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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”