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Cultural diversity and London’s economy

Max NathanLondon School of Economics | SERC | LSE Cities

LSE London seminar, 5 March 2012

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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

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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

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Context

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Population change in the UK

Source: ONS (2010)

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1992

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Year

Thou

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Natural change

Net m igration

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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)

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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 …

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London

Source: Hall (2011)

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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)

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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)

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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)

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Theory and evidence

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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)

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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’?

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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?

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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)

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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)

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What we did

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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

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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 (1)

Y = innovation, commercialisation, sales orientation, reasons for firm foundation / entrepreneurship

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

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Model (2) • Our vector of controls includes:

firm agefirm size, sq root R&D spendCollaborative activity ExportsPLC 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

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Innovation  New product /

serviceMod. product /

serviceNew

equipmentNew way of

working

       

Migrant diverse firm 1.238*** 1.192** 1.128 1.158

(0.084) (0.097) (0.117) (0.110)

       

Migrant firm 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

LL -3854.84 -3678.76 -3565.64 -3777.23

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%.

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Commercialisation

 New product / service and

sales growth

Mod. product / service and

sales growth

New equipment and sales

growth

New way of working and sales growth

       

Migrant diverse firm 1.122 1.111 1.163 1.042

(0.121) (0.132) (0.124) (0.097)

       

Migrant firm 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

LL -2346.35 -2068.58 -1986.53 -2119.61Source: 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%.

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What type of firms?  New product /

serviceMod. product /

service New equipment New way of working

       Migrant diverse firm 1.316*** 1.473*** 1.348* 1.363***  (0.073) (0.103) (0.218) (0.091)       

Migrant firm 1.025 1.221** 1.152 1.145*  (0.066) (0.105) (0.105) (0.088)       

Knowledge-intensive 1.127 1.034 0.978 1.173*(KI) firm (0.127) (0.07) (0.102) (0.100)       

KI * migrant diverse 0.888 0.728*** 0.636** 0.751**  (0.082) (0.067) (0.138) (0.084)       

KI * migrant firm 1.313*** 1.037 1.057 1.104  (0.129) (0.101) (0.146) (0.129)       Observations 7524 7524 7524 7524Log-Likelihood -4167.398 -3931.26 -3832.756 -4009.598

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%.

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Sales / markets Dependent variable % sales

Local National International       Migrant diverse firm 1.348 -3.666*** 2.318**  (1.532) (1.309) (0.934)       

Migrant firm 1.718 -4.128*** 2.410***  (1.290) (1.102) (0.786)       Observations 3089 3089 3089R2 0.281 0.205 0.185       Ethnic diverse firm 6.493*** -5.743*** -0.75  (1.305) (1.117) (0.800)       Observations 3089 3089 3089R2 0.286 0.207 0.182

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%.

depvar entrep lockout other (1) (2) (3) mig_founder 0.324** 0.147 -0.189 (0.137) (0.209) (0.125) Controls Y Y Y N 4327 4148 4345 Pseudo R2 0.004 0.008 0.004 Log-Likelihood -2517.722 -1291.322 -2565.523

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Entrepreneurship

Source: LABSStandard errors in parentheses. All specifications use HAC standard errors, include year and sector dummies.* p<0.1 ** p<0.05 *** p < 0.01

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Implications

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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

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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?

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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

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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?

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Thanks.

m.a.nathan@lse.ac.ukpersonal.lse.ac.uk/nathanm

squareglasses.wordpress.com@iammaxnathan

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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

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DescriptivesVariable Description N mean sd min max

prodin1 Firm introduces new product/service 7425 0.304 0.46 0 1

gprodin1 New product/service, ³1 0% revenue growth 7425 0.127 0.333 0 1

prodin2 Firm modifies product/service range 7425 0.257 0.437 0 1

gprodin2 Mod. product/service, ³1 0% revenue growth 7425 0.107 0.31 0 1

procin1 Firm introduces new equipment 7425 0.224 0.417 0 1

gprocin1 New equipment, ³1 0% revenue growth 7425 0.094 0.291 0 1

procin2 Firm introduces new way of working 7425 0.252 0.434 0 1

gprocin2 New way of working, ³1 0% revenue growth 7425 0.105 0.307 0 1

migown_ At least 1 non UK-born owner / partner 7425 0.386 0.487 0 1

ethown At least 50% owners/partners minority ethnic 7425 0.211 0.408 0 1

migfirm All non UK-born owners / partners 7425 0.219 0.414 0 1

Source: LABS

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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

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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 founderMGT = management ability controls (qualifications, courses, training, experience)

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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

Depvar prodin1 prodin2 procin1 procin2 migown_1or~e -0.25 -0.40 -0.05 -0.25 (0.244) (0.253) (0.211) (0.237) Controls Y Y Y Y N 2663 2663 2663 2663

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IV (2)

First stage results F (1, 2525) P-value 13.79 0.000

Depvar prodin1 prodin2 procin1 procin2 ethown 0.152 0.0848 0.16 0.528*** (0.141) (0.133) (0.132) (0.146) controls Y Y Y Y N 2663 2663 2663 2663

First stage results F (1, 2525) P-value 63.38 0.000