Cultural diversity
and London’s economy
Max Nathan
London School of Economics | SERC | LSE Cities
LSE London seminar, 5 March 2012
1
What I’m going to talk about
• The big picture
• Concepts and theory
• Evidence: labour markets, wider effects
• Cultural diversity and London firms
• Policy lessons
• I’m an economic geographer. I use a lot of economics
• There will be equations
2
Headlines
• London exhibits ‘super-diversity’
• Immigration and natural change twin drivers of this
• Policymakers need to think about both labour market effects
(of immigrants) and wider effects (of diversity)
• London’s diversity has economic benefits for the
capital’s businesses - for example, on innovation
• Economic effects of immigration on London and (some)
Londoners seem more mixed
• The current policy mix needs to change
3
Context
4
1 9 9 8 /9
1 9 9 7 /8
1 9 9 6 /7
1 9 9 5 /6
2 0 0 7 /8
2 0 0 6 /7
2 0 0 5 /6
2 0 0 4 /5
2 0 0 3 /4
2 0 0 2 /3
2 0 0 1 /2
2 0 0 0 /1
1 9 9 9 /2 0 0 0
-5 0
1 9 9 4 /5
1 9 9 3 /4
1 9 9 2 /3
1 9 9 1 /2
T ho u s a n d s
Population change in the UK
300
250
N a tu ra l c h a n g e
N e t m ig ratio n
200
150
100
50
0
Y ea r
Source: ONS (2010)
5
Stylised facts
• The UK has become more diverse over the past two
decades - alongside other ‘Western’ countries (Putnam 2007)
• Two drivers: immigration and natural change
-
2007: net immigration = 52% of UK population growth
-
2009: non-’white British’ groups = 1/6 of the English population
-
2050: minority ethnic groups = 21% of the UK population?
(ONS 2011, Wohland et al 2011)
• Diversity is urbanised: cities and urban areas have the
largest migrant and minority populations (Champion 2006)
6
Super-diversity?
• Vertovec: ‘diversification of diversity’ since the early 1990s
has led to super-diversity (Vertovec 2006, 2007)
-
1994: Ireland, India, Pakistan, Germany, USA
-
2008: Poland, Zimbabwe, China, ex-USSR, Czech Republic
-
Growth of ‘new migrant communities’ (Kyambi 2005)
-
ESOL: from c.10-20% of children, 2003-2009 (DfE 2011)
-
2011 Census: likely rise in mix of religions practised
• Super-diversity is an urban phenomenon, and largely a
London phenomenon …
7
London
Source: Hall (2011)
8
London
• London exemplifies the cosmopolitan world city:
-
Major centre of world financial system (still)
Contributes c.20% of UK GVA
London schoolchildren speak over 300 languages
Contains 40% of net migration to the UK, has around 48% of
England’s non-white populations
(GLA 2008, Gordon et al 2007, Champion 2006, Baker and Eversley 2000)
• London’s cultural diversity is seen as a social, economic
asset - by London government, Whitehall, Londoners
(GLA 2008, Legrain 2004, Leadbeater 2008)
9
London … is uncharted territory. Never have so many
different kinds of people tried living together in the same
place before. What some people see as the great
experiment of multiculturalism will triumph or fail here.
Benedictus (2005)
By the tenth century [the city] was populated by Cymric
Brythons and Belgae, by remnants of the Gaulish
legions, by East Saxons and Mercians, by Danes,
Norwegians and Swedes, by Franks and Jutes and
Angles, all mingled and mingling together to form a
distinct tribe of ‘Londoners’ …
Peter Ackroyd, London (2000)
10
Some more history
• 883AD: King Alfred bans the Danes from London. Sent to
live east of the Lea (Keith 2005)
• Complaints across medieval Britain that ‘foreigners were
practising their own customs’ (Vertovec 2007)
• 1610: Elizabeth I orders the expulsion of ‘negars and
Blackamoores’ from the capital (Sandu 2004)
• 1867: Times leader: ‘there is hardly such a thing as a pure
Englishman on this island … our national denomination, to
be strictly correct, would be a composite of a dozen national
titles’ (Sandu 2004)
11
Theory and evidence
12
Definitions
• ‘Cultural diversity’ = mix of ethnic / cultural identity groups
• Requires a prior notion of cultural identity
• Cultural identity = multifaceted, subjective, evolves over time
(Aspinall 2009, Michalopolous 2008, Ahlerup and Olsson 2007 etc.)
• This means that quantifying cultural identity and thus
cultural diversity is very hard
• Workaround = use ‘identity proxies’ such as country of birth,
ONS ethnic groups, and use simple shares or indices for
diversity (Ottaviano et al 2007)
13
Economics of diversity
• In theory, a ‘diversity shock’ to an area might have:
-
Effects in local labour markets (as e.g. immigrants arrive)
Wider effects on consumer markets, business performance,
entrepreneurship, trade etc. (as new communities form)
• Labour market analysis tends to be neo-classical, predicts
average effect on UK ‘native’ wages, employment is zero
• Labour market institutional change – occupational
clustering of migrants; low-quality employers become
‘migrant-dependent’; lock out of low-skill ‘natives’?
14
Economics of diversity (2)
• Wider effects are under-explored
• Multiple channels, operating both at firm and city level
• Production complementarities may include:
-
More diverse workforces = better mix of ideas; good for innovation?
Co-ethnic networks = facilitate international market access?
‘Ethnic entrepreneurs’ = more likely to found start-ups?
Versus lower social capital in diverse firms; discrimination?
• Urban consumer markets – cosmopolitan urban populations
demand new products; or urban crowding, competition?
15
Evidence: labour markets
• UK-wide studies suggest
-
Small and/or insignificant average effects of immigrants
Small negative effects on wages, jobs of low-skilled Britons
Links to casualisation of entry-level work
(MAC 2012, Green 2011, Cook et al 2011, Nathan 2011, Nickell and Salaheen 2008)
• London studies suggest
-
Suggestive evidence of wage pressure for low-skilled UK-born
Clear ‘migrant division of labour’: catering, cleaning, care 56-76%
foreign-born workforce vs. 34% London ave.
Importance of employment agencies, ‘hierarchies of hiring’
(Wills et al 2010, Gordon and Kaplanis 2012)
16
Evidence: wider effects
• Evidence on production complementarities suggests
-
Mixed evidence of workforce diversity on innovation
Stronger evidence that co-ethnic networks and entrepreneurs
help knowledge transfer, market access, trade
(Ozgen et al 2011, Mare et al 2001, Kerr 2009, Wadhwa et al 2007, Saxenian 2006)
• Evidence on urban markets suggests
-
Some links between population and service sector mix, mainly
from qualitative studies in single cities
Mixed evidence on immigration and housing costs
(Sa 2011, Nathan 2011, Mazzolari and Neumark 2009, Gordon et al 2007, Saiz 2003)
17
What we did
18
Data and sample
• Source – London Annual Business Survey (LABS)
-
Annual survey of firms across Greater London region
Provides very rich information on workforce and ownership
characteristics, business performance and constraints
• Years – 2005-2007, repeated cross-section
• Units – individual sites, with bias towards HQs
• Observations – 7,425 firms
• Context – 2004 EU expansion => very large increases in net
migration (and thus diversity) in UK, especially London
• Focus – business owners / partners = key decision-makers
19
Model
• Model is a simplified (knowledge) production function
• For firm i in sector j and year t, we estimate:
Yijt
= aDIVijt + CONTROLSijtb + SECTj + YEARt + ei
Y
= innovation, commercialisation, sales orientation,
reasons for firm foundation / entrepreneurship
(1)
DIV = proxies: ‘migrant-diverse’ and ‘ethnic-diverse’ firms
‘migrant diverse’ = mix of UK/non-UK born partners / owners
‘migrant firm’ = all non-UK born partners / owners
‘ethnic diverse’ = at least half minority ethnic partners/owners
20
Model (2)
• Our vector of controls includes:
firm age
firm size, sq root
R&D spend
Collaborative activity
Exports
PLC status
Management ability (qualifications, experience, training etc.)
Knowledge intensive business services (KIBS) dummy
SECT = one of 150 3-digit SIC sectoral dummies
YEAR = 2005, 2006 or 2007
21
Innovation
New product / Mod. product /
service
service
New
equipment
New way of
working
1.238***
1.192**
1.128
1.158
(0.084)
(0.097)
(0.117)
(0.110)
1.134
1.182**
1.188**
1.164**
(0.087)
(0.095)
(0.101)
(0.089)
Observations
7476
7457
7435
7441
Pseudo R2
0.088
0.071
0.041
0.059
-3854.84
-3678.76
-3565.64
-3777.23
Migrant diverse firm
Migrant firm
LL
Source: LABS. Notes: Results are odds ratios. HAC standard errors in parentheses. All specifications include controls, year and SIC3
dummies: some observations dropped because of perfect prediction groups. * = significant at 10%, ** 5%, *** 1%.
22
Commercialisation
New product / Mod. product /
service and
service and
sales growth sales growth
New
equipment
and sales
growth
New way of
working and
sales growth
1.122
1.111
1.163
1.042
(0.121)
(0.132)
(0.124)
(0.097)
1.023
1.081
1.185
1.08
(0.119)
(0.130)
(0.148)
(0.121)
Observations
7370
7301
7243
7305
Pseudo R2
0.097
0.099
0.075
0.087
-2346.35
-2068.58
-1986.53
-2119.61
Migrant diverse firm
Migrant firm
LL
Source: LABS. Notes: Results are odds ratios. HAC standard errors in parentheses. All specifications include controls, year and SIC3
dummies: some observations dropped because of perfect prediction groups. * = significant at 10%, ** 5%, *** 1%.
23
What type of firms?
New product /
service
Mod. product /
New equipment
service
New way of
working
Migrant diverse firm
1.316***
(0.073)
1.473***
(0.103)
1.348*
(0.218)
1.363***
(0.091)
Migrant firm
1.025
(0.066)
1.221**
(0.105)
1.152
(0.105)
1.145*
(0.088)
Knowledge-intensive
(KI) firm
1.127
(0.127)
1.034
(0.07)
0.978
(0.102)
1.173*
(0.100)
KI * migrant diverse
0.888
(0.082)
0.728***
(0.067)
0.636**
(0.138)
0.751**
(0.084)
KI * migrant firm
1.313***
(0.129)
1.037
(0.101)
1.057
(0.146)
1.104
(0.129)
7524
-4167.398
7524
-3931.26
7524
-3832.756
7524
-4009.598
Observations
Log-Likelihood
Source: LABS. Notes: Results are odds ratios. HAC standard errors in parentheses. All specifications include controls, year and SIC3
dummies: some observations dropped because of perfect prediction groups. * = significant at 10%, ** 5%, *** 1%.
24
Sales / markets
Local
% sales
National
International
Migrant diverse firm
1.348
(1.532)
-3.666***
(1.309)
2.318**
(0.934)
Migrant firm
1.718
(1.290)
-4.128***
(1.102)
2.410***
(0.786)
3089
0.281
3089
0.205
3089
0.185
6.493***
(1.305)
-5.743***
(1.117)
-0.75
(0.800)
3089
0.286
3089
0.207
3089
0.182
Dependent variable
Observations
R2
Ethnic diverse firm
Observations
R2
Source: LABS. Notes: HAC standard errors in parentheses. All specifications include controls, year and SIC3 dummies: some observations
dropped because of perfect prediction groups. * = significant at 10%, ** 5%, *** 1%.
25
Entrepreneurship
depvar
entrep
(1)
lockout
(2)
other
(3)
mig_founder
0.324**
(0.137)
0.147
(0.209)
-0.189
(0.125)
Y
4327
0.004
-2517.722
Y
4148
0.008
-1291.322
Y
4345
0.004
-2565.523
Controls
N
Pseudo R2
Log-Likelihood
Source: LABS
Standard errors in parentheses. All specifications use HAC standard errors, include year and sector dummies.
* p<0.1 ** p<0.05 *** p < 0.01
26
Implications
27
Toplines
• Topline = London’s cultural diversity helps London firms
• Diversity is linked to ideas generation, but not to
successful commercialisation of those ideas
• Diversity seems to have a stronger effect for less
‘knowledge-intensive’ firms (e.g. retail, consumer services)
• All-migrant teams also help ideas generation, with a stronger
effect in knowledge-intensive firms
• Migrant-headed and migrant-diverse firms sell largely to
international markets; ethnic diverse firms more localised
• Migrant status has a small but robust link to
entrepreneurial behaviour
28
Issues (1)
• Why do diversity and co-ethnicity not feed through to
commercialisation?
-
Bad ideas / different skill sets?
-
Management experience, skills are key barriers to growth for
minority ethnic firms (Lee 2012)
-
Have we measured commercialisation correctly?
-
Discrimination?
-
Other constraints on business growth: finance, space etc. … ?
-
Potential for stronger business support policies?
29
Issues (2)
• Is the immigration cap a good idea? Doesn’t look like it:
-
Restricts London’s talent pool; migrant status is linked to
entrepreneurial behaviour
Entrepreneurship Visa = £50k bond!
Restrictions on post-study visas may have similar effect
• Labour market institutions in London
-
If firms hire more diverse teams, workers who would have been
hired are ‘losers’ (Borjas and Doran 2012) … but probably get other jobs
-
Case for re-regulating some employment agency activity?
-
Better enforcement of minimum wage, working conditions
-
More effective employment and training for low-skilled Londoners
30
Knowledge gaps
• Who are the entrepreneurs? Is there a hierarchy of
entrepreneurship?
• Is there really a ‘commercialisation gap’? Need to explore
sectoral differences in more detail
• Would the same population have the same effects in other
cities? Comparative studies of same groups in London, NYC
• London is unique. Similar effects in other UK cities?
31
Thanks.
[email protected]
personal.lse.ac.uk/nathanm
squareglasses.wordpress.com
@iammaxnathan
32
Identification challenges
• Cities / positive selection, simultaneity – raise levels of
both innovation and DIV
=> exploit quasi-experiment conditions post-2004
• Individuals / ethnic entrepreneurs – ambitious / talented
people would be innovative anywhere
=> separate tests on company founders
• Firms / both-ways causation – more innovative firms may a)
hire b) attract a more diverse workforce
=> instrumental variables approach
33
Descriptives
Va ria b le
Descr ipt ion
prodin1
N
me a n
sd
m in
max
Fir m in troduce s ne w produ c t/service
7425
0.304
0.46
0
1
gprodin1
New produ c t/servic e, ³1 0 % rev e nu e grow th
7425
0.127
0.333
0
1
prodin2
Fir m mod ifie s produ c t/servic e rang e
7425
0.257
0.437
0
1
gprodin2
Mo d . pr o duc t/service , ³1 0% revenu e gro w th
7425
0.107
0.31
0
1
procin1
Fir m in troduce s ne w equ ipm e nt
7425
0.224
0.417
0
1
gprocin1
New eq u ip men t, ³1 0 % re v enu e grow th
7425
0.094
0.291
0
1
procin2
Fir m in troduce s ne w way o f workin g
7425
0.2 52
0.434
0
1
gprocin2
New wa y o f workin g, ³1 0 % rev e nu e grow th
7425
0.105
0.307
0
1
m igo w n_
A t le as t 1 n on U K -bor n ow n er / p artne r
7425
0.386
0.487
0
1
ethown
A t le as t 5 0 % owners /partner s m inor ity e th n ic
7425
0.211
0.408
0
1
mi g fi rm
Al l no n U K -bor n owner s / par tners
7425
0.219
0.414
0
1
Source: LABS
34
Market shares
• For firm i in sector j and year t, we estimate:
Yijt = a+ bDIVijt + CONTROLSijtb + SECTj + YEARt + ei
(2)
Y
= % local / national / international sales
DIV = No. of migrant / minority ethnic partners / owners
(migown_1ormore, ethown, migfirm)
CONTROLS = firm age, size, R&D spend, collaboration, mgt ability
SECT = one of 150 3-digit SIC sectoral dummies
YEAR = 2005, 2006 or 2007
• Estimate as seemingly unrelated regressions => provides
some efficiency gains over OLS
35
Company founders
• We isolate the subset of LABS respondents who are
involved in company formation
• LABS gives reasons for company formation. We classify
these as ‘entrepreneurial’, ‘locked out’ or ‘other’
• We look at whether migrant status affects reasons for
company formation
• For founder i, sector j, year t:
Pr(Yijt = 1) = aDIVijt + MGTijtb + DIV*MGTijtc + Sj + Tt + ei
(3)
Y
= Reason for firm formation (entrepreneuial / locked out / other)
DIV
= Dummy for migrant founder
MGT = management ability controls (qualifications, courses, training, experience)
36
IV
• Issue = both-ways causation in firms. Upwards bias on DIV
• Problem = not a true panel, hard to find suitable instrument
• Approach = exploit historic settlement patterns at LAD
level (cf Altonji and Card 1991). For firm i, borough j, year t:
pDIVijt = DIVjtbase
(4)
t = 2007, tbase = 2001 (using 2001 Census data)
• Estimate on 2007 data only, 2SLS with robust SE’s
• Caveats = single cross-section, instrument dummies with
continuous variables
37
IV (2)
Depvar
prodin1
prodin2
procin1
procin2
migown_1or~e
-0.25
(0.244)
-0.40
(0.253)
-0.05
(0.211)
-0.25
(0.237)
Y
2663
Y
2663
Y
2663
Y
2663
Controls
N
First stage results
F (1, 2525)
13.79
P-value
0.000
Depvar
prodin1
prodin2
procin1
procin2
ethown
0.152
(0.141)
0.0848
(0.133)
0.16
(0.132)
0.528***
(0.146)
controls
N
Y
2663
Y
2663
Y
2663
Y
2663
First stage results
F (1, 2525)
63.38
P-value
0.000
38
Descargar

Cultural diversity and innovation in London: Evidence from