REGIONAL DIVERSITY AND HIGH-GROWTH ENTREPRENEURSHIP

FREDRIK ANDERSSONSTATISTICS SWEDEN

NEDIM EFENDICSTOCKHOLM SCHOOL OF ECONOMICS

KARL WENNBERG STOCKHOLM SCHOOL OF ECONOMICS & RATIO

BACKGROUND

• While unevenly distributed, we know that High-Growth Firms (HGFs) exist in all industries and all regions

(Delmar, Davidsson & Gartner, 2003)

• Yet, research has merely began to grapple with regional factors that may correlate with the emergence of HGFs

(Teruel & de Wit, 2011)

• Immigrants tend to start more companies than natives (Dana, 2007) – but whether these firms grow or not contingent on several other factors (Hart & Acs, 2011)

THEORY AND PURPOSE

• Theories of regional science suggests that regional diversity, both in economic and non-economic terms, is conducive for economic growth (Quigley, 1998)

• While migration has been shown to facilitate firm formation, we do not know what type of firms (low-growth, high-growth)

(Levie, 2007; Pennings,

1982)

(1) How do diversity in income, ethnicity, and education shape the number of HGFs in a region?

(2) Do the same factors affect the likelihood that individual firms within a region will become an HGF?

Purpose

DATA

• Matched employee-employer data from Statistics Sweden

• Sample: All Swedish incorporated firms 2004-2009:– Fewer than 100 employees in 2004 – No public involvement– At least one employee in addition to founder

N=43,199

• Analyses:

(1) Region-level analyses on # of HGFs

(2) Firm-Level analyses on probability of becoming an HGF (same predictors as in analysis 1)

MEASURING GROWTH

• Gibrat-type regression (size-independent growth) where growth is a relative measure: (Sorenson, 2003; Delmar & Wennberg, 2010)

S t+1/St= Sty exp (βΧt + ε)

Where:

Xit= predictor variable X at time t

S = Size in turnover at time t

y = firm’s relative size to the industry (MES)

ε = error term

HIGH-GROWTH FIRMS IN A REGION AND INDUSTRY

1. Region level: Panel models on % HGFs in each municipality

2. Firm level: Growth among all firms…

3. Firm level: Quantile regression on each ”snippet” of growth…

4. Firm level: Logit models on 10% most rapidly growing firms

(Stam & Wennberg, 2009)

-1 -0.5 0 0.5 1 1.48 1.95 2.43 2.94 3.44 4.06 4.96 6.24 8.25 170

1,000

2,000

3,000

4,000

5,000

6,000

FIRMS AND CEO BACKGROUND: DESCRIPTIVES

2005 2006 2007 2008 2009

Native Swedish 90,1% 90,4% 91,1% 90,9% 90,1%

Immigrant 7,1% 7,4% 6,9% 6,6% 7,8%

Second generation immigrant

2,7% 2,2% 1,9% 2,3% 2,0%

REGIONS AND HGFS: DESCRIPTIVES

“Star Gazelles” - 1995-2002

Source: Fredric Delmar and Karl Wennberg (2010)

Industry Municipality Employees Turnover

(mil.€)

Motor vehicle manufacture Stockholm 702 176

Investigation and security Stockholm 962 57

Secretarial and translation Stockholm 1414 52

Private Health Care Malmö 1847 44

Software consultancy Göteborg 217 19

Software consultancy Uddevalla 142 15

Data processing Karlskoga 143 11

Debt collecting / credit rating Stockholm 180 11

Engineering consultancy Västerås 152 9

Software consultancy Motala 172 9

Page 9

REGIONAL-LEVEL PREDICTORSVariable Definition

Gini coefficient Diversity in income in a focial municipality, where 1=toally unequal and 0=totally equal

Median IncomeMedian Income^2

Median income in a focial municipality, and it’s squared term (Davidsson et al. 1994)

% first generation immigrants % of municipality residents born abroad whose parents were also born abroad

% second generation immigrants % of municipality residents born in Sweden whose parents were also born abroad

ln(inhabitants) Inhabitants in municipality (natural log) (Braunerhjelm & Borgman 2004)

% employees in service sector Share of individuals employed in the service industry in relation to the municipality’s population (Fritsch and Falck, 2007; Braunerhjelm and Borgmann, 2004; Van Stel and Storey, 2004)

% employed Share of individuals in municipality with paid employment

% post-secondary education Share of individuals in municipality with 3-year or longer College Degree

% HGFs in Swedish Municipalities, 2005-2008

% HGFs= #HGFs / all firms

REGIONAL-LEVEL ANALYSISVariables:

OLS (pooled) Random Effects

Fixed Effects

ln(inhabitants) 0.35** -0.21 -0.20 -6.35

(2.145) (-0.998) (-0.937) (-0.479)

% employees in service sector -0.0031 -0.016 -0.013 0.29**

(-0.202) (-0.968) (-0.747) (2.534)

% employed -0.032*** -0.022*** -0.023*** -0.045

(-3.023) (-2.832) (-2.832) (-0.372)

% post-secondary education 0.060 0.064 -0.54

(1.479) (1.567) (-1.386)

Gini coefficient 0.10* 0.094* 0.074

(1.792) (1.709) (0.821)

Median Income 0.22* 0.21* 0.14

(1.909) (1.879) (0.840)

Median Income^2 -0.001* -0.001* -0.001

(-1.886) (-1.876) (-0.948)

% immigrants -0.071 -0.079 -0.76

(-1.382) (-1.524) (-1.822)

% 2nd generation immigrants 0.56*** 0.59*** 2.99*

(2.614) (2.667) (1.951)

Constant 11.8*** -10.5 -9.84 38.0

(2.745) (-0.796) (-0.757) (0.304)

Observations 1,160 1,160 1,160 1,160

R2 0.119 0.155 0.167 0.175

FIRM-LEVEL VARIABLES

Variable Definition

First generation immigrant Born abroad, parents born abroad

Second generation immigrant

Born in Sweden, parents born abroad

Post-High School Education

Post-High School Education, 3 years or more

Firm’s relative size Number of employees / average number of employees in industry

Lagged DV (endogeneity control)

Size at t-1

Industry controls SIC-2 equivalent

DESCRIPTIVE RESULTS

2004 2005 2006 2007 2008 20090

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Mean turnover

Th

ou

san

d S

EK

Firms with Native CEO grows slower (”catching up effect”)

2005 2006 2007 2008 20090%

5%

10%

15%

20%

25%

Mean growth

Native CEO

First generation immigrant CEO

Second generation immigrant CEO

• Firms with Native CEO 10-20% larger

Firm-level Analysis

FIRM-LEVEL ANALYSIS: LOGIT ON BECOMING HGF

OECD Worskhop

Variables: Logit (1) Logit (2)CEO: Post-High School Education 0.18*** 0.18***

(5.284) (5.262)CEO: Women -0.36*** -0.36***

(-11.46) (-11.46)Firm’s relative size 0.099*** 0.099***

(14.361) (14.33)

First generation immigrant 0.055 0.055

(1.043) (1.046)

Second generation immigrant 0.26*** 0.26***(5.145) (5.150)

Firm Age -0.005*** -0.005***(-4.990) (-5.132)

Gini coefficient 0.014*** 0.017***(2.601) (3.054)

Median Income 0.002* 0.002(1.724) (1.601)

Median Income ² -0.001* -0.001**(-1.924) (-2.273)

% post-secondary education -0.0020 0.003(-0.477) (0.679)

Other controls: no yesIndustry Controls: yes yesObservations 181,380 181,380Pseudo R2 0.042 0.043

Firm-level Variables

Region-level Variables

RESULTS

• Regional factors exhibit a strong influence on the emergence of HGFs

• Diversity in incomes (gini) as wells as ethnicity (second generation immigrants) positively associated with %HGFs in a region

• The same region-level factors also affect the growth of individual firms

CONCLUSIONS AND FURTHER WORK

• Results indicate the regional aspects of HGFs is an underexplored are requiring more empirical and theoretical work

• Regional characteristics (e.g. diversity) important both for firm dynamics in the region and for the growth chances of the individual firm

• Analysis of growth patterns of individual firms also need to consider geographic factors (e.g. multi-level analysis)

• Current definition of HGFs limited to firms at the top of the cross-sectional growth rate distributions additional analyses needed to distinguish between ”persistent HGFs” (3 year+) and ”temporary growth firms”

QUESTIONS, COMMENTS, CRITICISM?

Thank you!

WHAT INDUSTRIES DO NON-NATIVE CEOS START BUSINESSES IN?

Industry Second gen. immgrant Swedish

First gen. immgrant

1 – Forestry and Agriculture 1,7% 4,2% 1,2%

2 – Manufacturing 12,1% 15,2% 12,4%

3 – Energy, water, and waste 0% 0,3% 0,1%

4 - Construction 12,8% 17,1% 8,1%

5 – Trade and communication 33,7% 36,0% 28,8%

6 – Financial Services and Consulting 20,5% 16,8% 16,3%

7 – Education and Research 1,7% 1,3% 1,5%

8 – Health Care 4,8% 3,5% 7,9%

9 – Personal Services 12,6% 5,6% 23,6%

Totalt: 4 029 208 685 15 826 

• First generation immigrants often start firms in ”personal services” (=restaurants)

• Second generation immigrants often start in oftare in ”Financial Services and Consulting” as well as in “Education and Research”