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Stock of Ideas

Chapter 3 The Determinants of Innovation

3.3 Stock of Ideas

The stock of ideas is given a central position in a group of endogenous growth models2 that started with Romer’s (1990) article (see section 2.4). The concept is similar to the concept of human capital3 used in growth accounting applications.

In Romer’s model, growth is driven by the firm’s desire to innovate through the accumulation of knowledge. They are able to profit from their innovations since monopolistic competition (through the patent system) makes knowledge a partially excludable input. Romer demonstrates that firms cannot survive under conditions of perfect competition if they are dependent to some degree on non-rival inputs (e.g. freely available knowledge). Romer’s model specifies that knowledge is non-rival and that the rate of growth in the economy as a whole benefits from knowledge spillovers (i.e. other firms are able to make use of increases in the total stock of knowledge, to commercially exploit new products or processes thus leading to economic growth).

Hypothesis 1

Rate of new ideas production increases with the stock of ideas.

3.4

Demand Pull versus Technology Push

The relative significance of ‘demand-pull’ versus ‘science and technology push’

was debated for much of the last century, particularly amongst scholars of industrial organisation (section 2.3.2). In the 1960s and 1970s demand led theories of innovation had considerable influence. An empirical survey of over 500 innovations by Myers and Marquis (1969) appeared to support the demand-pull approach while Schmookler (1966, p. 207) provided a detailed historical justification through detailed analysis of US patent statistics. He found that the peaks and troughs of inventive activity usually lagged behind similar movements in investment activity and concluded that the main stimulus to innovation came from the changing pattern of demand: “…inventions are usually made because men want to solve economic problems or capitalize on economic opportunities”.

2 Other important models in this area are detailed in Aghion (1992), Grossman (1991) and Jones (2000).

3 Generally measured as years of education or training.

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Various authors retested Schmookler’s data and found a weaker relationship than he had claimed e.g. Scherer (1982a) and Mowery and Rosenberg (1979). It has also been demonstrated that technology push is likely to be strong, particularly in the early stages of radical innovations. For example Katz and Phillips (1982) showed that technology push dominated in the early days of the development of the computer, with several influential industrialists predicting that there would be no demand for the technology. More recent empirical work has tended to support the view that both factors are important although their relative significance varies across different stages of the industry cycle (Rothwell, 1992). This balanced view had been summarised earlier by Rosenberg (1974, p. 103):

… the allocation of inventive resources has in the past been determined jointly by demand forces which have broadly shaped the shifting payoffs to successful invention, together with supply side forces which have determined both the probability of success within any particular time frame as well as the prospective cost of producing a successful invention.

Rothwell and Zegveld's interactive model (1985) usefully illustrates the balanced approach. In this model “the process of innovation represents the confluence of technological capabilities and market needs within the framework of the innovating firm", see Figure 3.1.

Figure 3.1 The Interactive Model of Innovation

New Need

Development Prototype production

Manufacturing Marketing and sales Needs of society and the marketplace

State of the art in technology and production Idea

generation

Market place

New tech

Source Rothwell (1992, p. 222)

Although it is now widely accepted that different industries are faced with different technological opportunities there is no general agreement on how these opportunities should be measured. One approach is to treat technological opportunity as the elasticity of unit cost with respect to R&D spending (Dasgupta

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& Stiglitz, 1980) but adequate data are rarely available to estimate technological opportunity in this way. Most other attempts are based on classification of industry into scientific or technological fields. While this can be effective in explaining variation in innovative activity such as patenting, this may be due to the effect of unspecified industry practices or demand effects rather than variation in technological opportunity (Cohen & Levin, 1989, p. 1084).

Hypothesis 2

Investment in innovation is determined jointly by technological opportunity and market demand.

3.5

Technological Certainty

R&D is an uncertain process at all stages from invention through to conversion of new knowledge and innovation into economic benefits. In general, the level of uncertainty is highest for more basic, long-term research and lower for more applied, short term research. Much of the early work on the effect of uncertainty on R&D and innovation was carried out by researchers within the field of industrial organisation (section 2.3.2). Although Nelson (1997) takes a rather different approach arguing that most empirical work on technological change has highlighted “the inability of the actors to foresee the path of development, even in broad outline, or the major surprises that occurred along the path”. As an illustration of the uncertain returns to academic research, David (1995) reported on a study of over 2000 patents licensed by Stanford University’s Office of Technology Licensing during the period 1970-1992; only 17% of patents produced any financial returns; 80% of revenue came from less than one per cent of patents; 46% of revenue came from one patent alone. However the risks of applied industrial R&D “are less formidable than corporate publicists and proponents of the hero theory of innovation would have us believe”4 (Scherer,

4 Scherer (1980, p. 416) quotes work by Mansfield and Brandenburg who analysed 70 projects carried out in the central R&D laboratory of a leading electrical equipment company and found that “in more than three-fourths of the cases, the ex ante probability of success had been estimated at 0.80 or higher”

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1980, p. 416). This is explained by the fact that businesses do not usually begin new product or process development until the principal technical uncertainties have been removed through less expensive research conducted either in-house or elsewhere.

None of this alters the main conclusions that can be drawn from this area of the literature; namely that investment in risky activities such as R&D (and particularly basic research) is expected to be sub-optimal in the absence of market intervention.

Hypothesis 3

Increased technological certainty encourages innovative activity.

3.6

Market Structure and Firm Size

Much of the early innovation literature concentrates on the links between innovation, market structure and firm size5. Schumpeter (1942) brought this research area to prominence by stressing the importance of large oligopolistic firms in innovation. His positive view of monopolies diverged from orthodox theory and policy making, thus generating a controversy on innovation, size of firm and market structure that continued for decades6.

Scherer (1980, p. 423-430) provides a useful description of the reasons why monopolists might be expected to innovate more. Most notably firms may invest in new products and processes because they expect this will allow them to achieve a monopoly and supra-normal profits. At the same time enterprises that have achieved monopoly power are more likely to be able to use resources in discretionary ways including investment in R&D. This leads to the suggestion that barriers to entry, higher profit levels, and economies of scale in R&D generally increase the level of R&D undertaken leading to persistence of innovation. On the

5 While Schumpeter was an evolutionary economist (see 2.5), much of this work falls under the industrial organisation heading (see 2.3.2) e.g. Scherer (1980) above.

6 See Freeman (1994, p. 467) for another review of this literature.

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other hand these conditions may encourage firms to become complacent and disinterested in change.

Hypothesis 4

Barriers to entry, higher profit levels, and economies of scale in R&D generally increase the level of R&D undertaken leading to persistence of innovation.

Schumpeter seems to have been “primarily impressed by the qualitative differences between the innovative activities of small entrepreneurial enterprises and those of large modern corporations with R&D laboratories. Nonetheless the empirical literature interpreted Schumpeter’s argument as a proposition that “there exists a continuous positive relationship between firm size and innovation”

(Cohen & Levin, 1989, p. 1067). Levin et al (1987) document two common rationales for the hypothesis that R&D intensity and innovation rates are significantly influenced by industrial concentration:

One common rationale for this hypothesis is that industry concentration enhances the potential for appropriation of R&D returns. A different view is that in the long run, concentration tends to be a consequence of industry evolution in a regime of abundant technological opportunity and a high degree of uncertainty associated with investment in R&D (Levin et al., 1987, p. 813).

The main reasons7 that have been put forward to explain why larger firms may spend proportionately more on R&D are:

i. the costs of innovation are so great that they can only be borne by large corporations; large firms with a balanced portfolio can balance successes and failures;

ii. R&D projects are risky – small firms place themselves in a dangerous position when they invest all their resources in a single innovative project;

iii. there may be economies of scale in R&D e.g. a big laboratory can justify purchasing all sorts of specialised equipment;

iv. R&D projects may benefit from scale economies realised in other parts of the large firm’s operations;

7 based largely on Scherer (1980, p. 413).

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v. large producers have an advantage in making process innovations.

However an extensive empirical literature does not provide universal support for these ideas. Cohen and Klepper (1992; 1996) have written extensively on the relationship between firm size, R&D and technological progress. Summarising the literature they find that “among R&D performing firms, the number of patents and innovations per dollar of R&D decreases with firm size and/or the level of R&D, and among all firms, smaller firms account for a disproportionately large number of patents and innovations relative to size”. They propose and test a simple model based on the idea of R&D cost spreading8, to explain their findings about the R&D firm size relationship.

Scherer (1980, p. 414) summarises some possible disadvantages of size:

i. decisions to bear the risks of R&D are made by individual managers not impersonal organisations so the argument on risk spreading may not hold water;

ii. large firms are biased against really imaginative innovations;

iii. inability to get ideas approved by management drives away the most creative individuals; and

iv. research in large laboratories becomes over-organised.

There is further support for this view in the literature on the economics of biotechnology, where it is argued that small firms have advantages in the development of areas of new knowledge and are able to commercialise radical innovations more quickly than larger established organisations (McKelvey, 2001).

More recently Romer (1990) extended arguments first proposed by Schumpeter and in the industrial organisation literature by suggesting that the rate of new ideas production depends on ‘the stock of ideas and effort.’ This is a related idea since indicators of innovative effort are likely to be closely related to indicators of firm size. Innovative effort is most commonly measured as ‘number of ideas workers’.

8 In their model “size conditions the returns to R&D by spreading the costs of R&D over the output of a given product” (Cohen & Klepper, 1996).

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It may well be the case that no single firm size is uniquely conducive to technological progress (Scherer, 1980, p. 418). In this case innovative output may be maximized by a diversity of sizes, each with its own special advantages and disadvantages. A particular focus of this paper is to investigate the effect of enterprise size since on innovative output in the New Zealand biotech sector hence we test the following hypothesis.

Hypothesis 5

Innovation output and innovation rate increase with enterprise size and the number of ideas workers.