Ambitious Entrepreneurs and Mobility:

Entrepreneurship and Migrationacross the local authorities in England and Wales

a Multi-Level Study

Mark Hart & Tomasz Mickiewicz

Aston Business School

Research Questions

• Are immigrants more likely to engage in high aspiration entrepreneurship?

• How do immigrants compare with internal migrants –is it international mobility or mobility full stop?

• Are these individual effects or local externalities?

• Is it ethnicity or mobility that matters?

• Are these effects conditional on other characteristics - human capital in particular?

Why Focus on Growth Ambitions?

Motivation:

– Theoretical: Growth starts with attitudes and characteristics of those who run companies: what owners-managers think matters first and foremost (Penrose, 1959)

– Empirical: evidence of significant positive correlation between aspirations and growth outcomes

– Practical: longitudinal datasets are narrow and typically cannot tackle selection problem - by focusing on aspiration we get a wider empirical base

Defining Ambitious Entrepreneurship

• GEM - Early stage entrepreneurs (i.e. either involved in start-ups or owners-managers of young companies up to 42 months)…

• who aim to increase employment by 50% or more over the next five years, and…

• will employ 10 people or more

• This definition combines two characteristics used alternatively in earlier literature:– Looking at the expected level of employment after 5 years (but

companies could start large, hence no dynamism)– Looking at expected percentage change, controlling for initial level

(but a self employed adding an employee produces lots of dynamics but little impact)

Data and Estimator

• Combined data from Global Entrepreneurship Monitor (GEM) UK: representative sample of working age population.

• 2003-2013 data, 81k-283k observations used in estimations, with location of respondents attributed to one of 326 local authorities (LA) & one of 39 LEPs at a higher level

• We apply a multilevel logit model, estimating a likelihood of being engaged in high ambition entrepreneurship (as already defined)

Developing an Appropriate Analytical Framework

• Multi-level analysis (an extension of linear regression analysis) seeks to control for a set of independent variables which operate at the individual level (i.e., age, gender education, attitudes etc)

• …..and those which operate at a ‘higher level’ (i.e., LEP or LA – contextual variables) and in particular to control for the fact that individual observations share joint factors across space.

• It is perhaps a reasonable starting assumption to make that the characteristics of a population in a particular local area differ from those in another.

Specifications: explanatory variables

• Indicator variables for being an immigrant and for being a regional migrant

• Averages for these two calculated at LA level (densities of both immigrants and regional migrants)

• Indicator for being a graduate and the LA average (density of people with HE)

• Ethnicity (15 categories)• Being female• Age (categorised into 7 intervals)• Annual dummies• Knowing other entrepreneurs, being an owner manager of existing

company, a business angel in past 3 years, shut down a business within 12 months,

• Random intercepts for LA areas

Results (1)

• Both immigrants and regional migrants are more likely to be engaged in high ambition entrepreneurship

• At the same time, the likelihood is (consistently) higher for regional migrants than for immigrants

• Contextual factors: the impact of quality of local human capital dominates: robust over different specifications; highly significant

• Contextual effects of migration become insignificant as soon as we control for local human capital, however at least they oscillate between significant positive and insignificant positive, therefore for sure there is no displacement effect; if anything, having migrants around has a positive spillover effect

Results (2)

• Ethnicity matters a lot for growth ambitions, but make no difference when interacted with migration – these two effects are both strong but clearly distinguishable

• Coming to an area rich in human capital enhances growth aspirations of regional migrants but has no effect on aspirations of immigrants (this effect is less robust than the other three)

• But for immigrants knowing other entrepreneursmatters more…

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Life-long residents Immigrants In-migrants

no yes

Knows other entrepreneurs

Migrant status interacted with Knowing other entrepreneurs

Knowing other entrepreneurs make more difference for immigrants….

More results / Robustness checks• We introduced control for ethnic diversity measure (calculated as a local

Herfindahl index of ethnic distribution): no effect• Results on migration robust over the business cycle; they do not change

significantly over time• We introduced a proxy for local dynamism: percentage change in

population, no effect (albeit measured at higher, LEP level)

• Interactions between migration and education (individual effects): higher education matter less for migrants

• Interactions between migration and education (contextual effects): an environment that combines human capital and presence of migrants is the most conducive to high aspirations entrepreneurship

• We explored if there are either overcrowding or wave effects: migration indicators interacted with local authorities migration averages – little evidence

• …but the interaction models could be over-specified or masking some other nonlinear effects

migrant 0.376** 0.311* 0.312* 0.290+ 0.388*

(0.123) (0.124) (0.124) (0.158) (0.163)

reg_migrant 0.501*** 0.453*** 0.456*** 0.459*** 0.635***

(0.076) (0.077) (0.077) (0.077) (0.099)

1.graduate 0.672*** 0.650*** 0.629*** 0.623*** 0.890***

(0.066) (0.067) (0.067) (0.074) (0.119)

1.migrant#1.graduateother 0.038 -0.230

(0.168) (0.192)

1.reg_migrant#1.graduate -0.414**

(0.147)

migr_avg 1.851*** 0.630 0.012 -0.436

(0.384) (0.589) (1.245) (1.257)

reg_migr_avg 1.104** 0.622 0.668+ -1.457

(0.337) (0.378) (0.386) (1.243)

graduate_avg 1.572** 1.282+ -3.084

(0.578) (0.776) (2.615)

migr_avg#graduate_avg 1.638 4.432

(2.909) (3.310)

reg_migr_avg#graduate_av 8.014+

White Irish 0.153 0.121 0.116 0.120 0.121(0.276) (0.276) (0.276) (0.276) (0.276)

White Other 0.468** 0.398* 0.389* 0.384* 0.377*(0.153) (0.155) (0.155) (0.156) (0.156)

Mixed Caribbean 1.162*** 1.099*** 1.106*** 1.109*** 1.108***(0.313) (0.314) (0.314) (0.314) (0.314)

Mixed African 1.201** 1.125** 1.139** 1.140** 1.126**(0.375) (0.376) (0.376) (0.376) (0.376)

Mixed Asian 1.197*** 1.143*** 1.144*** 1.147*** 1.141***(0.281) (0.281) (0.281) (0.281) (0.281)

Mixed Other 0.879** 0.803** 0.800** 0.801** 0.799**(0.291) (0.291) (0.292) (0.292) (0.292)

Indian 0.311 0.253 0.285 0.292 0.279(0.194) (0.196) (0.196) (0.197) (0.197)

Pakistani 0.704*** 0.697** 0.726*** 0.736*** 0.723***(0.213) (0.214) (0.213) (0.214) (0.214)

Bangladeshi 0.460 0.378 0.401 0.409 0.405(0.424) (0.425) (0.425) (0.425) (0.425)

Chinese -0.793 -0.813 -0.823 -0.825 -0.833(0.716) (0.716) (0.716) (0.717) (0.717)

Asian Other 1.007*** 0.942*** 0.956*** 0.960*** 0.952***(0.219) (0.220) (0.220) (0.220) (0.220)

Black Caribean 1.138*** 1.044*** 1.056*** 1.065*** 1.056***(0.224) (0.225) (0.225) (0.226) (0.226)

Black African 1.329*** 1.244*** 1.270*** 1.278*** 1.273***(0.177) (0.179) (0.178) (0.179) (0.179)

Black Other 0.666 0.551 0.566 0.572 0.559(0.511) (0.512) (0.512) (0.513) (0.513)

female -1.269*** -1.268*** -1.268*** -1.268*** -1.272***

(0.069) (0.069) (0.069) (0.069) (0.069)

1824.age9c 0.565+ 0.585+ 0.588+ 0.587+ 0.552

(0.336) (0.336) (0.336) (0.336) (0.337)

2534.age9c 0.585+ 0.615+ 0.621+ 0.619+ 0.581+

(0.328) (0.328) (0.328) (0.328) (0.328)

3544.age9c 0.628+ 0.662* 0.667* 0.665* 0.630+

(0.326) (0.326) (0.326) (0.326) (0.327)

4554.age9c 0.249 0.288 0.294 0.292 0.258

(0.329) (0.329) (0.329) (0.329) (0.330)

5564.age9c -0.277 -0.237 -0.230 -0.234 -0.268

(0.335) (0.335) (0.335) (0.335) (0.336)

6598.age9c -1.865*** -1.826*** -1.821*** -1.824*** -1.866***

(0.399) (0.400) (0.400) (0.400) (0.400)

2003b.yrsurv 0.000 0.000 0.000 0.000 0.000

(0.000) (0.000) (0.000) (0.000) (0.000)

2004.yrsurv -0.249+ -0.223 -0.233 -0.232 -0.232

(0.146) (0.146) (0.146) (0.146) (0.146)

2005.yrsurv -0.102 -0.099 -0.101 -0.099 -0.107

(0.134) (0.133) (0.133) (0.133) (0.133)

2006.yrsurv 0.019 0.021 0.016 0.020 0.016

(0.124) (0.123) (0.123) (0.123) (0.124)

2007.yrsurv -0.193 -0.179 -0.179 -0.176 -0.186

(0.127) (0.127) (0.127) (0.127) (0.127)

2008.yrsurv -0.276* -0.264+ -0.272* -0.270+ -0.271*

(0.138) (0.138) (0.138) (0.138) (0.138)

2009.yrsurv -0.182 -0.160 -0.169 -0.166 -0.167

(0.137) (0.137) (0.137) (0.137) (0.137)

2010.yrsurv 0.041 0.070 0.060 0.063 0.066

(0.196) (0.195) (0.195) (0.195) (0.195)

2011.yrsurv 0.274 0.252 0.240 0.242 0.242

(0.217) (0.216) (0.216) (0.217) (0.217)

2012.yrsurv 0.284 0.287 0.271 0.273 0.275

(0.184) (0.184) (0.184) (0.184) (0.184)

2013.yrsurv 0.036 0.005 -0.000 0.002 -0.012

(0.199) (0.199) (0.199) (0.199) (0.200)

Two main lessons

• Immigrants are characterised by high entrepreneurial ambitions - therefore those interested in promoting entrepreneurship should embrace immigration as a source of economic dynamism.

• At the same time it seems mobility itself is important. High inter-regional mobility has an even more positive effect on entrepreneurship than immigration.

• Note - not just the case of people moving their businesses after they became successful but of people engaging in entrepreneurship after they moved.