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Boon or Boondoggle? BusinessIncubation as Entrepreneurship Policy: A Report from the National Census ofBusiness Incubators...
Outline Why Business Incubation? Research Question Data Collection Findings Discussion and Questions                 ...
Business Incubator Definition An organization that provides free or below market  operating space to young businesses Of...
Why Incubators?                           Total Incubator Births By Year         150         125100  Frequency     75  50 ...
Incubators Cost Money Economic Development Administration   − 30 construction grants in 2010 with matching requirements  ...
Research Question Is this a good investment? Do incubated businesses outperform their  unincubated peers? Performance m...
Organizational Evolutionary Theory Emergence and survival of new organizations Liability of Newness   − Market   − Produ...
HypothesesH1.   Incubated new businesses will perform at higher levels      than their non-unincubated counterparts,      ...
Data Collection National Census of Business Incubators National Database of Incubated Businesses Matched Control Group ...
National Census of Business Incubators 950 business incubators   − 1,121 unique locations Sources   − National Business ...
Data on Incubated Businesses National Establishment Time Series Database (NETS)   − Annual snapshot of Dunn & Bradstreet ...
Data on Non-Incubated Businesses Challenge: Creating a valid comparison group without full  access to the NETS Stage 1: ...
Identification of Common Support               Propensity Score Histogram               Common Support when P-score<.5    ...
Incubated Firms Descriptive Statistics                                                  N       Average or %   Min        ...
Incubated Firms Descriptive Statistics                                                  N       Average or %   Min        ...
Incubated Firms Descriptive Statistics                                                  N       Average or %   Min        ...
Incubated Firms Descriptive Statistics                                                  N       Average or %   Min        ...
Incubated Firms Descriptive Statistics                                                  N       Average or %   Min        ...
Estimating Model I Sales and Employment Growth  Δgrowth ratei,t = β0i,t + γ1Δgrowth ratei,t-1 + β2Δincubation i,t +  β3Δp...
Estimating Model II Survival   Sθ(ti|xi) = [1+{exp(-β0 - xi βx) ti}1/γ]-θi Log-logistic distribution Frailty model   − ...
Measurement 1: Employment Growth                                          Model 1        Model 2        Incubation        ...
Measurement 1: Employment Growth                                          Model 1        Model 2        Incubation        ...
Measurement 2: Sales Growth                                          Model 1        Model 2        Incubation             ...
Measurement 2: Sales Growth                                          Model 1        Model 2        Incubation             ...
Measurement 3: Firm Survival                         Survival 1(a)Survival 2(a)(b) Logit         Incubation               ...
Measurement 3: Firm Survival                         Survival 1(a)Survival 2(a)(b) Logit         Incubation               ...
Survival Curves                             Loglogistic regression               1              .8              .6Survival...
Summary Employment Growth  −   Incubation is associated with an increase of 3.5 percentage      points in employment grow...
Thanks and Questions Thanks to Professors Stuart Bretschneider, Bruce Kingma,  David Popp, Johan Wiklund, and Peter Wilco...
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Boon or Boondoggle? Business Incubation as Entrepreneurship Policy: A Report from the National Census of Business Incubators and their Tenants

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Business has changed a lot since then and more recent research, such as “Boon or Boondoggle? Business Incubation as Entrepreneurship Policy” by Alejandro S. Amezcua of the Whitman School of Management has muddied the waters.

Amezcua’s study drew on a sample of approximately 35,000 incubated and unincubated businesses. He looked at sales growth, employment growth, and whether companies were able to survive independently.

According to his research, in terms of age and survival trends, the average incubated firm stays in business for 5 years and 42% of incubated firms close by the time they are 3.63 years old. In contrast, he found that only 4% of the sample or 655 incubated firms managed to exit their incubator, over an 18-year period, having spent an average of 3.84 years in their incubator. Incubators did have higher average sales than unincubated businesses in their first year: $693,000 versus $437,000 and also had a higher number of employees (average of 4.43 employees for incubated businesses versus 3.45 employees for unincubated businesses in their first year). But taking all of these figures together, the results seem to show that incubators have mixed results:

”Incubated firms outperform their peers in terms of employment and sales growth but fail sooner. These are important findings for policymakers who support incubation as a strategy to increase employment locally and for entrepreneurs who risk their livelihoods in order to earn a decent living.

Ultimately, he found overall that claims that incubators are highly successful are overstated.

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Transcript of "Boon or Boondoggle? Business Incubation as Entrepreneurship Policy: A Report from the National Census of Business Incubators and their Tenants"

  1. 1. Boon or Boondoggle? BusinessIncubation as Entrepreneurship Policy: A Report from the National Census ofBusiness Incubators and their Tenants Alejandro S. Amezcua, Ph.D. Post-Doctoral Fellow Whitman School of Management Syracuse University aamezcua@syr.edu
  2. 2. Outline Why Business Incubation? Research Question Data Collection Findings Discussion and Questions 2
  3. 3. Business Incubator Definition An organization that provides free or below market operating space to young businesses Often provide one or more of the following: shared administrative services, access to capital and financing, networks, and assistance with legal, technology transfer, and export procedures Motivated by the opportunity to create economic value by helping reduce the rate of failure of young businesses 3
  4. 4. Why Incubators? Total Incubator Births By Year 150 125100 Frequency 75 50 25 0 1960 1970 1980 1990 2000 2010 Year N=947 4
  5. 5. Incubators Cost Money Economic Development Administration − 30 construction grants in 2010 with matching requirements − Average grant size $1.5m − $3 million to build an incubator on average Other Federal Funding Sources − US Departments of Agriculture, Commerce, Energy, Health & Human Services, and Housing & Urban Development Average Operating Budget of an Incubator − $500,000 − $475 million annually − Paid primarily by state and local government sources 5
  6. 6. Research Question Is this a good investment? Do incubated businesses outperform their unincubated peers? Performance measures − Survival − Employment Growth − Sales Growth 6 6
  7. 7. Organizational Evolutionary Theory Emergence and survival of new organizations Liability of Newness − Market − Production − Management Selection − Organizations that can cover underneath the structure of larger organizations face different selection pressures − Organizations that stand-alone must rely on their own competencies to survive 7
  8. 8. HypothesesH1. Incubated new businesses will perform at higher levels than their non-unincubated counterparts, indicating incubation helps overcome the liability of newness.H2. Incubated firms will outperform their non-incubated counterparts post-incubation, indicating incubation helps firms adapt to the external environment. 8
  9. 9. Data Collection National Census of Business Incubators National Database of Incubated Businesses Matched Control Group of Unincubated Businesses 9
  10. 10. National Census of Business Incubators 950 business incubators − 1,121 unique locations Sources − National Business Incubator Association − State associations of business incubators − Economic development departments of all 50 states Useful data archives − Internet Archive, Dun & Bradstreet, National Center for Charitable Statistics, Lexis-Nexis Collected data on incorporation, founding year, university affiliation, and physical addresses 10
  11. 11. Data on Incubated Businesses National Establishment Time Series Database (NETS) − Annual snapshot of Dunn & Bradstreet databases Incubated firms identified through address matching − Approximately 19,000 − Observations span from 1990 to 2008 Verification process Key variables: survival, sales, employment, industry, gender, racial/ethnic identity, history of relocations 11
  12. 12. Data on Non-Incubated Businesses Challenge: Creating a valid comparison group without full access to the NETS Stage 1: Extract from NETS based on strata matching − 420 strata representing year founded, industry, county, & gender of the entrepreneur Stage 2: Propensity Score Matching − Measures the likelihood of incubation based on observed characteristics − Matched based on year founded, industry, county, and gender & ethnic/racial identity of the entrepreneur − Weighted data: 3 to 1 − Nearest neighbor selection method 12
  13. 13. Identification of Common Support Propensity Score Histogram Common Support when P-score<.5 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated Figure 1 13
  14. 14. Incubated Firms Descriptive Statistics N Average or % Min Max* Founding Year (ave) 18426 2000 1990 2006 Firm Failure (%) 18426 42% 0 1 Age (ave) 18426 5.03 1 18 Age of Surviving Firms (ave) 10761 6.03 2 18 Age of Failed Firms (ave) 7665 3.63 1 17 Years Spent in Incubator (ave) 18426 4.55 0 18 Graduates 655 4% 0 1 Failed Graduates 193 29% 0 1 Years Spent in Incubator (ave) 657 3.84 0 17 Age at Graduation (ave) 657 4.51 0 17 Initial Sales (ave) 18397 $ 692,783 $ 307 $ 805,000,000 Latest Sales (ave) 18397 $ 695,305 $ 500 $ 304,000,000 Initial Employment (ave) 18426 4.43 1 100 Latests Employment (ave) 18426 5.81 1 2500* Minority Owned (%) 18426 0.5% 0 1* Women Owned (%) 18426 6.1% 0 1* Finance & Insurance 18426 11% 0 1* Services 18426 59% 0 1 14
  15. 15. Incubated Firms Descriptive Statistics N Average or % Min Max* Founding Year (ave) 18426 2000 1990 2006 Firm Failure (%) 18426 42% 0 1 Age (ave) 18426 5.03 1 18 Age of Surviving Firms (ave) 10761 6.03 2 18 Age of Failed Firms (ave) 7665 3.63 1 17 Years Spent in Incubator (ave) 18426 4.55 0 18 Graduates 655 4% 0 1 Failed Graduates 193 29% 0 1 Years Spent in Incubator (ave) 657 3.84 0 17 Age at Graduation (ave) 657 4.51 0 17 Initial Sales (ave) 18397 $ 692,783 $ 307 $ 805,000,000 Latest Sales (ave) 18397 $ 695,305 $ 500 $ 304,000,000 Initial Employment (ave) 18426 4.43 1 100 Latests Employment (ave) 18426 5.81 1 2500* Minority Owned (%) 18426 0.5% 0 1* Women Owned (%) 18426 6.1% 0 1* Finance & Insurance 18426 11% 0 1* Services 18426 59% 0 1 15
  16. 16. Incubated Firms Descriptive Statistics N Average or % Min Max* Founding Year (ave) 18426 2000 1990 2006 Firm Failure (%) 18426 42% 0 1 Age (ave) 18426 5.03 1 18 Age of Surviving Firms (ave) 10761 6.03 2 18 Age of Failed Firms (ave) 7665 3.63 1 17 Years Spent in Incubator (ave) 18426 4.55 0 18 Graduates 655 4% 0 1 Failed Graduates 193 29% 0 1 Years Spent in Incubator (ave) 657 3.84 0 17 Age at Graduation (ave) 657 4.51 0 17 Initial Sales (ave) 18397 $ 692,783 $ 307 $ 805,000,000 Latest Sales (ave) 18397 $ 695,305 $ 500 $ 304,000,000 Initial Employment (ave) 18426 4.43 1 100 Latests Employment (ave) 18426 5.81 1 2500* Minority Owned (%) 18426 0.5% 0 1* Women Owned (%) 18426 6.1% 0 1* Finance & Insurance 18426 11% 0 1* Services 18426 59% 0 1 16
  17. 17. Incubated Firms Descriptive Statistics N Average or % Min Max* Founding Year (ave) 18426 2000 1990 2006 Firm Failure (%) 18426 42% 0 1 Age (ave) 18426 5.03 1 18 Age of Surviving Firms (ave) 10761 6.03 2 18 Age of Failed Firms (ave) 7665 3.63 1 17 Years Spent in Incubator (ave) 18426 4.55 0 18 Graduates 655 4% 0 1 Failed Graduates 193 29% 0 1 Years Spent in Incubator (ave) 657 3.84 0 17 Age at Graduation (ave) 657 4.51 0 17 Initial Sales (ave) 18397 $ 692,783 $ 307 $ 805,000,000 Latest Sales (ave) 18397 $ 695,305 $ 500 $ 304,000,000 Initial Employment (ave) 18426 4.43 1 100 Latests Employment (ave) 18426 5.81 1 2500* Minority Owned (%) 18426 0.5% 0 1* Women Owned (%) 18426 6.1% 0 1* Finance & Insurance 18426 11% 0 1* Services 18426 59% 0 1 17
  18. 18. Incubated Firms Descriptive Statistics N Average or % Min Max* Founding Year (ave) 18426 2000 1990 2006 Firm Failure (%) 18426 42% 0 1 Age (ave) 18426 5.03 1 18 Age of Surviving Firms (ave) 10761 6.03 2 18 Age of Failed Firms (ave) 7665 3.63 1 17 Years Spent in Incubator (ave) 18426 4.55 0 18 Graduates 655 4% 0 1 Failed Graduates 193 29% 0 1 Years Spent in Incubator (ave) 657 3.84 0 17 Age at Graduation (ave) 657 4.51 0 17 Initial Sales (ave) 18397 $ 692,783 $ 307 $ 805,000,000 Latest Sales (ave) 18397 $ 695,305 $ 500 $ 304,000,000 Initial Employment (ave) 18426 4.43 1 100 Latests Employment (ave) 18426 5.81 1 2500* Minority Owned (%) 18426 0.5% 0 1* Women Owned (%) 18426 6.1% 0 1* Finance & Insurance 18426 11% 0 1* Services 18426 59% 0 1 18
  19. 19. Estimating Model I Sales and Employment Growth Δgrowth ratei,t = β0i,t + γ1Δgrowth ratei,t-1 + β2Δincubation i,t + β3Δpost-incubationi,t + β4Δlag_sizei,t + β5Δfirm_agei,t + Δεi,t Double-difference model controls for the existence of unobserved heterogeneity Arellano-Bond system GMM estimator − Used to control for autocorrelation and endogeneity 19
  20. 20. Estimating Model II Survival Sθ(ti|xi) = [1+{exp(-β0 - xi βx) ti}1/γ]-θi Log-logistic distribution Frailty model − To control for firm level fixed effects − θI is defined as an unobserved observation-specific effect 20
  21. 21. Measurement 1: Employment Growth Model 1 Model 2 Incubation 0.0355*** (0.0023) Post-incubation 0.0665*** (0.0122) Employment growth lag -0.0077 -0.0073 (0.0071) (0.0071) Sales lag -0.0470*** -0.0498*** ( 0.0017) (0.0018) Firm age 0.0002 0.0004 (0.0003) (0.0003) Constant 0.6321*** 0.6553*** (0.0240) (0.0243) Number_obs. 147483 147483 Number_firms 35282 35282 Instruments 41 43 Model degrees of freedom 25 27 Wald chi-squared 995.3589 1068.9515 Wald chi-squared p-value <0.0001 <0.0001 AR(1) test statistic -27.5777 -27.5802 AR(1) p-value <0.0001 <0.0001 AR(2) Test Statistic -0.5786 -0.5104 AR(2) p-value 0.5629 0.6098 Hansen J statistic 20.6210 20.6767 Hansen J p-value 0.1117 0.1102 NOTES: Robust standard errors in parentheses. 21 * p<0.1, ** p<0.05, *** p<0.01
  22. 22. Measurement 1: Employment Growth Model 1 Model 2 Incubation 0.0355*** (0.0023) Post-incubation 0.0665*** (0.0122) Employment growth lag -0.0077 -0.0073 (0.0071) (0.0071) Sales lag -0.0470*** -0.0498*** ( 0.0017) (0.0018) Firm age 0.0002 0.0004 (0.0003) (0.0003) Constant 0.6321*** 0.6553*** (0.0240) (0.0243) Number_obs. 147483 147483 Number_firms 35282 35282 Instruments 41 43 Model degrees of freedom 25 27 Wald chi-squared 995.3589 1068.9515 Wald chi-squared p-value <0.0001 <0.0001 AR(1) test statistic -27.5777 -27.5802 AR(1) p-value <0.0001 <0.0001 AR(2) Test Statistic -0.5786 -0.5104 AR(2) p-value 0.5629 0.6098 Hansen J statistic 20.6210 20.6767 Hansen J p-value 0.1117 0.1102 NOTES: Robust standard errors in parentheses. 22 * p<0.1, ** p<0.05, *** p<0.01
  23. 23. Measurement 2: Sales Growth Model 1 Model 2 Incubation 0.0215*** (0.0025) Post-incubation 0.0513*** (0.0147) Sales growth lag -0.0527*** -0.0526*** (0.0181) (0.0181) Employment lag -0.0017*** -0.0017*** (0.0004) (0.0004) Firm age -0.0002 -0.0002 (0.0004) (0.0004) Constant -0.0131* -0.0239*** (0.0074) (0.0075) Number_obs. 147478 147478 Number_firms 35280 35280 Instruments 41 43 Model degrees of freedom 24 26 Wald chi-squared 1443.9119 1523.4264 Wald chi-squared p-value <0.0000 <0.0000 AR(1) test statistic -15.5638 -15.5634 AR(1) p-value <0.0000 <0.0000 AR(2) Test Statistic -0.9801 -0.9634 AR(2) p-value 0.3271 0.3353 Hansen J statistic 17.5076 17.4320 Hansen J p-value 0.2894 0.2937 NOTES: Robust standard errors in parentheses. 23 * p<0.1, ** p<0.05, *** p<0.01
  24. 24. Measurement 2: Sales Growth Model 1 Model 2 Incubation 0.0215*** (0.0025) Post-incubation 0.0513*** (0.0147) Sales growth lag -0.0527*** -0.0526*** (0.0181) (0.0181) Employment lag -0.0017*** -0.0017*** (0.0004) (0.0004) Firm age -0.0002 -0.0002 (0.0004) (0.0004) Constant -0.0131* -0.0239*** (0.0074) (0.0075) Number_obs. 147478 147478 Number_firms 35280 35280 Instruments 41 43 Model degrees of freedom 24 26 Wald chi-squared 1443.9119 1523.4264 Wald chi-squared p-value <0.0000 <0.0000 AR(1) test statistic -15.5638 -15.5634 AR(1) p-value <0.0000 <0.0000 AR(2) Test Statistic -0.9801 -0.9634 AR(2) p-value 0.3271 0.3353 Hansen J statistic 17.5076 17.4320 Hansen J p-value 0.2894 0.2937 NOTES: Robust standard errors in parentheses. 24 * p<0.1, ** p<0.05, *** p<0.01
  25. 25. Measurement 3: Firm Survival Survival 1(a)Survival 2(a)(b) Logit Incubation 0.9812*** 1.0616*** (0.0045) (0.0164) Post-Incubation 0.9070*** 1.2198** (0.0193) (0.1052) Employment lag 1.0024*** 1.0025*** 0.9997 (0.0006) (0.0006) (0.0006) Firm age 1.2850*** 1.2854*** 0.9223*** (0.0032) (0.0032) (0.0025) Women owned 1.1288*** 1.1282*** 0.5734*** (0.0104) (0.0104) (0.0186) Minority owned 0.9648* 0.9654* 1.2251** (0.0187) (0.0186) (0.1099) Constant 1.9668*** 1.9802*** 0.1437*** (0.0997) (0.0989) (0.0164) Gamma 0.2208*** 0.2207*** (0.0040) (0.0040) Rho .0000303 Frailty (theta) 0.000 Number_obs. 237274 237274 237274 Number_firms 36859.667 36859.667 46772.000 Log-likelihood -3.10e+04 -3.10e+04 -6.74e+04 AIC 62133.605 62098.600 1.35e+05 NOTES: (a)Weighted results (b)Robust standard errors in parentheses. 25 * p<0.1, ** p<0.05, *** p<0.01
  26. 26. Measurement 3: Firm Survival Survival 1(a)Survival 2(a)(b) Logit Incubation 0.9812*** 1.0616*** (0.0045) (0.0164) Post-Incubation 0.9070*** 1.2198** (0.0193) (0.1052) Employment lag 1.0024*** 1.0025*** 0.9997 (0.0006) (0.0006) (0.0006) Firm age 1.2850*** 1.2854*** 0.9223*** (0.0032) (0.0032) (0.0025) Women owned 1.1288*** 1.1282*** 0.5734*** (0.0104) (0.0104) (0.0186) Minority owned 0.9648* 0.9654* 1.2251** (0.0187) (0.0186) (0.1099) Constant 1.9668*** 1.9802*** 0.1437*** (0.0997) (0.0989) (0.0164) Gamma 0.2208*** 0.2207*** (0.0040) (0.0040) Rho .0000303 Frailty (theta) 0.000 Number_obs. 237274 237274 237274 Number_firms 36859.667 36859.667 46772.000 Log-likelihood -3.10e+04 -3.10e+04 -6.74e+04 AIC 62133.605 62098.600 1.35e+05 NOTES: (a)Weighted results (b)Robust standard errors in parentheses. 26 * p<0.1, ** p<0.05, *** p<0.01
  27. 27. Survival Curves Loglogistic regression 1 .8 .6Survival .4 .2 0 0 5 10 15 20 analysis time Control Group Non-Graduates Graduates 27
  28. 28. Summary Employment Growth − Incubation is associated with an increase of 3.5 percentage points in employment growth − Exiting the incubator is associated with an increase of 6.6% Sales Growth − Incubation is associated with an increase of 2.2 percentage points in sales growth − Exiting the incubator is associated with an increase of 5.13% Survival Analysis − Incubation is associated with a decrease of 2% in expected time to failure − Exiting the incubator is associated with a decrease of 10% in expected time to failure 28
  29. 29. Thanks and Questions Thanks to Professors Stuart Bretschneider, Bruce Kingma, David Popp, Johan Wiklund, and Peter Wilcoxen Thanks to the Kaufman Foundation Questions 29
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