context
clusters
calculator
cities
creativity
challenges
measuring creativity & innovation
from clusters to city-regions
greg spencer & tara vinodrai
department of geography &
munk centre for international studies
university of toronto
isrn annual meeting, toronto, canada - may 4, 2006
context
clusters
calculator
cities
creativity
challenges
background
• goals of cluster research (MCRI I)
– benchmark ISRN case studies to allow for comparison
– better our understanding of what makes for ‘successful’ clusters
– consider what (if any) impact clusters have on regional economic
performance
• goals of city-region research (MCRI II)
– profiles of the 15 city-regions to facilitate comparison and the
selection of case study sectors / occupational groups, etc.
– understand the relationship between economic performance,
diversity and the strength of local and non-local linkages and
knowledge flows
– explore the relationship between economic performance and
quality of place
from clusters to city-regions – spencer & vinodrai
context
clusters
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cities
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challenges
outline
• provide background and key findings from cluster research (MCRI I)
– quantitative methodology for identifying clusters
– analysis of cluster performance
• introduce and describe the cluster calculator database
– industry level database
• transition to city-region research (MCRI II)
– background information on ISRN case studies
– database re-design, development and tools
• provide some examples of how we might measure and analyze the
relationships between creativity, innovation and economic
performance in city-regions
• identify challenges and next steps
from clusters to city-regions – spencer & vinodrai
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cities
creativity
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key questions addressed
• how do we systematically define clusters in the Canadian context?
– functional boundaries?
– geographic boundaries?
– necessary for direct inter-cluster comparison/analysis
• does clustering make a difference?
– impact on industries/firms
– impact on city-regions
from clusters to city-regions – spencer & vinodrai
context
clusters
calculator
cities
creativity
challenges
defining clusters: level of analysis
• clusters determined inductively using consistent definitions and
systematic rules
– industry (300 industries)
• 1997 North American Industrial Classification System (NAICS)
• measured at the 4-digit level
– geography (140 cities)
• 27 Census Metropolitan Areas (CMAs, urban core ≥100,000)
• 113 Census Agglomerations (CAs, urban core ≥10,000)
– three step methodology:
• geographic concentration of industries
• systematic co-location of industries
• scale (1000+ employees), concentration (LQ≥1), scope (at least 50%
of individual industries with LQ≥1)
from clusters to city-regions – spencer & vinodrai
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clusters
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cities
creativity
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defining clusters: an overview
4-digit NAICS
(300 industries)
basic
geographically
concentrated
(218 industries)
step 1: identify industries
that tend to concentrate
in certain places
step 2: identify
industries that frequently
locate in the same places
(19 different groups)
step 3: criteria for
identifying clusters
in particular cities
clustered
scale, scope
& concentration
(263 cases)
clustering
geographic
co-location
(167 industries)
non-clustered
lack of scale, scope
or concentration
(2,397 cases)
from clusters to city-regions – spencer & vinodrai
non-basic
geographically
ubiquitous
(82 industries)
non-clustering
no geographic
co-location
(51 industries)
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clusters
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cities
creativity
challenges
defining clusters: canadian cluster universe
Agriculture
Plastics &
Rubber
Textiles &
Apparel
Maritime
ICT
Manufacturing
Automotive
Forestry &
Wood
Products
ICT
Services
Higher
Education
Biomedical
Mining
Business
Services
Finance
Food &
Beverage
Creative
& Cultural
Oil & Gas
Construction
from clusters to city-regions – spencer & vinodrai
Logistics
Steel &
Steel Products
context
clusters
calculator
cities
creativity
challenges
cluster count by city-region
-11 clusters
- 5 clusters
- 1 cluster
from clusters to city-regions – spencer & vinodrai
context
clusters
calculator
cities
creativity
challenges
Newfoundland
PEI
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
Canada
5
3 1
9 1
8
51 5
104 6
5 2
10 2
30 1
38 2
263 20
1
1
2
1
1
1
4
1
1
4 1 6
1
1 2
3 8 7
4 16 1 1 4
9 30 17 10 18
1
3
2
8
3
1
1
1
1
4
4
1 1
1 1
7 14
from clusters to city-regions – spencer & vinodrai
1
5 3 3 6
1 11 21 11
2
6
1
1
2
6
1
1
6 14 24 17 11
9
1
1
1
3
1
1
1
4
1
4
1
2
1
2 2
1 2
9 13
1
1
1
8
1
1
5
Higher Education
Creative & Cultural
Finance
Business Services
ICT Services
ICT Manufacturing
Biomedical
Plastics & Rubber
Automotive
Steel
Textiles
Food
Logistics
Construction
Oil & Gas
Mining
Forestry
Maritime
Agriculture
Total
cluster count by province
1
1
1
2
3
7
1
2
2
2
22
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cities
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Non-Clustering Industries
Clustering Industries
average income: clusters outperform non-clusters
$42,756
Clustered
Non-Clustered
$32,142
Basic
$36,709
Non-Basic
$-
$26,600
$10,000
$20,000
from clusters to city-regions – spencer & vinodrai
$30,000
$40,000
$50,000
til
e
Fo s &
re
st A p
ry pa
& re
IC
W l
T
o
M
an M od
R
ub ufa inin
be ct g
r & uri
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Fo
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A arit d
ut im
o
e
A mo
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ul
tu
C
LU S re
ST te
ER el
O IN
il
& G
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C
ed
re
at
i
iv Fi cal
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e
& an
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u
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C u c r al
on a
s ti
B IC tru on
us T c
in S tio
es er n
s vic
Se e
rv s
ic
es
Te
x
context
$60,000
clusters
calculator
from clusters to city-regions – spencer & vinodrai
cities
creativity
challenges
average income by industry: clustering vs. non-clustering
$70,000
Total
Clustered
Non-Clustered
$50,000
$40,000
$30,000
$20,000
$10,000
$-
context
clusters
calculator
cities
creativity
challenges
Non-clustering industries
Clustering industries
growth: clusters outpace non-clusters
3.9
Clustered
1.7
Non-Clustered
Basic
4.6
2.5
Non-Basic
0.0
1.0
2.0
from clusters to city-regions – spencer & vinodrai
3.0
4.0
5.0
e
Fo s &
re
A
st pp
ry
a
& rel
IC
W
T
oo
M
an M d
i
R
ub ufa nin
be ctu g
r & ri
n
Pl g
as
tic
F
M ood
a
A rit
ut im
om e
A
gr otiv
ic
ul e
tu
r
C
LU S e
ST te
ER el
O IN
il
G
&
Lo Ga
g s
B is
io tic
m
C
ed s
re
i
at
iv Fin cal
e
& an
C ce
u
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r
C uc al
on a
st tio
B IC ruc n
us T
in Se tion
es rv
s
i
Se ces
rv
ic
es
til
Te
x
context
6%
clusters
calculator
from clusters to city-regions – spencer & vinodrai
cities
creativity
challenges
growth by industry: clustering vs. non-clustering
8%
Total
Clustered
Non-Clustered
4%
2%
0%
-2%
-4%
context
clusters
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cities
creativity
challenges
average regional income by employment in clusters
$41,000
$39,000
Toronto
Ottawa - Hull
Windsor
Calgary
$37,000
Average Income (Region)
Oshawa
R2 = 0.4648
Hamilton
$35,000
Vancouver
Kitchener
$33,000
London
Edmonton
$31,000
Québec City
Montréal
Thunder Bay
Greater Sudbury
St. Catharines - Niagara
Kingston
Halifax
Victoria Regina
Chicoutimi - Jonquière
Winnipeg
St. John's
Saint John
Abbotsford
$29,000
Trois-Rivières
Saskatoon
Sherbrooke
$27,000
$25,000
0%
5%
10%
15%
20%
25%
30%
% of CMA Employment in Clusters
from clusters to city-regions – spencer & vinodrai
35%
40%
45%
50%
context
clusters
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cities
creativity
challenges
population growth by employment in clusters
15%
Calgary
R2 = 0.514
10%
Population Growth 1996-2001
Oshawa
Toronto
Edmonton
Abbotsford
Windsor
Ottawa - Hull
5%
Vancouver
Kitchener
Hamilton
Halifax
London
Saskatoon
Sherbrooke
Victoria
St. Catharines - Niagara
Québec City Kingston
Winnipeg
0%
Montréal
Regina
St. John's
Trois-Rivières
Saint John
Chicoutimi - Jonquière
Thunder Bay
-5%
Greater Sudbury
-10%
0%
5%
10%
15%
20%
25%
30%
% of CMA Employment in Clusters
from clusters to city-regions – spencer & vinodrai
35%
40%
45%
50%
context
clusters
calculator
cities
creativity
challenges
cluster database: data sources
• sources of data
– Census of Population, 2001
• social, demographic and economic data for the labour force
– Canadian Business Patterns, 1998-2005
• establishments by size category
– US Patent and Trademark Office (USPTO), 2000-2003
• number of patents
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cities
creativity
cluster database: structure and variables
• 154 geographies
– 113 census agglomerations (CAs)
– 27 census metropolitan areas (CMAs)
– 13 provinces/territories + national total
• 420 industries
– 300 4-digit NAICS level
– 99 3-digit NAICS level
– 20 2-digit NAICS level + total labour force
• 151 variables (for each industry/geography combination)
–
–
–
–
–
occupation (60)
educational attainment (12); major field of study (13)
mobility status (9); immigrant status (4); age (10)
labour force activity (5); class of worker (8); hours worked (6)
income (5); establishments (18); patents (1)
from clusters to city-regions – spencer & vinodrai
challenges
context
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cities
creativity
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cluster database: size
• 9,766,680 data points / cells
– 420 industries x 154 geographies x 151 variables
• BUT flexibility to define groups of industries, therefore there are a
large number of possible combinations of industries
– Σ(300Ck) = Σ (300!/[k! * (300-k)!]), where k=1 to 300
• SO … the database can generate ~ 47,638 x 1090 different
measurements on the fly
– ~2,037,000,000,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000 combinations of 4-digit level industries
x 154 geographies x 151 variables
from clusters to city-regions – spencer & vinodrai
context
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calculator
cities
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cluster database: indicators (60+)
• critical mass / specialization
– employment, establishments - absolute & relative size
– cluster scope (breadth)
• knowledge intensity
– occupation-based (e.g., professional, technical, trades, science &
technology occupations)
– education-based (e.g., highest level of schooling, field of
specialization)
• performance and dynamism
– establishment growth, 1998-2005
– average employment income
– patents, 2000-2003 (cumulative); patents per 1,000 labour force
– in-migration (domestic, foreign)
from clusters to city-regions – spencer & vinodrai
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from clusters to city-regions: database re-design
Cluster calculator
City-region database
Units
Industries
Cities (CMAs, CAs)
Universe
Labour force
Population
Data sources
2001 Census; Canadian
Business Patterns, USPTO
2001 Census; Canadian Business
Patterns, USPTO
Potential new sources: ???
Indicators
Narrow, primarily focused on
economic performance
Broad, incorporate place-based
measures of social inclusion,
inequality, well-being and
dynamics
Time
Limited change over time
Greater emphasis on social and
economic change over time
from clusters to city-regions – spencer & vinodrai
context
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cities
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key questions to address
• social dynamics of innovation
– what is the relationship between economic performance, economic
diversity and the relative strength of internal / external linkages?
– explore possibilities of measuring network structure and diversity
• social foundations of talent attraction/retention
– what are the relationships between creativity, economic
performance and quality of place?
• cultural dynamism, social diversity, openness and tolerance, social
inclusion and cohesion, socio-spatial polarization
• do these relationships hold across the urban hierarchy?
• socio-economic and demographic profiles of city-regions
– what are the socio-economic and demographic characteristics of
the 15 city-regions included in the ISRN study?
from clusters to city-regions – spencer & vinodrai
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cities
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ISRN case study city-regions
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context
clusters
calculator
cities
creativity
challenges
city-regions by population size (>100K)
City-region
Toronto
Montréal
Vancouver
Ottawa
Calgary
Edmonton
Quebec City
Winnipeg
Hamilton
London
Kitchener
St. Catharines-Niagara
Halifax
Victoria
Windsor
Oshawa
Pop. 2001
4,682,900
3,426,350
1,986,965
1,063,660
951,395
937,840
682,755
671,275
662,400
432,450
414,280
377,005
359,185
311,905
307,875
296,300
City-region
Pop. 2001
Saskatoon
Regina
St. John’s
Sudbury
225,930
192,805
172,915
155,600
Chicoutimi-Jonquière
Sherbrooke
Barrie
Kelowna
Abbotsford
Kingston
Trois Rivières
Saint John
Thunder Bay
Moncton
Guelph
Cape Breton
Chatham-Kent
Peterborough
154,490
153,810
148,480
147,735
147,370
146,835
137,510
122,680
121,985
117,725
117,340
109,330
107,710
102,425
from clusters to city-regions – spencer & vinodrai
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cities
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population distribution of city-regions
CMA/CA
1 million or more
4
Population
% Pop
11,159,875
37.2
250,000 to 999,999
12
6,404,665
21.3
100,000 to 249,999
18
2,583,125
8.6
50,000 to 99,999
22
1,572,970
5.2
25,000 to 49,999
37
1,334,210
4.4
10,000 to 24,999
47
784,230
2.6
CMA / CA
140
23,839,075
79.4
Non-CMA / CA
n/a
6,168,010
20.6
30,007,085
100.0
CANADA
from clusters to city-regions – spencer & vinodrai
context
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cities
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city-region profiles and database
• city-region profiler tool
– possible to create socio-economic and demographic profiles for all
27 CMAs and 113 CAs
• demographics, migration and population change
• education, employment, occupational structure
• industrial structure, clusters, establishments, income
• city-region schematic mapping tool
– represent socio-economic and demographic indicators
(geo)graphically for all 27 CMAs and 113 CAs
• city-region comparative database and tools
– currently under development
– emphasis on place-based characteristics and change over time
– data at the city-region level including measures of social and
economic diversity, social inclusion/cohesion, quality of place
from clusters to city-regions – spencer & vinodrai
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cities
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creativity, innovation & cities: some preliminary questions
• how can we measure creativity and innovation?
• what is the relationship between diversity, creativity and innovation?
• are creative / talented workers more mobile than other workers? how
can these patterns be understood within the context of broader
migration in Canada?
from clusters to city-regions
context
clusters
calculator
cities
creativity
challenges
how can we measure creativity & innovation?
• industry / cluster characteristics
– creative / cultural industries (e.g. fashion, film and television,
furniture, design, music, new media, publishing, etc.)
• levels of patenting
– absolute and relative measures at the city-region level and by
industry / cluster
• occupations
– artists, designers, ‘bohemians’
– science & technology workers
– knowledge workers, ‘creative class’
from clusters to city-regions
context
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cities
creativity
challenges
measuring creativity & innovation: patents
Ottawa - Hull
Kingston
Kitchener
Trois-Rivières
Windsor
Toronto
Saskatoon
Hamilton
Vancouver
Calgary
London
Montréal
Edmonton
Sherbrooke
Victoria
Winnipeg
Chicoutimi - Jonquière
St. Catharines - Niagara
Abbotsford
Québec
Oshawa
St. John's
Regina
Sudbury
Halifax
Thunder Bay
Saint John
-
5.0
10.0
15.0
20.0
Patents per 10,000 Labour Force
from clusters to city-regions
25.0
30.0
35.0
context
clusters
calculator
cities
creativity
challenges
measuring creativity & innovation: creative clusters
Motion picture &
video industries
Elect. & precision
equipment repair
& maintenance
Mfg & reproducing
magnetic &
optical media
Radio & television
broadcasting
Software
publishers
Other schools
& instruction
Technical &
trade schools
Specialized
design services
Independent
artists, writers &
performers
Performing arts
companies
Sound recording
industries
Grant-making &
giving services
Advertising &
related services
Promoters of
performing arts
& similar events
Agents …
for artists,
entertainers…
from clusters to city-regions
Spectator
sports
context
clusters
calculator
cities
creativity
challenges
measuring creativity: earnings in cultural industries
Toronto
Ottawa - Hull
Hamilton
Vancouver
Montréal
Greater Sudbury
Kitchener
Calgary
Oshawa
Windsor
Halifax
St. John's
Edmonton
Regina
Winnipeg
Saskatoon
Québec City
London
Kingston
St. Catharines - Niagara
Victoria
Abbotsford
Thunder Bay
Sherbrooke
Chicoutimi - Jonquière
$-
$5,000
$10,000
$15,000
$20,000
$25,000
Average Annual Earnings
from clusters to city-regions
$30,000
$35,000
$40,000
context
clusters
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cities
creativity
challenges
measuring creativity & innovation: ‘creative class’
Ottawa - Hull
Calgary
Toronto
Victoria
St. John's
Vancouver
Halifax
Québec
Kingston
Montréal
Regina
Edmonton
Hamilton
Saskatoon
London
Sherbrooke
Kitchener
Winnipeg
Thunder Bay
Trois-Rivières
Saint John
Oshawa
Sudbury
Chicoutimi - Jonquière
Windsor
St. Catharines - Niagara
Abbotsford
0.7
0.8
0.9
1
1.1
1.2
Creative Class Employment LQ
from clusters to city-regions
1.3
1.4
1.5
context
clusters
calculator
cities
creativity
challenges
Midland
Midland
geography
of the ‘creative
class’: golden horseshoe
Owen
Owen
Sound
Sound
Collingwood
Collingwood
Kawartha
Kawartha Lakes
Lakes
Barrie
Barrie
Peterborough
Peterborough
% Creative Class
2001
Over 45%
35% to 45%
25% to 35%
Under 25%
Cobourg
Cobourg
Oshawa
Oshawa
Toronto
Toronto
Guelph
Guelph
Kitchener
Kitchener
Stratford
Stratford
Hamilton
Hamilton
Woodstock
Woodstock
Brantford
Brantford
St.
St. Catharines
Catharines -Niagara
Niagara
don
don
Tillsonburg
Tillsonburg
Norfolk
Norfolk
from clusters to city-regions
Powell
Powell River
River
context
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cities
creativity
challenges
geography of the ‘creative class’: vancouver
% Creative Class
2001
ourtenay
ourtenay
Squamish
Squamish
Over 45%
35% to 45%
25% to 35%
Under 25%
Parksville
Parksville
Vancouver
Vancouver
Chilliw
Chilliw
Nanaimo
Nanaimo
Abbotsford
Abbotsford
Duncan
Duncan
Victoria
Victoria
from clusters to city-regions
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cities
creativity
challenges
geography of the ‘creative class’: montreal
Joliette
Joliette
Sorel-Tracy
Sorel-Tracy
% Creative Class
2001
Over 45%
35% to 45%
25% to 35%
Under 25%
Lachute
Lachute
Saint-Hyaci
Saint-Hyac
Montréal
Montréal
Saint-Jean
Saint-Jean
sur-Richelieu
sur-Richelieu
Salaberry-deSalaberry-deValleyfield
Valleyfield
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cities
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geography of the ‘creative class’: atlantic region
Campbellton
Campbellton
% Creative Class
2001
Over 30%
25% to 30%
20% to 25%
Under 20%
Bathurst
Bathurst
Summerside
Summerside
Charlottetown
Charlottetown
Fredericton
Fredericton
Cape
Cape Breton
Breton
Moncton
Moncton
New
New Glasgow
Glasgow
Truro
Truro
Saint
Saint John
John
Kentville
Kentville
Halifax
Halifax
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cities
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geography of the ‘creative class’: prairies
% Creative Class
2001
Wood
Wood Buffalo
Buffalo
Over 30%
25% to 30%
20% to 25%
Under 20%
Thompson
Thompson
Cold
Cold Lake
Lake
Edmonton
Edmonton
Camrose
Camrose
Wetaskiwin
Wetaskiwin
Lloydminster
Lloydminster
North
North
Battleford
Battleford
Red
Red Deer
Deer
Prince
Prince
Albert
Albert
Saskatoon
Saskatoon
Yorkton
Yorkton
Calgary
Calgary
Brooks
Brooks
Cranbrook
Cranbrook
Lethbridge
Lethbridge
Medicine
Medicine Hat
Hat
Swift
Swift
Current
Current
Moose
Moose Regina
Regina
Jaw
Jaw
Portage
Portage
Winnipeg
Winnipeg
Brandon
Brandon la
la Prairie
Prairie
Estevan
Estevan
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cities
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diversity, creativity and innovation
• hypothesis: places with high levels of diversity, openness and
tolerance, etc. will be more able to attract highly skilled, talent
workers and have higher levels of economic performance
– how can we operationalize this?
• unanswered questions:
– to what extent do these relationships hold in the Canadian case?
– do these relationships hold across the urban hierarchy?
– what possibilities exist for talent attraction/retention strategies
while maintaining goals of social inclusion/cohesion?
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creativity & diversity in canadian city-regions
50
40
% Creative Class
R2 = 0.04
30
20
10
n=27
0
-
5.0
10.0
15.0
20.0
25.0
% Foreign Born
from clusters to city-regions
30.0
35.0
40.0
45.0
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creativity & tolerance in canadian city-regions
50
2
R = 0.56
% Creative Class
40
30
20
10
n=27
0
1.0
1.5
2.0
2.5
3.0
3.5
Persons in same-sex common-law partnerships per 1000 population
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4.0
4.5
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Gender
Immigrant Status
Visible Minority
Status
gender, immigrant/visible minority status & ‘creative class’
Non-Visible
Minority
32.9%
Visible Minorities
32.6%
32.1%
Non-Immigrants
Immigrants
35.7%
Males
33.2%
Females
32.5%
0%
5%
10%
15%
20%
25%
30%
% of Labour Force in 'Creative Class'
from clusters to city-regions
35%
40%
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cities
creativity
challenges
talent attraction/retention: mobility of creative workers
• hypothesis: creative / talented workers are attracted to places with
high levels of diversity, openness and tolerance, etc.
• unanswered questions:
– little evidence that documents actual flows of talent between
places
– are creative / talented workers more mobile than other workers?
– what are their patterns of mobility?
• complex picture of migration flows
– distinctive and highly uneven geography of migration
– differences between domestic and international flows of talent
– characteristics of domestic and international migrants (e.g. age,
qualifications, occupation, etc.)
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flows of talent: creative workers are more mobile
24.0
Creative Occupations
20.5
Service Occupations
19.3
Trade and Manual Labour
12.4
Agricultural Workers
0.0
5.0
10.0
15.0
20.0
25.0
% Domestic and International Migrants, 1996-2001
from clusters to city-regions
30.0
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flows of talent: % migration by occupation – top 15
Occupations (3-digit NOCS)
Domestic
Int’l
Total
Managers in protective service
44.1
2.5
46.6
Other occupations in protective service
35.5
1.1
36.6
Other engineers
24.5
10.3
34.8
Transportation officers and controllers
31.8
2.6
34.4
Computer and information systems professionals
22.9
11.2
34.1
University professors and assistants
20.9
13.1
34.0
Mine service workers / oil & gas drilling operators
32.7
0.7
33.4
Life science professionals
28.5
4.7
33.2
Physical science professionals
23.4
9.7
33.1
Civil, mechanical, electrical & chemical engineers
23.3
8.8
32.2
Mathematicians, statisticians and actuaries
26.7
5.3
32.0
Announcers and other performers
27.3
3.2
30.4
Therapy and assessment professionals
26.3
3.6
29.9
Optometrists, chiropractors, health diagnosing prof.
22.0
7.0
29.0
Psychologists, social workers, clergy & probation officers
26.0
2.6
28.6
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flows of talent: % migration by occupation – bottom 15
Occupations (3-digit NOCS)
Domestic
Int’l
Total
Crane operators, drillers and blasters
16.3
0.8
17.1
Contractors, supervisors, trades & related workers
15.9
1.0
16.9
Occup. in travel, accommodation, amusement & rec.
15.0
1.7
16.7
Agriculture and horticulture workers
12.4
3.7
16.1
Secretaries, recorders and transcriptionists
14.0
1.6
15.6
Machine ops. & related in pulp & paper / wood processing
14.3
1.2
15.5
Upholsterers, tailors, shoe repairers, jewellers and related
11.8
3.5
15.3
Public works and other labourers, n.e.c.
14.4
0.9
15.3
Heavy equipment operators
14.8
0.4
15.2
Logging and forestry workers
14.7
0.3
15.0
Mail and message distribution occupations
13.1
1.8
14.9
Logging machinery operators
12.6
0.2
12.8
Contractors, supervisors in agric., hortic. & aquaculture
7.9
1.2
9.1
Other fishing and trapping occupations
8.0
0.3
8.3
Fishing vessel masters and skippers and fishermen
7.0
0.2
7.2
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flows of people: net domestic and international migration
Toronto
Vancouver
Montréal
Calgary
Ottawa - Hull
Edmonton
Hamilton
Kitchener
Oshawa
Windsor
Halifax
London
Victoria
St. Catharines - Niagara
Abbotsford
Winnipeg
Kingston
Saskatoon
Sherbrooke
Saint John
Trois-Rivières
Regina
St. John’s
Thunder Bay
Chicoutimi - Jonquière
Greater Sudbury
Québec
-50,000
0
50,000
100,000
150,000
200,000
Net Migration, 1996-2001
from clusters to city-regions
250,000
300,000
350,000
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cities
creativity
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flows of people: net domestic migration
Calgary
Edmonton
Ottawa - Hull
Oshawa
Hamilton
Halifax
Kitchener
Windsor
St. Catharines - Niagara
Abbotsford
Victoria
Kingston
London
Sherbrooke
Saskatoon
Trois-Rivières
Saint John
Thunder Bay
St. John’s
Chicoutimi - Jonquière
Regina
Greater Sudbury
Winnipeg
Montréal
Québec
Vancouver
Toronto
-50,000
-40,000
-30,000
-20,000
-10,000
0
10,000
20,000
Net Domestic Migration, 1996-2001
from clusters to city-regions
30,000
40,000
50,000
60,000
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cities
creativity
challenges
quality of place for whom? migration by age group
60 years and over
50-59 years
40-49 years
30-39 years
20-29 years
5-19 years
-30,000
-20,000
-10,000
0
10,000
Net Domestic Migration, 1996-2001
Toronto
from clusters to city-regions
Montréal
Vancouver
20,000
30,000
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cities
creativity
challenges
quality of place for whom? domestic migration by city size
Toronto, Montreal &
Vancouver
250,000 to 1,000,000
100,000 to 250,000
10,000 to 100,000
Rural (Under 10,000)
-80,000
-60,000
-40,000
-20,000
0
20,000
Net Domestic Migration, 1996-2001
20 to 29 years
from clusters to city-regions
50 years and over
40,000
60,000
80,000
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cities
creativity
challenges
next steps: data sources and metrics
• incorporation of additional data to support research around the
themes of the ISRN
– develop metrics based on data currently available
• measures of economic and social diversity, social inclusion, quality of
place, patents
• [insert your suggestion here]
– develop metrics based on new data sources
• measures of cultural assets, R&D data, firm dynamics, flows of people
and goods
• [insert your suggestion here]
– investigate new data sources
• Longitudinal Employment Analysis Program (LEAP)
• Community Innovation Indicators
• Airport Activity Statistics, Coastwise Shipping Survey, Marine
International Freight Origin and Destination Survey, 1996-2004
• [insert your suggestion here]
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next steps: analysis
• hypothesis testing and multivariate analysis
– what is the relationship between economic performance, economic
diversity and the relative strength of local and non-local linkages
and knowledge flows?
• diversity vs. specialization
• variations by size, proximity to major centres
– what is the relationship between economic performance and
quality of place?
•
•
•
•
attraction / retention of talented workers
social inclusion and socio-spatial polarization
change over time
variations by size, proximity to major centres
• explore possibilities for international comparisons (US, Europe)
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thank you
• we would like to acknowledge the assistance of Dieter Kogler and the
valuable comments and insights of the ISRN members – especially –
Deborah Huntley, Meric Gertler and David Wolfe.
• for further questions:
greg.spencer@utoronto.ca or tara.vinodrai@utoronto.ca
from clusters to city-regions – spencer & vinodrai