WOMEN-OWNED BUSINESSES AND ACCESS TO BANK CREDIT: A TIME SERIES PERSPECTIVE

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1 WOMEN-OWNED BUSINESSES AND ACCESS TO BANK CREDIT: A TIME SERIES PERSPECTIVE Monica Zimmerman Treichel Temple University Fox School of Business and Management Strategic Management Department 1810 North 13th Street 380 Speakman Hall Philadelphia, PA Telephone: (215) Fax: (215) Jonathan A. Scott Temple University Fox School of Business and Management Finance Department 1810 North 13th Street 205A Speakman Hall Philadelphia, PA Telephone: (215) Submitted to Entrepreneurship Theory and Practice March 2005

2 WOMEN-OWNED BUSINESSES AND ACCESS TO BANK CREDIT: A TIME SERIES PERSPECTIVE We test three questions related to access to commercial bank financing by women-owned businesses. First, are women-owned businesses less likely to apply for bank loans than businesses owned by men? Second are womenowned businesses less likely to be turned down in their most recent loan application. And finally, if approved on their most recent loan application, are loan terms more stringent for women-owned businesses. We found gender to be related to the application for bank loans as well as the size of the loans. Gender was not found to be significantly related to the denial of bank loans. The role of small businesses in generating employment growth in the U.S. economy has been well established. 1 Small firms produce 51 percent of the GDP and create an estimated two-thirds to three quarters of new jobs. Among these firms are 10.6 million women-owned businesses in the U.S. that represent an increasing share of the overall small business contribution to U.S. economic growth. The SBA defines a woman-owned business as one in which 50% or more of the equity is owned by a woman or women. Women-owned businesses generate $3.6 trillion dollars in US sales and account for 55% of all new US businesses. Over 19 million people are employed nationwide by women-owned businesses and one in seven people are employed by women-owned businesses (http://app1.sba.gov/faqs/). For all small firms, whether women- or men-owned businesses, bank lending is the most important overall source of external funding (Berger & Udell, 2002), and for womenowned businesses, securing bank loans is difficult (Chaganti, DeCarolis, & Deeds, 1995; Coleman, 2000). It is important for us to address the difficulties faced by women-owned 1 See for the latest statistics on the contributions of small business to the American economy. 2

3 businesses because of the increasing contribution of women-owned businesses to the growth of the U.S. economy. Although there is some literature addressing the financing of women-owned businesses, little is known about the attempts of such businesses to access bank loans and their success in securing those loans (Fabowale, Orser, & Riding, 1995). While there is a common belief that women-owned businesses face discrimination in securing loans from banks (Buttner & Rosen, 1992; Fabowale et al., 1995; Riding & Swift, 1990; Schwartz, 1979), the findings have been mixed after the firm size, years in business, industry, and other demographic factors are taken into consideration. More importantly, virtually all of the studies rely on a cross-sectional examination of a sample of borrowers at one point in time. While these results may be generalized, market conditions, structure and competition change over time. In this paper, we use a unique set of data to examine the ability of women-owned businesses to access debt markets. The data include three different samples of small business owners from the same national trade organization at three different points in time: 1987, 1995, and This period encompasses dramatic change in the U.S. banking markets. The number of banking organizations shrank from almost 14,000 to 8,000. A casual review of the annual reports of some of the nation s largest regional banks during this time would reveal a strong emphasis on marketing to mid-market and small businesses, in addition to the continued small business orientation of the 100+ new bank charters per year that were granted during this same period (www.fdic.gov/hsob). 3

4 With these data we test three questions related to access to commercial bank financing by women-owned businesses. 2 First, are women-owned businesses less likely to apply for bank loans than businesses owned by men? Second are women-owned businesses less likely to be turned down in their most recent loan application. And finally, if approved on their most recent loan application, are loan terms more stringent for women-owned businesses (using loan size as a proxy for loan terms). We begin with a review of literature on women-owned businesses and efforts to finance their business through commercial bank loans. Next we describe the survey data used to test our hypotheses. A discussion of the results is followed by implications for small business owners and suggestions for future research. LITERATURE REVIEW A significant body of research has examined the financing of small businesses. Evidence about the financing needs of small businesses is based mostly on surveys, primarily the Survey of Small Business Finance conducted by the Board of Governor s of the Federal Reserve System. Berger and Udell (2002), using data from the 1993 Survey of Small Business Finance, report that the average U.S. small business relies equally on debt and equity financing. Among equity sources, the largest category is insider funds or retained earnings (31 percent), followed by other equity (13 percent) that is likely provided by family and friends. Outside equity, whether angel investors or venture capital, represents a very small percentage of total funds (about 5.5 percent). Among debt sources, commercial banks are the largest source (19 percent of the total or 38 percent of all debt financing), followed by 2 We define women-owned businesses as businesses in which the principal owner is a woman. 4

5 trade credit (16 percent of the total or 21 percent of all debt financing). Trade credit, arises in the normal course of the sales cycle in financing working capital, while bank loans reflect a more discretionary outside source. 3 If trade credit is excluded from the debt financing, then commercial banks provide 54 percent of the total. Women-owned businesses are often self-financed -- women are less likely to go into debt or to sell shares to the public to secure capital -- and they launch businesses with less money than do men (Belcourt, Burke, & Lee-Gosselin, 1991). When they do secure debt funding, it is most often from savings and loans, family members, or banks (Schwartz, 1979). However, securing debt funding, especially from banks, is considered difficult for womenowned businesses (Chaganti et al., 1995). In the study of debt funding through commercial bank loans, there is a common belief that women-owned businesses face discrimination in accessing bank loans (Buttner & Rosen, 1992; Fabowale et al., 1995; Riding & Swift, 1990; Schwartz, 1979). The belief that such discrimination exists may prevent women-owned businesses from applying for loans and may limit the loan size applied for by women-owned businesses compared to those owned by men. If discrimination exists, the rate of approved loans to women-owned businesses compared to those owned by men may be affected. Cavalluzzo, Cavalluzzo, and Wolken (2002), using the 1993 Survey of Small Business Finance data, found that women-owned businesses experienced a higher incidence of unmet credit needs (turned down on their most recent loan or did not apply for fear of being turned down) and this shortfall increased with market concentration of lenders. However, empirical research provides evidence which 3 The other source of debt include other financial institutions, other business loans, owner loans, credit card, other individual loans. Together these sources comprise 16 percent of total external financing or 31 percent of total debt financing. 5

6 contradicts the presence discrimination (Buttner & Rosen, 1988; Coleman, 2000; Fabowale et al., 1995). It appears that differences in the application for, approval of, and size of loans to women-owned businesses and men-owned businesses are not due to gender but rather differences in business characteristics such as size, age, risk, and industry (Fabowale et al., 1995; Riding & Swift, 1990; Robb & Wolken, 2002). Research indicates that women-owned businesses are typically smaller than those owned by men (Fabowale et al., 1995; Orser, Hogarth-Scott, & Riding, 1994; Riding & Swift, 1990). Robb and Wolken (2002) found women-owned businesses were significantly smaller than men-owned businesses in terms of employment, assets, and sales. Smaller businesses typically have greater difficulty in securing bank loans (Fabowale et al., 1995). The age of the business is another characteristic that is related to the differences in access to commercial bank loans by women- and men-owned businesses. Women-owned businesses were found to be significantly younger than those owned by men (Riding & Swift, 1990; Robb & Wolken, 2002). Robb and Wolken (2002) found that women-owned businesses had significantly shorter relationships with their lending institutions than did menowned businesses which may be due to the younger age of women-owned businesses. This shorter relationship may influence the application for and approval of loans. A third characteristic related to differences in lending is that of risk. Women-owned businesses are typically considered riskier than those owned by men (Fabowale et al., 1995; Riding & Swift, 1990; Robb & Wolken, 2002). In addition, the growth rates of womenowned businesses were found to be less stable and lower than businesses owned by men (Fabowale et al., 1995; Riding & Swift, 1990). 6

7 A fourth characteristic that may explain differences in bank lending to men-and women-owned businesses is the industry in which the businesses operate. Women-ownedbusinesses are more likely to operate in retail and services than businesses owned by men (Belcourt et al., 1991; Fabowale et al ; Robb & Wolken, 2002), which may influence the need for and type of credit used. Service based businesses require little if any financing, and retail businesses allow for the use of trade credit. The literature is mixed as to whether or not differences exist in the application for bank loans between women-and men-owned businesses. Coleman (2000) argued that women-owned businesses may be less willing to seek external credit because the terms under which women typically obtain credit are less favorable than those given to men. However, Fabowale et al. (1995) found that in comparison to businesses owned by men, women-owned businesses are more likely to have applied for term loans. Cavaluzzo et al. (2002) found no difference in the application experience of women-owned businesses after controlling for credit history, assets, sales, and years in business. These results were corroborated using the 1998 Survey of Small Business Finance by Robb and Wolken (2002). Differences in the proportion of applications for bank loans by women- and men-owned businesses may be due to the reliance of women-owned businesses on other sources of credit such as credit cards and trade credit (Robb & Wolken, 2002). In summary, the evidence on whether women-owned businesses are disadvantaged in having access to commercial bank financing is mixed. A number of studies found that differences can be explained by business characteristics rather than gender of the primary business owner(s). The conflicting results may be due to the samples used in the previous studies; the most recent studies using the Board of Governor s survey data are based on data 7

8 collected in Our data set provides us with the opportunity to examine access to commercial bank financing of women- and men-owned businesses at three points in time, with the latest survey in With these data, we are able to examine women-owned businesses experience with commercial bank credit using three outcomes: 1) the likelihood of applying for bank loans, 2) outcome of the most recent loan (i.e. acceptance/ denial rates), and 3) the size of the loan as a proxy for terms. Our null hypothesis is that no significant differences exist in the experience of women- and men-owned businesses in accessing commercial bank credit. In other words, we expect that gender as an independent variable should not be significant in explaining the likelihood of applying for a bank loan, the outcome of the most recent loan, or the size of the most recent loan. METHODS The data in this study come from the Credit, Banks and Small Business (CBSB) survey conducted by the National Federation of Independent Business (NFIB) in 1987, 1995, and There were 1921 respondents in the 1987 survey, 3642 in the 1995 survey and 2223 in the 2001 survey. For each survey, the questionnaire was mailed twice within a twoweek interval to the random sample of members and duplicate responses were eliminated. The response rates have varied over time: 26% in 1987, 20 percent in 1995, and 18 percent in Although self-reported, there is no reason to believe that there is any systematic selfreporting bias in the responses to the survey questions. The data are similar to that gathered by the Board of Governors of the Federal Reserve System in their Survey of Small Business Finance (SSBF) through a commissioned telephone survey of small firms drawn from the 4 A similar decline in the response rate has been experienced with the Board of Governor s Survey of Small Business Finance. 8

9 Dun and Bradstreet files. Neither the NFIB nor the SSBF surveys attempt to verify the accuracy of the self-reported data. 5 A brief summary of the key firm demographic distributions for each survey is shown in the Appendix Insert Appendix about here Historically, there has been little survey response bias with respect to number of employees, sales, industry, and region of the NFIB membership. The characteristics of the NFIB membership do not show large differences from those of all small businesses. 6 Even if the survey respondents were not completely representative of the small business population, the objective of the research is only to identify significant correlation between the gender of the owner and selected banking outcomes. Representativeness of the data would be more important, for example, if the objective was to measure some population characteristic, such as average sales or employment. RESULTS Key demographic statistics are presented in Table 1 for each survey for all firms. In addition, the distributions are reported separately for women-owned and men-owned businesses. The proportion of women-owned businesses in the surveys has increased slightly over time from 10 percent in 1987 to 12 percent in Women-owned businesses are more frequently organized as proprietorships and less frequently organized as corporations, 5 The Fed collects a significant amount of income statement and balance sheet data that is cross-checked for consistency through internally developed algorithms. 6 A special report prepared for the Small Business Administration is available upon request from the author. 9

10 which may be related to size and age of the firm where women-owned firms are younger and smaller across all surveys. Several industry differences are also notable: women-owned firms are more likely to be in retail and businesses services and less likely to be in manufacturing and construction Insert Table 1 about here To test the likelihood of women-owned businesses applying for a bank loan, we used logistic regression to estimate the odds of applying for a bank loan and created a dependent variable for each survey that takes a value of 1 if the owner reports applying for a bank loan and 0 otherwise. 7 The key independent variables were gender of owner (women-owned, jointly-owned, with male-owned the omitted variable), years in business, size of the business, recent sales growth, form of business, and industry. Table 2 presents the model estimates for the 1987 survey in Panel A, the 1995 survey in Panel B, and the 2001 survey in Panel C Insert Table 2 about here Three different estimates are reported: only the gender variables are included in column 1; business size, years in business, and sales growth are added in column 2; and form of business and industry are added in column 3. Even after controlling for important business demographics, women-owned businesses are significantly less likely to apply for a 7 Excluded are applications made to non-bank sources. 10

11 bank loan compared to male-owned businesses, and this outcome persists over time. 8 These results are economically meaningful as well. Using the mean value of the number of owners that applied for a loan in 2002, the probability of a women-owned business of not applying for a loan at a bank is over.10. Logistic regression was also used to test the likelihood of women-owned businesses being turned down in their most recent loan application in comparison to men-owned businesses. The dependent variable for this test is the outcome of the most recent loan application at a bank (1= applied and turned down; 0= applied and approved) and the same set of independent variables were used. 9 Similar to Table 2, the estimates are presented in Table 3 for three sets of independent variables: gender variables only; gender and business size, age, and sales growth; and finally, gender, business size, age, sales growth, form of business, and industry. In the last specification, the location (MSA) and size of current bank (banks at CFI) are also added as explanatory variables. Both location and bank size have been shown to be important in determining the turn down rate on loan applications (Scott, in press) Insert Table 3 about here The model was also estimated with data that were weighted by United States Census Bureau proportions (using a 1997 base) for the 2001 survey results. There was no change in the significance of the key predictors shown in column 3. 9 The estimates in Table 3 may suffer from a selection bias. This bias could occur if some unobserved characteristic is more common among firms that apply than those that do not is correlated with the gender of the owner. While there are estimation techniques available to address this problem, the challenge is to find a variable that would identify the application equation separate from the loan application equation and still be uncorrelated with the gender of the applicant. We used location and MSA employment in a Heckit model and the results were unchanged, i.e. gender had no effect on the loan application outcome after taking other firm characteristics into consideration. 11

12 The logistic regression results, presented in Table 3, indicate that women-owned businesses are more likely to be turned down when the gender variables are the only predictors (column 1, Table 2), except for 2001 in which gender is not significant with any set of predictors. Gender is not a significant predictor when other firm characteristics are included in the estimates for all survey years. Thus, if female-owned businesses more frequently report being turned down for a loan, it appears that it is because of how size, firm age, growth, industry, location, or size of current bank factor into the credit decision, not but not because of gender. To test whether women-owned businesses, if approved on their most recent loan application, are given more stringent loan terms than businesses owned by men (using loan size as a proxy for loan terms), we ran an ordinary least squares regression. The dependent variable for this test is the log of the loan size reported if successful on the most recent application. Similar to Tables 2 and 3, the estimates are presented in Table 4 for three sets of independent variables: gender variables only; gender and business size, age, and sales growth; and finally, gender, business size, age, sales growth, form of business, and industry. In addition, a fourth set of independent variables related to loan terms is added to the equation. These terms include the collateral status (1 = required), purpose (1 = working capital needs), type of loan (1 = fixed term), and the log of the loan maturity. The results, presented in Table 4, indicate that women-owned firms persistently report a lower loan size compared to male-owned firms, even after adding both firm and loan term variables as explanatory variables. This effect persists across all surveys. It is possible that the gender effect is related to size, i.e. women-owned firms are demonstrably smaller and thus the negative association of loan size with gender may just be a firm size effect. To 12

13 test this effect, we created an interactive variable with gender and size (log of FTE) and reestimated the equation. Although not reported in Table 4, the negative and significant relationship between gender and loan size remained, indicating that the association of gender and loan size is not just an effect of firm size. This outcome may be due to some omitted variable that is not adequately captured by the set of independent variables, such as the incidence of home-based businesses or the role of personal assets in funding the business Insert Table 4 about here In sum, we found limited support for our null hypothesis that women-owned businesses do not have a significantly different experience across the three aspects of commercial bank lending. We found support for the hypothesis with regards to approval of bank loans but did not find support for the hypothesis with regard to application for bank loans and size of the loans. DISCUSSION AND CONCLUSION In this paper, we examined three questions related to women-owned business access to commercial bank financing. First, are women less likely to apply for bank loans than male-owned businesses? Second, are women-owned businesses less likely to be turned down in their most recent loan application? And finally, if approved on their most recent loan application, are loan terms more stringent for women-owned businesses? Although much of the research on gender and access to commercial bank financing indicates that gender does not explain differences between lending to women- and men- 13

14 owned businesses after controlling for business characteristics (Fabowale et al., 1995; Riding & Swift, 1990; Robb & Wolken, 2002), we found that gender does appear to be related to some aspects of access to bank financing. Specifically, we found that gender is significantly related to the application for bank loans as well as the size of the loans and these effects are persistent over time. In testing the likelihood of women to apply for bank loans, we found that even after controlling for important firm characteristics, women-owned firms are significantly less likely to apply for a bank loan. This finding may be related to the belief by women-owned businesses that they may face discrimination in the lending process. Concern for discrimination may prevent women-owned businesses from applying for commercial bank loans. It may also cause them to seek funds from other sources. In addition, many womenowned businesses are service or retail businesses and so may not need financing or may rely on sources of credit other than bank loans. Services may not require bank financing, and retail businesses may finance their inventory through trade credit. Another factor that may explain the significant relationship of gender and loan applications is the desire to retain control of the business. Women-owned businesses may choose to forego bank loans because of the monitoring of and requirements placed upon their business by the lender. In examining if women-owned businesses are less likely to be turned down in their most recent loan application, we found that gender was not significant. Thus, our results suggest that there is no likely difference in the approval of loans to women- and men-owned businesses after controlling for business characteristics. This confirms prior research. It appears that firm level characteristics account for differences in the approval of bank loans to women- and men-owned businesses. In our study, business size, age, growth, industry, 14

15 location, or size of current bank account for differences in the approval/ denial of a bank loan. In examining whether or not women-owned businesses are given more stringent loan terms than businesses owned by men, we found gender to be significant. Women-owned businesses reported a lower loan size compared to those owned by men, even after adding both business and loan term variables as explanatory variables. This may reflect discrimination of women-owned businesses. It may also reflect the types of businesses most frequently owned by women, i.e., service and retail businesses, and the limited need for bank loans for those businesses, although these variables were included in the multivariate analysis. Perhaps the relationship between the size of the loan and business type and size is more complex than the relationships tested in the model. Another explanation is that the smaller loan size of women-owned businesses relative to businesses owned by men is a difference in attitudes toward external funding, specifically debt funding. Thus, if a womanowned business requires external funding, it may minimize the amount of funds it secures. There are several implications for women-owned businesses and banks that can be derived from this study. Women-owned businesses may benefit from the finding that there appears to be no significant difference in the approval rates of women and men owned businesses. This may encourage women-owned businesses to apply for bank loans. For banks, the significant difference in application rates between women- and men-owned business suggests that there is potential for better marketing to women-owned businesses. Our sample consisted of over 7700 subjects collected at one of three time periods providing a wealth of data. Nonetheless, there are limitations to this study. First, in examining differences in access to commercial bank financing between women-and men- 15

16 owned businesses we controlled for economic variables. The differences may be due to an omitted variable an economic or a non-economic variable. Future research may benefit from expanding the set of variables, adding market structure variables that capture the extent of local bank competition. Another limitation is that we examined three samples of businesses. We did not study the same set of businesses over time. Future research would benefit form examining a panel over time that may provide a better perspective on access to bank financing over the life cycle of the business. 16

17 REFERENCES Belcourt, M., Burke, R., & Lee-Gosslin, H. (1991). The glass box: Women business owners in Canada. Ottawa: Canadian Advisory Council on the Status of Women. Berger, A. N., & Udell, G. F. (2002). The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle. Journal of Banking and Finance, 22, Buttner, E. H., & Rosen, B. (1988). Bank loan officers' perceptions of the characteristics of men, women, and successful entrepreneurs. Journal of Business Venturing, 3(3), Cavalluzzo, K. S., Cavalluzzo, L. C., & Wolken, J. D. (2002). Competition, small business financing, and discrimination: Evidence from a new survey. Journal of Business, 75(4): Chaganti, R., DeCarolis, D., & Deeds, D. (1995). Predictors of capital structure in small ventures. Entrepreneurship: Theory & Practice, 20(2): Coleman, S. (2000). Access to capital and terms of credit: A comparison of men- and women-owned small businesses. Journal of Small Business Management, 38(3): Fabowale, L., Orser, B., & Riding, A. (1995). Gender, structural factors, and credit terms between Canadian small businesses and financial institutions. Entrepreneurship: Theory & Practice, 19(4): retrieved on 3/31/05. Orser, B., Hogarth-Scott, S., & Riding, A. (1994). Performance, firm size, and management problem solving. Journal of Small Business Management, 38(4):

18 Riding, A., & Swift, C. (1990). Women business owners and terms of credit: Some empirical findings of the Canadian experience. Journal of Business Venturing, 5(5): Robb, A., & Wolken, J. (2002). Firm, owner, and financing characteristics: Differences between female- and male-owned small businesses. Unpublished manuscript. Schwartz, E.B. (1979). Entrepreneurship: A new female frontier. Journal of Contemporary Business, 4: Scott, J. A. (in press). Loan officer turnover and small business credit availability. Journal of Small Business Management. retrieved on 3/27/05. retrieved on 3/25/05. 18

19 Appendix. Distribution of NFIB survey respondent demographic characteristics The frequency distributions for selected firm characteristics from the 1987, 1995, and 2001 National Federation of Independent Business (NFIB) Credit, Banks and Small Business Survey. Form of Business Proprietorship Partnership Corporation S-Corporation No answer Full Time Equivalent Employees One No answer Years in Business or more No answer Industry Construction/mining Manufacturing Transportation Wholesale Retail FIRE Business services Professional services Agriculture No answer Sales Growth (past 3 years) Decline No change (-5 to 5%) Grew 6-10% Grew 11-20% Grew > 20% Too new to tell No answer

20 Table 1: Demographic Profile of Respondents to the National Federation of Independent Business' Credit, Banks and Small Business Surveys Survey Date Male/ Male/ Women Jointly Women Jointly Women Owned Owned All Owned Owned All Owned Male/ Jointly Owned Firm characteristics All Owner gender Female Male Joint Form of business Proprietorship Partnership S-corporation Corporation Industry Agriculture Manufacturing Construction Transportation Wholesale Retail FIRE (finance, insurance, real estate) Business services Professional services Full-time equivalent employees Years in business Most recent loan characteristics Applied for a loan Turned down on most recent loan Amount of loan ($000) $ 30.6 $ 17.3 $ 32.1 $ 54.8 $ 37.7 $ 56.8 $ 59.2 $ 33.4 $ 63.4 Fixed assets Refinance 20

21 Table 2 : Multivariate results of the decision to apply for a bank loan The dependent variable, applied for a loan at a commercial bank, takes a value of 1 if yes and 0 no application was made to any source. Logistic regression is used to estimate the model. All variables are defined in Table 1 and all data are taken from the National Federation of Independent Business' Credit, Banks and Small Business surveys. Panel A presents the estimation results from the 1987 survey, Panel B from the 1995 survey, and Panel C from the 2001 survey. (1) (2) (3) Panel A: 1987 Coef. Std Err Coef. Std Err Coef. Std Err Female owned *** ** * Jointely owned Log FTE *** *** Log years in business ** Sales growth Partnership * Proprietorship *** Agriculture Manufacturing Construction Transportation Wholesale FIRE Business services ** Professional Constant *** ** *** No. of obs 1,702 1,548 1,548 pseudo R-squared Panel B: 1995 Coef. Std Err Coef. Std Err Coef. Std Err Female owned *** *** *** Jointly owned Log FTE *** *** Log years in business ** Sales growth Partnership Proprietorship S-corporation Agriculture *** Manufacturing Construction Transportation Wholesale FIRE Business services Professional Constant *** No. of obs 2,776 2,776 2,776 pseudo R-squared

22 Table 2 (continued) : Multivariate results of the decision to apply for a bank loan Panel C: 2001 Coef. Std Err Coef. Std Err Coef. Std Err Female owned *** *** *** Jointly owned Log FTE *** *** Log years in business *** *** Sales growth * Partnership Proprietorship S-corporation Agriculture ** Construction FIRE Manufacturing Professional Business services Transportation Wholesale Constant *** * No. of obs 1,920 1,920 1,254 pseudo R-squared

23 Table 3 : Multivariate results of the result of the last loan application at a commercial bank The dependent variable, denied on last loan attempt at a commercial bank, takes a value of 1 if applied at a bank and approved and 0 if applied and turned down. Logistic regression is used to estimate the model. The key independent variable is "Female-owned", which takes a value of 1 if the respondent reports that the business is women-owned and 0 otherwise. The variables are defined in Table 1 and all data are taken from the National Federation of Independent Business' Credit, Banks and Small Business surveys. Panel A presents the estimation results from the 1987 survey, Panel B from the 1995 survey, and Panel C from the 2001 survey. (1) (2) (3) Panel A: 1987 Coef. Std Err Coef. Std Err Coef. Std Err Female owned ** Jointely owned Log FTE Log years in business *** *** Sales growth *** *** Partnership Proprietorship *** Agriculture Manufacturing Construction Transportation Wholesale FIRE Business services Professional ** Banks at CFI ** MSA location *** Constant *** * No. of obs 1,362 1,362 1,548 pseudo R-squared

24 Table 3 (continued) : Multivariate results of the result of the last loan application at a com bank Panel B: 1995 Coef. Std Err Coef. Std Err Coef. Std Err Female owned *** Jointly owned Log FTE * ** Log years in business *** *** Sales growth ** Partnership Proprietorship S-corporation Agriculture Manufacturing Construction Transportation ** Wholesale FIRE Business services ** Professional Banks at CFI *** MSA location *** Constant *** No. of obs 2,914 2,914 2,914 pseudo R-squared Panel C: 2001 Coef. Std Err Coef. Std Err Coef. Std Err Female owned Jointly owned Log FTE *** ** Log years in business *** ** Sales growth Partnership Proprietorship S-corporation ** Agriculture Construction FIRE Manufacturing Professional * Business services * Transportation Wholesale Banks at CFI ** MSA location Constant *** No. of obs 1,321 1,321 1,321 pseudo R-squared

25 Table 4 : Multivariate results of the size of the most recent loan received The dependent variable is the log of the loan size of the most recently obtained loan. Ordinary least squares regression is used to estimate the model. The variables are defined in Table 1 and all data are taken from the National Federation of Independent Business' Credit, Banks and Small Business surveys. Panel A presents the estimation results from the 1987 survey, Panel B from the 1995 survey, and Panel C from the 2001 survey. (1) (2) (3) Panel A: 1987 Coef. Std Err Coef. Std Err Coef. Std Err Female owned *** * ** Jointly owned * Log years in business *** *** Log FTE *** *** Sales growth Partnership Proprietorship *** *** Agriculture *** *** Manufacturing Construction Transportation Wholesale FIRE Business services ** Professional Collateral required *** Purpose - working cap ** Type - fixed rate Log of maturity *** Constant *** *** No. of obs R-squared

26 Table 4 (continued) : Multivariate results of the size of the most recent loan received Panel B: 1995 Coef. Std Err Coef. Std Err Coef. Std Err Female owned *** * Jointly owned Log years in business ** Log FTE *** *** Sales growth Partnership Proprietorship *** *** S-corporation ** Agriculture *** *** Manufacturing ** *** Construction Transportation Wholesale *** *** FIRE * Business services *** *** Professional Collateral required *** Purpose - working cap *** Type - fixed rate *** Log of maturity *** Constant *** *** *** No. of obs R-squared Panel C: 2001 Coef. Std Err Coef. Std Err Coef. Std Err Female owned *** ** ** Jointly owned Log years in business *** Log FTE *** Sales growth Partnership ** Proprietorship S-corporation Agriculture Manufacturing * Construction Transportation Wholesale *** ** FIRE Business services Professional * Collateral required *** Purpose - working cap ** Type - fixed rate Log of maturity *** Constant *** *** *** No. of obs R-squared

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