Transcript Title

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
OVERVIEW
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I will cover two recent research projects
which have analysed the use and
effectiveness of IP by Australian firms
1.
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)
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OBJECTIVES
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The Australian Govt. commissioned IPRIA:
“…to determine whether the level of intellectual
property protection by Australian SMEs is at suboptimal levels, and the reasons for this...”
There are 3 key components to the study:
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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?
METHODOLOGY
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The methodology involved:
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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)
AUSTRALIAN SMEs
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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:
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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
DATA ISSUES
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“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:
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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.
IP APPLICATION RATES
IPR Count/’000 Employees
Type of IPR
Large
SME
Patent applications, 2000/01
0.35
0.38
Trade mark applications, 2000/01
2.44
4.19
Design applications, 2000/01
0.22
0.32
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Note the use of a rate not just a count of IPRs
Controlling for the number of employees:
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SMEs’ use of patents/designs is comparable to large firms
SMEs apply for significantly more TMs than large firms
OBSERVATIONS
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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
SURVEY METHOD
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Two surveys of IP stakeholders were conducted:
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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
RESULTS: USE OF IPR
Rank-Ordered Reasons
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Score
Attract investors
4.47
Protection against imitation
4.45
Build competitive advantage
4.25
Protection in overseas markets
4.22
Protect brand value
4.02
Establish a foothold in the market
3.33
Increase market share
3.22
Send a signal to the market
2.94
OBSTACLES FOR SMEs
Rank-Ordered Factors
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Score
Cost of enforcement
4.27
Cost of application
3.75
Limited managerial resources
3.32
Nature of the technology
3.11
Uncertainty over whether IP rights will be upheld
3.04
Concerns regarding disclosure
3.00
Speed of product innovation
2.96
Uncertainty regarding benefits of IP protection
2.87
Lack of awareness of IP system
2.69
IP EFFECTIVENESS
Rank-Ordered Effectiveness
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Score
Patents
4.09
Licensing arrangements
3.83
Trade/service marks
3.17
Confidentiality agreements
3.13
Copyright
2.89
Business method patents
2.47
Innovation patents
2.43
Registered designs
2.35
CONCLUSIONS
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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
IP, Technological Conditions and New
Firm Survival
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MOTIVATION
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International empirical evidence suggests that:
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Problems with existing survival studies:
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Selection bias: only “successful” innovation considered
Omitted variable bias: technological conditions matter
Fail to capture industry dynamics
In this paper, we:
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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
Map patterns of entry/exit using data 1997-2003
Link these data with other firm-, industry- and macrolevel data in order to analyse the determinants of survival
OBJECTIVE
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We answer the following questions:
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Firm survival modeled using a piecewise-constant
exponential hazard function
Data: unbalanced panel of 260,000 companies alive at
some stage during 1997-2003
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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?
Numerous cohorts of entrants
Time-varying industry-level measure of tech conditions
Firm-level measures of IP stocks and flows
Some aggregate macroeconomic fluctuation
DATA
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Our dataset consists of:
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The data were linked (by company name) to:
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Cafes under-represented since company ≠ trading name
Yellow Pages filters out “non-trading” companies
The following ABS data also linked into the dataset:
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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
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261,262 companies alive during 1997-2003 as determined
by ASIC registration/deregistration data
Industry-level profit margin
GDP, interest rates and
ASX stock market index
DESCRIPTIVES
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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:
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Births are decreasing over the period
Deaths are increasing over the period
But net entry rate is positive overall
Year
Stock
(number)
Birth Rate
(% of stock)
Death Rate
(% of stock)
1997
219,318
12.1
1.6
1998
236,958
10.4
2.5
1999
250,911
9.5
3.0
2000
264,680
8.0
3.2
2001
269,864
5.7
4.2
2002
271,861
5.9
4.2
2003
272,576
6.1
4.1
Total
1,786,168
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EMPIRICAL MODEL
hi (t | x)  h0 (t ) exp(xi ' β)
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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:
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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
EMPIRICAL MODEL (2)
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Other explanatory variables are:
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Gross industry entry rate: # entrants relative to # incumbents
(proxies intensity of competition or barriers to entry)
Risk: industry profit margin over the tangible capitaloutput 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
RESULTS (1)
Dep. Var: Probability of Firm Death
Explanatory Variables
New Firms
Incumbent Firms
PATENTAPPS (f)
-0.088
0.257**
TMAPPS (f)
-0.034
-0.082*
PATENTSTOCK (f)
-0.026
-0.024**
TMSTOCK (f)
-0.059**
-0.029**
LARGE (f)
-0.451+
-0.450**
PRIVATE (f)
-0.297**
0.097*
SUBSIDIARY (f)
0.440**
0.263**
PARENT (f)
0.044
-0.387**
RISK (i)
-0.008
0.025**
GROSSENTRY (i)
0.046**
0.015*
INDINNOV (i)
-0.341**
0.014
INTEREST RATE (e)
0.697**
0.119**
MACRO (e)
-1.223**
-0.495**
STOCKMKT (e)
-0.331**
-0.016**
Yes
Yes
322,798
1,043,432
Industry dummies
No. of Observations
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MODEL 1
RESULTS (2)
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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:
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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
CONCLUSIONS
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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