IP and SMEs: Australian Evidence

1. IP and SMEs: Australian Evidence Dr. Paul H. Jensen University of Melbourne WIPO Expert Panel on IP and SMEs, Geneva, 17-18th September 2009 www.melbourneinstitute.com

2. OVERVIEW • I will cover two recent research projects which have analysed the use and effectiveness of IP by Australian firms • Factors Affecting the Use of Intellectual Property Protection by SMEs in Australia (Jensen & Webster 2006) • IP, Technological Conditions and New Firm Survival(Jensen et al. 2008; 2010)

3. OBJECTIVES • The Australian Govt. commissioned IPRIA: “…to determine whether the level of intellectual property protection by Australian SMEs is at sub-optimal levels, and the reasons for this...” • There are 3 key components to the study: • How does the existing level of IPR protection by SMEs compare with that of large companies? • What is the optimal level of IPR use? If there are differences in IPR use, does this imply market failure? • What inhibits SMEs’ use of the IPR system?

4. METHODOLOGY • The methodology involved: • Consultation with key stakeholders • Analysis of IP Australia database on patents, trade marks & registered designs to establish level of activity • Surveying 100 SME “Innovation Partners” and “Innovation Advisors” to identify factors inhibiting SMEs’ use of IPRs • Conduct 10 case studies of SMEs • I will focus on: IPR activity levels, survey results • Other results are available in Jensen & Webster (2006)

5. AUSTRALIAN SMEs • SME definition: <200 employees & <$200m assets • According to ABS data, there are 608,000 SMEs and 3,000 large firms in Australia • SMEs are important to the Australian economy: • Employ 69% of total workforce • Account for 49% of value-added • Own approximately 15% of business assets • SMEs: mainly in manufacturing, retail trade and business services

6. DATA ISSUES • “Matching” IP administrative data to IBISWorld and AOD data on firm characteristics, since there is no universal firm-level dataset in Australia • Excluding individuals from the analysis, the matching rates across the various IPRs were: • Patents (60% of Aust. company applications) • Trade marks (50% of Aust. company applications) • Designs (40% of Aust. company applications) • No evidence of any systematic bias. That is, matched sample is representative.

7. IP APPLICATION RATES • Note the use of a rate not just a count of IPRs • Controlling for the number of employees: • SMEs’ use of patents/designs is comparable to large firms • SMEs apply for significantly more TMs than large firms

8. OBSERVATIONS • Results seem to run counter to the conventional wisdom since SMEs do not appear to be disadvantaged in their use of IPRs • But we can’t draw any strong conclusions whether this represents “optimal” levels of IPR use. Why? • Because we don’t have an independent measure of innovative activity by large and small firms • It may be the case that SMEs do far more innovation, but don’t take out as many patents

9. SURVEY METHOD • Two surveys of IP stakeholders were conducted: • Innovation Partners (50 organisations): venture capitalists, CRCs, business incubators… • Innovation Advisors (50 organisations): IP lawyers, patent and trade mark attorneys, COMET advisors… • All were asked their view on factors affecting IP usage by SMEs • Response rate of 49% and no systematic bias across respondents • Respondents asked a number of questions and rated their responses on a 1-5 Likert scale

10. RESULTS: USE OF IPR

11. OBSTACLES FOR SMEs

12. IP EFFECTIVENESS

13. CONCLUSIONS • SMEs don’t appear to have a problem using the IP system vis-à-vis large firms • Enforcement costs are the most important inhibiting factor, but it is not clear whether these are more (or less) of a barrier than for large firms • Future work on innovation measurement may provide stronger conclusions • Availability of firm-level panel data continues to be a major obstacle to good empirical analysis

14. IP, Technological Conditions and New Firm Survival

15. MOTIVATION • International empirical evidence suggests that: • Firm survival has important effects on market structure, productivity growth and technological change • Innovation, firm size (size-at-birth) and organisational structure are important determinants of firm survival • Problems with existing survival studies: • Selection bias: only “successful” innovation considered • Omitted variable bias: technological conditions matter • Fail to capture industry dynamics • In this paper, we: • Map patterns of entry/exit using data 1997-2003 • Link these data with other firm-, industry- and macro-level data in order to analyse the determinants of survival

16. OBJECTIVE • We answer the following questions: • Firm Level: How does innovation shape survival for new vis-a-vis incumbent firms? • Industry Level: How does the speed of technological change in an industry affect relative survival rates? • Macro Level: Are new firms more susceptible to business cycle effects than incumbent firms? • Firm survival modeled using a piecewise-constant exponential hazard function • Data: unbalanced panel of 260,000 companies alive at some stage during 1997-2003 • Numerous cohorts of entrants • Time-varying industry-level measure of tech conditions • Firm-level measures of IP stocks and flows • Some aggregate macroeconomic fluctuation

17. DATA • Our dataset consists of: • 261,262 companies alive during 1997-2003 as determined by ASIC registration/deregistration data • The data were linked (by company name) to: • IP Australia data to construct IP stocks/flows • Yellow Pages in order to get ANZSIC codes • Parent/subsidiary concordance • Companies that changed name treated as ongoing entities • 67% of ASIC records matched to Yellow Pages • Cafes under-represented since company ≠ trading name • Yellow Pages filters out “non-trading” companies • The following ABS data also linked into the dataset: • Industry-level profit margin • GDP, interest rates and • ASX stock market index

18. DESCRIPTIVES • Death is defined as deregistration of an ACN or disappearance from the Yellow Pages • Age profile: companies vary from 0 to 124 yrs old • Trends in birth/death rates: • Births are decreasing over the period • Deaths are increasing over the period • But net entry rate is positive overall

19. EMPIRICAL MODEL • Piecewise exponential hazard function • Company age (years) is the unit of time analysis • Incumbents are defined as any company born prior to 1990 who we observe in 1997-2003 • New firms are defined as new ACNs 1997-2003 • Our set of explanatory variables xi consists of: • Patent/trade mark stocks (i.e. renewals): (log+1) yrs • Patent/trade mark flows (i.e. applications): lagged number of applications (log+1) (“Shadow of death”) • Size dummy (all IBIS firms are large) • Parent and subsidiary dummies • Private/public firm dummy • 1-digit ANZSIC industry dummies

20. EMPIRICAL MODEL (2) • Other explanatory variables are: • Gross industry entry rate: # entrants relative to # incumbents (proxies intensity of competition or barriers to entry) • Risk: industry profit margin over the tangible capital-output ratio (proxies capital intensity) • Industry innovativeness (i.e. technological conditions), a weighted index of R&D expenditure/employment, IP applications and labour productivity (to proxy process innovations). Measures the speed of technological change • Macro conditions: factor of ∆GDP and ∆∆GDP • Interest rate: 90-day bank bill rate • Stock market: ASX index • Model is estimated separately for incumbent/new firms and the relative effects are compared

21. RESULTS (1)

22. RESULTS (2) • Firm size (crudely measured) matters: larger firms are much more likely to survive • Entry begets exit, especially for new firms. Maybe low barriers to entry, but high barriers to survival • BUT: in industries characterised by rapid technological change, new firms are more likely to survive • All macro factors are significant, but the relative effect is greater for new firms: • Increase in interest rates increase hazard rate, but new firms are more vulnerable • Increase in GDP aids all firms, but provides a greater boost for new firms • New firms are more susceptible to stock market falls

23. CONCLUSIONS • No simple linear relationship between innovation and performance • Results demonstrate the importance of separating innovation investments (IP flows) from innovation capital (IP stocks) • New firms play an important role in technological change: in fast-moving industries, new firms drive the “gale of creative destruction” • New firms are particularly sensitive to changes in macroeconomic conditions