context clusters calculator cities creativity challenges
measuring creativity & innovationfrom clusters to city-regions
greg spencer & tara vinodraidepartment of geography &munk centre for international studiesuniversity of toronto
context
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
context
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity 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
context
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
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
clusters
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)
clusters
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
defining clusters: an overview
4-digit NAICS(300 industries)
basicgeographicallyconcentrated
(218 industries)
non-basicgeographically
ubiquitous(82 industries)
clusteringgeographicco-location
(167 industries)
non-clusteringno geographic
co-location(51 industries)
clusteredscale, scope
& concentration(263 cases)
non-clusteredlack of scale, scope
or concentration(2,397 cases)
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
clusters
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
defining clusters: canadian cluster universe
clusters
Oil & Gas Logistics
Steel &Steel Products
Automotive
Forestry &Wood
Products
Food &Beverage
Mining
Agriculture
Construction
Maritime
Plastics &Rubber
BusinessServices
Finance
Creative& Cultural
Textiles &Apparel
ICTManufacturing
ICTServices
BiomedicalHigher
Education
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
cluster count by city-region
clusters
-11 clusters- 5 clusters- 1 cluster
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
cluster count by province
clusters
To
tal
Agr
icul
ture
Mar
itim
e
For
estr
y
Min
ing
Oil
& G
as
Con
stru
ctio
n
Log
istic
s
Foo
d
Tex
tiles
Ste
el
Aut
omot
ive
Pla
stic
s &
Rub
ber
Bio
med
ical
ICT
Man
ufac
turin
g
ICT
Ser
vice
s
Bus
ines
s S
ervi
ces
Fin
ance
Cre
ativ
e &
Cul
tura
l
Hig
her
Edu
catio
n
Newfoundland 5 1 1 1 1 1PEI 3 1 1 1Nova Scotia 9 1 2 1 1 1 1 1 1New Brunswick 8 1 2 1 1 1 2Quebec 51 5 8 4 1 1 4 5 3 3 6 2 2 1 1 1 1 3Ontario 104 6 1 3 4 1 6 3 4 1 11 21 11 6 6 3 4 4 2 7Manitoba 5 2 1 1 1Saskatchewan 10 2 1 2 1 1 1 2Alberta 30 1 3 8 7 1 1 1 2 2 1 1 2British Columbia 38 2 4 16 1 1 4 1 1 1 1 2 1 1 2Canada 263 20 9 30 17 10 18 7 14 6 14 24 17 11 9 9 13 8 5 22
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
average income: clusters outperform non-clusters
clusters
$42,756
$32,142
$36,709
$26,600
$- $10,000 $20,000 $30,000 $40,000 $50,000
Clustered
Non-Clustered
Basic
Non-Basic
Clu
ster
ing
In
du
stri
esN
on
-Clu
ster
ing
In
du
stri
es
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
average income by industry: clustering vs. non-clustering
clusters
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
Textil
es &
Appar
el
Forest
ry &
Wood
Min
ing
ICT M
anufa
cturin
g
Rubber &
Pla
stic
Food
Mar
itim
e
Autom
otive
Agricultu
reSte
el
CLUSTERING
Oil & G
as
Logistic
s
Biom
edic
al
Finan
ce
Creat
ive
& Cultu
ral
Educatio
n
Constru
ctio
n
ICT S
ervi
ces
Busines
s Ser
vice
s
Total
Clustered
Non-Clustered
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
growth: clusters outpace non-clusters
clusters
3.9
4.6
2.5
1.7
0.0 1.0 2.0 3.0 4.0 5.0
Non-Basic
Basic
Non-Clustered
Clustered
No
n-c
lust
erin
g i
nd
ust
ries
Clu
ster
ing
in
du
stri
es
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
growth by industry: clustering vs. non-clustering
clusters
-4%
-2%
0%
2%
4%
6%
8%
Textil
es &
Appar
el
Forest
ry &
Wood
Min
ing
ICT M
anufa
cturin
g
Rubber &
Pla
stic
Food
Mar
itim
e
Autom
otive
Agricultu
reSte
el
CLUSTERING
Oil & G
as
Logistic
s
Biom
edic
al
Finan
ce
Creat
ive
& Cultu
ral
Educatio
n
Constru
ctio
n
ICT S
ervi
ces
Busines
s Ser
vice
s
Total
Clustered
Non-Clustered
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
average regional income by employment in clusters
clusters
Halifax
Saint John
Sherbrooke
Trois-Rivières
Montréal
Kingston
Oshawa
Toronto
Hamilton
London
Windsor
Greater Sudbury
Winnipeg
Calgary
Edmonton
Abbotsford
Vancouver
St. John'sChicoutimi - Jonquière
Québec City
Ottawa - Hull
St. Catharines - Niagara
Kitchener
Thunder Bay
Regina
Saskatoon
Victoria
R2 = 0.4648
$25,000
$27,000
$29,000
$31,000
$33,000
$35,000
$37,000
$39,000
$41,000
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
% of CMA Employment in Clusters
Ave
rag
e In
com
e (R
egio
n)
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
population growth by employment in clusters
clusters
Halifax
Saint John
Sherbrooke
Trois-Rivières
Montréal
Ottawa - Hull
Kingston
Oshawa Toronto
Hamilton
St. Catharines - Niagara
London
Windsor
Greater Sudbury
Winnipeg
Calgary
EdmontonAbbotsford
Vancouver
St. John's
Chicoutimi - Jonquière
Québec City
Kitchener
Thunder Bay
Regina
Saskatoon
Victoria
R2 = 0.514
-10%
-5%
0%
5%
10%
15%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
% of CMA Employment in Clusters
Po
pu
lati
on
Gro
wth
199
6-20
01
from clusters to city-regions – spencer & vinodrai
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
calculator
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
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)
calculator
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
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
calculator
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
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)
calculator
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
from clusters to city-regions: database re-design
calculator
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, USPTOPotential 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 clusters calculator cities creativity challenges
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?
cities
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
ISRN case study city-regions
cities
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
city-regions by population size (>100K)
cities
City-region Pop. 2001 City-region Pop. 2001
TorontoMontréalVancouverOttawa
4,682,9003,426,3501,986,9651,063,660
SaskatoonReginaSt. John’sSudbury
225,930192,805
172,915155,600
CalgaryEdmontonQuebec CityWinnipegHamiltonLondonKitchenerSt. Catharines-NiagaraHalifaxVictoriaWindsorOshawa
951,395937,840
682,755671,275
662,400432,450414,280377,005
359,185311,905307,875296,300
Chicoutimi-JonquièreSherbrookeBarrieKelownaAbbotsfordKingstonTrois RivièresSaint JohnThunder BayMonctonGuelphCape BretonChatham-KentPeterborough
154,490153,810148,480147,735147,370
146,835137,510122,680121,985117,725117,340109,330107,710102,425
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
population distribution of city-regions
cities
CMA/CA Population % Pop
1 million or more 4 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
CANADA 30,007,085 100.0
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
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
cities
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
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?
creativity
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’
creativity
from clusters to city-regions
context clusters calculator cities creativity challenges
measuring creativity & innovation: patents
creativity
from clusters to city-regions
- 5.0 10.0 15.0 20.0 25.0 30.0 35.0
Saint JohnThunder Bay
HalifaxSudbury
ReginaSt. John's
OshawaQuébec
AbbotsfordSt. Catharines - NiagaraChicoutimi - Jonquière
WinnipegVictoria
SherbrookeEdmonton
MontréalLondonCalgary
VancouverHamilton
SaskatoonTorontoWindsor
Trois-RivièresKitchenerKingston
Ottawa - Hull
Patents per 10,000 Labour Force
context clusters calculator cities creativity challenges
measuring creativity & innovation: creative clusters
creativity
Motion picture &video industries
Radio & televisionbroadcasting
Elect. & precisionequipment repair& maintenance
Technical &trade schools
Softwarepublishers
Mfg & reproducingmagnetic &
optical media
Sound recordingindustries
Performing artscompanies
Specializeddesign services
Other schools& instruction
Independentartists, writers &
performers
Grant-making &giving services
Agents …for artists,
entertainers…
Promoters ofperforming arts& similar events
Spectatorsports
Advertising &related services
from clusters to city-regions
context clusters calculator cities creativity challenges
measuring creativity: earnings in cultural industries
creativity
from clusters to city-regions
$- $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000
Chicoutimi - JonquièreSherbrooke
Thunder BayAbbotsford
VictoriaSt. Catharines - Niagara
KingstonLondon
Québec CitySaskatoon
WinnipegRegina
EdmontonSt. John's
HalifaxWindsorOshawaCalgary
KitchenerGreater Sudbury
MontréalVancouver
HamiltonOttawa - Hull
Toronto
Average Annual Earnings
context clusters calculator cities creativity challenges
measuring creativity & innovation: ‘creative class’
creativity
0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
Abbotsford
St. Catharines - NiagaraWindsor
Chicoutimi - Jonquière
SudburyOshawa
Saint JohnTrois-Rivières
Thunder BayWinnipeg
KitchenerSherbrooke
London
SaskatoonHamilton
EdmontonRegina
MontréalKingston
QuébecHalifax
Vancouver
St. John'sVictoria
TorontoCalgary
Ottawa - Hull
Creative Class Employment LQ
from clusters to city-regions
context clusters calculator cities creativity challenges
geography of the ‘creative class’: golden horseshoe
creativity
TorontoTorontoTorontoTorontoTorontoTorontoTorontoTorontoToronto
Kawartha LakesKawartha LakesKawartha LakesKawartha LakesKawartha LakesKawartha LakesKawartha LakesKawartha LakesKawartha Lakes
CobourgCobourgCobourgCobourgCobourgCobourgCobourgCobourgCobourg
St. Catharines -St. Catharines -St. Catharines -St. Catharines -St. Catharines -St. Catharines -St. Catharines -St. Catharines -St. Catharines -NiagaraNiagaraNiagaraNiagaraNiagaraNiagaraNiagaraNiagaraNiagara
GuelphGuelphGuelphGuelphGuelphGuelphGuelphGuelphGuelph
KitchenerKitchenerKitchenerKitchenerKitchenerKitchenerKitchenerKitchenerKitchener
OwenOwenOwenOwenOwenOwenOwenOwenOwenSoundSoundSoundSoundSoundSoundSoundSoundSound
MidlandMidlandMidlandMidlandMidlandMidlandMidlandMidlandMidland
BarrieBarrieBarrieBarrieBarrieBarrieBarrieBarrieBarriePeterboroughPeterboroughPeterboroughPeterboroughPeterboroughPeterboroughPeterboroughPeterboroughPeterborough
CollingwoodCollingwoodCollingwoodCollingwoodCollingwoodCollingwoodCollingwoodCollingwoodCollingwood
OshawaOshawaOshawaOshawaOshawaOshawaOshawaOshawaOshawa
HamiltonHamiltonHamiltonHamiltonHamiltonHamiltonHamiltonHamiltonHamilton
StratfordStratfordStratfordStratfordStratfordStratfordStratfordStratfordStratford
LondonLondonLondonLondonLondonLondonLondonLondonLondon
BrantfordBrantfordBrantfordBrantfordBrantfordBrantfordBrantfordBrantfordBrantfordWoodstockWoodstockWoodstockWoodstockWoodstockWoodstockWoodstockWoodstockWoodstock
NorfolkNorfolkNorfolkNorfolkNorfolkNorfolkNorfolkNorfolkNorfolk
TillsonburgTillsonburgTillsonburgTillsonburgTillsonburgTillsonburgTillsonburgTillsonburgTillsonburg
% Creative Class2001
Over 45%35% to 45%25% to 35%Under 25%
from clusters to city-regions
context clusters calculator cities creativity challenges
geography of the ‘creative class’: vancouver
creativity
AbbotsfordAbbotsfordAbbotsfordAbbotsfordAbbotsfordAbbotsfordAbbotsfordAbbotsfordAbbotsfordNanaimoNanaimoNanaimoNanaimoNanaimoNanaimoNanaimoNanaimoNanaimo
Powell RiverPowell RiverPowell RiverPowell RiverPowell RiverPowell RiverPowell RiverPowell RiverPowell River
SquamishSquamishSquamishSquamishSquamishSquamishSquamishSquamishSquamish
Port AlberniPort AlberniPort AlberniPort AlberniPort AlberniPort AlberniPort AlberniPort AlberniPort Alberni
VancouverVancouverVancouverVancouverVancouverVancouverVancouverVancouverVancouver
CourtenayCourtenayCourtenayCourtenayCourtenayCourtenayCourtenayCourtenayCourtenay
ParksvilleParksvilleParksvilleParksvilleParksvilleParksvilleParksvilleParksvilleParksville
ChilliwackChilliwackChilliwackChilliwackChilliwackChilliwackChilliwackChilliwackChilliwack
DuncanDuncanDuncanDuncanDuncanDuncanDuncanDuncanDuncan
VictoriaVictoriaVictoriaVictoriaVictoriaVictoriaVictoriaVictoriaVictoria
% Creative Class2001
Over 45%35% to 45%25% to 35%Under 25%
from clusters to city-regions
context clusters calculator cities creativity challenges
geography of the ‘creative class’: montreal
creativity
MontréalMontréalMontréalMontréalMontréalMontréalMontréalMontréalMontréal
Salaberry-de-Salaberry-de-Salaberry-de-Salaberry-de-Salaberry-de-Salaberry-de-Salaberry-de-Salaberry-de-Salaberry-de-ValleyfieldValleyfieldValleyfieldValleyfieldValleyfieldValleyfieldValleyfieldValleyfieldValleyfield
Saint-JeanSaint-JeanSaint-JeanSaint-JeanSaint-JeanSaint-JeanSaint-JeanSaint-JeanSaint-Jeansur-Richelieusur-Richelieusur-Richelieusur-Richelieusur-Richelieusur-Richelieusur-Richelieusur-Richelieusur-Richelieu
LachuteLachuteLachuteLachuteLachuteLachuteLachuteLachuteLachute
Sorel-TracySorel-TracySorel-TracySorel-TracySorel-TracySorel-TracySorel-TracySorel-TracySorel-Tracy
JolietteJolietteJolietteJolietteJolietteJolietteJolietteJolietteJoliette
Saint-HyacintheSaint-HyacintheSaint-HyacintheSaint-HyacintheSaint-HyacintheSaint-HyacintheSaint-HyacintheSaint-HyacintheSaint-Hyacinthe
% Creative Class2001
Over 45%35% to 45%25% to 35%Under 25%
from clusters to city-regions
context clusters calculator cities creativity challenges
geography of the ‘creative class’: atlantic region
creativity
CharlottetownCharlottetownCharlottetownCharlottetownCharlottetownCharlottetownCharlottetownCharlottetownCharlottetown
SummersideSummersideSummersideSummersideSummersideSummersideSummersideSummersideSummerside
Cape BretonCape BretonCape BretonCape BretonCape BretonCape BretonCape BretonCape BretonCape Breton
CampbelltonCampbelltonCampbelltonCampbelltonCampbelltonCampbelltonCampbelltonCampbelltonCampbellton
BathurstBathurstBathurstBathurstBathurstBathurstBathurstBathurstBathurst
FrederictonFrederictonFrederictonFrederictonFrederictonFrederictonFrederictonFrederictonFrederictonMonctonMonctonMonctonMonctonMonctonMonctonMonctonMonctonMoncton
TruroTruroTruroTruroTruroTruroTruroTruroTruroSaint JohnSaint JohnSaint JohnSaint JohnSaint JohnSaint JohnSaint JohnSaint JohnSaint John
New GlasgowNew GlasgowNew GlasgowNew GlasgowNew GlasgowNew GlasgowNew GlasgowNew GlasgowNew Glasgow
KentvilleKentvilleKentvilleKentvilleKentvilleKentvilleKentvilleKentvilleKentville
HalifaxHalifaxHalifaxHalifaxHalifaxHalifaxHalifaxHalifaxHalifax
% Creative Class2001
Over 30%25% to 30%20% to 25%Under 20%
from clusters to city-regions
context clusters calculator cities creativity challenges
geography of the ‘creative class’: prairies
creativity
SwiftSwiftSwiftSwiftSwiftSwiftSwiftSwiftSwiftCurrentCurrentCurrentCurrentCurrentCurrentCurrentCurrentCurrent
Prince Prince Prince Prince Prince Prince Prince Prince Prince AlbertAlbertAlbertAlbertAlbertAlbertAlbertAlbertAlbertNorthNorthNorthNorthNorthNorthNorthNorthNorth
BattlefordBattlefordBattlefordBattlefordBattlefordBattlefordBattlefordBattlefordBattleford
ReginaReginaReginaReginaReginaReginaReginaReginaReginaMooseMooseMooseMooseMooseMooseMooseMooseMooseJawJawJawJawJawJawJawJawJaw
CamroseCamroseCamroseCamroseCamroseCamroseCamroseCamroseCamroseWetaskiwinWetaskiwinWetaskiwinWetaskiwinWetaskiwinWetaskiwinWetaskiwinWetaskiwinWetaskiwin
PortagePortagePortagePortagePortagePortagePortagePortagePortagela Prairiela Prairiela Prairiela Prairiela Prairiela Prairiela Prairiela Prairiela Prairie
Wood BuffaloWood BuffaloWood BuffaloWood BuffaloWood BuffaloWood BuffaloWood BuffaloWood BuffaloWood Buffalo
ThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompsonThompson
Cold LakeCold LakeCold LakeCold LakeCold LakeCold LakeCold LakeCold LakeCold Lake
EdmontonEdmontonEdmontonEdmontonEdmontonEdmontonEdmontonEdmontonEdmontonLloydminsterLloydminsterLloydminsterLloydminsterLloydminsterLloydminsterLloydminsterLloydminsterLloydminster
SaskatoonSaskatoonSaskatoonSaskatoonSaskatoonSaskatoonSaskatoonSaskatoonSaskatoonRed DeerRed DeerRed DeerRed DeerRed DeerRed DeerRed DeerRed DeerRed Deer
CalgaryCalgaryCalgaryCalgaryCalgaryCalgaryCalgaryCalgaryCalgary YorktonYorktonYorktonYorktonYorktonYorktonYorktonYorktonYorkton
Medicine HatMedicine HatMedicine HatMedicine HatMedicine HatMedicine HatMedicine HatMedicine HatMedicine Hat
BrooksBrooksBrooksBrooksBrooksBrooksBrooksBrooksBrooks
WinnipegWinnipegWinnipegWinnipegWinnipegWinnipegWinnipegWinnipegWinnipeg
CranbrookCranbrookCranbrookCranbrookCranbrookCranbrookCranbrookCranbrookCranbrookBrandonBrandonBrandonBrandonBrandonBrandonBrandonBrandonBrandon
LethbridgeLethbridgeLethbridgeLethbridgeLethbridgeLethbridgeLethbridgeLethbridgeLethbridge
EstevanEstevanEstevanEstevanEstevanEstevanEstevanEstevanEstevan
% Creative Class2001
Over 30%25% to 30%20% to 25%Under 20%
from clusters to city-regions
context clusters calculator cities creativity challenges
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?
creativity
from clusters to city-regions
context clusters calculator cities creativity challenges
creativity & diversity in canadian city-regions
creativity
R2 = 0.04
0
10
20
30
40
50
- 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0
% Foreign Born
% C
rea
tiv
e C
las
s
n=27
from clusters to city-regions
context clusters calculator cities creativity challenges
creativity & tolerance in canadian city-regions
creativity
R2 = 0.56
0
10
20
30
40
50
1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
Persons in same-sex common-law partnerships per 1000 population
% C
rea
tiv
e C
las
s
n=27
from clusters to city-regions
context clusters calculator cities creativity challenges
gender, immigrant/visible minority status & ‘creative class’
creativity
32.5%
33.2%
35.7%
32.1%
32.6%
32.9%
0% 5% 10% 15% 20% 25% 30% 35% 40%
Females
Males
Immigrants
Non-Immigrants
Visible Minorities
Non-VisibleMinority
Gen
der
Imm
igra
nt
Sta
tus
Vis
ible
Min
ori
tyS
tatu
s
% of Labour Force in 'Creative Class'
from clusters to city-regions
context clusters calculator 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.)
creativity
from clusters to city-regions
context clusters calculator cities creativity challenges
flows of talent: creative workers are more mobile
creativity
12.4
20.5
24.0
19.3
0.0 5.0 10.0 15.0 20.0 25.0 30.0
Agricultural Workers
Trade and Manual Labour
Service Occupations
Creative Occupations
% Domestic and International Migrants, 1996-2001
from clusters to city-regions
context clusters calculator cities creativity challenges
flows of talent: % migration by occupation – top 15
creativity
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
from clusters to city-regions
context clusters calculator cities creativity challenges
flows of talent: % migration by occupation – bottom 15
creativity
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
from clusters to city-regions
context clusters calculator cities creativity challenges
flows of people: net domestic and international migration
creativity
-50,000 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000
Québec
Greater Sudbury
Chicoutimi - Jonquière
Thunder Bay
St. John’s
Regina
Trois-Rivières
Saint John
Sherbrooke Saskatoon
Kingston
Winnipeg
Abbotsford
St. Catharines - Niagara
Victoria
London
Halifax
Windsor Oshawa
Kitchener
Hamilton
Edmonton
Ottawa - Hull
Calgary
Montréal
Vancouver
Toronto
Net Migration, 1996-2001
from clusters to city-regions
context clusters calculator cities creativity challenges
flows of people: net domestic migration
creativity
-50,000 -40,000 -30,000 -20,000 -10,000 0 10,000 20,000 30,000 40,000 50,000 60,000
Toronto
Vancouver
Québec
Montréal
Winnipeg
Greater Sudbury
Regina
Chicoutimi - Jonquière
St. John’s Thunder Bay
Saint John
Trois-Rivières
Saskatoon
Sherbrooke
London
Kingston
Victoria
Abbotsford St. Catharines - Niagara
Windsor
Kitchener
Halifax
Hamilton
Oshawa
Ottawa - Hull
Edmonton
Calgary
Net Domestic Migration, 1996-2001
from clusters to city-regions
context clusters calculator cities creativity challenges
quality of place for whom? migration by age group
creativity
-30,000 -20,000 -10,000 0 10,000 20,000 30,000
5-19 years
20-29 years
30-39 years
40-49 years
50-59 years
60 years and over
Net Domestic Migration, 1996-2001
Toronto Montréal Vancouver
from clusters to city-regions
context clusters calculator cities creativity challenges
quality of place for whom? domestic migration by city size
creativity
-80,000 -60,000 -40,000 -20,000 0 20,000 40,000 60,000 80,000
Rural (Under 10,000)
10,000 to 100,000
100,000 to 250,000
250,000 to 1,000,000
Toronto, Montreal &Vancouver
Net Domestic Migration, 1996-2001
20 to 29 years 50 years and over
from clusters to city-regions
context clusters calculator 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]
challenges
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
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)
challenges
from clusters to city-regions – spencer & vinodrai
context clusters calculator cities creativity challenges
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:[email protected] or [email protected]
challenges
from clusters to city-regions – spencer & vinodrai
Top Related