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Other Factors Affecting Innovative Performance

In document the New Zealand Biotechnology Sector (Page 152-159)

Chapter 4 Biotechnology in New Zealand

4.9 Other Factors Affecting Innovative Performance

The 2001 survey asked respondents to rank a list of factors “that may affect the amount of innovation produced by your business”, see Table 4.25. Many of the results are unsurprising; for example ‘quantity of funds available for R&D’ is ranked most highly, followed by ‘number and quality of R&D staff’ and

‘appropriability (ability to profit from the innovation)’.

These mean scores hide some important patterns in the way that respondents answered this question. The most controversial question relates to the impact of

‘one or a few star scientists’; 18 respondents regarded this as being highly important (score 5), while 14 clearly disagreed giving this item a score of 1.

Table 4.25 Factors Affecting Business Performance 2002

Low High Mean


1 2 3 4 5

Conditions in your business

Quantity of funds available for R&D 1 2 9 16 31 4.3**

Quality of the R&D environment 4 2 9 19 25 4.0

Number and quality of R&D staff 3 3 5 18 28 4.1**

One (or a few) ‘Star Scientists’ 14 4 8 12 18 3.3

‘Science push’ or technological opportunity 5 4 16 20 10 3.5

Quality of links with other organisations in New Zealand 4 8 23 17 7 3.3 Quality of links with overseas organisations 4 5 11 21 16 3.7

Ownership of intellectual property 10 8 9 12 19 3.4

Appropriability (ability to profit from the innovation) 3 5 4 15 31 4.1**

Links with purchasers or consumers e.g. ‘demand pull’ 6 7 4 18 21 3.7 Conditions in New Zealand

Quality/quantity of Basic Science carried out 6 6 13 20 12 3.5 Quality/quantity of Applied Science carried out 5 6 11 23 12 3.5 R&D environment e.g. regulations, incentives, attitudes etc 5 5 13 13 20 3.7

Notes: The symbols ** mark sources ranked first to third, Modal scores for each source are shaded.

Source: Marsh (2002).

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Figure 4.19 illustrates some of the differences between industrial groups in the perceived importance of different factors. Enterprises in tertiary education place greater weight on ‘star scientists’ and ‘science push’ and regard ‘appropriability’

and ‘demand-pull’ as less important. By contrast, primary product enterprises rank

‘demand-pull’ and appropriability as being more important. Mean scores for manufacturers and scientific research organisations were similar for these four factors; perhaps reflecting the importance of commercial incentives for organisations in the scientific research category.

Figure 4.19 Mean Factor Scores by Industrial Group

Star Scientists

Science Push

Appropriability Demand Pull

Primary Products Manufacturers Scientific Research Tertiary Education

Source: Marsh (2002).

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4.10 Summary and Conclusions

4.10.1 Sector Size and Definition

Different interpretations of the terms biotechnology and biotechnology sector hinder attempts to measure biotech activity in a way that is comparable over time and across nations. Size estimates for the New Zealand biotech sector range from 30 core biotech companies with annual income of the order of $200 million, to many thousands of companies having annual production worth several billion dollars, when traditional food applications such as cheese, yoghurt and beer are included.

The economic literature since the mid 1980s has generally concentrated on modern biotechnology and the biotech sector is often taken internationally to refer to the population of ‘core’ private sector enterprises that conduct R&D into modern biotechnology. In this thesis the modern biotech ‘sector’ is defined as the population of private and public sector enterprises that carry out modern biotech R&D. Based on this definition, New Zealand’s modern biotech ‘sector’ consisted in 1998/99 of approximately 57 enterprises, employing around 1700 people. Most activity was concentrated in universities, Crown Research Institutes (CRI) and a small number of private sector companies e.g. Genesis, Virionyx, ViaLactia.

4.10.2 International Comparisons

The OECD has taken the lead in attempting to develop internationally comparable statistics on biotechnology. Data on public funding of biotechnology and patents were included in its Science, Technology and Industry Scoreboards for 2001 and 2003 (OECD, 2001, 2003a) but the variety of definitions and data collection methods make reliable comparisons almost impossible. In the 2003 scoreboard New Zealand is reported to put the highest proportional effort into biotech R&D (biotech R&D as a proportion of total R&D). This results both from New Zealand’s R&D specialisation in the primary sector and from use of a broad definition of what constitutes biotech R&D. Not surprisingly, a rather different picture emerges in absolute terms with New Zealand’s total biotech GBAORD being the third smallest of the 21 countries listed (see section 4.2.5)

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A more accurate international comparison can be made with Canada, based on data from the Statistics New Zealand biotech survey, since this was closely modelled on surveys carried out by Statistics Canada. New Zealand’s biotech revenue per million population (NZ$54 million) is rather lower than Canada's (NZ$94 million), but the difference is fairly small considering Canada’s higher per capita income and proximity to the United States. New Zealand has a rather lower mean revenue per biotech firm (NZ$5.3m vs. NZ$8.0m); consistent with the predominance of small firms in the New Zealand economy. New Zealand appears to have a significantly higher rate of biotech employment. There is some evidence that use of biotech processes in New Zealand is at an earlier stage with 72% being at the R&D stage against 49% in Canada.

4.10.3 Sector Characteristics

Enterprises in the modern biotech sector are split fairly evenly between the private sector and the public sector. They reported expenditure on biotech of NZ$202 million and income from biotech of NZ$236 million for 1998/99. This compares to enterprise income from all sources of NZ$2.1 billion i.e. biotech provided around 11% of income for the 57 enterprises. More recent data indicates that annual growth in expenditure may be as much as 20%.

Respondents to the 2002 biotech survey indicated that R&D constituted around 10% of total expenditure while expenditure on biotech R&D comprised around 80% of all biotech expenditures. Around 60% of all ‘biotech staff’ were engaged in R&D. Respondents spent far more on R&D than the industry average. For example the dairy industry is reported to spend around 1% of turnover on R&D, while R&D expenditure as a proportion of value added of manufactured products was 1.3 percent in 1999/2000 (see section

The government has been estimated to spend around NZ$127m a year on all biotechnology-related research, ranging from genomics to processing of natural products. Biotechnology-related research comprised around 15% of total government R&D spending in 2000.

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Data on the age distribution of biotech processes provides useful information on the development of the biotech industry over time and may also be compared with similar data from overseas. Average age in use in New Zealand is longer than in Canada for all but two process categories, possibly because of the lower number of new entrants in New Zealand. There are distinct differences between modern and older biotechnology processes. Genomics exhibits a typical age structure for a recent process; 56% have used this process for 5 years or less, 83% have used it for 10 years or less. Extraction/purification/separation is typical of a more mature technology; 24% started using this process within the last 5 years (often these are new enterprises). A further 24% have been using this process for at least 20 years (see section 4.3.3)

4.10.4 Innovative Output

In June 2001 Statistics New Zealand conducted the first economy wide Business Practices Survey (BPS). The BPS collected information on three aspects of business activity: use of information technology, innovation and management practices (Statistics New Zealand, 2002). Statistics New Zealand (2002) reports that: “the level of innovative activity carried out by New Zealand enterprises is at least equal, if not higher, than that indicated in a survey of European Union (EU) countries”. Review of the innovation literature suggests that Statistics New Zealand should be cautious of making such claims on the basis of one set of survey results. For example Tether (2001, p. 17) reports that comparisons between sectors and between countries are problematic for a number of reasons (section 4.5.2).

One indication of the rate of innovation by biotech respondents is provided by questions such as: “In the last 3 years, how many new or significantly improved products or processes has this business introduced on to the market?” Overall, 51% of respondents to the 2002 biotech survey reported implementation of a new product with the innovation rate being lowest for food manufacturers (33%) and highest for non-food manufacturers (79%). Process innovation rates were much lower with only 21% reporting implementation of a new process in the last 3 years; this is notably different to the 1998/99 survey when 33% of enterprises

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reported introduction of new products and the same percentage reported introduction of new processes.

Further work is required before definite conclusions can be reached on the relative innovative output of New Zealand biotech firms, although the evidence reviewed in section 4.5.2 suggests that New Zealand biotech firms do not have a particularly high rate of new product or process development relative to other New Zealand sectors or to other countries.

4.10.5 Data on Patenting

New Zealand’s rate of modern biotech patent applications over the five years to the end of 2002 was 3.7 per million of population, per year. This is below the average for the G7 (5.3) and for a reference group of small, developed OECD economies (5.5). Patent application rates range from a high of 15.2 for Denmark to a low of 0.5 for Italy. Overall New Zealand ranks eleventh out of 18 with a patenting rate above that found in France and Japan. However New Zealand’s performance is disappointing compared to other small countries with strong primary industries that it might hope to emulate e.g. Denmark (15.2), Switzerland (10.9), Netherlands (5.5), Australia (4.1).

Comparison of the three-year periods 1997-99 and 2000-02 reveals an average increase in patenting rates of 51%. New Zealand has increased its performance relative to the OECD and Australia; although the rapid change in patent application rates may, in part reflect an increased propensity to patent in universities and Crown Research Institutes.

Regressing C12N patenting rates against population produces a surprisingly good fit with 75% of the variation in applications being explained by population size.

Based on this analysis New Zealand is very close to the trend line; its C12N patent rate is close to the expected value, after adjusting for population size. There are few real outliers, although Denmark and Switzerland have a rather higher patenting rate than expected, while the rate for Italy and Ireland is lower than expected (see section 4.6).

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4.10.6 Biotech Alliances and Other Factors

Most enterprises (84%) responding to the 2002 biotech survey had at least one alliance involving biotech activity, 62% reported at least one New Zealand alliance while 41% reported an overseas alliance. Overseas alliances were most common in the tertiary education, and scientific research groups. The most commonly reported alliance purposes were product/process development; reported by 82% of respondents who had an alliance and basic research. Respondents were asked to rate ‘partnership outcomes to date’; 50% were described as ‘very productive’ and 44% as ‘somewhat productive’. Only 6% were reported to be ‘not very/not at all productive’. Further details of biotech alliances are reported in section 4.7.

Section 4.8 provides data on the main sources of information used by enterprises in the biotech sector while section 4.9 focuses on a range of other factors that affect innovative performance. There are some significant differences between industrial groups in the perceived importance of different factors. Enterprises in tertiary education place greater weight on ‘star scientists’ and ‘science push’ and regard ‘appropriability’ and ‘demand-pull’ as less important. By contrast, primary product enterprises rank ‘demand-pull’ and appropriability as being more important.

This chapter has provided a detailed description of the New Zealand biotech sector based on data collection and analysis carried out by Marsh (2001b; 2002) and a review of secondary sources. This description sets the scene and provides context for the study of the determinants of innovation described in Chapters 5 and 6. Prior to this analysis our knowledge of most sector parameters was very limited or completely lacking. There is a need for policy makers to make more use of the available data, rather than continuing to use less reliable estimates produced by organisations that have a vested interest in exaggerating the size of the sector.

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Chapter 5 Analysis of the Institutional

In document the New Zealand Biotechnology Sector (Page 152-159)