Regional Differences in Innovation Performance

Differences in Innovation 1

Regional Differences in Innovation and Economic

Performance in Canada’s ICT Industry

Submitted by

Ryan MacNeil

To

Dr. Peter Hall

In fulfillment of the requirements for
GEOG 611: Location Theory

April 7, 2005


Regional Differences in Innovation and Economic Performance in Canada’s ICT Industry

Endogenous growth theory attributes productivity growth to “innovations”. These can take

the form of product or service innovations, process innovations, and/or organizational innovations

(Bourgeois and LeBlanc, 2002 and Betts, 1998). Even when experiencing equal capital and labour

growth, firms that successfully implement innovations will see growth in output over those which do

not innovate (Carvalho, 2005). This revelation has encouraged governments to divert resources from

expensive capital-mobilization strategies to innovation catalyzing ones. However, innovation defies

simple government intervention. The default response is to increase public spending on research and

development through government laboratories, transfers to universities, and incentives to the private

sector. However, this response is limited. It is based on three myths: (1) that innovation only occurs

in R&D laboratories, (2) that it is unique to scientific and technical industries, and (3) that the

primary obstacle to innovation is financing. Many industries “acquire ideas not from in-house R&D

but by tapping into the knowledge and ingenuity of their workers, suppliers and customers – by

networking with research institutions, universities, competitors, governments, and other

stakeholders” (Bourgeois and LeBlanc, 2002, p. 18). Indeed, there is a burgeoning volume of research

on the social-embeddedness of innovation.

Noted academics like Saxenian (1994) argue that industrial networks encourage the free-flow

of ideas and therefore foster continuous innovation. Saxenian contends that the mechanism of Silicon

Valley’s success was its collaborative industrial structure. In contrast, she says that Route 128 in

Massachusetts, “came to be dominated by a small number of large, vertically integrated minicomputer

firms…that had minimal relationships with each other or with local or regional institutions”

(Saxenian, 1998, p. 2). If Saxenian is correct, regions with protean industrial structures (like the


versatile, horizontally networked system in Silicon Valley) will see greater economic growth than

regions with autarkic structures (like the closed, vertically integrated industrial system of Route 128).

The key difference between these regions would be their approach to innovation. The most successful

regions would be home to firms that collaborate with suppliers, customers, universities and

competitors. The least successful regions would be home to highly secretive firms that make full use

of the law to protect their intellectual property.

This paper presents empirical evidence to support these theories in the Canadian context. It

examines inter-provincial variations in the approach to innovation taken by the information and

communication technology service industry. In many ways these variations in innovation culture

explain variations in regional economic performance between 2001 and 2003. Maskell and Malmberg

(1999, p. 21) proposed this line of inquiry when they asked, “Do firms from different regions exhibit

different patterns of interaction and cooperation?” The evidence presented in this paper is drawn

from Statistics Canada’s “Survey of Innovation, 2003” and “Survey of Employment, Earnings and

Hours” (CANSIM Table 281-0023). The findings support the conclusion that provinces with higher

levels of collaboration outperform provinces with higher levels of secrecy. Indeed, a culture of

collaboration boosts innovativeness, creating a regional economic advantage.

Innovation and Economic Development

Neoclassical Economic Theory

Government policy in Canada is often informed by neoclassical economic theory (Carvalho,

2005). This theory states quite simply that regional output of a commodity (in the case of the one

sector model) is a function of the capital and labour employed in its production (see Equation 1). By

modifying this formula, it can be shown that an increase in productivity (defined as output per unit


Equation 1 Equation 2

Q= f (k , L ) Q
= f k
L L

of labour) is the result of an increase in the ratio of capital to labour (see Equation 2). Therefore,

increases in productivity can only be achieved two ways: labour must remain constant while capital

increases, or capital must increase faster than labour. Unfortunately this simplistic version of the

theory suggests that technological progress has no qualitative effect on productivity. Technology can

only manifest as additional capital inputs and reduced labour inputs.

Endogenous Growth Theory

Acs and Varga (2002, p. 137) compare neo-classical and endogenous growth theories and

explain that the latter allows for “the modeling of technological change as a result of profit-motivated

investments in knowledge creation by private economic agents.” They argue that neo-classical theory

is limited by its assumption of perfect competition and constant returns to scale. In fact, technology is

not a purely ‘public good’ since knowledge can be “sticky” (Bourgeois and LeBlanc, 2002) in time and

space. Patents and tacit knowledge can create disparity in technological diffusion. Firms and regions

that can develop “sticky” innovations gain market power and fixed-term monopoly profits (Bourgeois

and LeBlanc, 2002).

Endogenous growth theory attributes productivity growth above and beyond a change in the

capital-labour ratio to “innovations”. These can take the form of product or service innovations,

process innovations, and organizational innovations (Morgan, 1997; Bourgeois and LeBlanc, 2002; and

Betts, 1998). Product or service innovations can be incremental changes to existing products or

services, or entirely new ones. Process innovations can either reduce the costs or improve the quality

of production (for example, just-in-time inventory systems). Organizational innovations involve some


form of structural advantage, such as the way Walmart coordinates its distribution chain through

computerized inventory systems. Morgan (1997) credits Marx and Schumpeter for introducing the

idea that innovation is the root of regional development in capitalist economies. Schumpeter’s

seminal work (1943) on innovation and capitalism argues that economic growth requires innovation.

Even when experiencing equal capital and labour growth, firms that successfully implement

innovations will see growth in output over those which do not innovate (Carvalho, 2005). This

revelation has encouraged governments to divert resources from expensive capital-mobilization

strategies to innovation-catalyzing ones. However, innovation defies simple intervention.

Not Simply Research and Development

A typical government initiative might involve encouraging research and development. When

discussing the downfalls of typical job-creation strategies for declining regions, Hall (1984) suggests

using an existing or “deliberately implanted” research and development tradition to create an

entrepreneurial tradition. He is cautious, and notes, “such bold strategies may succeed, but they are

likely to take a long time to produce substantial results…no single strategy, but rather a combination

of different approaches, will be appropriate” (p. 35). Despite this hesitation, and the tradition of peer-

juried awarding of university research grants, Hall concludes with a call for “the establishment of

regional quotas to the Research Councils” (in the UK, USA and Canada). Indeed, there is evidence

that the Canadian government’s university research grants neglect disadvantaged regions. Over its

first five years, the Canada Foundation for Innovation invested only 3.2% of its total contributions in

Atlantic Canada (Beaudin and Breau, 2001, p. 133). But only measuring innovation in terms of gross

expenditures on research and development (GERD) is inappropriate. GERD is “meant to reflect the

degree of innovative effort and intent, not necessarily innovative potential and success” (Bourgeois


and LeBlanc, 2002, p. 170). Despite a low level of government R&D funding grants, Bourgeois and

LeBlanc found that Atlantic Canada firms in knowledge intensive industries (computer services,

engineering consultant services, and other scientific and computer services) have innovation rates

near the national average (2002, p. 71). However, this innovation is much less likely to involve the

introduction of new capital intensive technologies than elsewhere in Canada (financial capital is

lacking). They say that, “studies in the last ten years are increasingly rejecting R&D as a master key

that unlocks a linear innovation process, seeing it instead as one of several pieces to the innovation

puzzle” (p. 170).

There is a myth that innovation is unique to high-technology industries and only happens in

R&D laboratories. Bourgeois and LeBlanc, as well as Beaudin and Breau, note the importance of

innovation to firms in the primary and service sectors. For example, in the Atlantic fish processing

sector between 1988 and 1996, the number of labour-hours declined 40% but the value-added per

hour rose 35% (Beaudin and Breau, 2001, p. 89). These industries “acquire ideas not from in-house

R&D but by tapping into the knowledge and ingenuity of their workers, suppliers and customers – by

networking with research institutions, universities, competitors, governments, and other

stakeholders” (Bourgeois and LeBlanc, 2002, p. 18). Maskell and Malmberg (1999, p. 21) argue that

knowledge-based competition is forcing firms to place “a new premium on establishing cooperative

relations with firms and institutions with complementary competencies.”

There is a burgeoning volume of research on the social-embeddedness of innovation. Noted

academics argue that community networks encourage the free-flow of ideas and therefore foster

continuous innovation. Morgan (1997, p. 493) says that, “innovation is shaped by a variety of

institutional routines and social conventions.” The Danish Aalborg group of economists goes so far as


to say that “knowledge is the most strategic resource and learning the most important process” (Ibid.)

for regional development. This connects with research on Japanese organizational innovations that

recognizes tacit knowledge as highly personal and difficult to measure. At the national level, Japan

has strong intermediate institutions (like trade and professional associations). Nelson (1993) is

recognized as the pioneer of research on national innovation systems.1 He attributes the rise of

Japanese leadership in automotive and consumer electronics production in part to interfirm linkages

(Nelson, 1999, p. 5). Japan is renowned for unique supplier-customer partnership chains at the

interfirm level. And at the intrafirm level, the Japanese kaizen2 approach results in horizontal

information flows and decentralized learning. Storper (1992, 1994, 1995) is credited with relating

these issues of learning, innovation and institutions to the study of economic geography. His work

outlines the importance of untraded interdependencies in organizational learning.

Feldman and Florida (1994, p. 211) conclude that a broad case study literature, “encourages

scholars to shift focus from the firm-level to a consideration of innovation as a social process.” They

argue that innovation stems from an agglomeration of social and economic institutions which form

part of a broader social structure (Ibid., p. 220). Saxenian’s work contrasting Route 128 and Silicon

Valley support a similar view. She describes the innovation in these two regions as ecosystems,

Silicon Valley is like the rainforest. It’s a decentralized system with a complex and
continually diversifying mix of species, flora and fauna that spontaneously and repeatedly
cross-polinate. Route 128, by contrast, using this metaphor is like a plantation. It’s a more
centralized system dominated by large corporations that crowd out local opportunities for
new growth (Saxenian, 1998, p. 3).

Saxenian is critical of science parks and other strategies that aim to create replica Silicon Valleys. She

concludes that, “ultimately regions are best served by policies that help companies to learn and

1 Note that Nelson does not consider geography to be as important to innovation as the other authors referenced
here. Nelson (1999, p. 8) says, “it is the connections, not geographic proximity at all…”.
2
“…continuous improvement through interactive learning and problem-solving…” (Morgan, 1997, p. 494)


respond quickly to changing conditions – rather than policies that either protect or isolate them from

competition or external change” (1994, p. 166).

Method

Data Sources

The two data sources used in this study were supplied by Statistics Canada and accessed

through the Data Liberation Initiative at the University of Waterloo. Provincial employment trends

were extracted from the Survey of Employment, Earnings and Hours (Statistics Canada, 2004). This

survey’s population includes all business in Canada found on either Statistics Canada’s Business

Register or in Revenue Canada’s Business Number Database. From this dataset, provincial

employment levels in January 2001 and December 2003 were extracted for all NAICS 2002 (North

American Industrial Classification System) codes relating to the international standard information

and communication technology (ICT) service industries. Table 1 provides the list of NAICS categories

making up the ICT service industry. Total industry employment and total overall employment were

calculated for each province.

Data describing provincial approaches to innovation were drawn from the Survey of

Innovation, 2003 (Statistics Canada, 2003). The survey “is based on the Oslo Manual (OECD/Eurostat,

1997) which outlines proposed guidelines for collecting and interpreting innovation data at the level

of the firm” (Ibid.). Its population includes those establishments with more than 15 employees and

over $250,000 per year in revenues. Four broad industry classifications were sampled, including the

ICT service industry defined in Table 1. The results are published at the provincial aggregation.


Table 1. Information and Communication Technology Industries by NAICS 2002.

NAICS Description
4173 Computer and Communications Equipment and Supplier Wholesaler-Distributors
41791a Office and Store Machinery and Equipment Wholesaler-Distributors
5112 Software Publishers
5171 Wired Telecommunications Carriers
5172 Wireless Telecommunications Carriers (except Satellite)
5173 Telecommunications Resellers
5174 Satellite Telecommunications
5175 Cable and Other Program Distribution
5179 Other Telecommunications
518111b Internet Service Providers
518112b Web Search Portals
5182 Data Processing, Hosting, and Related Services
53242c Office Machinery and Equipment Rental and Leasing
5415 Computer Systems Design and Related Services
8112 Electronic and Precision Equipment Repair and Maintenance
Source: Statistics Canada, Survey of Innovation 2003, Methodology Note (p. 3).
a This classification is unavailable in CANSIM Table 281-0023. The higher level of classification, “4179 - Other

machinery, equipment and supplies wholesaler-distributors” is used.
b CANSIM Table 281-0023 combines these two classifications into “5181 - Internet service providers, web

search portals”.
c This classification is unavailable in CANSIM Table 281-0023. The higher level of classification, “5324 -

Commercial and industrial machinery and equipment rental and leasing” is used.

Procedure

The effect of an innovative culture on regional economic performance is not direct. The

literature suggests a causal relationship similar to that outlined in Figure 1. A high level of innovation

is predicted for regions where industry approaches innovation in a collaborative manner. Conversely,

a secretive approach that relies on strict intellectual property protection should result in a lower level

of regional innovation. In turn, endogenous growth theory predicts that the level of innovation will

influence a region’s economic growth.


Figure 1. The predicted pattern of causation for innovation and economic growth.

A number of variables are used to capture the variation in culture/attitudes toward

innovation among Canada’s provinces. Each variable represents the proportion of firms which

recognize the importance of, or are actively engaged in, a given innovation strategy. Summary

statistics for these variables are included in Table 2.

An additional variable representing the average firm size for each province was created by

dividing the total ICT employment in each province by the population of ICT firms identified in the

documentation for the Survey of Innovation (Statistics Canada, 2003). The mean firm size is 52

persons with a standard deviation of 15 persons.

Simple correlation was used to test the relationships in the model. To test the first set of

relationships (where the approach to innovation is said to influence the level of innovation), the

variables identified above were tested against the proportion of innovative ICT firms in each


province3. The Pearson’s product moment correlation coefficient (r) was calculated and interpreted

using the thresholds identified in Table 3.

Table 2. Variables defining a province’s culture/attitudes toward innovation.
Variable Mean SD
Proximity to knowledge institutions is highly or moderately highly important to
14.24 6.40
success (ProxKnow).
Proximity to knowledge institutions is moderately highly important to success
11.11 4.14
(ProxKnowMod).
Proximity to knowledge institutions is highly important to success
3.13 2.72
(ProxKnowHigh).
Involvement in industry associations is highly or moderately highly important to
25.00 9.62
success (IndAssoc).
Involvement in industry associations is moderately highly important to success
18.60 9.76
(IndAssocMod).
Involvement in industry associations is highly important to success (IndAssoc
6.40 3.24
High).
The use of partnerships, strategic alliances or joint ventures to acquire knowledge is
42.61 18.81
highly or moderately highly important to success (Partner).
29.65 6.36
moderately highly important to success (PartnerMod).
18.29 8.26
highly important to success (Partner High).
Collaborated and cooperated to develop new innovations (CollabTOT). 59.40 10.20
Collaborated with competitors to innovate (CollabCOMP). 29.66 8.54
Collaborated with universities or other higher education institutes to innovate
22.64 10.15
(CollabUNIV).
Used patents to protect intellectual property (Patents). 15.28 3.60
Used secrecy to protect intellectual property (Secrecy). 50.74 8.91
Used a lead-time strategy to protect intellectual property (LeadTime). 53.09 10.80

Table 3. Pearson’s Product Moment Correlation Coefficient Interpretation.

Association Absolute r-value
Perfect (P) 1.00
Strong (S) 0.75 – 0.99
Moderately Strong (M) 0.50 – 0.74
Weak (W) 0.01 – 0.49
None (N) 0.00
Source: Jean Andrey, Lecture Notes, ENVS 178 (p. 69)

3 The variable for “Percentage of innovative business units in Canada during the period 2001 to 2003”
(Innovators) has a mean of 74.67 and a standard deviation of 7.83.


A measure of relative regional economic performance was calculated to test the second half of

the model. The three elements of shift and share (see Newkirk, 2002) were calculated for the ICT

industry in each province for the period January 2001 – December 2003. The differential shift

coefficient is a standardized comparable measure of provincial performance. It represents the quality

of regional economic performance in the ICT industry separate from national economic growth and

national industry growth (decline). The full results of the shift and share analysis can be found in

Appendix A. Differential shift coefficients for each province were tested for correlation with the

proportion of innovative ICT firms in each province.

Limitations

Two key limitations to this method are acknowledged. First, there is broad recognition of the

time-lag inherent in the innovation model presented. Feldman and Florida (1994, p. 217) note that it

is difficult to measure the length of this time lag. However they cite a study by Mansfield (1991)

which suggests the lag is in the order of 7 years (with a standard deviation of 2 years) between an

academic research finding and commercial introduction of a new product. Clearly this is a concern.

The method presented above compares innovation data and economic data from the same time

period. However, the innovation data identifies a broader social context for innovation that should be

relatively persistent over time. The literature suggests that approaches to innovation are culturally

embedded. It is therefore unlikely that the regional approach to innovation varied significantly either

before or after the Survey of Innovation was conducted.

The second limitation is in using a provincial level of analysis. As Feldman and Florida (1994,

p. 216) explain, in the American context, “Using the state as the unit of analysis inevitably obscures


spatial processes that occur within a state or across state boundaries.” Unfortunately results from the

Survey of Innovation (or any similar data) are not available at a sub-provincial level.

An additional two limitations were addressed in the data analysis. First, most of the Survey of

Innovation results from Prince Edward Island have been suppressed under Statistics Canada’s privacy

policies. Unfortunately this meant that PEI could not be included in this study. Furthermore, the ICT

industry classification identified above included wired telecommunication companies. In many

Canadian provinces only one firm (the current or former crown telephone corporation) fits this

category. In those cases where more than one firm is found in the category the majority of the labour

force is still employed by the one dominant firm. This means that for many provinces employment

data in the 5171 NAICS category has been suppressed. In the case of Saskatchewan and Ontario this

data is only suppressed for the latter part of the 2001-2003 study period. This greatly exaggerated

employment declines. This NAICS category has therefore been excluded from this study’s ICT

industry definition.

Results

Innovation Across Canada

The results indicate a high level of innovation among ICT firms across the country. New

Brunswick has the greatest proportion of innovators (83.1%), followed by British Columbia (81.1%).

Saskatchewan has the lowest proportion of innovators (60.6%), followed by Newfoundland and

Labrador (63.5%). The other provinces have innovation rates ranging from 72-79% (see Figure 2).

These findings support Bourgeois and LeBlanc’s (2002) conclusion that information technology firms

in Atlantic Canada innovate at, or above, the national level. Innovation in Canada’s ICT industry does


Proportion of Innovative
ICT Firms by Province
(2001-2003)
63.5

81.1 74.8
60.6 72.7
77.5
79.5
79.2
83.1

Proportion of Innovative ICT Firms
50% 100%

Figure 2. Proportion of ICT firms that produced innovations between 2001 and 2003, by province.

not seem to follow typical lines of regional disparity. However, regional differences in innovativeness

are still evident and deserve closer examination.

Approach to Innovation vs. Innovativeness

It is very clear that Canada’s highest levels of collaboration are found in the east (see Figure

3). Although Newfoundland and Labrador had the second lowest level of innovation it has by far the

highest level of collaboration. Had these findings held tightly to the literature, Newfoundland and

Labrador’s collaborative environment would have led to a high level of innovation. There may

however be additional obstacles in that province. The collaboration is likely a cultural feature.

Further research should be conducted to identify barriers to innovation in the poorer provinces. The

literature suggests that these barriers might include a lack of venture capital. Additional research can

also explore any functional differences in collaboration across the country.

Some evidence did emerge to support the link between approaches to innovation and regional

innovativeness. Unfortunately many of the innovation variables were only weakly associated with

the level of innovation (see Table 4). Six variables did yield a moderately strong association.


Proportion of Collaborative
ICT Firms by Province
(2001-2003)
83.3

52.6
51.0 57.6 52.2
60.6
53.1
66.2
58.0

Proportion of Collaborative ICT Firms
50% 100%

Figure 3. Proportion of ICT firms that collaborated between 2001 and 2003, by province.

Surprisingly, a negative relationship was found for the importance placed on industry associations

(IndAssoc) and proximity to knowledge institutions (ProxKnow). This suggests that innovation is

lower where firms identified these success factors as highly and moderately important. Perhaps these

firms are not actually engaged in these collaborations but simply see them as important. This logic is

supported by additional findings. First, innovation was greater where firms noted the importance of

partnerships, strategic alliances and joint ventures (Partner). Also, a moderately strong positive

relationship was found between two measures of collaborative action and the level of innovativeness.

The more innovative provinces see higher levels of collaboration with competitors (CollabCOMP)

and with universities (CollabUNIV). These findings all point to the clear relationship between

collaboration and innovation. Unfortunately no strong evidence emerged to support or rebut the

proposition that secrecy strategies undermine regional levels of innovation.

Another surprising finding was that firm size has a moderately strong positive association

with innovativeness. The literature predicts that autarky will be present in regions where average

firm size is large. This may not be the case for the Canadian ICT industry since the provincial average


firm sizes are all below 75. The mean firm size is only 52 persons and there is little interprovincial

variation (a standard deviation of only 15 persons). There simply is not the same contrast in average

firm size among Canadian provinces as Saxenian (1998) saw between Route 128 and Silicon Valley.

There appears to be a small business bias in the Canadian ICT sector. Also, the positive correlation

may indicate the relative strength of larger SMEs. Larger small businesses may simply be mature.

Table 4. Correlation of Innovation Strategies with Innovators.
Variable r-value Variable r-value
ProxKnow -0.55 Partner High +0.05
ProxKnow Mod -0.43 CollabTOT -0.44
ProxKnow High -0.65 CollabCOMP +0.56
IndAssoc -0.40 CollabUNIV +0.65
IndAssoc Mod -0.53 Patents -0.44
IndAssoc High +0.39 Secrecy +0.22
Partner +0.58 LeadTime +0.45
Partner Mod -0.03 AvgFirmSize +0.71

Innovativeness vs. Economic Performance

Between 2001 and 2003 employment in the ICT service industry declined in every province

except Alberta and British Columbia. Nationally the change in ICT sector employment was

negligible. But employment in this industry underperformed with respect to the national economy.

The shift and share results indicate that each province should have seen a 7% regional share of

national growth. But the ICT industry experienced a very slight decline. As a result, the industry mix

coefficient is set to -7% to indicate that the national ICT industry’s performance offset the overall

performance of the economy. In addition to poor national performance, the industry performed

poorly regionally. Alberta and British Columbia were the only provinces to see a positive differential

shift. Their regional advantage in this sector resulted in the equivalent of a 4% increase in

employment. Other provinces saw negative differential shifts (see Figure 4). The provinces with low


Differential Shift for
Provincial ICT Industries
(2001-2003)
-0.11

0.04
0.04 -0.06 -0.09
-0.01
-0.10
-0.08
-0.12

Differential Shift Coefficient
-0.15 +0.15

Figure 4. Provincial ICT service industry differential shifts, 2001-2003.

differential shifts are located in the east, including New Brunswick, Newfoundland and Labrador, and

surprisingly Ontario. Again, this pattern does not follow the predicted lines of regional disparity. A

clue to explaining these results may be in the fact that the western provinces, particularly British

Columbia, have become the main destination for Asian immigrants to Canada. Saxenian’s current

work (see Saxenian, 1998, pp. 5-6) suggests that Silicon Valley’s ability to attract highly skilled Asian

immigrants has become its new regional advantage in the 1990’s and beyond. She claims that

immigration is the new mechanism for growth in the ICT sector.

The evidence found linking innovation to regional economic performance is weak at best.

The relationship between the proportion of innovators (Innovators) and differential shift (DiffShift) is

positive, but weak (r=0.17). New Brunswick is the greatest contradiction to the literature. Eighty-

three point three percent of its ICT firms innovated during this time period. But the province saw the

lowest differential shift. As in the previous section, there may be barriers preventing the translation

of innovations into commercially viable products and services. As previously noted, Bourgeois and


LeBlanc (2002) concluded that innovation in Atlantic Canada is much less likely to involve the

introduction of new capital intensive technologies because venture capital is lacking.

It is useful to note the results from a direct test of association between the innovation

approach variables and the differential shift. Although there is clearly an intermediary step in the

innovation process, one interesting finding emerged. The relationship of firm-university

collaboration to the differential shift is stronger (r=0.79) than its relationship to the level of

innovation (r=0.65). The former relationship was the strongest identified in this study. It suggests that

firms may not be only collaborating with universities to develop innovations. They may simply be

commercializing innovations already developed by university researchers. The industry-university

relationship is clearly vital to economic performance in the ICT industry.

Conclusions

Although the correlation results were not as strong as required in the interpretation rubric

(Table 3), they support the structure of the causal model being tested (see Figure 5). Provinces with

higher levels of collaboration with competitors saw a correspondingly high proportion of their ICT

firms innovating. Provinces with higher levels of patent use saw lower levels of innovation and

poorer regional economic performance. High levels of industry-university collaboration were

strongly positively related to regional economic performance. Unfortunately the evidence to support

the link between the level of innovation and economic performance was only weak. It has been

suggested that a lack of institutional infrastructure in some provinces may present barriers to the

commercialization of innovation. Clearly, more than the right attitude is needed for an innovation

strategy to materialize into economic growth.


Figure 5. The predicted pattern of causation with Pearson’s Product Moment Correlation Coefficients.

The findings presented here support the idea that regional approaches to innovation can

effect regional economic growth. Variation was found among Canada’s provinces in terms of their

innovation cultures, their level of innovation activity, and their relative employment growth/decline.

The most successful provinces were home to a greater proportion of firms that collaborate with

universities and competitors. The least successful provinces were home to a greater proportion of

highly secretive firms that used patents to protect their intellectual property. These findings support

the conclusion that provinces with higher levels of collaboration outperform provinces with higher

levels of secrecy. A culture of collaborative innovation can boost innovativeness, creating a regional

economic advantage.


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