local clusters of entrepreneurs - hv.se

CIRCLE, Lund University, Sweden

CENTER FOR INNOVATION, RESEARCH AND COMPETENCE IN THE LEARNING ECONOMY

Local Clusters of Entrepreneurs

- neighborhood peers effects in entrepreneurship

Martin Andersson* and Johan P Larsson**

*CIRCLE, Lund University and Blekinge Institute of Technology (BTH)

**CEnSE, Jönköping International Business School (JIBS)


Our question

• Does living in a neighborhood where many residents are

established entrepreneurs induce entrepreneurial behavior? • Local social interactions: individuals’ behavior depend on the behavior of others in

their environment (Glaeser and Scheinkman 2003)

• Peer/network effects in entrepreneurship (Minniti 2005, Nanda and Sorensen

2010, Bosma et al 2012)

Our finding:

• Yes!

– Local clusters of entrepreneurs at the neighborhood level

– The fraction of neigborhood residents that are established entrepreneurs

has an economic significant and robust effect on the probability that other

residents transcend from employment to entrepreneurship


Motivation and background

• ’Local peer effects in entrepreneurship an ”old” question:

– Social dimension of the decision to become an entrepreneur (Shapero and Sokol 1982,

Aldrich 2005, Licht and Siegel 2006, Nanda and Sorensen 2010), Giannetti and

Simonov 2009).

• … but it is an important one

– Geography of growth = f(geography of entrepreneurship)

– Persistent local clusters of entrepreneurship (cf. Fritsch and Whywhich 2013, Anderson

and Koster 2011)


Figure 2. The relationship between start-up rates in 2007 (Start_up_rate) and in

1987 (L20.Start_up_rate) across Swedish municipalities (new

establishments per inhabitant 16-64 years of age).

0

.01

.02

.03

.04

Sta

rt_up_ra

te

.01 .02 .03 .04L20.Start_up_rate

PERSISTENCE OF REGIONAL START-UP RATES



• Peer effects put forth as an explanation of the evolution and persistence of entrepreneurship

clusters: Minniti (2005):

– “relatively simple assumptions about peer effects and learning behavior suffices to produce

distinct local clusters of entrepreneurial activity”.

• Anna Lee Saxenian:

– Maintained that one important explanation for the divergent performance of Silicon

Valley (California) and Route 128 (Boston) is rooted in differences in regional

entrepreneurship culture

“In Boston, if I said I was starting a company, people would look at me and say: ‘Are you sure you want to take the risk?

You are so well established. Why would you give up a good job as vice president at a big company?’ In California, I

became a folk hero when I decided to start a company. It wasn't just my colleagues. My insurance man, my water deliverer

– everyone was excited. It’s a different culture out here.”

– Social interactions and peer effects one way in which ”culture” persists and transfers between

individuals in a locality.



• Policy relevant: social multiplier (Glaeser et al 2003):

– an exogenous shock induces not only a direct effect on individual

behavior, but also an indirect effect mediated by people adopting

the behavior of their peers.

– Potential for long-term policy effect



• Empirical evidence of local peer effects in entrepreneurship is

still limited: – Survey-based evidence uninformative as regards the magnitude of the peer effects in

quantitative terms (relative to other explanations), and few studies link local peer effects

to geographic outcomes.

• Regional analyses of persistence of entrepreneurship

– Peer effects often cited as an explanation, but:

» Political AND social dimension of ’culture’ (separation difficult)

» Regions NOT homogeneous to several important

fundamentals

– Identification issue:

• Manski’s (2000) reflection problem: separating the effects of the

behavior of peers on individual behavior from the effects of spatial

sorting


Our contribution

– Focus within-city clusters of entrepreneurs neigborhoods (1 square

kilometer)

• (1) Comes much closer to the conceptual notion of a neighborhood

as an arena for social interactions.

• corresponds to established findings of the distance-decay of inter-personal

contacts. 42% of frequent contacts occur between individuals who lives less

than 1 mile apart (Wellman 1996).

• (2) Identification: neighborhoods homogeneous with regard to any

determinant operating at the city (or municipality) level

spatial differentiation in outcomes in the absence of differences

in fundamentals is a key feature of any model of social

interactions (Glaeser and Scheinkman 2003, Minniti 2005)


#1: EMPIRICAL REGULARITY

clusters of entrepreneurs across neighborhoods within regions


d

within-city neighborhood clusters of entrepreneurs

Figure 2. Distribution of entrepreneurs within the Stockholm metropolitan area (left), and the Jönköping urban

region (right).


0

5

10

15

20

25

30

35

0 200 400 600 800 1000

Stockholm average Fraction entrepreneurs

0

5

10

15

20

25

30

35

0 20 40 60 80 100 120 140

Jonkoping average Fraction entrepreneurs

Figure 3. The fraction of entrepreneurs across neighborhoods in Stockholm (left) and Jönköping (right).

within-city neighborhood clusters of entrepreneurs


• Patterns consistent with local social interactions

• variance in entrepreneurship across neighborhoods within one

and the same city cannot be explained by city-wide

fundamentals, since those are shared by all neighborhoods in

the city.

– “Standard” supply- and demand-side determinants likely to operate

at the city (or region) level

» ex. local policy regime, market-size, labor supply


#2: MICROECONOMETRIC ANALYSIS

- Does living in a neigborhood where a large fraction of the residents are established

entrepreneurs influence the probability of transcending from employment to

entrepreneurship?


– Individuals that become entrepreneurs

– Extensive controls (individual, employer, geography)

– Inclusion of municipality-specific effects

• Parameters identified from variations across neighborhoods within

cities

– Isolate sub-groups to test of robustness of the results with regard to

the underlying identifying assumption (Lindbeck et al 2007)

• Age groups, immigrants, local market-dependent sectors


IDENTIFICATION STRATEGY

• Leave full-time employment for full-time entrepreneurship.

• All employees in 2007 (N= about 2.7 million)

• Full population matched employer-employee dataset for Sweden

Γxx 1ti,1ti, 1Pr ,tiE

(1)

ti, σRθΩγZβIΓx 1ti,1ti,1ti,1ti,1ti,

Individual Employer Neighborhood Region


Table 4. Determinants of leaving employment for entrepreneurship.

Variable

Fraction entrepreneurs in the neighborhood 0.0323***

(0.00137)

Neighborhood density (ln) -0.00994***

(0.00309)

Human capital (neighborhood) 0.451***

(0.0359)

Fraction entrepreneurs in the municipality 0.00717***

(0.00273)

Size of municipality (ln) 0.00511

(0.00326)

Stockholm (dummy) 0.0753***

(0.0103)

Years of schooling 0.0137***

(0.00170)

Tenure -0.00931***

(0.000669)

Wage (ln) -0.232***

(0.00373)

Establishment exit 0.114***

(0.0139)

Establishment employment size (ln) -0.120***

(0.00321)

Age (ln) 4.679***

(0.350)

Age squared (ln) -0.598***

(0.0470)

Male (dummy) 0.335***

(0.00748)

Immigrant (dummy) 0.00496

(0.00813)

Observations 2,735,407

Pseudo R-squared .146

Note: The table report coefficient of the model in (1) using a Probit estimator. The underlying data is a matched

employer-employee dataset for Sweden for the years 2007, covering all employees in the age interval 25-

64 that live in city areas. The dependent variable is a dummy which is 1 if the individual leave employment

to become self-employed in 2008, either through a sole proprietorship or ownership of an incorporated

business. The model include a full set of dummies for the educational specialization of individuals,

dummies for occupation at the 1-digit ISCO-88 standard and dummies for the industry the in which the

individual works in 2007 at the 2-digit NACE industry level. Robust standard errors are presented in

parentheses. *** p < .01.


Issues • Sorting?

– Individuals move to certain neighborhoods once the decision to

start a firm is taken.

• Migration of entrepreneurs and employees

• Immigrants

• Additional controls

• Start-ups with very local market (neighborhood)?

• Split by sectors (cafés, hairdressers etc.)

• Driven by agglommerated areas?

• Sample split (cities // countryside)

• Entrepreneurship/self-employment?

• incorporated business / self-proprietorship

• Artifact of age composition in neighborhood?

• Estimations by age groups


Mobility of entrepreneurs and non-entrepreneurs

across neighborhoods

0%

5%

10%

15%

20%

25%

30%

1 2 3 4 5 17+

Entrepreneurs Non-entrepreneurs


Test #1 Immigrants, neighborhood tenure, agglomeration and ”local-demand” sectors

Table 7. Estimated effects of neighborhood’s fraction of entrepreneurs on the decision to enter

entrepreneurship, by different sub-groups.

(1) (2) (3) (4) (5) (6) (7)

Selection

Imm

igra

nts o

nly

Imm

igra

nts

arriv

ed

afte

r

20

02

Neig

hb

orh

ood

ten

ure

≤ 5

years

Neig

hb

orh

ood

ten

ure

≤ 2

years

50

% le

ast d

en

se

neig

hbo

rhoo

ds

Ex

clu

din

g lo

cal

dem

an

d d

riven

secto

rs

Ex

clu

din

g lo

cal

dem

an

d d

riven

secto

rs an

d

reta

il

Fraction entrepreneurs

in the neighborhood

.0423***

(0.00329)

.0381***

(.00985)

.0376***

(.00287)

.0360***

(.00197)

.0260***

(.00161)

.0382***

(.00123)

.0380***

(.00128)

Average marginal effect .0007 .0007 .0007 .0007 .0005 .0006 .0006

N 437,844 40,458 575,588 1,144,774 1,333,577 2,718,206 2,716,150

Note: The model is identical to (1), the coefficients of which are presented in Table 4. Robust standard errors are

presented in parentheses. *** p < .01. Local demand driven sectors are defined as NACE 93, including

restaurants, and NACE 82, including hair dressers and beauty salons.


Test #2 Additional neighborhood controls

Table 6. Sensitivity analysis of the main specification in Table 4.

(1) (2) (3) (4)

Added control(s)

Neig

hb

orh

ood

mean

wag

e (ln)

Reg

ion

hu

man

capital

Neig

hb

orh

ood

fraction

entrep

reneu

rs

19

91

All o

f (1)-(3

)


in the neighborhood

.0302***

(.00138)

.0343***

(.00125)

.0328***

(.00131)

.0296***

(.00142)

Average marginal effect .0005 .0006 .0006 .0005

Note: Aside from the added control variables the model is identical to (1), the coefficients of which are presented

in Table 4. Robust standard errors are presented in parentheses. *** p < .01.


Test #3 Split by start-up type

Table 5. Estimated effects of neighborhood’s fraction of entrepreneurs on the decision to enter entrepreneurship,

by start-up type.

(1) (2)

Start-up type S

tartup

of

inco

rporated

bu

siness

Startu

p o

f sole

pro

prieto

rship


in the neighborhood

.0352***

(.00138)

.0312 ***

(.00202)

Average marginal effect .0004 .0002

Note: Aside from the added control variables the model is identical to (1), the coefficients of which are

presented in Table 4. Robust standard errors are presented in parentheses. *** p < .01. Out of all startups,

61 percent are sole proprietorships, and 39 percent are incorporated businesses.


Test #4 Estimations by age intervals

Table 8. Estimated effects of neighborhood’s fraction of entrepreneurs on the decision to enter

entrepreneurship, by age interval.

(1) (2) (3)

Age interval

Ag

e 25

-35

Ag

e 36

-55

Ag

e 56

-64

Fraction entrepreneurs in the

neighborhood

.0266***

(0.00299)

.0303 ***

(.00183)

.0312***

(.00311)

Average marginal effect .0004 .0005 .0005

N 745,201 1,446,622 525,481

Note: The model is identical to (1), the coefficients of which are presented in Table 4. Robust standard errors are

presented in parentheses. *** p < .01.


CONCLUSIONS

• Local social interactions (or peer effects) in entrepreneurship

may explain persistent local clusters of entrepreneurship

– emphasized in theoretical work on the emergence and evolution of clusters

(eg. Minniti 2005) as well as in cluster case studies, such as in Saxenian’s

(1994) work on the strengths of the Silicon Valley region.

• Imply potentially large policy effects (social multiplier):

– direct effects amplified by peer effects


CONCLUSIONS

• We employed geo-coded matched employer-employee data and showed:

– (i) clusters of entrepreneurs at the neighborhood level within cities =>

consistent with local social interactions.

– (ii) micro-econometric evidence of a significant feedback effect in which

existing entrepreneurs in a neighborhood breeds new local entrepreneurs

• Overall => consistent with local social interactions effects.

• Social interactions appear as relevant in explaining the emergence and

persistence of local clusters of entrepreneurs.

• Provides an example of how characteristics of a local environment induce

entrepreneurial behavior at the individual-level, that then feeds back on the

environment (social multiplier)

local clusters of entrepreneurs - hv.se

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