Entrepreneurship and social...
Transcript of Entrepreneurship and social...
DIMETIC Session October 9, 2008
Prof. Olav SorensonUniversity of Toronto
Entrepreneurship and social networks
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Four studies
• Danish residents (1995-2001)
• U.S. Footwear manufacturers (1940-1989)• Sorenson & Audia, American Journal of Sociology, 2000
• U.S. Computer workstation manufacturers (1980-1996)
• U.S. Dedicated biotechnology firms (1978-1996)
• Stuart & Sorenson, Research Policy, 2003
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Exp. 1: Footwear manufacturing 1940
MA-281
NY-264
MO-69
CA-13
IL-68
NH-69
PA-103
WI-56ME-60
Source: Sorenson & Audia, American Journal of Sociology, 2000
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Footwear manufacturing 1989
CA-77
MA-51
ME-40
MO-48
NY-55
PA-67
WI-25
TX-36
Source: Sorenson & Audia, American Journal of Sociology, 2000
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Typical explanations
• Transportation costs
• Agglomeration externalities
• Pooled labor
• Shared suppliers
• Information spillovers
• Implication: Efficiency of spatial distribution
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What do social networks do for entrepreneurs?
• Provide access to information
• Opportunity identification aided by access to private information which travels through social networks
• Firm building aided by access to accumulated tacit knowledge which travels through social networks
• Provide access to critical resources
• Resource mobilization facilitated by information transfer, the ability to monitor and sanction, and trust – all of which depend on social networks
• Offer social support
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Social networks facilitate information access
• Burt & Raider (2002): Structurally diverse networks positively related to entrepreneurship
• Rezulli, Aldrich & Moody (Social Forces, 2003): Those at intersecting positions in different networks found firms at higher rates
• Elfring & Hulsink (Small Business Economics, 2003): Weak ties facilitate opportunity identification
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Social networks facilitate resource access
• Financial capital: Fried & Hisrich (Financial Management, 1994), Hsu (Journal of Finance, 2004), Sorenson & Waguespack (Administrative Science Quarterly, forthcoming)
• Human capital
• Intellectual capital: Singh (Management Science, 2004), Sorenson, Rivkin & Fleming (Research Policy, 2006)
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How do networks relate to geography?
• Social ties concentrate in space
• Both in physical space and social space (e.g., industry)
• Space shapes opportunities for contact, as well as the costs of maintaining a tie
• …leading industries to cluster
• Entrepreneurs most aware of opportunities and best able to formulate and execute plans for new businesses where they live in the industries in which they work
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Evidence from the individual level
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• IDA + supplemental information on entrepreneurs
• Spatial unit: 275 kommunes
Source: Dahl & Sorenson, “Home sweet home,” 2007
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8,400 observations
Inexperienced Experienced
KommuneIndustry
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• Conditional logit (McFadden choice model)
• One location actually chosen
• Compared to characteristics of 274 locations not chosen
• Control for founder characteristics
Location choice estimates
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Entrepreneurs do not often move
Average effects Average effects Anchors Sirens
Home 0.914 0.388
Work 3.43 2.46
Distance to home -1.45 -1.47
Region tenure 0.252 0.247
Ln (rivals) 1.02 0.987 0.944
Population -0.231 -0.561 -0.015
Source: Dahl & Sorenson, “Home sweet home,” 2007
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• Discrete time probability of failure
• Include controls for firm heterogeneity
• Age - number of years since founding (splined)
• Limited liability - corporate form
• Ln (size) - ln of number of employees
• Includes controls for entrepreneur experience in industry
• X home - potential test for industry-specific social capital
Firm performance estimates
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Firms founded by entrepreneurs with experience in the region survive longer
Home 0.176 0.130 0.189
Work -0.114 -0.069 -0.085
Distance to home 0.043 0.048 0.051
Region tenure -0.083 -0.064 -0.064
Ln (rivals) 0.066 0.066
Spin out -0.507 -0.389
Spin out X home -0.184
Source: Dahl & Sorenson, “Home sweet home,” 2007
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Firms founded by entrepreneurs with experience in the region recruit better employees and grow faster
No tenure Tenure < 6yrs Tenure > 5 years
Profits (t = 1) 33K DKK 84K DKK 102K DKK
Profits (t = 3) -125K DKK 60K DKK 84K DKK
Size (t = 1) 3.5 2.8 2.8
Size (t = 3) 6 4.9 2.5
% Local 16% 26% 32%
% Industry 18% 18% 20%
Source: Dahl & Sorenson, “Home sweet home,” 2007
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Evidence from the industry level
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Footwear manufacturing 1989
CA-77
MA-51
ME-40
MO-48
NY-55
PA-67
WI-25
TX-36
Source: Sorenson & Audia, American Journal of Sociology, 2000
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Weighted density variables
• i is focal location
• j are all other location
• x is a vector of location characteristics
• Number of firms
• Exp.: size
• d is a vector of distances between i and j
• reduces to (usual) density when d = 0 for all ij pairs
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Weighted density variables – Construction example
d = 10
d = 11
AB
d = 3
For location A, local density = 1.083 =
1 + 0 (0/11) + .083 (1/12)
For location B, local density = .341 =
0 + .091 (1/11) + .25 (1/4)
For location C, local density = 1.083 =
1 + 0 (0/4) + .083 (1/12)
C
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Entry, exit and concentration
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 8 9
Entry rate
Exit rate
Mul
tiplie
r of
bas
e ra
te
Local density of shoe manufacturersSource: Sorenson & Audia, American Journal of Sociology, 2000
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Time to equilibrium
1956
1956
1956
1963
1964
2047
Exit with random entry
Exit with no entry
Exit with structured entry
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Exp. 2: Computer workstations in 1984
Source: Sorenson, 2004
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Computer workstations in 1996
Source: Sorenson, 2004
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Local density and entry in workstations
0
5
10
15
20
25
30
35
40
0 10 20 30 40 50 60
Source: Sorenson, 2004
Entry rate
Exit rateMul
tiplie
r of
bas
e ra
te
Local density of workstation manufacturers
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Exp. 3: Biotechnology
Source: Stuart & Sorenson, Research Policy, 2003
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Relative resource importance
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
BT VC FirmsVC Firms Patents Universities
Source: Stuart & Sorenson, Research Policy, 2003
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Fig. 1.—The small world of markets and organizationsSource: Burt, American Journal of Sociology, 2004
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