1. DOUGLAS CUMMING AND EILEEN FISCHER YORK UNIVERSITY SCHULICH SCHOOL OF BUSINESS MARCH 2011 P UBLICLY F UNDED B USINESS A DVISORY S ERVICES AND E NTREPRENEURIAL O UTCOMES

2. Motivation Massive government spending to promote entrepreneurship and innovation around the world  View from OECD and World Bank, among others: entrepreneurship and innovation will be the key driver to growth and prosperity in the 21 st century Growing role of publicly funded business advisory hubs (e.g., MARs in Toronto, ISCM in Markham)  What is the value added?  How to most effectively deliver services?

3. Additional Motivation

4. Prior Research on Public Funded Advisory Hubs No direct evidence of whether advisory services can effectively enhance entrepreneurial outcomes when they are deliberately targeted to innovation and growth oriented firms. Scant evidence on impact of the intensity or volume of advisory services Scant evidence considering endogenity and selection econometric issues

5. In this paper.... Unique dataset from Investment Network  Affiliated with Innovation Synergy Center, Markham (ISCM)  Thanks to Catarina von Maydell (detailed records) Examine the impact of mentor hours on entrepreneurial outcomes  Sales  Patents  Financing  Alliances Control for endogeneity and selection effects

6. Investment Network ISCM started in 2003 Investment Network in 2006-Q4, subprogram of ISCM Focus on companies that:  Generating revenue or will generate revenue within 12 months  Have the capacity to generate a minimum of $2M in revenue within 3 to 4 years  Have a sustainable competitive/technical advantage  Have a current company valuation of less than $2M  Will be looking for up to $500,000 in financing within 24 months Appropriate program to assess impact of advisory services on entrepreneurial outcomes among firms with growth and innovation intentions and potential

7. Hypotheses The amount of advising a growth-oriented firm receives is positively and significantly related to entrepreneurial outcomes subsequently attained by the firm  Sales  Patents  Financing  Alliances Controlling for issues of causality and sample selection

8. Data 228 firms in contact with Investment Network 101 entered the Investment Network Program, 2006-Q4 to 2009-Q2 Types of Variables (over 100 variables in dataset)  (1) dependent variables  sales, patents, financing, alliances  (2) factors that influence whether or not the firm is part of the Investment Network  Referral sources, market conditions  (3) value added provided by advisors  Hours spent, number of mentors, number of companies advised / mentor  (4) entrepreneurial firm characteristics  Industry, incorporation date, business acumen, coachability, etc  (5) top management team characteristics  Age, race, experience, etc.  (6) market conditions  Public market returns over investment horizon, year effects, etc.

9. Some Costs and Benefits As at June 2009 early-stage entrepreneurial firms had raised $6,545,000 in financing The program costs were totaled at $662,360 Ratio of financing raised per dollar of cost is $0.10 From a public policy perspective, therefore, the program is highly efficient and cost effective

10. Table 2. Comparison Tests This table presents comparison of means and medians tests for high (>20) versus (<20) number of hours that the advisor(s) spent with the firm. Percentage changes for sales are for the subset of firms (48 in total) that had sales in 2008. Variables are defined in Table 1. P-value presented for the median test. *, **, *** Significant at the 10%, 5% and 1% levels, respectively. Variable Advisors >20 hoursAdvisors <20 hours Comparis on of Proportio ns Comparis on of Means Compar ison of Medians Number of Observations MeanMedian Number of Observations MeanMedian Sales 08/0928106.67472.32120360.91535.417 - 104.282* ** P<=0.00 0*** Financing530.7551480.58311.834* Patents530.8871480.54213.870*** Strategic Alliances530.4720480.37500.981

11. Econometric Tests: Tables 4 and 5 Step 1: Is the firm part of the Investment Network Program? (Logit) Step 2: How many mentor hours does the firm receive accounting for step 1? (Heckman Selection) Step 3: What is the impact of advisor hours on outcomes, controlling for steps 1 and/or 2? (Instrumental Variables)  (Sales, Patents, Financing, Alliances)  Use log (hours) to account for diminishing effect

12. Steps 1 and 2: Findings More help needed in bad markets  A 1-standard deviation increase in quarterly stock returns lowers the probability that a firm will join the Investment Network by 10%.  An decrease in market conditions by one standard deviation increases the total number of hours by 10 Referral source matters  If the firm is referred to the network by the Network’s coordinators or a governmental organization then the probability that the firm becomes part of the Investment Network by 56% and 35%, respectively.

13. Steps 1 and 2: Findings (Continued) Entrepreneur characteristics matter  Entrepreneurs of Middle East origin receive on average 15.5 more hours of advice  Entrepreneurs with a Masters degree receive on average 20 more hours of advice  Females receive on average 16 fewer hours advice Harder to measure personal traits also matter  More advice is provided to firms with higher rankings in terms of coachability and business acumen  Less advice is provided to firms with more people that are part of the top management team

14. Key Findings from Regressions: Sales 08/09 Additional mentor hours  greater sales  Regardless of controls for selection and endogeneity  Move 10-11 hours increases sales by 13.3%  Move 20-21 hours increases sales by 6.8% Interaction terms with advisor hours and other variables that reflect the potential learning capacity of a young firm  E.g., age, size of top management team and business acumen  All statistically insignificant.

15. Key Findings for Patents Accounting for the possible endogeneity of hours spent:  Hours do not statistically increase the probability of patents. Without controlling for endogeneity:  There is a statistical association between hours and patents which is significant at the 5% level of significance.  Move 10-11 hours increases the probability of patents by 0.6%  Move 20-21 hours increases the probability of patents by 0.3%.

16. Key Findings for Financing Additional mentor hours  higher probability of financing  Regardless of controls for endogeneity  Accounting for endogeneity:  A move from 10-11 hours increases the probability of angel financing by 0.7%,  A move from 20-21 hours increases the probability of angel financing by 0.4%.  Not controlling for endogeneity:  A move from 10-11 hours increases the probability of angel financing by 0.6%  A move from 20-21 hours increases the probability of angel financing by 0.3% Interaction term between hours and business acumen  Reduces effect of hours on financing by 10%  Due to learning capacity of the firm

17. Key Findings for Alliances Positive association between hours and obtaining a strategic alliance  Statistically insignificant with controls for endogeneity  Statistically significant without controls for endogeneity  Move 10-11 hours increases the probability of an alliance by 0.6%  Move 20-21 hours increases the probability of an alliance by 0.3%

18. Conclusions and Takeaways

19. Primary Conclusions Advising hours significantly and positively impact sales and financing, regardless of econometric controls for sample selection and endogeneity. There is a positive association between hours and patents and alliances, but the causality is more ambiguous. Cost effective:  Entrepreneurial firms had raised $6,545,000 in financing  The program costs were totaled at $662,360  Ratio of financing raised per dollar of cost is $0.10

20. Takeaways What's working here?  Selecting firms with high potential  Intensive advising rather than minimal advising Should more advising programs be sponsored with public dollars?  Ideally, we need more research with larger panels and equal attention to data collection  Record keeping (and likely advice) differs across advisors / programs  Need to be willing to target selective firms; can't expect advising to pay off equally for all types of firms  Need to recognize that minimal advising is likely to have minimal payoffs; UP TO A POINT, more is better