REGIONAL DIVERSITY AND HIGH-GROWTH
ENTREPRENEURSHIP
FREDRIK ANDERSSON
STATISTICS SWEDEN
NEDIM EFENDIC
STOCKHOLM 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?
Purpose
(2) Do the same factors affect the likelihood that individual
firms within a region will become an HGF?
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
Region level: Panel models on % HGFs in each municipality
Firm level: Growth among all firms…
Firm level: Quantile regression on each ”snippet” of growth…
Firm level: Logit models on 10% most rapidly growing firms
(Stam & Wennberg, 2009)
6,000
5,000
4,000
3,000
2,000
1,000
0
-…
-79%
-58%
-37%
-16%
5%
26%
47%
68%
89%
110%
130%
150%
169%
190%
209%
229%
250%
272%
293%
314%
335%
355%
384%
410%
450%
488%
531%
589%
650%
716%
826%
108…
152…
236…
345…
1.
2.
3.
4.
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
Industry
Motor vehicle manufacture
Investigation and security
Secretarial and translation
Private Health Care
Software consultancy
Software consultancy
Data processing
Municipality Employees
Stockholm
702
Stockholm
962
Stockholm
1414
Malmö
1847
Göteborg
217
Uddevalla
142
Karlskoga
143
Debt collecting / credit rating Stockholm
Engineering consultancy
Västerås
Software consultancy
Motala
Source: Fredric Delmar and Karl Wennberg (2010)
180
152
172
Turnover
(mil.€)
176
57
52
44
19
15
11
11
9
9
REGIONAL-LEVEL PREDICTORS
Variable
Definition
Gini coefficient
Diversity in income in a focial municipality, where
1=toally unequal and 0=totally equal
Median Income
Median 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
Page 9
REGIONAL-LEVEL ANALYSIS
OLS (pooled)
Variables:
ln(inhabitants)
% HGFs in
Swedish
Municipalities,
2005-2008
% employees in service sector
% employed
0.35**
(2.145)
-0.0031
(-0.202)
-0.032***
(-3.023)
% post-secondary education
Gini coefficient
% HGFs=
#HGFs / all firms
Median Income
Median Income^2
% immigrants
% 2nd generation immigrants
Constant
Observations
R2
11.8***
(2.745)
1,160
0.119
-0.21
(-0.998)
-0.016
(-0.968)
-0.022***
(-2.832)
0.060
(1.479)
0.10*
(1.792)
0.22*
(1.909)
-0.001*
(-1.886)
-0.071
(-1.382)
0.56***
(2.614)
-10.5
(-0.796)
1,160
0.155
Random
Effects
-0.20
(-0.937)
-0.013
(-0.747)
-0.023***
(-2.832)
0.064
(1.567)
0.094*
(1.709)
0.21*
(1.879)
-0.001*
(-1.876)
-0.079
(-1.524)
0.59***
(2.667)
-9.84
(-0.757)
1,160
0.167
Fixed
Effects
-6.35
(-0.479)
0.29**
(2.534)
-0.045
(-0.372)
-0.54
(-1.386)
0.074
(0.821)
0.14
(0.840)
-0.001
(-0.948)
-0.76
(-1.822)
2.99*
(1.951)
38.0
(0.304)
1,160
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
Firm-level Analysis
DESCRIPTIVE RESULTS
Mean growth
Mean turnover
14,000
25%
Native CEO
Thousand SEK
12,000
20%
10,000
15%
8,000
6,000
Second generation
immigrant CEO
10%
4,000
5%
2,000
0
2004
•
First generation
immigrant CEO
2005
2006
2007
2008
2009
Firms with Native CEO 1020% larger
0%
2005
2006
2007
2008
2009
Firms with Native CEO grows
slower (”catching up effect”)
FIRM-LEVEL ANALYSIS: LOGIT ON BECOMING HGF
Variables:
CEO: Post-High School Education
CEO: Women
Firm-level
Variables
Firm’s relative size
First generation immigrant
Second generation immigrant
Firm Age
Gini coefficient
Region-level
Variables
Median Income
Median Income ²
% post-secondary education
OECD Worskhop
Other controls:
Industry Controls:
Observations
2
Logit (1)
0.18***
(5.284)
-0.36***
(-11.46)
0.099***
(14.361)
0.055
Logit (2)
0.18***
(5.262)
-0.36***
(-11.46)
0.099***
(14.33)
0.055
(1.043)
(1.046)
0.26***
(5.145)
-0.005***
(-4.990)
0.014***
(2.601)
0.002*
(1.724)
-0.001*
(-1.924)
-0.0020
(-0.477)
no
yes
181,380
0.26***
(5.150)
-0.005***
(-5.132)
0.017***
(3.054)
0.002
(1.601)
-0.001**
(-2.273)
0.003
(0.679)
yes
yes
181,380
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?
•
•
First generation
immigrants often start
firms in ”personal
services” (=restaurants)
Industry
Second gen.
immgrant Swedish
1,7%
4,2%
12,1%
15,2%
1 – Forestry and Agriculture
2 – Manufacturing
Second generation
3 – Energy, water, and waste
immigrants often start in
4 - Construction
oftare in ”Financial
Services and Consulting” 5 – Trade and communication
6 – Financial Services and
as well as in “Education
Consulting
and Research”
7 – Education and Research
8 – Health Care
9 – Personal Services
Totalt:
First gen.
immgrant
1,2%
12,4%
0%
12,8%
33,7%
0,3%
17,1%
36,0%
0,1%
8,1%
28,8%
20,5%
1,7%
4,8%
12,6%
16,8%
1,3%
3,5%
5,6%
16,3%
1,5%
7,9%
23,6%
4 029
208 685
15 826