1. Cole and Mehran (2010) Gender and Credit Gender and the Availability of Credit to Privately Held Firms Rebel A. Cole DePaul University Hamid Mehran Federal Reserve Bank of New York Academy of Entrepreneurial Finance September 16 – 17, 2010

2. Cole and Mehran (2010) Gender and Credit Background There is a growing portion of the literature on small-firm finance that notes significant differences in their findings for firms controlled by male vs. female owners. There is a growing portion of the literature on small-firm finance that notes significant differences in their findings for firms controlled by male vs. female owners. For example: For example: Robb (2002) finds that the survival rates are lower for firms controlled by women than for firms controlled by men.Robb (2002) finds that the survival rates are lower for firms controlled by women than for firms controlled by men. Cole and Mehran (2008) find that female CEOs pay themselves significantly less than do male CEOs.Cole and Mehran (2008) find that female CEOs pay themselves significantly less than do male CEOs. Cole (2008) finds that firms with female controlling owners use less leverage in their capital structure.Cole (2008) finds that firms with female controlling owners use less leverage in their capital structure.

3. Cole and Mehran (2010) Gender and Credit Background These studies (and many, many more) point to the need for a better understanding of how female-owned firms systematically differ over time from male-owned firms. These studies (and many, many more) point to the need for a better understanding of how female-owned firms systematically differ over time from male-owned firms. It is the goal of this research to provide such an understanding by developing a comprehensive set of “stylized facts” regarding the differences in firms controlled by women and firms controlled by men. It is the goal of this research to provide such an understanding by developing a comprehensive set of “stylized facts” regarding the differences in firms controlled by women and firms controlled by men. We accomplish this by analyzing data from four independent and nationally representative surveys of small U.S. firms that span 15 years, from 1987 to 2003—the FRB’s SSBFs. We accomplish this by analyzing data from four independent and nationally representative surveys of small U.S. firms that span 15 years, from 1987 to 2003—the FRB’s SSBFs.

4. Cole and Mehran (2010) Gender and Credit Contributions to the Literature Most importantly, we establish a set of “stylized facts” about differences in small U.S. firms controlled by men and women. Most importantly, we establish a set of “stylized facts” about differences in small U.S. firms controlled by men and women. These are differences that have been consistently observed across the 15 years spanned by the SSBFs.These are differences that have been consistently observed across the 15 years spanned by the SSBFs. We also document how credit-market outcomes do (or do not) differ across U.S. firms controlled by men and women. We also document how credit-market outcomes do (or do not) differ across U.S. firms controlled by men and women. Finally, we document how firm-creditor relationships differ across U.S. firms controlled by men and women. Finally, we document how firm-creditor relationships differ across U.S. firms controlled by men and women.

5. Cole and Mehran (2010) Gender and Credit Implications for Policymakers Our research has important implications for policymakers who contemplate (or enact) regulations promoting employment and the availability of credit. For example: Our research has important implications for policymakers who contemplate (or enact) regulations promoting employment and the availability of credit. For example: Our research offers insights into why so few CEOs of public firms are female.Our research offers insights into why so few CEOs of public firms are female. Our research suggests that disparate credit- market outcomes are attributable to other differences in male- and female-controlled firms, such as firm size, owner experience and owner education.Our research suggests that disparate credit- market outcomes are attributable to other differences in male- and female-controlled firms, such as firm size, owner experience and owner education.

6. Cole and Mehran (2010) Gender and Credit Methodology Weighted Descriptive Statistics by Gender Weighted Descriptive Statistics by Gender Univariate tests for differences in weighted means. Univariate tests for differences in weighted means. Multivariate tests using weighted probit regression: Multivariate tests using weighted probit regression: where the dependent variable is equal to one if the firm is controlled by a female owner and is equal to zero otherwise.where the dependent variable is equal to one if the firm is controlled by a female owner and is equal to zero otherwise. where the dependent variable is one of three credit-market outcomes.where the dependent variable is one of three credit-market outcomes.

7. Cole and Mehran (2010) Gender and Credit Methodology: Who Needs Credit and Who Gets Credit? (Cole 2009) Figure 1: Who needs and who gets credit? A sequential model (1) Need Credit? (2) Apply for Credit? (3) Get Credit? No Yes No Non-Borrower Discouraged Borrower Denied Borrower Approved Borrower

8. Cole and Mehran (2010) Gender and Credit Methodology: Correction for Sample-Selection Bias At steps 2 and 3, we correct for potential sample-selection bias using a bivariate- probit selection model developed by Van de Ven and Van Praag (1981) and refined by Green (1992, 1996). At steps 2 and 3, we correct for potential sample-selection bias using a bivariate- probit selection model developed by Van de Ven and Van Praag (1981) and refined by Green (1992, 1996). At step 2, our selection equation is Need Credit (1=Yes, 0=No) and our equation of interest is Discouraged.At step 2, our selection equation is Need Credit (1=Yes, 0=No) and our equation of interest is Discouraged. At step 3, our selection equation is Apply for Credit (1=Yes, 0=No) and our equation of interest is Denied.At step 3, our selection equation is Apply for Credit (1=Yes, 0=No) and our equation of interest is Denied.

9. Cole and Mehran (2010) Gender and Credit Data We extract data from each of the FRB’s SSBFs: We extract data from each of the FRB’s SSBFs: Four surveys:Four surveys: Cross sections as of 1988, 1993, 1998, 2003 Cross sections as of 1988, 1993, 1998, 2003 Broadly representative of 5 million privately held firms with fewer than 500 employees. Broadly representative of 5 million privately held firms with fewer than 500 employees. Stratified random samplesStratified random samples Oversample large and minority-owned firms. Oversample large and minority-owned firms. Also stratify by census region Also stratify by census region Cannot use results from unweighted descriptive statistics or from OLS to make inferences about the populationCannot use results from unweighted descriptive statistics or from OLS to make inferences about the population

10. Cole and Mehran (2010) Gender and Credit Explanatory Variables: To select our explanatory variables, we rely primarily upon the existing literature on the availability of credit, as this may be the most vexing issue facing small firms. These variables are motivated by and used in Cole (1998), Cole, Goldberg and White (2004), and/or Cole (2009). To select our explanatory variables, we rely primarily upon the existing literature on the availability of credit, as this may be the most vexing issue facing small firms. These variables are motivated by and used in Cole (1998), Cole, Goldberg and White (2004), and/or Cole (2009). We group these variables into four vectors: We group these variables into four vectors: Firm CharacteristicsFirm Characteristics Market CharacteristicsMarket Characteristics Owner CharacteristicsOwner Characteristics Firm-Creditor Relationship CharacteristicsFirm-Creditor Relationship Characteristics

11. Cole and Mehran (2010) Gender and Credit Explanatory Variables: Firm Characteristics Size (Sales, Assets, Employment) Size (Sales, Assets, Employment) Age Age Organizational form (C-Corp, S-Corp, Partnership, Proprietorship) Organizational form (C-Corp, S-Corp, Partnership, Proprietorship) Creditworthiness (Firm Delinquent Obligations, Firm Bankruptcy, Firm Judgments, D&B Credit Score, Paid Late on Trade Credit) Creditworthiness (Firm Delinquent Obligations, Firm Bankruptcy, Firm Judgments, D&B Credit Score, Paid Late on Trade Credit) Financial performance and condition (profitability, leverage, liquidity) Financial performance and condition (profitability, leverage, liquidity)

12. Cole and Mehran (2010) Gender and Credit Explanatory Variables: Market Characteristics Very limited information on firm location because of confidentiality concerns. Can only use what is available from the SSBFs. Very limited information on firm location because of confidentiality concerns. Can only use what is available from the SSBFs. Banking concentrationBanking concentration Categorical representation with three levels, which we convert into dummy variables for low, medium and high concentration Categorical representation with three levels, which we convert into dummy variables for low, medium and high concentration Urban/Rural Location of the FirmUrban/Rural Location of the Firm Binary indicator variable Binary indicator variable

13. Cole and Mehran (2010) Gender and Credit Explanatory Variables: Owner Characteristics Age Age Experience Experience Education (Grad, College, Some College, High School) Education (Grad, College, Some College, High School) Personal Wealth Personal Wealth Personal Creditworthiness (Delinquent Obligations, Judgments, Bankruptcy) Personal Creditworthiness (Delinquent Obligations, Judgments, Bankruptcy) Race and Ethnicity (Asian, Black, Hispanic) Race and Ethnicity (Asian, Black, Hispanic)

14. Cole and Mehran (2010) Gender and Credit Explanatory Variables: Firm-Creditor Relationship Characteristics Type of Primary Financial Institution (Commercial Bank, Savings Association, Finance Company, or “Other”) Type of Primary Financial Institution (Commercial Bank, Savings Association, Finance Company, or “Other”) Distance from firm HQ to Primary FI. Distance from firm HQ to Primary FI. Length of Relationship with Primary FI. Length of Relationship with Primary FI. Total Number of FIs (also split by number of Commercial Banks and number of Non- Bank FIs. Total Number of FIs (also split by number of Commercial Banks and number of Non- Bank FIs.

15. Cole and Mehran (2010) Gender and Credit Results: Descriptive Statistics The percentage of female-owned firms has steadily increased across time, almost doubling from 14.0% in 1987 to 26.3% in 2003.

16. Cole and Mehran (2010) Gender and Credit Results: Descriptive Statistics Increase has not been uniform across size quartiles.

17. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics

18. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics

19. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics

20. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female-owned firms are much smaller as measured by Sales, Assets or Employment.

21. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female-owned firms are younger and much more likely to organize as Proprietorships rather than S- or C-corporations.

22. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female-owned firms are less creditworthy, except as measured by paying late on trade credit.

23. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female-owned firms are disproportionately over-represented among service and retail trade firms, under-represented among construction, manufacturing, transportation and wholesale trade firms.

24. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female-owned firms are over-represented in concentrated banking markets.

25. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female owners are younger, less experienced and less educated than their male counterparts.

26. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female-owners are more likely to also be African American.

27. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female owners are less creditworthy and have less personal wealth.

28. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (2003) Female-owned firms have fewer banking relationships and are less likely to choose a bank as their primary financial institution.

29. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) Female-owned firms are consistently larger as measured by annual sales, total assets and employment.

30. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) Female-owned firms are consistently younger and more likely to be organized as proprietorships and less likely to be organized as corporations.

31. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) Female-owned firms are non significantly different from male-owned firms in terms of credit quality.

32. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) Female-owned firms are consistently more likely to be in retail trade and business services, and less likely to be in construction, manufacturing and wholesales trade.

33. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) There are no consistent differences by urban/rural location or banking market concentration.

34. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) Female-owned firms have owners who are consistently younger, less experienced and less educated.

35. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) There are no consistent differences by race or ethnicity.

36. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) There are no consistent differences by the credit quality of the primary owner.

37. Cole and Mehran (2010) Gender and Credit Results: Weighted Descriptive Statistics (All) Female-owned firms are consistently less likely to rely upon a commercial bank as their primary source of financial services, to have shorter relationships with their primary source, and to have fewer sources of financial services.

38. Cole and Mehran (2010) Gender and Credit Results: Multivariate Logit (Female=1, Male=0) These results largely confirm univariate differences in means. These results largely confirm univariate differences in means. However, a number of these differences lose statistical significance because they are collinear with other differences. However, a number of these differences lose statistical significance because they are collinear with other differences. Among firm characteristics, only size is consistently significant. Among firm characteristics, only size is consistently significant. Among owner characteristics, experience and education remain significant, but age flips sign from positive to negative. Among owner characteristics, experience and education remain significant, but age flips sign from positive to negative.

39. Cole and Mehran (2010) Gender and Credit Results: Multivariate Probit (No Need=1, Need=0) No consistent and significant differences in male-owned and female-owned firms that need credit.

40. Cole and Mehran (2010) Gender and Credit Results: Multivariate Probit (Discouraged=1, Applied=0) Highly significant univariate differences disappear when we control for other factors in a multivariate setting.

41. Cole and Mehran (2010) Gender and Credit Results: Bivariate Probit with Selection (Denied=1, Approved=0) Only significant difference shows female-controlled firms were less likely to be denied credit in 2003.

42. Cole and Mehran (2010) Gender and Credit Summary and Conclusions In this study, we have analyzed data from four nationally representative surveys of privately held U.S. firms for evidence on how firms controlled by males and females differ. In this study, we have analyzed data from four nationally representative surveys of privately held U.S. firms for evidence on how firms controlled by males and females differ. We have established a baseline set of stylized facts about female-controlled firms. We have established a baseline set of stylized facts about female-controlled firms.

43. Cole and Mehran (2010) Gender and Credit Summary and Conclusions Female-controlled firms are much smaller, no matter whether we measure size by sales, assets or employment. Female-controlled firms are much smaller, no matter whether we measure size by sales, assets or employment. Female-controlled firms are younger, more likely to be organized as proprietorships and less likely to be organized as corporations. Female-controlled firms are younger, more likely to be organized as proprietorships and less likely to be organized as corporations. By industry, female-owned firms are more likely to be in retail trade and business services, but less likely to be in construction, secondary manufacturing and wholesale trade. By industry, female-owned firms are more likely to be in retail trade and business services, but less likely to be in construction, secondary manufacturing and wholesale trade. Female owners are younger, less experienced and less educated. Female owners are younger, less experienced and less educated. Female-controlled firms have fewer sources of financial services and shorter relationships. Female-controlled firms have fewer sources of financial services and shorter relationships.

44. Cole and Mehran (2010) Gender and Credit Summary and Conclusions With respect to credit-market outcomes, female-controlled firms are significantly more likely to report that they are “discouraged” borrowers. With respect to credit-market outcomes, female-controlled firms are significantly more likely to report that they are “discouraged” borrowers. However, this difference disappears once we control for firm and owner characteristics in a multivariate framework. However, this difference disappears once we control for firm and owner characteristics in a multivariate framework. Our results suggest that observed gender differences in credit-market outcomes are attributable to differences in other firm and firm- owner characteristics. Our results suggest that observed gender differences in credit-market outcomes are attributable to differences in other firm and firm- owner characteristics.