Understanding innovation - The role of policy intervention
Understanding Innovation: The Role of
Policy Intervention
A Report for Victorian Department of
Treasury and Finance
Gaétan de Rassenfosse, Paul Jensen and Elizabeth Webster
Intellectual Property Research Institute of Australia
Melbourne Institute of Applied Economic and Social Research
University of Melbourne VIC 3010
* We would like to thank Claire Thomas, Steve Martin, Anne Leahy and Russell Thomson for thoughtful comments and corrections to our text, and Phil Ruthven and Rob Bryant from IBISWorld for the use of their data. This paper represents the view of the authors and not those of the
Department.
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Contents
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NIS
R&D
SBIR
SME
USPTO
VC
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List of acronyms
ABS
ATO
BLD
CIS
EPO
Australian Bureau of Statistics
Australian Taxation Office
Business Longitudinal Data
Community Innovation Surveys
European Patent Office
National Innovation System
Research and Development
Small Business Innovation Research
Small- and medium-sized enterprise
United States Patent and Trademark Office
Venture Capital
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Introduction
This Report presents a comprehensive overview of the effects of policy intervention on innovation.
In doing so, we draw on theoretical and empirical literature from around the world. Of course, there are numerous facets of public policy which affect the rate and success of innovative investment – from monetary policy which shapes the financial climate through to education policies which provide the skills and training of the next generation of innovators. For the most part, these policies lie outside the scope of this Report. The decision to innovate and the outcome of innovative efforts also depend on the social, cultural and legal aspects pertaining to the broader environment in which research takes place. By extension, these factors also affect the need for and the effectiveness of policy interventions. These environmental factors also lie outside the scope of this Report. Instead, we focus specifically on direct innovation policy interventions including policies that affect firm- and government-financed R&D; intellectual property (IP); and venture capital finance.
With regard to analysis of innovation policy interventions, we apply the standard “test” applied to all public policy interventions: i) there should be sufficient robust evidence indicating that market failure exists; ii) there should be a rationale for choosing amongst the different possible policy interventions; and iii) the distortions introduced by the intervention should be smaller than the original market failure problem. This last point is generally overlooked, but it is of crucial importance when considering market failure. In other words, the existence of market failure is a necessary but
not sufficient condition for public policy intervention. Since distortions are very difficult to quantify, our conclusions regarding the net benefits of policy interventions are necessarily tentative.
One of the other difficulties faced when trying to analyse the relative merits of a range of different innovation policy interventions that have been implemented around the world is the availability of comprehensive micro data. Without such data, it is impossible to conduct detailed evaluations of specific policy instruments. As a result, the evidence required to assist governments on where to get the “most bang for their buck” remains elusive. The conclusions we draw in this Report, therefore, should be seen in this light. If governments are serious about improving the evidence base for innovation policy, they should make data available on their specific policy mechanisms. This ambit claim for greater access to data is not hollow – the importance of such policy evaluation is well known and access to data is standard in other areas of economics such as labour and health economics. We strongly believe that the same should be done in innovation economics. Without this, policymakers will continue to work in an evidence-scarce zone.
We start – in the following section – by providing some definitions of the key concepts used in the
Report including ‘innovation’ and ‘innovative activities’. In Section 3, we then provide some background on the nature of the innovative process – how innovation actually occurs – and the characteristics of innovative Australian firms. Section 4 outlines the key rationales for the need for policy intervention in innovation – which broadly relate to the presence of market failures. Section 5 presents the key policy interventions that have been implemented around the world and the evidence supporting their effectiveness.
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Definitions
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Innovation
An authoritative definition of innovation is provided in the Oslo manual (OECD 2005), a methodological manual by the OECD on the measurement of technological innovation. An innovation is ‘the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations.’ This definition is inspired by Schumpeter (1934). Note the subtle difference between invention and innovation. An invention is an idea made manifest, whereas an innovation is an idea applied in practice.
Although the various components of innovation are not mutually exclusive, using this rubric enables us to get a clearer picture of what we are referring to when we talk of ‘innovation’. Product innovation refers to the creation of new (or improved) goods or services that are launched on to the market. Whilst both goods and services are included in this aspect of innovation, much of the literature is dominated by innovations in physical goods. Process innovations, on the other hand, refer to changes in the way in which goods and services are produced. Market innovation refers to improved ways of sourcing supplies of raw inputs or intermediate goods and services, as well as opening up new market opportunities (which could relate to either creating new domestic or export markets). A final type of innovation is organisational innovation, which refers to changes in the architecture of production, and accounts for innovations in: management structure; corporate governance; financial systems; and changes in the way workers are paid.
Innovation is thus a broad phenomenon not confined to technological product and process innovations. In fact, Fagerberg (2009) argues that many of the most important innovations throughout history have been of the organisational kind such as, for instance, how the Japanese automotive industry reorganised the entire value chain in the period following the end of the Second
World War.
A further important distinction in the meaning of innovation is whether the innovation is new-tothe-world or new-to-the-firm. Overwhelmingly, most innovations are new-to-the-firm and the dominant source of productivity is new-to-the-firm innovations. We expect that firms that engage in new-to-the-world innovations also engage in many innovations that imitate and copy other firms.
However, the dominant view, based more on casual empiricism than hard data, is that the most profitable firms are the largest source of new-to-the-world innovations.
This report focuses on technological innovations. Market innovations, which are influenced by trade and investment policies; and organisation innovations, which are influenced by regulation and the public provision of education, health and transport services, inter alia, represent large and diverse policy areas. While we believe a review of these policies is beyond the scope of this report, the reader should bear in mind that reforms and innovations from these sources are as important for societal well-being as technological innovations.
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Measuring innovation
While the aim of government policies is always to optimise certain activities, innovation policies almost always aim to increase the level of innovation. Technological innovation activities are ‘all of the scientific, technological, organisational, financial and commercial steps, including investments in new knowledge, which actually, or are intended to, lead to the implementation of technologically
new or improved products and processes’ (
The innovation process is not limited to investment in R&D. According to
expenditures on R&D represent about one quarter of total innovation expenditure, and half
of the latter consists of investment in capital expenditures. According to the OECD (2002) non-R&D innovation activities include ancillary services (i.e. scientific and technical education and training, libraries and museums, translation and editing of S&T literature, surveying and prospecting, data collection on socio-economic phenomena, testing, standardisation and quality control, client counselling and advisory services) and innovation activities which occur after the experimental development stage (i.e. patent filing and licensing, market research, manufacturing start-up, tooling up and redesign for the manufacturing process).
Measured R&D depends on the accounting and measurement policies of individual firms as there are few rules about how firms must organised their journals for innovation expenses. However, most firms are guided either by national accounting standards or national taxation rules – the two are not the same.
The Generally Agreed Accounting Principles (GAAP) define R&D according to whether it meets the definition of an ‘asset’ and can thus be said to contribute towards intangible assets. GAAP requires that asset expenditures: are separable (i.e., implying contractual or property rights); have the power to obtain future economic benefits; have the power to restrict the access of others to the benefits; have a 50 per cent or higher probability that future benefits will eventuate; and have been a cost from an external party. Research costs generally fail this test and are therefore normally expensed.
This means that they are not separately accounted for and cannot be distinguished from production wages and administrative costs. Only downstream portions of the commercialisation process which have high probability of generating future income will be included as an R&D asset.
The ABS by contrast follows that Frascati manual definition, which includes risky, unsecured upstream research but excludes downstream activities. The former comprise basic research, applied research and experimental development .
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The ATO typically follows the ABS definition.
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The upshot
1 ‘Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view. Applied research is also original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific practical aim or objective. Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed. R&D covers both formal
R&D in R&D units, and informal or occasional R&D in other units (OECD 2002, p. 30). It excludes: Education and training;
Other related scientific and technological activities; Other industrial activities (such as scientific, technical, commercial and financial steps, other than R&D, necessary for the implementation of new or improved products or services, and the commercial use of new or improved processes, which include acquisition of technology (embodied and disembodied), tooling up and industrial engineering, industrial design n.e.c., other capital acquisition, production start-up and marketing for new and improved products); Administration and other supporting activities (OECD 2002, pp. 31–33).
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is that the differences in standards make it unclear which guidelines firms are following in their own internal accounting, and what they include when they complete company reports and ABS surveys.
In addition, changes over time to ATO rules, including the financial incentive to claim tax rebates, have meant that data based on this measure will not necessarily give a consistent series of data over time. Table 1 gives a potted summary of the different measures of R&D.
Table 1: Measures of R&D
R&D Measure
Accounting principles
(GAAP)
ABS (Frascati)
Research
X
Development
X
(unless certain)
(external patent costs)
(prototypes, design, tests if part of further research )
Commercialisation
X
ATO X
As there is no universally agreed upon measure of innovation, researchers rely on proxy measures such as patents, R&D expenditure, research personnel, and technology balance of payments. Each of these measures captures a particular aspect of the process of technological change. Patent and trade mark data has been increasingly used in research because of its increased availability through the release of electronic administrative databases, but has been criticised because it’s scope if limited to patentable subject matter technologies, inter alia. Griliches (1990, p. 1661) however notes that ‘…in this desert of data, patent statistics loom up as a mirage of wonderful plenitude and objectivity’. Schmookler agrees with the general sentiment of Griliches ‘…we have a choice of using patent statistics cautiously and learning what we can from them, or not using them and learning nothing about what they alone can teach us’‖ (Schmookler 1966, p. 56). (cited in Gedik 2010, p. 102).
The ad hoc nature of much innovation data means that researchers can be limited in their choice of dataset. What is available either narrows the research question or forces researchers to ‘shoe-horn’ available data into their preferred economic model (which typically results in estimates with large standard errors). However, it also means we have no way of distinguishing between the absence of an economic relationship and measurement error. To overcome this problem, work continues on developing new and improved measures of innovation. The papers by Dodgson and Hinze (2000),
Kleinknecht et al. (2002) and Jensen and Webster (2009a) advocated the use of multiple indicators of innovation in order to overcome the problems of single indicators. Appendix A provides a summary of existing innovation measures: their uses and shortcomings.
2 According to the ATO ‘Research and development tax concession schedule instructions 2009’, Research and development activities means: systematic, investigative and experimental activities that involve innovation or high levels of technical risk and are carried on for the purpose of acquiring new knowledge (whether or not that knowledge will have a specific practical application), or creating new or improved materials, products, devices, processes or services, or other activities that are carried on for a purpose directly related to the carrying on of activities of the kind referred to in the paragraph above. http://www.ato.gov.au/taxprofessionals/content.asp?doc=/content/00189551.htm&page=60#P2405_127616.
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The ‘ideal’ innovation policy
There can be two rationales for innovation policy: i) to solve a market failure; and ii) to build and sustain the national innovation system (NIS). In the market failure-based perspective, every policy measure must be justified both by the identification of some form of market failure, and by an argument that explains how the policy can bring the system closer to its optimal state (Soete et al.
2010). The national innovation system has been defined by Lundvall (1992) as ‘... the elements and relationships which interact in the production, diffusion and use of new, and economically useful, knowledge ... and are either located within or rooted inside the borders of a nation state.’ This second approach recognises that innovation is supported by non-market-based institutions such as socio-economic, political and cultural systems that work together and are an essential ingredient in innovation outcome. The extensiveness of innovation is affected by the societal education level and entrepreneurial base, as well as the regulatory environment, amongst other things. Policies justified on the grounds of the NIS perspective do not seek to address a specific market failure. Instead, they are primarily aimed at ensuring that all the elements of the system are in place and working smoothly with each other.
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Many of the NIS policies relate to programs under the control of the
States.
Both the market failure and NIS theories of industrial economics have their underpinnings of the work of 19 th century economist Alfred Marshall. In his two manifestos, The Principles of Economics and Trade and Industry, Marshall gave extensive descriptions and analyses of the role of knowledge, immaterial capital (which we now refer to as ‘intangible assets’ or ‘intangible capital’), learning-bydoing and networks, along with one of the first exposés of marginal analysis.
What can be called the market failure approach has evolved as an abstraction of Marshall’s ideas using the method of limiting tendencies. Essential elements of the economy are reduced to properties such as non-exclusivity, non-rivalry, pure competition and uncertain risk. These traits are viewed as abnormalities to be compared with the normal or ‘ideal’ (purely competitive) situation. An extreme version of this school adheres to assumptions of hyper-rationality – that is, the assumption that firms have perfect foresight and will literally always behave in a profit-maximising way.
However, most industrial economists do not employ this extreme assumption, thus leaving more room for policy intervention.
The NIS approach also has its roots in Marshall but is based more on descriptive reality and uses the method of representative conditions. It views the economy as being in a constant state of transformation and thus either assumes the absence of equilibrium, unstable equilibrium or multiple equilibria. In this ‘evolutionary’ view of the economy, the industrial system is regarded as a whole rather than a set of properties that may operate separately from one another. These economists put most emphasis on a package of policies that are complementary and work towards moving a market
(or technology area) from a low to high equilibrium point.
Although distinct in some ways, these branches of the economics discipline can overlap and are not necessarily independent of each other. In fact, we argue – based on the logic articulated by Marshall
– that they are not distinctly different. Therefore, we treat the “systems failure” arguments as a
3 See Furman et al. (2002) and de Rassenfosse and van Pottelsberghe de la Potterie (2009) for empirical evidence on the role that the national innovation system plays in enhancing R&D productivity.
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subset of “market failure” – a subset which we refer to as “coordination failures”.
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Although the literature on coordination failures is far less advanced than the literature on other market failures, we will draw upon all of the relevant streams of the economics literature, where necessary, in order to draw out the key insights from the literature.
Our over-arching theoretical framework is that a policy intervention is deemed ‘ideal’ if:
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It eliminates large deadweight losses;
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Displacement effects are small (displacement = innovation that would otherwise take place);
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It does not create perverse moral hazard incentives (e.g. by encouraging rent-seeking or gameplaying); and
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It does not adversely distort private costs and benefits elsewhere in the system.
Figure 1: Losses due to an Externality and Subsidy
$
Deadweight loss
Subsidy
{
Displacement loss
MPB
MC
MSB
O
1
O* Output
Figure 1 presents a situation where an innovative activity is the output. We assume there is a positive externality on the consumption of that activity (therefore there is a difference between marginal social benefit [MSB] and marginal private benefit [MPB]). MC is the marginal cost each
‘piece’ of innovative activity. Under a market situation we have a deadweight loss as indicated by the triangle. If a public per unit subsidy is invoked to drive production to the optimal level (O*), then we eliminate the deadweight loss, but create a displacement effect (where production that will be undertaken under a free market situation receives a subsidy). In general, there will always be displacement effects from a program. Eliminating them completely would incur excessive costs in terms of complexity and administration (if indeed possible at all).
4 Part of the issue is that the system failure literature does not use a consistent terminology or set of definitions and does not articulate their analysis in a tight and consistent manner.
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A good policy intervention will achieve net benefits for the community after taking account of all the impacts. Note that, in practice, efforts to address market failure are never perfect. They suffer from government failure in implementation (lack of knowledge to target the intervention, inability to provide incentive-neutral financing, political pressure by interest groups for beneficial treatment, etc.) and might have unintended side-effects, creating collateral costs that outweigh the benefits
(Ketels 2009, p. 21). Important characteristics of successful innovation policies are their certainty and their stability. Without these, business decision makers are unable to assess risk and opportunity and make the trade-offs necessary for investment in new technologies (Marcus 1981).
An overly strong focus on market failures, as in neoclassical economic models, to justify government intervention may not be desirable. Opponents of the market-failure approach often argue that the innovation process is subject to so many market failures that neoclassical economic theory is an inappropriate tool for analysing the dynamics of innovation. The NIS approach is often put forward as an alternative paradigm to justify policy intervention (see e.g. Six Countries Programme 2009 and
Dodgson et al. 2010). In this report, we adopt a pragmatic approach to innovation policy. We acknowledge that the viability of many policy instruments designed to address market failures is often substantially dependent on a well-functioning Australian system of innovation.
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However, in many cases, it is necessary to use the abstract marginal analysis approach to illustrate why nonintervention is sub-optimal.
It is also important to mention that, in addition to market failures, there are potential government failures. That is, even though the ideal policy interventions can be designed, the government may not be able to implement them due to problems associated with bounded rationality, principalagent problems between the policy makers and the bureaucracy, and imperfect information. In our
Report, we ignore these issues despite their obvious importance.
Figure 2: On Specific Targets in Innovation Policy
It is tempting for policymakers to set explicit goals. Such performance indicators allow one to measure the success of a policy and to move from abstract policy recommendations to concrete actions. For instance, the Small Business Innovation Research (SBIR) program enacted in the United
States in July 1982 mandated that all federal agencies spending more than $100 million annually on external research set aside 1.25 per cent of these funds for awards to small business. Congressional effort was made to ensure geographic dispersion of awards. More recently, China set itself the goal to reach two million annual patent filings by 2015 whereas the ‘Europe 2020’ strategy aims to achieve the target of investing 3 per cent of GDP in R&D by 2020. In Australia, universities can be rewarded on the basis of patent application targets.
Such explicit goals may be misleading in many different ways. For one, they may have unintended consequences. China’s boast about rising patent filings echoes similar behaviour by the EPO and the
USPTO in the 1990s. These patent offices are now complaining about excessive backlogs of unprocessed applications and decreasing patent quality. In addition, targets may turn out to be too simplistic to be effective. For instance, Lerner (1999) suggests that the political pressure faced by managers of the SBIR program to make geographically diverse awards could explain the low effectiveness of the program in regions with few high-technology firms. Europe’s target that each country must reach a 3 per cent R&D intensity does not account for the heterogeneity of industry
5 See Cutler (2008) and Australian Government (2009) for a comprehensive review of the Australian innovation system.
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structure across countries. It may be more efficient to spend marginal R&D dollars from non-R&D intensive countries in R&D intensive countries (see Azele and van Pottelsberghe de la Potterie,
2008).
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The Innovation Process
Innovation is a dynamic process which has no beginning and no end: it is a continual process in which products and processes are in a constant state of flux. The innovation process follows a complex pathway which involves feedback loops generated by: learning by doing; trial and error; and discontinuities in production. That said, in order to make discussions tractable and clear, we often speak of a spectrum of activities from high-end or upstream basic science through to applied application, development and commercial activities.
A good understanding of the innovation process is needed to identify sub-optimalities. In this section we explain how an idea progresses from concept to the creation of a new product/process, all the way through to a market launch (or even export to a foreign market). In addition, we will examine the operation of the ‘market for technology’, where we will focus our attention on how technology is traded: how buyers and sellers meet, and the importance of long-term collaboration as a way of building trust between buyers and sellers. Finally, we examine our understanding of the determinants of successful collaborative arrangements – whether that be between firms (such as a joint venture) or between universities and firms.
In Figure 3 we present a brief snapshot of some recent Australian innovations.
Figure 3: Examples of Australian Innovations
Ciba Vision. Within a collaborative research centre, the ‘holy grail’ of optometry was created: a contact lens that could be worn for 30 days without removal and without any associated swelling or irritation. Due to the lens’ high oxygen transmissibility, the eye receives sufficient oxygen while the wearer is asleep.
Compumedics. This company developed the first paperless system to collect and store the data generated during sleep apnoea diagnosis. This overcame the need for doctors to record analogue sleep chart recorders. The system was first installed at the Epworth Hospital and has revenues in excess of $35 million per annum.
Ausmelt. The company developed an upright cylindrical smelting bath that has been hailed internationally as one of the most important technological innovations in metallurgy in the past 50 years. The new technology produces higher quality reaction products and is able to deal with more difficult ore bodies.
Vision Systems. This company created an aspirating smoke detector which works by continually drawing air into a pipe network with a highly efficient aspirator and then taking a sample of air into a laser detection chamber. This method is significantly more sensitive than traditional smoke detectors. The technology was initially developed by CSIRO.
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International Catamaran. This Hobart-based company created lightweight, high-speed catamarans that are driven by water jets rather than propellers. The boats are large, fast and highly manoeuvrable. The company has bounced back after difficult financial times in the aftermath of the
9/11 attacks.
Source: Cebon (2008)
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Why do innovations occur?
Innovation is arguably the way firms compete. The ability of firms to compete via price reduction is limited to what is financially sustainable and therefore price cutting can only be a short-term competitive strategy. If firms want to cut costs over the long term, or increase profit margins via the production of better products or by developing more markets, then they must innovate. Most profit maximising activity is essentially about creating a monopoly advantage for the firm, and innovation, which begins by being something new, is the genesis of this advantage.
This said, not all firms or all industries, innovate (or compete) with equal vigour and success. The overwhelming majority of innovations are those that are new-to-the-firm rather than new-to-theworld. In this section, we review theories about what drives firms to innovate. A good starting point is to look at studies which try to understand what makes some firms decide to attempt to undertake innovation activities and other firms to not do so. And what skills and capabilities shape this decision? In a recent article, Woerter (2008) summarised a number of hypotheses that have been proposed to address the issue:
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The Schumpeterian hypotheses (Schumpeter 1934, 1975), which focus on the size of the firm and the level of concentration in which the firm operates as key factors determining its innovative behaviour;
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The demand-pull hypothesis (Schmookler 1966), which proposes the significant role that market conditions play such as the size of the market and changes in prices;
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The technology-push hypothesis (Phillips 1966; Rosenberg 1976) which argues that conditions underlying knowledge production processes are essential;
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The financial-constraints hypothesis (Nelson 1959) and the related hypotheses which are based on issues surrounding the risks of R&D and the risk preferences of the institutions involved
(Mansfield 1968); and
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The technology-related, supply-side factors hypothesis, which combines appropriability, tacitness of knowledge, technological opportunities and uncertainties (Dosi 1988).
Woerter (2008) then continued by citing recent empirical studies, based on the European
Community Innovation Survey (CIS) data, aimed at providing formal tests of these hypotheses.
Support for the ‘firm-size hypothesis’ is at best inconclusive, with a tendency for the effect to be negative. This is consistent with other non-CIS empirical studies which tried to sort out whether
6 See also Cohen (1995) and Cohen and Levin (1989).
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firms acquired market power because of successful innovation or whether market power enabled firms to make innovation profitable (i.e. Kamien and Schwartz 1982; Mansfield 1984; Levin and Reiss
1984; Acs and Audretsch 1987, 1988, 1991). Many studies that find market structure and/or firm size to be significant determinants of R&D intensity do not control for the underlying conditions of opportunity and appropriability (Phillips 1966; Sutton 1991; Scherer 1967; Cohen 1995; Bosworth and Rogers 2001).
In contrast, both the demand-pull and technology-push hypotheses have some support. There have been a series of economic studies that have tried to estimate the role of more deep-seated determinants such as the opportunities proffered by the scientific sector and how easily firms can appropriate their R&D profits (Levin and Reiss 1984; Pakes and Schankerman 1980). This avenue of research appears to have produced more consistent results than the earlier studies, in part because the theoretical directions of the effects are less ambiguous. However, it still leaves open the question of what governs scientific opportunity and natural appropriability. For example, it may be that size, and the underlying financial resources it implies, enhances the scope of an enterprise’s opportunity and appropriability sets.
Another smaller but concurrent stream of economic research considers why firms do not innovate rather than why they do. The key hypothesis concerns the financial hurdle for firms which desire to invest in highly uncertain and collateral-free projects such as R&D. As with the scientific opportunity and appropriability theories, there is a clear a priori prediction of the effects of retained earnings and gearing levels, and therefore empirical studies tend to find reasonably robust findings. There is a widespread view, and solid evidence, that raising debt to fund R&D is particularly costly and difficult because large R&D activities typically produce uncertain and distant collateral which, more often than not, are absent from balance sheets (Schumpeter 1943; Hall 2005; Scellato 2007; Canepa and
Stoneman 2008; Carreira and Silva 2010). Evidence from Australia supports this: Palangkaraya et al.
(2010) find that internal funding is the most common way patent applicants fund their research.
However, Thomson (2010) used Australian data and did not find that working capital influenced the level of R&D.
There are few econometric Australian studies which explore the determinants of firm innovation; exceptions are Griffiths and Webster (2010) and Thomson (2010). The former found that most of a firm’s R&D activity is explained by internal factors such its managerial style and its competitive and appropriation strategies. The growth in product demand and internal sources of funds were significant, but smaller, in magnitude. The importance of the internal operation of the firm is also supported by Woerter (2008). He finds evidence in support of another hypothesis related to fifth point above. This hypothesis argues that if firm innovation is driven by its perceptions about the problems it faces, and if the firm’s perceptions depend on working routines which are influenced by the characteristics of the firm – such as the size of its employment and physical capital – then one could expect that industries with a greater variety in terms of firm characteristics would be relatively more innovative than industries with more homogeneous firms.
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How do innovations occur?
Some innovations occur ‘automatically’ within the firm in the sense that the managers see a problem or opportunity and then devise a process for producing the design, undertaking its development and then oversee the ‘manufacture’ and sale of the idea. A few firms have clear pathways for this process to occur within their organisation. Other innovations are not automatic and their successful conclusion depends on serendipity rather than planning. This is especially true when several distinct parties are involved in the value-added chain, or where prior experience by the players in the area is limited. Typically these are ideas that emanate from public sector organisations and SMEs. In these situations, the efficacy of brokers in the market for technology, or go-betweens, can be critical.
Apart from specific case studies, we are limited in our knowledge about how innovation occurs to large-scale surveys of different facets of the innovation process. We only have facets because the very heterogeneous nature of innovation means it is hard to generate stylised facts about common pathways (compared with say, the pathways for skill acquisition). Unfortunately, there is also a tendency for the literature to suffer from ‘high-tech myopia’ (the idea that economic growth and employment is mostly the result of research-intensive industries, as defined by their R&D intensity) and then to concentrate only on firms undertaking R&D or patenting. However, since we do not have comprehensive and widely reported measures of innovative activity, we cannot verify whether these so-called research intensive firms are the major contributors to productivity. How biased R&D or patent counts are as a measure of the broader term ‘innovation’ depends on the use to which the data is put (see Jensen and Webster 2009a). Often the estimated measurement bias is based on the researchers ‘expert opinion’ rather than empirical work. An exception is the work by Kirner et al.
(2009), who used the 2006 German Manufacturing Survey data of 1663 firms and find that, while
‘low-technology’ manufacturing firms lag behind medium- and high-tech firms with respect to their product innovation, they may perform better at process innovation. This finding is perhaps explained by the tendency for low-tech innovations to involve processes which are not primarily based on formal research and technological development. Instead they tend to be practical and experiencebased, usually involving implicit knowledge (Heidenreich 2009).
There is a view that variation between firms’ innovative intensity depends on the maturity of the industry. However, the importance of this source of variation may alter from one setting to another, as illustrated by the finding of McGahan and Silverman (2001). In their study they investigate the activity of US publicly-traded firms during the 1980s through to mid-1990s. They find that firms’ patenting activity does not decrease as the industry in which they operate matures in terms of the underlying technology life cycle. They conclude that industry maturity does not appear to lead to a switch from product to process innovation, nor does it imply lower firm innovative activities compared to those in emerging industries.
Our qualitative experience suggests that there are three main types of innovation process.
1.
A problem or opportunity is identified, solved and implement within the firm. Many incremental process innovations are of this type.
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2.
A problem or opportunity is identified by a firm but parts of the innovation process are subcontracted to specialist research, design and manufacturing organisations who undertake research, development, translation, extension and pre-commercial activities on the originating firm’s behalf.
3.
A problem or opportunity is identified by a research organisation who then sells or licenses the findings to subsequent organisations with complementary specialist capabilities. The innovation process is owned by a series of parties who share in the risk of the whole process.
Many of these innovations are radical and involve new scientific discoveries.
One well-documented facet of how innovation occurs is the source of knowledge which inventors build upon. In 2007, IPRIA surveyed all Australian inventors listed on patent applications submitted to the Australian Patent Office between 1986 and 2005. This survey collected information on commercialisation outcomes of 3,736 inventions.
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Essentially, the Inventor Survey provides us with a picture of innovation beyond that which can be provided by firm-level data such as R&D expenditure and patenting counts. It reveals that customer product users are the most important source of ideas and knowledge for Manufacturing and, to a slightly lesser extent, Resources and Services inventors
(see Figure 4). Scientific literature is predominantly important for Resources and Services sector firms.
Figure 4: Importance of Knowledge Sources, Patent Applications Filed by Organisations Located in
Australia by Sector, 1986–2005
Source: Figure 6.6. Palangkaraya et al. (2010).
Table 2 uses data from the Melbourne Institute Business Survey to compare the sources of knowledge used by companies in Australia with a similar US survey. The sources of knowledge are classified into open-learning (networks, customers and suppliers) and closed-learning (licensing of new technologies; hiring other organisations’ workers; patent disclosures; publications and technical meetings; and firm-sponsored R&D). This comparison shows that, on average, Australian firms rate open-learning styles more highly than closed-learning styles.
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Among the former, networks with
7 See Jensen and Webster (2011) for details.
8 Knowledge garnered through formal or informal networks, suppliers and customers are forms of ‘open learning’ since they involve reciprocity or mutual engagement with other organisations. ‘Closed learning’ styles on the other hand include
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other organisations was the most highly rated source of information, while among the latter, the most highly rated learning source was hiring skilled workers. All pair-wise means are statistically significant. Learning styles in our survey show little variation across firms either by size or by industry
(although these results are not presented here). However, the responses from the US Levin et al. survey show little relation to the Australian list. In an analysis of the Australian data, Jensen and
Webster (2009b) found that firms which favour closed-learning practices tend to rely more upon patents and secrecy and eschew lead-time and brands as ways to capture profits. Firms that favour open styles of learning operate in the opposite manner. The manner in which firms seek to stem the flow of knowledge from their firm affects how they behave and learn from other firms.
Table 2 : Rated Importance a of Firms’ Sources of Learning
Melbourne Institute
Business Survey
Australia, 2001-2006
Yale Survey
Learning Style US, 1983
OPEN
Networks b
Suppliers & customers c
Mean
Products and Processes
4.78
4.05
Mean
Products
4.07
Mean
Processes
4.07
CLOSED
Licensing technologies 2.90 4.62 4.58
Publications
R&D e d
Hiring skilled workers
3.41
3.28
4.06
4.01
4.92
3.88
4.42
Researchers
Sample
Jensen and Webster
1340
Levin et al.
650
Notes: a Scale is based on a Likert scale with anchors 1 (= very ineffective) and 7 (= very effective); b Informal networks with other organisations and Formal cooperation/networks with other organisations; c Lead suppliers and customers; d Patent disclosures and
Publications or technical meetings; e In-house R&D and Reverse engineering.
Source: Jensen and Webster (2009b).
Figure 5 reveals that compared with other firms, Manufacturing firms, especially large ones, spend a smaller amount of time researching the ideas behind their inventions. Public sector organisations
(which are not classified into sectors) spend the most amount of time researching the idea behind the patent. All in all, SMEs appear to spend more time on research than large firms, which may reflect greater overall caution by SMEs when it comes to patenting compared to large firms.
Alternatively, it is possible that SMEs are slower at generating patented inventions compared with large companies.
Given the importance of networks and learning for innovation, one would expect to see high levels of collaboration among innovating firms. However, we do not find this. Figure 6 illustrates the propensity of the ABS BLD firms to collaborate with other businesses for innovation purposes.
F
9
F
Collaboration helps firms tackle some of the important barriers to innovation, such as the inability to learning through the licensing of new technologies; hiring other organisations’ workers; patent disclosures; publications and technical meetings; and firm-sponsored R&D. The distinction between these sources is whether or not they involve trust and reciprocity between parties; that is, whether knowledge can be transmitted without any pecuniary quid pro quo.
9 The IPRIA Scoreboard database does not have any information on collaboration and therefore the analysis of collaboration-innovation link for large companies is not possible.
16
obtain access to finance due to the uncertainty of innovation and the inability to absorb external technology due to the partly tacit nature of the underlying knowledge. Collaboration can be especially important for smaller firms, which are more likely to face these difficulties. However, collaboration exposes these firms to contracting and transaction costs (Coase 1937), pernicious opportunistic behaviour (Williamson 1985) and expropriation of intellectual property (Arrow 1962).
Figure 5: Average Years Spent Researching the Invention, Patent Applications Filed by
Organisations Located in Australia, 1986 to 2005
Source: Figure 6.8. Palangkaraya et al. (2010)
A recent study by Thomson and Webster (2011) found that Australian firms were more likely to collaborate over the development stage (of innovation) if they: were an SME or a large but highly geared firm; had relatively limited experience patenting in that technology; or considered the particular innovation to be technologically risky. Frenz and Letto-Gillies (2009) reviewed relevant international empirical studies from the empirical literature and found that almost all of them confirmed the significance of external collaboration with the users and external sources of technical expertise. The studies they reviewed also pointed to the importance of both formal and informal networks for innovation. Despite these potential benefits, Figure 6 demonstrates that collaboration is not very common among Australian innovating SMEs. On average across size categories and industry groups, only about four per cent of innovators report innovation-related collaboration. The figure also indicates that, in comparison to small innovating firms, medium innovators are more likely to collaborate in their innovative activities.
17
Figure 6: Proportion of Innovating SMEs which Collaborate with Other Businesses for the Purpose of Innovation 2006–07, by Employment Size and Industry Group
Note: An innovating firm is a binary variable =1 if the firm has introduced any new or significantly improved goods/services or operational process in the respective year; =0, otherwise.
Source: Figure 4.6. Palangkaraya et al. (2010)
Data on what firms are actually doing are consistent with Tether (2002) who finds that most UK firms still appear to develop their new products, processes and services without forming (formal) cooperative arrangements with other firms or institutions. The extent of co-operative research arrangements for innovation may depend on the type of firm, and on what is meant by innovation.
For example, firms which conduct R&D in order to introduce innovation which is ‘new-to-themarket’ rather than ‘new-to-the-firm’ are much more likely to engage in co-operative arrangements for innovation. Otherwise, most firms still appear to develop their new products, processes and services without forming (formal) co-operative arrangements with other firms or institutions.
An econometric analysis conducted by the Commonwealth Department of Industry, Tourism and
Resources investigates how collaboration and other factors influence innovation novelty in
Australian businesses. The analysis uses information from the Innovation Survey 2003, presented in
ABS (2005). The database comprises only innovating firms, which are identified with a variable that is constructed by counting the number of different innovation-related activities, such as: the acquisition of machinery and equipment; training related to new goods or services; and substantial new design work. The analysis employs an ordered categorical probit model with the probability of introducing the highest degree of novelty (new-to-the-world innovation) as the dependent variable.
The model predicts that collaboration, while controlling for other firm characteristics such as size, foreign ownership and R&D intensity, is associated with a statistically significant increase in the chance of achieving new-to-the-world novelty. Collaboration was found to be more common and important to frontier and creative innovation than to relatively minor modification of goods, services and processes and purely adoptive innovation. The principle conclusion is that, in comparison to non-collaborating businesses, collaborating firms are more likely to introduce new-to-the-world innovation (however we cannot attribute causality here).
The persistence of innovation – the frequency and consistency of innovation produced by a particular firm – also gives us a clue to how innovation is occurring. In theory, there are a number of reasons why particular firms become persistent innovators. First is the cumulative causation or
18
‘success-breeds-success’ phenomenon (Nelson and Winter 1982). That is, innovative success yields profits that are reinvested in R&D. The second possible source of persistence is related to the idea that knowledge accumulation is intrinsically cumulative. If we look at Freeman’s list above, one may expect that successful innovators who satisfy the identified characteristics at one period of time may indeed satisfy the list at different periods.
Figure 7 defines large companies as ‘persistent innovators’ if they have at least one patent or design application in three (or more) consecutive years over the period from 1996 to 2007; ‘One-time innovators’ as those firms which had only one patent or design application during the period; and
‘sporadic innovators’ covers the remaining firms (i.e. firms with two patent or design applications over the period, as well as those firms with three or more applications which were not made in consecutive years).
Figure 7: Persistent Innovators, Large Firms, 2003–04 to 2006–07
Share in terms of number of firms Share in terms of number of innovations
Source: Processed from the IPRIA Scoreboard database
Note: An innovating firm is a binary variable = 1 if filed at least one patent or design application at IP Australia in the respective year; =0 otherwise. Persistent innovators are innovators in three or more continuous year in 1996-2007. One time innovators are innovators only in one of the years. Sporadic innovators are innovators who are not classified as persistent or one time innovators.
The first panel in Figure 7 shows the distribution of innovating firms according to the above definition of persistent innovation. As shown, there is variation across sectors: 20 per cent of the innovating firms in the Services industry are persistent innovators compared with around 30 per cent of the innovators in the Resources industry and 40 per cent in Manufacturing.
The second panel gives the breakdown of firms by type, but this time weighted by the total number of innovations (i.e. number of patent/design applications). For each industry group, persistently innovating firms account for the majority of innovations. For Resources and Manufacturing, respectively, more than 90 and 95 per cent of innovations are implemented by persistently innovating firms, while the number of persistent innovators in Services represents slightly more than two-thirds of the total number of innovations. Both sets of findings in Figure 7 are consistent with
Geroski et al. (1997), Cefis (1999), and Cefis and Orsenigo (2001).
F
10
10 Cefis (1996) uses a transition probability matrix approach to study the importance of persistence in UK firms’ innovation performance and finds that there is little persistence in innovation among most firms. However, she also finds that there is
19
13B
Who are the innovative firms?
We also know something about the innovative activities of small, medium and large Australian companies during the financial years from 2003–04 to 2006–07. We use BLD data (for small and medium companies) and IPRIA’s R&D and Intellectual Property Scoreboard data (for large companies), in three broad industry groups: Resources, Manufacturing and Services.
F
11
F
The discussion focuses on: how many and which firms innovate as shown by the share of innovating firms; how this share has changed over time; and how the activities vary across different industries.
In our discussion we also investigate if size and financial constraints appear to be influences on firms’ innovative activities.
In our discussion, the characteristics of innovators and non-innovators are contrasted. Due to the use of different sources of data for small and medium companies and for large companies, our definition of ‘innovators’ differs slightly depending on the type of information provided. For the small and medium companies (SMEs), an ‘innovator’ is defined as any company which reported as having introduced a new product or process in the specified year. For the large companies, an
‘innovator’ is defined as any company which filed at least one patent or design application in the specified year. Thus, the definition of innovator for large companies is stricter than that for the
SMEs.
In addition, within each of the two company groupings, the analyses also focus on the variation across size of employment. For the SMEs, the companies are classified into three groups: 1–4 employees, 5–19 employees and 20–199 employees. For the large companies, the size classifications are: less than 200 employees, 200–500 employees, 500–1000 employees, and more than 1000 employees. We note that the population of large companies with ‘less than 200 employees’ according to IPRIA Scoreboard data is different from the small companies in the BLD database, even though all BLD companies have less than 200 employees, and thus one should not make a direct comparison between the two groups.
Because large firms will do more of (almost) every activity, it is not remarkable that large firms are more likely to have done an innovative activity than their smaller counterparts. It does not mean they are more innovation intensive. What is remarkable, however, are situations where a small or medium-sized firm is more likely to report undertaking an innovative activity than large firms. significant heterogeneity across sectors and size, with significant persistence exhibited by the largest and the smallest innovating firms. Geroski et al. (1997) link the length in patenting spell and the initial level of patenting and, after controlling for various factors (such as parent/subsidiary status, ownership status, growth in manufacturing output, employment, and innovation spillovers), find that very few firms are persistent innovators and tha\t the threshold level to become a persistent innovator is very high. They also find that persistently innovative firms account for a very large share of total patents produced by the firms in their sample. Finally, Cefis and Orsenigo (2001) use (1) autoregressive parameters and (2) dynamics of cross-section distribution functions, i.e. transition probabilities matrix, as extensions to Cefis (1999) and find that: (a) there is a low degree of persistence in innovative activities at the firm level, which declines overtime; (b) there are important differences across countries, sectors and firm size; and (c) that because inter-sectoral differences are rather invariant across countries, persistence is (at least partly) a technology-specific.
11 We use the ANZSIC 1993 industry divisions to define these sectors as follows: resources (A&B), manufacturing (C), and services (E, F, G, H, I, J, L, P and Q). Small and medium-sized companies excluded from services are Electricity, Gas, and
Water Supply (D), Finance (K), Government (M), Education (N), Health (O), and other services (92, 96, 97). These exclusions are due to the design of BLD survey used for these types of companies. For large companies, however, utilities, health and finance are included.
20
Nonetheless, our basic measure of whether a firm is innovative or not can be useful in comparing firms across industries and over time.
We start by looking at the extent and patterns of resources used for innovation, particularly as reflected by R&D expenditure. The measure of innovation used here (positive R&D expenditure) is binary, as with the other Australian innovation surveys. Figure 8 shows the proportion of SMEs which reported having positive R&D expenditure in the financial year 2004–05, by industry group and employment size class. The figure shows that Manufacturing and Resources have significantly higher proportions of firms undertaking R&D than Services. The Manufacturing sector, followed by
Resources, is more likely to have higher a tendency to report R&D expenditures than Services. This finding is in line with previous work using Australian data such as DITR (2007). Note that R&D expenditure is not the sole input in the innovation process and it would be misleading to conclude that the Manufacturing industry is more innovative than the Services industry. Using European data,
Mothe and Nguyen Thi (2010) have shown that product innovation in service firms is more dependent on organisational and marketing innovations, which are typically not accounted for by statistics on R&D.
Two other important observations can be made regarding the propensity to undertake R&D shown by Figure 8 and Figure 9. First, it appears from Figure 8 that small firms in the Resources industries with 5–19 employed persons are more likely to engage in R&D than medium-sized firms (20–199 persons). Second, as shown by Figure 9, there is in general a reduction in the proportion of large firms which report positive R&D expenditures during the financial years from 2003–04 to 2006–07.
F
12
Figure 8: Proportion of SMEs with R&D Expenditure 2004-05, by Employment Size Class and
Industry Group
Source: BLD CURF database
12 Unfortunately, the BLD surveys did not ask the R&D question consistently over time so that we could not make any similar observation regarding the trend in R&D propensity of SMEs.
21
Figure 9: Proportion of Large Firms with R&D Expenditure 2003-04 to 2006-07, By Employment Size
Class and Industry Group
Source: IPRIA Scoreboard database
Figures 10 and 11 present the proportion of innovating firms, where innovation is defined as the introduction of a new product or process (for BLD firms) or having made at least one application for a patent or registered design (for R&D Scoreboard firms). According to Figure 10, around 25 per cent of SMEs in the BLD report having introduced a new product or process in one of the financial years from 2004–05 to 2006–07. There is also a consistent drop across all sectors and each size group in the percentage of innovative firms from 2005–06 to 2006–07. Figure 11 reveals that large manufacturing firms with more than 1,000 employees are significantly more likely to apply for a patent or registered design than smaller firms.
Figure 10: Proportion of Innovating SMEs 2004–05 to 2006–07, by Employment Size Class and
Industry Group
Source: BLD CURF database
Note: An innovating firm is a binary variable =1 if the firm has introduced any new or significantly improved goods/services or operational process in the respective year; =0, otherwise.
In contrast with Figure 10, Figure 11 suggests that the proportion of innovating firms in Resources and Services does not rise monotonically with size, as we have expected given that large firms do more of most activities. One possible reason for the difference is that the innovation question in the
22
BLD survey refers new-to-the-firm products or processes while, in the R&D Scoreboard, a patent or design implies a new-to-the-world products or processes. Thus, caution should be used when attempting to make direct comparisons between the two graphs.
Figure 12 shows that average R&D expenditure by large innovating firms actually increased between
2003–04 and 2006–07 in all industries (although the effect is quite small in the Manufacturing industry).
F
13
F
The figure also shows that as we would expect, the average innovating large firms spent a lot more on R&D than non-innovating firms.
Figure 11: Proportion of Innovating Large Firms 2003–04 to 2006–07, by Employment Size and
Industry Group
Source: IPRIA Scoreboard database
Note: An innovating firm is a binary variable = 1 if filed at least one patent or design application at IP Australia in the respective year; =0 otherwise.
13 Unfortunately, the BLD database does not provide any information on the amount of R&D expenditure so it is not possible to look at the relationship for SMEs.
23
Figure 12: Average R&D Expenditure by Large Firms, 2003–04 to 2006–07, by Industry Group and
Innovation Status
Source: IPRIA Scoreboard database
Note: An innovating firm is a binary variable = 1 if filed at least one patent or design application at IP Australia in the respective year; =0 otherwise.
14B
Characteristics of (successful) innovators
Freeman (1991) summarises an interesting list of characteristics which make for successful innovators. According to this list, successful innovators are those who:
-
Pay attention to the special needs and circumstances of users;
-
Integrate the development, production and marketing activities;
-
Link with external sources of scientific and technical information and advice, even when they typically have their own in-house R&D;
-
Commit high quality R&D resources to the innovative project; and
-
Bestow high status upon the ‘business innovator’ and, particularly in large organisations, have strong commitment from top management.
Terziovski et al. (2002) conduct a case study of a product development project (the ‘Bushranger’
Project) at Varian Australia Pty Ltd (a company with $140 million turnover which exports 95 per cent of its products). They find that Varian Australia focuses on optimising two critical success factors of product innovation, namely (a) meeting and exceeding customer needs and expectations by innovating new products and accelerating the cycle time from conceptualisation to market launch, and (b) establishing cross-functional, multi-disciplinary teams. Cebon (2008) summarises the findings from 11 Australian case studies and finds that successful innovators: paid attention to the market needs rather than the technology; managed risks, including IP risks, well; attuned corporate governance to innovation needs; had active support from, and at least considered understanding of their situation by, financiers; and were able to launch the product into the Australian market before
24
going overseas. These success factors seem to confirm items (1), (2) and (5) in Freeman’s list above.
In addition, Figure 13 outlines some principles of systematic innovators as presented in Samson
(2010).
Figure 13: Principles Common to Systematically Innovative Companies
Based on a careful analysis of 10 Australian companies that have built their competitive strategy on their innovation capability, Samson (2010) proposes a list of factors that are associated with a successful innovation capability, which we summarise below.
1.
Strong, determined, energetic, dynamic leadership of the organisation seems a necessary prerequisite;
2.
These firms have a particularly strong sense of customer focus and value creation;
3.
Innovation involves implementing change, so change management capability and readiness for regular change are prerequisites;
4.
Sustainable development factors, such as waste reduction, staff well-being, and environmental output improvement, go hand in hand with innovation;
5.
Innovation is often done well by involving partners from outside the organisation: open innovation works;
6.
Innovation helps firms to win in the labour market;
7.
Innovation can be leveraged throughout the supply chain;
8.
Innovation can become fully embedded in a firm’s ‘DNA’, innovation becomes part of everyone’s mindset in the best of firms;
9.
To become systematically innovative, a firm must be at least competent at ‘quality management;
10.
If the innovation is driven by a small group of leaders/ executives, then the risk occurs that once they are gone, the innovation capability and priority will dissipate;
11.
Innovation can be incremental (small) or radical (large), and when it is large it is usually followed by a series of incremental improvements;
12.
Innovation is not free. It requires investment in capability building, training and experimentation;
13.
Innovation means taking risk – technical, market risk etc. – so managing risk prudently, along with costs and benefits, is a core capability. This means an understanding and willingness to accept some failures, along with successes;
14.
Customers generally like to do business with innovative companies as it is intrinsically attractive, however customers do not generally like to share or bear any of the risk of innovation.
25
4.
3B
Sub-optimal innovation
As explained above, we adopt a broad definition of ‘market failure’ in order to understand the many ways in which the unfettered market may fail to supply the socially-optimal level of innovative activities. This broad conception incorporates traditional market failure explanations and also incorporates some that are more often referred to as ‘system failures’. Market failure occurs in the context of neoclassical economic theory when market mechanisms are unable to yield to the socially optimal level of investment in innovation either because agents are not getting or seeing the right signals, or, there are barriers to diffusion and adoption. These failures arise from three particularities of knowledge goods: uncertainty, indivisibility and non-excludability, as pointed out by Nelson
(1959) and Arrow (1962). Market failure in innovative activities can be classified into four groups:
-
Non-excludability;
-
Non-rivalry;
-
Coordination; and
-
Risk.
Coordination can be viewed as a subset of the non-excludability problem. Non-excludability creates externalities and spillovers
F
14
F
and can imply a level of ‘production’ or activity above or below the socially optimal level. Firms and individuals can fail to coordinate properly when there are externalities and spillovers in the provision of information and the activity of search. However, because coordination is specially relevant to the failure of markets to support enough innovation, we discuss coordination failure as a separate category.
Barber (2009) cites barriers to market entry (knowledge assets can provide very effective barriers to entry) and capital market failures (it is difficult to obtain external finance for investments in longterm knowledge creation) as additional failures in innovative activities. We, however, believe that the failures addressed in this report are sufficient to understand and address a large range of failures in innovative activities. For instance, difficulties obtaining external finance for R&D programs (the
‘capital market failure’ referred to by Barber) can be explained by: asymmetric information
(investors are unable to differentiate between good and bad projects); or lack of tangible collateral associated with R&D and the high uncertainty of the innovation process. In addition, other potential sources of market failure have also been identified. These include (Arnold 2004; Tsipouri et al. 2008;
Klein Woolthuis et al. 2005):
-
Capability failures: inadequacies in companies’ ability to act in their own best interests, for example through lack of ‘absorptive capacity’;
-
Institutional failures: inadequacies in other relevant NIS actors such as universities, research institutes or the patent office;
-
Network failures: problems in the interaction among actors in the innovation system;
14 An externality occurs when a cost or benefit arising from a market transaction is imposed on an uncompensated (and sometimes unwitting) third party. A spillover occurs when an activity creates indirect costs or benefits on another activity. The spillover can be internal to the same owner (and thus the costs and benefits are internalised) and the activity does not have to be mediated through the market. There is however a large overlap between spillovers are externalities.
26
-
Framework failures: gaps and shortcomings of regulatory frameworks, intellectual property rights, health and safety rules etc., as well as other background conditions;
-
Policy failures: deficiencies in the ‘governance system’ (policy making, evaluation and learning processes); and
-
Infrastructure failures: physical infrastructure that actors need to function (such as IT, telecom, and roads) and the science and technology infrastructure.
Although these issues are obviously relevant and important, our focus in the Report rests squarely on the four main sources of market failure outlined above which we argue captures all of these issues. The next sections explain the four key market failures in greater detail.
15B
Non-excludability
At the limit, knowledge creation is subject to the market failure of non-excludability: once created, knowledge can be appropriated by others. Thus, the unfettered market may not provide enough incentives for private parties to produce new knowledge at the socially optimal level. Put another way, non-excludability creates an externality or spillover, and therefore a gap between the private and social rates of return of specific piece of R&D. The first consumer of knowledge cannot prevent other (third) parties from enjoying the benefits of that piece of knowledge. This knowledge may be codified scientific knowledge, general know-how, or knowledge that is ‘in the air’ (knowledge acquired from other parties’ demonstrations and doing). The policy debate surrounding these spillovers and externalities is primarily conducted in cross-sectional terms rather than in terms of the bigger, inter-temporal issue of the optimal investment decision. That is, can we identify, and thereby give additional public support to, investments with large positive knowledge spillovers and externalities vis-à-vis those with negligible spillovers and externalities?
F
15
It is important to note that spillovers and externalities from a new idea include both the benefits to other firms from being able to copy or adapt this idea, and the benefits to consumers who are now able to buy the same good at a lower price or buy hitherto unknown goods. The latter are called rent or pecuniary spillovers. Value from rent spillovers typically last in perpetuity (hence to convert the flow of benefits to a present value we need to assume a positive rate of discount). For example, consumers still benefit from the invention of the wheel; its value has not been made obsolete.
The presence of spillovers and externalities provide the rationale for publicly funded or supported activities (government money grants, matching-fund schemes, direct government provision, subsidies etc.) and legislation for intellectual property (IP) rights, including copyright, patents, trade secrets (contract law), trademarks and plant variety rights. Government grants raise the level of innovative activities by increasing the profitability of these activities. The main purpose of the IP system is to provide innovators with the right to prevent others from copying their technology, thereby allowing technology owners to recoup their initial R&D investment through insisting on their
15 On the other hand, rent or market spillovers are created when competition follows technological change and leads to a fall in consumer prices (and a rise in consumer surplus). These spillovers represent an important end goal of innovation.
However, the optimal level of market spillovers is part of the optimal level of investment question. That is, how much should society save and invest today for enjoyment tomorrow? This is a very complex general equilibrium question and we will not consider this issue further.
27
IP rights. However, sometimes legally enforceable excludability can negate the very rationale for wanting to encourage more innovation per se (that is, to promote spillovers).
Arguments for government intervention are usually cast in extreme or limiting case terms where imitation is costless or impossible to prohibit (because monitoring the use of knowledge is not feasible). In reality, excludability ranges from 100 to zero per cent with, arguably, most cases being in the middle. As such, much newly created knowledge is partly protected either by the sheer difficulty and cost of reverse engineering, or by the power of secrecy. In addition, excludable complementary assets can facilitate the appropriation of returns from otherwise non-excludable technology assets (as highlighted by Richardson 1972; Teece 1986). Government interventions and
IP rights may not be required where natural excludability is high, since the incentive to invent is not deficient.
In a static framework, inhibiting the diffusion of productivity-enhancing technology represents a welfare loss to society as a whole because the marginal cost of using existing technology is zero (at the limit). This is because technology is non-rival, which implies that its use does not deprive anyone else from using the same technology. As a result, once a new technology has been created, it is welfare maximising to employ it wherever it has a positive use. This is the basis of the classic IP policy trade-off: IP rights provide an incentive to innovators by granting them market power, but as a consequence, firms can charge a higher price which ultimately reduces the utilisation and diffusion of the innovation. Balancing the tension between the dynamic benefits (encourage innovation) and the static losses (limit diffusion) is one of the key objectives of innovation policy. Total benefits to society as a whole include the private benefit net of any losses faced by other agents in the economy. While the private benefits are important, IP policy development is generally based on the net social benefits.
There is overwhelming evidence for the existence of spillovers. In an early literature review, Griliches
(1992) concluded that ‘the overall impression [is] that spillovers are both prevalent and dominant.’
Since then, economic research has constantly confirmed the existence of spillovers. In a more recent review of the literature, Dowrick (2003) concludes that there is a large enough body of evidence that social rates of return are about twice the private rates which indicates that, even given significant government interventions to increase innovation, significant knowledge spillovers still remain. For a summary of the private and social rates of return across U.S. manufacturing industries, see Table 3.
Technology transfer and knowledge diffusion are the very same activities as imitation, copying or expropriation; they are two sides of the same coin. Dissemination of socially costless knowledge is beneficial per se and should only be discouraged where it has a clear detrimental effect on the incentive to invent. Two types of empirical studies exist on how firms stop technology transfer or diffusion: the first reports on what firms do to stop their own knowledge from leaking out, and the second derives concrete estimates of the effect of the patent system on the incentive to invent.
28
Table 3: Private and Social Marginal Rates of Return on Investment in US Manufacturing
Industries, 1985 (before tax, net of depreciation)
Industry
Chemical Products
Fabricated Metal
Non-electrical
Machinery
Electrical Products
Transport Equipment
Scientific Instruments
Investment in R&D
Private Returns Social Returns
1
20%
21%
24%
46%
21%
40%
18%
26%
28%
31%
35%
86%
Investment in Physical Capital
Private Returns
22%
21%
25%
27%
23%
28%
Note: ‘Social’ returns are defined as private returns plus spillovers to the other industries covered in the study.
Source: Dowrick (2003) cited from Bernstein and Nadiri (1991) Table 6.
The first type of study uses survey evidence to discover the main mechanisms firms use to stop their important knowledge diffusing to other firms. If these mechanisms are efficient, then the size of market failure on account of non-excludability should be small. Standard measures to stop expropriation include the use of: patents; secrecy; lead time (including moving quickly down the learning curve); control over the distribution process and brand names and marketing; and organisational know-how and capabilities.
F
16
F
Note that some of these measures are naturally occurring in the sense that they require no government intervention, while others, such as patents and trade marks, require supporting government institutions. The average rating of these knowledge-capture mechanisms for Australia is presented in Table 4, along with comparable results from previous studies of this ilk by Levin et al. (1987) and Harabi (1995).
F
17
F
This table shows that naturally occurring organisational know-how was the highest rated form of knowledge capture, closely followed by distribution and brand names. All pair-wise means are statistically different except for Lead time and Distribution & brand names.
As in the Levin et al. and Harabi studies (and the follow-up study by Cohen et al. 2000), patents are reported as being relatively weak tools for preventing expropriation. There was some variation in the relative effectiveness of each appropriation strategy across industry groups in our data. In particular, know-how was the most commonly cited knowledge-capture mechanism for: Mining;
Manufacturing; Electricity, gas and water; Construction; and Property and business services. For the remaining service industries, know-how was second to distribution and brand names.
16 Capabilities are the firm’s ability to appropriate, integrate and organise internal skills to meet the requirements of the market.
17 Studies such as Cohen et al. (2000) which rank effectiveness are not strictly comparable to the studies mentioned here which rate effectiveness.
29
Table 4: International Ratings of the Effectiveness a of Knowledge-Capture Mechanisms
Melbourne Institute
Survey
Yale Survey Swiss Survey
Knowledge-Capture Mechanism Australia, 2001-2006 US, 1983 Switzerland, 1988
Mean Mean Mean Mean Mean Mean
Products Processes Products Processes Products Processes
Patents
Secrecy
Lead time b
Moving quickly down the learning curve
Distribution & brand names c
Organisational know-how d
Sales and service efforts
Researchers
3.09
3.54
4.16
2.84
3.52
4.11
4.21
4.69
4.05
4.65
Jensen and Webster
4.33
3.57
5.41
5.09
3.52
4.31
5.11
5.02
5.6
Levin et al.
4.55
3.44 2.76
3.25
5.37
4.56
5.2
Harabi
358
3.60
5.63
4.42
5.7
Sample size 1136 650
Note: a Based on a Likert scale with anchors of 1 (= very ineffective) and 7 (=very effective). b For Australia, Lead time and Moving quickly down the learning curve have been combined. c Control over distribution and Brand name and marketing. d Organisation knowhow, capabilities and production complexity.
Source: Jensen and Webster (2009b).
As mentioned, we expect that natural leakage of knowledge (or conversely the natural excludability of knowledge) will vary by industry and technology since some types of ideas are more easily codified and copied, while other forms of knowledge can only be revealed through example. A number of studies have supported this view and found that concern with inadvertent leakage is highest in the pharmaceutical and the chemical industries, where knowledge can be conveyed into algorithms. Significant leakage also exists to a lesser extent in machinery and electrical equipment.
The second strand of empirical research estimates the effect of patent protection on either the rate of copying or the financial benefits that accrue to the individual innovating firm. Mansfield (1986) used survey data from US firms and found that 60 per cent of inventions in the pharmaceutical industry would not have been developed in the absence of patent protection (corresponding figures in other industries were 38 per cent in the chemical industry, 17 per cent in the machinery industry, and 11 per cent in the electrical equipment industry).
The patent system will not increase the incentive to invent unless it enhances the private value of the invention. That is, the firm investing in the invention must (expect to) be able to appropriate sufficient returns to invest in the first place. Noting this, empirical researchers have attempted to estimate the implicit subsidy, for inventors, embodied in the patent system. There are four general approaches to estimating the value or incentive effect of patents.
The first is the renewal approach. This approach begins with the premise that firms will renew their patent if the net private value of patent protection, over the renewal period, is larger than the costs of renewal (Schankerman 1998; Gronqvist 2009). To estimate the effective rate of subsidy,
Schankerman (1998) divides the aggregate value of patents registered in a given year by the R&D expenditure in the previous year. The results suggest that the patent system provides an incentive
30
equivalent to a 25 per cent subsidy rate to R&D. Lanjouw (1998) performs analogous estimates for
West German patents, estimating an equivalent subsidy rate of 10 per cent. Another approach to estimating patent value is based on the effect of registering a patent on firm market value (see
Griliches 1981; Bosworth and Rogers 2001; Hall 1993; Griffiths and Webster 2006). Bessen (2009) aims to estimate the value of patent rights in this way, while controlling for ‘quality adjusted’ R&D stock. He estimates the effective subsidy rate for different industries and finds that it lies between 6 and 76 per cent. The upper estimates occur in pharmaceuticals and chemicals.
The third, more recent, approach uses survey data to understand the incentive power of patent protection. As an example of this approach, a recent study by Arora et al. (2008) models the patent premium as a random variable that has a firm-specific mean. Their results suggest that for most industries, patents provide no additional incentive (relative to the second best means of appropriation). In instances that firms do apply for a patent, they estimate the average patent premium to be 50 per cent. A recent study by Jensen et al. (2010) considers data from a survey of
Australian inventors. By comparing the self-reported value of inventions that are protected by patent with those not protected by patent, they estimate that patent protection represents an implicit subsidy of about 48 per cent. Finally, an Australian study of the likelihood that a patentable invention will progress to different commercialisation stages found that about 40 per cent of all inventions advanced to the point of market launch and mass production. Furthermore, being refused a patent (for those who applied) lowered the probability of attempting market launch and mass production by about 13 percentage points (Jensen and Webster 2011b).
16B
Non-rivalry
Most knowledge is non-rivalrous since it can be used simultaneously by many people without affecting another’s ability to use it. This characteristic means that once the original item is produced, the invention is not scarce. Price, therefore, has no role as a rationing device. In an efficient market
(in the economic sense), the good should be provided either under perfect price discrimination or free provision. In either case, no person with a valuation above cost, is priced out of the market (i.e. prevented from consuming the invention or its embodiment because of price). However, perfect price discrimination is not viable if the good is also non-excludable. Hence, only the free provision option remains. The free provision solution implies that the means to create the original item must be supplied by the public purse.
F
18
F
However, reliance on the public purse involves costs not only in the form of taxation distortions, but also from suboptimal selection mechanisms. These properties of non-rivalry and non-excludability are used to justify public subsidies for R&D.
17B
Coordination
Markets neither operate in a vacuum nor are the only institutions governing the inter-linking of the various stages of the valued-added chain. Civic institutions, such as professional societies, universities and schools, bureaucratic agencies and other informal networks exist to produce and diffuse non-rivalrous information and enable participants to garner ideas and search broadly. To
18 Strictly, this applies only if there are net costs to the creator. The provision of free software on the internet is one example where there are few net costs to the creator and no additional incentive is required from the government.
31
paraphrase Owen-Smith and Powell (2004), ‘networks are the plumbing of markets’. What we don’t know from the literature is the value of these organisations.
In a recent report on the new nature of innovation, the OECD (2009) cites ‘Global knowledge sourcing and collaborative networks’ as one of the important drivers of innovation in a world characterised by increased global openness. In explaining why, the report notes (p. 10):
‘Companies will form collaborative networks and engage themselves in binding innovation partnerships. No single company – regardless of size – will possess all the knowledge and resources needed to innovate on its own. Therefore, companies will have to access and combine globallydispersed knowledge on a larger scale than ever seen before. Transnational companies have always sourced knowledge globally, but in the future even the smallest companies will have the opportunity to source knowledge on a global scale. This will be necessary to respond to global competition. [...]
The new global search for knowledge bears important policy implications. In the industrial era, the free movement of commodities and capital was, and still is, crucial, but in the global innovation economy the free movement of knowledge workers will also be critical. Codified knowledge can be shared at a distance, but tacit or hidden knowledge can only be shared through physical presence.’
[our emphasis].
The rise in popularity of these collaborative networks is partly explained by the increasing complexity of the innovation process, which requires that firms interact with other organisations such as suppliers, customers, competitors, or universities and research institutes. To be state-of-art, an Australian organisation must be dealing with the best and most frontier organisations in the world. In only a few technology areas will these be in Australia.
Christensen et al. (2001) suggest that firms collaborate to:
-
Enhance learning, both the timeliness and quality of learnt material;
-
Reduce uncertainty;
-
Increase the ability to capture external knowledge; and
-
Increase the ability to handle complex technical issues and complementarities.
However they also note the costs of collaboration and networking, including:
-
Time costs;
-
Cost of disclosing valuable information.
There are various reasons coordination failures occur. According to Oxera (2005), coordination failures generally occur because, even though it can be in the common interest of parties to coordinate (the cooperative solution is optimal from a private return perspective), private returns are low if only some of the relevant participants cooperate. That is, there are spillovers and external benefits to other parties from one firm’s coordination efforts. Coordination may fail, not least because of a lack of information about possible useful partners but because agents do not have all the necessary knowledge (about markets, technologies, and the state of the world) to efficiently design, evaluate, choose and implement the activities they wish to carry out (Barber 2009). Such information is often dispersed across many different actors, especially if there is no interactive dialogue and communication between them. Spillovers are the norm not the exception.
32
As already illustrated, Australian SMEs have a low level of collaboration. This observation is reinforced by the recent report by Dodgson et al. (2010) who note that:
‘Although the importance of Australia’s NIS [National Innovation System] is widely appreciated and contains a large number of different elements, it is a disconnected system where there are few bridges between its major players [...] This finding is confirmed by OECD evidence that places
Australia lowest amongst its members on capacities for collaboration between firms and between firms and higher education, and second lowest on collaboration between firms and government’.
Given that R&D cooperation is usually found to significantly increase firm performance (see e.g.
Belderbos et al. 2004; Czarnitzki et al. 2007), a deeper investigation of the causes and consequences of the low propensity of Australian firms to collaborate is particularly recommended. At present, all we know is that Australian firms which engage in formal networks are on average 13 percentage points more likely to carry out R&D – but this is not necessarily a cause and effect relationship (see
Palangkaraya et al. 2010).
According to a survey of firms in Australia, Austria, Denmark, Norway and Spain by Christensen et al.
(2001), between 30–50 per cent of the surveyed firms had established a co-operative link with consultancies, technological service firms, universities and so on. At the core of knowledge-intensive services are specialised expert knowledge, research and development abilities, problem-solving know-how, amongst other things (Strambach 1997). Knowledge-intensive services provide a diversity of specialist expertise which may enhance a firm’s ability to adjust more rapidly to changing environments.
18B
Risk
The innovation process involves both actuarial risk, over which reliable statistical probabilities can be formed,
F
19
F
and ‘uncertain’ risk, over which no objective probabilities exist.
F
20
F
By definition, the level of actuarial risk facing society can be reduced by aggregation, since repeating an event an infinite number of times leads to a certain ex ante outcome. Uncertain risk, however, cannot be reduced: it can only be transferred from one party to another.
If risk cannot be separated from production, then the market will deliver a suboptimal level of production because the most efficient producer will not necessarily be the optimal bearer of risk.
The market has responded to this inefficiency by creating industries that effectively separate actuarial risk from production activities and then pool it. However, these pooling institutions – insurance agencies, stock markets, large business conglomerations and the like – cover only a small range of activities. Furthermore, this remedy is not flawless. The purchase and pooling of actuarial risk can create moral hazard problems; will always incur contracting costs; and, in extreme cases, may overwhelm the incentive to offer the service.
Unlike actuarial risk, uncertain risk cannot be theoretically reduced through pooling. However, if the marginal cost of bearing risk increases with the amount of risk held, the total cost of a given level of uncertain risk can be reduced by spreading it across the whole population (Arrow and Lind 1970).
19 An example of actuarial risk is the probability of errors from mechanical production lines.
20 An example of uncertain risk is the predicted outcome from a war.
33
For the same reason that governments institute special levies on a population to spread the cost of large crises (i.e. flood levy, sugar levy, gun buy-back levy) beyond the directly affected group, governments fund risky R&D.
While spreading the cost of a simple project among many parties minimises the social costs of uncertain risk, the matter becomes more complicated for production sequences involving many investment stages (i.e. long series of dated labour inputs). One strategy for dealing with uncertain situations with long time trajectories is to proceed ‘one step at a time’. This is because the value of each step is only revealed as it unfolds, so it is not possible to know the ultimate value of production and thus use backwards induction to put a value (and price) on earlier stages. Complex sets of tacit and analytic skills cause positive feedback mechanisms and uncertainty about how to next proceed, and how to imitate a more successful rival. This means that using price signals, in particular, the lure of high downstream prices, to motivate upstream inventors will be a poor and blunt instrument.
Future monopoly rents are simply too uncertain to enter current calculations. Other mechanisms are needed for deciding what, and how much, early stage production to do.
As indicated, policies to address uncertain risk include spreading the risk across multiple parties and using non-market mechanisms to select upstream projects. If the good is also non-rivalrous, then deadweight losses at the upstream stage can become compounded. Decisions in early stages can amplify and have disproportionate effects on downstream outcomes. The market is particularly poor at dealing with uncertain risk and financial intermediaries have few reliable metrics and yardsticks to evaluate the financial risk of specific R&D projects. The result is that R&D tends to be either internally financed, financed through equity or debt financed only when the firm has other, tangible collateral. As previously mentioned, there is solid evidence, that raising debt to fund R&D is particularly difficult because large R&D activities typically produce uncertain and distant collateral which, more often than not, are absent from balance sheets (Schumpeter 1943; Himmelberg and
Peterson 1994; Hall 2005; Canepa and Stoneman 2008; Carreira and Silva 2010; Czarnitzki and
Hottenrott 2010; de Rassenfosse 2011). Ample evidence shows that SMEs have difficulty attracting
R&D investment funds, ceteris paribus.
5.
4B
Policy Interventions
This section presents a list of the main policy interventions used to correct suboptimal levels of innovation. The rationale for each type of intervention is explained and empirical evidence from
Australia or abroad is discussed. An overview of the various policy tools is presented in Table 5. With regard to analysis of these policy interventions, we apply the standard “test” applied to all public policy interventions: i) there should be sufficient robust evidence indicating that the market failure exists; ii) there should be a rationale for choosing amongst the possible policy tools; and iii) the distortions introduced by the intervention should be smaller than the original market failure problem. This last point is generally overlooked, but it is of crucial importance when considering market failure. In other words, the existence of market failure is a necessary but not sufficient condition for public policy intervention. The obvious difficulty here is that any distortions introduced by intervention in the market are typically revealed ex post and are very difficult to quantify. So, our conclusions regarding the net benefits of policy interventions are necessarily tentative.
34
Policy interventions are not independent of each other since some interventions can be reinforced or weakened by others. For instance, providing seed funding to start-ups may prove to be more successful for start-ups located in technology clusters. Over the last two decades many European governments have pursued ambitious R&D policies with the aim of fostering innovation and economic growth in peripheral regions of Europe. There is a view that economies of scope are very important to the success of individual programs. Success may depend on reaching a minimum threshold level of research, achieving returns to scale, and the surrounding socio-economic context.
Bilbao-Osorio and Rodríguez-Pose (2004) conducted an empirical study of R&D investment and R&D education in peripheral regions of the European Union and found that success was contingent upon region-specific socioeconomic characteristics which affected the capacity of each region to transform R&D investment into innovation and, eventually, innovation into economic growth.
In the remaining parts of this section, we review the main policy interventions grouped under headings. The reader should bear in mind that these groupings are not precise and many programs cut across several boundaries. In most cases, there are few economic valuations of programs that are rigorous enough to enable the policy maker to estimate a rate of return from a program or to compare the relative effectiveness of programs. Many so-called evaluations are descriptive reports of limited analytical content. Where they exist, most evaluations are from the US or Europe. There are only a number of published Australian evaluations which include a properly constructed counterfactual.
Table 5: Interventions and Sub-optimal Innovation Characteristics
Policy type
R&D support schemes for industry
Service provision schemes
Entitlement schemes
Competitive schemes
Public research
Collaboration
University-industry linkages
R&D consortia and inter-firm networks
Industry R&D Corporations
Public procurement
Financial support schemes
Cluster formation and networks
Non-excludability
(private incentives too low)
Market failure characteristic
Non-rivalry
(diffusion too narrow)
Coordination
(broken linkages)
Risk
(need to share risk)
19B
R&D support schemes for industry
There are three types of industry R&D support schemes:
1.
Service provision schemes: information or advice to firms on a walk-in-the-door basis.
2.
Entitlement schemes: all firms that meet specified threshold criteria qualify for support (the most common being the R&D tax concession).
35
3.
Competitive schemes: support is awarded by committees only to the highest ranked eligible firms.
Service provision schemes
Governments can play an important role in providing information to participants in the innovation process. This can take a number of different forms. For example, many governments devote resources to activities such as ranking of potential R&D projects, or new technologies once created.
Since this latter knowledge is highly specialised and easy (costless) to distribute once known, there may be a role for governments to aid the uptake/adoption of new technology in this way. This is a type of ‘demonstration effect’, and it can potentially have significant economic benefits since the speed with which new technology is adopted can have obvious benefits for aggregate productivity growth. However, more analysis needs to be undertaken as to why this role isn’t undertaken efficiently by the market.
An information-related initiative in Australia (as part of Commercialisation Australia) is the provision of grants to subsidise specialist advice and services to build the skills, knowledge and linkages required to successfully commercialise new products, processes and services. Commercialisation
Australia is an Australian Government initiative that assists researchers, entrepreneurs and innovative companies to convert intellectual property into successful commercial ventures. The support provided by Commercialisation Australia is designed to help successful applicants through the commercialisation process. Assistance is tailored to the needs of each successful applicant and is structured around the key development stages in the commercialisation pathway. The rationale is premised on the view that the right information and advice is critical to commercialising a new product or service.
Other advice schemes typically offer information or advice to firms on a ‘walk in the door’ basis.
Extended advice involving firm visits can sometimes be rationed. Examples include the Australian
Institute for Commercialisation, INNOVIC, C21, Enterprise Connect and ASEA. We do not focus on service-based schemes in this report. Enterprise Connect, which is one of the largest schemes in
Australia, sends experts to companies to provide free or subsidised business advice on technical issues and opportunities and the location of suitable expertise, inter alia. We are not aware of publicly available evaluations of these Australian services.
Entitlement schemes
The majority of R&D subsidy entitlements are either extraordinary taxation concessions; accelerated depreciation on R&D or plant and equipment; or tax credits and tax rebates. These subsidies are given to all firms which qualify, they are not competitive. In Australia, it is common for R&D expenses to attract taxation concessions of over 100 per cent. R&D subsidies often target commercial R&D projects with large expected social benefits, but with inadequate expected returns to private investors due to the existence of spillovers (Klette et al. 2000). This is especially true where government has a strategic objective (such as reducing dependency on dirty energy sources).
These policies can also be linked to other strategic objectives. For example, in recognising that
36
collaboration can alleviate the problems associated with spillovers, the Finnish Government decided to force R&D subsidy recipients to actively collaborate with SMEs (Georghiou et al. 2003).
F
21
F
R&D support schemes – which include tax concessions and grants – are the most evaluated government policies in the world. In a recent review, Thomson (2010) argues that tax incentives are not the dominant determinant of real R&D expenditure; other factors such as demand conditions and technological opportunities probably dominate (which is one reason it is difficult to isolate the effect statistically). That said, Thomson argues that the literature suggests that each additional dollar of government revenue foregone generates approximately one additional dollar of private sector
R&D expenditure (with a confidence interval of between $1.40 and $0.30). That is, there is neither additionality nor displacement.
F
22
F
He notes that if the response is $1, the same increase in national
R&D could be achieved by funding an equivalent value of R&D directly (e.g. via the CSIRO or universities). However, the difference between these two strategies (tax policy versus direct funding) is, of course, the type of R&D projects that are funded. R&D undertaken by private firms using the tax incentive will be more commercially focused and immediately useful. At the same time, technology created by private firms may produce fewer spillovers since the knowledge created is not put into the public domain. He argues that there are good reasons to hedge between these policy options, because some beneficial projects will be funded by each.
As is the case with many policy interventions, the effectiveness of a policy may hinge on the implementation of other complementary policies. For example, one of the key issues with regard to the effectiveness of R&D subsidies in promoting an increase in real R&D effort relates to the elasticity of the supply of R&D workers. If the supply of R&D workers is low, R&D subsidies may simply be captured by R&D workers in the form of higher wages (see Thomson and Jensen 2010 for more on this issue). If the government fails to provide the infrastructure (and training programs) required to produce new R&D workers, then any policy designed to stimulate real R&D effort by providing simple R&D subsidies will be largely ineffective in the short term. In the long run however, we expect the scale of research capabilities to grow.
In sum, R&D subsidies are unlikely to leverage government funds, in the sense of leading to more total R&D across the economy. However, to the extent we believe that private sector R&D is either superior in quality or complementary to public sector R&D, these subsidies will generate more valuable or successful R&D outputs.
Competitive schemes
Competitive grants are usually based on the outcome of a formal application process. It is common for grants to be based on ‘matching’ money from the private sector applicant. In Australia, this has been as low as 3 : 1 (private : public), but 1 : 1 is more common. Matched competitive grant schemes for R&D are common throughout the developed world. As with entitlement schemes, key questions
21
It is unclear how this particular policy was enforced. That is, whether the subsidy was only paid at the end of a
‘successful’ collaboration – however defined – between the two parties.
22 On average, European studies are more likely to find additionality and US studies are more likely to find crowding out.
These differences could be due to the program mix or the prejudices of the researcher. There is no evidence that schemes designed to induce additionality increased R&D activity by more that schemes not designed in this way. See Hall and Van
Reenen (2000); Wallensten (2000); Klette and Moen (2010); Lach (2002); Conzalez, Jaumandreu and Pazo (2005); Almus and Czarnitzki (2003); Blanes and Busom (2004); Aerts and Schmidt (2008); Clausen (2009); Ebersberger (2005); Bayona-
Saez and Garcia-Marco (2010); David, Hall and Toole (2000); Lindstrom and Heshmati (2005).
37
about grant schemes relate to additionality (does the private sector spending rise?) and displacement or crowding out (does the private sector spending fall?). As mentioned above, these questions have been the subject of numerous macroeconomic and microeconomic studies in Europe and the United States (note that many macroeconomic studies do not separate competitive grants from total public sector R&D spending).
Grants can vary according to a number of criteria, for example: industry/technology scope; selection personnel; matching formulae; and amount. However, while there is very little empirical evaluation on the effects of these criteria, we make a few comments about the design of grant schemes based on research by Thomson and Webster (2011). There are four design parameters common to any form of government support for R&D:
Firm engagement — How does the scheme recruit business interest?
Project selection — What criteria are used and who selects the projects?
Payment structure — How is financial support structured?
Administrative costs — How to minimise the burden.
Competitive grants experience problems in all four aspects. Thomson and Webster (2011) report that the cost of knowing about industry grant schemes is larger than most people imagine. While
R&D managers in large firms are often not aware of large R&D grant programs, SMEs are even less informed. The problem is especially acute for programs that change often. Six to eight years may represent a long-lived program, but this is generally not long enough to become well-known in industry (many recent programs exist for only 3–4 years).
Thomson and Webster (2011) also argue that project selection is a major issue in the Australian environment. Government appointed selection panels, in the current form, are unlikely to possess a sufficient level of expertise in all the technology, industry and market areas they are required to cover. This problem can be a function of Australia’s small size and we should therefore be cautious about copying schemes from the United States or Europe without accounting for this handicap. The design of the structure of payments, however, can make many of the selection criteria redundant.
For example, co-contribution – matching funds – aligns the incentives of grant applicants and the selection committee/unit with respect to technological feasibility and private benefits. If the selection committee/unit cannot claim better knowledge than the applicant, these criteria should be omitted from the decision. The relative skills and access to information between applicant and committee is likely to vary. Sophisticated firms are likely to have better information about the technology and market than independent award committees, whereas this may not be the case regarding backyard inventors.
Applicants, however, have different incentives from selection committees with regard to project additionality and spillovers. A common objective of competitive R&D grant schemes is to avoid subsidising projects which would proceed in the absence of government support – that is, to target
‘additionality.’
F
23
F
In practice, applicants are asked to show evidence that they were unable to acquire funding from other sources. Thomson and Webster (2011) argue that using selection criteria to
23
For example, both the R&D Start Program and Commercial Ready include merit criteria related to the need for funding.
38
target marginal projects is unlikely to be successful, and will introduce considerable inefficiencies.
They first note that project level additionality is not a requisite for program additionality. That is, if a firm’s best project is awarded a grant, this frees up resources to fund other more marginal projects
(i.e. lowers the average cost of capital). There is a cascading effect. Hence, trying to second guess whether the project will or will not go ahead without the grant is not going to identify additionality.
F
24
F
Furthermore, all government programs – for education, the labour market, health – involve displacement (i.e. crowding out). A goal of zero displacement is not reasonable. We note however that about half of unsuccessful grant applications (in their survey) reported that the project did eventually go ahead, albeit in reduced form.
While the design of the structure of payments can avoid the need for technological feasibility and private benefits to be part of the selection criteria, to avoid over engineering schemes, ‘additionality’ should not included either. The only remaining ‘valid’ assessment criterion therefore relates to external benefits or spillovers.
Entitlement schemes and competitive grants also have obvious differences in regards to administrative costs, both to government and to applicants. The cost to government is lower in the case of entitlement schemes. For example, in 1998–99 the ratio of administration costs to program expenditure was three times higher for R&D Start (6 per cent) than for the R&D tax concession (2 per cent).
F
25
F
Evidence also suggests firm compliance cost for R&D Start was also higher for the R&D tax concession. An IPRIA-Melbourne Institute survey of 61 grant Recipients found that firms typically devote 2–3 weeks of staff time, with about 20–30 per cent of firms engaging an external consultant
(Thomson and Webster 2011).
20B
Public research
The OECD (2010, p. 125) provides a comprehensive definition of ‘public research’:
‘The public research system can be loosely defined as the institutions that depend on various forms of public support and carry out basic and applied research as well as experimental development. These institutions include world-class research universities, small regional universities, colleges of technology, public hospitals and clinics, government research laboratories and government establishments engaged in activities such as administration, health, defence and cultural services as well as technology centres and science parks. Some are mainly involved in the production of knowledge, others are more closely tied to firms and industrial innovation, and still others deal with public goods, such as standards, weather forecasting or developing test methods.’
The OECD considers the public research system as essential to strong economic performance. Not only does the public research system plays many roles in innovation systems (including education, creation and diffusion of knowledge, and storage and transmission of knowledge), it also performs much ‘blue sky’ science or basic research, and undertakes activities that support innovation such as: development work; certification; monitoring and measurement; creating links between scientific fields; and establishing multidisciplinary knowledge bases, such as gene banks and quality-assured scientific collections. Because basic research is subject to many of the market failures already
24
Fellner (1992) found that most program administrators have difficulty predicting additionality in proposals. See also Lach
(2002).
25 Evaluation of the R&D Start Program, the Allens Consulting Group.
39
discussed (low appropriability, high spillovers, strong uncertainty), there is a strong case for the public funding of basic research.
While it is relatively clear that direct funding of public sector research will lead to research outputs
(of greater or lesser value), most studies go beyond this to ask: what effect does public sector research have on national productivity and private sector innovation? That is, what are the public sector research spillovers? A number of studies have identified positive effects of public sector research on the private sector. In a review of the evidence, David et al. (2000) note that the majority of studies report overall complementarity between public and private R&D.
Using US survey data, Cohen, Nelson and Walsh (2002) found that public research is critical to industrial R&D in a small number of industries and, importantly, affects industrial R&D across much of the manufacturing sector. Contrary to the notion that university research primarily generates new ideas for industrial R&D projects, the survey responses demonstrate that public research both suggests new R&D projects and contributes to the completion of existing projects in roughly equal measure overall. The results also indicate that the key channels through which university research has an impact on industrial R&D include: the publication of papers and reports; public conferences and meetings; informal information exchanges; and consulting. They also found that, after controlling for industry, the influence of public research on industrial R&D is disproportionately greater for larger firms as well as start-ups.
The review conducted by Salter and Martin (2001) concluded that the public sector research economic benefits are substantial. Their study also highlighted the importance of spillovers and the existence of localisation effects in research. The relative importance of these different forms of benefit apparently varies with scientific field, technology and industrial sector. Consequently, no simple model of the economic benefits from basic research is possible. Dowrick’s review (2003) noted that some studies of the productivity effects of publicly funded R&D have suggested that returns were lower than those estimated on business R&D - for instance Lichtenberg and Siegel
(1991) and Nadiri (1993). More recently, both the OECD (2001) and Bassanini and Scarpetta (2001) have reported cross-country regressions that suggest a negative return on public sector R&D - implying that public sector R&D may displace private sector R&D. However, subsequent research suggests that the results of these studies may be misleading for two reasons. First, they failed to distinguish between different types of publicly funded research. Second, they failed to account for the time delay between productivity outcomes and the performance of public R&D, which tends to be focused more on the research than on the development side.
Studies which incorporate lagged effects and distinguish between the types of public R&D find significant positive productivity effects which suggest that the timelines for public sector research benefits are long. Mamuneas and Nadiri (1996) examine the cost-reducing benefits of publicly funded R&D according to whether it is done by business or the public sector in fifteen industries over the period 1956–1988 in the United States. They find that both forms of publicly financed R&D generate statistically significant benefits, albeit with the stronger reduction in marginal costs coming from R&D performed within the business sector. Jaffe and Trajtenberg (1996) provide one the first large-scale analyses of knowledge spillovers using patent citation data as a measure of spillovers.
Among their findings, they report that federal government patents are cited significantly less than corporate patents, but they seem to have a longer lasting impact on research (as evidenced by the
40
fact that citations to government patents have a ‘staying power’ over time). Nadiri and Mamuneas
(1994) estimate that the ‘social’ rate of return to publicly-funded US R&D stocks is 6–9 per cent.
Guellec and van Pottelsberghe de la Potterie (2001) estimate the long-run elasticity of productivity with respect to public R&D as 0.17 over their sample of sixteen OECD countries. This elasticity is higher for countries with a relatively large share of university-performed research compared to government laboratory research. They claim that ‘... much government performed R&D is aimed at public missions that don’t impact directly on productivity (health, environment), whereas universities are providing the basic knowledge that is used in later stages by industry to perform technological innovation’ (p. 116). The elasticity of public research is also higher where the business
R&D intensity is relatively high, indicating that the spillover benefits of public research are complementary with corporate research activities. In a subsequent study using data from 17 OECD countries over the period 1981-96, Guellec and van Pottelsberghe de la Potterie (2003) find that government funding stimulates business R&D expenditure (BERD) if the government research is contracted to the business sector, but that it tends to crowd out BERD when it is performed in government laboratories. BERD is not affected by university research. Finally, Jensen and Webster
(2011a) find evidence that the level of public sector research positively affects the propensity of patents to be commercialised in Australia.
An important factor that is missing in the evaluations reviewed above is information on the mechanisms in place for transferring public sector knowledge to the private sector. It is relatively well known that the transfer of scientific discoveries from universities to the corporate sector is far from seamless: few university patents are commercialised and industry generally do not rate universities as important sources of knowledge for their own innovations (Jensen and Webster
2011b). There has been much discussion about the best ways to improve the engagement between the public and private sectors. As with R&D competitive grants for industry (reviewed above), factors critical to the success of public grants include: the design; qualifying criteria; scale of benefits; selection criteria; selection of personnel; and reporting requirements, inter alia. While documents indicate that some schemes are more effective than others, empirical literature which has seriously evaluated generic design criteria are hard to find. Most program evaluation reports are confidential or ‘glowing’ and are of limited credibility.
In sum, these studies support the view that general funding for public sector research has significant benefits for the economy, although the benefits depend on the nature and scope of public-private engagement programs. However, there is no clear guide as to how much governments should commit to public research or suggest programs that can be advanced to increase public benefits.
21B
Collaboration
University–industry linkages
Collaboration between industry and universities, it is often argued, is one of the main areas where considerable scope exists for improving the performance of innovation. From the firm’s point of view, the main rationale for collaboration with universities naturally concerns innovation, in terms of new or improved products or processes. The public policy rationale behind these programs is that collaboration, whether formal or informal, enables innovating firms to reduce costs by eliminating
41
duplication and by achieving economies of scale. Collaboration also facilitates the process of finding, adapting and acquiring information relevant for innovation, as well as spreading the risk and maximising the rewards associated with innovation. Collaboration with a larger partner may tackle some of the barriers to innovation, such as the inability to obtain access to finance due to the uncertainty of innovation and the inability to absorb external technology due to the partly tacit nature of the underlying knowledge. For an overview of the evidence on industry–university partnerships, see Poyago-Theotoky et al. (2002).
According to the well-known Carnegie Mellon Survey of R&D laboratories in the early 1990s (
), the most effective channels through which US firms benefited from university research
are publications, open scientific communication, and consulting. Colyvas et al. (2002) analyse case studies from Columbia University and Stanford University to understand how innovation gets into practice. They find that intellectual property rights are most important for the transfer of embryonic inventions which require further development, and unimportant for inventions that would be useful to industry straight ‘off the shelf’ (although patents allow universities to collect revenues).
Survey data by Broström (2010) suggest that interaction rationales go beyond the pursuit of innovation per se. The author reports that firms also work with university researchers to access academic networks, to develop its human capital and to realise direct business opportunities.
F
26
F
H
Broström (2010)
H
recommends that co-founding of collaborative research should not be restricted to settings that emphasise outcomes of a concrete, direct nature (number of patents, number of spinoffs etc.) for funding evaluation. This stems from the observation that a large part of the selfreported benefits of industry–science collaboration is of an indirect nature and not as easily measurable as ‘concrete’ innovations. He also somewhat controversially argues that it may be necessary to consider supporting collaborative R&D in forms that compromise academic standard norms to some extent in order to meet the broader need of industry. In particular, he recommends forms of co-funding where demands of fundamentality and novelty are combined with demands on industry participation in the formulation of research. Laursen and Salter (2004) examine the role of different strategies firms use in partnering with universities. They found that firms who adopt ‘open’ search strategies and invest in R&D are more likely than other firms to collaborate with universities, indicating that managerial choice matters in shaping the propensity of firms to draw from universities.
Linkages between universities and industry have become stronger in recent decades for a variety of reasons. The science base is obviously an important source of ‘basic’ research which industry can employ and commercialise, some of which leaks out to industry but some of which requires formal collaboration in order to capture tacit knowledge. Governments around the world have also offered incentives for universities and industry to collaborate in many other ways – including science parks, technology incubators and the like. In Australia, the government has introduced formal programs such as ARC Linkage Grants and Cooperative Research Centres (CRCs). This latter program has been evaluated on numerous occasions – most recently by Mary O’Kane in 2008 – and has been shown to have ‘...delivered significant, identifiable economic and social benefits, particularly through end-user application of research’ (p.xi).
26 In fact, Fagerberg (1999) found that the most important effect of collaboration is not that they subsidize research but that they facilitate the sharing of ideas.
42
The other primary way in which universities and industry have increased collaborative arrangements is through the creation of technology transfer offices. These offices have sprung up in the last 20 year or so, such that almost every major research institution around the world now has one. The objective of these offices is to try and find commercial partners to invest in new technologies that have been created in the university. Part of the reason for the emergence of technology transfer offices lies in the Bayh-Dole Act 1980 which was enacted in the United States to allow universities to patent research outputs from federally-funded research projects.
F
27
F
The underlying rationale was that firms would not invest in university technology without some form of intellectual property – normally a patent (or patent application). Thus, potentially valuable university inventions would be under-utilised or simply left on the shelf. However, there are many other potential rationales for the creation of technology transfer offices. For example, Macho-Stadler et al. (2007) argue that they serve as a way of pooling inventions which enhance reputation building for universities, and Hellman
(2007) argues that technology transfer costs are lower than the average search costs associated with looking for a commercial development partner.
Although there are many inventions which have been commercialised via a technology transfer office – including the Boyer-Cohen gene-splicing patent – it remains to be seen how effective technology transfer offices have been in commercialising university inventive outputs. New models for commercialising public sector science are being proposed which co-locate university academics alongside industry researchers in order for a more informal and continual interaction to take place
(such as Carlton Connect at the University of Melbourne).
R&D consortia and inter-firm networks
Collaboration is important in more than just the university–industry nexus. In fact, there is a wide range of different collaboration linkages that can be fostered by careful public policy intervention, including: R&D contracts, inter-firm networks, and research joint ventures. In the Six Countries
Programme report (2009), there is a strong emphasis on the need to promote collaboration and knowledge exchange. They argue that innovators today increasingly collaborate with external partners – including suppliers, customers and universities – in order to tap into new knowledge, expand their reach or share risks and costs. Policy can facilitate such collaboration, which is increasingly global, by, for example, lowering barriers to international knowledge flows and encouraging the development of knowledge markets. The OECD (2009) suggests that in order to strengthen the research environment, there are a number of policy actions that governments should implement. First, they should seek to facilitate global partnership and network activities, and consider loosening the national restrictions applying to government-funded research programs; for instance, they could introduce collaborative R&D credits across borders. In general, tax credits are one important way that governments can promote R&D cooperation.
Japan is often regarded as the market leader in the formation of R&D consortia, an example being the Very Large Scale Integrated (VLSI) circuit project which was designed by the Japanese
27 Careful analysis of university patenting and licensing before and after the introduction of the Bayh-Dole Act, for example, suggests that university-industry collaboration was strong in many institutions prior to 1980.
Thus, it is important not to over-state the role of the Bayh-Dole Act in university licensing since 1980 (Mowery et al. 2001). In Australia, universities can claim ownership of inventions arising from publicly funded research from government funding agencies such as the ARC or the NHMRC. Accordingly, the conditions existing in the
US prior to the Bayh-Dole Act do not apply here.
43
Government to aid in the development of the Japanese semiconductor industry. This project – 22 per cent of which was funded by the Japanese Government – was hailed as an international success and was subsequently imitated in both Europe and America (see Sakakibara 1997). Most of the research on the performance of R&D consortia has been based on case studies, focusing on big R&D projects like SEMATECH in the United States, ESPRIT in Europe and VLSI in Japan. This research provides a rich set of information about successful consortia, but it doesn’t facilitate an understanding of the systematic forces which determine i) which firms cooperate, and ii) the outcome of cooperation. This requires large, cross-sectional (or panel) datasets. This type of systematic analysis has been done within countries (for Japan, see Branstetter and Sakakibara 2002) and across countries (for a comparison of Japanese and Korean industrial policies relating to R&D consortia, see Sakakibara and Cho 2002). The Australian R&D corporations, which are a form of R&D consortia, have not been formally evaluated.
The evidence presented in this stream of the literature provides us with a comprehensive overview of the causes and effects of collaboration amongst R&D firms. In an important evaluation of the impact of the Advanced Technology Program (ATP) in the United States, Sakakibara and Branstetter
(2003) examine the effect of cooperation on the ex post productivity performance of participants.
This program was designed to fund pre-commercial, high risk technologies created via research consortia. Sakakibara and Branstetter (2003) analyse data on 96 ATP-funded research consortia and find that there is a strong positive association between participation in the program and the overall research productivity of participants (regardless of whether productivity is measured at the firm level or the consortium level). Moreover, this effect is stronger when the cooperating firms are closer to each other in technological specialisation. Link, Paton and Siegel (2002) also evaluate the
ATP consortia and find that it was successful in inducing firms to engage in additional (privatelyfinanced) research joint ventures.
In a similar study evaluating the effect of research consortia in Japan, Branstetter and Sakakibara
(2002) examine the research productivity of 145 research consortia who participated in the government-sponsored program. They examine research productivity (as measured by the number of patents in the targeted technology area) before, during and after participating in the consortia, and show that outcomes are positively associated with the level of potential R&D spillovers within the consortium (as measured by technological proximity). Moreover, the consortia have stronger positive effects when the research is more basic in nature (as opposed to applied research). These results are broadly consistent with the desired goals of such R&D consortia and predicted theoretical outcomes (see Spence 1984 for the seminal contribution).
There is also some evidence suggesting that the design of the R&D consortia is important:
Branstetter and Sakakibara (2002), for example, state that ‘...the design of a consortium matters much more than the level of resources expended on it’ (p.156). However, this study is silent on the exact design characteristics of the scheme which contribute to the increased performance. For more evidence on direct comparisons of different schemes, Sakakibara and Cho (2002) compare Japanese and Korean policies aimed at promoting R&D cooperation. They find that organisational and institutional factors negatively affect the implementation of policies aimed at promoting R&D cooperation in Korea relative to Japan. In his study of Taiwan’s R&D consortia, Mathews (2002) argues that Taiwan has also been very successful in achieving its goals – through technological catch-
44
up and widespread diffusion of cutting edge technologies, rather than the more common approach of promoting simple risk sharing and cost reduction.
Industry R&D Corporations
R&D corporations are co-operative industry owned groups that fund R&D for the benefit of the industry (members). Typically, government support RDCs by the provision of matched funding but in some cases it will also instigate them. While most Australian R&D corporations are based on the agricultural industries, some also exist in mining and manufacture. In Australia, R&D corporations are generally (but not always) funded from a mix industry levies, membership fees and government funds.
F
28
F
Strategic research priorities are identified by the industry through a range of consultative activities and the research is targeted at specific industry needs. Their approach is fundamentally one of problem solving – identifying key challenges facing the industry, isolating sub components and funding projects which address those which have the best likelihood of making the biggest impact on the nominated problem.
Industry R&D corporations are individually crafted to suit the specific industry structure. Many are composed of small independent operators (such as primary producers), for whom technology is not a usual nexus of competition. However, there are several examples of industry R&D groups comprising small numbers of technologically sophisticated manufacturers and miners who are otherwise in direct competition with one another.
F
29
F
These tend only to fund R&D that is common to all members, such as basic research or research focused on industry-wide issues (like health and safety). For such organisations to work, members must have similar technological needs and be able to find areas of common technological interest, where the benefits of cooperation outweigh competitive considerations. A priori, the most suitable industries are: those not dominated by one main player; those with many price takers; those able to levy members in a way that is perceived as fair and equitable; and those for which technology is not the primary nexus of competition.
There is widespread acknowledgement of fundamental trade-offs between doing incremental research which may generate modest returns in the short term, versus pursuing more strategic radical innovation. R&D groups also acknowledge the inherent tension between common benefit and the reality of ongoing competition between members. For instance, sometimes a firm which is leading in a particular field may not want to work on a given issue. By funding more fundamental or basic research and leaving applied or commercial research for the individual firms, R&D corporations can support projects with industry-wide benefit.
Because the R&D corporations are owned by industry, there is good engagement between firms and the executive. The corporations and the committees know the researchers well and there is an enduring relationship with the relevant research community. The committee that determines funding allocations are in the industry (the parties contributing the levies) and have an intimate knowledge of the technologies and applications. The committee has an active role in shaping the research, so it is not a ‘one-hit game’. Because the funds are coming from industry, probity concerns are less and there is limited oversight from government.
28 Rural RDCs are funded 1:1 But other industry groups differ.
29 Such as Dairy Innovation Australia, the Australian Coal Association Research Program and the Australian Mineral
Industries Research Association Limited. These operate in a range of ways but each with the general objective of investing in intangibles, largely technology, for the good of the industry.
45
Expertise on selection committees appears to be the strength of the R&D corporation model.
Similarly, a range of governance structures are possible which align the incentive of the award committee and the industry members (e.g. committees can comprise member representatives, or allocation can be made by direct ballot). The collaborative nature of decision making means funding will be targeted to R&D not being undertaken by individual member firms. That is, funding is implicitly directed toward projects with the most intra-industry spillovers.
The Australian R&D corporations are not well evaluated.
22B
Public procurement
An increasingly common area of public policy intervention relating to innovation is the strategic use of normal government activities to underpin private sector innovation. Innovation procurement is very different from normal government policies to favour local firms when outsourcing for materials and services. While the latter aims to give local firms the chance to achieve economies of scale, innovation procurement is very much about de-risking ‘blue-sky’ research. Innovation procurement includes pre-commercial demonstration, regulation, and the development of technology standards.
The goal of these policies is to aid the process of market formation by stimulating demand for (and adoption of) new technologies. There are several possible mechanisms.
A secure demand for their products can reduce the risks early adopters face when employing a new technology that has not been fully operationalised.
Early adopters of new ideas can be demonstrators for other local firms. This reduces the risk for subsequent firms.
Regulations can secure demand from the private sector.
Governments can help create new, general purpose technologies through their technology procurement strategies.
With respect to the last point, it is not clear that Australian governments can develop new, general purpose technologies and new technology standards in the same way that US, Japanese and
European governments can. We suggest that a more realistic role for Australian governments would be to use procurement, pre-commercial demonstration and regulation to spread the risk of early adoption and innovation.
There is some evidence that public procurement is an effective intervention. For example, in the
United States there is evidence that the government’s defence procurement policies have led to higher levels of private R&D investment, increased spin-offs, and growth in the computer software industry (see Mowery and Langlois 1996).
F
30
F
Famous US examples of targeted policies include the
Defense Advanced Research Projects Agency (DARPA); ARPA-E (Advanced Research Projects Agency-
Energy); and the Advanced Technology Program.
F
31
F
There is also some documented evidence
30 The Advanced Research Projects Agency was founded in 1958 by the US Government to develop transformative technologies in the space and defence areas.
31 Feldman and Kelley (2003); Feldman and Kelley (2002); Nagano (2006); Feldman, Kelley, Schaff and Farkas (2000); Link and Scott (2005); Breznitz (2007).
46
indicating that the development of Nokia’s platform technology in the cellular switching business
(and other important facets of its technology) were shaped by the Finnish Government’s public procurement policies (see Palmberg 1998). Analysis of these cases suggests that public procurement may be more effective in instances where the technology is embryonic – rather than maturing – as this is where risk and uncertainty are most prominent.
The costs of using government procurement, demonstration and regulation to achieve innovation- related ends are unclear. There is some evidence which suggests that public innovation procurement is a more cost-efficient policy instrument than R&D subsidies (see Geroski 1990, for example).
Moreover, there is evidence that demand is one of the most important sources of innovation: in a survey of more than 1000 organisations, more than 50 per cent respondents stated that demand was the main driver of innovation (BDL 2003). However, this is one area of research which is still in a rather formative stage of development. One of the potentially significant problems with using public procurement to spread early adoption is that it may introduce undesirable distortionary price signals to firms. In other words, it doesn’t rely on market price signals to guide firms about where/how to invest their money. As far as we are aware, there are no studies which address this potential cost of this policy intervention. Therefore, the best we can conclude about these studies is that there may be some benefits, but that the implied costs of these policies (and therefore the net effects) are unknown.
Most countries around the world have focused more attention on supply-side policies to stimulate innovation than on demand-side policies, but this has been changing in recent years. In Europe, for example, there are a range of new demand-side policies including: a) The Lead Market Initiative, which was launched in 2006 to provide industries with an opportunity to develop niche export markets; b) A pre-commercial procurement policy designed to encourage public procurement agencies to share risks associated with development of new inventions; and c) The Environmental Technologies Action Plan (ETAP) which attempts to improve market conditions for emerging green technologies.
Sweden has been very active in the ‘catalytic procurement’ policy domain. The Swedish energy agencies NUTEK and STEM have implemented a complex set of schemes aimed at accelerating the diffusion of new energy-efficient (i.e. green) technologies. They employed a mix of different policies
– including public procurement, awareness measures, organised discourses with user communities and in some instances direct subsidies to procurers. Policy evaluation of this program suggests that the scheme was not universally successful for all technologies, but that diffusion was substantially accelerated (see Edler and Georghio 2007; Neji 1999). As there is very little use by Australian governments of innovation procurement policies, there are no apparent evaluations.
23B
Financial support schemes
Venture capital (VC), a type of private equity, is finance provided to high-potential,
H start-up companies
H
. The hallmark of a venture capital
H fund
H
is that it takes
H equity
H
in the target companies and then exits, about 8–10 years later, through an initial public offering (
H
IPO
H
) or direct sale. VC is
47
attractive for new, small companies that are unable to list on the stock market or secure a
H bank loan
H
. In exchange for accepting high risk, VC owners usually get significant control over company decisions, in addition to significant ownership. While VCs can be totally private affairs, governments can support them by directly investing in the VC firm. The VC firm is thus a wedge between the bureaucrat and the high-tech company. Most people argue that government support for VCs is justified by the market failure associated with risk (i.e. Dodgson et al. 2010).
The successful rise of the Israeli ITC sector over the last three decades has often been (partly) attributed to VC schemes. However, Breznitz (2007) notes that the flourishing VC schemes, which began in 1992 under the Government’s Yozam program, followed a decade of successful Israeli policies to ‘introduce’ Israeli firms to the US market and many years of successful Israeli IPOs on the
NASDAQ. In other words, the VC policies did not lead the growth of successful IT firms but, rather, they reinforced an existing strength and capability.
There are a range of non-VC financial support schemes which have been employed around the developed world. Examples in Australia include: the Innovation Investment Fund, which has a total of $358 million for investment in technology-based firms; the Pooled Development Fund, which has invested over $550 million in Australian firms; and the Pre-seed Fund ($78.7 million), which targets the early stage finance gap. One of the recommendations put forward in the Cutler Report concerns the introduction of a Competitive Innovation Grants Program to assist innovative firms (with limited access to capital) in the high risk, proof-of-concept and development stages (Cutler 2008).
One of the most well-known examples of a financial support scheme is the Small Business Innovation
Research (SBIR) program enacted by the US Congress in the early 1980s. The SBIR provides a mandate to the major R&D agencies in the United States – including, but not limited to, the
Department of Agriculture, Department of Education, Department of Energy, and Department of
Defense
F
32
F
– to allocate a share of the research budget to small innovative firms. There is some evidence suggesting that the SBIR program has been quite successful in stimulating academic entrepreneurship. A recent paper by Toole and Czarnitzki (2007) highlights two main characteristics of the program that make it attractive as an entrepreneurship policy: early-stage financing and scientist involvement in commercialisation. Using data on NIH-supported biomedical researchers, they describe the characteristics of these individuals. To explore the importance of early-stage financing and scientist involvement, they complement individual-level data with information on scientist-linked and non-linked SBIR firms. Their results suggest that firms associated with scientists perform significantly better than other, non-linked SBIR firms in terms of a range of different performance metrics, including follow-on venture capital funding, SBIR program completion, and patenting.
32 This list is certainly not exhaustive. For a complete list of the agencies which allocate SBIR funds to innovative small companies in the US, see
H http://www.sbir.gov/federal_links.htm
H
. The nature and structure of the programs varies across agencies. However, most of the funds are allocated on a competitive basis. For example, the National Institute of Food and Agriculture has an SBIR program that provides funds of “...up to
$100,000 for Phase I and up to $400,000 for Phase II. Success rates for applicants have been about 15% for
Phase I and 50-60% for Phase II. Projects dealing with agriculturally related manufacturing and alternative and renewable energy technologies are encouraged across all 2010 SBIR topic areas.... Proposals are reviewed through a confidential peer review process using outside experts from non-profit organizations. All applicants receive verbatim copies of reviews.”
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In a review of the evidence on the performance of the SBIR program, Audretsch (2003) lists the following benefits:
-
It provides a source of funding for scientists to launch start-up firms that otherwise would not have had access to alternative sources of funding;
-
The survival and growth rates of SBIR recipients have exceeded those of firms not receiving SBIR funding; and
-
It induces scientists involved in research to change their career path. By applying the scientific knowledge to commercialisation, these scientists shift their career trajectories away from basic research towards entrepreneurship.
More recent research suggests that the VC market has performed poorly during the last ten years and is in need of radical restructuring if it is to be an effective way of promoting successful commercialisation of new technologies. In a recent issue of the Harvard Business Review, Ghalbouni and Rouzies (2010) summarise evidence published demonstrating that i) VC-backed firms failed to outperform the NASDAQ during the boom period of the 1990s and ii) entrepreneurs who obtain funding from angel investors are more likely to survive to at least four years and have superior performance (based on research by Josh Lerner and John Cochrane, respectively). Buzzacchi, Scellato and Ughetto (2011) analysed VCs performance in the EU and found that the higher public ownership in the VC funds, all else being equal, the longer the investment periods, particularly when the analysis is focused on those deals that ended with a default. That is, unsuccessful investments.
Publicly available evaluations of Australia’s VCs are scarce.
24B
Cluster formation and networks
The economics of cluster formation begins with Marshall (1890) and is therefore one of most longlived concepts. A cluster can be defined as a group of firms, related economic actors, or institutions that are located near one another and that draw productive advantage from their mutual proximity and connections.
F
33
F
They may be connected by functional relationship (e.g., suppliers and purchasers, producers and distributors) or by competition for similar markets (U.S. Council on Competitiveness
2007, p. 1). Recent findings from research on the commercialisation of innovation suggest that the technical systems and institutions of a region play an important part in determining the success of the commercialisation of new inventions (Shane and Venkataraman 2003). Research from overseas has consistently found that managers believe informal networks to be the most important of these sources (Levin et al. 1987; Cohen et al. 2000). There is considerable evidence that clusters can form the basis of knowledge transfer, but their effectiveness depends on the creation of institutions within the cluster to support such transfer (Feldman and Francis 2004). Clusters tend to form when there is supportive social capital, venture capital, entrepreneurial support services and when the firms are actively engaged with the university sector and other publicly funded R&D organisations.
Merely clustering organisations in close physical proximity does not ensure the capture of knowledge spillovers.
33 The institutions include venture capitalists, education and training organisations, the intellectual property rights and enforcement environment, and industry programs for certain industry/region networks.
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Uncodified, early-stage knowledge often requires proximity for transmission. In general, the higher the degree of knowledge integration within a cluster, the better the cluster performs economically
(see Morosini 2004 for an extensive review of this literature). Although clusters can form spontaneously, government intervention is rarely far behind. A government may have provided the institutional infrastructure or even positive incentives.
spontaneous cluster formation that is accompanied by specific government intervention.
The rationale for government intervention in the creation and support of clusters lies in the following: spillovers (or externalities), information asymmetries, and coordination failures (European
Commission 2008). First, actors in a cluster may ignore future (inter-temporal) spillovers because of the time lag in reaping the benefits (path dependency). Second, information asymmetries may exist concerning the steps to be taken for deriving maximum overall value. Such information is often is dispersed across many different actors, especially if there is no interactive dialogue and communication between them. Third, coordination failures may arise because individual companies consider the impact only on themselves, not on others, when making decisions, be it whether to locate in a cluster or what investments to undertake once there. (European Commission 2008; Ketels
2009).
Boekholt and Thuriaux (1999) also provide a list of system failures pertaining to clusters. They argue that government intervention can be justified on the grounds that: SMEs do not grasp the opportunities for collaboration (with other firms) which could increase their interactive learning and resource base; firms, particularly SMEs, cannot access strategic knowledge when operating in isolation; firms do not utilise the expertise of knowledge suppliers, while knowledge suppliers are not sufficiently equipped to market their knowledge; and existing or potential clusters lack identity and self-awareness.
Figure 14: The Silicon Roundabout in East London
In 2008 followers of the UK technology industry began to talk excitedly about a cluster of internet start-ups in east London. There are now around a hundred high-tech businesses in what has been dubbed Silicon Roundabout. This example provides an interesting case of a cluster that emerged without government support, or even direct links with universities.
Silicon Roundabout’s firms are generally small-to-medium sized and highly specialised. One makes software for the fashion industry; another runs an online dictionary. There is a hotel-comparison website, a firm specialising in 3D and interactive content, and several digital-design firms with their own niches.
The congregation owes something to the advantages of London generally: its wealth; its appeal to global talent; and the English language. But the entrepreneurs in Silicon Roundabout stress the unique draw of the east of the city, with its bohemian atmosphere and relatively cheap rents, for young, creative workers. “If we were in west London, we would be a completely different type of business,” says Steve Hardman, the co-founder of SocialGo, which helps clients build their own online communities.
David Cameron recently unveiled a plan to support it. As well as tinkering with the legal framework in which such businesses operate – by creating updated “entrepreneur visas” to bypass ill-judged new immigration restrictions, for example – the government has persuaded technology titans such
50
as Google and Cisco to invest. Soon BT will bring super-fast broadband to the area; McKinsey, a consultancy, will share management expertise. The aim is to build a ‘Tech City’ stretching from
Shoreditch to the 2012 Olympic Games site farther east. David Cameron’s plan also includes measures to help with finance. Silicon Valley Bank, a Californian lender, will open up in Britain;
Vodafone will bring its venture-capital arm to the area; and the government will provide £200m in equity finance.
Source: Adapted from The Economist,
‘Jumping on the roundabout’, November 25, 2010. Available online at http://www.economist.com
Government intervention in fostering the development of clusters or increasing their economic benefits spans a number of policy areas: business network policy; FDI attraction policy; export promotion policy; sector-oriented industry policy; science and education policy; and competition and market integration policy. Cluster policy can take many different forms. For instance, it might involve appointing brokers and intermediaries who organise dialogue between the various actors in the system; it might also provide local services for firms, such as financial advice, marketing and design services; or it might attract investors and businesses to fill gaps in the cluster value chain and strengthen demand and supply links (Martin and Sunley 2003; Brown 2000). In a recent review in
Europe, Sölvell (2008: 53) provided six generic objectives of cluster policies, as summarised below.
Human resources upgrading enhances the available skills pool and involves, for example, vocational training and management education. Such efforts can focus on different target groups of people. One type is intended to attract and retain students for the region–and sometimes, for selected sectors – to ensure the future supply of a skilled workforce. Another type targets managers through management training programs; this is typically not sectorspecific. A third type is sector-specific vocational training and technical training.
Cluster expansion aims to increase the number of firms, through incubators or by promoting inward investment within the region. This can be done by promoting the formation of new firms, and by attracting existing firms to the region. Incubators are a vital element of cluster policies. They often combine provision of physical facilities with assistance in setting up business plans and financial plans, and help entrepreneurs get in touch with financiers and potential customers.
Internationalisation promotes firm operations, for example through export promotion.
Commercial cooperation encourages firms to interact with each other, for example through joint purchasing or sharing services.
Innovation objectives promote product, services and process innovation, for example through increased commercialisation of academic research. There are two general approaches to innovation, and these are often combined. One is to promote innovation through enhanced cooperation and networking between firms. The other is to enhance cooperation between the business sector and the research/university sector in order to commercialise academic research.
Business environment objectives, finally, aim at enhancing the conditions for business, through improving the legal and institutional setting or improving the physical infrastructure. Improving the business environment means that conditions outside firms are
51
improved. Business environment objectives therefore focus on issues that are in the hands of government, rather than working with firms directly. There are two main aspects of the environment that can be addressed: the physical/technical infrastructure; and the legal/institutional setting. In addition, region branding is an objective that can be assigned to this category.
6.
5B
Conclusions
Innovation is the key to long-run productivity growth and economic prosperity. However, it is wellknown that the market will under-supply innovative activities if left to its own devices. This market failure has been well documented and government around the world – including Australia – have attempted to correct it via the introduction of a broad range of policy mechanisms. In this Report, we review the available theoretical and empirical evidence on innovation and policy intervention.
Although economists have made much progress on understanding the relationship between innovation and economic growth and prosperity, it is fair to say that they haven’t made the same progress with regard to understanding the effectiveness of specific policy mechanisms aimed at addressing the market and systemic failures. It should also be apparent from our review that there are many instances where the available evidence is contradictory: that is, there is no unambiguous answer to the research questions posed. In these instances, we would caution the reader not to draw sweeping conclusions from one study.
Policy interventions aimed at correcting market failure should meet three criteria: i) there should be sufficient robust evidence indicating that the market failure exists; ii) there should be a rationale for choosing amongst the different possible policy interventions; and iii) the distortions introduced by the intervention should be smaller than the original market failure problem. This last point is generally overlooked, but it is of crucial importance when considering market failure: there is absolutely no logic in introducing a policy intervention which may correct market failure but which also introduces substantial distortions. Much cannot be said because either governments do not commission or publish evaluations; evaluations are not conducted in a rigorous manner or because independent researchers are prevented from accessing data to conduct an evaluation. So, we would like to note that – in the absence of evidence regarding the distortions of policy interventions – it is not possible to provide an iron-clad conclusion about their effectiveness.
The main area where repeated and intensive evaluation evidence exists is for R&D support schemes
(tax concessions or competitive grants). For these, the evidence suggests that each additional dollar of government revenue foregone generates approximately one additional dollar of private sector
R&D expenditure. That is, there is neither additionality nor displacement. The main difference between tax policy versus direct funding is the type of R&D projects that are funded. R&D undertaken by private firms using the tax incentive will be more commercially focused but it may produce fewer spillovers. Thus, there are good reasons to hedge between tax policy and direct funding because some beneficial projects will be funded by each.
Quality research on the presence of externalities or spillovers is more prevalent. There is strong and robust evidence that public sector research economic benefits are substantial. Much of the evidence has highlighted the importance of spillovers and the existence of localisation effects in research. The
52
relative importance of these different forms of benefit apparently varies with scientific field, technology and industrial sector. Consequently, no simple model of the economic benefits from basic research is possible. Despite the strong evidence in support of public research, the benefits depend on the nature and scope of public-private engagement programs. They do not, however, give a clear guide as to how much governments should commit to public research or suggest programs that can be advanced to increase public benefits. The elasticity of public research is also higher where the business R&D intensity is relatively high, indicating that the spillover benefits of public research are complementary with corporate research activities.
Collaboration between industry and universities enables innovating firms to reduce costs by eliminating duplication and by achieving economies of scale. Collaboration also facilitates the process of finding, adapting and acquiring information relevant for innovation, as well as spreading the risk and maximising the rewards associated with innovation. The most effective channels appear to be via publications, open scientific communication, and consulting. IP rights are most important for the transfer of embryonic inventions which require further development, and unimportant for inventions that would be useful to industry straight ‘off the shelf’. Collaboration also enables firms to work with university researchers and otherwise access academic networks, to develop its human capital and to realise direct business opportunities.
Collaboration is important in more than just the university–industry nexus. In fact, there is a wide range of different collaboration linkages that can be fostered by careful public policy intervention, including: R&D contracts, inter-firm networks, and research joint ventures. Policy can facilitate such collaboration by lowering barriers to international knowledge flows and encouraging the development of knowledge markets. Most of the research on the performance of R&D consortia suggests they have been very successful. However, they are largely based on case studies which doesn’t help us to understand the systematic forces which determine i) which firms cooperate, and ii) the outcome of cooperation. This requires large, cross-sectional (or panel) datasets. This research suggests that there is a strong positive association between participation in the program and the overall research productivity of participants, although design of the R&D consortium is important.
There is one final important conclusion from this review of the empirical evidence on innovation and policy intervention. Part of the difficulty with documenting this issue at the firm level has been the availability of suitable, robust and comprehensive micro data. Without such data, it is impossible to conduct detailed evaluations of specific policy instruments. And, therefore, the evidence required to assist governments on where to get the ‘most bang for their buck’ remains elusive. Our Report, therefore, should be seen in this light. If governments are serious about improving the evidence base for innovation policy, they should make data available on their specific policy mechanisms as they have done in other areas of economics such as labour and health economics.
53
6B
Appendix A - Commonly-used innovation proxies
Below we discuss the coverage of commonly-used proxies of innovation, including: expenditure data
(such as R&D expenditure); count-based data (such as patent and trade mark applications, and new product launches); and qualitative innovation assessments (such as surveys). Each innovation proxy has its relative strengths and weaknesses in terms of coverage. That is, some proxies are more suited to capturing product rather than process innovations (such as counts of trade marks and new product launches), while others are more suited to measuring radical rather then incremental innovation (such as patents). We draw upon a review by Jensen and Webster (2009a) here in order to aid the interpretation of the results in the subsequent empirical chapter.
25B
R&D data
R&D expenditure and employment data have been frequently used to measure innovation. These data come from two main sources: census data (R&D employment – e.g. Scherer 1965) and accounting data (R&D expenditure – e.g. Grabowski and Mueller 1978; Griliches 1986). The use of
R&D expenditure as a proxy for innovation is first and foremost problematic because the lack of mandatory reporting for R&D expenditure precludes systematic data collection. Second, whether or not R&D expenditure is reported, and what is reported, will vary according to a firm’s strategic motivations. Depending on how the firm wants to temporally distribute its earnings and profits (for tax benefits and to inform the stock market), it may or may not avail itself of the opportunity to capitalise rather than expense R&D. Finally, not all firms that collect R&D data formally report it in their annual reports. As a result, it is unclear whether the apparent high incidence of missing R&D data from most accounting-based firm data sets is due to: the intentional exclusion of R&D data for strategic reasons; the fact that R&D spending is positive but below a certain threshold; or is that
R&D truly zero.
F
34
F
From time to time, national governments operate R&D incentive programs which require formal documentation of R&D activities. Often the R&D that is required to be recorded is a subset of the accounting definition; in some countries, such as currently in Australia, the tax rebate program is skewed towards research rather than development. The empirical literature also suggests that larger firms more accurately report R&D than small firms, and that listed companies are more likely to report R&D due to the fact that they are subjected to a higher level of regulatory scrutiny. Brouwer and Kleinknecht (1997) argue that R&D has a manufacturing bias. Nevertheless, R&D typically relates to innovative activities undertaken in the early and middle stages of the product/process life cycles.
The other major problem with using R&D data is that its coverage is limited to product and process innovations. That is, R&D expenditure typically excludes organisational and market innovations.
26B
IP counts
Counts of IP administrative data – such as patents and trade marks – have also been commonly used in the innovation literature (e.g. Griliches 1981; Greenhalgh and Longland 2001). IP applications – patents, designs and trade marks – are popular measures of innovation, but it is widely accepted that ‘…patents appear to be a good indicator for …inventive activity…[only] at a very aggregated level’ (Griliches 1995, p.54). Nonetheless, using administrative data on IP provides certain benefits
34 Griffiths & Webster (2004) found that over the period 1989 to 2004, 8.3 per cent of large firms filed for a patent but never reported R&D expenditure. Abrahams and Sidhu (1998) also report information on this.
54
for the researcher since long time series of firm-level datasets can be merged in, and simple counts of applications for registration of IP at the firm level provide information on inventions that are both new-to-the-firm (trade marks) and new-to-the-world (patents, designs).
Proxies for innovation based on registered IP also have problems, since particular types of registered
IP tend to be used more intensively in some industries than others. For example, it is well documented that patents are infrequently used: by firms working in technical fields that are not well covered by patent laws (e.g. services); where inventions can easily be protected by other methods
(e.g. secrecy, unregistered copyright or keeping ahead of competitors); and where inventions are otherwise hard to imitate (e.g. knowledge is tacit). In fact, Arundel and Kabla (1998) claim that patents are reasonable as a measure of innovation only in sub-sectors of manufacturing
(pharmaceuticals, chemicals, machinery and precision instruments). Similarly, firms without the resources to support litigation and enforcement (such as smaller firms) are expected, a priori, to have a relatively lower correlation between patents and innovation (see Griliches 1990; Arundel and
Kabla (1998), however under-use by SMEs was not found in Australia (Jensen and Webster 2006).
Beyond this, each type of IP right differs in what it purports to measure. Patents and design applications are only granted for inventions or designs that are new-to-the-world. Trade marks, on the other hand, can be used to herald the formal launch of a product which is merely new-to-thefirm, or to the local market. While registered designs and trade marks are most clearly applied to product innovations only, there is also some evidence that the use of patents for process innovations is low (see Levin et al. 1987; Cohen et al. 2000).
Applications for patents, trade marks or registered designs represent an unknown proportion of the original set of ideas developed by a firm. As such, we expect that IP counts are biased towards successful innovation. Furthermore, ‘IP counts’ is not a measure that can be meaningfully aggregated, since we know from our limited information that the distribution of patent value is highly skewed.
F
31
F
While hedonic indexes can be used in relation to heterogeneous goods and services, this is only possible where there is enough of the old in each successive activity to splice onto the new. This is difficult to achieve with patents since there are no established ways of combining products that, in addition to heterogeneity, may also have no overlap with the previous period’s activities or products.
Moreover, IP data do not cover all innovative activities – for instance, patents almost totally exclude organisational and market innovations. Furthermore, patents can only apply to an idea that is
‘manufacturable’, which accordingly excludes most industries, particularly service sector industries.
While trade marks may have some coverage of product and market innovations, they would rarely apply to organisational and process innovations. Registered designs apply only to a select group of goods and therefore exclude services.
31 As a result, there have been several moves to systematically value-adjust patent applications by weighting application counts according to whether they have been granted (sealed); how often they had been renewed (Lanjouw et al. 1998); or how often they have been cited by subsequent patents (Jaffe et al. 1998). In practice, weighting individual firm patents by forward-citations or renewal rates is not without difficulty because citation rates for recent patents may be unreliable (see
Narin 1999) and renewals can take several years to occur. The Australian patent office does not systematically record prior art citations and therefore our data set cannot be used in the same was as the US patent data.
55
27B
Surveys of managers
Since the 1980s, a number of survey-based innovation measures have been devised – the most welldeveloped of which are the European Community Innovation Surveys (CIS) (see Baldwin et al. 2002).
These surveys require managers to quantify or rate the firm’s innovative activities during a defined time period, using measures such as the number of new products, the extent of introduction of new processes and technologies, and the type of R&D activity.
Responses from surveys of management have broad coverage across all of the dimensions of innovation since surveys can be addressed at any aspect of the firm’s activities, whether it be organisational innovation or the proportion of money spent on R&D. This is one major attraction of innovation surveys since activities such as changing the work culture can have a demonstrable effect on productivity, but are almost impossible for the economist to detect through administrative data.
Most surveys do not ask for details of money spent on innovative activities since the absence of consistent accounting standards across firms means that a reliable figure would either be impossible to extract or impose an undue response burden. Accordingly, surveys often seek qualitative Likertscale responses, which cannot strictly be aggregated and moreover do not represent flows over a given time period.
F
32
F
While surveys can have much broader coverage than other innovation proxies, there are some inherent problems with survey-based proxies. The main problems are potential sample selection and non-response bias—it is often difficult to identify the population of firms to be surveyed, and more successful innovators may be more likely to respond to the survey than unsuccessful innovators.
Such selection and non-response bias can be dealt with in a couple of ways—one is to survey the population of firms rather than a sample (i.e. conduct a census of firms), and the other is to undertake surveys of non-respondents in order to detect the magnitude of any non-response bias.
Both steps have been undertaken by Smith et al. (2007) in their census of innovation in Tasmania.
28B
New product launches
This measure of innovation counts the number of new product launches by searching for product launch announcements in trade journals. The oldest example of trade journal-based innovation counts is the US Small Business Administration’s Innovation Data Base compiled in 1982 by the
Futures Group and used by Acs and Audretsch (1993). The method has subsequently been employed in the Netherlands, Ireland, the United Kingdom and Austria [see the collection of papers in
Kleinknecht and Bain (1993) and see Brouwer and Kleinknecht (1996)]. The advantages of this measure are that it does not require firm compliance (which introduces considerable selection bias); it is relatively cheap to collect; and a time series can be collated ex post since historic records are usually available. Furthermore, journal-based counts are not subject to the same technical and economic bias that shape the patenting decision (the cost of being reported in a journal is negligible and articles are not limited to patentable innovations).
Nonetheless, this measure also has several shortcomings. First, it is unlikely that these measures can distinguish between true inventions and imitated products and thus market leaders. In addition, while journal counts can be reasonable records of product innovation, they are considered to represent relatively poor sources of information about process innovation. Firms have clear incentives to publicise product innovations but also to conceal new processes. Given this,
32
Arundel et al. (1998) discuss ways to account for Likert-scale biases in modelling approaches.
56
Kleinknecht (1993) suggests that these data should be primarily regarded as sources of product innovation, and that attention should be paid to possible bias across industry or market areas and over time arising from varying journal coverage rates. This type of measure typically does not adjust for quality and generally represents the successful end of the innovation pathway.
29B
Summary of innovation proxy coverage
A summary of the dominant coverage of different innovation proxies is presented in Table A.1. In this table, a cross indicates that the innovation proxy covers the innovation type listed in the first column. Note that the table is supposed to be indicative of the overall coverage rather than an exact representation. For instance, a specific trade mark may be new-to-the-world, but in general trade marks are best considered as new-to-the-firm. Thus, we indicate that the coverage of trade marks is new-to-the-firm by placing a cross in the relevant cell of the table. There is one clear conclusion to be drawn from this table: the most difficult innovative activities to measure are process, organisational and market innovations. As a measure of innovation, surveys can be designed to have the broadest coverage of all innovation proxies.
Table A.1: Summary of the Dominant Coverage of Commonly-used Innovation Proxies
Coverage includes…
Type of innovation
Product
Process
Organisation
Market
New to firm
New to world
Stage of innovation life cycle
Early
Middle
Late
Firm characteristic
Large firms
Small firms
Manufacturing firms
Service firms
Other features
Selection/response bias
Cheap to collect
Incremental/radical nature
R&D data
X
X
X
X
X
X
X
X
X
Patent applications
X
X
X
X
X
X
X
Trade mark applications
X
X
X
X
X
X
X
X
X
Design applications
X
X
X
X
X
X
X
Product launches
X
X
X
X
X
X
X
Survey of managers
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
57
7B
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