Contextclusterscalculatorcitiescreativitychallenges measuring creativity & innovation from clusters to city-regions greg spencer & tara vinodrai department

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


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


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



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



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



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



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



cluster count by city-region

clusters

-11 clusters- 5 clusters- 1 cluster



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



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



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



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



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



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)



population growth by employment in clusters

clusters

Halifax

Saint John

Sherbrooke

Trois-Rivières

Montréal

Ottawa - Hull

Kingston

Oshawa Toronto

Hamilton


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



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



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



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



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: 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



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



ISRN case study city-regions

cities



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



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



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



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


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



measuring creativity & innovation: patents

creativity


- 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


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



measuring creativity: earnings in cultural industries

creativity


$- $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


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


SudburyOshawa

Saint JohnTrois-Rivières

Thunder BayWinnipeg

KitchenerSherbrooke

London

SaskatoonHamilton

EdmontonRegina

MontréalKingston

QuébecHalifax

Vancouver

St. John'sVictoria

TorontoCalgary

Ottawa - Hull

Creative Class Employment LQ



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%



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


Over 45%35% to 45%25% to 35%Under 25%



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


Over 45%35% to 45%25% to 35%Under 25%



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


Over 30%25% to 30%20% to 25%Under 20%



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


Over 30%25% to 30%20% to 25%Under 20%



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



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



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



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'



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



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



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



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



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


Thunder Bay

St. John’s

Regina

Trois-Rivières

Saint John

Sherbrooke Saskatoon

Kingston

Winnipeg

Abbotsford


Victoria

London

Halifax

Windsor Oshawa

Kitchener

Hamilton

Edmonton

Ottawa - Hull

Calgary

Montréal

Vancouver

Toronto

Net Migration, 1996-2001



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


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



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


Toronto Montréal Vancouver



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


20 to 29 years 50 years and over



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



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



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


Documents

Contextclusterscalculatorcitiescreativitychallenges measuring creativity & innovation from clusters to city-regions greg spencer & tara vinodrai department