Making Competitive Creative Knowledge Cities

Making Competitive Creative Knowledge Cities: Urban Challenges Capital p Criativo numa

Regi達o Capital Lisbon 6 December 2011

Sako Musterd Urban Geography University of Amsterdam Accommodating creative knowledge

Source: Buiilt Environmentt 2004, 30 (3)

The Making of the C Creative ti Knowledge City

O tline Outline • • • • •

ACRE project Definitions Theory Empirical p Evidence ((and methodology) gy) Conclusions

ACRE – Accommodating Creative g – Competitiveness p of Amsterdam knowledge Barcelona European Metropolitan Regions Birmingham • • • •

EU 6th framework p programme g October 2006-2010 4.5 Million Euro 13 urban b regions i / partners t

Budapest B d t Dublin H l i ki Helsinki Leipzig Milan Munich Poznan Riga Sofia Toulouse

ACRE project objecti objective e Objective: to learn more about the conditions that are important to the development of creative and knowledge intensive industries in various European urban regions

Creative industries Advertising, architecture, arts and antiques, a t ques, crafts, c a ts, design, des g , designer des g e fashion, as o , video, film, music, photography, visual and performing arts, arts publishing publishing, computer games, software and electronic publishing, radio di and d TV

Creative industries Advertising, architecture, arts and antiques, a t ques, crafts, c a ts, design, des g , designer des g e fashion, as o , video, film, music, photography, visual and performing arts, arts publishing publishing, computer games, software and electronic publishing, radio di and d TV

Knowledge intensive industries Law (legal sector, accounting, bookkeeping, auditing, etc), financial sector, t R&D, R&D ICT ICT, hi higher h education d ti

Knowledge intensive industries Law (legal sector, accounting, bookkeeping, auditing, etc), financial sector, t R&D, R&D ICT, ICT higher hi h education d ti

Theoretical framework: two steps • Deep structural factors and pathways • Contemporary p y factors: hard (classic) conditions clustering l t i networks soft conditions

Three target groups Settlement considerations of • Managers g • High-skilled employees • High-skilled Hi h kill d ttransnational ti l migrants i t

Theoretical framework: five interrelated theories • • • • •

Path dependence theory ‘Classic’ location theoryy ((hard conditions)) Cluster theory Individual trajectories and network theory Soft conditions theory (‘talent’) ( talent )

Path dependence theory; deep structural factors • The question whether the urban region has had a key political or economic decisionmaking function • Position due to the development of the European city system • The impact of the industrial revolution on the urban region Boschma, Martin Boschma Martin, Sunley, Sunley Malecki Malecki, Lees Lees, Hohenberg, Camagni, Capello

‘Classic’ Classic location theory ((hard a d conditions) co d t o s) • • • • •

Labour and skills Capital availability ( ) Institutional context (subsidies) Regulations (tax regime) Infrastructure (airports (airports, telecommunication, telecommunication universities) • Accessibility • Availability of good quality locations and spaces Sassen, Taylor, Glaeser

Cluster theory • Geographic g p concentrations at a certain scale of interconnected industries and institutions, public and private services, context, agglomeration l ti economies i • Shaped over time (path dependence) and profit fi from f available il bl infrastructure, i f universities, etc. (also see hard conditions) Porter, Wu, Saxenian, Bathelt

Individual trajectories and network theory • • • • •

Personal ties (also transnational) Local social relations Individual trajectories Organisational affiliations Competition and cooperation Grabher Beccatini Grabher, Beccatini, Ganne

Soft conditions theory (conditions (co d t o s for o attracting att act g ‘talent’) ta e t ) • • • • • • • •

Urban amenities Quality of life g quality q y public p space p High Urban atmospheres (‘look and feel’) Tolerance Openness Diversity Housing market (availability, affordability) Florida, Landry, T. Clark

Empirical evidence

Urban region

Quasi hypothetical ranking of urban regions on deep p structural positions

Amsterdam Barcelona Dublin Munich Helsinki Budapest Milan Riga Leipzig Sofia Toulouse Poznan Birmingham

Known as established international political and economic decision making centre

Internationally known as strong historical centre for education, government and commerce

Known as strong high hightech centre or early service centre

Manufacturing industry was never dominant

Confronting theoretical positions based on deep structural positions with employment and GDP per region g information p 13 E European metropolitan t lit regions i (data for 2000-2006)

City regions Theoretical top (structural positions)

Employment in knowledge GDP per capita in in creative intensive the region industries (%) industries (%)

Amsterdam

+++

8

18

High (50,000+)

Barcelona

+++

12

10

Medium (25 (25-50,000) 50 000)

Dublin

+++

11

10

High

Munich

+++

8

21

Hi h High

Helsinki

+++

13

18

Medium

B d Budapest t

++

13

16

L Low (< 25 25,000) 000)

Milan

++

14

17

Medium

Ri Riga

++

6

23

L Low

Leipzig

++

9

16

Low

S fi Sofia

+

8

19

L Low

Toulouse

+

6

16

Medium

Poznan

+

7

11

Low

Birmingham +

6

19

Medium

City regions Theoretical top (structural positions)

Employment in knowledge GDP per capita in in creative intensive the region industries (%) industries (%)

Amsterdam

+++

8

18

High (50,000+)

Barcelona

+++

12

10

Medium (25 (25-50,000) 50 000)

Dublin

+++

11

10

High

Munich

+++

8

21

Hi h High

Helsinki

+++

13

18

Medium

B d Budapest t

++

13

16

L Low (< 25 25,000) 000)

Milan

++

14

17

Medium

Ri Riga

++

6

23

L Low

Leipzig

+

9

16

Low

S fi Sofia

+

8

19

L Low

Toulouse

+

6

16

Medium

Poznan

+

7

11

Low

Birmingham +

6

19

Medium

Some impact of deep structural conditions; other variance may be due to: contemporary p y ‘hard’ and/or cluster conditions … or to t contemporary t ‘soft’ ‘ ft’ conditions diti … or to ‘network’ network conditions and individual trajectories …

Integrated Methodology Methodolog • • • • • • •

‘Comparative’ ‘C ti ’ ‘Similar’ sectors ‘Similar’ target groups ‘Si il ’ questionnaires ‘Similar’ i i ((common d design) i ) ‘Similar’ Similar item lists Systematic approach (more robust results) Including different theoretical perspectives (path p clustering, g classic conditions, soft dependence, conditions, networks)

Three field work ork tests • Survey among high-skilled employees (n is 2500) • In depth interviews among managers (n is 300) • In-depth interviews among transnational migrants (n is 300)

Some empirical results based on the ACRE large-scale surveys among high-skilled hi h kill d employees, l managers and transnational managers, migrants

High-skilled g employees; p y ; Concepts p Networks b born iin region i family lives here studied in city proximity to friends

Hard conditions moved because of my job moved because of partner's job good employment opportunities hi h wages higher size of city good transport links presence of good universities

Soft conditions weather/climate proximity to natural environment housing affordability housing availability housing quality safe for children open to different people open minded and tolerant gay/lesbian friendly language overall friendliness diversity of leisure & entertainment cultural diversity diversity of built environment

High-skilled employees ranking indicators as most important, classified as indicators for networks networks, hard hard, and soft factors Networks

Soft Hard conditions conditions

Total percentage

N

Amsterdam

38

35

26

100

221

Barcelona

62

27

11

100

200

Birmingham

57

38

5

100

165

Budapest

71

24

5

100

197

Helsinki

51

39

10

100

191

Leipzig

43

50

8

100

159

Munich

30

60

10

100

178

Poznan

74

23

3

100

155

g Riga

80

17

4

100

132

Sofia

91

10

100

200

Toulouse

47

42

10

100

191

Milan

64

32

4

100

183

Dublin

57

42

1

100

201

T t l Total

58

34

8

100

2373

Special categories

Networks

age <35

59

36

income <1000

70

self employed

Hard Soft conditions conditions

%

Total

6

100

1239

24

6

100

339

58

31

11

100

542

less than 1 yr in the city

21

76

3

100

90

more than 1 yr in the city

60

32

8

100

2283

fi firm size i < 10

60

30

10

100

659

All survey

58

34

8

100

2373

C rrent debates highlighted Current Employment Opportunities moved because of my job moved because of partner's job good employment opportunities

Diversity diversity of leisure & entertainment cultural diversity

Openness p and Tolerance open to different people open p minded and tolerant gay/lesbian friendly

Personal Networks born in region family lives here studied in city proximity to friends

Relative share of respondents that ranked indicators as among the th four f mostt important i t t from f a list li t off 26 indicators, i di t assembled in specific dimensions, per urban region 100%

personal networks diversityy openness and tolerance employment opportunities Total T

Du ublin

Milan M

Toulo ouse

Sofia S

Riga R

Pozznan

Mun nich

Leip pzig

Helssinki

Budap pest

Birmingh ham

Barcellona

Amsterd dam

0%

Managers • • • •

Infrastructure L b Labour pooll Universities Networks (personal, business) – Less for consultants (for them: accessibility) – More for film p production,, video,, media,, webdesign, internet and games activities – More for smaller firms; less for larger firms

Transnational migrants â&#x20AC;˘ Employment opportunities â&#x20AC;˘ Personal trajectories and networks â&#x20AC;˘ Difference between creative industries (more networks) and knowledge intensive industries (more inner firm related)

Preliminary conclusions • Deep structural and contemporary factors are key to understand ci and ki development • Context Context-specific specific pathways and historically grown place specific characteristics are important • Personal trajectories and networks networks, and job opportunities are key contemporary variables • Classic ‘hard’ conditions remain essential essential: infrastructure (telecommunication, airport), universities i iti

Additional considerations â&#x20AC;˘ Urban amenities, high-quality urban space, urban atmospheres, atmospheres and proper residential accommodation are not important to attract creative knowledge g workers;; but may y be important to retain them p clusters,, also in the existing g city, y, â&#x20AC;˘ Specialised may be relevant (not fully researched in ACRE): spatial patterns of creative industry i Amsterdam in A d suggest importance i off specific urban milieus

Urban Orientation of employees in Creative Business Services (brown) and in Law Firms (blue) in Amsterdam 2005 CBS in region: 2% of employment CBS in concentrations : 22% (avg) Law in region: 4% of employment Law in concentrations : 44% (avg)

Urban Orientation of employees in the Arts Sector in Amsterdam 2005 Arts in region: 1% of employment Arts in concentrations 2005: 31% (avg)

Urban Orientation of the Arts Sector in Amsterdam 1995 (blue), 2005 (brown) Arts in region: 1% of employment Arts in concentrations 2005: 31% (avg) Arts in concentrations 1995: 30% (avg)

Spatial Association between the Arts Sector and Old Urban Structures brown 2005 arts, blue 2008 old structures Arts in region: 1% of employment Arts in concentrations: 31% Old structures: 80% b built il b before f 1920

Lessons for Urban Restructuring • Historically grown contexts are conditioning hard conditions are still relevant; • Classic ‘hard’ invest in infrastructure (telecommunication, airport), a po t), universities u e s t es • Urban amenities, high-quality urban space, urban atmospheres, and proper residential accommodation may be important to retain creative knowledge g workers • Specialised clusters, also in the existing city, seem to be relevant to nurse creative and knowledge intensive industries

PPCC-T instead of 3 T’s • • • •

Pathways P th Personal Networks Classic conditions Clusters Tailored Policies

Sako Musterd - Urban Geographies - University of Amsterdam

http://acre.socsci.uva.nl/

Critical comments (on Florida) • Storper and Manville (2006): not skills, but fi firms (agglomeration ( l ti economies) i )d drive i g growth • Peck (2005): Florida is merely a neolib l h liberal hype • Hall (2004): Building innovative cities takes time • Markussen (2006); Pratt (2005); etc.

Status of the city National/ regional capital + important decision making

Considered as a world city of any rank

Economic profile Competitive spirit (cities with a 'drive')

Diversified economic profile

No negative industrial heritage or image

Historical/cultural background Powerful high-tech clusters

PathPath ways with periods of glory

Important internation al function in the past

W.Europe Amsterdam

3

3

2

3

3

2

3

3

Barcelona

3

2

3

3

2

2

3

3

Birmingham

1

2

2

2

1

1

2

1

Helsinki

3

2

2

2

3

3

1

1

Leipzig

1

2

1

2

2

1

3

2

Munich

3

2

3

3

3

3

3

2

Toulouse

1

1

1

2

3

3

1

1

Milan

2

3

2

3

3

1

3

3

Dublin

3

2

2

3

2

1

2

2

Budapest

3

2

3

3

2

1

3

3

Poznan

1

1

3

3

1

1

1

1

Riga

3

2

3

3

1

1

2

2

Sofia

3

2

3

3

1

1

1

1

E.Europe

3 – strong 2 – medium 1 – relatively weak or no score

Some impact of deep structural conditions; other variance may be due to contemporary ‘hard’ and/or cluster conditions …

Estimated positions on these conditions (ranked) • Milan, Mil M Munich, i h A Amsterdam t d • Helsinki,, Barcelona,, Dublin,, Leipzig p g • Birmingham, Budapest, Riga, Toulouse • Poznan, P S Sofia fi

… or to contemporary ‘soft soft conditions … conditions’ Estimated strong position on ‘soft’ conditions • W.Europa: Amsterdam, Munich, Barcelona • E. Europa: Budapest, Leipzig, Riga

… or to t ‘network’ ‘ t k’ conditions diti and d individual trajectories …

?