Local Clusters of Entrepreneurs - neighborhood peers effects in - - PowerPoint PPT Presentation

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)

CIRCLE, Lund University, Sweden

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

CIRCLE, Lund University, Sweden

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)

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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).

.01 .02 .03 .04 .01 .02 .03 .04 L20.Start_up_rate

PERSISTENCE OF REGIONAL START-UP RATES

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Motivation and background

• 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.

CIRCLE, Lund University, Sweden

Motivation and background

• 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

CIRCLE, Lund University, Sweden

Motivation and background

• 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

CIRCLE, Lund University, Sweden

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)

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#1: EMPIRICAL REGULARITY

clusters of entrepreneurs across neighborhoods within regions

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d

within-city neighborhood clusters of entrepreneurs

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

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5 10 15 20 25 30 35 200 400 600 800 1000 Stockholm average Fraction entrepreneurs 5 10 15 20 25 30 35 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

CIRCLE, Lund University, Sweden

• 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

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#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?

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– 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

CIRCLE, Lund University, Sweden

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

 

 

Γ x x

1 t i, 1 t i,  

   1 Pr

,t i

E

(1)

t i,

            

    

σ R θ Ω γ Z β I Γ x

1 t i, 1 t i, 1 t i, 1 t i, 1 t i,

Individual Employer Neighborhood Region

CIRCLE, Lund University, Sweden 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

CIRCLE, Lund University, Sweden

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

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Mobility of entrepreneurs and non-entrepreneurs across neighborhoods

0% 5% 10% 15% 20% 25% 30% 1 2 3 4 5 17+ Entrepreneurs Non-entrepreneurs

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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) ( Selection Immigrants only Immigrants arrived after 2002 Neighborhood tenure ≤ 5 years Neighborhood tenure ≤ 2 years 50% least dense neighborhoods Excluding local demand driven sectors sectors and retail Fraction entrepreneurs in the neighborhood .0423*** (0.00329) .0381*** (.00985) .0376*** (.00287) .0360*** (.00197) .0260*** (.00161) .0382*** (.00123) .038 (.00 Average marginal effect .0007 .0007 .0007 .0007 .0005 .0006 .00 N 437,844 40,458 575,588 1,144,774 1,333,577 2,718,206 2,71 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.

CIRCLE, Lund University, Sweden

Test #2

Additional neighborhood controls

Table 6. Sensitivity analysis of the main specification in Table 4. (1) (2) (3) (4) Added control(s) Neighborhood mean wage (ln) Region human capital Neighborhood fraction entrepreneurs 1991 All of (1)-(3) Fraction entrepreneurs 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.

CIRCLE, Lund University, Sweden

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 Startup of incorporated business Startup of sole proprietorship Fraction entrepreneurs 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.

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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 Age 25-35 Age 36-55 Age 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.

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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

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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)