David B. Audretsch
Prepared for the OECD
Copenhagen, March 2012
3/23/2012
High-Growth
Entrepreneurship
• What constitutes a “high-growth firm”?
• How prevalent are high-growth firms?
• What is their (economic) impact?
• What are determinants of high growth firms?
• Firm-specific
• Locational
• What are policy implications?
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Research Questions
• “All enterprises with average annualized growth
greater than twenty percent per annum, over a
three-year period, and with ten or more
employees at the beginning of the observation
period. Growth is thus measured by the number
of employees and by turnover.”
• the OECD-Eurostat Manual on Business
Demography Statistics (2007)
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What Constitutes a
High Growth Firm?
• “All enterprises up to five years old with average
annualized growth greater than twenty percent
per annum over a three-year period, and with
ten or more employees at the beginning of the
observation period.”
• OECD-Eurostat Manual on Business
Demography Statistics (2007)
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Gazelle Firms
• Less than 5 percent of firms in U.S. (Birch and
Medoff , 1994)
• Between 2-4 percent of firms in U.K. (BERR,
2008)
• Less than one percent of enterprises in most
countries (OECD, 2007)
• Less than two percent of turnover in most
countries (OECD, 2007)
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Prevalence
• Birch and Medoff (1994 )1988-1992, around 70
percent of all new jobs in the United States created
by existing firms (rather than new startups) were
accounted for by only four percent of the firms. This
same four percent of the firms accounted of 60
percent of all new jobs in the entire U.S. economy.
• U.K. government study finds between two to four
percent of all firms account for most of the growth
in employment (BERR, 2008)
• Account for high share of employment created in
any time period
• OECD (2007)
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Economic Impact
• Theoretical Framework
• Empirical Evidence
• Firm Specific
• Locational Specific
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Determinants
• Underlying Assumption: Opportunities are
randomly distributed
• Sizeit = (1 +et) Sizeit-1
• Prediction – Firm growth is unpredictable,
randomly distributed and not specific to firm or
locational characteristics
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Theoretical Framework –
Gibrat’s Law
• Knowledge created in one organizational context
but not fully commercialized triggers
entrepreneurial startups
• Entrepreneurship provides conduit for spillover
of knowledge from organization creating
knowledge to new firm commercializing it
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Framework of Knowledge
Spillover Theory of
Entrepreneurship
• New & firms account for high share of employment
created
• Prediction that high growth should be systematically
related to
• High knowledge contexts (firm & locational specific)
• Negatively related to firm age (firm specific)
• Negatively related to firm size (firm specific)
• (Contrary to Gibrat’s Law)
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Framework of Knowledge
Spillover Theory of
Entrepreneurship
• For largest firms, Gibrat’s Law holds
• Not systematically related to firm-specific characteristics of size
and age
• For broader distribution of firm size,
• Growth rates are higher for younger enterprises
• Growth rates are higher for smaller enterprises
• Growth rates are even higher for small and young enterprises in
knowledge-intensive industries
• Caves , Journal of Economic Literature (1998)
• Sutton, Journal of Economic Literature (1997)
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Empirical Evidence on
Firm Growth
• Consistent with Jovanovic’s theory of noisy
selection (1982) and the knowledge spillover
theory of entrepreneurship
• Robust across countries
• Caves , Journal of Economic Literature (1998)
• Sutton, Journal of Economic Literature (1997)
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Empirical Evidence
Performance
D
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Entrepreneurship & Firm
Growth
Survival Trajectory
- Returns
- Wages
B
Incumbent Firm
B
A
B
Failure Trajectory
C
Time
Temporal Impact of Entrepreneurship on
Employment Growth in the United States
(Source: Acs and Mueller, 2007)
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2.5
2.0
1.5
1.0
0.5
0.0
0
1
2
3
Lag (year)
4
5
6
• Firm-Specific Determinants
• High Growth Firms Young
• High Growth Firms Small
• Birch and Medoff (1994), Henrekson and
Johansson (2010), Storey (1994)
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Determinants of
High-Growth Firms
• Henrekson and Johansson (2010, p. 1), “net employment
growth rather is generated by a few rapidly growing
firms—so-called gazelles—that are not necessarily small
and young. Gazelles are found to be outstanding job
creators. They create all or a large share of net new jobs.
On average, gazelles are younger and smaller than other
firms, but it is young age more than small size that is
associated with rapid growth.”
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Firm-Specific
Determinants
• Acs, Parsons and Tracy (2008)
• American Corporate Statistical Library (ACSL),
from Corporate Research Board
• 1994-2006
• Linked to DMI file from Dun & Bradstreet, the
United States Bureau of Labor Statistics’ Industry
Occupation Mix, and the PUMS file from the
United States Census Bureau
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Contradictory Evidence
• Most high impact firms are small
• Large high-impact firms account for most of the
employment creation
• High-impact firms are not young (typical high-impact firm
not a startup)
• Mean age 25 years old
• Survived startup & adolescent phases prior to being
classified as high impact
• High-impact firms found in most sectors of economy
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Key Findings of Acs, Parsons
& Tracy (2008)
Table 1: U.S. Gazelles
Number of
Number of Employees Period
Job Change
Revenue Change ($1,000s)
1-19
20-499
500-plus
Total
1994-1998
309,160
3,018,440
$577,533,025
1998-2002
301,275
3,573,918
$716,504,242
2002-2006
283,308
2,883,475
$589,072,471
1994-1998
43,342
3,014,683
$762,963,829
1998-2002
42,390
3,291,048
$957,923,241
2002-2006
39,617
2,130,682
$1,014,653,361
1994-1998
1,547
5,063,517
$1,195,977,664
1998-2002
1,665
4,515,417
$1,841,396,607
2002-2006
1,485
2,514,558
$1,663,635,336
1994-1998
354,049
11,096,640
$2,536,474,518
1998-2002
345,330
11,380,383
$3,515,824,090
2002-2006
324,410
7,528,715
$3,267,361,168
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Gazelles
Number of High-Impact
Number of Employees
Period
Job Change
Revenue Change ($1,000s)
1-19
20-499
500-plus
Total
1994-1998
327,397
3,170,729
$346,038,292
1998-2002
278,190
3,577,111
$423,042,570
2002-2006
359,289
4,041,099
$425,041,975
1994-1998
23,464
2,788,969
$503,059,203
1998-2002
20,601
2,966,647
$570,102,604
2002-2006
16,523
2,001,835
$549,674,434
1994-1998
1,253
5,501,049
$1,110,073,562
1998-2002
1,182
5,192,558
$1,657,759,197
2002-2006
793
2,966,826
$1,060,128,527
1994-1998
352,114
11,460,747
$1,959,171,057
1998-2002
299,973
11,736,316
$2,650,904,371
2002-2006
376,605
9,009,760
$2,034,844,936
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Firms
1994-1998
1998-2002
Firm Size (No. of
Firm Size (No. of
Employees)
Employees)
500-
2002-2006
Firm Size (No. of Employees)
500-
1-19 20-499
1-19 20-499
1-19 20-499
500-plus
plus
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plus
Age of Firm
0-4
2.83
0.67
0.56
4.13
0.9
1.35
5.55
0.89
0.38
5-7
16.72
7.94
4.89
22.42
9.89
9.73
23.26
10.19
6.2
8-10
16.81
11.49
7.94
15.46
11.56
7.7
17.3
13.04
10.63
11-14
17.85
16.82
14.6
15.08
13.92
9.98
14.34
13.82
10.76
15-19
15.22
16.19
13.95
13.75
16.09
15.57
11.95
14.41
13.04
20-24
10.51
11.49
9.22
9.61
11.68
11.68
8.59
12.44
9.75
25-29
6.75
9.13
9.3
6.24
8.43
6.77
6.09
8.62
7.72
30-39
6.62
9.96
11.39
6.54
10.72
10.58
6.74
10.97
10.89
40-49
3.32
6.12
6.82
2.98
5.75
5.33
2.67
5.47
6.96
50-69
2.42
6.31
10.67
2.4
6.3
8.63
2.27
5.46
9.49
70-99
0.95
3.9
10.67
0.94
3.4
7.02
0.86
3.2
7.85
0
0
0
0.45
1.36
5.67
0.39
1.48
6.33
100-plus
• United Kingdom 2008 study by Department for
Business Enterprise and Regulatory Reform
(BERR)
• Broad range of sectors
• entrepreneurs & management teams with higher
skill levels & educational attainment
• greater propensity to hold intellectual property
and intangible assets, including trademarks
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Additional Evidence
• Superior access to finance (high prevalence of
venture capital finance)
• Cultural context promoting high growth
• High social capital component – networks,
partnerships, relationships & linkages to other
firms and institutions ( supply chains, formal
strategic alliances)
• BERR (2008)
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Additional Evidence
• High level of human capital (education)
• BERR (2008); Baum et al. (2001); Baum
&Locke (2004); Vivek et al. (2009)
• Experience as entrepreneur
• Baum &Locke (2004)
• Experience as employee in high growth firm
• Klepper (2009 ); Agarwal et al. (2004)
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Characteristics of
Entrepreneur
• High levels of experience in industry
• Baum et al. (2001); Baum &Locke (2004)
• Gender (male)
• BERR (2008
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Characteristics of
Entrepreneur
• Size of founding team
• Stability of the team members
• Time together as a team
• Heterogeneity of background
• Cohesiveness
• Eisenhardt & Schoonhoven, 1990
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Characteristics of Founding
Team of Entrepreneurs
• No tradition in research & management
• Journal of Economic Literature surveys by
Sutton (1997) and Caves (1998)
• Existence of cluster or agglomeration of
complementary economic activity & supporting
institutions
-- Porter (1998)
• Empirical evidence identifying higher growth
rates for entrepreneurial startups within a
cluster
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Locational Characteristics
• Empirical evidence identifying higher growth
rates for entrepreneurial startups within a
cluster
• Gilbert et al. (2006 & 2008); Lechner and
Dowling (2003)
• Geographic proximity facilitates accessing and
absorbing localized knowledge spillovers
-Jacobs (1969); Jaffe et al. (1993); Audretsch &
Feldman (1996)
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Empirical Evidence
• Worker mobility
• Almeida and Kogut (1999); Saxenian (1990);
Lee, Miller, Hancock and Rowen (2000)
• Entrepreneurial startups (Audretsch, Keilbach &
Lehmann, 2006)
• Localized networks, linkages & social capital
• Saxenian (1990)
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Localized Spillover Conduits
• Acs, Parsons and Tracy (2008)
• High-impact firms found in almost every U.S.
location
• City
• SMSA
• State
• Region
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Empirical Evidence
Empirical Evidence
•
•
•
•
Location with close geographic proximity to urban area important
High impact firms found not only in urban areas
Importance of urban area decreasing over time
No discernible difference in spatial location of high- and lowimpact firms
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• Role of Geographic Proximity to Urban Area
Table 4a. High-Impact Firm Geographic Location
1994-1998
1998-2002
2002-2006
Distance from Central Business
Number
Percent
Number
Percent
Number
Percent
In CBD
36,758
10.48
28,085
9.38
33,249
8.84
1-5
31,771
9.06
27,547
9.20
33,966
9.03
6-10
59,279
16.90
50,357
16.82
63,458
16.88
11-15
35,154
10.02
31,476
10.52
39,269
10.45
16-20
26,307
7.50
23,018
7.69
30,169
8.02
21-25
27,998
7.98
24,197
8.08
30,383
8.08
26-30
15,579
4.44
13,507
4.51
18,014
4.79
31-35
10,377
2.96
9,661
3.23
12,866
3.42
36-40
10,180
2.90
8,941
2.99
11,046
2.94
41 or more
14,432
4.12
15,004
5.01
19,515
5.19
Rural
82,840
23.62
67,549
22.57
84,008
22.35
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District (Miles)
Table 4b. Low-Impact Firm Geographic Location
1994-1998
1998-2002
2002-2006
Distance from Central Business
Number
Percent
Number
Percent
Number
Percent
In CBD
983,126
9.83
1,197,286
8.24
1,345,903
7.92
1-5
879,598
8.79
1,318,135
9.07
1,538,320
9.05
1,660,875
16.60
2,461,005
16.93
2,921,467
17.19
11-15
984,786
9.85
1,513,943
10.41
1,794,170
10.55
16-20
722,589
7.22
1,122,682
7.72
1,359,973
8.00
21-25
762,361
7.62
1,180,531
8.12
1,373,575
8.08
26-30
438,348
4.38
662,607
4.56
801,096
4.71
31-35
290,937
2.91
443,464
3.05
562,935
3.31
36-40
279,359
2.79
411,190
2.83
483,402
2.84
41 or more
434,649
4.35
714,863
4.92
877,225
5.16
2,566,109
25.65
3,513,281
24.16
3,941,502
23.19
6-10
Rural
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District (Miles)
• Promote entrepreneurship capital
• Audretsch, Lehmann & Keilbach (2006)
• Promote access to finance
• Lerner & Gompers (2010)
• “There is strong evidence that a heavy regulatory burden
negatively impacts new companies’ into the market and
thereby contributes to reduced competitive pressure and
less entrepreneurship.”
• Swedish Agency for Growth Policy Analysis (2010, p. 8)
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Policy Implications
• High impact entrepreneurship plays key role in
growth & job creation in OECD
• Systematic firm-specific characteristics of postadolescent & large firms contribute the most to
employment growth
• Entrepreneurial characteristics of human capital,
experience, access to finance & social capital
important
• Policy can facilitate high impact
entrepreneurship
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Conclusions