use of the knowledge. The private sector then
capitalises on the products of the scientific advancement, turning the science into innovations
and new products to bring to market. In particular it is perceived that large corporations have
the capacity and the ability to perform this role (Schot and Steinmueller, 2018, p. 1557).
Innovation also is imagined to happen without regard to context- the value produced is the
same no matter where it occurs (Laranja, Uyarra and Flanagan, 2008, p. 826).
Any problems that may emerge as a result of technology are seen as soluble by further
technological advancement or through some sort of regulatory agencies. These regulatory
agencies perform risk assessments- which are seen as a sort of add-on to the scientific process
(Schot and Steinmueller, 2018, p. 1556). Much of the regulation in this paradigm is ex-post
5
and issues that arise may be described as necessary costs of progress (Schot and Steinmueller,
2018, p. 1557)
The role of Policy in this view
Given that technological progress is said to be one of the key determinants of economic
development, the question is then how best to promote the incentives of market actors to
produce the desired level of scientific advancement? However, as the knowledge goods
produced by research and development can be easily appropriated by competitors, it is
considered difficult to incentivise firm-level R&D. In this way the private rate of return is lower
than the social return
6
and the gap between these must be bridged (Hall and van Reenen,
1999, p. 1). Thus the production of these knowledge goods may be therefore subject to a
market failure and government can intervene (Laranja, Uyarra and Flanagan, 2008, p. 825).
Another market failure includes the high risk hurdles that must be surmounted by firms
engaging in basic R&D; research is an uncertain business and many firms will be put off by
this uncertainty (Guellec and Van Pottelsberghe De La Potterie, 2003, p. 226). In response to
these issues, it is argued that public support should be provided to fundamental R&D to
compensate for the misallocation of resources (Schot and Steinmueller, 2018, p. 1556). In
5
Meaning “after the fact”
6
An externality or market failure
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Major Framings in R&D Policy
this frame, latter stage research, such as the research necessary to bring a product to market,
is not subject to the same risk of appropriation by market competitors (Schot and
Steinmueller, 2018, p. 1556). Hence later stages of research are understood as requiring less
government support to correct market failures.
Typical policy instruments in this frame that attempt to support R&D often involve fiscal
incentives, such as tax credits or the creation of otherwise favourable conditions for firms
engaging in R&D. With the supply-oriented model of innovation in which benefits flow to
society inevitably from public investment, key indicators of R&D policy are research intensity
as a percentage of GDP (Schot and Steinmueller, 2018, p. 1557).
There remain practical limits to the amount of money that can be spent on R&D as all
government budgets are ultimately limited. Technology foresight exercises
7
allow
governments to make strategic decisions, and these exercises may contain some
consideration of societal factors, though they are often dominated by technological factors
(Schot and Steinmueller, 2018, p. 1557).
Challenges to this view
One of the principle problems associated with this viewpoint is the limited understanding of
the potential pathologies associated with scientific development. A reaction against a
perceived form of mindless technological optimism was fomented in the 1960s, growing
throughout the 80s spurred on by iconic science-related disasters such as Chernobyl. This in
turn led to the creation of new regulatory agencies or the expansion of their powers; for
example, the US FDA gained powers to regulate pharmaceuticals after the Thalidomide
disaster (Schot and Steinmueller, 2018, p. 1556).
The linear model of innovation pays little attention to where the producers of knowledge are
situated- it is as if innovation is occurring everywhere and nowhere in particular. Given that
one of the primary determinants of technological advancement in this model is state funding,
those richer governments able to provide greater support to fundamental research will see
greater amounts of technological progress. This creates a distinction between the creators
and the consumers of innovations, essentially the idea that there are winners and losers in
the innovation game, particularly at a national scale (Schot and Steinmueller, 2018, p. 1557).
Some national governments, particularly in east Asia, deployed more targeted versions of the
policies advocated for by this narrative and observations of the success of these programmes
lead in part to the emergence of the second narrative we review.
7
Discussed in section 2.3.5
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Major Framings in R&D Policy
1.2 ‘The National Ecosystem’
Innovation happens in different ways in different contexts- it is also not
always global and relies on having a skilled workforce and a supportive
ecosystem of different organisations. Consequently, for nations to
successfully harness the benefits of innovation and stay competitive with
others, they must pay close attention to the way that innovation occurs
in their country- innovation in Japan is not the same as innovation in
France. Innovation also relies on the network of private and public actors-
we need to facilitate link ups between academia and private industry to
allow the easy commercialisation of the knowledge produced in research.
Origins of the Narrative
In the 1980s, there dawned a gradual realisation that spill-overs from innovation were not
distributed evenly across the world: winners and losers were clear. Further to this, the
disparity in outcomes was self-reinforcing: under a Schumpeterian economic analysis, these
uneven advancements may lead to ever increasing returns on R&D investment thus a positive
feedback loop causes a process of widening inequality in the outcomes of national R&D
(Laranja, Uyarra and Flanagan, 2008, p. 826).
In response to the perceived issues with winners and losers, many national governments
considered how not to be ‘left behind’ in the struggle for national technological competitivity.
The differential success in the process of technological globalisation placed a new emphasis
on the importance of fostering more effective local or national innovation policies and nit
relying on the spill overs from other, wealthier nations (Schot and Steinmueller, 2018, p.
1558).
Furthermore, oil price shocks and the early-80s recession brought countries into greater
strategic competition with each other. Concerns grew about the ability of richer countries to
hoard the knowledge and benefits accrued through their more generously funded R&D
programmes. Critical examination of the ideas associated with the ‘endless frontier’ framing
lead to a number of observations (Schot and Steinmueller, 2018, p. 1558):
• Knowledge has a ‘stickiness’ and can be accrued by a nation, rather than flowing
evenly throughout the world.
• Absorptive capacities
8
are important for understanding how firms can capitalise on
innovations. R&D will be ineffective at driving growth if the skills necessary to
incorporate it into business models are not there.
8
“The ability of a firm to recognize the value of new, external information, assimilate it, and apply it to
commercial ends is critical to its innovative capabilities.”(Cohen and Levinthal, 1990)
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Major Framings in R&D Policy
• These absorptive capacities are not only driven by education levels, but also by the
value placed on entrepreneurship
9
by society.
• Innovation is path dependent. Disruptive innovations move the path of innovation and
cumulative innovations reinforce it
10
.
These observations about path dependence and the importance of local context were
reinforced by the tremendous economic growth of east-Asian economies such as Japan,
followed by the four “Tigers”: Taiwan, South Korea, Singapore and Hong Kong (Schot and
Steinmueller, 2018, p. 1558). The growth of these countries economically and technologically
was seen to be the result of their strong local innovation systems, each appropriate to their
particular national context.
Variations of this framing have proved very popular over recent decades and narratives
around innovation systems are predominant in many countries (Sá and Litwin, 2011, p. 425).
View on the Innovation System
In this framing, the key to innovation is the cultivation of ecosystems of mutually supportive
actors. This frame focusses less on the state providing support for early-stage or pre-
commercial R&D, and more on the creation and maintenance of relationships between actors
in the innovation system.
The previous frame did not consider the role of users or consumers of a technology. Here
users have an inflated role and legitimacy and some basic level of consultation is considered
appropriate.
Innovation is said to occur through the increasingly intertwined interactions of different
actors. Thus, innovation can occur when academia interacts with business. The role of
entrepreneurship is elevated in this view, concordant with the idea that absorptive capacity
relies on the strong social capital of entrepreneurialism (Schot and Steinmueller, 2018, p.
1560).
The role of Policy in this view
Policy in this view has a focus around developing and maintaining networks of relationships
between actors such as government, business and academia. However, there is a lack of
consensus of what sorts of policies are most effective within this view. Hence a number of
policy approaches have been made, including such policies as (Schot and Steinmueller, 2018,
p. 1560):
9
The social capital of entrepreneurship
10
These two kind of innovation can be imagined as revolution versus evolution
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Major Framings in R&D Policy
• Policies that aim to align actors. An example is ‘funding conditionality’ in which
funding for R&D is made conditional on cooperation with other actors.
• Support for New Technology Based Firms (NTBF) which are able to expand the based
of R&D activities beyond what would be possible within the internal R&D systems of
major firms.
• Attempts to stabilise demand for new products and services to promote technological
diffusion. Though government procurement is generally less favoured
• Education of workforces that build absorptive capacity and human capital
The creation of networks might also be facilitated through policies such as the creation of
science parks, which allow collaboration between industry and university researchers
(Martin, 2016, p. 162). Other prominent strategies may involve the creation and promotion
of spatially distinct innovative zones
11
, though there is controversy as to whether these
clusters can be reliably brought about deliberately as a result of targeted policy intervention
or whether the process that leads to their successful creation is more organic (Laranja, Uyarra
and Flanagan, 2008, p. 827)
Challenges to this view
One of the key challenges to this view is that these national systems of innovations fail to
open the discussion about options to the wider group of people who are affected by
innovation (Schot and Steinmueller, 2018, p. 1561). Funding for R&D and innovation is still
seen, relatively uncritically, as a good in itself. Issues associated with this innovation can be
considered by regulators, but the variety of options and pathways is not open for discussion.
Against this uncritical optimism, the next frame considers how a broader franchise of
stakeholders, including marginalised groups, can be used to improve the innovation process
and bring it into line with a number of other social or environmental goals.
In recent times similar framings have been manifested on regional and local levels with
regional and local governments pursuing independent innovation strategies (Martin, 2016, p.
165).
11
Areas where similar technology companies and researcher can be in close contact.
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1.3 ‘The Great Challenge’
Innovation does not always necessarily mean social progress. We are
faced with a wide variety of profound shared challenges, unconfined to
national borders, from climate change to cybersecurity that demand our
attention and require new ways of thinking. To unlock the potential for
innovation to deal with these problems we must recognise that there is
no one pathway to achieving a given goal and that the best strategy is to
allow experimentation. We must open up the innovation process to a
broader group of participants and collectively focus on solving large
societal challenges. Identifying grand challenges can assist in the
coordination of all of these actors and give social legitimacy to research
& development.
Origins of the Narrative
Recognising the failure of national innovation systems to consider the role of a broader range
of actors, and the shortcomings of the system in producing answers to the challenges of
inequality, poverty and environmental collapse, a new frame emerged in the 2000s. It
recognises that previous paradigms have been unable to manage the externalities associated
with growth. The previous paradigms may exacerbate inequalities as they aim innovation
towards the production of goods for consumers with higher purchasing power (Schot and
Steinmueller, 2018, pp. 1561–2).
This new frame emphasises the importance of working towards grand societal challenges in
areas such as energy, food, transport, water and healthcare, and is embodied in programmes
such as the EU’s horizon 2020 and the challenges laid out in the UN’s Sustainable
Development Goals (SDGs). Responding to these challenges require more than just doing
more science and creating new technology products- it is also necessary to consider the path
that society wants to take with innovation in a way that pays attention to the concurrent
behaviour and social changes. For example, the development of the electric car is desirable,
but needs to be twinned with a consideration of the changes of human behaviour that come
with it and whether simply replacing all fossil-fuelled vehicles is the best aim of transforming
the mobility sector (Schot and Steinmueller, 2018, p. 1562). In these so called ‘socio-technical
transitions’
12
there are interlinked and concurrent changes in aspect of society such as
technology, skills, infrastructure, regulations, cultural predilections, among others.
This narrative challenges traditional views of technological advancement furthering society
and the ability of government to ensure a reduction of inequality through a redistribution of
12
The process in which new technologies are incorporated into society, and society re-adapts itself with the new
technology
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benefits of innovation. To deal with issues such as climate change, governments are able to
invest in green technologies. However, for these investments to be successful there must be
long term persistent or patient capital (Schot and Steinmueller, 2018, p. 1561). Is the state
able to ensure a redistribution of the benefits of innovation, control tax avoidance and ensure
this long term investment?
View on the Innovation System
In this model, the pathway to innovation is negotiated between a number of different actors-
no one person is at the steering wheel of the innovation system. Actors experiment with
different possibilities, recognising that there is no one route to the desired outcome. Through
this process of experimentation, there may be an accumulation of knowledge and experience.
This experimentation is said to be particularly productive in niches, which can then be
upscaled to the whole system
13
(Schot and Steinmueller, 2018, p. 1563).
A key challenge in this model is how to manage incumbent networks and destabilise lock-in
14
.
Incumbents may include existing industrial actors and elements of civil society (Schot and
Steinmueller, 2018, p. 1563). This lock-in manifests itself in vested interests, pre-existing
commitments and the cognitive challenges of accepting new paradigms.
In this framing, the goals of the sociotechnical transition are not met through redistribution
of the surpluses of innovation, but through a co-creation
15
. Also the focus of innovation is
global, as the problems that the transitions seek to solve are global (Schot and Steinmueller,
2018, p. 1564).
The role of Policy in this view
The role of policy in this view is to allow innovation as experimentation or as a search process
on the system-level. The role of experience is values and the innovation process must be
inclusive to gather as much value from diverse experience as possible.
Often the policy prescriptions involve the formation of some new public mission, though it is
recommended that the missions are formed in some way that allows experimentation and
different viewpoints (Schot and Steinmueller, 2018, p. 1564). Mechanisms must be developed
to allow the participation of users in the system and for the creation of new demands and
markets.
The process of anticipation is also important in this view. Deliberative processes may help
identify the technological, social and environmental impacts of innovation (Schot and
Steinmueller, 2018, p. 1564). However, these anticipatory processes cannot discover all
13
Though the mechanism of up-scaling from niches is said to be under-explored by the literature
14
Where incumbents are unable to escape from old modes of doing things
15
Where different parties negotiate, formally or informally, a mutually desired outcome
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potential outcomes, thus experimentation must be allowed to find the unexpected. Through
putting experiments into practice, learning can occur, which can help reset incumbent
mindsets. Thus, the three key processes that policy should facilitate in this view are:
• Anticipation
• Experimentation
• Learning
However, as this paradigm is still somewhat emergent, there do not exist a great deal of policy
examples for study.
Criticisms of the first two frames included the idea that they are not properly attentive to the
problems that arise as a result of innovation. This third frame notes the interlinked nature of
different policy problems and linked STI policy to these issues in a more meaningful way. Thus
R&D policy begins to overlap with more and more policy domains. This means that it may
become more difficult to isolate the effect of a given policy instrument on the R&D or
innovation system of a country.
Challenges to this view
There exist a number of challenges to this view:
• It may lead to stagnation as consultations slow down decision making
• It may provide excuses for a withdrawal of R&D funding
• It places additional requirements on marginalised actors in the system through
participatory processes
• These processes themselves may be difficult to administrate and expensive
• Many of the ideas involved in this framing are new, and hence some policy
instruments associated with this are less tried and tested
1.4 Comparison of the Frames
Table 1, overleaf, gives an Intercomparison of these three primary frames, considering the
roles of different actors in the innovation system and summarising the general features of the
conceptual models that guide thinking within these frames.
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Narrative
The Role of…
Associated
Economic
Models
Models and
Metaphors of
Innovation
Governance
Structures
Preferred Policy
Instruments
Public Sector
Private
Sector
Science
Users
1:
Generous funder
Harnesser of
Wellspring of new
No Significant
Neo-classical
Linear: Government
Research follows
Support for fundamental
The Endless
of Science
scientific ideas
ideas and
Role
Economics; (also
funds, science
scientific
research.
Frontier
for shared
progress
innovations
Schumpeterian
Growth Theory)
produces, private
sector capitalises
Funding>Innovation>
Capitalisation> Growth
curiosity.
Standard model
of governance, or
revised-standard
model.
2:
The National
Ecosystem
Coordinator of the
ecosystem,
moderate risk
taker, creator of
collaborative
industrial
community
Participant in
national
ecosystem.
Entrepreneurs
drive
innovations.
Entrepreneurial
science engaging
with government
and business and
developing spin-
offs from
fundamental
research
Limited, but
noted role: To
provide
feedback and
light input
Schumpeterian
Growth Theory;
Neo-Marshallian
Interaction of actors
produces knowledge
Triple Helix: of
cooperation between
academia, business
and government
Coordination>
Strengthening>
Competativity
Consultative
Aiding the alignment of
actors’ incentives;
creation of ecosystems/
clusters like silicon valley,
silicon roundabout
(London) etc.;
3:
Investing for the
One player
One player among
Significant role:
Systemic
Experimentation
Consultative or
Destabilisation of
The Great
long term and
among many.
many. Research
consulted or
Institutional
drives disruption and
Co-constructive
incumbents. Facilitation of
Challenge
regulating the ill-
effects of
modernity,
Identifier of
technology failure,
risk taker
Diverse forms of
firms exist.
and expert
knowledge are
produced in a
societal context.
invited into the
innovation
process
Approaches;
Evolutionary
change, overseen with
healthy scepticism.
A search process
aiming for particular
goals.
Anticipation>
Experimentation>
Learning
communication between
producers and consumers
(e.g. knowledge
brokerage).
Table 1: Comparison of three key framings for R&D Policy
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1.5 Additional Influential Frames and Models
The three framings described in detail above are not all encompassing, though they provide
grand overarching stories about what innovation or R&D is, situating the role of the state and
other stakeholders within this, there are other frames and models which are prominent in
discourse that are worthwhile paying at least a little attention to. This sub-section gives very
brief unconnected vignettes for a number of other influential ideas about STI policy and
innovation.
New Public Management
New Public Management (NPM) refers to a philosophy of government and a normative
movement that seeks to restructure public sector organisations to make their operations
more business-like (Eakin et al., 2011). One particular exhortation of this view is that
policymakers should ‘steer, not row’ (Peters, 2000, p. 40).
Rationales behind the movement include an improvement of managerial efficiency, a
reduction of misaligned goals and an improved citizen access to services (Haque, no date).
Increasing the productivity of public service provision through efficiency and value-for-money
are key in the philosophy of NPM. Typical routes to achieving this efficiency include the
marketisation of public services, competition for public contracts and the disaggregation of
public services into their constituent components for cost optimisation (Robinson, 2015).
NPM-style reforms have been applied to research funding in a number of ways, in particular
in the UK and the Netherlands, though NPM-style research policy reforms can also be seen in
other countries that have not so vigorously adopted NPM in other areas of public life, such as
Germany. Typical reforms may include (Ferlie, Musselin and Andresani, 2008, pp. 334–336):
• Market-based reforms such as competition for students and research funding
• Hardening of budget constraints
• Target setting as a means of control
• Performance Related Pay for researchers
Open Innovation
Pioneered in a book by Henry Chesbrough (2003), the concept of ‘Open Innovation’ is in
opposition to mindsets about innovation and research that emphasise secrecy and the
excludability of knowledge
16
. Advocates of open innovation want greater cooperation and
transparency of research. It is noted that firms and researchers do not rely solely on their own
research, but incorporates the ideas of others at every stage. By opening innovation and
intellectual property, entrepreneurs can be afforded a wider menu of ideas that they can
draw upon in innovative activities.
16
So-called ‘closed innovation’
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Evidence for the value of open innovation came from the low returns seen in ‘fordist’
17
American companies due to unavoidable spillovers and the (Herstad, Bloch and Ebersberger,
2010, p. 114). Typical policies that aim to promote open innovation involve the promotion of
networking or collaborations.
Many ideas around open innovation would sit comfortably in the second (somewhat) and
third framings discussed earlier.
Responsible Research and Innovation
Responsible Research and Innovation (RRI) is a recent movement that attempts to reorganise
the social contract between science, policy and society, acknowledging the inherent ethical
and other values dimensions in scientific research (de Saille, 2015). Given that science and
society are viewed as being inextricable, some argue that research agendas should be shaped
by society. This responsible innovation must be reactive to concerns about the environment,
ethics and other social sensitivities. RRI is found in operation in programmes such as the EU’s
Horizon 2020.
The Innovation Policy Dancefloor
An interesting metaphor for understanding the innovation policy process was developed by
Kuhlmann et al. (2013). This systemic perspective casts the practice of science policy as an
interaction between the actors involved in innovation practice (such as firms, researchers),
public intervention strategies (policies), and innovation theory (the ideas guiding both). In the
dancefloor that lies between these the challenges can be o create harmony of theory, practice
and policy. The ways in which the three interact with and learn from each-other.
There are similarities with this idea and the idea of the “Triple Helix” of innovation. However
the dancefloor metaphor stretches further: there can be changing music fashions that effect
all actors- consider the three frames detailed in this section and how actors can be all aligned
when each of these frames is dominant.
Table 2 provides a map of some of these systemic interactions
17
Modes of company organisation that involve high levels of top-down organisation and in-housing of all
activities
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Table 2: Some typical interactions between different actors on the Innovation Dancefloor. Adapted from Table 1.2 in
(Kuhlmann, Smits and Shapira, 2013)
The Learner
Learning from..
Practitioners
Policy
Theory
Practitioners
Policymakers
evaluate the
impact of their
interventions, they
interact with
businesses and
gather opinions
Researchers
conduct real world
experiments and
research on firms,
Researchers gather
experiences of
actors
Policy
Businesses adapt to
policy measures
Researchers gather
experiences of
policy actors,
research asses
policy programmes
Theory
Entrepreneurs adapt
their mental models
and adapt business
models, Researchers
act as consultants for
entrepreneurs
Policymakers adapt
mental models
form theory,
researchers act as
consultants to
policymakers
The Entrepreneurial State
The concept of the Entrepreneurial State was developed by Economist Mariana Mazzucato
and most famously popularised in a book of the same name (Mazzucato, 2013). The book
examines how many key technological innovations of recent decades had underappreciated
roots in government-associated research programmes and were not simply the result of
private R&D. Examples of this include many of the key components of modern smartphones
(GPS, touch screens, voice recognition). The moral of this story is that states have the ability
to be risk takers and that innovation should not be left solely to private enterprise. The books
has prompted significant levels of debate.
Experimental Policymaking in Science Policy
Policy experiments vary from constituting research, to an approach for the incremental
implementation of policy programmes. They have seen particular popularity with the rise of
behavioural economics, which often advocates real world testing of small interventions such
as testing ‘nudges’ in Randomised Controlled Trials (RCTs). They also find broader applications
outside of behavioural economic in a variety of domains. Other policy experiments may
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involve the restricted implementation of given policy to, say, a town and closely monitoring
the results before rolling out the policy more widely. This experimentation phase provides an
opportunity for learning.
Policy experiments may be popular with proponents of the third narrative previously
examined, as it allows additional opportunities for creativity and learning.
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2 Policy Instruments for R&D Policy
In order to understand the policy options available, it is necessary to provide somewhat of a
map of the terrain. Martin (2016) notes that although the effectiveness of individual
instruments has been often well researched, relatively little systematic evidence exists that
compares the relative effectiveness of different instruments, side-by-side. However, this
report will endeavour to consider the linkages with other large ideas about STI policy when
discussing these instruments.
This section considers how the different kinds of policy instruments relevant to R&D can be
disaggregates and what ideas they may be associated with. This is to give an overview of how
a policy mix or portfolio of policies can be charted. It then looks at individual policy
instruments and the research that has considered their benefits and drawbacks. This will also
in greater detail into R&D funding policies and the considerations particular to them.
2.1 The Policy Cycle
Instrument exists at all stages of the so-called policy cycle
18
(Howlett, 2011). Though the
primary focus on instrument selection occurs during policy formation.
Figure 3: The policy cycle. Adapted from Howlett (2011)
18
It should be noted that the policy cycle is a useful conceptual tool to frame the policymaking process, but
nonetheless faces some limitations
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During each of these phases different activities are undertaken (Howlett and Fraser, 2009, p.
26);
Agenda setting: Problem recognition
Policy formation: Proposal of solutions
Decision-making: Choosing a solution
Policy Implementation: Putting the solution into effect
Policy evaluation: Monitoring Results
2.2 Mapping out instruments
To understand the space of policies available for selection one can think of the map in a
number of ways. This subsection provides a brief classification of tools to help frame the
ensuing analysis and exploration.
A naïve and intuitive way of classifying the options available to a policymaker is to think of
carrots (encouragements/ incentives) and stick (regulations and punishments). However this
is very minimal and does not necessarily not give a feel for all of the range of options available
in a context such as R&D policy (Martin, 2016, p. 160). One more extensive and popular
system for understanding the range of policy instruments available is the ‘NATO system’,
developed by Robert Hood, which classifies policy instruments according to the government
resource expended in their operation.
Name
Nodality/
Information
Authority
Treasure
Organisation
Resource
Information
collection and
dissemination
Command and
Control
Regulation
Grants and
Loans (Fiscal
incentives)
Direct Provision
of goods and
services
Examples
Advice,
advertising,
promotional
campaigns.
Commissions
and inquiries
Standard setting,
self-regulation,
consultations
Tax relief,
incentive
schemes
Use of
community
organisations,
market creation,
governmental
reform
Figure 4: Adapted from (Howlett and Fraser, 2009, fig. 1), with own additions and modifications
Howlett also provides another way of thinking about policy tools, according to what style of
governance they require (Howlett, 2011, pp. 128–130):
Legalistic: A preference for direct government
Corporatist: Preference for state-owned enterprises and delegated regulation, the use of
subsides
Market: Preference for deregulation and out-contracting
Network: Creation of interest groups, clientele agencies
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DEPARTMENT OF SCIENCE, TECHNOLOGY, ENGINEERING
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Using these two frameworks we can classify the different R&D instrument options available
to policymakers in a matrix, shown in Table 3, overleaf:
Table 3: Overview of R&D Policy Instruments, organised according to NATO and governance style. Inspired by Table 9.1 in
(Howlett, 2011, p. 129)
Style of Governance
Legal
Corporatist
Market
Network
Resources Deployed
Nodality
(Info)
Patent protection
laws
Government
information campaigns
Production of R&D
statistics/indicators
Promotion of research
information sharing,
Foresight Exercises
Authority
Ethical Standards,
Research
Transparency and
Accountability Rules
Stakeholder
conferences
Treasure
Subsidies for R&D,
Research Grants,
Funding of Science
and TEchnology
Institutes
Tax Incentives for
R&D, Procurement
Policies
Funding of inter firm
R&D collaborations
Organisation
Creation of Public
private partnerships
Promotion of Scientific
Mobility, Engagement
with International
Networks
Thus we can see that through an appreciate of the different styles a broad range of policies
are in fact available beyond carrots and sticks. Policymakers have a broad menu of options
for steering an R&D system.
2.3 Review of instruments
In this subsection we first consider the different ways in which funding can be allocated to
academic research, before using the framework above to consider some of the other popular
options. In particular we look at:
• Academic Funding R&D Policies
o The role of different state-steering models
o Performance Related Funding
• Financial Incentives for Private Industry
o Direct Subsidies
o Tax Credits
• Network Based Instruments
o Policies to foment collaboration
o Polices to Promote Clusters
• Foresight Exercises
• Public Engagement
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There exists much research on the relationship between science and policy from an economic
point of view, but less rigorous Intercomparison from a policy perspective. Much research
focusses on the ‘trinity’ of policies: direct government funding of research (for government
labs and HE), government funding of private R&D (through grants or procurement) and tax
incentives for private R&D (Guellec and Van Pottelsberghe De La Potterie, 2003). We extend
our review beyond finical instruments by considering a number of network-based
instruments and the role of foresight exercises as a policy tool.
Each subsection of this overview begins with a simple statement of the mechanism of the
policy, followed by additional commentary.
Figure 5, overleaf , is a dendrogram showing the many types of policies organised by target
and style of intervention.
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DEPARTMENT OF SCIENCE, TECHNOLOGY, ENGINEERING
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' '
' '
' '
Instruments
Instrument Target
Resources Deployed
Policy
Instruments
Treasure
Public
Government
Institutes
HE Funding
Private
Direct Subsidies
Grants Procurement
Tax Incentives
Organisational
Instruments
Network
Intramural Extramural
Clusters Collaborations
International
Nodality
Information
Environment
Foresight
Exercises
Figure 5: Map of different policy instruments relevant to R&D (and also partially Innovation Policy)
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Policy Instruments for R&D Policy
2.3.1 Government Laboratories
Governments may directly commission research to be carried out at public sector research
institutes or centres
National labs are often a popular choice for the production. The logic for their creation is fairly
simple as there is a direct line from a policy agenda to the commissioning and performance
of thematic research. Prominent examples of national laboratories include CNRS (Centre
National de la Recherche Scientifique) in France, NREL (National Renewable Energy
Laboratory) in the USA and British Antarctic Survey (BAS) in the UK.
There may be issues with ‘crowding out’ of fundamental research. This is particularly true in
some sectors such as defence, where public sector expenditure reduces private expenditure
in the sector significantly (Guellec and Van Pottelsberghe De La Potterie, 2003).
2.3.2 HE Funding Allocation Policies
Governments give money to support research in Higher Education institutions, though the
mechanism for providing this funding may vary significantly.
There exist a number of different fundamental approaches to the funding of academic
research. Generally speaking, Universities have far greater autonomy in comparison to
national research institutes (Guellec and Van Pottelsberghe De La Potterie, 2003, p. 227).
Higher education funding also does not have the crowding out effects of other capital
intensive instruments (Guellec and Van Pottelsberghe De La Potterie, 2003, p. 237).
Key aims of policy makers for HE funding include cost effectiveness of programmes and to
achieve larger amounts of academic outputs, often measures in terms of publications or
citations (see section 5) (Himanen et al., 2009). Policymakers may use competition for
resources as a policy level to steer universities and this is perceived as an effective instrument
by many. However, the relationship between the state, markets, universities and other
stakeholders is not universally regarded in the same way.
State-Steering Models
What are the different ways that governments can steer and how do these relate to ideas
about styles of governance? It is important here to note that views on how HE funding is
administrated come with them very articular ideas about what the relationship between the
state and HE should be.
Himanen et al. (2009) use a framework for administration styles described by Olsen (1988) to
analyse how different approaches to research funding administration may produce different
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Policy Instruments for R&D Policy
results. These different administration styles are documented in the table below and can be
seen to loosely align these with the governance framework from Table 3.
Table 4: Summary of different state steering models and how they come to play in HE funding policy
Steering Style
Sovereign
Institutional
Supermarket
Corporate-
pluralist
Similar
Governance
Style
Legal
Corporatist
Market
Network
Decision-
making
Top-down
Delegated
Marketised
Distributed
Role of State
Director
Hands-off funder
Minimal
One of many
stakeholders
View of HE
Tool of government
Uphold standards
and cultural
authority
Delivery of research
and education
services
Defined by
constellation of
interests
Assessment of
functionality of
Universities
Political
Effectiveness
Maintenance of
norms
Efficiency, value for
money
Criteria set by
multiple
stakeholders
The institutional steering model may be related to Mertonian
19
idea that government should
have a limited role in organising science, aside from ensuring its transparency and autonomy
(Ferlie, Musselin and Andresani, 2008, p. 327). Such an idea corresponds well to the ‘endless
frontier’ framing detailed in Section 1.1.
The sovereign steering model sees academic research as little different to other forms of
public service delivery. Therefore academia is treated in a similar way to other parts of the
public sector.
The supermarket steering model sees research outputs as a marketable commodity, rather
than as a public good. This is very consistent with the NPM reforms of Higher Education in
some countries, such as the United Kingdom.
The corporate-pluralist model may be possible to separate into different such as ideas about
the devolution and decentralisation of decision-making in the ‘hollowing out’ of the nation-
state or more democratic forms of higher education governance (Ferlie, Musselin and
19
American sociologist Robert Merton (1942) set out some very influential ideas about what the ethos of
modern science should be: communism (in the sense that scientists have common ownership of intellectual
goods), universalism (the socio-political attributes of the scientist should have no bearing on knowledge),
disinterestedness (acting in the common good, not personal gain) and organised scepticism (methodology and
conduct should be subject to close scrutiny)
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Andresani, 2008, pp. 332–334) (the latter may have resonance with the ‘grand challenge’
narrative frame section 1).
Himanen et al. (2009) tracked the role of these four models in influencing research outputs
in five OECD countries and found that the relationship between the administration approach
and research effectiveness was not always straight-forwards. In essence, the way that
governments administer Higher Education or University R&D funding has a complicated
relationship to the outcomes for that country’s R&D system. Steering models have also been
influenced by the rise of new public management (see section1.5), which pushes more
towards the supermarket steering model and emphasises the use of performance related
funding.
Funding Allocations and Performance Related Funding
Aside from administering programmes, decisions need to be made as to how allocation of
funds necessary for research will exactly occur. Funding for research activities may come from
government directly, or externally from sources such as private industry or other forms of
revenue raising, such as charitable foundations. Direct funding from central government,
which funds the things necessary for research to take place (such as staff, buildings,
equipment etc.), may depend on different allocation criteria. Typically these criteria may
describe the input to the university or the outputs of research.
The way these allocation criteria are organised will affect how competitive the system is
(Himanen et al., 2009, p. 421). There are also differences between what the UK government
terms ‘resource funding’ for everyday running the research, and ‘capital funding’
20
for new
equipment, research centres and so on (Royal Society, 2018).
Figure 6: Examples of Input and Output criteria for funding allocation decisions
20
These are essentially Operational and Capital Costs
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De Boer et al. (2015) List a number of key indicators used by 13 polities in the allocation of
funding.
Table 5: Summary of typical performance indicators adapter from de Boer et al (2015)
Indicator
Polity
COMMON INDICATORS
Number of Bachelor/Masters Degrees
Austria, Finland, Netherlands, North Rhine
Westphalia, Thuringia, Tennessee
Number of exams passed or credits earned
by students
Austria, Denmark, Finland, Tennessee,
Louisiana, South Carolina
Number of students from underrepresented
groups
Australia, Ireland, Thuringia, Tennessee
Study Duration
Austria, Denmark, the Netherlands,
Tennessee
Number of PhD graduates
Australia, Denmark, Finland, Thuringia,
Netherlands
Research Productivity
Australia, Denmark, Finland, United
Kingdom (England, Scotland)
Research performance in terms of winning
(research council) contracts
Australia, Finland, Hong Kong, Ireland,
Scotland, Tennessee
Third Party Income
Australia, Denmark, Finland, North-Rhine
Westphalia, Thuringia, Hong Kong
Revenues from knowledge transfers
Australia, Austria, Scotland
LESS COMMON INDICATORS
Internationalisation (student or staff)
Finland
Quality of education based on student
surveys
Finland, Tennessee
Employability indicators, e.g. the number of
employed graduates
Finland
Research quality
United Kingdom (England, Scotland).
It is generally considered that systems which focus funding dependent to a greater degree on
output criteria and have greater shares of external funding are more competitive. Though it
should be noted that competitivity does not necessarily entail effectiveness. Indeed some
incentives may have very short lived effects or perhaps negative effects (Himanen et al., 2009,
p. 429). The law of unintended consequences is at work.
The final word in this subsection is to say that although funding allocation is a very large lever
of policy, it is not the sole determinant of university research performance (Himanen et al.,
2009, p. 420). Research funding policy does not occur in a vacuum. It exists as part of a policy
mix and this should be taken into mind when designing allocation policies.
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Alternative Funding Models
A simple input-output model of funding is not the only basis for deciding how to allocate
funding. It may be noted that not all research funding is utilitarian in nature and that research
has a number of spill overs to other parts of life such as culture.
Performance related funding has a disadvantage that benefits are given after the fact, so that
funding trails performance. An increasingly popular model is the use of Performance
Agreements in which are signed between funding authorities and individual universities
(Jongbloed et al., 2018).
The Role of Research Councils
Governments may (partially) delegate funding decisions to research councils- often Quasi-
Autonomous Non-Governmental Organisations (QUANGOs). This may be borne out of the
acknowledgement that funding decisions should be placed in the hands of those better
qualified than politicians to make the decisions.
Take the UK as an illustrative example, United Kingdom Research and Innovation (UKRI) is
funded through the science budget of the Department of Business, Energy and Industrial
Strategy (BEIS). This in turn is composed of seven thematic research councils, many of which
have a long history in the UK. These research councils then autonomously assign funding
through mechanisms such as peer review panels, grant panels, grant competitions and
funding calls. Separate to UKRI, Innovate UK runs a variety of programmes to help in the
commercialisation of R&D, through catapults and others.
2.3.3 Fiscal Incentives
Governments may also wish to incentivise R&D outside of an academic context by focusing
on firm-level R&D. Firms may produce both basic and applied research. A perceived benefit
to incentivising firm R&D is that firms are closer to the marketisation of innovations and
therefore the beneficial economic impacts of R&D may be more immediate.
These measures are generally viewed to be effective at stimulating additional private sector
R&D (additionality). However, this is only believed to be true up to a point- over generous
incentives tend to lead to greatly diminishing returns. The two types of incentive considered
here, tax incentives and direct subsidies, are also substitutes, meaning that they replace each
other and may have diminish effects in combination (Guellec and Van Pottelsberghe De La
Potterie, 2003).
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Tax Incentives
A softening of tax burden on a company based on some measure of the R&D expenditure
or intensity of a firm’s R&D activities.
Tax credits have been a popular mechanism to encourage innovative behaviour in companies
by different governments for a long time. As previously discussed, much of the rationale for
this policy relies on ideas about the market undervaluing basic R&D (market failure rationale).
As these policies fall under the tax system, they often do not place additional burdens on
ministries responsible for R&D promotion, but rather an overall burden on the tax system
shared by the whole government. Further to this administration of the schemes is cheap as it
falls under tax, so alterations of the scheme do not require great effort (Köhler, Larédo and
Rammer, 2012). These tax incentives are also targetable towards particular forms of R&D that
a government may wish to incentivise.
!"#$ "& #'ℎ)*) = ,)'-).#) / 0.1 -)2)34)# + !"#$ "& 6/*73#$-.$7"3
Though cheap for the agency or ministry designing the scheme, these tax policies can be
costly for governments overall. Though this can be limited with caps on potential tax benefits,
and minimum R&D thresholds (Köhler, Larédo and Rammer, 2012). There are, of course, a
number of different types of tax incentive that can be employed. They can be differentiates
by their type of incentive and how the incentive is calculated (Köhler, Larédo and Rammer,
2012, sec. 2).
TYPE OF TAX INCENTIVE
Accelerated Depreciation: Allow accelerated depreciation on assets used in R&D (this is an
early form of scheme)
R&D Allowance: Allow more than 100% deduction of R&D expenditure from
income
Special Exemptions: Allow deduction of particular costs, such as R&D labour
Tax Credits: Allow deduction of R&D expenses from corporate tax liabilities
“Patent Box”: Tax relief on income from intellectual property generated by
R&D (this is a new form of scheme)
CALUCLATION OF TAX INCENTIVE
Volume based: Relief based on the volume (all of) of R&D in a fiscal year
Incremental Schemes: Relief based on increases to R&D in a fiscal year
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Further to this, policymakers must decide:
• How generous the scheme is to be? This is a primary determinant of cost
• What definitions are in operation? This includes the definition of who is eligible to
receive the incentives (one may wish to target SMEs for example), what activities are
eligible?
• The duration of the scheme
• Other scheme eligibility criteria, such as regional options
A report by NESTA, examining studies R&D schemes in 12 countries found that positive
benefits are highly variable across national contexts (Köhler, Larédo and Rammer, 2012). They
also note that:
• Volume based incentives and tax credits tend to be the most successful, but this must
be balanced against their costs
• Incremental schemes are less effective under poor market conditions (such as during
recessions)
• There is little evidence whether R&D tax incentives increase overall firm productivity
Direct Subsidies
Direct grants, either completive or non-competitive to firms engaged with R&D projects
Governments may simply decide to implement schemes that provide direct finding for R&D
activities. There are of course different ways through which grants can be given out. These
may be given out either on a competitive basis, or on a qualification basis. Under a
competitive basis, firms must organise bids to secure the funding. On a qualification basis, all
firms that satisfy some criteria may be eligible to receive the funds.
Similarly to research into tax incentives, much research in this space focusses on crowd-out
effects and whether additionality (Görg and Strobl, 2007 See for example). Also, research that
has attempted to understand the effect of grants has uncovered somewhat of a ‘chicken and
egg’ situation (Wallsten, 2000). Is the correlation between R&D intensity and the reception
of research grants due to the additional funding may increasing research activities, or is it that
research intensive firms are more successful at obtaining grants?
These direct grants have the benefit of being very tailored and can be very highly targeted
towards sectors considered worthy of support.
• The formation of research alliances (Guellec and Van Pottelsberghe De La Potterie,
2003, p. 227)
• Minimum standards for research transparency
A key purported disadvantage is that is suggested that in providing grants, governments may
be less efficient at allocating resources than markets (Guellec and Van Pottelsberghe De La
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Potterie, 2003, p. 226). Thus, there may be an effectiveness issue. Also, as will be discussed
in section 6.2, there is a tendency of a number of governments to provide targeted to support
to technological research streams to which their innovation systems are not suitable to
support
21
.
Procurement
The government can use its purchasing power to support particular innovations, firms,
sectors or products
Public Technology procurement (PTP) involves the government supporting particular
technologies, or firms conducting R&D, through the procurement of their goods. Government
procurement of goods and services more generally makes a very significant proportion of
many economies (16% of GDP in EU15) so the potential of this policy lever is very significant
(Nyiri et al., 2007) Not all PTP is intended to support R&D or innovation per se, as it can be
used to simply support domestic industry. Also PTP that is directed towards R&D may either
support existing technologies, or provide a stable economic environment that allows
domestic firms to justify the R&D necessary to create the procured products.
PTP requires the coordination of departments responsible for supporting R&D/Innovation
and those responsible for procurement. Forming such collaborations can be non-trivial. It
faces a number of barriers, such as low political risk appetite (Nyiri et al., 2007).
Different nations, for example the United States and Japan have made more effective use of
PTP as a policy lever in comparison to many European nations. Also, in some sectors, many
nations have failed to support niches and have instead supported simple improvements to
existing technologies (Nyiri et al., 2007).
The recipient of the funding in this case need not necessarily be the producer of the research
that lead to the creation of the product (Guellec and Van Pottelsberghe De La Potterie, 2003,
p. 227). This can create complex and skewed market environments.
2.3.4 Network-based Policies
Policies that aim to increase the amount of may be understood to:
• Increases knowledge share Intramurally (bringing collaboration and expertise into
firms)
• Nationally or Regionally, through the creation of collaborations or research-intensive
localities (see for instance the trend for “silicon-{valley, motorway, oasis,
roundabout})
21
Ideas about ‘smart specialisation’ have attempted to ameliorate this issue
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Policy Instruments for R&D Policy
• Internationally, through collaborations with international firms or research
institutions
Collaborations
The government can use its organising and funding power to create collaboration between
public and private sector actors.
When considering the potential for commercialisation, the funders need to be cognisant of
the potential for exaggeration in commercialisation or spill overs as there can be disconnects
between universities and entrepreneurs (Martin, 2016, p. 162).
Policies can aim to facilitate university and business research linkages (such as in the model
of the triple helix) (Martin, 2016, p. 162). Further to this there are a growing range of policy
tools that are focussed on promoting the commercialisation of policy tools. Though a
drawback of this is said to be the focussing of research efforts on short term gains, rather
than long-term outcomes.
Some policies may aim to facilitate inter-firm R&D collaboration. Some research has
suggested that R&D subsidies are more effective for firms that collaborate internationally, as
well as SMEs (Hottenrott and Lopes-Bento, 2012).
Clusters
The government can facilitate the creation of special districts with a beneficial business
environment and similar firms or related research institutions in close proximity
Popular in recent discourse around innovation is the idea of clustering. Inspired by the success
of Silicon Valley or Northern Italian industrial districts, policymakers have become sensitive
to the idea of creating clusters to promote innovation and R&D. However, there may exist a
gap between the vogueishness of the concept and the understanding of how to best promote
clusters (Uyarra and Ramlogan, 2016). Terms that describe these geographic areas are science
parks, new industrial spaces, regional innovation systems and industrial districts (Martin,
2016, p. 162; Uyarra and Ramlogan, 2016). These clusters generally entail specialisation,
rather than simply being high-technology.
Clusters may emerge in a variety of ways and recognition of specialist clusters have existed
for a long time. Generally, clusters may have some historical antecedents, such as the
existence of some other industry in the area beforehand leaving a skilled workforce behind
or the existence of a military base, for example. However, there is controversy over the extent
to which policymakers can reliably found sustainable clusters or whthether these clusters are
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more organic in their origin. Here we see the importance of pre-existing human capital as a
precondition for the creation of strong specialisation.
Actions to promote clusters may include (Rosenfeld, 2002):
• Identification of clusters, and the mapping of systemic relationships can provide a
roadmap for supporting them
• The formalisation of communications channels within and to the cluster
• Providing state services to support the cluster
• Development of specialist workforces and human capital necessary to support cluster
o The use of the cluster as place of learning
• The encouragement of local entrepreneurs
• marketing and branding a region to encourage inward investment
o Also promotes exports
• Provide fiscal incentives for collaborations within a cluster
2.3.5 Foresight Exercises
The government can commission foresight exercises to anticipate future technological
needs and to identify key challenges in the future. This informs smart policy development
and can contribute to the coordination of a number of actors within the innovation system.
Foresight exercise can help in the formulation of policy strategy, identifying nation-specific
opportunities and anticipate future technology demands. Through such exercises,
governments can also attempt to manage the risk from innovation and research. Foresight
can provide scenarios, forecasts and roadmaps (Meissner and Rudnik, 2017, p. 457).
The value in foresight exercises for STI policy is not limited to the increase in policy analysis
capacity that they bring- they also provide opportunities for stakeholder engagement and the
coordination of a number.
There are a wide number of forms that these foresight exercises might take and may be
informed by activities such as modelling, technological assessment and public consultations.
Recent approaches have begun looking at more ‘integrated’ approached to technology
assessment, including a greater deal of information on societal considerations (Meissner and
Rudnik, 2017, p. 457)
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2.3.6 Public Engagement
Public Engagement can be a useful tool to shape innovation systems and ensure that R&D
and innovation activities are in line with broader social ideals
Consistent with ideas about responsible innovation, governments can commit to public
engagement and consultation to ensure that R&D and innovation is aligned with societal
goals. Consultations can take a number of forms, from tokenistic engagement to full
consultations where a broad variety of stakeholders are brought into the decision-making
process.
2.4 Policy Mixes in R&D Policy
There exists relatively little research on how R&D policies can be mixed and the choices faced
in R&D policy selection (Martin, 2016, p. 164). R&D policies may interact in often unexpected
ways (Martin, 2016, p. 167)
One of the key trends in policy choices and mixes m sophistication of policy instruments is
said to have increased over time
22
and the areas which these R&D policies cover has
increased
23
. Broadening has been seen as R&D and Innovation policy has come to overlap
with more and more domains of policy in recent years. In particular, policymakers have looked
to innovation policy to aide in achieving a number of other societal goals
24
.
When considering policy mixes, one may also wish to consider the various interactions
between policies. For example, the two types of incentive considered here, tax incentives and
direct subsidies, are also substitutes, meaning that they replace each other and may have
diminish effects in combination (Guellec and Van Pottelsberghe De La Potterie, 2003).
Perhaps part of the difficulty in producing systematic analyses of instruments, is the fact that
instruments never exist on their own, but always in a mix. Their efficiency is best captured
through an examination of the system as a whole, though this is clouded by the sticky issue
of how to determine if a policy has been successful (Guellec and Van Pottelsberghe De La
Potterie, 2003, p. 228).
The effectiveness of all of the instruments is not only affected by their design but also the
level of funding available
25
and the stability of the policy environment- integrating
instruments into a larger long-term framework reduces uncertainty. Further policy
22
Known as deepening
23
Known as widening
24
Such an approach could be seen as being at least partially consistent with the ‘the great challenge’ narrative
earlier
25
Too little will have no effect. Too much will crowd out. This forms an ‘inverted U’ shape
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instruments need be consistent, which requires the coordination and cooperation of a
number of administrative departments involves in their functioning (Guellec and Van
Pottelsberghe De La Potterie, 2003, p. 238).
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Table 6: Overview of different policy instruments relevant to R&D
Instrument
Targeted at…
Instrument Resources
and
Associated
Governance Style?
Purported Benefits
Purported Disbenefits
Typical
Indicators for
Monitoring
Government
Public Sector
Corporatist
• Targeted research on policy-relevant
• Crowding out of Private R&D
Citations,
Research
themes
• Does not increase private R&D
Publications,
Institutes
expenditure (though this is not
Public
considered the purpose of this
Re
instrument)
HE Funding
Public Sector
Various
• Low or no crowd-out of private R&D
• Does not increase private R&D
Citations,
search
• Universities are tried and tested
institutions
expenditure (though this is not
considered the purpose of this
Publications,
Doctorate
•
instrument)
produced
• Knowledge produced may not be
Treasure
immediately commercially relevant
Grants
Firms
Various
• Allows governments to ‘pick winners’
• Market forces may allocate
Direct
(Carrot)
• Large number of potential criteria for
eligibility can steer R&D
resources for R&D more efficiently
than government
Subsidies
• Particular national sectors and goals
can be targeted
• Firms who are not recipient of
government funding may be
deterred to begin similar research
projects
• “Picking winners” is not universally
liked
Government
Firms
Various
• Can create distorted market
Procurement
environments
• Requires collaboration of both R&D,
Innovation and procurement parts of
government
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Tax Incentives
Firms
Market
• Simple administration through existing
tax system.
• Cheap for ministry organising scheme
(as burden is on overall government
revenues).
• Wide range of design features allow
policy targeting to particular forms of
R&D
• Easier to access for SME or new
innovative businesses when compared
of grants
• May strengthen absorptive capacity of
recipient firms
• Can be expensive overall for
government with reduced tax
revenues
• “Crowding out effects” of private
R&D
• Benefits are received after R&D is
performed so have less of an effect
on overall strategy
• The money received/saved by the
firm will not necessarily be spent on
anything with a large social rate of
return
• Tax break not necessarily
accessible to companies with low
tax burden, such as new firms with
large investment and low sales.
• Project that should be promoted are
ones with largest gap between
social and private return. Tax
system does not allow this focus
Input
additionality:
Change in
R&D
expenditure
as a result of
incentive.
(measured
through
natural
experiments);
Response
Elasticity
Network Policies
Collaborations
Firms and
Regions
Organisati
on
Market/
Network
• Collaborations can increase the
•
Clusters
• Clusters can improve marketability of
products they produce and stimulate
inward investment
• There is controversy over the extent
to which governments can
authentically create new clusters, or
if the growth process must be more
organic
Foresight
Exercises
System-wide /
Policy
Formation
Nodality
(Informatio
n)
Network
• Can promote the alignment of a number
of different actors
• Foresight exercises may be limited
when they consider only a small
number of narrow factors, such as
purely technological variables
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Key Policy Considerations and Policy Values
3 Key Policy Considerations and Policy Values
What are the key criteria and decisions that are made when selecting a policy? In this section
we consider how the criteria for selection of a policy may be typically thought about. We then
consider a number of trade-offs and dilemmas that typically arise in the formation of R&D
policy.
3.1 General Policy Selection Criteria
Peters (2000) provides seven trade-offs or criteria for policy instrument selection. We
consider each of these trade-offs in the context of R&D policy. Many of these choices will
define the style of governance being enacted from more hierarchical, legal, or corporatist to
markets or network-based measures.
Directness Vs Indirectness
This is a measure of how directly the policy is trying to influence the policy situation
Visibility
Visible tools can be more effective in setting the tone for a policy area, whereas less visible
tools can manifest less political opposition.
Capital Vs Labour Intensity
A trade-off between spending on capital projects or labour-intensive activities. Labour
intensive activities may require more managerial styles of governance to control personnel,
whereas capital intensive [projects may require more ex-post evaluation.
Automaticity/ Level of administration Required
Will the programme ‘run on its own” or will it require updating?
Level of Universality
Is the programme applicable to all stakeholders?
Information Vs Coercion
How reliant is the programme on Persuasion vs Enforcement?
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Key Policy Considerations and Policy Values
Forcing Vs. Enabling Nature
Does the programme compel stakeholders to behave in a certain way, or make it easier for
them to behave in a certain way?
Potential policies can be evaluated on criteria such as these to help determine their suitability
for a government’s particular context.
3.2 STI-Specific Policy Considerations
Here are a number of additional considerations that are particularly relevant to the domain
of STI policy.
3.2.1 Linear and Systemic Thinking
As previous discussed in section 1.4, the transition between the frames in recent years has
coincided with differing policy concerns. One of the most pronounced is a move from thinking
about innovation systems in a linear way (i.e. government funds, science produces and society
benefits) to a systemic way that either considers the innovation ecosystem or how a broad
range of stakeholder participate in innovation (Martin, 2016, p. 162).
The question for policymakers is then: how to ensure that R&D policy instruments are
sufficiently target at the systemic changes they seek to encourage?
3.2.2 Area of Governance
In section 2.4 we discussed how policy portfolios have broadened to include a larger range of
policies. Policy portfolios for R&D policy also increasingly overlap with other policy areas as
policymakers hope that innovation can ameliorate other societal issues. This has always been
true in defence, but it extends to other areas. Take climate change for example: there exists
significant governmental in many polities funding for green innovations such as new
renewable energy technologies; thus energy and environment policy links up with innovation
policy. This bleed of R&D policy into other is particularly relevant when considering the ‘grand
challenges’ policies (Martin, 2016, p. 166).
The question for policymakers is then: which other policy priorities are to have a bearing on
R&D policy?
3.2.3 The Level of Governance
In the early days of R&D policy, the most significant actors were national governments. This
is not the case anymore as regional governments and a smorgasbord of other actors are
involved in steering innovation systems. This trend has moved alongside greater calls for
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Key Policy Considerations and Policy Values
accountability of publicly funded projects (such as in NPM) and the growing need to involve
the public on issues that may involve risk (Martin, 2016, p. 166).
3.2.4 The inventive unit: Researchers, Teams, Firms and Networks
Alongside the move from linear to systemic thinking has been a move from the unit that R&D
and innovation policy attempts to influence. A previous focus had been on the units that
produce research: firms, researchers, laboratories etc. Now focus is shifting to various forms
of collaborations or multi-actor systems: PPPs, clusters and networks (Martin, 2016, p. 166).
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Implementation, Institutional and Governmental Change
4 Implementation, Institutional and Governmental Change
There exists relatively little literature that considers exactly how R&D or innovation policy is
formulated, implemented and the common challenges met along the way. Here we gather
some of that which is available and scan the literature for solutions to these issues.
This section will be built upon after the workshop to include the primary research we have
conducted and to better tailor to the UAE national context.
4.1 Challenges in Priority Setting
Having a firm rationale is key to aligning the necessary actors. However, creating a rational
that is compelling, yet widely accepted may be a challenge.
4.2 Challenges in Implementation
Implementing STI, Innovation and R&D policy can be challenging, especially when there are
not great existing domestic research capacities.
The implementation of STI policy may be on a very sectoral basis. But there need to be
mechanisms in place to allow the coordination of these (Oyewale, Adebowale and Siyanbola,
2013).
4.3 Challenges in Institutional Reform and cooridnation
The challenge is not only in guiding institutions but managing the interactions in existing
institutions. For example, in Nigeria, the implementation of STI policy was hampered by the
lack of cooperation between ministries (Oyewale, Adebowale and Siyanbola, 2013).
Discontinuities between governments can also be a challenge as long term incentives can be
disrupted and the agenda setting work done during policy formation can be disrupted.
Better coordination can be achieved with the use of a number of instruments to coordinate
both from national to local levels and across themes. The Basque Country, a region of Spain,
is notable for its successful application of instruments such as these to overcome issues
associated with conflictual agencies. It utilised a “coordination mix” of instruments such as
agencies, networks, business associations (Magro, Navarro and Zabala-Iturriagagoitia, 2014)
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STI policy poses a number of unique issues as it overlaps with other areas of policymaking
such as economic policy, foreign policy, agricultural policy and education policy. Figure 7,
adapted from Margo et al, illustrates the layers of governance to coordinate, both
horizontally (across themes) and vertically (across levels of government) .
Figure 7: Layers of complexity in STI policy. Taken from Figure 1 in (Magro, Navarro and Zabala-Iturriagagoitia, 2014)
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Measurement and Monitoring of Outcomes
5 Measurement and Monitoring of Outcomes
5.1 Why Monitor?
As has hopefully been conveyed earlier in this primer, the process of policymaking for STI and
R&D policy involves interacting with a complex system with a wide variety of stakeholders. As
such the relationships between policy inputs and system responses can be complex. For
effective programmes it is recommended that programmes monitor their success to
determine if they are having the desired outcome and to guide amendments to these
programmes. However, there are pitfalls in approaches that focus on narrow indicators that
do not properly represent success.
5.2 How is R&D, Innovation monitored?
5.2.1 Input Metrics
These are metrics that measure the levels of financial or human capital going into theR&D
system.
These include: % GDP spent on R&D funding, Number of researchers per million population
5.2.2 Intermediate Metrics
These are metrics which indicate the level of research production, but pre-commercialisation.
These include: Patents produced
5.2.3 Output Metrics
These measure new economic activity brought about as a result of innovation.
These include: Numbers of new Products brought to market
5.2.4 Frameworks
A number of prominent frameworks exist for collecting data on and measuring innovation at
a national scale, such as the Olso framework (OECD and Eurostat, 2005) and the Frascati
manual
26
(OECD, 2015). These frameworks have been heavily used and are very standardised
providing a standard candle for comparisons across nations and regions.
26
Possibly the most well known system
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Measurement and Monitoring of Outcomes
There is also the National Innovation System approach which recommends monitoring
information flows between actors, such as:
• Flows between firms (e.g. patent transfers)
• Flows between firms and public institutions (e.g. number of firm-university
collaborations)
• Diffusion to companies
• Mobility of technical personnel
However, as these relationships can be complex, the NIS method is not as developed as the
Oslo or Frascati and there are fewer cross-national comparisons (Hao, van Ark and Ozyildirim,
2017).
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National Models of R&D policy and Innovation
6 National Models of R&D policy and Innovation
This section considers the role of national models. It does so from two perspectives. Firstly, it
takes a brief look at some case studies to illustrate how the idea of national models can be
seen shaping R&D and Innovation policy in a number of countries. One should recogniss that
some level of replication is always inevitable, but wholesale imitation of policy portfolios is
likely undesirable.
Secondly it considers the re-application of set models to a number of other nations- in essence
is borrowing and appropriating models of innovation systems from other contexts effective?
What are the advantages and pitfalls of such an approach?
6.1 Policy Importation/Replication as a practice
Policy Importation is the practice of taking elements and policies or whole systems and
attempting to transplant them into a different national context. Practices such as this have
been commonplace in the GCC. For example, in the realm of education policy the idea of
importing models from elsewhere, such as Singapore, Finland and the UK, was seen as an
innovative practice by many GCC nations (Kirk, 2015). However, by importing frameworks and
adhering to them rigidly, factors that are unique to the nations particular context.
6.2 The Importation of National policy models
Given the success of many national systems of innovation and one’s ability to survey their
benefits and weaknesses, it may be tempting for a policymaker planning R&D and Innovation
system reform to consider the importation or reapplication of successful schemes. This
importance of learning from other national contexts and developed economies on economic
policy has been emphasised since the 1800s (Varblane, 2012, p. 1) Much of the existing
analysis focusses on
• Institutions
• Actors, networks
• Knowledge, technologies
6.2.1 R&D Policy Importation/Replication
Policy importation has taken place for many years in the area of R&D policy and even
countries such as the United States are not immune to it: in the early days of the American
state, there was an attempt to copy elements of the German research system and in the 20
th
century the US copied the of large-scale public finance models for research of other European
countries.
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National Models of R&D policy and Innovation
Replication can take place form the simple copying of the mechanisms of instruments to
attempts to import carbon-copy instruments. Clearly the former is commonplace- there is no
need to reinvent the wheel overtime a policymaker develops a policy intervention. On the
other hand, the very idea of a national innovation system is a comparative concept, meaning
there is no one set model that can be transplanted wholesale (Varblane, Dyker and Tamm,
2007, p. 108), thus all replication is on a spectrum. The question is how successful can more
widespread replication be?
Informative examples of what happens when policymakers attempt to replicate innovation
systems can be found in Eastern Europe both after the fall of the Iron Curtain and upon the
entry of many states into the EU. Upon the integration of New member States (NMS) to the
European Union, there was a strong belief in the linear innovation model (frame 1) (Varblane,
2012, p. 7). Produced by these countries focussed mainly on the creation of new high-tech
industries and policymakers in many NMS believed that simply focussing on particular high-
tech sectors and pouring in money, there would be successful R&D, innovation and growth.
This bias towards certain high-technology sectors was ultimately unsuccessful. This is because
there was a mismatch between R&D and education policy: without an alignment between
Human Capital development and the research system, it will be difficult to meet industry
needs. Thus NMSs fell into a trap of policy imitation, but without analysis (Varblane, 2012, p.
9). Varblane argues that some of blame for this lies with the NMSs acceptance of mediocre
advice from the IMF and the bland mix of policies
Another issue with these replications of policies is that they were not implemented. The
copying of supermarket-style policies took away much of the necessary emphasis on local
capacity building and evaluation (Varblane, 2012, p. 11).
The next subsection explores why naïve policy imitation in the area of STI be additionally
problematic: it fails to pay attention to the particulars of national context.
6.2.2 The Intractability of National context
When forming policy focus on the institutions in the ‘triple helix’
27
may be too narrow.
Understanding the context in which R&D and Innovation is taking place requires an analysis
that includes the religious, cultural, scientific, and entrepreneurial culture that surrounds the
institutions (Varblane, 2012, pp. 2–3). One must also pay attention to economic systems,
education and research systems and political systems (Varblane, 2012, p. 4)
So why is context so critical to evaluation of policy replication? Consider this rather forced
metaphor: Much like the transplantation of a plant into a new soil bed must take into account
he moisture content of the soil, the acidity of the soil, the shade and the rainfall, if you
transplant institutions into a different context, they may not thrive successfully.
27
Of business, academia and policy. See the second framing
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National Models of R&D policy and Innovation
Path dependent processes are at play which mean that economic, cultural and social
development paths must be taken into account. Much international experience and research
on STI policy focusses on developed economies with a strong knowledge base (Varblane,
2012, p. 5). Path dependency means these experiences cannot be wholly transplanted.
6.2.3 Replicating STI policy: some relevant notes for the UAE context
There are a number of nascent lessons that can be drawn together for the UAE context from
the existing literature. Some of these are briefly detailed here, though more contextual
exploration will be developed in the final report, taking into account experience from the
workshop.
Paying attention to National Context
States should consider what makes their knowledge base unique (Varblane, 2012, p. 17).
Often precursor industries can be developed and expertise be transferred to new industries
(an example being the attempted transfer from offshore oil and gas to offshore wind in the
North Sea).
Resisting the temptation to de-specialise towards voguish industries
Strategic importance is often given by some nations in a knee-jerk way to fashionable
technologies such as bio, nano- and info technologies (Varblane, 2012, p. 17), It can be better
to think more broadly about what sectors are genuinely most advantageous to support.
Developing Human Capital Alongside STI policy
Bell and Pavit (1997) say you can’t just build the plant- you have to absorb the technology.
Absorptive capacity requires attention to human capital. Latecomer countries don’t just face
technology divide, but a learning divide (Varblane, 2012, p. 7).
A vision to develop a diverse knowledge base is needed.
Developing Institutions Alongside STI policy
Ne cannot neglect the role of active learning in developing the innovation system (Varblane,
Dyker and Tamm, 2007, p. 107)
Latecomer countries don’t just face technology divide, but a learning divide (Varblane, 2012,
p. 7)
FDI must be used as a knowledge transfer mechanism (Varblane, 2012, p. 8)
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National Models of R&D policy and Innovation
The benefits of being a latecomer
There are a number of advantages to being a latecomer
28
economy as cointries can capitalise
on technologies that already exists and avoid some of the risks associated with being a first
mover. Paying attention to these advantages is widely appreciated as being of great
importance. Latecomer advantages involve (Varblane, 2012, p. 5): (cite A Gerschenkron 1962)
• Acquisition of technologies at lower costs
• Inward investment and the recruitment of skilled people
• Skipping of certain stages of technological development
• Take advantage of markets created by others (don’t face uncertainty of making
markets anew)
This latecomer advantage is not automatic and according to Abramowitz (1994) relies on
• Technological congruence: how similar are the leader and catcher in terms of market
size, factor supply
• Social Capacity: the capabilities of the country for catching up
6.3 Illustrative Case Studies
This section details two brief informative case studies of catching-up countries and their
national innovation systems.
6.3.1 National Snapshot: Estonia
The example of Estonia shows a nation that attempted, unsuccessfully to replicate much of
another nations R&D system (Finland). However, later efforts to focus on Estonia’s strategic
position were more successful.
Until second half of 1990s Estonia had what is described by some as a ‘no-policy’ policy with
respect to STI (Varblane, 2012, p. 11). Estonia then began looking to its Baltic and linguistic
neighbour, Finland. Such an approach made some sense as the two nations share much
cultural and economic history: Estonia has a long history of receiving Foreign Direct
Investment (FDI) from Finland and there had even been small aspirations from some to see
unification between the two countries in the early 20
th
century.
Initially the Estonian government made two plans: the Estonian State Innovation Programme
and the National Development Plan for 2000-2002. However, these plans never reached
implementation as policymakers didn’t pay attention to the different in starting points of the
28
Not in a pejorative sense. In the sense that the STI system is less established
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National Models of R&D policy and Innovation
two countries: Estonia recovering form communism and Finland as an established high-GDP
country.
The Estonian parliament passed an R&D strategic plan by end of 2001 (Varblane, 2012, p. 12).
This plan proposed a list of sectors to support: user-friendly IT, biomedical sciences and
material science. Such a broad and loosely defined list of sectors to support was similar to
those of larger nations and did not pay attention to fact that the Estonian economy was not
geared up the perform this research. The Estonian economy did not have strong research
capacity. So as R&D despecialised with a focus on broad themes,
The plan further suffered from non-realistic targets that focussed on increasing R&D
expenditure as a percentage of GDP. The risk such targets is that as nations increase R&D
expenditure as a share of GDP, they also tend to see the share of R&D financed by private
industry increasing. Thus such a target is difficult and costly to force through
Estonia has seen greater success in developing its R&D sector in recent years thought context-
appropriate innovation. In particular they have developed their oil-shale
29
industry , long
existing since the 1920s (Varblane, 2012, p. 19). This was brought about in part by their state
oil company (EESti Energia ) brought in a forward-thinking management team. This R&D came
in close collaboration with the Tallinn University of Technology, developing cleaner
technologies for the extraction of oil from oil shale.
6.3.2 National Snapshot: South Korea
Perhaps the three of the greatest national economic success stories of the past 50 years are
South Korea, Singapore and Taiwan. Emerging from the devastation of the Korean war, South
Korea (henceforth simply Korea) was one of the poorest nations on Earth, relying on
subsistence farming. In a phases known as ‘compressed growth’ has quickly caught up with
developed nations to become one of the world’s most prosperous nations (Lee, Im and Han,
2016).
The Korean success story in terms of STI policy is one that demonstrates the importance of
learning and building local capacity and avoiding over-reliance on models drawn from outside
of the country.
During the initial phase of Korea’s STI deveoment there was a deliberate and concerted focus
on developing local expertise and know-how. This involved not simply the usual route of
promotion of FDI, but the importation of turn-key factories (to be run by Korean companies),
then expansion by local talent (Feinson, 2003). Rather than imply acquiring technologies they
went the hard route with an effort to find mechanisms where capital goods could be imported
and later reverse engineered.
29
Not to be confused with “shale-oil” which is associated with hydro-fracking
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National Models of R&D policy and Innovation
An example of this absorptive capacity building is the acquisition of badly-needed
telecommunications technology from Erikson. Korea ensured that local engineers were
involved at all stages of the process of building the national telecoms system. Much of this
expertise eventually went to benefit local company Samsung. This is in contrast to nations
such as Uruguay, who acquired the same technology form the same company at the same
time and failed to incorporate the expertise into their national knowledge base.
Assimilation of technical expertise was further supported with locally-appropriate measures,
such as tax breaks and exemptions from military service for certain personnel (Feinson, 2003).
Policymakers also initially focussed on supporting R&D into goods with a short cycle time
(such as IT systems). These have lower latecomer barriers as expertise can be built more
rapidly and there would be a lower reliance on the expertise bases of established countries.
This also facilitates the creation of niche markets. Though some of this pursuit of short cycle
life industries was the result of policymakers looking forwards to opening up as many
emerging markets as they could (Lee, Im and Han, 2016).
An outward export-orientated in their production systems meant that firms were forced to
maintain competitive edge through R&D. To allow smaller firms to compete they established
strategic industries exempt from taxes. They balanced the removal of special protections to
incentive firms to innovate, without exposing them to early international competition.
(Feinson, 2003)
All of this was undergirded with a strong education system to develop human capital, in fact
a significant proportion of South Korea’s national budget was spent on education, raising
literacy rates to need 100% from around 21% after the Korean war (Feinson, 2003)
This rise in econmic productivity came with it a rise in wage rates, leading in the 1980s to
something known as the middle income trap where the prices of its products increased and it
struggled in competition with other East Asian countires (Lee, Im and Han, 2016). A stall in
the innovative nature of its products meant that they were unable to adequately differentiate
Korean products from equivalent cheaper products made in countries with lower wages.
Taiwan and South Korea escaped this trap with further investment in R&D from the 1980s.
This stimulates private R&D to surpass publiclly funded R&D (Lee, Im and Han, 2016).
In recent times Korea has experienced another slow-down in economic growth rate. Some
reserachers have attributed this to attributed to the focus on the Chaebols
30
and the focus on
export growth, whilst leaving behind SMEs.
30
Large state-linked Korean firms such as Samsung
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Summary
7 Summary
This primer has provided an extremely broad overview of many of the key areas of concern
in the process of creating STI policy. Using the policy cycle as a framework it firstly laid out
what are the key narratives that have floated over thinking about R&D and Innovation over
the last century. These narratives and other ideas are key for the rationales that
policymakers build to support policy interventions. With a clear rationale for action, policy
can be shown to have a clear direction and help organise actors within an innovation
system.
The next phase of the report gave an overview of the typical instruments used to support
STI. Financial instruments, such as tax breaks, have been the most popular. The instruments
available to support research, development and innovation are by far not only limited to
instruments relying on large capital expenditure. Practice and research over the last few
decades has shown the value in instruments that facilitate the coordination, learning and
interactions between stakeholders in the innovation system.
In producing STI policy a number of trade-offs may be experienced. The eventual mix of
policy may be in part influence by the style of administration and governance preferred by
policymakers. Assessing the impact of mixes of policy is non-trivial due to the complex
interaction between the multitude of participants in an innovation system. However,
monitoring can be carried out using a variety of metrics and highly standardised processes,
such as those designed by the OECD.
When selecting policy mixes, the experiences of a number of countries can be taken into
account. However, the literature cautions against wholesale copying of policy portfolios as
this can lead to misalignment with local capabilities. Instead, tailored policies that pay
attention to group local intellectual capital bases are favoured.
This primer serves as a basis for discussion, provides a common language to discuss ideas in
this policy space and as background setting for the final reports. This report does not reflect
the opinion of the UCL STEaPP or the UAE Office of Advanced Sciences. It provides an
overview of a number of different strands of thinking.
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Key Terms and Acronyms
Key Terms and Acronyms
Additionality Change in R&D Spending per unit of tax relief
Co-creation A process where different parties negotiate,
formally or informally, a mutually desired
outcome
Crowding Out Where government-funded research may have a
knock-on effect of reducing private resaearch
FDI Foreign Direct investment
Invention The creation of a new idea
Innovation The development of new ideas into products,
goods or services
Diffusion The spreading of the use of the innovation
Ex-post After the fact
HE Higher Education
NATO System A system of policy instrument classification:
Nodality, Authority, Treasure, Organisation
New Public Management A movement in public administration
emphasising marketisation and goal setting. See
section 1.5
NESTA National Endowment for Science, Technology
and the Arts: a UK foundation that promotes
innovation through partnerships and other
mechanisms
NIS National Innovation System
NMS New Member States (of European Union)
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DEPARTMENT OF SCIENCE, TECHNOLOGY, ENGINEERING
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Key Terms and Acronyms
NSF National Science Foundation- US government
agency that supports around ¼ of federally
funded basic research in the US
NPM New Public Management
NTBF New Technology Based Firms
PPP Public Private Partnership
PTP Public Technology Procurement
QUANGO Quasi-Autonomous Non-Governmental
Organisation
Rationales Framings and narratives can provide justification
for policy actions through rationales
RDI Research, Development and Innovation
SME Small and Medium Sized Enterprise
Social Return The value of a good to society at large
Socio-technical Transitions The process in which new technologies are
incorporated into society, and society re-adapts
itself with the new technology
Spillover Effect Where the research of one organisation
indirectly benefits other organisations. Such
effects can benefit innovation and research
systems as a whole, but reduce private returns
for the organisation conducting the research.
STI Science, Technology and Innovation
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References
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